improvement of short range quantitative …...combined data assimilation with radar and satellite...

Combined Data Assimilation with Radar and Satellite Retrievals and Ensemble Modelling for the

Improvement of Short Range Quantitative Precipitation Forecasts

The founders of DAQUA-ensemble are

Clemens Simmer

Meteorologisches Institut Rheinische Friedrich-Wilhelms-Universität Bonn

Hendrik Elbern

Rheinisches Institut fuer Umweltforschung Universität zu Köln

Joseph Egger

Meteorologisches Institut Ludwig-Maximilians-Universität München

George Craig

Institut für Physik der Atmosphäre Deutschen Zentrum für Luft- und Raumfahrt (DLR)

Werner Wergen

Deutscher Wetterdienst (DWD)

Andrea Rossa MeteoSchweiz

Universität

MeteorologischesInstitut

Bonn

1.2 Topic (Thema) Quantitative precipitation forecasts, ensemble forecast modelling, Bayesian chains, evolutionary algorithms, variational assimilation, physical initialisation nudging, application of Radar and satellite data 1.3 Code name (reference) (Kennwort) DAQUA-Ensemble

1.4 Scientific discipline and field of work (Fachgebiet und Arbeitsrichtung) Meteorology, data assimilation, numerical weather forecast

1.5 Scheduled duration in total (Voraussichtliche Gesamtdauer) 6 years

1.6 Application period (Antragszeitraum) 24 month

1.7 Date when funding should begin (Gewünschter Beginn der Förderung) 1. April 2004

1.8 Summary (Zusammenfassung) We propose the improvement of short range quantitative precipitation forecasting by regional high resolution weather forecast models. We will combine improved regional ensemble modelling with best member selection based on the most recent remote sensing information, with further broadening and narrowing of the distribution by a new evolutionary approach, followed by improved data assimilation and further forecast integration. The former step will introduce a Monte Carlo technique to invert a highly nonlinear and critically discontinuous microphysical problem in a novel way in cloud and rain assimilation. Physical initialisation techniques, nudging techniques and variational approaches with the most timely available information from remote sensing including Radar reflectivities, cloud parameters, and water vapour content will be employed. With this stacked procedure we expect to substantially reduce the influence of phase errors in the background field, which currently impede the successful assimilation of observations for short range precipitation forecasting by regional numerical weather forecast models. The project will establish an advanced capability for ensemble forecasting in the German research community. Validation efforts will explore and quantify the sources of uncertainty in forecasts especially under convective conditions. The differing requirements for the forecasting system under different meteorological conditions will be explored in the first instance by examining a set of case studies of convective storms in environments with orography of varying degrees of steepness. Some of the case studies will be orientated around the likely location of the field experiment, to aid in planning of the operations and to prepare for real-time forecasting.

2 State of the art, preliminary work (Stand der Forschung, eigene Vorarbeiten)

2.1 State of the art (Stand der Forschung) While efforts to improve the skill of precipitation forecasts have increased in recent years, even moderate demands for predictive quality remain largely unsatisfied on all time scales. More accuracy is required, in terms of amount, timing, and location of rainfall. Progress has been made over the last decade in general numerical weather forecasting, cloud microphysics modelling, and exploitation of the increasing computing power for higher model resolutions. More complete explicit microphysical schemes have been devised, and space borne land and sea surface observations with much more detailed information about energy and humidity transfer between the Earth surface and the atmosphere have become available in the last decade. At the same time both routine weather satellites (e.g. TOVS, HIRS, AMSU-B onboard recent NOAA satellites, water vapour radiance channels onboard the geostationary satellites METEOSAT 5,7, GOES 8, 10, SSMI radiances from DMSP satellites, and in the future SEVIRI onboard MSG) and research sensors with increasing sophistication (MIPAS onboard ENVISAT, AIRS onboard AQUA, IASI onboard METOP, and GRAS and ACE+ making use of GPS radio-occultation measurements with exceptionally high vertical resolution) have become available. Most important for short range quantitative precipitation forecasting (QPF) are Radar data, which are nowadays available from many national and even international Radar networks almost immediately after the measurement process, with horizontal resolution of only a few kilometres and temporal resolution of a few minutes. The German Weather Service (DWD) operates one of the most advanced Radar networks in the world. In the near future operational Radars will be able to distinguish between different microphysical particles by using different polarisations. Together with the anticipated improvements in the treatment of cloud and precipitation processes in weather forecast models this will enlarge dramatically the potential of Radar information for assimilation and validation. Despite this wealth of potentially useful information, QPF has hardly been able to demonstrate a significant success during the last decade, while forecasts of other weather parameters steadily increased in skill (Ebert et al. 2003). This seems to give some support to the assumptions of Lorenz (1969), who found from studies on initialization errors with a two-dimensional model of the vorticity equation, that the period of useful forecast skill decreases with the scale, with a predictability of less than 1 hour for spatial scales smaller than approximately 40 km. Nevertheless, the vast achievements in numerical weather forecasts have extended the range of useful predictability considerably further than the rather pessimistic view of Lorenz. Several arguments which suggest reasons for additional predictability are discussed in detail in Mass et al. (2002). It is likely that the true potential of remote sensing data has not yet been fully exploited owing to inadequate assimilation techniques for the meso-scale as described below.

CURRENT PROBLEMS IN DATA ASSIMILATION As more weather satellite platforms and denser weather Radar coverage become available, advanced cloud and rain assimilation algorithms are often touted as the solution to the problem of mispredicted rainfall (Macpherson 2002; Maréchal and Mahfouf 2002, Fillion and Mahfouf 2002; Hou et al. 2001). Zupanski et al. (2002) demonstrated for a Great plains tornado outbreak that the NCEP 4Dvar system is well able to analyse precursor features for tornado activity, including wind shear, humidity, high and low level jet streaks, and convective available potential energy, although they also admit that model errors are a critical limitation. However, even sophisticated and development-intensive data assimilation algorithms like the four-dimensional variational method (4D Var) fail to demonstrate the desired performance, although they are regarded as ideally suited

to the assimilation of remote sensing observations. For example, at ECMWF inconsistencies in the model physics between the inner and outer loop of the incremental 4D Var algorithm were identified as a significant problem in the variational assimilation system (Maréchal and Mahfouf 2002). Spatial interpolation and large values of rainfall Jacobians were further problem sources. Park and Droegemeier (1999, 2000) assessed the impact of error sources on the evolution of convective storms and rainfall. They found the phase error to contribute more to the total error than the amplitude error for a domain integrated accumulated rainfall score. Furthermore, errors in temperature, pressure and water vapour fields have different sensitivities to evolution times, with the temperature field being of highest impact. There are several theoretical reasons for the disappointing performance gains provided by 4D Var assimilation schemes: as for legacy Optimal Interpolation (OI) and all versions of the Kalman filter, 3D and 4D variational algorithms rest on the assumption of Gaussian error distributions. The error characteristics of cloud process models with highly nonlinear features cannot be expected to comply with this assertion. A theoretically sound treatment of non-Gaussian error characteristics has been provided by van Leeuwen (2001) in the context of an ensemble Kalman smoother, with formulas introducing high complexity and poorly known PDFs. On a more ad hoc basis this problem has so far been addressed in practise at ECMWF by Anderson et al. (1998) for radiosonde assimilation, in Simmons et al. (1999) for stratospheric humidity analysis, and in Anderson et al. (2000) for water vapour sensitive radiance observations. A means to ease the problem for water vapour has been devised by Dee and da Silva (2003), who introduced a pseudo-relative humidity, where the mixing ratio is scaled by the background saturation mixing ratio. Nevertheless discontinuities cannot be removed. Discontinuities due to modelled water phase transitions not only violate the assumption of Gaussian error characteristics, but also render tangent linear approximations (TLA), and hence adjoint calculations questionable. A more fundamental discussion of this fact is presented by Douady and Talagrand (1990). Park and Droegemeier (1997) estimated the validity limits of the TLA in a moist convective cloud scheme and found it highly sensitive to the amount of input modification (amplitude of the applied perturbation). As expected, the authors found total failure of the TLA in the vicinity of regime changes, with the largest errors near cloud tops. In a model study with artificial parameter perturbations, Park (1999) found predictability limits beyond 140 minutes. Fillion and Mahfouf (2002) re-examined diagnostic and prognostic cloud schemes, aiming at smoothing by suitable simplifications for a better applicability in the variational context. The Tiedke (1998) operational convection scheme at ECMWF has been subject to a modification towards reduced complexity, which is less prone to non-linearities, and making assimilation of space based SSMI, TRMM, and GPM data more feasible.

TREATMENT OF PHASE ERRORS The lack of differentiability (lack of smoothness) also leads to TLA errors due to the restricted vertical and horizontal extent of cloud formation (location problem) and finite lifetime (timing problem). It is partly true that these errors can be significantly reduced by well analysed synoptic scale fields with advanced data assimilation methods, especially on the on the meso-β-scale, particularly in regard to the vertical extent of a convective system. However, sophisticated data assimilation algorithms still cannot exclude severe misplacement errors in the timing and horizontal location of convective systems. Difficulties in using meso-scale forecasted fields often stem from conditions where convection is properly developed in the model but improperly positioned. This may be due to erroneous propagation velocities, phase errors, or mis-positioning of the embedding meso-scale structure. Attempts to address the problem using techniques originally developed for image processing have been made by several researchers. Hoffman et al (1995) defined and estimated distortion errors of forecasted fields, which were extended to SSM/I precipitable water fields by Hoffman and Grassoti (1996). Mehrkorn et al. (2003) further developed this method to decompose analysis and forecast errors into phase error, amplitude error,

and residual error, calling their procedure Feature Calibration and Alignment (FCA). The method has, however, only been applied to large-scale two-dimensional meteorological fields. In addition to quantification and separation of the forecast errors in general, such methods also hold promise for correcting phase errors, and may be applicable also to high resolution fields. Alexander et al (1998) employed a “digital warping” method, which was adopted from animated motion picture production. Using observed and synthetic satellite images, Keil et al. (2003) applied a 'pyramidal image matching' technique to determine displacement vectors of image elements at various scales. This allows for the detection of phase errors of key meteorological features recognisable in satellite imagery. After introducing a misplacement cost function to suitably estimate the phase error based on Radar observations, Brewster (2003a, 2003b) proposed three placement correcting techniques: a single correction step by simply imposing the observed Radar feature at the right observed location, by a stepwise correction within an assimilation window, and by introducing of an artificial wind term in the advection equation. The problem of phase errors is of course an addition to the more conventional assimilation problem of analysing the embedding synoptic scale features, typically pressure systems with the associated temperature, wind and moisture fields.

MODEL RESOLUTION

The particular focus on convectively driven precipitating systems places a specific requirement on the representation of convective-scale precipitation in the model used. There is a large body of literature on numerical simulations of convective storms that suggest - as a rule of thumb - that mesh sizes of the order of 1 km will suffice to simulate deep moist convection (e.g. review by Wilhelmson and Wicker 2001). A recent study of Bryan et al. (2003) advocates that in order to realistically represent deep convection, mesh sizes of the order of 100 m are necessary, while they concede that simulations on a 1 km grid are able to reproduce the basic convective circulation itself, even if aspects of it (e.g. precipitation amount, system phase speed) are incorrect. Most numerical studies on severe convection have been performed for the USA, so the applicability of the results in other regions, particularly mountainous areas, remains to be shown.

Approaches

i) Meso-scale Ensemble Forecasting A data assimilation system that can deal with significant non-Gaussianity in the statistics will almost certainly be based on an ensemble of model forecasts. The first crucial problem is therefore to construct a representative ensemble, based on the numerous sources of uncertainty in the forecast. An example of this process is given by Stensrud et al. (2000), who tested different sets of ensemble conditions separately, with both perturbed initial conditions and different model physical parameterisations. In general, a limited area model will be influenced by the following: 1. Boundary conditions: uncertainty in the synoptic and meso-scale environment that influences

the limited area model through the boundary conditions obtained from a global model 2. Initial conditions: uncertainty due to structures not seen by the observing system, due to limited

resolution or other instrument limitations 3. Physical parameterisations: uncertainty resulting from the model formulation of convection,

cloud microphysical, planetary boundary layer, or other processes A number of different methods have been proposed to generate ensembles that reflect these various sources of uncertainty. The most obvious way to account for boundary condition uncertainty is to

use a set of boundary conditions generated by a global ensemble forecasting system. A brute-force approach that uses every member of the global ensemble is likely to be inefficient, however, since much of the variability in the global ensemble may be confined outside of the domain of the limited-area model. One solution to this problem is to cluster global forecasts that are similar in the target region, and use only a single representative set of boundary conditions for each cluster. This approach has been implemented in the Consortium for Small-scale MOdelling Limited-area Ensemble Prediction System (COSMO-LEPS) developed in Italy using the Lokal Modell (LM) of DWD (Molteni et al. 2001, Tibaldi et al. 2003). It was found in this system that most of the variability in the 51 member ECMWF EPS for a region centred on the Alps can be retained by as few as five members. In medium range ensemble prediction, the most important source of uncertainty is probably the initial conditions, and a number of methods have been developed to generate perturbations that reflect this uncertainty. These include lagged and scaled lagged average forecasting (Hoffmann and Kalnay 1983, and Ebisuzaki and Kalnay 1991, respectively), the breeding method (Toth and Kalnay 1997), and a singular vector (SV) approach (Molteni et al. 1996; Buizza 2000). The latter methods in particular attempt to identify rapidly growing linear perturbations. In the case of convective storms, methods based on linear growth of errors may be less appropriate, however. The timescale for growth - about an hour for convective systems - is short compared to the forecast length, which may be from several hours up to several days, and the errors may have long-since saturated. This is more analogous to seasonal prediction than the classical problem of medium-range weather forecasting. In this case, a more random distribution of initial conditions may be required to explore the parameter space, with predictability coming via the surface and lateral boundary conditions, rather than preferred rapidly growing structures during the initial part of the forecast. Various studies concerning the effects of model errors have also been published. Further improvements for the ensemble approach can be expected by additionally applying error regression techniques on the ensemble members, demonstrated with great success by Krishnamurti et al. (1999). Shin and Krishnamurti (2003) tested super-ensemble approaches based on 3 different composition methods: multi-cumulus scheme, multi-model scheme, and a multi-analysis scheme, which achieved a QPF improvement of about 20%, with the multi-model configuration being the most effective. Verlinde and Cotton (1993) investigated the possibility and computational costs of parameter optimisation with intermittent observations, which are formulated as functions of prognostic variables, as given by reflectivity and liquid water path. Hou et al. (2001) investigated the ensemble approach in the framework of the Storm and Meso-scale Ensemble Experiment (SAMEX) with resolutions down to 3 km. Apart from the beneficial impact of multi-model ensembles, the authors found improved results with perturbations to model physics parameterizations. A detailed investigation of the relative contributions of initial value versus model physics perturbations for forecast skill is given in Stensrud et al. (2000), demonstrating that both act, but primarily on different time scales: model physics variance acts much faster during the first 12 hours than initial condition. A promising approach for parameterized convection is presented by Grell and Dévényi (2002). Ensembles are generated by a suite of different parameterisation schemes, for which domain-averaged precipitation rates show significant improvements with respect to single ensemble members. An effect is exploited here which makes use of better statistics, first demonstrated by Leith (1974). Grell and Dévényi (2002) showed the beneficial impact of Bayesian PDF construction with assimilated observations, to diagnostically reduce the precipitation bias.

ii) Assimilation of remote sensing data There has been considerable research in recent years on the assimilation of Radar wind data. Notably, Weygandt et al. (2002a, 2002b) demonstrated some degree of predictability for a supercell thunderstorm by sequential variational assimilation of single-Doppler velocity retrievals (SDVR). The predicted storm evolution was found to be highly sensitive to the initial moisture field. The authors introduced a moving reference frame, as was done in Caya et al. (2002), who found the assimilation result to improve significantly with a 1 hour extended assimilation window. Physical initialization (PI), pioneered by Donner (1988) and especially Krishnamurti et al. (1991, 1993) on the large scale, and developed by Haase et al. (2000) and Ament et al. (2001) with Radar and satellite data, respectively, on the meso-γ-scale, is a very time efficient alternative to variational approaches. PI is thus especially suited for the timely assimilation of very recent observation data. The method uses assumptions about the structure of the 3-dimensional flow in the cloudy and/or rainy atmosphere and tries to match or impose this structure on the observed or assumed flow in the model at the start of the forecast run. The advantage of the method is its extremely high time efficiency, because only the first model simulation time step is modified subject to the observations. The method has also been used in the framework of the nudging technique described below. A somewhat less time-efficient method is the nudging (NG) approach (Manobianco et al. 1994, Jones and Macpherson 1997, Haase 2002). NG is often called the poor-mans-approach to 4DVar. During the assimilation time window the model state is “nudged” by artificial forcing terms in the relevant model equations. With Radar reflectivity data, Latent Heat Nudging (LHN) is a popular approach. LHN adds the heat produced by the precipitation processes in observed meteorological structures as an additional heat source in the model equations. The LHN scheme is conceptually simple, makes no assumptions regarding the linearity of the systems to be assimilated nor does it rely on the Gaussianity of their error distributions. Is an inherently 4D data assimilation scheme that easily handles data with high temporal resolution, and is computationally inexpensive. On the other hand, like nudging in general, it is an heuristic approach of weighing observation and model information into an analysis. The handling of observed quantities that are not directly related to the model prognostic variables is not straightforward. The LHN approach has been shown to have good potential to improve short-range QPF in NWP models with a mesh size of the order of 10 km (e.g. Macpherson 2001). The appropriateness of the LHN scheme when applied on a scale of 1 km needs to be shown, although successful studies have already been performed on a 2.8 km grid with the LM (Haase 2001). LHN assumes that most of the latent heating takes place in the column where precipitation is observed. While this seems to be a good approximation on a grid with a mesh size of the order of 10 km or larger, this assumption may be weaker for a mesh size of the order of 1 km. Furthermore, the meso-scale environment has been shown to be critical for the correct simulation of convective systems which have been forced at the storm scale (e.g. Ducrocq et al. 2000). Therefore, it is essential to have a meso-scale ensemble which possesses enough spread at analysis time and forecast ranges of +6h to +24h in order to cover the uncertainties in the key parameters governing the convective-scale processes (see discussion of ensemble forecasting above).

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Zupanski, D. , M. Zupanski, E. Rogers, D.F. Parrish, and G.J. DiMego, 2002: Fine-resolution 4DVAR data assimilation for the Great Plains tornado outbreak of 3 May 1999. Weather and Forecasting, 17, 506-525.

2.2 Preliminary work (Eigene Vorarbeiten)

METEOROLOGISCHES INSTITUT DER RHEINISCHEN FRIEDRICH-WILHELMS-UNIVERSITÄT BONN (MIUB) The Meteorological Institute of the University Bonn is a part of the faculty of Mathematics and Natural Science and responsible for teaching the complete curriculum for studies aiming at the degree of Diplom-Meteorologe. Working groups at the institute do research on various topics related to QPF like cloud modelling, model output statistics, use of remote sensing data, and general assimilation issues. The working group on Remote Sensing and Meso-scale Modelling, lead by Clemens Simmer who coordinates this proposal, concentrates on the use of remote sensing data for validation of and assimilation into meso-scale weather forecast models. Most relevant for the proposed work is a series of studies assimilating Radar and satellite remote sensing data into the Lokalmodell (LM), which has been conducted in close cooperation with DWD. A method was developed to initialize the surface module of the LM, TERRA, with Radar derived rainfall and satellite derived surface radiation (Gross et al. 2000). This method has been applied and extended to other satellite data types in the framework of the EU-FP5-project ELDAS in cooperation with ECMWF in order to determine the most probable soil moisture distribution as an initial condition for atmospheric models (Seuffert et al. 2003). A simplified rain fall model was developed to use Radar data for the initialisation of precipitation in the LM (Haase et al. 2000, Haase 2002). Radar information was transferred to surface rainfall, which is then used to constrain a simplified rain model, which predicts vertical velocity as the key precipitation-forming process. The model was further constrained by observations of cloud height and base from different sources. Finally, the LM was initialized with the derived vertical velocity fields. The method is able to reproduce almost exactly Radar derived rainfall at initialisation time. The impact of the Radar information fades with increasing time steps but improves quantitative precipitation forecast significantly for the first few hours depending on the synoptic situation. In a Diploma thesis a similar method was derived to initialize the LM with cloud information from METEOSAT (Ament et al. 2001). We achieved the almost identical representation of the METEOSAT cloud fields during the first time steps; the impact did, however, fade away even faster, a fact which we could attribute in part to the inflow of information from the boundaries. In the proposed project, we will - as a first step - combine both methods and the information of both data sources in order to achieve an initial condition of the model that is consistent with both the Radar and satellite information. In the BMBF-AFO2000 project 4DCLOUDS, coordinated by Clemens Simmer, the working group developed an evolutionary approach to create up to three-dimensional representations of clouds from limited cloud measurements obtained from various sources (ground-based microwaves and cloud Radar, satellites, aircraft probes). The approach creates arbitrary structures by mutation and selection where the selection is governed by a fitness function, which is driven by a set of arbitrarily chosen a priori known (and desired) statistical properties of the structure (Venema et al. 2003). In the project we will use this approach – as a second step - to generalize the cloud/precipitation model, which is the basis of PI, to the one used in the meso-scale model. Other work beneficial to the proposed project relates to our expertise in remote sensing. To simulate Radar reflectivities from forecasts of the LM a model was developed (Haase und Crewell 2000; Meetschen et al. 2000, Gerstner et al. 2002), which is currently used for validation of the LM at DWD. Both the Finnish (FMI) and the Swedish weather services (SMHI) currently plan to implement the Radar simulation model for validation of the HIRLAM forecast model environment. Radar data were assimilated together with METEOSAT data into the soil module of the LM (as part of SFB 350) to determine regional evaporation (Braun et al. 2001). The LM itself was two-

way coupled with the hydrological model TOPLATS to study the sensitivity of the weather forecast towards a more accurate soil moisture distribution (Seuffert et al. 2002). Research has also been done in the general statistical properties of precipitation in Steinhorst et al. (2003) by developing a statistical model to reconstruct ground based precipitation measurements. Another research subject of the working group from which the proposed project can benefit is the remote sensing of the cloudy atmosphere (Löhnert et al. 2001, Czekala et al. 2001a,b, Czekala and Simmer 2002).

References Ament, F., G. Haase and C.Simmer, 2001: Intialisierung von Wolken im Lokal-Modell aus Meteosatdaten,

DACH-MT, Österr. Beitr. Meteorol. und Geophysik, Heft 27 Braun, P., B. Maurer, G. Müller, P. Gross, G. Heinemann, and C. Simmer, 2001: An integrated approach for

the determination of regional evapotranspiration using mesoscale modelling, remote sensing and boundary layer measurements. Meteor. Atm. Phys., 76, 83-106.

Crewell, S., H. Czekala, U. Löhnert, C. Simmer, Th. Rose, R. Zimmermann, and R. Zimmermann, 2001: Microwave Radiometer for Cloud Carthography: A 22-channel ground-based microwave radiometer for atmospheric research. Radio Science, 36, 621-638.

Czekala, H. and C. Simmer, 2002: On precipitation induced polarization of microwave radiation from space. Meteorologische Zeitschrift, 11, 49-60.

Czekala, H., S. Crewell, C. Simmer, A. Thiele, A. Hornbostel, and A. Schroth, 2001a: Interpretation of polarization features in ground based microwave observations as caused by horizontally aligned oblate rain drops. Journal of Applied Meteorology, 40, 1918-1932.

Czekala, H., S. Crewell, C. Simmer, and A. Thiele, 2001b: Discrimination of cloud and rain liquid water path by groundbased polarized microwave radiometry. Geophysical Research Letters, 28, 267-270, 2001.

Gerstner, T., D. Meetschen, S. Crewell, M. Griebel, and C. Simmer, 2002: A case study on multiresolution visualization of local rainfall from weather radar measurements, In H. Pfister and M. Bailey (eds.), Proc. IEEE Visualization 2002, IEEE Computer Society Press, 533-536.

Gross, P., C. Simmer, G. Müller, and G. Haase, 2000: Use of remote sensing for validation and assimilation in mesoscale models. In: IAHS-AISH Red Book "`Remote Sensing and Hydrology 2000, Owe, M., K. Brubaker, J. Ritchie, and A. Rango (Eds.) ISSN 0144-7815, 192-196.

Haase, G., and S. Crewell, 2000: Simulation of radar reflectivities using a mesoscale forecast model. Water Resour. Res., 36, 2221-2230.

Haase, G., S. Crewell, C. Simmer und W. Wergen, 2000: Assimilation of radar data in mesoscale models: Physical Initialization and latent heat nudging. Phys. Chem. Earth (B), 25, 1237-1242.

Haase, G., 2002: A physical initialization algorithm for non-hydrostatic weather prediction models using radar derived rain rates, PhD Thesis, Meteorological Institute, University of Bonn, 98 pp.

Löhnert, U., S. Crewell, A. Macke, and C. Simmer, 2001: Profiling cloud liquid water by combining active and passive microwave measurements with cloud model statistics. Journal of Atmospheric and Oceanic Technology, 18, 1354-1366.

Meetschen, D., 1999: Erkennung, Nutzung und Entfernung von Clutter zur Verbesserung der Niderschlagsmessung mit dem Bonner Radar. Dipomarbeit am Meteorologischen Institut der Universität Bonn, 66 Seiten.

Meetschen, D., S. Crewell, P. Gross, G. Haase, C. Simmer, and A. van Lammeren, 2000: Simulation of weather radar products from a mesoscale model. Phys. Chem. Earth (B), 25, 1257-1261.

Müller, G. und C.Simmer, 1998: Regenerkennung und Bestimmung von Regenraten aus Messungen mit dem SSM/I-Radiometer über Land durch Kalibrierung mit in-situ Regenmessungen. Annalen der Meteorologie, Deutsche Meteorologen-Tagung 1998, 14.-18. September 1998, München, Selbstverlag des Deutschen Wetterdienstes. Deutsche Meteorologische Gesellschaft, 37, 259-260.

Seuffert, G., P. Gross, C. Simmer, and E.F. Wood, 2002 : The influence of hydrologic modeling on the predicted local weather : Two-way coupling of a mesoscale weather prediction model and a land surface hydrologic model. Journal of Hydrometeorology, 3, 505-523,

Seuffert, G., H. Wilker, P. Viterbo, J-F. Mahfouf, M. Drusch, J-C. Calvet, 2003: Soil moisture analysis combining screen-level parameters and microwave brightness temperature: A test with field data.Geophys. Res. Lett., 30, 5/1-5/4

Steinhorst, H., C Simmer, and H.-D. Schilling, 2003: A statistical-dynamic analysis of precipitation data with high temporal resolution. In: Neugebauer and Simmer (Eds.), Dynamics of Multiscale Earth Systems. Lecture Notes in Earth Sciences, Springer Verlag, 337-350, 2003.

Venema, V., C. Simmer, S. Crewell, U. Löhnert, S. Gimeno Garcia, and S. Meyer, 2003: Iterative Amplitude Fourier Transform (IAAFT) surrogat Cloud Fields (paper in preparation)

RHEINISCHES INSTITUT FÜR UMWELTFORSCHUNG AN DER UNIVERSITÄT ZU KÖLN (RIU) The “Rheinisches Institut für Umweltforschung an der Universität zu Köln“ (Rhenish Institute for Environmental Research at the University of Cologne) is a public organisation founded to support environmental research at the University of Cologne in close collaboration with the university. The Executive Committee presently consists of two university professors, a university administration officer, and a senior scientist of the Rhenish Institute. The association is acknowledged as a non-profit organisation. The institute is controlled by the association and its Executive Committee. It is structured into divisions dealing with different fields of environmental research. RIU has a leading role in the development of the 4D Var tropospheric chemistry data assimilation technique: In Elbern et al. (1997) a first development of a tropospheric variational data assimilation scheme based on a gas phase mechanism has been presented and shown to be skill-improving in a real case experiment. The first full Eulerian 4D Var systems were presented in Elbern and Schmidt (1999) to demonstrate the feasibility in terms of Observation System Simulation Experiments (OSSE). In a real case application the forecast improvement after variational assimilation has been demonstrated in Elbern and Schmidt (2001), where by the initial value estimates chemical state variables were the optimisation parameters. However, as tropospheric chemistry is largely controlled by emissions, Elbern et al. (2000) demonstrated the feasibility to select emission rates as control parameter, after introduction of temporal regularisation rules, first as an Observation System Simulation Experiment (OSSE). A first real case application is presented in Elbern and Schmidt (2002), where success could be demonstrated by routine network in situ observations. Recent research is directed to combine both optimisation of emission rates and initial values in a joint optimisation space (Elbern, 2002). Presently the methodology of variational data assimilation is introduced in the chemistry general circulation model SACADA (Synoptic Analyses with Chemical Advanced Data Assimilation) with the icosahedral grid and meteorological driver from the DWD’s global model GME. Here a four-dimensional variational data assimilation scheme is developed which will be applied to trace gas measurements from ENVISAT sensors SCIAMACHY, MIPAS, and GOMOS. Further work is in progress to include a complex soil vegetation atmosphere transfer scheme including frost and snow aging in a 4D Var scheme in the German DEKLIM program. MM5 is the main meteorological driver of the EURAD model (European Air Pollution Dispersion Model) at the University of Cologne. Since 1987 we gained lots of experience with the structure and the physical parameterizations of MM5, especially for the moist processes (Jakobs et al. 1990; Hantel et al. 1995). MM5 provided the dynamics and moist physics for the Chemical transport calculation for many case studies (Jakobs et al 1991; Jakobs et al. 1995; Hass et al. 1995; Ebel et al. 1997) The EURAD project also gained experience with the LM model, using it at the meteorological driver for chemical transport calculations within a network of different institutions (Jakobs et al. 2002).

References Ebel, A., M. Memmesheimer, H.J. Jakobs, Regional modelling of tropospheric ozone distribution and

budgets, In: Global Environmental Change, ed. by C. Varotsos, NATO ASI Series, Subseries I, Vol. 53, Springer Verlag, pp. 39-59, 1997.

Elbern H., H. Schmidt, and A. Ebel; Variational data assimilation for tropospheric chemistry modeling. J. Geophys. Res., 10, D13, 15,967-15,985, 1997.

Elbern, H., J. Hendricks, A. Ebel; A climatology of tropopause folds by global analyses. Theor. and Appl. Clim., 59, 181-200, 1998.

Elbern, H., and H. Schmidt; A four--dimensional variational chemistry data assimilation scheme for Eulerian chemistry transport modeling. J. Geophys. Res., 104, 18 583--18 598, 1999.

Elbern, H., H. Schmidt, A. Ebel, Implementation of a parallel 4D-variational Data Assimilation scheme. Environ. Manag. Health, 10, 236-244, 1999.

Elbern, H., H. Schmidt, O. Talagrand, A. Ebel; 4D-variational data assimilation with an adjoint air quality model for emission analysis. Environ. Mod. Softw., 15, 539-548, 2000.

Elbern, H., and H. Schmidt; Ozone episode analysis by four-dimensional variational chemistry data assimilation. J. Geophys. Res., 106, D4, 3569-3590, 2001.

Elbern, H., Emission rate estimates by variational assimilation of surface and satellite data, Proceedings of the EUROTRAC Symposium 2002, P. Midgley and M. Reuther (Eds.), Margraf Verlag, 2002.

Elbern, H., and H. Schmidt; Chemical 4D variational data assimilation and its numerical implications for case study analyses; IMA volumes in Mathematics and its Applications, Atmospheric Modeling, 130, 165-184, Edts: David P. Chock and Gregory R. Carmichael, 2002.

Elbern, H., Emission rate estimates by variational assimilation of surface and satellite data, Proceedings from the EUROTRAC-2 Symposium 2002, P.M. Midgley, M. Reuther (Eds.), Margraf Verlag, Weikersheim, 2002.

Jakobs, H.J., N. Mölders, M. Laube, A. Ebel, Validation of Rainfall during Chernobyl period in EURAD, Annales Geophysicae XV General Assembly, Copenhagen, 23.-27. April 1990, Ganthier-villers, Montouge Cedex, France, 1990, p. 136-137.

Jakobs, H.J., H. Hass, M. Memmesheimer and Y.-H. Kuo, Effects of spatial resolution on tracer field simulations with an Eulerian Long-range transport Model (EURAD), In: Air Pollution Modelling and its Application VIII, eds. H. van Dop and D.G. Steyn, Plenum Press, 225-232, 1991.

Jakobs, H.J., H. Feldmann, H. Hass, M. Memmesheimer, The use of nested models for air pollution studies: an application of the EURAD model to a SANA episode, J. Appl. Met., Vol. 34, No. 6, 1301-1319, 1995.

Hass, H., H.J. Jakobs, M. Memmesheimer, Analysis of a Regional Model (EURAD) Near Surface Gas Concentration Predictions using Observations from Networks, Meteorol. Atmos. Phys., 57, 173-201, 1995.

Hantel, M., H.-J. Jakobs, Y. Wang, Validation of Parameterized Convective Fluxes with DIAMOD, Meteorol. Atmos. Phys., 57, 201-227, 1995.

Hoelzemann, J., H. Elbern, and A. Ebel; PSAS and 4D-var data assimilation for chemical state analysis by urban and rural observation sites, Phys. and Chem. of the Earth, 26, 807-812, 2001.

DEUTSCHES ZENTRUM FÜR LUFT UND RAUMFAHRT (DLR) The Institut for Atmospheric Physics of the Deutsches Zentrum für Luft- und Raumfahrt (DLR) is one of more than 30 institutes within DLR, which in turn is part of the Helmholz Gemeinschaft. Directed by Prof. Dr. Ulrich Schumann, the institute has approximately 100 workers in five departments, and carries out research into the physical and chemical processes occurring in the troposphere and lower stratosphere. There has been long experience at DLR-PA in using the short-range NWP models of DWD for detailed investigations of numerous severe weather events, including the Odra flood of 1997, studied with the Deutschland-Modell (Keil et al. 2000), the Pfingst-Hochwasser of 1999 and other convective events in Bavaria (Keil 2000, Keil and Volkert 2000), and the winter storm Lothar of 1999 using the LM (Keil et al. 2003). For the validation of the model results remote-sensing data (Radar, satellite) have been extensively used. In particular, synthetic Radar and satellite imagery has been generated from model output fields. In addition to visual inspection, an image matching technique has been developed and applied to objectively evaluate forecast quality (Tafferner et al. 2002, Keil et al. 2003). The Institute has recently been joined by Dr. George Craig, formerly of the University of Reading in the UK, who brings with him extensive experience in high resolution and ensemble modelling of

convective storms. This experience includes cloud-resolving modelling of convection in radiative-convective equilibrium over a homogeneous surface (Tompkins and Craig 1998a), where one can precisely determine the statistics of convective variability (Cohen and Craig 2003a,b) and the timescales of convective response (Tompkins and Craig 1998b, Cohen and Craig 2003c). Ensemble simulations have been made for two meso-scale convective systems over the UK, using the meso-scale version of the Met Office Unified Model (Done 2002). In each case the synoptic scale flow provided a region of instability (significant CAPE), and a 12-member ensemble was generated by adding random variability to the low-level equivalent potential temperature to influence the locations of convective triggering. In the case with no orography in the region of convective initiation, the different ensemble members showed storms located randomly within the unstable region. For the second case, with significant orography, all ensemble members produced storms in one or both of only two locations. This preliminary investigation suggests that the large-scale flow will provide a range of possible outcomes for QPF, with the precise predictability depending crucially on local forcing that a high-resolution model may or may not resolve. Craig, G. C. and B. G. Cohen: Fluctuations in an equilibrium convective ensemble. Part I: Theoretical basis.

J. Atmos. Sci., submitted, 2003a. Cohen, B. G. and G. C. Craig: Fluctuations in an equilibrium convective ensemble. Part II: Experimental

validation. J. Atmos. Sci., submitted, 2003b. Cohen, B. G. and G. C. Craig: The response time of a convective cloud ensemble to a change in forcing.

Quart. J. Roy. Meteor. Soc., in press, 2003c. Done, J.M.: Predictability and representation of convection in a mesoscale model. PhD thesis, University of

Reading, Reading, UK, 2002. Keil, C. , H. Volkert, D. Majewski: The Oder flood in July 1997: Transport routes of precipitable water

diagnosed with an operational forecast model, Geophys. Res. Lett., 26, 235-238, 2000. Keil, C. and H. Volkert: Precipitation in the northern Alpine region: Case-study-type validation of an

operational forecast model, Meteorol. Atmos. Phys.72, 16-173, 2000. Keil, C.: Numerische Simulation von Starkniederschlagsereignissen mit mesoskaligen

Wettervorhersagemodellen - Überprüfung mit Radar-Daten und Diagnose der atmosphärischen Wasserbilanz, Dissertation der Ludwig-Maximilians-Universtät München, DLR Forschungsbericht FB2000-14, ISSN 1434-8554, 133 S., 2000.

Keil, C. , A. Tafferner, H. Mannstein, U. Schättler: Evaluating High-Reolution Model Forecasts of European Winter Storms by Use of Satellite and Radar Observations, Weather and Forecasting, 18, 732-747, 2003.

Tafferner, A., Mannstein, H., Paccagnella, T., Marsigli, C., Montani, A., Nerozzi, F., 2002: On Finding the Best Forecast out of an Ensemble by Satellite Image Matching for MAP IOP2b. 10th Conf. on Mountain Meteorology. 17-21 June, Park City, Utah, USA, AMS, 201-204

Tompkins, A. M., and G. C. Craig: Timescales of adjustment to radiative-convective equilibrium in the tropical atmosphere. Quart. J. Roy. Meteor. Soc., 124, 2693-2713, 1998a.

Tompkins, A. M., and G. C. Craig: Radiative-convective equilibrium in a three dimensional cloud ensemble model. Quart. J. Roy. Meteor. Soc., 124, 2073-2097, 1998b.

METEOROLOGISCHES INSTITUT DER UNIVERSITÄT MÜNCHEN (MIM) The Meteorological Institute of the University Munich is part of the faculty of physics and responsible for teaching the complete curriculum for students working towards the degree of Diplom-Meteorologe. At MIM there is considerable experience in numerical modelling. In particular, a wide range of orographic effects has been studied (Egger and Hoinka 2000; Egger et al. 2002; Egger 2003). In what follows, main emphasis will be given to the group's expertise in modelling moist processes. Regional climate simulations in the Alpine region were performed with the MM5 and precipitation played hereby an important role (Grell et al. 2000). Winter and summer season precipitation distributions from the regional-scale simulations are compared with the global simulation and observed precipitation climatologies. It was shown that the degree to which the terrain is resolved in the various runs significantly alters the simulation of the precipitation

climatologies. This work was done within the Bavarian climate research program which was coordinated at our Institute (Egger). Currently the theoretical meteorology group is engaged in the projects CLIMESTO, COBI, GLOWA and VERTIKATOR. There is ongoing work in the BMBF-AFO2000 project VERTIKATOR to investigate orographically induced diurnal vertical motion (Gantner et al. 2003). Both dry and wet processes are of interest in this project. Some promising simulations were performed for the VERTIKATOR case of 9. July 2002 when a storm cell was moving through the target area – the Bavarian Alps and foreland southwest of Munich - during an IOP. Furthermore, a model test - including the models LM, KAMM and different versions of MM5 - was carried out for idealized conditions showing that some models exhibit serious numerical errors leading to strong precipitation where none should occur (Zängl et al. 2003). In the GLOWA-DANUBE project we are working on quantitative precipitation forecasting on a climatic time scale because precipitation is one of the key parameters in the hydrological cycle. A sensitivity study is close to completion. The aim of this study is to find out which configuration of parameterizations works best for the Upper Danube catchment area (Schipper 2003). Furthermore, Zängl performed recently some studies for cases of observed precipitation. There was an intercomparison of simulated precipitation fields of the MAP/IOP2B with different high-resolution models (Richard et al. 2002). Another focus was the sensitivity of orographic precipitation simulation to model details other than cloud microphysics (Zängl 2003a). This study considered MAP/IOP10 during which substantial precipitation occurred on the southern side of the Alps. In addition to investigating the use of different parameterizations of the boundary layer and cumulus convection, the implementation of horizontal diffusion and the specification of the vertical coordinate, the impact of using different data as initial and boundary conditions was tested. The different data were the MAP Reanalysis dataset provided by the ECMWF and standard operational ECMWF data. The results show that the side effects arising from the model numerics and from the physics parameterizations not related to cloud microphysics can be as large as or even larger than the impact of the microphysical parameterization itself. Another study deals with numerical simulations of the flooding events 2002 in eastern Germany (Zängl 2003b). High-resolution numerical simulations were carried out and sensitivity experiments were performed with coarser resolution, different cloud microphysical parameterizations and with a different time of initialization. The results show that the high-resolution runs are very successful in reproducing the observed structure of the precipitation fields. The location of the rainfall maximum is correct within 10km, but the amount of precipitation tends to be under predicted. Finally, the ensemble approach is used at our Institute for the prediction of hurricane tracks (Weber 2003). This system is based on a statistical analysis of the annual performance of numerical track prediction models. Another method used is an ensemble system using a barotropic model with 21 members created by perturbing deep layer means. Our contribution to the CLIMESTO project uses three-dimensional master equations for predicting the distribution of an ensemble of initial conditions in the famous Lorenz system (Lorenz 1963) extended with a stochastic forcing.

References Egger, J. and K.-P. Hoinka (2000): Mountain torques and the equatorial components of global angular

momentum, J. Atmos. Sci., 57, 2319-2331 Egger, J. S. Bajrachaya, R. Heinrich, P. Kolb, S. Lämmlein, M. Mech, K. Reuder, W. Schäper, P. Shakya, J.

Schween, and H. Wendt (2002): Diurnal Winds in the Himalayan Kali Gandaki Valley. Part III: Remotely Piloted Aircraft Soundings, Mon. Wea. Rev., 130, 2042-2058.

Egger, J. (2003): Valley Winds, Elsevier Science Ltd., 2481-2490 Gantner, L., M. Hornsteiner, J. Egger, and G. Hartjenstein, 2003: The diurnal circulation of Zugspitzplatt:

Observations and modeling. Meteorol. Zeitschrift, 12, 95-102 Grell, G., L. Schade, R. Knoche, A. Pfeiffer, and J. Egger, 2000: Nonhydrostatic climate simulations of

precipitation over complex terrain. J. Geophys. Res., 105, 29,595-29,608. Lorenz, E.N.(1963): Deterministitc Nonperiodic Flow, J.Atmos. Sci. 20, 130-141

Richard, E., N. Asencio, R. Benoit, A. Buzzi, R. Ferretti, P. Malguzzi, S. Serafin, G. Zängl, and J.-F. Georgis, 2002: Intercomparison of the simulated precipitation fields of the MAP/IOP2b with different high-resolution models. Proc. 10th AMS Conf. On Mountain Meteorology, Park City, UT, 8.3, 167-170

Schipper, H. et al., 2003: Sensitivity of MM5 results to various parametrizations. In preparation Weber, H. C., 2003: Hurricane track prediction using a statistical ensemble of numerical models. Mon.

Wea. Rev. (in press) Zängl, G., L. Gantner, G. Hartjenstein, and H. Noppel, 2003: Numerical errors above steep topography: A

model intercomparison. Submitted to Meteorol. Zeitschrift, Zängl, G., 2003a: The sensitivity of simulated orographic precipitation to model details other than cloud

microphysics. Submitted to Q. J. R. Meteorol. Soc. Zängl, G., 2003b Numerical simulations of the 12-13 August 2002 flooding event in eastern Germay.

Submitted to Q. J. R. Meteorol. Soc.

METEOSWISS MeteoSwiss has an operational implementation of the LM at the Swiss Centre for Supercomputing (CSCS) in Manno, Switzerland and enjoys ample resources for experimentation. The LHN scheme has been adapted for the current operational LM version, as well as for an idealized version of the LM. Indeed, real case and idealized experiments with the LHN scheme have already been performed. Real cases have been investigated using the operational 7 km mesh size, with a convective parameterization scheme. Good results have been obtained for a squall line case over the Swiss Plateau, in that the LHN scheme was capable of assimilating the system into the LM. The positive impact on the free forecast lasted for three hours, as the system decayed more quickly in the simulation than in reality (Leuenberger and Rossa 2003). Daniel Leuenberger, currently finishing his PhD at MeteoSwiss, is the candidate to work on the proposed work. He thoroughly knows the modelling and assimilation system and would be productive from day 1. Moreover, verification of LM QPF with Radar Quantitative Precipitation Estimation (QPE) over Switzerland, stratified following a weather classification was performed for two years (Rossa et al. 2003). A clear dependency of the LM QPF errors on the prevalent flow was found, particularly in relation with topography. Daniel Leuenberger also implemented a new vertical coordinate which has better characteristics over complex terrain (Schär et al. 2002, Leuenberger 2002). Studies have been undertaken in collaboration with the University of Berne on the use of GPS-derived vertically integrated water vapour for validation (Guerova et al. 2003a) and assimilation purposes (Guerova et al. 2003b, Guerova et al. 2004). Guerova, G., E. Brockmann, J. Quiby, F. Schubiger, and C. Mätzler, 2003a. Validation of NWP mesoscale

models with Swiss GPS networkk AGNES. J. Appl. Meteor., 42, 141-150. Guerova, G., J. Bettems, E. Brockmann, and C. Mätzler, 2003b. Assimilation of the GPS-derived Integrated

Water Vapour (IWV) in the MeteoSwiss Numerical Weather Prediction model - a first experiment. Physics and Chemistry of the Earth (in press).

Guerova, G., J. Bettems, E. Brockmann, and C. Mätzler, 2004. Assimilation of COST 716 Near Real Time GPS data in the nonhydrostatic limited area model used at MeteoSwiss. Meteor. Atmos. Phys. (submitted).

Leuenberger, D., and A. M. Rossa, 2003. Assimilation of radar information in aLMo. COSMO Newsletter, 3, 165-172.

Leuenberger, D., 2002. The SLEVE coordinate in LM . COSMO Newsletter, 2, 105-110. Rossa, A., M. Arpagaus, and E. Zala, 2003. Weather situation-dependent stratification of precipitation and

upper-air verification of the Alpine Model (aLMo). COSMO Newsletter, 3, 123-138. Schär, C., D. Leuenberger, O. Furher, D. Lüthi, and C. Girard, 2002. A new Terrain-Following Vertical

Coordinate Formulation for Atmospheric Prediction Models. Mon. Wea. Rev., 130, 2459-2480.

3 Goals and work schedule (Ziele und Arbeitsprogramm)

3.1 Goals (Ziele)

GENERAL OBJECTIVE The overall objective of the proposed work is to improve the predictive skill of short term quantitative precipitation forecasts with regional models in general with a focus on convective rain. A fundamental prerequisite for successful numerical weather prediction is the initialisation of the model fields using independent measurements. Area-covering remote sensing observations from Radar networks and meteorological satellites are a major step towards filling existing gaps through their high spatial and temporal resolution. Besides the highly developed Radar network of DWD especially the new generation of satellite instruments allows measurements of cloud properties and water vapour with unprecedented accuracy and resolution. The general methodology of the project rests on advanced space-time data assimilation techniques for Radar and satellite data combined with novel ensemble generation techniques for meso-scale limited-area models. The final goal is to predict as correctly as possible by timely introducing observations from Radar and satellites in a multi-stage forecasting approach combining elements of ensemble forecasts, evolutionary methods, and a range of different assimilation techniques. Using different models (MM5 in addition to LM) will lead to a multi-model ensemble, which has shown the potential (see state of art) to significantly improve QPF. Further progress is also expected in the course of the project life time after further refinements and complementing the system with advances gained in other projects of the SPP and introduction of additional observations.

SPECIFIC OBJECTIVES In contrast to synoptic situations with Gaussian error characteristics, second order statistical moments can only describe PDF sections closely around some maxima, assuming an unrealistically skilful first guess simulation. Thus variational assimilation approaches of any kind fail, when the first guess includes significant phase errors. Ensemble simulations are principally able to shape probability density distribution much more realistic. But due to the high and still increasing computational demands due to high resolution and the large data amounts to be processed in the assimilation chain, complete ensemble simulations together with expensive assimilation methods are not feasible with high-resolution limited-area models. The alternative data assimilation system to be developed in the project addresses the principal problems described in chapter 2.1 with the following strategy: 1. The pronounced non-linearity and the discontinuity associated with hydrological processes in

the atmosphere, together with the non-Gaussian nature of prediction errors is accounted for by an ensemble approach within the assimilation system. Ensemble modelling as a version of Monte Carlo method is adopted as the only feasible means to handle the non-linear processes. However, non-Gaussian error prevalence renders the use of covariances by second moment ensemble statistics of very limited value, as higher moments or multimodality must be accounted for. With ensemble simulation as a method for Monte Carlo modelling a technique is available (e.g. the Bayesian approach, see Tarantola and Valette 1982), which can also be extended to nonlinear problems (Mosegaard and Tarantola, 1995).

2. With the degree of freedom of today’s meso-scale models of O(106-107), the necessary number of ensemble integrations renders the pure ensemble approach as practically infeasible in a straight forward way. Since the convective-scale processes and associated precipitation act as “fast response” processes, which can be considered as “slaved” to “slow response” meso-scale

and synoptic-scale processes, an adiabatic decoupling (in a generalised sense, following synergetics terminology by Haken (1983)), can be applied (see also Saltzman 2002, for climatological applications). The ensemble approach is therefore split into the meso-scale phase in 1. with few ensemble members (~10), for which Gaussian error assumptions can be applied, and a convective-scale phase, for which detailed simulations of about 100-1000 must be made to approximate the PDF. Monte Carlo approaches by an evolutionary algorithm in a Bayesian context are taken to estimate this PDF. Remote sensing information of the state of the atmosphere is nowadays available from remote sensing instruments with ever decreasing time delay and increasing horizontal and temporal resolution. The enhanced ensemble member simulations are then compared with available Radar and satellite data of the most recent past in order to select the most probable members. Thus we fold the PDF with the condition of good representation of available information. By this means we are able to select the most probable prevailing synoptic situation at the start of the forecast.

3. Having met the assumption of a skilful background forecast, highly efficient assimilation methods adapted to Radar and satellite field information (physical initialisation, nudging, variational approaches) resting on this assumption will be applied to further constrain the simulated state of the atmosphere to reality.

QPF of the best members after assimilation will be evaluated using remote sensing data and available surface precipitation estimates against the operational DWD forecast and forecasts based on results obtainable from different stages and alternative methods followed in the project (e.g. initial ensemble, refined ensemble without assimilation, different assimilation techniques). Meso-scale ensemble generation (Step 1) A data assimilation system that can handle the nonlinearities and non-Gaussian statistics inherent in the QPF problem will be based around an ensemble of possible forecasts. This ensemble will need to take account of several sources of uncertainty, as described in section 2.1. An important conceptual simulation described in 2.1 is the separation of time and often space scales between the large-scale synoptic or meso-scale flow and the small-scale variability in the precipitation, whether convective or embedded in frontal zones. Since the large-scale flow is to a great degree imposed on a limited area by the boundaries, the first priority is to vary the boundary conditions. We propose to use the COSMO-LEPS system, described briefly in 2.1, to provide sets of boundary conditions for the case studies. Each will form the basis of a sub-ensemble, where the individual members are generated by adding small-scale variability through changes in the initial conditions or model physics. The number of sub-ensembles will be chosen according to the degree of clustering of the ECMWF ensemble members, but experience with the COSMO-LEPS system suggests that the number will not need to be much larger than the operational choice of five. Small scale variability will also provide an important source of ensemble spread. Details unresolved by the observing system and small scale instabilities within the model domain will rapidly lead to large forecast uncertainties. As discussed earlier, a simple method that gives some idea of the rapidly growing perturbations is the error-breeding method. This will be implemented in this project. However, since the small-scale instabilities will rapidly saturate, the fast-growing modes at the initial time may not be the only relevant instabilities at the forecast time. Therefore, a simple method for adding random variability will be implemented first as a reference. To obtain a first estimate of the influence of model error two meso-scale models will be employed, using the same initial and boundary data from the ECMWF ensemble system: the German weather service's local model (LM), and the Pen State/NCAR meso-scale meteorological model 5 (MM5).

In addition, the models will be tested in various configurations with different choices of parameterisations. The non-hydrostatic Lokal-Modell (LM) (Doms and Schättler 1998; Saito et al. 1998) has been the operational short range weather forecasting tool at (DWD) since December 1999. In the operational configuration, with a horizontal mesh size of 7 km on a 325x325 grid, the model domain encompasses all of Central Europe. The resolution will be increased in the near future to 2.8 km. The LM has a generalized terrain-following vertical coordinate, which divides the atmosphere into 35 layers from the bottom up to 20 hPa. The prognostic model variables are the wind vector, temperature, pressure perturbation, specific humidity, and cloud liquid water, while the geopotential height, rain and snow flux are currently diagnostic variables. The model physics include a level-2 turbulence parameterization, a delta-2-stream radiation scheme, and a multi-layer soil model. The model includes a grid-scale cloud and precipitation scheme as well as a parameterization of moist convection (Tiedtke 1989). The LM distinguishes between water in clouds and precipitation. To parameterize the conversion terms, cloud water is treated as a bulk phase without spectral distribution, whereas size distribution functions are specified for rain and snow. Experimentally a prognostic ice phase and the Kain and Fritsch scheme (Kain and Fritsch,1990; Kain, 2002) are implemented. The LM has not yet been widely tested and validated on the scale of 1 km, yet plans are underway in COSMO (LM COnsortium for Small-scale MOdelling) to tackle some of the current limitations (e.g. 3D turbulence, prognostic treatment of precipitation, vertical coordinate). It is therefore needful and appropriate to investigate the LM’s capability to explicitly simulate convective systems with a mesh size of the order of 1 km. The PSU/NCAR meso-scale model as well is a limited-area, non-hydrostatic or hydrostatic (Version 2 only), terrain-following sigma-coordinate model designed to simulate or predict meso-scale and regional-scale atmospheric circulation. It has been developed at Penn State and NCAR as a community meso-scale model and is continuously being improved by contributions from users at several universities and government laboratories. The Fifth-Generation NCAR/Penn State Meso-scale Model (MM5) is the latest in a series that developed from a meso-scale model used by Anthes at Penn State in the early 70's that was later documented by Anthes and Warner (1978). Since that time, it has undergone many changes designed to broaden its usage. These include (i) a multiple-nest capability, (ii) non-hydrostatic dynamics, which allows the model to be used at a few-kilometre scale, (iii) multitasking capability on shared- and distributed-memory machines, (iv) a four-dimensional data-assimilation capability, and (v) more physics options. MM5 is the main meteorological driver of the EURAD model (European Air Pollution Dispersion Model) at the University of Cologne. It has several options for the precipitation physics: 1. Cumulus parameterization schemes: Anthes-Kuo (Anthes et al. 1982); Grell (Grell et al. 1991); Kain-Fritsch (Kain and Fritsch 1990); new Kain-Fritsch including shallow convection physics (Kain 2002); Betts-Miller (Betts and Miller (1986); Arakawa-Schubert (Grell et al. 1994). 2. Resolvable-scale microphysics schemes: Removal of supersaturation; Hsie's warm rain scheme ; Dudhia's simple ice scheme (Dudhia 1989); Reisner's mixed-phase scheme (Reisner et al. (1998)); Reisner's mixed-phase scheme with graupel; NASA/Goddard microphysics with hail/graupel (Tao et al. (1993); Schultz mixed-phase scheme with graupel (Schultz 1995) The multi-model approach in combination with different physical parameterizations is superior to the single model/single parameterization approach since 1. the most effective improvement in QPF is found for the multi-modelconfiguration (Shin and

Krishnamurti 2003), 2. changes in model physics parameterizations should bring larger effects near surface than other

approaches, and 3. model physics variations act much faster than initial conditions changes (Stensrud et al. 2000)

The initial analysis of the ensemble results will focus on understanding the relative importance of the different sources of variability. The benefit of this understanding is the ability to design an optimal ensemble that captures the key aspects of the forecast distribution with the minimum computational effort. The preliminary work described in 2.2 has shown that the degree and source of forecast uncertainty will depend on the meteorological situation, in particular the ability of orography and small-scale circulations driven by the surface to determine the location of precipitation in a predictable way, even in the absence of direct observations of the incipient storm. A sequence of three case studies with differing strength of orographic forcing will therefore be considered. This work will contribute to the aims of the project by: 1. By establishing a capability for ensemble forecasting in regional modelling in the German research community: Q1. Is the observed event within the range of ensemble, for a reasonable ensemble size? 2. By exploring and quantifying the sources of uncertainty in forecasts of convective storms: Q2. How does variability associated with small scale uncertainty compare with uncertainty from larger-scale environment (boundary conditions)?

Q3. Is small-scale uncertainty in the initial conditions more important than at later times throughout the forecast?

3. By exploring the role of orography in the predictability of convective storms. Q4. Does increasingly strong orographic influence remove the uncertainty due to small-scale variability? Q5) How is an ensemble system best deployed for field experiment planning?

Ensemble-broadening (Step 2) For the provision of convective and microphysical scale-ensembles, another approach is selected: With a number of optimisation parameters of O(100), this is feasible for 1-D convective cloud modules applied in meso-scale models. With available observations, here taken from Radar and from satellites, a number of Monte Carlo techniques is devised to solve the following inversion problem: Given a sequence of Radar signals, what is the required and most probable meso-scale parameter input to best fit the Radar data? A meanwhile well established technique in geophysics is the Markov Chain Monte Carlo method, with different algorithmic implementations (Metropolis-Hastings or Gibbs algorithms, see e.g.. Mosegaard and Tarantola 1995), which is able to compose a posteriori Bayesian PDFs. Markov Chain methods exploit previous knowledge, which is useful only within the validity of some smoothness assumptions. However, with discontinuities due to phase transitions in microphysics, an alternative is the application of genetic algorithms (e.g. Whitley 1994). The Monte Carlo based generation of cloud module ensembles will use genetic algorithms, which have been applied with success in solid earth geophysics. Venema et al. (2003) introduced this method successfully for cloud statistics modelling. This approach is applied here in a Bayesian context where the aim is to sample the a posteriori PDF. The overall assimilation and prediction procedure has to synthesize the following sources of information: An initial value ensemble for the meso-scale simulation, producing an a priori PDF for the cloud module integration with subsequent Radar observation operator, and the Radar observations. With given non-Gaussian error characteristics the formal procedure adopted here is partly analogous to the Bayesian formulation taken by McKague and Evans (2002), however with greater generality to allow for the combination of meso-scale models with legacy data and temporal evolution observations of clouds by Radar and satellites. In our case, the mathematically rigorous processing of the heterogeneous information sources resorts to Bayesian estimation, which, in the present multi-step case, makes use of the concept of Bayesian networks (Neapolitan 1990).

Member selection and assimilation (Step 3) Given observations with Radar and satellites, a PDF of the cloud model ensemble, a PDF of the meso-scale simulation, a combination of these three information items is necessary. The most flexible approach for this task is the application of Bayesian networks (e.g. Pearl 1988, Dean and Wellman 1991). This will enable us to select the best members of the enhanced ensembles. The further upgrade of the best ensemble members by assimilation of Radar and satellite information will be done by a selection of methods. 1. The physical initialisation (PI) methods developed by Haase et al. (2000) for Radar and Ament

et al. (2001) for satellite cloud information will be combined and extended to columnar water vapour (total column in clear sky areas, above clouds in cloud areas) in a first step along the methodology already developed. In addition, constraints on the mass balance (see Göber et al. 2003) will be imposed for the modification of the horizontal wind field to better allow for the adaptation of PI-induced changes in the vertical wind field modifications. We plan to use this methodology as a first iteration of the PI formalism because of its simplicity and ease of application. In a second step, the evolutionary approach already used in Step 2 will be adopted to find the best conditions to initialize the observed clouds and precipitation structures. This methodology assures that the conditions enforced in the atmospheric model to simulate observed cloudiness and precipitation is automatically consistent with the atmospheric model in use.

2. Based on the NCAR MM5 adjoint model the optimal cloud inversion results will also be variationally assimilated into the nearest ensemble members of the MM5 set up. The phase discrepancies will be adjusted by the Brewster (2002) shift technique, following the Bayesian chain formalism. The resulting PDF will be evaluated later against the final observations of the convective and rainfall processes.

3. The main principle of the latent heat nudging (LHN) is to correct the model’s latent heating at each time step by an amount derived from observed and model estimated precipitation. The appropriateness of the LHN scheme to be applied on the scale of 1km needs to be shown, as it assumes that most of the latent heating takes place in the column where precipitation is observed, without specifying the vertical distribution of the former. While this seems to be a good approximation on a grid of the order of 10 km or larger, this assumption may be weaker for a 1 km grid. It is a goal of the project to document and assess the LHN scheme’s performance on the convective scale. In addition, the scheme is open to be complemented with the assimilation of other remotely-sensed data as they become available

3.2 Work schedule (Arbeitsprogramm) The proposed work is scheduled for 3 times 2 years duration. The overall outline is summarized below:

YEARS 1 AND 2 Implementation of the basic two step dual-model ensemble system and validation in an Observation System Simulation Experiment (OSSE) context (RIU, DLR, MIM, MIUB). Combination of the PI methodologies for Radar and satellite cloud information, extension to water vapour information and constraining the velocity field (MIUB). Characterization of the LHN scheme for the LM with a mesh size of order of 1 km with idealized and real case experiments, including OSSE, and assess the realism of LHN forced convective-scale precipitation systems.(MeteoSwiss).

YEARS 3 AND 4 Preliminary combination of Step 3 with Steps 1 and 2. Validation and refinement of the assimilation system with the GOP data. Implement Ensemble Kalman Filter in LM ensemble system as reference for comparison with nonlinear methods. Using feature objective alignment procedures to correct phase errors on the regional scale.

YEARS 5 AND 6 System refinement and introduction of efficiency measures for operational use. Implementation of the system on the LM 24-hour ensemble environment at DWD. General studies on predictability of precipitation with meso-scale limited area models.

WORK PACKAGES FIRST TWO YEARS Within the first two years, the following work packages are scheduled:

WP1. Design and validation of the basic ensemble system (Coord. Craig) WP1.1 LM Implementation(DLR) WP 1.1.2 Set up basic ensemble system WP 1.1.2. Characterise the small-scale variability and use it to generate sub-ensembles WP1.2 MM5 Implementation (MIM) WP 1.2.1 Set up of MM5 for use of DLR-ensemble environment WP 1.2.2 Implement and test breeding in the MM5 part of the ensemble system WP1.3 Validation (DLR/MIM) WP 1.3.1 Explore influence of orography (DLR) WP 1.3.2 Investigate sensitivity of system 1.2 to different physical parameterizations(MIM) WP 1.3.3 Investigate benefits of fine-scale modelling using nesting techniques (MIM)

WP2. Cloud Model Monte Carlo Method (Coord. Elbern) WP2.1 (RIU) Design and implementation of genetic algorithm for ensemble broadening

WP 2.1.1 Installation of the Reisner resolvable-scale microphysics scheme in a stand-alone environment WP 2.1.2 Installation and coupling of the microphysics cloud scheme with the Radar forward observation operator WP 2.1.3 Implementation of the genetic algorithm and adjustment of interfaces with the microphysics scheme WP2.1.4 Definition of the input and output selection rules WP2.1.5 Operation of the genetic algorithm WP2.2 (RIU) Implementation of the Bayesian chain formalism WP2.2.1 PDF retrieval of the meso-scale ensemble output of cloud and cloud related parameters WP2.2.2 PDF retrieval of the genetic algorithm output, conditioned to the meso-scale PDF WP2.2.3 A posteriori PDF retrieval of the genetic algorithm output, conditioned to the meso-scale PDF and the observation PDF

WP2.3 (MIUB) Design of a constrained assimilation method based on genetic algorithm

3. New data assimilation methods (Coord. Rossa) WP3.1 (MIUB) Combination of the PI-methodologies for Radar reflectivity, satellite

cloud cover and water vapour assimilation with constraints on the 3D-velocity field WP3.1.1 Merging of the PI-methodologies for precipitation, clouds, and water vapour

WP3.1.2 Implementation of constraints on the 3D wind field WP3.1.3 Validation and Improvement WP3.2 (MeteoSwiss) Latent heat nudging WP3.3 (DLR) Evaluate forecasts using image matching algorithms

DETAILED WORK PACKAGES

WP1. Design and validation of the basic ensemble system (Coord. Craig) WP1.1 LM Implementation (DLR) WP1.1.1 Set up basic ensemble system: The COSMO-LEPS system running at ECMWF to generate sets of boundary conditions for LM ensemble. Preliminary agreement has been reached with ARPA-SMR to make the system available for this and other projects. Initial testing would be carried out for a case with strong orographic influence, since preliminary work (Done 2002) shows it is likely to be more predictable and less influenced by small scale variability (to be introduced in WP1.1.2). The overall performance of the ensemble system would be evaluated by a conventional comparison with all available data (list data sources), with particular attention to Q1 above. WP1.1.2. Characterise the small-scale variability and use it to generate sub-ensembles: The small-scale variability in the initial conditions will be isolated using a high-pass filter, then quantified by correlation length and time scales, and mean amplitude. Sub-ensembles will then be generated by creating random fields with these statistical properties, which are then superimposed on the model fields. Q2 will be addressed by measuring ensemble spread associated with these and the boundary condition perturbations (WP1.1.1) using conventional methods (e.g. rms error) and image matching techniques that are more suitable to situations dominated by phase errors (WP1.3.1). Experiments will be done with variability introduced at the initial time only, and continuously throughout the forecast period (Q3). WP1.2 MM5 Implementation (MIM) WP1.2.1 Set up of MM5 for use of DLR-ensemble environment: An interface will be created to run the MM5 with the same ECMWF initial and boundary conditions chosen using the COSMO-LEPS system in WP1.1.1. Software exists to utilize data from the ECMWF deterministic model as initial and boundary conditions for MM5, and will be modified and extended to use ECMWF ensemble prediction system data. It is planned to run the MM5 model on Linux machines in Munich and also at the Munich Supercomputer Center (LRZ) or if possible on an ECMWF computer. To do the runs at LRZ/ECMWF MM5 must be installed there. When this preparatory work is completed we will create a multi-model ensemble using MM5 to conduct model runs with the initial and boundary conditions from a subset of the cases chosen for the DLR LM runs. The multi-model ensemble is expected to substantially enhance the variability. WP1.2.2 Implement and test breeding in the MM5 part of the ensemble system: We will implement breeding through the following steps: • add a small perturbation to the initial conditions of MM5; • integrate the model with both perturbed and unperturbed initial conditions; • calculate the difference of the two forecasts; • scale this difference and add this to the new initial fields.

After the first raw implementation, the breeding needs to be tested and some adaptations will we undertaken if necessary. WP1.3 Validation (DLR/MIM) WP1.3.1 Explore influence of orography (DLR): Three case studies will be examined in order answer Q4 above: Case A) Strong orography (e.g. VERTIKATOR 9.7.02 in Alpine Vorland) Case B) Moderate orography (e.g. VERTIKATOR Schwarzwald case or other Mittelgebirge) Case C) Weak orography (oceanic or case from UK CSIP experiment) A particular emphasis will be placed on the study of Case B), since this will be chosen to resemble the location of the planned IOP within 1167. Requirements for a real-time system to aid planning of the experiment will be identified, including, domain, resolution, number of ensemble members based on different boundary conditions and different realisations of small-scale variability (Q5). WP1.3.2 Investigate sensitivity of system 1.2 to different physical parameterizations (MIM): For selected ensemble members, runs will be performed with different parameterizations for cumulus convection, PBL, and different parameters, with the following aims: • Perform a thorough sensitivity analysis of case studies starting with the strong orography case

(Case A above) by selecting different parameterizations of cumulus convection (e.g. Grell and Kain-Fritsch), boundary layer (e.g. Gayno-Seaman and Blackadar) and using different values for soil moisture availability.

• Investigate the possible gain in variability hereby and compare this sensitivity with the influence of the different boundary and initial conditions

• By comparing the runs with different parameterizations gain an indication of whether the breeding approach could be sensitive to the choice of parameterizations.

• Compare the amplitude of the fields resulting from different parameterizations with that from the initial condition small-scale variability obtained from 1.1.2.

WP1.3.3 Investigate benefits of fine-scale modelling using nesting techniques (MIM): Investigate the feasibility and the expected benefits of finer-scale modelling in the context of the ensemble system by applying two-way interactive or one-way nesting, especially in cases with expected heavy precipitation or with strong orographic influence. Results can be applied to defining a 'refinement on demand' for sub-domains for an ensemble forecast. This work should be started in the first phase using Case A.

WP2 Cloud Model Monte Carlo Method In this work package the probability density distributions are compiled to be jointly evaluated in a Bayesian chain formalism. The course of logics is that from the meso-scale ensemble a PDF of the cloud module input parameters is to be retrieved, resulting in a dependent PDF for cloud “profile” parameters for Radar echo and related satellite radiances, which form the a priori PDF for the Bayesian formula. The corresponding likelihood function is then shaped by the genetic algorithm. Both are taken for the a posteriori estimation of the PDF of the cloud input parameters. These are then available to be assimilated into the respective meso-scale ensemble member with the smallest discrepancy to these parameters. This completes the three link Bayesian chain. WP2.1 Design, implementation, and operation of genetic algorithm for ensemble broadening (RIU) This work subpackage focuses on the provision of the likelihood function (PDF) of the Radar and satellite observations subject to the constraint of cloud input parameters by the non-linear Monte Carlo inversion of the cloud module. The input quantities are the prognostic parameters of the

meso-scale models at the cloud columns of interest including water contents (cloud water, precipitation water, ice, …), cloud profile parameters important for Radar and satellite radiance comparisons like thickness, cloud top height, liquid or ice water paths, effective radii, soil moisture, and stability parameters. The cloud parameters are subject to inversion by the genetic algorithm, given the Radar and satellite observations. The likelihood function is then provided to the related step in the Bayesian chain formalism in WP 2.2 WP2.1.1 Installation of the Reisner resolvable-scale microphysics scheme in a stand-alone setting This includes software engineering items like input/output code for the correspondence with the meso-scale environment, and graphical software for display of the cloud evolution, and installation of the tangent linear and adjoint version of the cloud module for later use in sensitivity tests. WP2.1.2 Installation and coupling of the microphysics cloud scheme with the Radar forward The Radar forward observation operator (Radar Simulation Model, RSM by Haase and Crewell (2000), available at MIUB) will be implemented and coupled to the cloud module, along with related software to perform observation-model discrepancies in observation space WP2.1.3 Implementation of the genetic algorithm and adjustment of interfaces with the microphysics scheme The coupled cloud module - Radar forward observation operator will be included in the genetic algorithm for population(=ensemble) generation of 100-1000 members WP2.1.4 Definition of the input and output selection rules Test runs of the genetic algorithm will be made to calibrate the genetic system by defining fitness rules for population members in terms of observation-model discrepancies and viability rules for input parameter. The latter measure excludes unlikely parameter configurations for computational efficiency. WP2.1.5 Operation of the genetic algorithm The calibrated genetic algorithm setting is applied for the case studies. It approximates the likelihood function of the Radar and satellite observations subject to the constraint of cloud input parameters. The operation is made with a temporal sequence of the Radar observations to further constrain the probabilities of admissible input parameters. WP2.2 (RIU) Implementation of the Bayesian chain formalism We combine the a priori PDFs of the meso-scale simulation and the genetic algorithm to form a posteriori estimates of the cloud input parameters and meso-scale conditions WP2.2.1 PDF retrieval of the meso-scale ensemble output of cloud and cloud related parameters The meso-scale ensemble parameters as restricted to the cloud development areas of interest span the probability density regions in cloud input parameter space, which serve as a priori PDF for the genetic algorithm. WP2.2.2 PDF retrieval of the genetic algorithm output, conditioned to the meso-scale PDF Data extracted from the ensembles are subject to an empirical orthogonal function expansion, to enforce simulated smoothness in the vertical and to reduce the optimisation space as a further constraint of the genetic algorithm.

WP2.2.3 A posteriori PDF retrieval of the genetic algorithm output, conditioned to the meso-scale PDF and the observation PDF The Bayesian product of the a posteriori PDF retrieval of the genetic algorithm output with a priori meso-scale PDF of input parameters form the a posteriori distribution of the related cloud areas in the meso-scale ensemble. Two cases may occur to define a good fit between meso-scale ensemble members and well fitted population members: first, the location of the convective cells are well simulated but with poor fit to the observed cloud evolution, or vice versa. With a suitably defined cost function an optimised small sub-ensemble can be created, which assimilates the cloud parameters and shifts toward the convective system toward the observed location. WP 2.3 Design of a constrained assimilation method (MIUB, RIU) In WP 3.1 we will combine the PI-methodologies for Radar (as developed by Haase et al. 2000, Haase 2002) and satellite cloud cover (as developed by Ament et al. 2001) and extend the method by water vapour information and constraints on the 3D wind field. In that approach, changes are made to the initial field, to make it more coherent with the measurements, using prescribed steps that maintain the model balance. A more general approach, which would also be in general independent of the NWP model used, would be to make a constrained search for thermodynamic fields that are close to the model fields, are coherent with the measurements (as was done in Venema et al. 2003), and maintain the model balance. Basically, we do not program the algorithm; instead we program, what the algorithm should do. A genetic search algorithm (Holland 1976) would be ideal for this task, as they are robust and flexible. Genetic algorithms (GAs) are able to handle even very badly behaved cost functions (non-linear, noisy, discontinuous; Back 1996); they do not require information on its shape (or its derivative). An advantage of GAs above other global search methods is that you store information on previously successful strategies in its genes; the search does not have to start from scratch each time. This application may be a showcase for the usefulness of diploid genes (Goldberg 1989), which store information in the recessive genes on strategies that were useful once (e.g. a convective case), but not needed for this case (e.g. a dreary rainy day). The implementation of the constrained search in the forecast system will be done in the 3rd and 4th year. We expect to learn from this method better and physically more consistent ways (consistent with the cloud model in use) of doing PI assimilation. For efficiency, this better way will have to be implemented directly; the constrained approach will be too slow for an operational environment. In this work package, we want to investigate if it is possible to describe the constraints in an efficient manner, using what we have learned in WP2.1, were a GA is used to broaden the ensemble. WP3.1 Combination of the PI-methodologies for Radar reflectivity, satellite cloud cover and water vapour assimilation (MIUB) This work package will consist of 3 sub-packages WP3.1.1 Merging of the PI-methodologies for precipitation, clouds, and water vapour In this work package we will combine the PI-methodologies for Radar (Haase et al. 2000, Haase 2002) and satellite cloud cover (Ament et al. 2001) subject to additional cloud and water vapour information available from MSG and other meteorological satellites. Ament made minimal changes to improve the analysis with respect to the cloud cover and cloud top height measured by Meteosat/MSG. The cloud water profiles and cloud bases were taken from the model analysis. The new scheme developed here will put more weight on the observations. In the rain-PI of Haase the rain rate measurements are used to update the vertical wind field between the cloud boundaries calculated from the model analysis. To merge the two approaches the measurements from Radar and Meteosat/MSG will have to be optimally merged and inconsistencies removed. We expect a substantial improvement on this merging, when high-quality cloud parameter retrievals from the

proposed QUEST project becomes available. The merging will, amongst others, require temporal interpolation of the satellite measurements to the Radar data (DWD-network). To make the improvements of the analysis more durable the overall structure of the full analysis will have to be improved. Therefore, we will in addition incorporate measurements of humidity (in the former approaches humidity has been modelled in the simplified PI-cloud and precipitation schemes) into the PI methodology. WP3.1.2 Implementation of constraints on the 3D wind field In a final step we need to make the overall dynamics (3D wind field) of the PI-analysis better correspond to the measured hydrological fields. One important goal is to minimize the occurrence of gravity waves generated by the imposed vertical wind field, which spoil part of the Radar information in the Haase-PI approach. A first option will be to also change the horizontal wind field distribution consistent with the changes in the vertical wind field subject to a constraint on the mass balance as used e.g. by Göber et al. (2003). However, in the end a scale-sensitive approach will be called for: at large scales the discrepancies are most likely due to errors of the average vertical wind (large scale subsidence or the opposite), the remaining errors at smaller scales can then be improved upon by updating the humidity fields. WP3.1.3 Validation and Improvement With the extended and improved PI-system set up, we will make extensive tests, first with artificial fields, then with the case studies provided by the ensemble work packages. This step will hint then at obvious short-comings and give rise to the final improvement of the methodology. WP3.2 Latent heat nudging (MeteoSwiss) This work package has three main goals: WP3.2.1 Prepare the LM and the LHN for 1 km grid, including Swiss and German Radar data for this resolution. The questions to be answered are: 1. Is convection sufficiently realistic in the LM with this mesh size? 2. Are the assumptions of the LHN scheme still acceptable, i.e. that latent heating takes place

where precipitation is observed? What is the impact on forced convective systems? WP3.2.2 Perform idealized and real-case simulation to characterize the LHN scheme, and include other remotely-sensed data as they become available (cloud and humidity analyses). Questions to be answered are: 3. How realistic are the LHN-forced precipitation system in the simulation? 4. What are the key sensitivities of the LHN scheme, particularly with respect to the success of

the subsequent free forecast? How important is the vertical structure of the forced LHN? 5. Is the LM QPF using a grid O(1km) superior to the now operational version with a 7 km grid

(links to Wernli, Uni Mainz, novel verification measures for QPF)? 6. Is the LM, along with the LHN scheme, able to reproduce the diurnal cycle of precipitation;

particularly in connection with topography (e.g. is the interaction of the Valley wind system with convection mapped in the model)?

7. How does LHN compare with other assimilation methods for the convective scale like the PI in 3.1 (also potential links to S. Bauer, Uni Hohenheim)?

WP3.2.3 Investigate ensemble analyses and short-range forecasts produced by DLR by LHN. 8. How substantial is the spread on the meso-β-scale going to be after 6, 12, 24 hours of forecast

in terms of the key parameter influencing convection? 9. Do these ensembles provide a large enough variability in the key parameters identified in 2 as

to result in significant differences in the LHN analysis and the subsequent forecast? 10. Can LHN nudging be used to select the best members in an ensemble?

11. How does a short-range ensemble produced with the LM nudging analysis (EnNUD) on a grid of O(10km) with a set of perturbed observations compare to the ensemble as provided by DLR?

12. Can the key processes that lead to large error growth be identified from these ensembles? The particular benefit of the MeteoSwiss contribution is its complementary focus on storm-scale LM and LHN studies. A thorough characterization of the LHN scheme, not studied by other proposing groups, at this scale is the basis for a comparison with other, more sophisticated but much more expensive methods, as for instance 4DVar. The close collaboration within this bundle is particularly helpful as the LHN scheme can be investigated in the framework of meso-scale ensembles. WP3.3: Evaluate forecasts using image matching algorithms (DLR) Generation of synthetic Radar and satellite imagery and comparison with observed remote-sensing data allows for a validation of the forecast quality and the accuracy of certain ensemble members. Using image matching techniques the 'best' ('worst') members of the ensemble can be objectively identified by analysing displacement vectors of key meteorological features. Generation of synthetic Radar and satellite imagery and comparison with observed remote-sensing data allows for a validation of the forecast quality and the accuracy of certain ensemble members. Using image matching techniques the 'best' ('worst') members of the ensemble can be objectively identified by analysing displacement vectors of key meteorological features. The work will be accomplished in three stages: 1. Set up generation of synthetic satellite images of the LM ensemble simulations. 2. Adaptation and application of 'pyramidal image matching' technique to determine displacement

errors in ensemble environment. 3. Specification of error measures to quantify the forecast quality objectively

TIME PLAN To coordinate the research in this project, we have planned 4 general meetings: 2nd month: kick-off in Bonn (details of cooperation, agreement of case studies) 9th month: Progress meeting (focus ensemble quality and new assimilation methods) 16th month: Progress meeting (focus validation) 22nd month: Final meeting (finalising papers, preparation of 2nd phase) WP3.1.1 Merging of the PI-methodologies for precipitation, clouds, and water vapour

Time period Tasks 1st year 2nd year

WP1 Design and validation of the basic ensemble system Set up basic ensemble system (1.1.1) Add variability to initial conditions (1.1.2) Validate output for Case A (1.1.2, 1.1.3) Install MM5 in ensemble environment (WP1.2.1) Run MM5 in ensemble system Install breeding (WP1.2.2) Run Cases B and C and quantify orographic influence (1.3.1) Sensitivity to physical parameterizations (WP1.3.2) Investigate benefits of fine-scale modelling (WP1.3.3) WP2 Cloud Model Monte Carlo Methods Design & implementation of GA for ensemble broadening (WP2.1) Reisner scheme in a stand-alone environment (WP2.1.1) Installation & coupling cloud scheme with Radar operator (WP2.1.2)

Implementation of the genetic algorithm (WP2.1.3) Definition of the input and output selection rules (WP2.1.4) Operation of the genetic algorithm (WP2.1.5) Implementation of the Bayesian chain formalism (WP2.1.6) Design of a constrained assimilation method (2.2) WP3 New data assimilation methods Combination of the PI-methodologies (3.1) Merging PI-methods (precipitation, clouds, and vapour) (3.1.1) Implementation of constraints on the 3D wind field (3.1.2) Validation and improvement (3.1.3) High resolution Latent heat nudging (3.2) Prepare the LM and the LHN for a mesh size of 1km grid (3.2.1) Perform idealized and real-case simulation (3.2.2) Investigate ensemble analyses and short-range forecasts (3.2.3) Evaluate forecasts using displacement vectors (3.3) Generate synthetic satellite images and apply image matching Specify error measures

Table 3.1 Time plan with all work packages; the project meetings Are indicated by the red line.

COOPERATION AND COORDINATION C. Simmer (MIUB) will be responsible for organizing the cooperation between the partners along the lines of the overall project goals. In close interaction with the SPP-coordination team and W. Wergen (DWD) C. Simmer will evaluate the prospect of the developed tools within the DWD environment and promote their timely testing in the quasi-operational test system. He will further the close cooperation of the project partners with components 1 (Assimilation and Models), 2 (LM-

Short-range Forecasting), and 3 (Nowcasting and Model Interpretation) of the “Aktionsprogramm 2003” of DWD (Coordinator Dr. Kurz). This cooperation will integrate the system to be developed in the 24h high-resolution ensemble planned by DWD. Pending funding the coordination efforts will also encompass the interaction with other relevant projects within the SPP. Of particular relevance for this project are the planned proposals QUEST (Crewell et al., MIM) for data and assimilation methodology and prob-qpf (Hense et al., MIUB) for ensemble strategies, modelling of PDFs, and probabilistic precipitation forecast. All cooperation partners will work on the same case studies, with the same Radar, and satellite data, as well as the same ensembles from DLR/MIM in the first project phase. The measurement data will be made available by an FTP-server in Bonn. The satellite and Radar data will be provided by DWD. If the QUEST project is funded, they will provide high quality satellite data and allow us the use of their FTP-server at the Free University of Berlin (Prof. Fischer). Especially the high-quality cloud information has a high potential impact on the PI and NG-schemes developed and tested in WP3 because this data provides substantial higher constraints on the analysis than the much simpler satellite cloud analysis planned to use by MIUB and MeteoSwiss. The products from the QUEST project are summarized in Table 3.2. Table 3.2 Satellite instruments and products for water vapour and cloud properties

Resolution Product* Instrument Platform Spatial (km) Temporal Day Night

SEVIRI MSG 4 15 min 1, 2, 3, 4, 5, 6 1, 3, 6 MERIS Envisat 0,25 1 / day 1, 2, 3, 6 6 (land)

MODIS TERRA, AQUA 1 2-3 / day,

2-3 / night 1, 2, 3, 4, 5, 6 1, 3, *Product codes: 1 = cloud mask, 2 = cloud optical thickness, 3 = cloud top pressure, 4 = cloud droplet effective radius, 5 = liquid water path, 6 = vertical integrated water vapour under cloud free conditions.

Ensembles The global scales ensembles will be made by the DLR in cooperation with the ECMWF. The breeding scheme written by MIM for MM5 will be written for easy porting to the LM by the DLR. MIM plans to run the MM5 model on Linux machines in Munich and at the Munich Supercomputer Center (LRZ). The multi-model ensembles using the LM and MM5 are, of course, made in close cooperation of MIM and DLR.

New assimilation methods

The RIU will use an evolutionary algorithm to broaden the ensembles provided by DLR. Of course, the experience with evolutionary search algorithms of Dr. Venema of MIUB will very useful here to get a quick start, as the implementation of genetic algorithms is still somewhat of an art. Later in the project, MIUB will benefit of the experience of RIU in evolutionary search algorithms in the context of hydrological fields. As the three assimilation methods (evolutionary broadening, PI and LHN) are tested on the same cases, a good comparison of the benefits of the various methods will be possible. DLR will evaluate the quality of these assimilation methods with their displacement method.

DWD The DWD will help and advice the project with their long experience in assimilation and take part in the project meetings. The DWD-team consists of Dr. Erdmann Heise for ensemble physics, Dr. Volker Renner for interpretation and Werner Wergen for assimilation. Furthermore, the DWD will

provide observational data, model fields and the use of the DWD LM-development environment (‘Experimentier System’).

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