Format for NMM International project proposals 1. Title of the proposed project :

Improving Monsoon Predictions with a Coupled Ensemble Kalman Filter Data Assimilation System

2. Brief information about Principal Investigator (PI) and Co-PI(s) :

PI: Name: Prof. Eugenia Kalnay Date of birth: 1 October 1942 Institution: University of Maryland Official website: www.atmos.umd.edu/~ekalnay E-mail Id: ekalnay@atmos.umd.edu

Qualification: Ph.D. (Meteorology) at MIT under Jule G. Charney (1971). Distinguished University Professor, former Director of NCEP’s Environmental Modeling Center, Member of the National Academy of Engineering, WMO 2009 IMO Prize and many other prizes, author of Atmospheric Modeling, Data Assimilation and Predictability (Cambridge Univ. Press, translated to Chinese and Korean), first author of the most cited paper in all Geosciences about the NCEP-NCAR Reanalysis, Kalnay et al., 1996.

Co - PI (1): Name: Prof. Takemasa Miyoshi Date of birth: April 6, 1977 Institution: University of Maryland Official website: http://www.atmos.umd.edu/~miyoshi/ E-mail Id: miyoshi@atmos.umd.edu

Qualification: Ph.D. (Meteorology) from University of Maryland (2005) Co - PI (2): Name: Dr. C. Gnanaseelan Date of birth: 1 June 1966 Institution: Indian Institute of Tropical Meteorology Official website: www.tropmet.res.in E-mail Id: seelan@tropmet.res.in

Qualification: Ph.D. (Mathematics) Indian Institute of Technology, Kharagpur (1998) Co - PI (3): Name: Prof. James A. Carton Date of birth: July 10, 1954 Institution: University of Maryland Official website: www.atmos.umd.edu/~carton

E-mail Id: Carton@atmos.umd.edu Qualification: Princeton University

Co - I (1):

Name: Stephen G. Penny Date of birth: 30-May-1980 Institution: University of Maryland, College Park E-mail Id: Steve.Penny@noaa.gov Official website address: www.aosc.umd.edu/~spenny Qualification: University of Maryland Ph.D.(Oceanic and Atmospheric Science, 2011)

Co-I (2): Name: Dr. S. A. Rao

Institution: Indian Institute of Tropical Meteorology Official website: www.tropmet.res.in E-mail ID: surya@tropmet.res.in Qualification:

3. Project Summary (1 page) : (a) Intellectual merits of the proposed work

We propose to implement a coupled data assimilation system based on an advanced Ensemble Kalman Filter (EnKF) developed at UMD, the Local Ensemble Transform Kalman Filter (LETKF, Hunt et al., 2007) that is 100% parallel and algorithmically very efficient. Co-PI Prof. Miyoshi has developed a widely used LETKF code with MPI tested on several platforms (available on code.google.com/p/miyoshi/). The LETKF algorithm has been followed by the development of important additional methodologies such as Running in Place (RIP) that extracts more information from the observations, Ensemble Forecast Sensitivity to Observations (EFSO), that determines the forecast impact of observations, Adaptive Inflation (AI), estimation of observational errors, efficient assimilation of precipitation, and estimation of model systematic errors. These advantages led the National Weather Services of Germany, Italy, Brazil and Argentina, to adopt the LETKF for their operational data assimilation. Other countries are also testing the LETKF (e.g., Japan, Taiwan, Korea). Co-I Dr. Steve Penny carried out a 7-year ocean reanalysis coupling the ocean model MOM-2 to the LETKF. The results were so good compared to the widely used Simple Ocean Data Assimilation that he was invited by NCEP to couple the LETKF with the MOM-4 for operational use. Experiments with a simple coupled ocean-atmosphere model (Singleton, 2011) show that the best forecasting results for weather and climate timescales are obtained with advanced coupled data assimilation, using either 4D-Var or EnKF. LETKF is efficient and simple to couple, but coupled assimilation using 4D-Var is essentially unfeasible because it is computationally too expensive and complicated. For the real monsoon system, improving weather and climate prediction also requires the use of advanced coupled atmosphere-land-ocean data assimilation, which is made possible by the use of an efficient EnKF like the LETKF. (b) Broader impacts of the proposed work

We propose to train at UMD two advanced students and one postdoc from India, and one UMD student working on efficient assimilation of rain in the coupling the LETKF with the atmosphere and ocean components of the NCEP CFS V2 in collaboration with Co-PI Dr. Gnanaseelan from IITM and Co-I Dr. Rao. This will involve coupling the atmospheric component of the CFS (under Co-PI Miyoshi) and the MOM-4 (under Co-PI Prof. Carton and Co-I Dr. Steve Penny). After these are completed, they will be coupled and initially tested at relatively low resolution using cluster computers. They will be ported to an Indian super-computer for testing high resolution forecasting experiments. Such advanced coupled ocean atmosphere-land data assimilation should significantly improve the skill of weather and climate monsoon predictions. The students will write a doctoral thesis during this research, and like the postdocs, will spend part of the year at IITM. Since the LETKF and models are already available, we expect that this project could be carried out in 3 years. In addition, the use of powerful new techniques such as effective assimilation of precipitation and estimation of model errors should contribute significantly to the improvement of monsoon forecasts both for weather and for climate, thus bringing to India the most advanced methodology for data assimilation and model improvement.

Project Description:

1. Research Objectives The main research objective is “to develop a coupled ocean-land-atmosphere data assimilation based on the advanced and widely used LETKF developed by our team at the University of Maryland (UMD) coupled with the NCEP Coupled Forecast System Version 2”. Indian students and postdocs will carry out this research under the direction of PI and Co-PIs Kalnay, Miyoshi and Carton, and Co-I Penny, in collaboration with IITM scientists, Co-PI Gnanaseelan and Co-I Rao. An additional UMD student (Guo-Yuan Lien) will contribute to this project by developing an effective approach to assimilate rain. The system we propose will be based on two major codes already available: 1) the LETKF of Hunt et al. (2007) with a widely used code developed by Co-PI Miyoshi (code.google.com/p/miyoshi/) that has been coupled to many models, from operational atmospheric and ocean models, Chesapeake Bay model, a model of Mars, the SPEEDY GCM, to simple quasi-geostrophic and toy models (see Miyoshi’s references for more detail). 2) The CFS V2 developed at NCEP, including the global spectral model and the MOM-4 ocean model. We note that coupling the LETKF to a model is relatively straightforward because the LETKF is like a black box with inputs and outputs (Fig. 1), and (unlike 4D-Var), when the model is updated, only those inputs and outputs need to be modified. The system that we propose has a number of additional advantages: • It allows the implementation of Running in Place (RIP, Kalnay and Yang, 2010,Yang

et al., 2012), allowing the extraction of more information from the observations, accelerating spin-up, and reducing the problems of non-Gaussianity in the ensemble. RIP was used by Co-I Penny in a 7-year global ocean reanalysis (Penny et al, 2012). The forecasts from LETKF-RIP have ocean temperatures and salinities that are much closer to the observations than the Simple Ocean Data Assimilation (SODA, Carton et al., 2000a, 2000b, 2007), or even the LETKF with IAU (Incremental Analysis Update, Bloom et al., 1996) (Figure 2).

(Start with initial ensemble)

LETKF Observation operator

Model

ensemble analyses

ensemble forecasts

ensemble “observations”

Observations Figure 1: Schematic flow chart of the LETKF showing it as a “black box” independent of the details of the model

• It allows the implementation of the Ensemble Forecast Sensitivity to Observations (EFSO, Liu and Kalnay, 2008, Li et al. 2010, Kalnay et al., 2012). Ota et al., (2013) have used this system to assess the relative impact of all the different types of observations on the 24 hour forecast (Fig. 3). Note that these observations can be further subdivided into regions, so that is will provide a powerful tool to estimate whether observations are properly assimilated.

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Figure 2: RMS differences between observations and both forecast B (background) and Analysis A for a 7-year reanalysis. The methods of data assimilation compared are 1) a Free Run (grey), 2) SODA (green), 3) LETKF with IAU (black), and 4) LETKF with RIP (blue). Top: temperature Bottom: Salinity (Penny, 2011)

• It allows the effective assimilation of observed precipitation, with very encouraging

positive impacts. Unlike methods used so far for the assimilation of precipitation, which modify the temperature and moisture fields to force the model to rain as observed, our system modifies the potential vorticity. As a result, the system “remembers” the changes introduced by the assimilation of rain and improves significantly the medium range forecasts (Fig. 4). Guo-Yuan Lien, also supported by this project, is carrying out this work as a regular UMD student. See section 2.2.1 for more details.

Figure 3: Estimation of the contribution of each type of observation on the reduction of moist total energy of the 24hr forecast averaged from 12Z 21 October 2010 to 06Z 28 October 2010, using the ensemble forecast sensitivity to observations of Kalnay et al., 2012. Courtesy of Yoichiro Ota, presented at the 5th WMO Workshop on Forecast Sensitivity to Observations, Sedona, AZ, May 2012.

Figure 4: Left: Global analysis errors and Right: average 0-5 day forecast errors in the NH, SH, tropics and the globe for the three basic experiments: 1) Only Raobs (black); 2) PP-transform, where all variables are modified by the assimilation of precipitation (blue) and 3) PP-transform, but with only moisture analyzed (orange).

Negative indicates reduction of the moist total energy of the 24 hr forecast error due to all the observations of the given type.

• The Adaptive Inflation (AI) method developed by Co-PI Miyoshi (2011) has been shown to increase the accuracy of the analysis and forecasts, and avoids the painful and costly “hand-tuning” that was necessary before.

• Li et al. (2009) showed that it is possible to estimate observation error statistics simultaneously with the adaptive inflation.

• The LETKF data assimilation offers an excellent platform to estimate model errors (Danforth et al. 2007), both constant (bias), seasonal varying, and state dependent errors (like during the presence of El Niño). Danforth and Kalnay (2009) showed that “correcting” the model equations with these empirically determined errors substantially reduced systematic errors in the medium range forecasts. This application is a powerful tool that will be very useful in improving monsoon forecasts.

1.1 Intellectual merit of the proposed work We propose to implement a coupled data assimilation system based on an advanced Ensemble Kalman Filter (EnKF) developed at UMD, the Local Ensemble Transform Kalman Filter (LETKF, Hunt et al., 2007) that is 100% parallel and algorithmically very efficient. Co-PI Prof. Miyoshi has developed a widely used LETKF code tested on several platforms (available on code.google.com/p/miyoshi). Moreover, the LETKF algorithm has made possible the development of important additional methodologies such as Running in Place (RIP) that extracts more information from the observations, Ensemble Forecast Sensitivity to Observations (EFSO), that determines the forecast impact of observations, Adaptive Inflation (AI), estimation of observational errors, efficient assimilation of precipitation, and estimation of model systematic errors. These advantages led the National Weather Services of Germany, Italy, Brazil and Argentina, to adopt the LETKF for their operational data assimilation. Other countries are also testing the LETKF (e.g., Japan, Taiwan, Korea). Co-I Dr. Steve Penny carried out a 7-year ocean reanalysis coupling the ocean model MOM-2 to the LETKF. The results were so good compared to the widely used Simple Ocean Data Assimilation (based on OI/3D-Var) that he was invited to couple the LETKF with the MOM-4 at NCEP for operational use. Experiments with a simple coupled ocean-atmosphere model (Singleton (2011) show that the best forecasting results for weather and climate timescales are obtained with advanced coupled data assimilation, using either 4D-Var or EnKF. However, whereas LETKF is efficient and simple to couple, coupled assimilation using 4D-Var is essentially unfeasible because it is computationally too expensive and extremely complicated. Similarly, improving weather and climate prediction of the monsoon should require the use of advanced coupled atmosphere-land-ocean data assimilation, a feat made possible by the use of an efficient EnKF like the LETKF. 1.2 Broader Impact of proposed work We propose to train at UMD two advanced students and two postdocs from India to couple the LETKF with the atmosphere and ocean components of the NCEP CFS V2 in collaboration with Co-PI Dr. Gnanaseelan and Co-I Dr. Rao from IITM. This will

involve coupling the atmospheric component of the CFS (under Co-PI Miyoshi) and the MOM-4 (under Co-PI Prof. Carton and Co-I Dr. Steve Penny). After these are completed, they will be coupled and initially tested at relatively low resolution in local supercomputers. They will be ported to an Indian super computer for high resolution forecasting experiments. Such advanced coupled ocean atmosphere-land data assimilation should significantly improve the skill of weather and climate monsoon predictions. The students will write a doctoral thesis during this research, and like the postdocs, will spend part of the year at IITM. Since the LETKF and models are already available, and the coupling is well under development, we expect that this project can be carried out in 3 years. In addition, the use of powerful new techniques such as effective assimilation of precipitation (Figure 4, Lien et al. 2012) and estimation of model errors Danforth and Kalnay (2007) should contribute significantly to the improvement of monsoon forecasts both for weather and for climate, thus bringing to India the most advanced methodology for data assimilation and model improvement. 2. Technical Section

We discuss in section 2.1 the current NCEP approach to the CFS coupling as carried out ion the CFS Reanalysis, and then our plans for an advanced LETKF data assimilation for the atmospheric model (section 2.2), for the ocean model (section 2.3), preliminary plans for an optimal coupling of the two systems (section 2.4) and other advanced research that can contribute to model improvement in section 2.5.. 2.1 Brief introduction to the CFS and the coupling used in the CFSR (from Saha et al., 2010) Models: The atmospheric component of the CFS is the Global Forecast System (GFS) of NCEP, run operationally at a resolution of T574/L64, and at T382/L64 in the Coupled Forecast System Reanalysis (CFSR). The ocean component is MOM4 (Griffies et al., 2004) at 1/4o zonal resolution, with a rotated bipolar grid north of 65oN that places two poles over land and avoids the singularity in the North Pole. There are 40 layers in the vertical, 27 of them in the top 400m. Data assimilation: Both the atmospheric and the oceanic data assimilation are performed every 6 hrs. The atmospheric component is based on the GSI, implemented in the GFS in May 2007 (Kleist et al., 2009) with some additions. In 2009, when the original CFSR was completed, the operational GSI was adopted for use in the CFSR run in real time, with minor changes. The ocean data assimilation is the Global Ocean Data Assimilation System (GODAS, Behringer et al., 1998, 2007). It is based on the 3D-Var scheme of Derber and Rosati (1989), and assimilates temperature and salinity profiles from the last 10 days, giving less weight to older observations. The top layer of the model (5m) is relaxed to the daily mean sea surface temperature (SST) and salinity (SSS) fields obtained using Optimal Interpolation, interpolated to the ocean model grid. Coupling: The GFS and MOM4 run independently but they exchange data through a Coupler that receives and sends back data from both models. The coupler receives accumulated surface fluxes from the GFS (derived from the atmospheric variables and

the SST) and sends them to the MOM-4, while receiving data from the ocean and ice model and sending it to the CFS. Over land, in the CFSR, the model precipitation is replaced by observed precipitation, which increases the accuracy of the hydrological cycle, but cannot be done in a forecasting mode. 2.2 Coupling of the LETKF to the CFS Atmospheric Model (GFS) Co-PI Miyoshi will lead this important component of the project. He has already ported the operational GFS model at T62/L64 resolution to a UMD cluster computer. Prof. Miyoshi has already successfully led the coupling of the LETKF to many models, from toy models (Lorenz, 1996), through intermediate GCMs (like the SPEEDY model, Molteni, 2003, Kucharski 2010) and the GFDL Mars GCM, to the state of the art WRF model. The minimal number of ensemble members required is about 20, which will stretch our computational resources. The LETKF-GFS will include several novel algorithms: 1) adaptive inflation (Miyoshi, 2011) that has improved results in a number of systems; Running in Place (RIP, Kalnay and Yang, 2010, Yang et al. 2012) that allows extracting more information from observations, estimation of observation errors (Li et al., 2009), and most recently, effective assimilation of precipitation (Lien et al., 2012). 2.2.1 Effective assimilation of precipitation in the GFS We developed a new approach to do global data assimilation of observations of precipitation that for the first time could significantly improve numerical weather beyond a few hours using TRMM (and future GPM) observations. The method is designed to modify the model potential vorticity and therefore makes possible for the model to “remember” beyond several hours the changes made by the precipitation observations. Current nudging and variational methods used to assimilate precipitation modify the model moisture and temperature profiles, in order to either enhance or reduce short-term precipitation according to the model parameterization of precipitation. It is generally accepted that this approach has not been successful beyond 6-24 hours (e.g., Errico et al., 2007, Bauer et al. 2011). We introduced three major changes in the assimilation of precipitation leading to the success of the new method: (1) The use of an Ensemble Kalman Filter (EnKF) that automatically modifies all model variables, and hence potential vorticity, leading to better analysis and forecasts, and avoids linearization of the precipitation processes. (2) A Gaussian transformation of precipitation that allows EnKF to efficiently assimilate both “precipitation” and “no precipitation” observations. (3) Requiring at least one ensemble member to precipitate at a grid point in order to assimilate precipitation information, which avoids a singularity leading to poor results. The results obtained in a simulation mode with this effective assimilation of precipitation (EAP) method are very encouraging (Figure 4), and student Lien has started working with real TMPA/TRMM estimated precipitation (a combination of estimation of rain from several microwave and infrared sensors and TRMM). Figure 5a shows a sample of TMPA precipitation on 1 October 2009 00Z, at a high resolution of 0.25o in space and 3 hr, and 5b the climatological probability of no rain in the TMPA/TRMM for the summer season. The Gaussian preprocessing of the precipitation is thus underway.

Once the GFS is coupled with the LETKF, we will perform data assimilation and medium range weather forecasts experiments (2010-2012 period) to test whether, as expected, effective assimilation of precipitation improves the tropical forecasts. 2.3 LETKF coupled to Ocean Model As indicated before, Co-I Steve Penny successfully completed coupling the LETKF with the MOM-2 global ocean model (Penny, 2011), Penny et al., 2012). He compared four types of runs: 1) a free run without data assimilation, used as a baseline. 2) The Simple Ocean Data Assimilation (essentially an Optimal Interpolation/3D-Var system designed after Derber and Rosati, 1989) which has been used to create long reanalyses that have been very widely used (Carton et al., 2000, 2007). 3) The LETKF coupled with Bloom et al. Incremental Analysis Update that allowed the use monthly assimilation windows as in SODA. 4) The LETKF coupled with Running in Place (Kalnay and Yang,2010, Yang et al., 2012) that allows the repeated use of observations and thus extracting more information. With the LETKF-RIP Penny was able to use assimilation windows of just 5 days, rather than one month. The results for the RMS differences between the forecast and the observations (labeled as background or B), and between the analyses and the observations (labeled as analysis or A) averaged for temperature and for salinity (Figure 2) are very striking indeed. LETKF-RIP B is almost indistinguishable from LETKF-RIPA, and far smaller than the RMS differences in the analysis for the other two methods (SODA-A, LETKF-A), which in turn are much better than their corresponding background RMS differences with observations. Dr. Penny is now working at NCEP coupling the ocean component of the CFS (namely the MOM-4) with the LETKF. He plans to test the use of RIP combined with a coarse resolution LETKF with interpolation of the LETKF weights, since Yang et al. (2010)

Fig. 5b: Probability of no rain of the 3 hourly precipitation based on 14 years of TMPA/TRMM observations.

showed that the weights have much larger scales than the analysis fields, and even more so than the analysis increments, and that such a coarse analysis actually is more accurate than the full resolution analysis since it reduces the small scale sampling errors. Dr. Penny’s research will be essential for the carrying out of the coupling of the MOM-4 component of CFS V2, and he will lead this effort. 2.4 Coupling the LETKF Ocean and Atmospheric Data Assimilations Although the coupling of the individual atmospheric and ocean models with the LETKF are very complex, the path to an optimal system is rather clear. The choices are less clear on how to do coupled ocean-atmosphere data assimilation. The complications come from the boundary conditions between the ocean and the atmosphere. Physically, it is clear that the fluxes between the ocean and the atmosphere should be equal and opposite to the fluxes between the atmosphere and the ocean, but the way the models are coupled is not symmetric. For example, the MOM-4 ocean model, as discussed in section 2.1, is relaxed towards a 2-dimensional SST daily analysis obtained with 1/4o resolution Optimal Interpolation. Then the atmospheric model computes the surface heat flux from the ocean SST and the surface air temperature, and this surface flux is applied with the opposite sign to the ocean. Although this approximation is probably accurate due to the large difference in density between the ocean an the atmosphere, in principle it would be better to estimate the fluxes between the two media simultaneously, as well as to assimilate the SST observations in the ocean as part of all the observations assimilation. Singleton (2011) estimated the optimal approach to coupled system data assimilation using a toy coupled ocean atmosphere system. She found that the best results were obtained with a fully coupled advanced data assimilation system (either EnKF or 4D-Var) but that the results depended on the length of the assimilation window (Fig. 6). The coupled ETKF with Quasi-Outer Loop (QOL, similar to RIP) was optimal for short windows. The best results for coupled 4D-Var were obtained for longer windows, and required using the Quasi-static Variational data Assimilation (QVA) approach (Pires et al., 1996) in order to choose the correct minimum in the cost function. Since short windows are more efficient, this is an advantage of ensemble approaches.

Fig. 6: RMSE error of data assimilation with a toy coupled ocean atmosphere model (Singleton, 2011). Compares for different window lengths, fully coupled ETKF-QOL (green), 4D-LETKF (orange), LETKF (red), 4D-Var (fully coupled). The dashed lines are the observation error levels.

In summary, the coupling of the atmospheric and oceanic data assimilations presents different possible paths to follow, and the determination of a choice that is both accurate and computationally feasible will require significant experimental research: a) The optimal (and conceptually the simplest) approach is to consider a model that includes both the atmosphere and ocean variables, and assimilate with the EnKF all the observations simultaneously. This approach gave the best results with the toy model experiments of Singleton (2011) that also determined the fluxes between the atmosphere and the ocean within her system. However, this approach may not be computationally optimal or even feasible. b) The traditional approach of performing an atmospheric assimilation where the atmosphere first “sees” the SST field (estimated independently) and estimates the surface fluxes. Then the ocean data assimilation is driven by the surface fluxes estimated from the atmospheric assimilation. This is simple approach but it is based on a number of assumptions, and does not guarantee that the fluxes cancel out. Usually the SST field is obtained from a more detailed analysis (like NCEP’s OI at 0.25o resolution, and is also used to forces the top ocean model, rather than doing a direct ocean assimilation of SST observations. c) Some combination of these and other methods that is computationally efficient, assimilates all observations in each system simultaneously, and couples correctly the two systems. 2.5 Other advanced research related to model improvement

Fig. 7: Climatological winter mean (DJF) bias (model-observation) of the SST (°C) for a Sys4, b CFSv2; and of PRCP (mm/day) for c Sys4 and d CFSv2 (Fig. 1 of Kim et al., 2012)

Figure 7, (fig. 1 from Kim et al., 2012) shows the climatological winter bias (model minus observation) of the ECMWF System4 (Sys4) and the NCEP CFSv2 for both SST and precipitation. Figure 8 (fig 2 from Kim et al., 2012) show the time correlation between the seasonal reforecasts and the reanalysis of the seasonal predictions of 2m temperature and precipitation. It is clear that both systems have (as expected) model bias and geographical variability in their ability to reproduce the evolution. Both systems show a strong cold bias in the SH, and patterns of precipitation bias that are very distinct and somewhat different. The precipitation bias is small over the monsoonal land areas, but large and positive in the South Indian Ocean. The forecast skill, measured by the time correlation, is large in the tropics but not over India and the Asian continent. Although both forecasting systems show quite respectable skill and biases that are not large, it is clearly important to have the ability to further improve their biases and model realism. We will include research about model improvement by taking advantage of the tremendous opportunity that data assimilation in general and the LETKF in particular, offer in estimating and reducing model biases and improving the parameters used in physical parameterizations. We indicated that student Guo-Yuan Lien will be dedicated to the problem of assimilation of precipitation with the GFS, having already completed very successful simulation experiments. This subproject is important for this project because assimilating precipitation should allow applying the parameter estimation ability of the LETKF (Ruiz, August 2012). The parameters that need to be tuned to better represent the observations are simply added to the state vector (a method known as state vector augmentation). Then the Ensemble Kalman Filter estimates the error covariance between

Fig. 8: Correlation coefficients of (left) 2 meter temperature and (right) precipitation for (top) Sys4 and (bottom) CFSv2 for the period of 28 years from 1982 to 2009 winter (Fig. 2 of Kim et al, 2012)

the state vector and the added parameters, and makes changes in the parameters to reduce the errors. Similarly, the LETKF estimation of model bias is rather straightforward (an average of the observations minus the 6hr forecast (background). Danforth and Kalnay (2009), Greybush et al. (2012), Kang et al., (2011) showed that these estimated bias over 6 hours can be used to empirically correct the model during the forecast and reduce the forecast biases. This is done by simply adding to the forecast equations for variables showing large biases, the estimated bias (or time averaged analysis increment) divided by 6 hours.

3. Statement of Work (methodology to be adopted)

As indicated in Section 2 we have an ambitious program to create a completely coupled Earth System data assimilation system that should substantially improve the medium range and intra-seasonal prediction of the Monsoon. The coupled model (Atmosphere with Land and Ocean model) to be used is the NCEP CFSv2, and the data assimilation system is the Local Ensemble Transform Kalman Filter (LETKF). This project is made feasible by the fact that both those codes have been developed and tested, and that our group of senior personnel has much experience on using the LETKF. It will also be helped by the presence of two outstanding students and postdocs, especially selected by IITM to participate in this project. Our work plan is as follows:

Year 1:

• Port the GFS at T62L64 to the UMD computer cluster.

• Couple the LETKF to the GFS

• Test one year (2010 or later) of atmospheric reanalysis

• Verify the quality through 6hr observation-forecast statistics.

• Perform 12 seasonal forecasts and compare with state of the art systems

• Start porting the MOM4 at 1o resolution to the UMD computer cluster

Year 2:

• Couple the LETKF to the MOM4

• Test one year (2010 or later) of ocean reanalysis with and without including

relaxation to SST fields as currently done.

• Verify the quality through observations-forecast statistics for both experiments.

• Test MOM4 with RIP+low analysis resolution and interpolation of weights.

• Verify the quality through observations-forecast statistics for experiment and

control.

• Start experimental coupling of the atmospheric and oceanic data assimilation.

Year 3:

• Complete coupling with the best approach

• Perform 1 year reanalysis with coupled system

• Compare results with uncoupled reanalysis verifying both obs-forecast and

seasonal forecasts.

• Prepare and test codes with higher resolution

• Start porting high resolution system to Indian supercomputers for operational

testing and implementation.

3.1 Schedule (Year wise) (see details above)

Year Expected Outcome Deliverables

Year – 1 GFS-LETKF coupling 1 yr atmospheric Reanalysis

Year – 2 MOM4-LETKF coupling 1 yr ocean Reanalysis

Year – 3 CFSv2-LETKF coupling Port to India supercomputers

3.2 Team Composition and expertise *

Investigator Qualification Expertise PI Eugenia Kalnay Dist. Univ. Professor NWP, EnKF Co-PI(1) Takemasa Miyoshi Assistant Professor LETKF, coupling with models Co-PI(2) C.Gnanaseelan Ph.D. IITKharagpur (1998) Ocean modeling, data assimil.

and air-sea interaction Co-PI (3) Jim Carton Professor and Chair Ocean modeling, data

assimilation Co-I (1) Steve Penny Research Associate Ocean data assimilation

*MoES encourages to include one Indian partner in the project proposal, preferably

from MoES institutes.

3.3 Connections to Operational forecast and Human resource development

This project should significantly improve monsoon prediction. The coupled data assimilation will be tested at Indian institutes with different resolutions and observations and is expected to provide improved monsoon forecast. It also aims to train Indian students and postdocs (young scientists) apart from the active interaction among different participating institutions and so contributes considerably to the human resource development.

4. Related works and project assessment : Steve Penny is developing the LETKF-MOM4 global ocean analysis at NCEP.

4.1 National status Data assimilation in seasonal forecasting is relatively a new development in India. The existing system in India is based on 3DVAR and has several limitations and so a system similar to 4DVAR or LETKF is very much required for better forecasting. 4.2 International status

The LETKF has been adopted by a number of countries for operational use.

4.3 The mechanisms adopted in your institute for internal review (assessment) and validation of this Project Proposal. This proposal was internally reviewed by experts and by the institutional ORAA.

5. Results from prior MoES support (if any) [Describe any prior MoES funded work by the PI, Co-PI(s)]

No previous support for the UMD PI, Co-PIs Investigator MoES grant no. Title Year Description PI Co-PI 6. Facilities available at the workspace

Currently we have access to a computer cluster shared with the whole Department of Atmospheric and Oceanic Science of the University of Maryland.

7. Total Budget ( $ ) requirements ** (with justifications)

S.No Item Name 1st Year 2nd Year 3rd Year Total Justification A)

Man Power

1)Keypersonnel: Kalnay (0.5 mo) Carton (0.5 mo) Miyoshi (1 mo) Penny (6 mos)

(salary +fringe benefits) $7,851+$1,592=$9443 $7,495+$1,918=9413 $7,990+$1,861=9851 $29,000+$10,744=39744

8087+1645=9732 7720+1989=9709 8230+1927=10157 29870+11183=41053

8329+1699=10028 7951+2062=10013 8477+1995=10472 30766+11640=42406

$29,203 $29,135 $30,480 $123,203

See attached

2)Other personnel GRA Lien

(salary+tuition+benefits) $52,641

$54,484

$56,667

$163,792

See attached

3)Technical Asst

Total budget for Manpower

$121,092 $125,135 $129,586 $375813

B) Travel 1) DomesticTravel (USA) $3,000 $3,000 $3,000 $9,000 See attached

2) Foreign Travel (India) $10,000 $10,000 $10,000 $30,000 See attached

Total budget for Travel

$13,000 $13,000 $13,000 $39,000

C. Additional Computational Requirement

1) Cluster computer $16,000 *** For 16 x 4-core CPU, 8 x 32 GB RAM (see attached)

2) Storage $10,000*** For 48 TB RAID (see attached)

Grand Total (for each year) $150,092 $148,135 $142,586 $440,812

** As per MoES guidelines, we will not be paying any additional overheads for international proposals. Funding is available only for

manpower (not exceeding $1, 00,000/- per year/per person) and travel.

*** Additional expenditure for cluster computer and storage are projected in the budget for the better implementation of the project.

Budget Justification

Year 1: Personnel: The proposal will support 0.5 months of PI Kalnay; 0.5 months of Co-I Carton, 1 month of Co-I Miyoshi, 6 months of Co-I Research Associate Penny and 12 months of Graduate Student Guo-Yuan Lien. Travel: We request $3000/year to partially support the attendance two of the senior personnel and a student to the Fall Meeting of the American Geophysical Union (AGU), and two of the senior personnel and two students to the AMS Annual meeting. We also request $10,000/year to support two senior personnel traveling to IITM and spending 2-3 weeks interacting with Indian scientists. Cluster Computer: We request $16,000 for a Cluster computer: for 16 x 4-core CPU, 8 x 32 GB RAM, essential in order to implement the T62/L64 coupled with the LETKF with 20 members ensemble. This will be coupled with the MOM4-LETKF being developed by Steve Penny at NCEP with half resolution. Year 2: Personnel: The proposal will support 0.5 months of PI Kalnay; 0.5 months of Co-I Carton, 1 month of Co-I Miyoshi, 6 months of Co-I Research Associate Penny and 12 months of Graduate Student Guo-Yuan Lien. Travel: We request $3000/year to partially support the attendance two of the senior personnel and a student to the Fall Meeting of the American Geophysical Union (AGU), and two of the senior personnel and two students to the AMS Annual meeting. We also request $10,000/year to support two senior personnel traveling to IITM and spending 2-3 weeks interacting with Indian scientists.

Storage: We request $10000 for a 48 TB RAID storage device, essential in order to store the results of the CFSv2-LETKF experiments. Year 3: Personnel: The proposal will support 0.5 months of PI Kalnay; 0.5 months of Co-I Carton, 1 month of Co-I Miyoshi, 6 months of Co-I Research Associate Penny and 12 months of Graduate Student Guo-Yuan Lien. Travel: We request $3000/year to partially support the attendance two of the senior personnel and a student to the Fall Meeting of the American Geophysical Union (AGU), and two of the senior personnel and two students to the AMS Annual meeting. We also request $10,000/year to support two senior personnel traveling to IITM and spending 2-3 weeks interacting with Indian scientists.

7. Bio-data (CV) of the Investigators :

7.1 PI Biography (Kalnay) Name: Eugenia Kalnay Date of birth: 1 October 1942 Institution: University of Maryland Address (Residence): 56 Lakeside Dr, Greenbelt, MD 20770 Tel. No: 301-313-0208 Mob No: 240-506-0231 E-mail Id: Address (Office):3431 CSS, College Park, MD, 20741-2425 Tel. No: 301-405-5370 FAX: 301-314-9482 Official E-mail Id: ekalnay@atmos.umd.edu Official website address: www.atmos.umd.edu/~ekalnay Educational Qualification:- School/College/University Degree Year Main subjects Division/Class Univ. Of Buenos Aires Licencia 1965 Meteorology MIT Ph. D. 1971 Meteorology Awards / Honors / Fellowship etc.: 2009 WMO IMO Prize, and many others (please see www.atmos.umd.edu/~ekalnay) Appointments (Professional experience/employment record): Organization Designation / Position Duration ( Year / date) Univ. of Uruguay Assist Prof 1971-1973 MIT Associate Prof 1975-1978 NASA GMAO Branch Chief 1979-1987 List of important and relevant research publications: The paper Kalnay et al., 1996 (Bull. Of the American Metorol. Soc.) about the NCEP-NCAR Reanalysis that Kalnay directed is the most cited paper in all geosciences Other publications: The book “Atmospheric Modeling, Data Assimilation and Predictability” (Cambridge University Press, 2003) is widely used as a numerical weather prediction text throughout the world and has been translated and published in Chinese and Korean.

Participation in Conference/Seminar/Workshop/ Summer Schools Over 100 invited participations. Recent collaborations: Miyoshi, Yang, Ide, Surja Sharma, and many others Research Guidance: Advisor at MIT: Jule G. Charney No. of Ph.D. students enrolled/ completed 18 completed, 5 enrolled Synergistic Activities: Wrote Atmospheric Modeling, Data Assimilation and Predictability (CUP) For other activities please see www.atmos.umd.edu/~ekalnay 7.2 Co-PI (1) Biography (Miyoshi) Name: Takemasa Miyoshi Date of birth: April 6, 1977 Institution: University of Maryland Address (Residence): 56 Lakeside Dr., Greenbelt, MD 20770, USA Tel. No. : 301-405-7797 Mob No: 240-441-0741 E-mail Id: miyoshi@atmos.umd.edu Address (Office): 3423 Computer and Space Sciences Building, College Park, MD 20742, USA Tel. No. : 301-405-7797 FAX: 301-314-9482 Official E-mail Id: miyoshi@atmos.umd.edu Official website address: http://www.atmos.umd.edu/~miyoshi/ Educational Qualification: School/College/University Degree Year Main subjects Division/Class University of Maryland Ph.D. 2005 Meteorology University of Maryland M.S. 2004 Meteorology Kyoto University B.S. 2000 Physics Awards / Honors / Fellowship etc.: 2008 Yamamoto-Syono Award (Meteorological Society of Japan) Appointments (Professional experience/employment record): Organization Designation / Position Duration ( Year / date) University of Maryland Assistant Professor 2011/July 1 – present

University of Maryland Assistant Research Professor 2009/January 5 – 2011/June 30

UCLA Visiting Assistant Researcher 2009/January 5 – 2009/December 31

Japan Meteorological Agency Scientific Official 2000/April 1 – 2008/December 31

List of important and relevant research publications: Miyoshi, T. and M. Kunii, 2012: The Local Ensemble Transform Kalman Filter with the

Weather Research and Forecasting Model: Experiments with Real Observations. Pure and Appl. Geophys., 169, 321-333. doi:10.1007/s00024-011-0373-4

Miyoshi, T., 2011: The Gaussian Approach to Adaptive Covariance Inflation and Its Implementation with the Local Ensemble Transform Kalman Filter. Mon. Wea. Rev., 139, 1519-1535. doi:10.1175/2010MWR3570.1

Miyoshi, T., Y. Sato, and T. Kadowaki, 2010: Ensemble Kalman filter and 4D-Var inter-comparison with the Japanese operational global analysis and prediction system. Mon. Wea. Rev., 138, 2846-2866. doi:10.1175/2010MWR3209.1

Miyoshi, T. and S. Yamane, 2007: Local ensemble transform Kalman filtering with an AGCM at a T159/L48 resolution. Mon. Wea. Rev., 135, 3841-3861. doi:10.1175/2007MWR1873.1

Miyoshi, T., S. Yamane, and T. Enomoto, 2007: The AFES-LETKF Experimental Ensemble Reanalysis: ALERA. SOLA, 3, 45-48. doi:10.2151/sola.2007-012

Other publications: Miyoshi, T., E. Kalnay, and H. Li, 2012: Estimating and including observation error

correlations in data assimilation. Inverse Problems in Science and Engineering, in press.

Kunii, M., T. Miyoshi, and E. Kalnay, 2012: Estimating impact of real observations in regional numerical weather prediction using an ensemble Kalman filter. Mon. Wea. Rev., 140, 1975-1987. doi:10.1175/MWR-D-11-00205.1

Miyoshi, T., T. Komori, H. Yonehara, R. Sakai, and M. Yamaguchi, 2010: Impact of Resolution Degradation of the Initial Condition on Typhoon Track Forecasts. Weather and Forecasting, 25, 1568-1573. doi:10.1175/2010WAF2222392.1

Miyoshi, T. and T. Kadowaki, 2008: Accounting for Flow-dependence in the Background Error Variance within the JMA Global Four-dimensional Variational Data Assimilation System. SOLA, 4, 37-40. doi:10.2151/sola.2008-010

Miyoshi, T. and Y. Sato, 2007: Assimilating Satellite Radiances with a Local Ensemble Transform Kalman Filter (LETKF) Applied to the JMA Global Model (GSM). SOLA, 3, 37-40. doi:10.2151/sola.2007-010

Participation in Conference/Seminar/Workshop/ Summer Schools July 2011 Invited Presentation, Session J-M02: Data assimilation and ensemble forecasting for weather and climate, IUGG 2011, Melbourne, Australia. August 2010 Invited Presentation, Session IN04: Inverse problems in geosciences, The Meeting of the Americas of the American Geophysical Union, Foz do Iguacu, Brazil.

(also invited to "Session A06: Data assimilation techniques" and "Session OS20: Operational ocean prediction and data assimilation") May 2010 Invited Presentation, The 5th international workshop on ensemble Kalman filter for model updating, Bergen, Norway. November 2008 Invited Award Lecture, Biannual Meeting of the Meteorological Society of Japan, Sendai, Japan. November 2008 Invited Presentation, WMO/WWRP/THORPEX Workshop on 4D-VAR and ensemble Kalman filter inter-comparisons, Buenos Aires, Argentina. May 2008 Invited Presentation, Session J245: Advances in inversion techniques, Japan Geosciences Union Meeting 2008, Makuhari, Japan. January 2008 Invited Presentation, Third WMO/WCRP International Conference on Reanalysis, Tokyo, Japan. Recent collaborations: LETKF with the Coupled Ocean-Atmosphere GCM for the Earth Simulator (CFES): Prof. Takeshi Enomoto (Kyoto U, Japan), Dr. Nobumasa Komori (Earth Simulator Center, Japan) Tropical Cyclone research: Drs. Craig Bishop and Peter Black (NRL) Mesoscale ensemble-based data assimilation: Drs. Kazuo Saito and Hiromu Seko (MRI/JMA, Japan) Aerosol and chemistry data assimilation: Drs. Thomas Sekiyama and Taichu Tanaka (MRI/JMA, Japan) Research Guidance: LETKF with the WRF model: Masaru Kunii (visiting researcher from MRI/JMA, Japan) No. of Ph.D. students enrolled/ completed 1 enrolled, none completed No. of Graduate/Postgraduate students enrolled/ completed None Synergistic Activities: Activity 1 Dr. Miyoshi developed the MPI/OpenMP parallel Fortran90 code for the Local Ensemble Transform Kalman Filter (LETKF) system from scratch and tested it on various computing platforms, including the Japanese Earth Simulator supercomputer system, the Japan Meteorological Agency (JMA) operational supercomputer system, the Japanese Meteorological Research Institute (MRI/JMA) supercomputer system, the Jet Propulsion Laboratory (JPL) cluster system, a Linux cluster system, and a Linux computer. The latest version of the MPI/Fortran90 code and B-shell run scripts is available to public through http://code.google.com/p/miyoshi and has been downloaded by members of several international operational and research organizations. Many studies have been published using the data assimilation system. This system is also useful for educational purposes, and has been used in graduate-level courses at UMD as well as intensive data assimilation courses internationally.

Activity 2 2012-present Associate Editor of Monthly Weather Review 2010-present Editor of Journal of the Meteorological Society of Japan 2008-present Editor of Scientific Online Letters on the Atmosphere (SOLA) 2008-2009 Member of the International Scientific Organizing Committee, Fifth

WMO International Symposium on Data Assimilation (WMODA5) 8.3 Co-PI (2) Biography (Dr. C. Gnanaseelan) Name: C. Gnanaseelan Date of birth: June 1, 1966 Institution: Indian Institute of Tropical Meteorology Address (Residence): C-26, Whispering Wind Pashan Baner Link Road, Pune-411021 Tel. No. : 91-20-46778714 Mob No: 91-9823619582 E-mail Id: gseelanc@gmail.com Address (Office): Indian Institute of Tropical Meteorology, Dr. Homibhabha Road, Pune-411008 Tel. No. :- 91-20-25904273 FAX:- 91-20-2586-5142 Official E-mail Id:- seelan@tropmet.res.in Official website address:- www.tropmet.res.in Educational Qualification: Indian Institute of Technology, Kharagpur Ph.D. (Mathematics, 1998) School/College/University Degree Year Main subjects Division/Class

Indian Institute of Technology,

Kharagpur

Ph.D. 1998 Mathematics

Indian Institute of Technology,

Kharagpur

M.Tech. 1991 Meteorology I

Madurai Kamaraj University M.Sc. 1989 Mathematics I

Madurai Kamaraj University B.Sc. 1987 Mathematics I

Awards / Honors / Fellowship etc.: 2011 Young Scientist Award with the Certificate of Merit from MoES, Govt. of India Appointments (Professional experience/employment record): Organization Designation / Position Duration ( Year / date) Indian Institute of Tropical Meteorolgy

Scientist-E 2010 onwards

,, Scientist-D 2007-2010 ,, Scientist-C 2003-2007 ,, Scientist-B 1999-2003

List of important, recent relevant research publications: 1. C. Gnanaseelan, Aditi Deshpande, and M.J. McPhaden, ‘Impact of Indian Ocean

Dipole and El Niño/Southern Oscillation wind forcing on the Wyrtki jets’, Journal of Geophysical Research (Oceans), 2012, 117, doi:10.1029/2012JC007918

2. J.S. Chowdary, C. Gnanaseelan and S.P. Xie, (2009), ‘Westward propagation of barrier layer formation in the 2006-07 Rossby wave event over the tropical southwest Indian Ocean’ Geophysical Research Letters, 36, L04607, 1-5.

3. B. Thompson, C. Gnanaseelan and P.S. Salvekar, ’Variability in the Indian Ocean Circulation and Salinity and their Impact on SST anomalies During Dipole Events’, Journal of Marine Research, 2006, 64, 853-880.

4. J.S. Chowdary and C. Gnanaseelan, ‘Basin wide warming of the Indian Ocean during ElNino and Indian Ocean Dipole years’, International Journal of Climatology, 2007, 27, 1421-1438.

5. Soumi Chakravorty, J.S. Chowdary, and C. Gnanaseelan, ‘Spring asymmetric mode in the tropical Indian Ocean: role of El Niño and IOD’, Climate Dynamics, 2012, DOI 10.1007/s00382-012-1340-1

6. J.Vialard, A. Jayakumar, C.Gnanaseelan, M. Lengaigne, D. Sengupta, B.N. Goswami, ‘Processes of 30-90 day sea surface temperature variability in the Northern Indian Ocean during boreal summer’, Climate Dynamics, 2012, 38, DOI: 10.1007/s00382-011-1015-3, 1901-1916

7. A. Parekh, C. Gnanaseelan, A. Jayakumar, "Impact of improved momentum transfer coefficients on the dynamics and thermodynamics of the north Indian Ocean", Journal of Geophysical Research (Oceans), 2011, 116, C01004, doi:10.1029/2010JC006346.

2012 8. A. Jayakumar and C. Gnanaseelan, ‘Anomalous intraseasonal events in the

thermocline ridge region of Southern Tropical Indian Ocean and their regional impacts’, Journal of Geophysical Research, 2012, 117, C03021, doi:10.1029/2011JC007357.

9. P. Sreenivas, C. Gnanaseelan, and K.V.S.R. Prasad, ‘Influence of El Niño and Indian Ocean Dipole on sea level variability in the Bay of Bengal’ Global and Planetary Change, 2012, 80-81, 215–225.

10. Jayakumar, A, C. Gnanaseelan, and T.P. Sabin, ‘Mechanism of intraseasonal oceanic signature in the region off southern tip of India during boreal summer’, International Journal of Climatology, 2012 (in press)

11. R. Deepa, C. Gnanaseelan, M. Deshpande and P.S. Salvekar, ‘ A model study on understanding the influence of Arabian Sea mini warm pool on monsoon onset vortex formation’ , Pure and Applied Geophysics, 2012, 169,  1693–1706.

12. A. Jayakumar and C. Gnanaseelan, ‘Chlorophyll–a variability in the Southern tropical Indian Ocean using an OGCM’, Marine Geodesy, 2012 (online).

Recent collaborations: Dr. M.J. McPhaden, Prof. J.P. McCreary, Prof. S.P. Xie, Prof. T.N. Krishnamurti, Dr. J. Vialard. Research Guidance: No. of Ph.D. students enrolled/ completed: 10 (4 enrolled/6 completed) No. of Graduate/Postgraduate students enrolled/ completed: 14 (1 enrolled/13 completed) Synergistic Activities: Activity 1: Heading the programme ‘Ocean model development and data assimilation’ at Indian Institute of Tropical Meteorology, Pune. Activity 2: Adjunct Professor of University of Pune, actively involved in teaching and guiding the graduate and doctoral students of IITM and University of Pune. The subjects he teaches include ‘Data Assimilation and objective analysis’.

8.4 Co-PI (3) Biography (Dr. J. Carton) Name: James Alfred Carton Date of birth: July 10, 1954 Institution: University of Maryland Address (Residence): 8601 Long Acre Ct., Bethesda, MD 20817 Tel. No. : 301-365-3928 Mob No:301-728-9529 E-mail Id:carton@atmos.umd.edu Address (Office):3413 Computer and Space Sciences Bldg., Stadium Dr., University of Maryland, College Park, MD 20742 Tel. No. :301-405-5391 FAX:-301-314-9482 Official E-mail Id:-carton@atmos.umd.edu

Official website address:-www.atmos.umd.edu/~carton Educational Qualification: School/College/University Degree Year Main subjects Division/Class Princeton Univ. BSE 1976 Comp. Sci. University of Washington MS 1979 Oceanography Princeton University PhD 1983 Atmos. Ocean sci. Awards / Honors / Fellowship etc.: Manfred Pika physics prize, Princeton University, 1973. General Research Board Award, University of Maryland, 1986. University of Maryland Travel Award, 1988. College of CMPS teaching award, 2002. Fellow of the American Meteorological Society, 2009. Appointments (Professional experience/employment record): Organization Designation / Position Duration ( Year / date) Harvard Univ. Research Fellow Jan., 1983 – Sept., 1985 Univ. Maryland Assist. Professor Sept., 1985 – Jul., 1990 Univ. Maryland Assoc. Professor Jul., 1990 – Jul., 1997 NOAA/NOS Visiting Scientist 1994-1997 Univ. Wisc. Visiting Faculty 1992 Univ. Maryland Full Professor Jul., 1997 - pres GFDL Visiting Scientist 2007-2010 Univ. Maryland Dept. Chair Jul., 2007 - pres List of important and relevant research publications:

Carton, J.A., G. Chepurin, X. Cao, and B.S. Giese, 2000a: A Simple Ocean Data Assimilation analysis of the global upper ocean 1950-1995, Part 1: methodology, J. Phys. Oceanogr., 30, 294-309.

Carton, J.A., G. Chepurin, and X. Cao, 2000b: A Simple Ocean Data Assimilation analysis of the global upper ocean 1950-1995 Part 2: results, J. Phys. Oceanogr., 30, 311-326.

Carton, J.A., and A. Santorelli*, 2008: Global upper ocean heat content as viewed in nine analyses, J. Clim., 21, 6015–6035. DOI: 10.1175/2008JCLI2489.1

Carton, J.A., and B.S. Giese, 2008: SODA: A Reanalysis of Ocean Climate, Mon. Wea. Rev., 136, 2999-3017.

Giese B.S., G.A. Chepurin, J.A. Carton, and H.F. Seidel, 2011: Impact of Bathythermograph Temperature Bias Models on an Ocean Reanalysis, J. Clim., 24, 84-93, DOI: 10.1175/2010JCLI3534.1.

Grodsky S.A., R. Lumpkin, and J.A. Carton, 2011: Spurious trends in global surface drifter currents, J. Geophys. Res., 38, L10606. DOI: 10.1029/2011GL047393.

Santorelli*, A.; R.T. Pinker, A. Bentamy, K. B. Katsaros, W. M. Drennan, A. M. Mestas-Nuñez, and J. A. Carton, 2011: Differences between two estimates of air-sea turbulent heat fluxes over the Atlantic Ocean, J. Geophys. Res., 116, C09028 DOI: 10.1029/2010JC006927.

Carton, J.A., H.F. Seidel, and B.S. Giese, 2012: Detecting historical ocean climate variability, J. Clim., in press.

Other refereed publications (since 2009): Hoffman*,M. J., E. Kalnay, J. A. Carton, and S. C. Yang, 2009: Use of breeding to detect

and explain instabilities in the global ocean, Geophys. Res. Letts., 36, L12608, doi:10.1029/2009GL037729.

Grodsky, S.A., A. Bentamy, J.A. Carton, and R.T. Pinker, 2009: Intraseasonal latent heat flux based on satellite observations, J. Clim., 22, 4539-4556, DOI: 10.1175/2009JCLI2901.1,

Liu*, H., S. A. Grodsky, and J.A. Carton, 2009: Observed subseasonal variability of oceanic barrier and compensated layers, J. Clim., 22, 6104-6119.

Stammer, D., et al, 2009: Ocean Variability evaluated from an Ensemble of Ocean Syntheses, Ocean Obs 2009 White Paper CWP-2A-17

Lagerloef, G., 2009: Resolving the global surface salinity field and variations by blending satellite and in situ observations, Ocean Obs 2009 White Paper, CWP-4B-08.

Heimbach, P., et al., 2009: Observational requirements for global-scale ocean climate analysis: lessons learnt from ocean state estimation, Ocean Obs 2009 White Paper, CWP-4C-07.

Lee, T., et al., 2009: Ocean State Estimation for Climate Research, Ocean Obs 2009 White Paper CWP-4C-09

Carton, J.A., B.S. Giese, G.A. Chepurin, and H. Seidel, 2009: Bias in the bathythermograph records and its impact on ocean climate analysis Ocean Obs 2009 AC-2B-05.

US CLIVAR Decadal Predictability Working Group, 2009: What additional skill we can expect from the initialized decadal predictions, and why?, Bull. Atmos. Sci., submitted.

Grodsky S.A., A. Bentamy, J.A. Carton, and R.T. Pinker, 2009: Intraseasonal Latent Heat Flux Based on Satellite Observations, J. Clim., 22, 4539-4556 DOI: 10.1175/2009JCLI2901.1

Liu, H., S.A. Grodsky, and J.A. Carton, 2009: Observed subseasonal variability of oceanic barrier and compensated layers, 22, 6104-6119 DOI: 10.1175/2009JCLI2974.1.

Hoffman*, M.J., E. Kalnay, J.A. Carton, and S.C. Yang, 2009: Use of breeding to detect and explain instabilities in the global ocean, Geophys. Res. Letts., 36, L12608 DOI: 10.1029/2009GL037729.

Giese B.S.; G.P. Compo, N.C. Slowey, P.D. Sardeshmukh, J.A. Carton, S. Ray, and J.S. Whitaker, 2010: The 1918/19 El Nino, 91, 177-183 DOI: 10.1175/2009BAMS2903.1

Chen*, H., D.L. Zhang, J.A. Carton, and R. Atlas, 2011: The Rapid Intensification of Hurricane Wilma (2005). Part I: Model Prediction and Structural Changes, Wea. Forecasting, 26, 885-901, DOI: 10.1175/WAF-D-11-00001.1

Carton, J.A., G.A. Chepurin, J. Reagan*, and S. Hakkinen, 2011: Interannual to decadal variability of Atlantic Water in the Nordic and adjacent seas, J. Geophys. Res., 116, C11035 DOI: 10.1029/2011JC007102.

Santorelli*, A.; R.T. Pinker, A. Bentamy, K. B. Katsaros, W. M. Drennan, A. M. Mestas-Nuñez, and J. A. Carton, 2011: Differences between two estimates of air-sea turbulent heat fluxes over the Atlantic Ocean, J. Geophys. Res.,116, C09028 DOI: 10.1029/2010JC006927.

Carton, J.A., and S. Hakkinen, 2011: Introduction to: Atlantic Meridional Overturning Circulation (AMOC), Deep-Sea Res. part II, 58, 1741-1743. DOI: 10.1016/j.dsr2.2010.10.055.

Grodsky S.A., R. Lumpkin, and J.A. Carton, 2011: Spurious trends in global surface drifter currents, J. Geophys. Res., 38, L10606. DOI: 10.1029/2011GL047393.

Solomon A., L. Goddard, A. Kumar, L. Goddard, A. Kumar et al., 2011: Distinguishing the roles of natural and antroprogenically forced decadal climate variability: Implications for Prediction, Bull. Amer. Meteor. Soc., 92, 141-156. DOI: 10.1175/2010BAMS2962.1

Ricko*, M., J.A. Carton, and C. Birkett, 2011: Climatic Effects on Lake Basins. Part I: Modeling Tropical Lake Levels, J. Clim., 24, 2983-2999, DOI: 10.1175/2010JCLI3602.1.

Giese B.S., G.A. Chepurin, J.A. Carton, and H.F. Seidel, 2011: Impact of Bathythermograph Temperature Bias Models on an Ocean Reanalysis, J. Clim., 24, 84-93, DOI: 10.1175/2010JCLI3534.1.

Carton, J.A., H.F. Seidel, and B.S. Giese, 2012: Detecting historical ocean climate variability, J. Clim., in press.

Chepurin, G.A., and J.A. Carton, 2012: Sub-arctic and Arctic sea surface temperature and its relation to ocean heatcontent 1982-2010, submitted.

Ricko, M., C.M. Birkett, J.A. Carton, and J.-F. Cretaux, 2012: Intercomparison and Validation of Continental Water Level Products Derived from Satellite Radar Altimetry, submitted.

Grodsky, S.A., J.A. Carton, S. Nigam, and Y. Okumura, 2012: Tropical Atlantic biases in CCSM4, J. Clim. Special CCSM4 issue, in press.

Goddard, L., A. Kumar, A. Solomon et al., 2012: A verification framework for Interannual-to-decadal prediction experiments, Bull. Amer. Meteorol. Soc., in press.

BOOKS EDITED Wang, C., S.-P. Xie, and J. A. Carton, ed.s 2004: “Ocean-Atmosphere Interaction and

Climate Variability”, American Geophysical Union Press. CHAPTERS IN BOOKS Carton, J.A., 1993:"Circulation of the Tropical Atlantic", in The Oceans: Physiochemical

Dynamics and Resources, edited by S.K. Majumdar, et al., Pennsylvania Academy of Sciences Press.

Foltz*, G., and J.A. Carton, 2002: Latent Heat, Fusion, and Evaporation, in the Encyclopedia of Oceanography and Marine Sciences, Moschovitis Group, in press.

Foltz*, G., and J.A. Carton, 2002: Sun, Radiant Energy of, in the Encyclopedia of Oceanography and Marine Scienc Moschovitis Group, in press.

Xie, S.-P., and J.A. Carton, 2004: Tropical Atlantic variability: patterns, mechanisms, and impacts, in “Ocean-Atmosphere Interaction and Climate Variability”, edited by C. Wang, S.-P. Xie, and J. A. Carton, AGU Press. Participation in Conference/Seminar/Workshop/ Summer Schools (1991-2006) Trieste Inst. Theor. Phys Summer School Fellow 1991 Member NASA ESTAR (a surface salinity sensor) evaluation team, 1991 Member TOGA/Coupled Ocean-Atmosphere Response Experiment (CORE) Modelling and data assimilation subworking group, 1989-1993 Member, World Ocean Circulation Experiment, Numerical Modelling panel, 1988-1993. Member, NASA TOPEX Tides Committee Panel, 1989-1993. Member Western Tropical Atlantic Experiment (WESTRAX), 1991-3. Member TOPEX Science Team, 1990-1998. Member, NOAA Atlantic Climate Change Program (ACCP) Steering Committee, 1992-1996. co-Chair, NOAA Atlantic Climate Change Program (ACCP) Steering Committee, 1994-1996. Member NRC panel on earth gravity, 1997. Co-author Ocean Observations 1999 position statement on Atlantic Climate Variability Co-author Ocean Observations 1999 position statement on A Climate Observing System for the Tropical Atlantic Member, AMS STAC committee on air-sea interaction 2000-2002. Member, NSIPP science team 2000-2002. Member NOAA moored instrument program, Review Committee 2001. Member review committee for NASA satellite salinity proposal (Aquarius), 2001. Co-Chair ONR-sponsored workshop: “Progress and Prospects of Data Assimilation in Ocean Research”, Williamsburg, VA, 2003. Member Scientific Steering Committee of the UCAR Community Climate System Model, 2003-2006. Member, Scientific Steering Committee, US CLIVAR, 2004-2005. Member, Steering Committee, African Monsoon Multidisciplinary Analysis (AMMA), 2004-2005. Session Chair, Spring Meeting of the American Geophysical Union, 2004 Session Chair, Fall Meeting of the American Geophysical Union, 2004 Member, Joint Center for Satellite Data Assimilation (JCSDA), 2005-pres. Member, NASA Jason-1 Science Working Team, 2004-pres. Member NASA Winds Science Working Group, 2005-pres. Member Tropical Atlantic Circulation Experiment Steering Committee, 2005-pres Member Science Steering Committee, USCLIVAR, 2006. Member POS panel, USCLIVAR, 2006-7. Member Phenomena, Observations, and Synthesis panel, USCLIVAR, 2006-pres. Co-Chair, USCLIVAR subcommittee on ocean salinity, 2005-pres. Co-Convener, special sessions on ocean salinity, Ocean Sciences Meeting, 2006. Co-Convener, Workshop on Ocean Salinity, WHOI, May, 2006.

Recent collaborations: (a) Collaborators. M. Hoffman (RIT), E. Kalnay (UMD), S.C. Yang (NTU), A. Bentamy

(IFREMER), R. Pinker (UMD), H. Liu (NOAA/AOML), S. Grodsky (UMD), H. Seidel (TAMU), B. Giese (TAMU), G. Compo (NOAA/ESRL), P. Sardeshmukh (NOAA/ESRL), J. Whitaker (NOAA/ESRL), D. Zhang (UMD), R. Atlas (NOAA/AOML), J. Reagan (NOAA/NODC), S. Hakkinen (NASA/GSFC), K. Katsaros (UW), A. M. Mestas-Nuñez (TAMU), R. Lumpkin (NOAA/AOML), C. Birkett (UMD), Y. Okumora (NCAR), F. Bryan (NCAR)

(b) Graduate and Postdoctoral Advisors: Dr. David Halpern, JPL/NASA; Professor George Philander, Princeton University; Professor Alan Robinson, Harvard University

Research Guidance:

28 MS students No. of Ph.D. students enrolled/ completed

Enrolled: five Completed: 15 PhDs (B. Huang, J-M. Yoo, S. Babin, Z. Zhou, M. Taylor, A. Ruiz-Barradas, J. Wang, T. Janjic, G. Foltz, A. Borovikov, H. Liu, S. Penny, A. Santorelli, M. Hoffman, M. Ricko)

No. of Graduate/Postgraduate students enrolled/ completed

4 postdoctoral fellows (F. Vossepoel (Fullbright), R. Raghu, B. Giese, G. Chepurin, and S. Penny)

Synergistic Activities: Activity 1

Member of the NASA Winds, SST, and salinity Science Working Groups Activity 2

Member, US AMOC Activity 3

Member, USCLIVAR Decadal Prediction Working Group Activity 4

Member, CLIVAR GSOP ocean state estimation/assimilation intercomparison 8.5 Co-I Biography (Dr. S. Penny) Name: Stephen G. Penny Date of birth: 30-May-1980 Institution: University of Maryland, College Park Address (Residence): 5704 Goucher Drive Berwyn Heights MD, 20740 Tel. No. : 703-467-2382

Mob No: 703-474-1354 E-mail Id: Steve.Penny@noaa.gov Address (Office): Tel. No. :- 301-405-8022 FAX:- 301-314-9482 Official E-mail Id:- Steve.Penny@noaa.gov Official website address:- www.aosc.umd.edu/~spenny Educational Qualification: School/College/University Degree Year Main subjects Division/Class University of Maryland Ph.D. 2011 Applied

Mathematics and Scientific Computation

Atmospheric and Oceanic Science

University of Maryland M.S. 2008

James Madison University B.S. 2002 Mathematics Pure and Applied

Awards / Honors / Fellowship etc.: NASA Goddard/ESSIC Fellowship (2005-2010), Metron Applied Mathematics Award (2002) Undergraduate Mathematics and Statistics Research Award (2002), Meritorious Ranking on 2002 COMAP Mathematical Modeling Contest (top 17% worldwide), President's List, Dean's List, Metron Aviation ‘Rated M’ award (2004). Appointments (Professional experience/employment record): Organization Designation / Position Duration ( Year / date) University of Maryland

Research Associate May 2011 to present

Metron Aviation Analyst June 2002 to June 2009 List of important and relevant research publications: Penny, S., E. Kalnay, J.A. Carton, B. Hunt, K. Ide, T. Miyoshi, G. Chepurin, (2012): Comparison

of the 4D Local Ensemble Transform Kalman Filter and the Simple Ocean Data Assimilation in a Global Ocean. Tellus A, (accepted pending revisions).

Other publications: Penny, S., Hoffman, R., Krozel, J., Roy, A. Classification of Days in the NAS using Cluster

Analysis. Air Traffic Control Quarterly Journal, Vol 13, Num 1. Krozel, J. Penny, S., Prete, J., and Mitchell, J.S.B., Automated Route Generation for Avoiding

Deterministic Weather in Transition Airspace, Journal of Guidance, Control, and Dynamics, Vol. 30, No. 1, Jan./Feb., 2007.

Krozel, J., Jacobovits, R., Penny, S., An Algorithmic Approach for Airspace Flow Programs. Air Traffic Control Quarterly Journal, Summer 2006.

Participation in Conference/Seminar/Workshop/ Summer Schools Presentations: Invited Talk: “Weather Prediction: How we combine computer models with real world

observations to make better forecasts.” (June 14, 2012) Metron Aviation Inc., an Airbus company.

National Centers for Environmental Prediction (NCEP) Environmental Modeling Center (EMC) Seminar, NOAA Science Center (World Weather Building) (Oct. 11, 2011): Data Assimilation of the Global Ocean using the Local Ensemble Transform Kalman Filter

Conferences: 4th World Climate Research Programme International Conference on Reanalyses (ICR4), May7-11,2012 JCSDA 9th Workshop on Satellite Data Assimilation, May 24-25, 2011 Recent collaborations: Research Guidance: No. of Ph.D. students enrolled/ completed N/A No. of Graduate/Postgraduate students enrolled/ completed N/A Synergistic Activities: Activity 1 Development of the Oceanic-LETKF for NOAA/NCEP as an upgrade to the Global Ocean Data Assimilation System (GODAS). The new system is applied to GFDL’s Modular Ocean Model version 4.1 (MOM4p1). Activity 2 Development and implementation of new data assimilation techniques into the Oceanic-LETKF system, including adaptive observation errors and constraints applied to the LETKF algorithm. 8. List of supplementary documents : ( e.g., authorization letter from the Head of the organization, endorsements etc.) N/A

9. References Cited :

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Symp. on Integrated Observing and Assimilation Systems for Atmosphere, Oceans, and Land Surface, San Antonio, TX, Amer. Meteor. Soc., 3.3. [Available online at http://ams.confex.com/ams/ pdfpapers/119541.pdf.]

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Carton, J.A., and B.S. Giese, 2008: A reanalysis of ocean climate using Simple Ocean Data Assimilation (SODA), Mon. Wea. Rev., 136,2999-3017

Danforth, C. M., E. Kalnay, T. Miyoshi. 2007: Estimating and Correcting Global Weather Model Error. Monthly Weather Review, 135, No. 2, 281–299.

Danforth, C. M., E. Kalnay. 2008: Using Singular Value Decomposition to Parameterize State-Dependent Model Errors. Journal of the Atmospheric Sciences, 65, No. 4, 1467-1478.

Danforth, C. M., E. Kalnay. 2008: Impact of Online Empirical Model Correction on Nonlinear Error Growth. Geophysical Research Letters, 35, L24805.

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Localization Techniques. Mon. Wea. Rev., 139, 511–522. Griffies, S.M., M.J. Harrison, R.C. Pacanowski, and A. Rosati, 2004: A TECHNICAL GUIDE TO MOM4

GFDL OCEAN GROUP TECHNICAL REPORT NO. 5. NOAA/Geophysical Fluid Dynamics Laboratory. Version prepared on August 23, 2004. Available online at www.gfdl.noaa.gov

Griffies, S. M., M. Schmidt, M. Herzfeld. 2010: Elements of MOM4p1. GFDL Ocean Group Technical Report No. 6, NOAA/Geophysical Fluid Dynamics Laboratory. Available online: http://www.gfdl.noaa.gov/cms-filesystem-action/model_development/ocean/mom-guide4p1.pdf

Brian R. Hunt, Eric J. Kostelich, Istvan Szunyogh, Efficient data assimilation for spatiotemporal chaos: A local ensemble transform Kalman filter, Physica D: Nonlinear Phenomena, Volume 230, Issues 1–2, June 2007, Pages 112-126. Available online: http://www.sciencedirect.com/science/article/pii/S0167278906004647

Kalnay, E., 2003: Atmospheric Modeling, Data Assimilation, and Predictability. Cambridge University Press, 342 pp.

Kalnay E., and S.-C. Yang, 2010: Accelerating the spin-up of Ensemble Kalman Filtering. Q. J. Roy. Meteor. Soc., 136, 1644-1651.

Kalnay et al. (2012) A Simpler Formulation of Forecast Sensitivityto Observations: Application to Ensemble Kalman FiltersTellus, in press. Kang, J.-S., E. Kalnay, J. Liu, I. Fung, T. Miyoshi, and K. Ide, 2011: “Variable localization” in an

ensemble Kalman filter: Application to the carbon cycle data assimilation. J. Geophys. Res., 116, D09110.

Kleist, D. T., D. F. Parrish, J. C. Derber, R. Treadon, R. M. Errico, and R. Yang, 2009: Improving incremental balance in the GSI 3DVAR analysis system. Mon. Wea. Rev., 137, 1046–1060.

Kunii, M., T. Miyoshi, and E. Kalnay, 2012: Estimating impact of real observations in regional numerical weather prediction using an ensemble Kalman filter. Mon. Wea. Rev., 140, 1975-1987

Li, H., E. Kalnay, and T. Miyoshi, 2009: Simultaneous estimation of covariance inflation and observation errors within an ensemble Kalman filter. Quart. J. Roy. Meteor. Soc., 135, 523-533.

Lien, G.-Y., E. Kalnay, and T. Miyoshi: Ensemble Kalman Filter Assimilation of Precipitation with a Simplified AGCM. Poster presentation, The 5th EnKF Workshop, Albany, NY, May 21-24, 2012.

Liu, J. and E. Kalnay, 2008: Estimating observation impact without adjoint model in an ensemble Kalman filter. Q. J. R. Meteorol. Soc., 134, 1327-1335.

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Miyoshi, T. and M. Kunii, 2012: The Local Ensemble Transform Kalman Filter with the Weather Research and Forecasting Model: Experiments with Real Observations. Pure and Appl. Geophys., 169, 321-333.

Miyoshi, T., 2011: The Gaussian Approach to Adaptive Covariance Inflation and Its Implementation with the Local Ensemble Transform Kalman Filter. Mon. Wea. Rev., 139, 1519-1535.

Miyoshi, T., Y. Sato, and T. Kadowaki, 2010: Ensemble Kalman filter and 4D-Var inter-comparison with the Japanese operational global analysis and prediction system. Mon. Wea. Rev., 138, 2846-2866.

Miyoshi, T. and S. Yamane, 2007: Local ensemble transform Kalman filtering with an AGCM at a T159/L48 resolution. Mon. Wea. Rev., 135, 3841-3861.

Miyoshi, T. and K. Aranami 2006: Applying a Four-dimensional Local Ensemble Transform Kalman Filter (4D-LETKF) to the JMA Nonhydrostatic Model (NHM). SOLA, 2, 128-131.

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Penny, S., 2011: Ph.D. Thesis, UMD. Penny, S., E. Kalnay, J.A. Carton, B. Hunt, K. Ide, T. Miyoshi, G. Chepurin, (2012): Comparison of the 4D

Local Ensemble Transform Kalman Filter and the Simple Ocean Data Assimilation in a Global Ocean. Tellus A, (accepted pending revisions).

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Singleton, T., 2011: Ph.D. Thesis, UMD. Yang, S.-C., E. Kalnay and B. Hunt, 2012: Handling nonlinearity in an Ensemble Kalman Filter:

Experiments with the three-variable Lorenz model. Mon. Wea. Rev, 140.