towards an analysis ensemble for nwp-model verification
DESCRIPTION
TOWARDS AN ANALYSIS ENSEMBLE FOR NWP-MODEL VERIFICATION. Manfred Dorninger, Theresa Gorgas and Reinhold Steinacker. OUTLINE Motivation The analysis tool VERA The JDC Observational Data Set The ensemble method First results Summary and outlook. Deterministic FC. EPS. MOTIVATION. - PowerPoint PPT PresentationTRANSCRIPT
Department of Meteorology and Geophysics
University of Viennasince 1851 since 1365
TOWARDS AN ANALYSIS ENSEMBLE FOR NWP-MODEL VERIFICATION
Manfred Dorninger, Theresa Gorgas and Reinhold Steinacker
Dorninger, et al. Joint COPS/CSIP-meeting Cambridge
OUTLINE
• Motivation
• The analysis tool VERA• The JDC Observational Data Set• The ensemble method• First results• Summary and outlook
Dorninger, et al. Joint COPS/CSIP-meeting Cambridge
MOTIVATION
Verification
Reference (obs., ana.)Deterministic FC
EPS
Forecast
„truth “
Forecast Uncertainty
Dorninger, et al. Joint COPS/CSIP-meeting Cambridge
MOTIVATION
observation representative observational (known) (true) value deviation
(wanted!)
)t~,~,t̂,ˆt,,()t~,~t,,()t̂,ˆt,,( rrrrrrr oro
IVre
IIIse
IIsn
Ibo ,, t),()()t~,~t̂,ˆt,()~,ˆ,( rrrrrrrr
I subscale bias (e. g. urban heat island)II random subscale effects (meteorological noise)III systematic error (technical, calibration, sensor)IV random error (technical, sensor, data processing)
t̂,r̂ …observation scale t~,~r …representative scale
Dorninger, et al. Joint COPS/CSIP-meeting Cambridge
MOTIVATION
Verification
Reference (obs., ana.)Deterministic FC
EPS
Forecast
EOS, EAS
„truth“
Forecast Uncertainty Verification Uncertainty
Dorninger, et al. Joint COPS/CSIP-meeting Cambridge
The analysis tool VERA
Does not include first guess fields – „NWP-model independent“
(Vienna Enhanced Resolution Analysis)
need:
• sophisticated QC procedure
• very high station density
suitable for NWP-model verification
BUT
Dorninger, et al. Joint COPS/CSIP-meeting Cambridge
The analysis tool VERA
(Vienna Enhanced Resolution Analysis)
Further reading: Steinacker, et al. 2000 (MWR), Steinacker, et al. 2006 (MWR)
Potential Temperature
Equivalent – Pot. Temperature
Precipitation:Accumulated to
1h, 3h, 6h, 12h, 24h
Wind
MSL - pressure
Analysed Surface Parameters:
Post processing:- Mixing Ratio- Moisture Flux
Divergence
Data quality control scheme+
Thin-Plate-Spline algorithm+
Downscaling via the „Fingerprint“ method
Dorninger, et al. Joint COPS/CSIP-meeting Cambridge
Joint D-PHASE and COPS (JDC) data set
• 28 data providers• GTS-Stations: 1232
• NGTS-Stations: 10811
• Mean station distance: GTS: ~ 36km GTS+Non-GTS: ~ 12km
Frames: D-PHASE (large) & COPS (small) areas
Red: Non-GTS stations Blue: GTS stations
•Collection of operational network data of National Weather Services initiated in the framework of the WWRP COPS (RDP, Wulfmeyer, et al., 2008, BAMS) and D-PHASE (FDP, Rotach, et al., 2009, BAMS)
•Available at WDCC Hamburg following MAP Data Policy (http://cera-www.dkrz.de/WDCC/ui/Index.jsp) DOI in the near future
•Task performed in cooperation of U Vienna and U Hohenheim Dorninger, et al., 2009
32
>13300
Dorninger, et al. Joint COPS/CSIP-meeting Cambridge
Ensemble methodKey question:
How to define the analysis ensemble ?
• the QC scheme of VERA produces a correction proposal every analysis time• this results in 8760 correction proposals for hourly analysis in 2007
deviations of potential temp. for 06/2007 deviations of msl-pressure for 06/2007
Dorninger, et al. Joint COPS/CSIP-meeting Cambridge
Estimation of uncertainties in VERA analyses - a very first approach
Steps towards ensemble analyses
Correct station observation values by removing biases dereived from deviations proposed by quality control
Analyse bias-corrected observations = reference analysis
Generate normal distribution fitted to distribution of quality control outputs
Create a number of sets of (gaussian) randomized observation values
Use perturbated data to create ensemble analyses
Schematic randomisation procedure performed for each station and parameter
First experiments: Choose sets for 10 Ensemble Members
Ensemble method
Dorninger, et al. Joint COPS/CSIP-meeting Cambridge
Analysis RR 1h acc.
Stdev. of Ens. Members (10)
Stdev. of Ens. Members (10) – Max: 2.9 K
2007062112 8km RR [mm/h] 2007062112 8km Pot. Temp. [K]
Analysis Pot. Temp.
Dorninger, et al. Joint COPS/CSIP-meeting Cambridge
Summary and outlook
• Observed values do not represent the truth• QC module of NWP-model independent VERA system is used to create ensemble members (perturbations)• uncertainty of analysis highest in regions of strong gradients• joint WWRP COPS and D-PHASE activity to collect fine-scale JDC data set• JDC data are shared at the multi user database at WDCC in Hamburg
• correction proposals are not necessarily Gaussian distributed• implementation of “alternative” analysis methods (e.g., Cressman, Barnes, Kriging) to produce “poor man ensemble analysis”• increase number of ensemble members to 50• define uncertainty of basic verification measures
Dorninger, et al. Joint COPS/CSIP-meeting Cambridge
VERITA
NWP model verification over complex terrain with VERA
SPP 1167
Study of the process chain and predictability of precipitation by
combining the D-PHASE ensemble and the COPS data
sets in the COPS domain
Thank you for your attention !Contact: [email protected]