The 2nd International Workshop on GPM Ground Validation TAIPEI, Taiwan, September 2005 GV for ECMWF's Data Assimilation Research Peter ECMWF, Reading, UK NWP and data assimilation Validation options Requirements for GV

The 2nd International Workshop on GPM Ground Validation TAIPEI, Taiwan, September 2005 What will NWP systems focus on in the GPM timeframe?

The 2nd International Workshop on GPM Ground Validation TAIPEI, Taiwan, September 2005 Example of NWP Prediction Skill Development Improvement of model spatial/temporal resolution due to increased computer power. Improvement of physical parameterizations (diabatic, land/ocean-atmosphere etc.). Increased satellite data usage! 2-day skill improvement Elimination of NH-SH discrepancy

The 2nd International Workshop on GPM Ground Validation TAIPEI, Taiwan, September 2005 Expected Future Developments in NWP ECMWF now:- 40 km (T511/T159), 60 model levels; - Two analysis suites (6-hour, 12-hour window); - Two 10-day forecasts initialized at 00 and 12 UTC; - 50-member EPS (50 km, T255); - Radiances/products from ~20 different satellite sensors assimilated; - Assimilation of rain-affected radiances operational since 28/06/2005. ECMWF late 2005:- 25 km (T799/T255), 91 model levels; - Two 14-day forecasts initialized at 00 and 12 UTC. ECMWF ~2010:- 15 km (EPS 30 km); - towards longer assimilation window analyses; - towards unified ensemble prediction system (medium-range, monthly, seasonal); - towards coupled data assimilation (land-ocean-atmosphere); - towards environmental monitoring; - towards focus on severe weather forecasting. General: - NWP systems will become much better in (physically) resolving even meso- scale synoptic systems. - NWP systems will become much better in assimilating cloud and rain affected observations (see recent JCSDA workshop on Cloud and Precipitation, ).

The 2nd International Workshop on GPM Ground Validation TAIPEI, Taiwan, September 2005 Why do observations in cloud and precipitation have potential?

The 2nd International Workshop on GPM Ground Validation TAIPEI, Taiwan, September 2005 Forecast sensitivity to Cloud and Rain-affected Observations Current systems produce rather good precipitation forecasts without assimilating any (!) direct precipitation or cloud observation. However: There are indications that key analysis errors occur in areas that are influenced by clouds and precipitation. t days Model H t 0 t days Sensitivity of FCST error Adjoint Model H* to perturbations FCST t days in t 0 Optimum perturbations to minimize FCST error KEY ANALYSIS ERROR TRACKING (Rabier et al. 1996, Klinker et al. 1998)

The 2nd International Workshop on GPM Ground Validation TAIPEI, Taiwan, September 2005 Mean Dec hPaT-perturbations modifiedT-perturbations modified T-perturbationsby high cloud coverby low cloud cover Mean profile of Dec 1999 Mean Dec 1999 high cloud cover Mean Dec 1999 low cloud cover T-perturbations Forecast sensitivity to Cloud and Rain-affected Observations (McNally 2002)

The 2nd International Workshop on GPM Ground Validation TAIPEI, Taiwan, September 2005 What are the validation options for clouds and precipitation in NWP systems?

The 2nd International Workshop on GPM Ground Validation TAIPEI, Taiwan, September 2005 Direct vs. Indirect Validation of NWP-System Performance: Small-scale Direct validation of ECMWF cloud-cover analyses with LITE observations Indirect validation of ECMWF cloud/rain profiles with ground-based (ARM) 35-GHz radar observations ECMWF LITE ECMWF ARM Latitude Time [h] Z e [dBZ] Cloud water (solid) and ice (dashed) mixing ratio [g/kg] (Lopez et al. 2005)

The 2nd International Workshop on GPM Ground Validation TAIPEI, Taiwan, September 2005 Direct validation of ECMWF Indirect validation of ECMWF precipitation forecasts with cloud/rain fields with SSM/I BMRC rain gauge analyses GHz (h) observations ECMWF SSM/I ECMWF CTRL ECMWF EXP BMRC mm Direct vs. Indirect Validation of NWP-System Performance: Large-scale

The 2nd International Workshop on GPM Ground Validation TAIPEI, Taiwan, September 2005 What could be the specific GV requirements?

The 2nd International Workshop on GPM Ground Validation TAIPEI, Taiwan, September 2005 Conceptual Model of GV for Data Assimilation Purposes Rain assimilation at ECMWF became operational in June 2005: 1) 1D-Variational retrieval of integrated moisture using SSM/I radiances over ocean. 2) 4D-Variational assimilation of integrated moisture in analysis system. (Bauer et al. 2005a, b) Main validation requirements: Cost function minimization in variational assimilation calculates:  J (x) = B -1 (x-x b ) + H T R -1 [H(x) - y o ] where R includes modelling (of H) (also representativeness) and observation errors. H comprises moist physical parameterizations and radiative transfer models but R is required in units K because y o consists of radiances! Initialize single column Validate model (H) forecastPerform validation over long time with 3-d observations of with radiometric and other series and different forecast lengths: T, q, u, v, …. observations: model error small direct estimation of R in units K representativeness error estimate areas of improvement for H model error growth estimate Single column model experiment at GV site:

The 2nd International Workshop on GPM Ground Validation TAIPEI, Taiwan, September 2005 Potential Ground Validation Requirements NWP systems perform analyses to optimally initialize model forecasts of the entire atmospheric state: Example 1: bad moisture analyses will produce bad cloud and precipitation forecasts. Example 2: good moisture analyses with bad diabatic models will produce bad cloud and precipitation forecasts. Ideally, the entire 3-D meteorological environment should be observed to validate the initial conditions, the model parameterizations, and the observation operators that are employed in data assimilation. Large-scale (satellites, networks): Direct validation with derived products from independent observations (example: PR, GPCP, Cloudsat products, …). Indirect validation with radiances (operational, simple, requires interpretation but prepares for radiance assimilation). Small-scale (GV): Accurate and continuous (to address representativeness issue) observations of derived key meteorological parameters and radar/radiometry (example: conceptual model).