Improving Short Range Precipitation Forecasts with Combined Radar and Satellite Data Assimilation - Phase III

1. Combined Data Assimilation with Radar and Satellite Retrievals and Ensemble Modelling for the Improvement of Short Range Quantitative Precipitation ForecastsPHASE III Clemens Simmer (coord.)+ Victor Venema +Marco Milan Meteorologisches Institut Rheinische Friedrich-Wilhelms-Universität Bonn George Craig+Christian Keil Institut für Physik der Atmosphäre Deutschen Zentrum für Luft- und Raumfahrt (DLR) Hendrik Elbern+Elmar Friese Rheinisches Institut für Umweltforschung Universität zu Köln Mathias Rotach +Daniel Leuenberger MeteoSchweiz Werner Wergen+Klaus Stephan +Stefan Klink Deutscher Wetterdienst (DWD)

2. Combined Data Assimilation with Radar and Satellite Retrievalsand Ensemble Modelling for the improvement of Short Range Quantitative Precipitation Forecasts General DAQUA Goal: Improvement of short and very short (nowcasting) quantitative precipitation forecasting based on regional high resolution weather forecast models Problems: • Predictions can be far from truth at local scale. • Effects of nonlinear dynamics (e.g. discontinuous processes like convection) might dominate the development. • non-Gaussian error distributions • variational approaches lack their basis Plans for Phase II: • First setup of a combined ensemble based data assimilation system (e.g. EnTKF) (DLR) • Use of GPS tomography to derive and assimilate humidity profiles (MeteoSwiss) • Finalisation of the Physical Initialisation data assimilation tool method for nowcasting(Uni-Bonn) • Finalisation of the genetic data assimilation for column-based cloud ensembles (Uni-Cologne) • Setup and first test of a regional convective-scale ensemble-based data assimilation system based on the Sequential Importance Resampling Filter (DWD + Uni-Bonn)

3. DAQUAAchievements Phase II • Development of spatial measures for quality assessment of precipitation and cloud forecasts (DLR) • Test of physical consistency and improvement of a Latent Heat Nudging data assimilation technique for radar data and its operational implementation (DWD + MeteoSwiss) • Setup and test of a combined highly time-efficient assimilation scheme for radar and satellite data (PIB) based on a physical initialisation technique suited for nowcasting (Uni-Bonn) • Setup and evaluation of a genetic data assimilation for column-based cloud ensemble for MM5/WRF (Uni-Cologne) • Setup and first test of a regional convective-scale ensemble-based data assimilation system driven with COSMO-LEPS meso-scale ensemble using LHN (DLR + Meteo Swiss)

4. Plans for Phase III- Basics of SIRF - system state ←obs x x x x x x ←obs ←obs x x x time Sequential Importance Resampling Filter (SIRF) • runs an ensemble of forecasts • compares sequentially the forecasts with observations → Bayesian weights, importance • removes members with low weights and replaces them by better performing members according to their weight → resampling • SIRF handles major challenges on the convective scale data assimilation: • Non Gaussian PDF • Highly nonlinear processes • Model errors • Direct and indirect observations with highly nonlinear observation operators and norms

5. Plans for Phase III- Planned Implementation - time Driving EPS (COSMO-SREPS) HREAS (High Resolution Ensemble AssimilationSystem, COSMO-DE, MM5/WRF) Best-Member-Selection 2 Working on HREAS, based on Satellite and Radar Data conventional observations Ev. Assimilation Increments Best-Member-Selection 1 Working on driving EPS, based on Satellite and Radar data conventional observations Ensemble Enhancement/Resampling SIRF: basic SIRF L-SIRS: Localized SIRS G-SIRF: Guided SIRF

6. Plans for Phase III- General Goals - Goals: • Implementation and test of standard SIRF with COSMO-DE in the DWD Km-Scale Ensemble-based Data Assimilation (KENDA, based on LETKF) environment • Move from COSMO-LEPS ensemble to COSMO-SREPS as driving ensemble (better mesoscale prediction, consistency with KENDA) • Implement and test the Guided SIRF variant (GSIRF) to „cheaply“ enhance ensemble size and spread • Test coupling of SIRF and GSIRF with conventional data assimilation to keep ensemble closer to observations • Develop and test the Localized Sequential Ensemble Resampling Smoother (LSIRS) with MM5/WRF as km-scale model (because 4DVAR is a necessary component)

7. Plans for Phase III- Workpackages - WP1: Evaluation of classical and spatial metrics for the determination of weighting schemes for mesoscale and convective-scale ensemble members (DLR, MeteoSchweiz, ½+½ Postdoc) • Implement and test classical and spatial metrics on COPS events • Investigate correlation of metrics between models of different resolution • Investigate persistence of skill in different metrics WP2: Setup of a standard and G-SIRF-based COSMO-DE ensemble assimilation system with and without standard data assimilation (MIUB, DWD, 1 Postdoc) • Setup and test of a first version of the standard SIRFand the Guided SIRF • Evaluate the impacts of conventional DA on ensemble development • Implement optimal stepping to a new driving mesoscale ensemble WP3: Setup of a LSIRS-based MM5/WRF/COSMO-DE ensemble assimilation system (RIU, DWD, ½ Postdoc) • Identification of observed and modelled convective cell • Assimilation by genetic optimisation of mini-models (cell-wise instead of column-wise) • Gluing of genetic algorithm optimized mini-model results by 4DVar All Partners: Apply EnDa systems to the GOP/COPS period

8. Planned Tasks for Third Phase in WP2 by DLR Evaluation of classical and spatial metrics for the determination of weighting schemes for ensemble members • Implement spatial metrics and evaluation on selected COPS events: e.g. FQM of Keil and Craig (2007), SAL (Wernli et al. 2008), and spatial measures in the fuzzy verification package of Ebert (2007). • Investigate correlation of metrics between models of different resolution: e.g. quantify the extent to which performance in the coarser resolution model carries over to the nested high-resolution forecasts • Investigate persistence of skill in different metrics and different meteorological situations: e.g what combination of metrics provides the most useful weighting for resampling in the SIRF?

9. Planned Tasks for Third Phase in WP2 by MeteoSwiss Best member selection for SIRF and GSIRF • Used for selection of best members of both Driving members (LBC) and Fine scale members (reduction of population) • Use of conventional observation (surface observations, radiosondes) for cloud/precipitation free regions (pre-convective regions) • Classic quadratic metric for conventional observations at the meso-g scale • Investigate relative importance of conventional observations and cloud/precipitation based observations (radar, satellites) • Investigate “tuning” of observation error covariance matrix with relative weights of observations • Strong synergies with Local Ensemble Transform Kalman filter project of DWD and COSMO consortium (KENDA)

10. LSIRS: Local Sequential Importance Resampling Smoother (RIU) convective cells ensemble # 1000 LSIRS introduces two distinct features: Localisation: reduce horizontal model size drastically (only local convective cells simulated, mini-models), but increase ensemble size drastically (> 1000). ECMWF ensemble(#50) T, T+DT, T+2DT, … Smoother: the fit to all observations from initial time will be enforced until the end of the assimilation interval. Convective cell mini models with genetic algorithm based SIRS selection. Blue configuration prove fittest. extinct at obs time 2 likely estimates extinct at obs time 1

11. SIRS-Approach (RIU)Localisation by Minimodel approach 7x7 grid columns 13:00 14:00 15:00 16:00 mini-model MM5 radar (for genetic algorithm)