Combined Data Assimilation with Radar and Satellite Retrievals and

Ensemble Modelling for the Improvement of Short Range

Quantitative Precipitation Forecasts

PHASE III

Clemens Simmer (coord.)+Victor Venema +Marco Milan

Meteorologisches InstitutRheinische Friedrich-Wilhelms-

Universität Bonn

George Craig+Christian KeilInstitut für Physik der AtmosphäreDeutschen Zentrum für Luft- und

Raumfahrt (DLR)

Hendrik Elbern+Elmar FrieseRheinisches Institut für

UmweltforschungUniversität zu Köln

Mathias Rotach+Daniel Leuenberger

MeteoSchweiz

Werner Wergen+Klaus Stephan+Stefan Klink

Deutscher Wetterdienst (DWD)

Combined Data Assimilation with Radar and Satellite Retrievals

and 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 distributionsvariational 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)

DAQUA forecasting system

Convective scale ensemble generation

Convective scale ensemble broadening

PI or LHN

COSMO-LEPS ensemble

Meso scale ensemble generation

Final (precipitation) analysis

Importance resampling

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)

Plans for Phase III- Basics of SIRF -

Sequential Importance Resampling Filter (SIRF)

time

systemstate

←obs

←obs

←obsx xx

x xx

xxx

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 PDFHighly nonlinear processes Model errorsDirect and indirect observations with highly nonlinear observation operators and norms

Plans for Phase III- Planned Implementation -

Best-Member-Selection 1Working on driving EPS, based on Satellite and Radar data conventional observations

Best-Member-Selection 2Working on HREAS, based on Satellite and Radar Dataconventional observationsEv. Assimilation Increments

HREAS(High Resolution Ensemble AssimilationSystem,COSMO-DE, MM5/WRF)

timeDriving EPS(COSMO-SREPS)

Ensemble Enhancement/ResamplingSIRF: basic SIRFL-SIRS: Localized SIRSG-SIRF: Guided SIRF

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)

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

Planned Tasks for Third Phase in WP2 by DLR

Evaluation of classical and spatial metrics for the

determination of weighting schemes for ensemble members

1. 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).

2. 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

3. 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?

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- 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)

)()( 1 xyRxy HH T

Planned Tasks for Third Phase in WP2 by MeteoSwiss

LSIRS: Local Sequential Importance Resampling Smoother (RIU)

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.

extinctat obs time 1

extinctat obs time 2

likely estimates

convective cells ensemble # 1000

SIRS-Approach (RIU)Localisation by Minimodel approach 7x7 grid columns

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