Post on 19-Dec-2015
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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