KENDA (Km-Scale Ensemble-based Data Assimilation) COSMO General Meeting, Offenbach, 7 – 11 Sept. 2009 KENDA christoph.schraff@dwd.de Contributions / input by: Hendrik Reich, Andreas Rhodin, Klaus Stephan, Werner Wergen (DWD) Daniel Leuenberger, Tanja Weusthoff (MeteoSwiss) Marek Lazanowicz (IMGW) Mikhail Tsyrulnikov (HMC) PP Kenda : Status Report [email protected]Deutscher Wetterdienst, D-63067 Offenbach, Germany • status & outlook • general issues in the convective scale experiments for assessing importance of km-scale details in IC • deterministic analysis
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KENDA (Km-Scale Ensemble-based Data Assimilation) COSMO General Meeting, Offenbach, 7 – 11 Sept. 2009 KENDA [email protected] Contributions / input.
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Task 1: General issues in the convective scale and evaluation of COSMO-DE-EPS
Purpose: Guides decision how resources will be spent on/ split betw. LETKF and SIR
(COSMO-NWS and universities); part of the learning process
main disadvantage of LETKF: assumes Gaussian error distributions
Task 1.1.A: investigate non-Gaussianity by means of O – B statistics (convective / larger scales, different forecast lead times): provides an upper limit estimate of the non-Gaussianity to deal with
talk by Daniel Leuenberger:Statistical characteristics of high-resolution COSMO Ensemble forecastsin view of Data Assimilation
Task 1.2: investigate non-Gaussianity by examining perturbations of very-short range(2009) forecasts from COSMO-DE-EPS
Task 2: Technical implementation of an ensemble data assimilation framework / LETKF
analysis step (LETKF) outside COSMO code ensemble of independent COSMO runs up to next analysis time separate analysis step code, LETKF included in 3DVAR code of DWD
read ensemble of NetCDF feedback files + ensemble of COSMO S-R forecast Grib files
perform LETKF (based on obs–fg values around each grid pt.,calc. transformation matrices and analysis (mean & pert.))(adapt: C-grid, specific variables (w), efficiency)
write ensemble of COSMO S-R analysis Grib files + NetCDF feedback files with additional QC flags ( verif.)
exp.system
Task 2.3: finished
Task 2.2: almost finishedTask 2.4: not yet done
However: scripts written to do a few stand-alone cycles with LETKF → preliminary tests can start soon
Task 2.5: - in progress: include above libraries in 3DVar environment, (translate COSMO data structure into 3DVar data structure and vice versa)
- not yet started: extend flow control (e.g. reading several Grib files and temporal interpolat.)
Task 2: Technical implementation of an ensemble data assimilation framework / LETKF
for verification: ‘stat’-module: compute model (forecast) – obs :adapt verification mode of 3DVar/LETKF package
Advantages:
– COSMO obs operators available in 3DVAR/LETKF environment 3DVar/ EnKF approaches requiring 3DVar in principle applicable to COSMO LETKF for ICON will require COSMO obs operators in the future
– 1 common code for GME/ICON and COSMO to produce input for diagnostics / verif..
Disadvantages: – more complex code for this diagnostic task
– possibly additional transformation from COSMO data structure into 3DVAR data structure and vice versa required for new COSMO obs operators.
observation operators H with QC
modules for reading obs from NetCDF
Task 2.6: Adapt ensemble-related diagnostic tools : not yet started
address primary scientific issues related to the LETKF on the convective scale(using only in-situ data at first)
• investigate / control noise, adapt in view of non-hydrostatic aspectsif mixing of hydrostatically balanced model states (ensemble forecasts) varies with height (vertical localisation), the resulting model analysis will usually not be hydrostatically balanced
→ get version that can be used to do first tests with radar radial winds
• primary scientific issues:– model model (physical) perturbations– multiplicative covariance inflation– localisation (multi-scale DA)– additive covariance inflation with red noise, backscatter, statistical forecast error
covariances (‘3DVAR-B’) ‘– bias removal– noise control (update frequency, digital filter initialisation, lateral boundary cond.)– (convection initiation (warm bubbles, LHN)
Task 4.4: Cloud info based on satellite and conventional data(DWD: applied for Eumetsat fellowship, start end of 2010)
• derive incomplete cloud analysis, use obs increments of cloud or humidity• use SEVIRI brightness temperature directly in LETKF in cloudy (+ cloud-free) conditions
Set up standard and G-SIRF with and without standard data assimilation (MIUB, DWD)
• assess impact of conventional DA (LHN, PIB) on ensemble development (spread generation, keeping ensemble on track)
• implement optimal stepping to a new driving mesoscale ensemble
Evaluate classical and spatial (object oriented, fuzzy) metricsfor weighting mesoscale (SREPS) and km-scaleensemble members (DLR, MCH)• assess correlation of metrics betw. models of different res.• assess persistence of skill in different metrics