introduction to kenda as cosmo priority project

[email protected] to KENDAKENDA Mini-Workshop., Munich, 28 Feb. 2014 1

Introduction to KENDA as COSMO Priority Project

Christoph Schraff

Deutscher Wetterdienst, D-63067 Offenbach, Germany

• Motivation, implementation, status

• Current & future work

KENDA: Km-scale ENsemble-based Data Assimilation

[email protected] to KENDAKENDA Mini-Workshop., Munich, 28 Feb. 2014

ensemble-based data assimilation component missing & required

convection-permitting NWP: after ‘few’ hours, a forecast of convection is a long-term forecast deliver probabilistic (pdf) rather than deterministic forecast need ensemble forecast and data assimilation system

(strategic aims in COSMO)

forecast component: COSMO-DE EPS developed & operational at DWD

perturbations: LBC + IC + physics

GME, IFS, GFS, GSM

perturb.

Motivation : Why develop Ensemble-Based Data Assimilation ?

replace current nudging-based DA by state-of-the-art DA with flow-dependent B


GermanyGreece

ItalyPoland

RomaniaRussia

Switzerland

similar

configurationsx = 1 – 3 km ~ 2016 : x 2 km , LETKF

Local Ensemble Transform Kalman Filter (LETKF, Hunt et al., 2007) ,(because of its relatively low computational costs)

Motivation : Why develop Ensemble-Based Data Assimilation ?

data assimilation: priority project within COSMO consortium

Km-scale ENsemble-based Data Assimilation (KENDA):


• analysis step (LETKF) outside COSMO code ensemble of COSMO runs, collecting obs – f.g. 4D -LETKF separate analysis step code, LETKF included in 3DVAR package of DWD

LETKF (km-scale COSMO) :implementation

ensemble

K

deterministic

• analysis for a deterministic forecast run : use Kalman Gain K of analysis mean

deterministic run must use same set of observations as the ensemble system ! deterministic run may have higher resolution (not optimal if deterministic f.g. deviates strongly from ensemble mean f.g.)

xA = xB + K [yo – H(xB)]


• CNMCA (Lucio Torrisi Lucio Torrisi et al.) : LETKF for 10-km COSMO operational

• perturbed lateral BC : IFS EPS (MCH, ARPA-SIM)

Lateral BC /other LETKF implementations

lower resolution analysis

ensemble (40 members)

high resolution deterministic

analysis

(or at DWD) hybrid EnVar for ICON (GME)variational formulation

(Buehner et al 2005)


main development of LETKF at DWD (Hendrik Reich Hendrik Reich , Andreas Rhodin),main implemented features:

•adaptive multiplicative covariance inflation (based on Desroziers statistics)

•adaptive estimation of obs errors in obs space

•adaptive estimation of obs errors in ensemble space (to account for limited Nens)

•adaptive localisation to keep effective Nobs constant (to account for limited Nens)

•multi-step analysis

implementation of LETKF featuresin KENDA


• DWD:

stand-alone scripts for 2-day period: many LETKF tests, e.g. adaptive methods

LETKF in operational experimentation system NUMEX slow (archive)

‘BACY’ (basic cycling scripting environment for KENDA, Hendrik ReichHendrik Reich):

• fast (speed: DA with BACY ~ 1 – 2, i.e. ~ 5 – 10 times faster than with NUMEX)

• largely portable (if obs / GME fields provided)

• automatic plotting suite

• model equivalent calculation (MEC) from forecasts for input to verification potential: tool to ease collaboration with academia

• scripting environments for LETKF DA cycle also at

MeteoSwiss: 1-hourly LETKF DA cycle for 1 month using conventional obs

ARPA-SIM: first tests, setting up OSSE (Chiara MarsigliChiara Marsigli)

implementation & LETKF tests(so far using TEMP, aircraft, surface, wind profiler)


Main aim: reach operationability in (mid/end) 2015

•system complete (e.g. ana + perturb surface / soil) + robust + efficient

•quality KENDA ≥ quality nudging-based opr. DA (incl. LHN) (deterministic)(using similar obs set)

•additional: provide IC perturbations for EPS

evaluation of EPS:

•EPS: how to use KENDA IC perturbations for EPS (COSMO-DE-EPS) (PP COTEKINO / Richard KeaneRichard Keane, DWD) replace or rather combine with current IC perturbations

HErZ LMU: structure & impact of KENDA IC perturbations (Florian HarnischFlorian Harnisch)

•Diagnostics: FSO (forecast sensitivity of observations) (Matthias SommerMatthias Sommer, LMU)

KENDA : main short-term goal


KENDA : short-term tasks

• general testing, tuning, optimization of LETKF setup

specification of observation errors

use of adaptive methods (localisation, cov. inflation, R in ensemble space),

multi-step and multi-scale analysis with different obs / localisation scales

ensemble size (40 ?),

update frequency at ? RUC 1 hr at 15 min ! (high-res. obs)

non-linearity vs. noise / lack of spread / 4D property ?• inclusion of additive covariance inflation,

probably using self-evolving perturbations (Lucio TorrisiLucio Torrisi, CNMCA)

• testing SPPT in DA cycle, possibly also perturbed physics parameters

• inclusion of LHN (latent heat nudging) (as long as reflectivity not ready for use)

• robustness: create new ensemble members, if few crash


Extended Use of Observations (1)

Aim: (implementation,) forecast improvements from using these observations

•3D radar radial velocityComplete obs operator and efficient approximations suitable for DA developed,thinning and superobbing strategies implemented, preliminary DA cycles

Yuefei Zeng, Uli Blahak Yuefei Zeng, Uli Blahak (DWD)(Status of Y. Zeng after June 2014 or other resources at DWD unclear)

•3D radar reflectivity (direct use)Complete obs operator and efficient approximations suitable for DA developed,thinning and superobbing strategies implemented, preliminary DA cycles

Virginia Poli, Tiziana Paccagnella Virginia Poli, Tiziana Paccagnella (ARPA-SIM); Klaus Stephan Klaus Stephan (DWD), Theresa Bick Theresa Bick (U. Bonn)


Extended Use of Observations (2)

• GPS Slant Path DelayObs operators (incl. ray tracer) implemented in DWD global 3DVar;Aim: implement complete and efficient obs operator in COSMO by end of 2014

Michael Bender ; Erdem Altunac Michael Bender ; Erdem Altunac (tomography) (DWD)No resources available yet after 2014 for use in LETKF(challenge to use horizontally + vertically non-local obs in LETKF)

• Cloud Top Height (CTH) derived from Meteosat SEVIRI Fully implemented, single-obs experiments, cycled DA with dense obs for low-stratus cases

Annika Schomburg Annika Schomburg (DWD, talk on Monday)

• Direct use of SEVIRI IR window channels in view of assimilating cloud infoObs operator (RTTOV) + data flow implemented, next monitoring + DA tests

Africa Perianez Africa Perianez , DWD, until Feb. 2015, no resources yet thereafter

• Exploratory: SEVIRI VIS/NIR window channels (Leonhard ScheckLeonhard Scheck. LMU)


Extended Use of Observations (3) :Future

• Mode-S (high-resolution) wind and temperature data (from aircraft)and application to high-res airport model COSMO-MUC (with radar data)Heiner Lange, Tijana Janjic-Pfander Heiner Lange, Tijana Janjic-Pfander (HErZ LMU)

• Screen-level observations (T-2m, q-2m, uv-10m) (C. Schraff, DWD) (+ Master Thesis at MeteoSwiss on station selection)

• Direct use of SEVIRI WV channels (for T, qv; for cloud info; linked to IR window)Great interest by HErZ-LMU for a project, starting 2015


thank you for your attention


• implementation following Hunt et al., 2007

• basic idea: perform analysis in the space of the ensemble perturbations

– computationally efficient, but also restricts corrections to subspace spanned by the

ensemble

– explicit localization (doing separate analysis at every grid point,

select only obs in vicinity and scale R-1)

– analysis ensemble members are locally linear combinationsof first guess ensemble members

LETKF for km-scale COSMO :method

analysis membersforecastmembers


KENDA : Analysis & Perturbation of Lower Boundary Fields

• Snow cover and depth, idea:apply snow analysis independently to ensemble members (with perturbed obs ?)

• Sea surface temperature (SST), idea: add perturbations to deterministic analysis

• Soil moisture (soil temperature) perturbations only: as in EPS (COTEKINO)

Longer-term additional tasks

•Soil moisture (soil temperature) analysis, by using screen-level obs ; 2 ideas: add 1 analysis level in LETKF for the soil, and

apply strong localization for calculating the transform matrix for this level use the ensemble in current stand-alone variational SMA (perturbations ?)

•Soil moisture analysis (+ perturbations) using satellite soil moisture data in LETKFEumetsat fellowship at CNMCA

introduction to kenda as cosmo priority project

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