Introduction to KENDA as COSMO Priority Project

1. Introduction to KENDA as COSMO Priority Project Christoph SchraffDeutscher Wetterdienst, D-63067 Offenbach, Germany KENDA: Km-scale ENsemble-based Data Assimilation • Motivation, implementation, status • Current & future work

2. perturbations: LBC + IC + physics perturb. GME, IFS, GFS, GSM Motivation : Why develop Ensemble-Based Data Assimilation ? 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  ensemble-based data assimilation component missing & required • replace current nudging-based DA by state-of-the-art DA with flow-dependent B

3.  similar configurations x = 1 – 3 km ~ 2016 : x  2 km , LETKF Motivation : Why develop Ensemble-Based Data Assimilation ?  data assimilation: priority project within COSMO consortium Km-scale ENsemble-based Data Assimilation (KENDA): Germany Greece Italy Poland Romania Russia Switzerland  Local Ensemble Transform Kalman Filter (LETKF, Hunt et al., 2007) , (because of its relatively low computational costs)

4. 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)] LETKF (km-scale COSMO) :implementation • 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 ensemble K

5. Lateral BC / other LETKF implementations • perturbed lateral BC : IFS EPS (MCH, ARPA-SIM) (or at DWD) hybrid EnVar for ICON (GME) variational formulation (Buehner et al 2005) high resolution deterministic analysis lower resolution analysis ensemble (40 members) • CNMCA (LucioTorrisiet al.) : LETKF for 10-km COSMO operational

6. implementation of LETKF features in KENDA • main development of LETKF at DWD (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

7. implementation & LETKF tests (so far using TEMP, aircraft, surface, wind profiler) • 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 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 Marsigli)

8. KENDA : main short-term goal 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 Keane, DWD) • replace or rather combine with current IC perturbations • HErZ LMU: structure & impact of KENDA IC perturbations (Florian Harnisch) • Diagnostics: FSO (forecast sensitivity of observations) (Matthias Sommer, LMU)

9. 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 (LucioTorrisi, 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

10. Extended Use of Observations (1) • Aim: (implementation,) forecast improvements from using these observations • 3D radar radial velocity • Complete obs operator and efficient approximations suitable for DA developed, • thinning and superobbing strategies implemented, preliminary DA cycles • 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,TizianaPaccagnella(ARPA-SIM); • Klaus Stephan (DWD), Theresa Bick (U. Bonn)

11. Extended Use of Observations (2) • GPS Slant Path Delay • Obs operators (incl. ray tracer) implemented in DWD global 3DVar; • Aim: implement complete and efficient obs operator in COSMO by end of 2014 • Michael Bender ; ErdemAltunac(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(DWD, talk on Monday) • Direct use of SEVIRI IR window channels in view of assimilating cloud info • Obs operator (RTTOV) + data flow implemented, next monitoring + DA tests • Africa Perianez, DWD, until Feb. 2015, no resources yet thereafter • Exploratory: SEVIRI VIS/NIR window channels (Leonhard Scheck. LMU)

12. 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, TijanaJanjic-Pfander(HErZ LMU) • Screen-levelobservations (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

13. thank you for your attention

14. forecast members analysis members LETKF for km-scale COSMO :method • 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 combinations of first guess ensemble members

15. 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 LETKF • Eumetsat fellowship at CNMCA