slide1 n.
Download
Skip this Video
Loading SlideShow in 5 Seconds..
Introduction to KENDA as COSMO Priority Project PowerPoint Presentation
Download Presentation
Introduction to KENDA as COSMO Priority Project

Loading in 2 Seconds...

play fullscreen
1 / 15

Introduction to KENDA as COSMO Priority Project - PowerPoint PPT Presentation


  • 107 Views
  • Uploaded on

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

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'Introduction to KENDA as COSMO Priority Project' - daphne-daniel


Download Now An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
slide1

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
slide2

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
slide3

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)

slide4

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

slide5

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
slide6

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
slide7

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)
slide8

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)
slide9

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
slide10

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)
slide11

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)
slide12

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
slide14

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

slide15

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