LACE Data assimilation activities EWGLAM meeting 2016

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1 LACE Data assimilation activities EWGLAM meeting 2016 Mate Mile and many LACE DA colleagues

2 LACE data assimilation systems dx OI OI+OI+ 4 OI+ Blend OI+ VAR OI VAR CAN PACK OI+ OI+ OI Blending OI anal freq. Horizontal and vertical resolution upgrades Increasing assimilation cycle frequency More and more observations in use Limitations of error characteristics and representations Challenges on mesoscales System upgrade, validation and maintenance issues

3 Outline Highlights of LACE DA systems Surface assimilation with EKF Two-way coupling of ocean-atmosphere in DA system The use of aircraft observations in LACE data assimilation systems GNSS ZTD assimilation in 3

4 Surface assimilation activities The Extended Kalman-Filter approach is being further studied in LACE. There are ongoing developments to utilize: Conventional observations for operational purposes and to replace OI (control variables: TG1, TG2, WG1, WG2, observations: T2m, RH2m) Satellite observations for special project purposes (new projects: control variables: WG1, WG2, TG1, observations: combined SWI, LST) During these studies AROME or ALARO and SURFEX models are used. 4

5 EKF with conventional observations The analysis of EKF method seems realistic, however more careful validation is ongoing. The following issues, one has to be investigated: generation of gridded observations analysis and assimilation window optimization work to determine operationally feasible framework Increments TG1 Increments TG2 Increments WG1 synop EKF Courtesy of Helga Toth Increments WG2 synop EKF

6 EKF with satellite observations There are also two EKF surface assimilation studies utilizing satellite observations: Sentinel-1 and ASCAT soil moisture product (combination of the two) Sentinel-3 LST (soon started) Spatial resolution of the data is 1km and temporal resolution is 1 day. 6 Courtesy of Stefan Schneider

7 Two-way coupled DA system In Slovenia the two-way coupling of atmosphere and ocean model was investigated. For ocean model, the combination of POM (Adriatic sea) and MFS was used. Atmosphere component: ALARO 4.4km L87, 3h RUC Parameters exchanged during each time step. 7 Courtesy of Benedikt Strajnar

8 Two-way coupled DA system Studying different options, the two-way coupling in both DA and forecasting system has been found the most appropriate for convective case studies. Coupled assim only 8 Coupled forecast only Courtesy of Benedikt Strajnar Coupled assim+forecast

9 The use of aircraft observations inside LACE The Mode-S observations have wider and wider network in LACE. The Slovenian Mode-S MRAR and KNMI collected EHS observations are already (re)distributed through common observation preprocessing system (OPLACE). Mode-S observations from Czech Republic and Austria are also collected and tested. The use of Mode-S in LACE DA systems: In Slovenia, Slovenian MRAR (results showed in last EWGLAM) In Croatia, Slovenian Mode-S MRAR In Austria, Slovenian and Austrian Mode-S MRAR In Czech Republic, Czech Mode-S MRAR In Hungary, Slovenian MRAR 9

10 The use of aircraft observations inside LACE The Mode-S observations from Slovenia tested in AROME/Hungary 6-31 Dec ETS 24h precipitation 00UTC +30h AROME oper AROME mrar Model domain + Verif domain AROME OPER 06UTC +6h 10 AROME MRAR 06UTC +6h Courtesy of Viktoria Homonnai RADAR 12UTC

11 The use of aircraft observations inside LACE The Mode-S MRAR observations and its error characteristics were studied in ALARO/CHMI (4.7km, L87) DA systems. Due to the reduction of error correlations, optimal thinning distance and error inflation have been determined (using Desroziers). Optimal thinning distance: error corr. less or equal than (Liu and Rabier, 2003) Horizontally ~25-35km, vertically ~20hPa Error inflation: 2.0 (studying best forecast impact) 11 Courtesy of Patrik Benacek

12 The assimilation of GNSS ZTD in AROME The use of GNSS ZTD was continued in AROME. Revision of SGO1 EGVAP network and its measurements Reassess the whitelist generation procedure (thinning 40km, biasmax 15mm, stdevmax 15mm) The ZTDs from SGO1 network have good quality and coverage over Hungary For bias correction both static and VARBC were tested. VARBC with whitelist of active stations, but with zero initial bias information Additionally VARBC was extended with more predictors (pred3 and pred4) in varbc_pred. 12

13 The assimilation of GNSS ZTD in AROME The impact of GNSS ZTD was evaluated for spring 2016 comparing OPER, STATIC and VARBC experiments. 13

14 Thank You for your attention! Questions? Cheers from last LACE Data Assimilation Working Days 14