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Towards Rapid Update Cycling for Short Range NWP Forecasts in the HIRLAM Community

Towards Rapid Update Cycling for Short Range NWP Forecasts in the HIRLAM Community WMO/WWRP Workshop on Use of NWP for Nowcasting UCAR Center Green Campus, Boulder, Colorado, USA 24-26 October, 2011

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Towards Rapid Update Cycling for Short Range NWP Forecasts in the HIRLAM Community

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  1. Towards Rapid Update Cycling for Short Range NWP Forecasts in the HIRLAM Community WMO/WWRP Workshop on Use of NWP for Nowcasting UCAR Center Green Campus, Boulder, Colorado, USA 24-26 October, 2011 Magnus Lindskog, Siebren de Haan, Sibbo van der Veen, Sigurdur Thorsteinsson, Shiyu Zhuang, Tomas Landelius and Kristian Pagh Nielsen

  2. The HIRLAM consortium Developments towards Rapid Update Cycling Experimental results Concluding remarks Structure

  3. ALADIN/HIRLAM close collaboration started in 2005 Lithuania

  4. Model domains in HIRLAM consortia HIRLAM 7.3 RCR (15 km hor res, 60 vertlev) HIRLAM 7.4 RCR (7 km hor res, 65 vert lev) SMHI HARMONIE (ALARO) (5.5 km hor res, 60 vert lev) DMI HARMONIE (AROME) (2.5 km hor res, 65 vertlev) HIRLAM ref DA: 4D-Var HARMONIE ref DA: 3D-Var Focus is moving towards frequently updated short-range km-scale forecasts

  5. Investigate effects of increasing frequency of data assimilation cycles and of shortening observation cut-off time Utilization of new types of observations Handling of balances Algorithmic developments Towards Rapid Update Cycling (RUC)On-going data assimilation developments

  6. RADAR radial winds and reflectivities, GNSS (GPS) ZTD, Mode-S, satellite based radiances (IASI, SEVIRI,ATOVS), GPS RO, derived satellite based cloud-products, Scatterometer,… ASCAT, SMOS, MODIS, GLOBSNOW, … New types of observations Upper-air: Surface: Illustration upper-air observation types Illustration derived cloud products

  7. Handling of Balances (seasonal variation of coupling of humidity background errors with errors of other variables as derived for km-scale model over Danish domain) SUMMER (12 UTC) WINTER ( 12UTC) VORTICITY DIVERG. T and Ps Air-mass/flow dependence to be represented

  8. Algorithmic developments • EKF for surface DA • 4D-Var • ETKF, EnDA • Hybrid DA • Handling of non-additive errors

  9. Non-additive errors (phase-/displacement-/alignement-/timing errors) Handling – two step method • Estimate the phase error (displacement field) and warp the background state. • Minimize the additive error using standard VAR-method. Warp Estimate Example H(fg) Estimated T SEVIRI

  10. HIRLAM model RUC parallel experiments Parallel experiments over H11 and U11 domains Summer period:1 May 2010- 5 September 2010 Winter period:13 January 2011- 28 February 2011 Domains D11/H11/U11: 11 km hor res., 60 vert lev. Experimental Design

  11. Verification of Rainfall forecasts Verification area

  12. Parallel experiments U11 RUC with and without cloud initialization siebren.de.haan@knmi.nl 1. Transfer of MSG cloud cover to 3D cloud cover in HIRLAM model: • cloud cover N from NWC SAF • cloud base from (interpolated) synoptic observations • cloud top from MSG (10.8 micron channel) 2. Translate N to humidity Cloud forecast Verification scores Verification results by comparison of Hirlam cloudiness to synoptic observations (bias and standard deviation of errors) (large verification area over Europe) REF: Hirlam reference run MSG: Hirlam run with MSG cloud initialisation

  13. HARMONIE system parallel experiments Two parallel exp. for July & August 2009 and January & February. 2010: • 6 h intermittent data assimilation cycle • 3 h intermittent data assimilation cycle Model domain: SMHI pre-oper. Horizontal resolution: 5.5 km Vertical levels: 60 LBC: 3 hourly with ECMWF fc Surface DA: Optimal Interpolation Upper–air DA: 3D-Var Observation usage: SYNOP, SHIP, DRIBU, TEMP, PILOT, AIREP, AMDAR, Conv.+ATOVS AMSU-A Initialization: IDFI

  14. Scores for verification against observations (summer period) Temperature (K) RMS/BIAS of + 12 h forecasts as function of vertical level Surface pressure (hPa) RMS/BIAS as function of forecast range 6h cycle 3h cycle Assimilation of ATOVS AMSU-A crucial for positive impact of 3h data assimilation cycle in this parallel experiment

  15. Utilization of observations with high resolution in space and time important for RUC. Encouraging first results from initializing clouds for RUC, applying a simple approach. Significant seasonal variations of balances revealed for a km-scale model. Future plans include investigation of air-mass and flow dependent balances. Imbalances and associated spin-up need further investigations. Algorithmic developments for handling of non-linearities, complex observation types and non-additive errors are on-going. Co-ordinated impact studies planned to assess the impact of new observation types and to optimize the handling of these. Conclusions and Future Plans

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