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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,

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Presentation on theme: "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,"— Presentation transcript:

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 October, 2011 Magnus Lindskog, Siebren de Haan, Sibbo van der Veen, Sigurdur Thorsteinsson, Shiyu Zhuang, Tomas Landelius and Kristian Pagh Nielsen

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

3 ALADIN/HIRLAM close collaboration started in 2005 Lithuania

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

5 Towards Rapid Update Cycling (RUC) On-going data assimilation developments 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

6 New types of observations 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, … Upper-air : Surface : Illustration derived cloud products Illustration upper-air observation types

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) Air-mass/flow dependence to be represented SUMMER (12 UTC) VORTICITY DIVERG. T and Ps WINTER ( 12UTC)

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

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

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

11 Verification of Rainfall forecasts Verification area

12 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 Parallel experiments U11 RUC with and without cloud initialization 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 Cloud forecast Verification scores

13 HARMONIE system parallel experiments 6 h intermittent data assimilation cycle 3 h intermittent data assimilation cycle Two parallel exp. for July & August 2009 and January & February. 2010: 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) 6h cycle Surface pressure (hPa) RMS/BIAS as function of forecast range Temperature (K) RMS/BIAS of + 12 h forecasts as function of vertical level 3h cycle Assimilation of ATOVS AMSU-A crucial for positive impact of 3h data assimilation cycle in this parallel experiment

15 Conclusions and Future Plans 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.


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