towards rapid update cycling for short range nwp forecasts in the hirlam community wmo/wwrp workshop...
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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 NowcastingUCAR 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
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Structure
•The HIRLAM consortium
•Developments towards Rapid Update Cycling
•Experimental results
•Concluding remarks
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ALADIN/HIRLAM close collaboration started in 2005
Lithuania
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Model domains in HIRLAM consortia
DMI HARMONIE (AROME) DMI HARMONIE (AROME) (2.5 km hor res, 65 vert lev)(2.5 km hor res, 65 vert lev)
HIRLAM 7.4 RCR HIRLAM 7.4 RCR (7 km hor res, 65 vert lev)(7 km hor res, 65 vert lev)
HIRLAM 7.3 RCR HIRLAM 7.3 RCR (15 km hor res, 60 vert lev)(15 km hor res, 60 vert lev)
SMHI HARMONIE (ALARO) SMHI HARMONIE (ALARO) (5.5 km hor res, 60 vert lev)(5.5 km hor res, 60 vert lev)
HIRLAM ref DA: 4D-VarHIRLAM ref DA: 4D-VarHARMONIE ref DA: 3D-VarHARMONIE ref DA: 3D-Var
Focus is moving towards frequently updated short-range km-scale forecastsFocus is moving towards frequently updated short-range km-scale forecasts
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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
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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 productsIllustration upper-air observation types
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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)
VORTICITYDIVERG.T and Ps
WINTER ( 12UTC)
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Algorithmic developments
•EKF for surface DA
•4D-Var
•ETKF, EnDA
•Hybrid DA
•Handling of non-additive errors
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Estimate the phase error (displacement field) and warp the background state.
Minimize the additive error using standard VAR-method.
Non-additive errors
bHx
y
)(sxb
)( Tsxb T
Estimate Warp
Handling – two step method
(phase-/displacement-/alignement-/timing errors)
H(fg) Estimated T SEVIRI
Example
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Domains D11/H11/U11: 11 km hor res., 60 vert lev.
HIRLAM model RUC parallel experiments
Summer period: 1 May 2010- 5 September 2010 Winter period: 13 January 2011- 28 February 2011
Parallel experiments over H11 and U11 domains
Experimental Design
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Verification of Rainfall forecasts
Verification area
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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
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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:Model domain: SMHI pre-oper. SMHI pre-oper.Horizontal resolution:Horizontal resolution: 5.5 km 5.5 kmVertical levels: Vertical levels: 6060LBC:LBC: 3 hourly with ECMWF fc 3 hourly with ECMWF fcSurface DA:Surface DA: Optimal Interpolation Optimal InterpolationUpper–air DA:Upper–air DA: 3D-Var 3D-VarObservation usage:Observation usage: SYNOP, SHIP, SYNOP, SHIP, DRIBU, TEMP, PILOT, AIREP, AMDAR, DRIBU, TEMP, PILOT, AIREP, AMDAR, Conv.+ATOVS AMSU-A Conv.+ATOVS AMSU-A Initialization:Initialization: IDFI IDFI
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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
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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.