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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, Boulder, Colorado, USA 24-26 October, 2011

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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, 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

slide2
The HIRLAM consortium

Developments towards Rapid Update Cycling

Experimental results

Concluding remarks

Structure
model domains in hirlam consortia
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

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

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

New types of observations

Upper-air:

Surface:

Illustration upper-air observation types

Illustration derived cloud products

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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)

SUMMER (12 UTC)

WINTER ( 12UTC)

VORTICITY

DIVERG.

T and Ps

Air-mass/flow dependence to be represented

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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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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

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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

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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

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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

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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

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.

Conclusions and Future Plans