Development of a data assimilation system with HRM model and 3DVAR

technique

Kyoto, 03 - 2008

Le DucVietnam National University of Hanoi

Vietnam National Center for Hydro-Meteorological Forecast

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Outline

Introduction to the numerical weather prediction system in Vietnam HMS

Overview of the data assimilation system with HRM model using 3DVAR technique

Verification

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1. Numerical weather prediction in Vietnam

HMS

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Probabilistic Forecast

Multi-model multi-analysis SREF:Models: BOLAM, Eta, HRM Initial and boundary conditions: GEM (CMC), GFS (NCEP), GME (DWD), GSM (JMA), NOGAPS (US Navy)LAF to increase members

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Deterministic Forecast

Model: HRM

Data assimilation: 3DVAR and IDFI technique

Boundary conditions: GME (DWD)

MOS: multivariate regression and Kalman filter

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2. Data assimilation system in Vietnam

HMS

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Components of a data assimilation system

Quality control system

An objective analysis program

An initialization program

A model

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Quality control system

Observations: SYNOP, SHIP, DRIBU, TEMP, PILOT, AMDAR, AMV (METEOSAT5, MTSAT-1R), sea winds (QuikSCAT, ASCAT), radiances (in near future)

Observation database

Simple quality checks

Variational quality control: this module comes from the 3DVAR program and can be considered as a firstguess check

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Conventional data

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Satellite data

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Radiance Data

Upper profile extrapolation: the top level that RTTOV uses is higher than one of HRM and we want to derive meteorological values above the model top from satellite data by linear regression.Bias correction: scan angle correction and air mass correction.

NOAA data receiver will be set in Vietnam HMS in March 2008. Current works:

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Objective analysis (3D-VAR) program

Source: The 3D-VAR program has developed at DWD for GME model and we adapted it for HRM model

Assimilation space: observation space (3D-PSAS)

Observation error matrix R: diagonal, taken from IFS (ECMWF) or the old OI analysis system (DWD)

Background error matrix B: analytical functions for computing B elements

Control variables: ps, u, v, T, rh

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Initialization program

Digital filter initialization (DFI): this program is included inHRM as a module. DFI will smooth out meteorological fields in regions without observations.We take IDFI (Incremental DFI) approach: ANA(IDFI) = ANA(DFI) + FG - FG(DFI)Here ANA is an abbriviation for the analysis, FG for the firstguess field. In data-sparse areas, ANA is equal FG then ANA(DFI) is equal FG(DFI). That means ANA(IDFI) and FG are the same and initialized analyse retain small-scale processes.

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NWP model

HRM model has developed at DWD (known as EUROPA model). It is a hydrostatical NWP model for α and β meso-scale phenomena on limited areas. HRM is built on rotated lat/lon grids with Arakawa C grid type. For vertical coordinate, HRM uses hybrid coordinate.The prognostic variables are surface pressure ps, wind components u and v, temperature T, specific humidity qv, cloud water qc, cloud ice qi.

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3. Verification

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Experiment

Domain: 0.1250

resolution, 161x161 grid points, 31 layersTime: 2005Observations: conventional dataInitialisation: DFIComparison: with HRM run used GME analyses as ICs

Domain

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Case study: KAITAK typhoon 00Z-31/10/2005

Mean sea level pressure(GME analysis)

Mean sea level pressure(HRM analysis)

Best track: 14.6N, 111.8E, 950mb

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MSL pressure forecast verification

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2m temperature forecast verification

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Rainfall forecast verification

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Remark

A significant improvement in forecasting during the first 12 hours.A marginal/neutral improvement on forecasts with the forecast range more than 12 hours.A small impact on 24h precipitation forecast with increment in correlations and reduction in RMSE and false alarm rates. A significant improvement in storm intensity (mean sea level pressure at storm centre) forecast. Improvement mainly comes from background fields.

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Case study with some modifications in DA system

Domain: 0.150

resolution, 201x161 grid points, 31 layersTime: 11/2007 (Hagubis typhoon)Observations: conventional and satellite dataInitialisation: IDFIBoundary conditions: test with GSM forecasts as BCs

Domain

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HAGUBIS 00Z-23/11/2007

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HAGUBIS 00Z-23/11/2007

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Future works

Running the new data assimilation system operationally

Assimilating radiance data

Specifying B matrix with NMC method

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Thank you for your attention

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Case study: VICENTE typhoon 00Z-17/9/2005

Mean sea level pressure(GME analysis)

Mean sea level pressure(HRM analysis)

Best track: 13.5N, 114.5E, 990mb