Status and Plans of using the scatterometer winds in JMA's Data Assimilation and Forecast System

Similar documents
Operational Use of Scatterometer Winds at JMA

Operational Use of Scatterometer Winds in the JMA Data Assimilation System

Study for utilizing high wind speed data in the JMA s Global NWP system

Scatterometer Utilization in JMA s global numerical weather prediction (NWP) system

The Impact of Observational data on Numerical Weather Prediction. Hirokatsu Onoda Numerical Prediction Division, JMA

Satellite Soil Moisture Content Data Assimilation in Operational Local NWP System at JMA

Impact of METOP ASCAT Ocean Surface Winds in the NCEP GDAS/GFS and NRL NAVDAS

Assimilation of Himawari-8 Atmospheric Motion Vectors into the Numerical Weather Prediction Systems of Japan Meteorological Agency

Observing system experiments of MTSAT-2 Rapid Scan Atmospheric Motion Vector for T-PARC 2008 using the JMA operational NWP system

Assimilation of GNSS Radio Occultation Data at JMA. Hiromi Owada, Yoichi Hirahara and Masami Moriya Japan Meteorological Agency

Global and Regional OSEs at JMA

Use of satellite radiances in the global assimilation system at JMA

AN OBSERVING SYSTEM EXPERIMENT OF MTSAT RAPID SCAN AMV USING JMA MESO-SCALE OPERATIONAL NWP SYSTEM

Scatterometer Wind Assimilation at the Met Office

Masahiro Kazumori, Takashi Kadowaki Numerical Prediction Division Japan Meteorological Agency

Upgraded usage of MODIS-derived polar winds in the JMA operational global 4D-Var assimilation system

Data Short description Parameters to be used for analysis SYNOP. Surface observations by ships, oil rigs and moored buoys

STATUS ON THE USE OF SCATTEROMETER DATA AT METEO FRANCE

The Improvement of JMA Operational Wave Models

Advances in weather modelling

OSSE to infer the impact of Arctic AMVs extracted from highly elliptical orbit imagery

Recent Developments of JMA Operational NWP Systems and WGNE Intercomparison of Tropical Cyclone Track Forecast

ASSIMILATION OF ATOVS RETRIEVALS AND AMSU-A RADIANCES AT THE ITALIAN WEATHER SERVICE: CURRENT STATUS AND PERSPECTIVES

IMPACT STUDIES OF AMVS AND SCATTEROMETER WINDS IN JMA GLOBAL OPERATIONAL NWP SYSTEM

The role of GPS-RO at ECMWF" ! COSMIC Data Users Workshop!! 30 September 2014! !!! ECMWF

Observing System Impact Studies in ACCESS

Impact of GPS and TMI Precipitable Water Data on Mesoscale Numerical Weather Prediction Model Forecasts

Recent Developments of JMA Operational NWP Systems and WGNE Intercomparison of Tropical Cyclone Track Forecast

Satellite Radiance Data Assimilation at the Met Office

WIND PROFILER NETWORK OF JAPAN METEOROLOGICAL AGENCY

Numerical Weather prediction at the European Centre for Medium-Range Weather Forecasts

APPLICATIONS OF SEA-LEVEL PRESSURE

Use of DFS to estimate observation impact in NWP. Comparison of observation impact derived from OSEs and DFS. Cristina Lupu

Marine Meteorology Division, Naval Research Laboratory, Monterey, CA. Tellus Applied Sciences, Inc.

Scatterometer winds in rapidly developing storms (SCARASTO) First experiments on data assimilation of scatterometer winds

Towards the assimilation of all-sky infrared radiances of Himawari-8. Kozo Okamoto 1,2

ECMWF. ECMWF Land Surface Analysis: Current status and developments. P. de Rosnay M. Drusch, K. Scipal, D. Vasiljevic G. Balsamo, J.

SATELLITE DATA IMPACT STUDIES AT ECMWF

Recent Data Assimilation Activities at Environment Canada

A two-season impact study of the Navy s WindSat surface wind retrievals in the NCEP global data assimilation system

Bias Correction of Satellite Data in GRAPES-VAR

HIGH SPATIAL AND TEMPORAL RESOLUTION ATMOSPHERIC MOTION VECTORS GENERATION, ERROR CHARACTERIZATION AND ASSIMILATION

The Coupled Earth Reanalysis system [CERA]

The satellite winds in the operational NWP system at Météo-France

STATUS OF JAPANESE METEOROLOGICAL SATELLITES AND RECENT ACTIVITIES OF MSC

THE USE OF SATELLITE OBSERVATIONS IN NWP

OSI SAF Sea Ice products

AVIATION APPLICATIONS OF A NEW GENERATION OF MESOSCALE NUMERICAL WEATHER PREDICTION SYSTEM OF THE HONG KONG OBSERVATORY

Stability in SeaWinds Quality Control

Direct assimilation of all-sky microwave radiances at ECMWF

Introduction. Recent changes in the use of AMV observations. AMVs over land. AMV impact study. Use of Scatterometer data (Ascat, Oceansat-2)

Assimilation of Scatterometer Winds at ECMWF

J16.1 PRELIMINARY ASSESSMENT OF ASCAT OCEAN SURFACE VECTOR WIND (OSVW) RETRIEVALS AT NOAA OCEAN PREDICTION CENTER

Activities of Numerical Weather Prediction for Typhoon forecast at Japan Meteorological Agency

GPS Meteorology at Japan Meteorological Agency

Una Mirada a la Teledetección Satelital y la Asimilación de Datos a Través de los Dispersómetros

Current status and plans of JMA operational wind product

Changes in the Arpège 4D-VAR and LAM 3D-VAR. C. Fischer With contributions by P. Brousseau, G. Kerdraon, J.-F. Mahfouf, T.

NCMRWF Forecast Products for Wind/Solar Energy Applications

Development of 3D Variational Assimilation System for ATOVS Data in China

11 days (00, 12 UTC) 132 hours (06, 18 UTC) One unperturbed control forecast and 26 perturbed ensemble members. --

The JRA-55 Reanalysis: quality control and reprocessing of observational data

GOES-16 AMV data evaluation and algorithm assessment

Assimilating only surface pressure observations in 3D and 4DVAR

IMPORTANCE OF SATELLITE DATA (FOR REANALYSIS AND BEYOND) Jörg Schulz EUMETSAT

Microwave-TC intensity estimation. Ryo Oyama Meteorological Research Institute Japan Meteorological Agency

The Application of Satellite Data i n the Global Surface Data Assimil ation System at KMA

Meteorological Service of Canada Perspectives. WMO Coordination Group on Satellite Data Requirements for RAIII/IV

Impact of hyperspectral IR radiances on wind analyses

Recent improvements on the wave forecasting system of Meteo-France: modeling and assimilation aspects

Assimilation of the IASI data in the HARMONIE data assimilation system

The NWP SAF: what can it do for you?

Analysis of an Observing System Experiment for the Joint Polar Satellite System

Satellite data assimilation for Numerical Weather Prediction II

Satellite data assimilation for Numerical Weather Prediction (NWP)

The satellite winds in the operational NWP system at Météo-France

Recent development at JMA (short-range and medium-range NWP)

Current and Future Experiments to Improve Assimilation of Surface Winds from Satellites in Global Models

OBSERVING SYSTEM EXPERIMENTS ON ATOVS ORBIT CONSTELLATIONS

Jordan G. Powers Priscilla A. Mooney Kevin W. Manning

Value of Satellite Observation Sensitive to Humidity and Precipitation in JMA s Operational Numerical Weather Prediction

Evaluation and assimilation of all-sky infrared radiances of Himawari-8

Update on SCOPE-Nowcasting Pilot Project Real Time Ocean Products Suman Goyal Scientist-E

JMA s atmospheric motion vectors

Do Differences between NWP Model and EO Winds Matter?

ECMWF Data Assimilation. Contribution from many people

Interaction of GPS Radio Occultation with Hyperspectral Infrared and Microwave Sounder Assimilation

The Use of Hyperspectral Infrared Radiances In Numerical Weather Prediction

Observation usage in Météo-France data assimilation systems

Development of JMA storm surge model

Introduction to Reanalysis and JRA-55

A New Global Ice Analysis System

IMPACT OF IASI DATA ON FORECASTING POLAR LOWS

Assessment of AHI Level-1 Data for HWRF Assimilation

RECENT UPGRADES OF AND ACTIVITIES FOR ATMOSPHERIC MOTION VECTORS AT JMA/MSC

Development of the Canadian Precipitation Analysis (CaPA) and the Canadian Land Data Assimilation System (CaLDAS)

Use and impact of satellite data in the NZLAM mesoscale model for the New Zealand region

The Contribution of Locally Generated MTSat-1R Atmospheric Motion Vectors to Operational Meteorology in the Australian Region

Wind tracing from SEVIRI clear and overcast radiance assimilation

Inter-comparison of 4D-Var and EnKF systems for operational deterministic NWP

Transcription:

Status and Plans of using the scatterometer winds in 's Data Assimilation and Forecast System Masaya Takahashi¹ and Yoshihiko Tahara² 1- Numerical Prediction Division, Japan Meteorological Agency () 2- Meteorological Satellite Center, NASA Ocean Vector Wind Science Team Meeting, Seattle, 19-21 November 28

Outline s NWP models - Status of NWP models - History and use of scatterometer winds Experiment - Metop-A/ASCAT assimilation Summary and future plans

Status of s Operational NWP Models RADAR OBS. GSM MSM Model Global Model (GSM) Mesoscale Model (MSM) Resolution H/V(top height) Forecast range (Initial time) Target Data Assimilation (outer/inner loop) TL959 (2km)/6 (.1hPa) 84h (,6,18UTC) 216h (12UTC) 1~7 day forecast Aeronautical forecast 4D-Var (TL959/T159 or 2km/8km) 5km/5 (21.8km) 15h (,6,12,18UTC) 33h (3,9,15,21UTC) Disaster prevention information 4D-Var (1/2km)

Data Assimilated in Global Model Data Coverage Map 28/1/24 UTC SYNOP, Ship, Buoy Radiosondes and Wind Profilers Aviation, Australian BOGUS MW Scatterometer (QuikSCAT) Atmospheric Motion Vector WV ch radiances of geo. sat. imagers T-Sounder (AMUS-A) H-Sounder (AMUS-B, MHS) MW Imager (SSMI, TMI, AMSR-E)

Data Assimilated in Mesoscale Model Data Coverage Map 28/1/23 12UTC SYNOP, Ship, Buoy Radiosondes and Wind Profilers Aviation MW Scatterometer / AMV MW-Sounder MW-Imager

History of OVW Data Use in NWP at QuikSCAT winds are used in the both global and mesoscale models. Operational use of Metop-A/ASCAT winds will start in 29. 1998 1999 2 21 22 23 24 25 26 27 28 29 Global Analysis (Scheme) Sep. Feb. 3D-OI 3D-Var 4D-Var Jul. Jan. May ERS2 / AMI QuikSCAT / SeaWinds Metop-A / ASCAT Meso-scale Analysis Mar. Mar. (Scheme) 3D-OI 4D-Var Jul. QuikSCAT / SeaWinds Metop-A/ASCAT 4D-Var, 3D-Var: Four or Three dimensional variational scheme 3D-OI: Three dimensional optimum interpolation

Use of QuikSCAT Winds Winds that pass quality control procedures and are thinned are assimilated. Quality control for QuikSCAT winds - Rain flag, Land/sea flag check - Sea ice check - Ambiguity removal Select the closest wind to s forecast - Gross error check Reject large Obs. Background (forecast) winds w.r.t. wind speed, direction QuikSCAT winds after preprocessing (Global analysis, 12UTC 25 Jul. 28). Thinning interval and total QuikSCAT number assimilated. Global analysis Meso analysis Total number observed 2, 3,~1, Data thinning 1km 5km Total number assimilated 8,~12, 5~1,5

Experiments Data assimilation with Metop-A/ASCAT winds and forecast experiment in global model

Experimental Setup 4 experimental runs: Assimilated scatt. winds QuikSCAT only (nearly operational) ASCAT only Both ASCAT and QuikSCAT No- Scatterometer Example of scatt. data used in one analysis Low resolution global model: Experimental Operational Resolution H/V (top height) TL319 (6km) / 6 (.1hPa) TL959 (2km) / 6 (.1hPa) Data Assimilation (outer/inner loop) 4D-Var (TL319/T16 or 6km/11km) 4D-Var (TL959/T159 or 2km/8km) One month data assimilation experiment (Aug 27). 9-day forecasts to see analysis accuracy enhanced. Assimilated scatterometer: selected winds by ambiguity removal Quality control for ASCAT is the same as that for QuikSCAT. Assimilated observational data are the same as those of the operational model except for scatterometer winds. (satellites data such as AMV, Mw-Sounder, Mw-Imager are used)

Evaluation of QC passed Scatterometer Winds Win nd Speed Comparison of scatterometer winds (after QC) with s 6 hour forecast. ASCAT QuikSCAT - Both scatterometer winds have high accuracy enough to be assimilated. - ASCAT winds have low wind speed bias against s forecast at high wind speed (>15m/s). Wind Direction Standard deviation of wind speed O-B for ASCAT, QuikSCAT, Buoy (period mean) Standard deviation (m/s) 2 1.5 1.5 ASCAT QuikSCAT Buoy (Not height corrected)

Forecast Improvement Rate Against No-Scatt. Run Rate of Improvement = RMSE(No Scatterometer) RMSE(Test) [%] RMSE(No Scatterometer) Im mprove Rate of Improvem ment (%) Rate of Improvement (%) Best improvement: Both ASCAT and QuikSCAT run Increase of data coverage provides more reliable analysis field, and it leads to improvement of forecast. 2.5 2.5 2 1.5 1.5 1.5.5 2-day forecast N.H. Psea T85 Z5 WS85 WS25 2.5 2.5 2 1.5 1.5 1.5.5 5-day forecast N.H. 3 2 1 1-1 -2-1 -3-3 -4-5 -5 3 21 1-1 -1-2-3-3-5 Psea ps T85 t85z5 z5ws85 w85 WS25 w25 TR. Psea T85 Z5 WS85 WS25 TR. Psea T85 Z5 WS85 WS25 : QuikSCAT (nearly operational) : ASCAT : Both ASCAT and QuikSCAT 3 2.5 22 2 1.5 1.5 -.5-1 3 2.5 2 1.5 1.5 S.H. Psea T85 Z5 WS85 WS25 S.H. Psea ps T85 t85 Z5 z5ws85 w85 WS25 w25

Impact on Typhoon Track Forecasts In addition to the increase of data coverage, assimilation of less contaminated winds by rainfall has positive impact on typhoon position forecasts. 9-day typhoon position forecast for typhoon FITOW -: QuikSCAT -: ASCAT -: ASCAT and QuikSCAT -: No Scatterometer -: Analyzed Track Initial: 12UTC 7/8/28 QC-passed (blue) and rejected (red) winds QuikSCAT (Initial: 6UTC 7/8/28) ASCAT (Initial: 12UTC 7/8/28) Tokyo Assimilated winds QuikSCAT ASCAT

Impact on Typhoon Track Forecasts Scatterometer winds provides significant improvement for typhoon track forecasts. Averaged position error of typhoon track forecasts -36 case for 6 typhoons. Positional Error (km) -QuikSCAT -ASCAT -ASCAT and QuikSCAT -No Scatterometer Forecast Time (hour)

Summary and Future Plans has used scatterometer winds in the operational global and meso-scale models. Metop-A/ASCAT data assimilation and forecast experiments have been carried out to evaluate the impact of scatterometer winds. Both ASCAT and QuikSCAT assimilated run provided the best improvement. After the evaluation of the experiments with high resolution global model, ASCAT will be used at.

Thanks for your attention.

Background Weather map analysis Use of scatterometer data at Numerical Weather Prediction, wave analysis, Weather Forecast Wave Analysis (Wave Chart) Typhoon Track Forecasts In particular, scatterometer winds give positive impacts on our analyses and forecasts. To improve them further, has plans to - Start the use of Metop-A/ASCAT winds in 29. - Upgrade assimilation method (use ambiguous winds instead of selected winds by an ambiguity removal).

Reduction of Lower Forecast Bias (against initial) Scatterometer winds reduce the bias of s wind speed at lower troposphere. Both ASCAT and QuikSCAT run provides the best improvement. 1day fcst. Time series of wind speed bias at 85hPa (global mean) (m/s) -: QuikSCAT (nearly operational) -: ASCAT -: ASCAT and QuikSCAT -: No Scatterometer Period mean. 5day fcst. 1AUG 6AUG 11AUG 16AUG 21AUG 26AUG 31AUG 5day fcst. 1day fcst. -.4 -.3 -.2 -.1.1 Better -.4 -.3 -.2 -.1 QS AS AS & QS No Scatt QS AS AS & QS No Scatt