Recent Developments in assimilation of ATOVS at JMA 1.Introduction 2.1DVar preprocessor 3.Simple test for 3DVar radiance assimilation 4.Cycle experiments.

Slides:



Advertisements
Similar presentations
Adaptive Estimation and Tuning of Satellite Observation Error in Assimilation Cycle with GRAPES Hua ZHANG, Dehui CHEN, Xueshun SHEN, Jishan XUE, Wei HAN.
Advertisements

ECMWF/EUMETSAT NWP-SAF Satellite data assimilation Training Course 1 to 4 July 2013.
Gergely Bölöni, Roger Randriamampinanina, Regina Szoták, Gabriella Csima: Assimilation of ATOVS and AMDAR data in the ALADIN 3d-var system 1 _____________________________________________________________________________________.
2. Description of MIIDAPS 1. Introduction A Generalized Approach to Microwave Satellite Data Assimilation Quality Control and Preprocessing Here, we present.
1 Met Office, UK 2 Japan Meteorological Agency 3 Bureau of Meteorology, Australia Assimilation of data from AIRS for improved numerical weather prediction.
EUMETSAT04 04/2004 © Crown copyright Use of EARS in Global and Regional NWP Models at the Met Office Brett Candy, Steve English, Roger Saunders and Amy.
Slide 1 IPWG, Beijing, October 2008 Slide 1 Assimilation of rain and cloud-affected microwave radiances at ECMWF Alan Geer, Peter Bauer, Philippe.
Satellite SST Radiance Assimilation and SST Data Impacts James Cummings Naval Research Laboratory Monterey, CA Sea Surface Temperature Science.
ECMWF CO 2 Data Assimilation at ECMWF Richard Engelen European Centre for Medium-Range Weather Forecasts Reading, United Kingdom Many thanks to Phil Watts,
1 ATOVS and SSM/I assimilation at the Met Office Stephen English, Dave Jones, Andrew Smith, Fiona Hilton and Keith Whyte.
1 Impact study of AMSR-E radiances in NCEP Global Data Assimilation System Masahiro Kazumori (1) Q. Liu (2), R. Treadon (1), J. C. Derber (1), F. Weng.
Numerical Weather Prediction Division The usage of the ATOVS data in the Korea Meteorological Administration (KMA) Sang-Won Joo Korea Meteorological Administration.
ECMWF – 1© European Centre for Medium-Range Weather Forecasts Developments in the use of AMSU-A, ATMS and HIRS data at ECMWF Heather Lawrence, first-year.
Data assimilation of polar orbiting satellites at ECMWF
Status of operational NWP system and satellite data utilization at JMA APSDEU-8 Montreal, Canada, October 10-12, 2007 Masahiro KAZUMORI Numerical Prediction.
Recent activities on utilization of microwave imager data in the JMA NWP system - Preparation for AMSR2 data assimilation - Masahiro Kazumori Japan Meteorological.
Recent developments in data assimilation for global deterministic NWP: EnVar vs. 3D-Var and 4D-Var Mark Buehner 1, Josée Morneau 2 and Cecilien Charette.
© The Aerospace Corporation 2014 Observation Impact on Mesoscale Model Forecast Accuracy over Southwest Asia Dr. Michael D. McAtee Environmental Satellite.
Satellite Application on Weather Services in Japan Yasushi SUZUKI Japan Weather Association 12nd. GPM Applications Workshop, June/9-10/2015.
Towards Utilizing All-Sky Microwave Radiance Data in GEOS-5 Atmospheric Data Assimilation System Development of Observing System Simulation Experiments.
Advances in the use of observations in the ALADIN/HU 3D-Var system Roger RANDRIAMAMPIANINA, Regina SZOTÁK and Gabriella Csima Hungarian Meteorological.
MIIDAPS Status – 13 th JCSDA Technical Review and Science Workshop, College Park, MD Quality Control-Consistent algorithm for all sensors to determine.
Moisture observation by a dense GPS receiver network and its assimilation to JMA Meso ‑ Scale Model Koichi Yoshimoto 1, Yoshihiro Ishikawa 1, Yoshinori.
Five techniques for liquid water cloud detection and analysis using AMSU NameBrief description Data inputs Weng1= NESDIS day one method (Weng and Grody)
Slide 1 EUMETSAT Fellow Day, 9 March 2015 Observation Errors for AMSU-A and a first look at the FY-3C MWHS-2 instrument Heather Lawrence, second-year EUMETSAT.
Current status of AMSR-E data utilization in JMA/NWP Masahiro KAZUMORI Numerical Prediction Division Japan Meteorological Agency July 2008 Joint.
Page 1 Validation by Model Assimilation and/or Satellite Intercomparison - ESRIN 9–13 December 2002 Monitoring of near-real-time SCIAMACHY, MIPAS, and.
Satellite Bias Correction for CFSRR Haixia Liu, Russ Treadon, Robert Kistler, John Derber, Suru Saha and Hua-lu Pan Nov. 7, 2007 with input from Jack Woollen,
30 November December International Workshop on Advancement of Typhoon Track Forecast Technique 11 Observing system experiments using the operational.
Global and regional OSEs at JMA Ko KOIZUMI Numerical Prediction Division Japan Meteorological Agency.
Research and development on satellite data assimilation at the Canadian Meteorological Center L. Garand, S. K. Dutta, S. Heilliette, M. Buehner, and S.
Assimilation of Satellite Radiances into LM with 1D-Var and Nudging Reinhold, Christoph, Francesca, Blazej, Piotr, Iulia, Michael, Vadim DWD, ARPA-SIM,
1 Hyperspectral Infrared Water Vapor Radiance Assimilation James Jung Cooperative Institute for Meteorological Satellite Studies Lars Peter Riishojgaard.
USE OF AIRS/AMSU DATA FOR WEATHER AND CLIMATE RESEARCH Joel Susskind University of Maryland May 12, 2005.
Development of ATOVS Data Assimilation for Regional Forecast System Eunjoo Lee NWPD, KMA.
A Generalized Approach to Microwave Satellite Data Assimilation Quality Control and Preprocessing Kevin Garrett 1 and Sid Boukabara 2 11 th JCSDA Science.
Introduction of temperature observation of radio-sonde in place of geopotential height to the global three dimensional variational data assimilation system.
Recent activities on AMSR-E data utilization in NWP at JMA Masahiro Kazumori, Koichi Yoshimoto, Takumu Egawa Numerical Prediction Division Japan Meteorological.
25 th EWGLAM/10 th SRNWP Lisbon, Portugal 6-9 October 2003 Use of satellite data at Météo-France Élisabeth Gérard Météo-France/CNRM/GMAP/OBS, Toulouse,
The Impact of Data Assimilation on a Mesoscale Model of the New Zealand Region (NZLAM-VAR) P. Andrews, H. Oliver, M. Uddstrom, A. Korpela X. Zheng and.
The Hyperspectral Environmental Suite (HES) and Advanced Baseline Imager (ABI) will be flown on the next generation of NOAA Geostationary Operational Environmental.
Layered Water Vapor Quick Guide by NASA / SPoRT and CIRA Why is the Layered Water Vapor Product important? Water vapor is essential for creating clouds,
Concordiasi Satellite data assimilation at high latitudes F. Rabier, A. Bouchard, F. Karbou, V. Guidard, S. Guedj, A. Doerenbecher, E. Brun, D. Puech +
MIIDAPS Application to GSI for QC and Dynamic Emissivity in Passive Microwave Data Assimilation.
Recent development of all-sky radiance assimilation at JMA Kozo Okamoto, Masahiro Kazumori Japan Meteorological Agency (JMA) The 3 rd Joint JCSDA-ECMWF.
NCAR April 1 st 2003 Mesoscale and Microscale Meteorology Data Assimilation in AMPS Dale Barker S. Rizvi, and M. Duda MMM Division, NCAR
A step toward operational use of AMSR-E horizontal polarized radiance in JMA global data assimilation system Masahiro Kazumori Numerical Prediction Division.
MODIS Winds Assimilation Impact Study with the CMC Operational Forecast System Réal Sarrazin Data Assimilation and Quality Control Canadian Meteorological.
One-dimensional assimilation method for the humidity estimation with the wind profiling radar data using the MSM forecast as the first guess Jun-ichi Furumoto,
1 3D-Var assimilation of CHAMP measurements at the Met Office Sean Healy, Adrian Jupp and Christian Marquardt.
Satellite Data Assimilation Activities at CIMSS for FY2003 Robert M. Aune Advanced Satellite Products Team NOAA/NESDIS/ORA/ARAD Cooperative Institute for.
ITSC-12 Cloud processing in IASI context Lydie Lavanant Météo-France, Centre de Météorologie Spatiale, BP 147, Lannion Cedex France Purpose: Retrieval.
NCEP Assessment of ATMS Radiances Andrew Collard 1, John Derber 2 and Russ Treadon 2 1 IMSG at NOAA/NCEP/EMC 2 NOAA/NCEP/EMC 1NPP ATMS SDR Product Review13th.
Xiujuan Su 1, John Derber 2, Jaime Daniel 3,Andrew Collard 1 1: IMSG, 2: EMC/NWS/NOAA, 3.NESDIS Assimilation of GOES hourly shortwave and visible AMVs.
Global vs mesoscale ATOVS assimilation at the Met Office Global Large obs error (4 K) NESDIS 1B radiances NOAA-15 & 16 HIRS and AMSU thinned to 154 km.
Status on Cloudy Radiance Data Assimilation in NCEP GSI 1 Min-Jeong Kim JCSDA 9th Workshop on Satellite Data Assimilation, May 24-25, 2011, M-J. Kim 2.
The assimilation of satellite radiances in LM F. Di Giuseppe, B. Krzeminski,R. Hess, C. Shraff (1) ARPA-SIM Italy (2) IMGW,Poland (3)DWD, Germany.
Assimilation experiments with CHAMP GPS radio occultation measurements By S. B. HEALY and J.-N. THÉPAUT European Centre for Medium-Range Weather Forecasts,
Station lists and bias corrections Jemma Davie, Colin Parrett, Richard Renshaw, Peter Jermey © Crown Copyright 2012 Source: Met Office© Crown copyright.
© Crown copyright Met Office Assimilating infra-red sounder data over land John Eyre for Ed Pavelin Met Office, UK Acknowledgements: Brett Candy DAOS-WG,
June 20, 2005Workshop on Chemical data assimilation and data needs Data Assimilation Methods Experience from operational meteorological assimilation John.
ECMWF/EUMETSAT NWP-SAF Satellite data assimilation Training Course
Tony Reale ATOVS Sounding Products (ITSVC-12)
Tadashi Fujita (NPD JMA)
Assimilation of Cloudy AMSU-A Microwave Radiances in 4D-Var
FSOI adapted for used with 4D-EnVar
Use of ATOVS at DAO Joanna Joiner, Donald Frank, Arlindo da Silva,
Infrared Satellite Data Assimilation at NCAR
Validation of NOAA-16/ATOVS Products from AAPP/IAPP Packages in Korea
Comparison of different combinations of ensemble-based and variational data assimilation approaches for deterministic NWP Mark Buehner Data Assimilation.
Presentation transcript:

Recent Developments in assimilation of ATOVS at JMA 1.Introduction 2.1DVar preprocessor 3.Simple test for 3DVar radiance assimilation 4.Cycle experiments 5.Conclusion and plan Kozo Okamoto, Yoshiaki Takeuchi, Yukihiro Kaido, Masahiro Kazumori NWP Division, Forecast Dept, Japan Meteorological Agency

Recent Change in the JMA NWP system Mar : Replace the supercomputer (768GFlops, 640GByte, 80node) GSM T213L30 => T213L40 (model top : 10=>0.4 hPa) Sep : Global 3DVar system started in operational data assimilation system Mar : Meso 4DVar system is going to start in operational data assimilation system (H.Res.: 10km, Assimilation window: 3h)

Use of ATOVS in the JMA assimilation system NESDIS/MSC T,Q retrievals ・ conversion ・ QC ・ select region Present Status Retrieval Use 3DVar NESDIS 120km BUFR TBB ・ QC ・ Channel Selection ・ Obs Error Assignment ・ Bias Correction Plan TBB Use 3DVar 1DVar as preprocessor dZ( -1000hPa) Bias Corrected TBB Tskin

ATOVS 1DVar as Pre-processor (1) Quality Control (QC) Geographical check : reject data over the coast, lake and river.. Edge scan check: reject data with outer edge swath Gross check : reject data for TBB >400K or <100K Rogue check-1: reject data including some channels with |dTBB|>a*Ostd Minimize check: reject data not converged within 12 iterations Jend check: reject data with Jend>8*used channel number Rogue check-2: tighter Rogue check-1

ATOVS 1DVar as Pre-processor (2) Bias Correction The TBB bias for each channel j can be described by –y: background TBB (TBbg) of AMSU-5,7,10 –TPW: background total column precipitable water – : satellite scan angle, Ts:skin temperature –overbar represents spatial and temporal mean The regression coefficients a ji are updated every day using previous 2 weeks data and calculated for NH/Trop/SH and each analysis time. The bias-correction is not applied to HIRS11,12,AMSU13,14 because of large systematic errors in the JMA forecast model

AMSU-A ATOVS 1DVar as Pre-processor (3) Channel Selection and Observation Errors The channels to be used and observation errors for each observation condition : Clear/Cloudy and Sea/Ice/Land –Clear Sea : HIRS1-8, HIRS10-16, AMSU5-14 –Land : only HIRS1-3 and AMSU 8-14 are used. Observation errors used in 3DVar are multiplied by 1.5. At the moment, –Cloud detection is based on NESDIS flag –Ice detection based on SST<1K and the classification is corrected as sea when TBob - TBbg <-50 for AMSU1 HIRS

Surface type and TBob-TBbg Due to mis-classimication of surface type, TBbg is quite different from TBob. –The mis-classification of the coast accounts for 95% of data with TBob- TBbg >50K –The mis-classification of the sea ice accounts for 98% of data with TBob- TBbg <-50K Distribution of data with large TBob-TBbg for AMSU A1 (10 Oct - 11 Nov 2001)

JMA 3DVar Incremental method –Outer loop : T213L40 –Inner loop : T106L40 Background error covariance is calculated by using the NMC method –Horizontal homogeneous Observation operator for radiance data –RTTOV6 ADJ and TL model

Evolution of Cost function J and Gradient of J with iteration The minimization is continued for 100 iterations Case of 12Z on 18th Dec Radiance AssimilationRetrievals Assimilation Cost J |gradJ| All Radiance Others All Z

Cross Section along observation longitude(137E) Q[g/kg ] U[m/s ] Analysis Increment for 1ch-1point observation Only one HIRS4 observation with TBB departure of +10*Observation error STD is assimilated at the point of 35N,137E Analysis Increments are large in the stratosphere because of the large background error covariance and wide spread RT sensitivity. T[K ] Z[m ]

Analysis Increment for 1ch-1point observation At the 35th level of JMA eta level (around 10hPa) Q[g/kg ] T[K ] Z[m ] U[m/s ]

ATOVS Radiance Assimilation Impacts on NWP -Parallel Assimilation Experiments (Jul 2001)- TEST : 1DVar preprocessor + 3DVar Radiance Assimilation CNTL: 3DVar Retrieval Assimilation Data Configurations –TEST : ATOVS TBB from 120km BUFR note: All HIRS and AMSU-14 radiances from NOAA15 are not used due to instrumental problems –CNTL: ATOVS NESDIS retrievals (BUFR + SATEM) System –6hourly intermittent data assimilation –forecast model : T106L40 (model top 0.4hPa) global spectral model, 216h forecasts for 12Z initial –analysis model : 3DVar Incremental method 1 month run

RMSE and Bias of Analysis/Guess verified against radiosonde Temperature on the standard pressure levels from 1000 to 10 hPa Case of 30th Jul 2001 Test Anal Cntl Anal Test Gues Cntl Gues BiasRMSE N.H. Trp. S.H.

RMSE and Bias of Analysis/Guess verified against radiosonde Wind Speed on the standard pressure levels from 1000 to 10 hPa Case of 30th Jul 2001 Test Anal Cntl Anal Test Gues Cntl Gues BiasRMSE N.H. Trp. S.H.

Forecast Errors verified against radiosonde for 500hPa Z Improvements especially in the S.H. But in the N.H. and Tropics, the improvements diminish beyond day 5 of the forecast. Test Cntl BiasRMSE N.H. Trp. S.H.

Forecast Errors verified against radiosonde for 250hPa Wind Speed Nearly Neutral Impact on forecast Test Cntl BiasRMSE N.H. Trp. S.H.

Averaged Zonal Mean for Forecast Error at day 5 and Analysis difference Average during 13th - 29th Jul 2001 Large systematic forecast errors around 10 hPa and above 3hPa, especially in the S.H. are obvious.The value is positive around 10hPa while negative above 3hPa. Averaged analysis difference is also obvious. Unfortunately Test fits radiosonde worse than Cntl for the 10hPa temperature. 10hPa Averaged Zonal Mean Forecast error (Fcst - Init ) at day 5 for temperature from 850 to 1 hPa Averaged Zonal Mean Analysis difference between Test and Cntl for temperature from 850 to 0.4 hPa 10 1hPa N90S 1hPa N90S

Conclusion and Plan JMA global 3DVar started operationally since Sep At the moment NESDIS and MSC thickness retrievals are assimilated. The direct radiance assimilation system is being developed. QC, channel selection and bias correction are performed in the 1DVar pre-processing system. Parallel assimilation experiments have been run. Some improvements for analyses and forecasts are given but are not found beyond day 5 of the forecast. The problem can be attributed to QC, observation error assignment and data selection ( thinning ). Besides forecast systematic error in the stratosphere probably have something to do with it. We have other plans to –assimilate AMSU-B radiance –improve QC –use level 1B data

AMSU-B Assimilation : initial results Accuracy of AMSU-B 1DVar products verified against radiosonde observations for specific humidity below 100 hPa Studying the impact of AMSU-B radiance on analysis and forecast AMSU-B retrieval First Guess Bias RMSE N.H. Trp. S.H.

Improve QC (1) Detect clear/thin cloud/thick cloud/rain using only observation information (not guess) The system is based on AAPP. Cloud detection J = ( y-m ) T C -1 ( y-m ) –y: TBob of HIRS1-4, 13-15, AMSU4-5 for thin cloud detection AMSU1-3 for thick cloud detection –m:average clear TBB, C: clear TBB covariance designate as cloudy when J>J 0 STD of clear TBob-TBbg over land Histogram of TBob-TBbg for HIRS8 over sea Clear Thin cloudy Thick Cloudy TBob-TBbg

Improve QC (2) Rain detection : Scattering Index SI = TBcal(A15) - TBob(A15) –TBcal(A15) is calculated based on a statistical regression approach with predictors of AMSU1-3 designate as rainy when SI > 10. –The threshold 10 is determined based on collocated TRMM TMI and PR rain TBob-TBbg STD of each HIRS and AMSU channel for clear/cloudy/rain over sea