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Assimilation of ATOVS level1C radiance and MODIS polar winds at JMA

Assimilation of ATOVS level1C radiance and MODIS polar winds at JMA. Masahiro Kazumori Japan Meteorological Agency / EMC Visiting scientist. RSM. MSM. GSM. Global Model (GSM). Regional Model (RSM). Meso-scale Model (MSM). Purpose:. 3-7day forecast Boundary for RSM. 1-2day forecast

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Assimilation of ATOVS level1C radiance and MODIS polar winds at JMA

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  1. Assimilation of ATOVS level1C radiance and MODIS polar winds at JMA Masahiro Kazumori Japan Meteorological Agency / EMC Visiting scientist EMC Sack Lunch Seminar

  2. RSM MSM GSM Global Model (GSM) Regional Model (RSM) Meso-scale Model (MSM) Purpose: 3-7day forecast Boundary for RSM 1-2day forecast Boundary for MSM Disaster prevention information Resolution, Forecast time: 55km, 40levels (0.4hPa) 90 hours (00UTC) 216 hours(12UTC) 20km, 40levels (10hPa) 51 hours (00,12UTC) 10km, 40levels (10hPa) 18 hours (00,06,12,18UTC) Assimilation system: 4D-Var (Since Feb. 2005) 4D-Var (Since 2003) 4D-Var (Since 2002) Introduction of JMA NWP models EMC Sack Lunch Seminar

  3. Satellite data in JMA global data assimilation system Now using operationally Now testing • Radiance • AIRS (Aqua) • SSMI,TRMM,AMSR-E • GOES-9,METEOSAT,MTSAT-1R • Wind • METEOSAT(8),MTSAT-1R • SSMIS(DMSP-16) • HIRS,AMSU-A,HSB(NOAA-18) • IASI (Metop) • ASCAT(Metop) • Radiance • AMSU-A(NOAA15/16, Aqua) • AMSU-B(NOAA15/16/17) • Wind • GOES(9/10/12),METEOSAT(5/7) • MODIS(Terra/Aqua) • QuikSCAT Planning EMC Sack Lunch Seminar

  4. Progress on global data assimilation in 2004-2005 • May 2004 : • Assimilation of polar winds from Terra/MODIS and Aqua/MODIS in the north polar region started. • Sep. 2004 : • Assimilation of polar winds from Terra/MODIS and Aqua/MODIS in the south polar region started. MODIS polar winds • Dec. 2004 : • Direct assimilation of ATOVS level-1C data replaced that of level-1D data • The radiative transfer model for the radiance assimilation was upgraded from RTTOV-6 to RTTOV-7 • Feb. 2005 : • Introduction of 4D-Var in operational system • March. 2005: • Start of Aqua/AMSU-A radiance data assimilation ATOVS level 1C radiance 4D-Var EMC Sack Lunch Seminar

  5. Assimilation of ATOVS level 1C radiance Masahiro Kazumori Hiromi Owada Kazuyo Fukuda Numerical Prediction Division Japan Meteorological Agency EMC Sack Lunch Seminar

  6. ATOVS level 1C radiance data Since May 2003, ATOVS radiance data have been assimilated directly into the global model with JMA 3D-Var system. But…, it had been using ATOVS level 1D radiance data • Dec. 2004 • ATOVS data was changed from level-1D data(NESDIS pre-processed data) to level-1C data(unmapped instrument data) • At the same time, RTM upgrade was performed. • JMA uses RTTOV (Saunders et al. 1998) as RTM • The update was from RTTOV-6 to RTTOV-7 • RTTOV-7 treats each sensor separately • RTTOV-7 has an ability to calculate AIRS radiance EMC Sack Lunch Seminar

  7. AAPP & MSC direct read out data AAPP（ATOVS and AVHRR Processing Package） Direct broadcast data are received at JMA Meteorological Satellite Center. JMA Global level 1B data from NESDIS Level 1C radiance AAPP Direct read out data (HRPT) at MSC Level 1D radiance decode Data coverage of ATOVS level 1c for early analysis. Blue points are the global data from NESDIS and red points are direct read out data at MSC at JMA. 12UTC 31 July 2004. The limit of data receiving. EMC Sack Lunch Seminar

  8. Difference of ATOVS data between level 1C and level 1D Level 1D data Level 1D data No high latitude data. Thinned and removed by NESDIS NESDIS provide some quality flag for observation Level 1C data Blue：NOAA15Red：NOAA16 Level 1C data All observation data are available. thinning and quality check are needed. EMC Sack Lunch Seminar

  9. Difference of ATOVS data between level 1C and level 1D Level 1D data Level 1D data No high latitude data. Thinned and removed by NESDIS Level 1C data Level 1C data All observation data are available. Original data thinning and quality check are needed. EMC Sack Lunch Seminar

  10. Mapping (computing sounder data to another sounder grid) FOV HIRS AMSU-A In level 1D data, Mapping of AMSU-A FOV to HIRS FOV are conducted. In level 1C data, We can assimilative those data in their original observation points. EMC Sack Lunch Seminar

  11. Assimilation scheme of ATOVS 1C radiance • Data thinning • 240km for AMSU-A, 180km for AMSU-B • Land sea decision: 0.25*0.25degree map and considering the maximum size of FOV for each sensor. • Quality Control • Rain detection ( based on scattering index) • Cloud detection( based on amount of cloud liquid water) • Observation error • Remove the adjustment for each FOV • Bias correction • Scan bias correction ( fixed for each channel) • Air mass correction (fixed for all season) • Predictor: Calculated brightness temperature of AMSU-A Ch5,7,10, Surface temperature. • Coefficient was calculated from collocated data set with RAOB discontinued EMC Sack Lunch Seminar

  12. AMSU-A CH4 Departure(monthly mean) A Observation point Land [K] 0.25degree Sea Contamination by land 0.25degree Land sea grid Field of View Land Sea Mask based on FOV Land sea mask are modified based on FOV size for each sensor. EMC Sack Lunch Seminar

  13. Results of cloud, rain detection Level 1C Clear →Thin cloud For level 1C, HIRS can not be used for cloud detection (no mapping) Used Retrieval Algorithm for QC CLW from AMSU-A (English et al. 1997) > 100g/m^2 rejected RAIN (Scattering index method) AMSU-A (over ocean) SI = ETB15 – TB15 >10 rejected AMSU-B ( over ocean) SI = TB1-TB2>3 and Median filter QC for CH1 Level 1D Level 1C data There are many rain observation （Level 1C have original all observation data） Many cloud around high latitude in winter. EMC Sack Lunch Seminar

  14. Mean departure for each scan position Level1C (AMSU-A) Level1D (AMSU-A) Stop the use of Ch14 Red：W Bias correction Blue：W/O Bias correction Level 1D data has an effect of mapping to HIRS field of view EMC Sack Lunch Seminar

  15. Difference between level 1C and level 1D • Example: AMSU-A Channel 7(2004/10/07/12UTC) Level 1C Level 1C data All observation data are available. Data thinning and quality check should be done by ourselves. Level 1D Level 1C data No high latitude data. Thinned and removed by NESDIS (NESDIS pre-processed data) EMC Sack Lunch Seminar

  16. Monthly mean O-B distribution (AMSU-A) level1C level1D The bias of level1C seems smaller than 1D It means the QC of level 1C works better. EMC Sack Lunch Seminar

  17. Assimilation experiment of ATOVS 1C • Period: • Summer：From July 13, 2004 To September 9, 2004 • Verification Period： From July 26, 2004 to August 31, 2004 • Winter: From December 27 2003 to February 2004 • Verification Period: From January 1,2004 to January 31 2004 • Setting of experiments Analysis：Global 3D-Var, Forecast：T213L40GSM0407NAPEXR111 CNTL: same with operational（For summer, with MODIS polar winds in the S.H.) TEST:CNTL + replace of ATOVS data ( from 1D to 1C) Update of RTM from RTTOV-6 to RTTOV-7 • Used level1C NOAA15,16 AMSU-A,B, NOAA17AMSU-B • Discontinued level 1D data NOAA15,16AMSU-A,B, NOAA16 HIRS EMC Sack Lunch Seminar

  18. CNTL GUESS TEST-CNTL TEST GUESS TEST ANAL CNTL ANAL TEST-CNTL ANAL-GUESS ANAL-GUESS Summer Z500 EMC Sack Lunch Seminar

  19. Impact on analysis • TEST: • ATOVS level 1C data • RTTOV-7 • CNTL: • ATOVS level 1D data • RTTOV-6 Zonal mean difference of Temperature for August 2004 TEST-CNTL Change at the high latitudes in the troposphere And large change in the stratosphere. EMC Sack Lunch Seminar

  20. 2004 Aug. N.H. 2004 Aug.S.H. 2004 Jan.S.H. 2004 Jan.N.H. Impacts on forecast Z500RMSE level 1C level 1D Operational use of level 1C data started on 2 Dec. 2004 Improvement of RMSE of 500hPa Geopotential height EMC Sack Lunch Seminar

  21. Where can I find the improvements? Difference of RMSE between TEST and CNTL (blue color means improvement) summer winter 1day S.H. and the polar region were improved. 3day 5day EMC Sack Lunch Seminar

  22. Comparison of typhoon track prediction • Target The 11 typhoon in the summer test period ( maximum sample number 62 ) red：TEST, green：CNTL T0409 T0410 T0411 T0412 T0413 T0414 T0415 T0418 T0416 T0417 There are some difference between TEST and CNTL. But, statistically, the impact was almost neutral. T0419 EMC Sack Lunch Seminar

  23. Summary JMA use ATOVS level-1C radiance since Dec. 2004 operationally. The use of ATOVS level-1D radiance was discontinued. • Land sea mask are modified for each sensor. • Cloud, rain detection scheme are introduced. • Use of direct read out data received at MSC/JMA • Change of the RTM from RTTOV-6 to RTTOV-7. • OSE showed the improvement of forecast score on 500hPa height. EMC Sack Lunch Seminar

  24. Plan for 2005 on ATOVS at JMA KMA and JMA are going to exchange the direct received ATOVS data. Red: JMA(Tokyo) Blue:KMA(Seoul) For Early analysis, These data make the data coverage expand. Data will be available within 50 min. after the observation At present, data impact study is being conducted. EMC Sack Lunch Seminar

  25. Plan for 2005 on ATOVS at JMA Japan has a ground station(ice station) in the Antarctic. SHOWA-kichi JMA is getting the direct received ATOVS data in the Antarctic through National Institute of Polar Research (NIPR). The data is available within 50 min. after observation. The data is coming through International Mobile Satellite Organization. Data acquisition is going well. EMC Sack Lunch Seminar

  26. Exchange of ATOVS Direct Broadcast Data in Eastern Asia • JMA will contribute to establish a RARS together with the associated inter-regional data exchange mechanisms; • - JMA is willing to perform its part of the re-transmission functions through the GTS and/or the Internet, in co-operation with CMA, ABoM and other centres; • - The APSDEU forum should be used to co-ordinate the Asian RARS activities; • - Responsibility for implementation of functions and operations to be shared between centres. Reference:CGMS/WMO REGIONAL ATOVS RE-TRANSMISSION SYSTEM(RARS) WORKSHOP REPORT EMC Sack Lunch Seminar

  27. Data coverage EARS(EUMETSAT ATOVS Retransmission Service ) RARS(Eastern Asia) http://www.eumetsat.int/en/dps/atovs/images/coverage.gif If we use both, we will get much data in Early analysis. EMC Sack Lunch Seminar

  28. Next… EMC Sack Lunch Seminar

  29. Assimilation of MODIS polar winds at JMA Masahiro Kazumori Yoshiyuki Nakamura Numerical Prediction division Japan Meteorological Agency EMC Sack Lunch Seminar

  30. N.H. S.H. MODIS polar winds • AMVs from Geostationary Satellites have been used at JMA • But, the polar regions have been remained as data poor regions because no winds from geostationary satellite for these regions. RAOB and Aircraft-network are also sparse. And ATOVS 1D data had no high latitude data. • Since July 2002, CIMSS(Cooperative Institute for Meteorological Satellite) at Univ. of Wisconsin have been produced AMVs from MODIS on Terra and Aqua for the polar regions. Data distribution of AMVs from Satellite Orange Terra Green Aqua MODIS polar winds fill the gap of observation and will improve the accuracy of analysis in the polar regions, and bring a better forecast in the mid-latitudes. EMC Sack Lunch Seminar

  31. Difference between CIMSS and NESDIS • Terra/MODIS WV 400hPa N.H. Wind Speed O-B and number CIMSS NESDIS O-B Period: From 27 December 2003 To 9 February 2004 Difference in Quality And Coverage Number JMA use CIMSS MODIS winds. EMC Sack Lunch Seminar

  32. ALL data QC Pass Quality of MODIS Polar Winds • BIAS and RMSE of Wind Speed against first guess EMC Sack Lunch Seminar

  33. MODIS polar winds data assimilation Experiments • Period of the experiments • From 27 June 2003 to 9 August 2003 • From 27 December 2003 to 9 February 2004 • Configurations JMA Global Spectral Model (GSM)+ 3D-Var T213L40 CNTL the same as JMA operational run. MODIS polar winds were passively monitored. TEST CNTL+Terra/MODIS+Aqua/MODIS • Used MODIS polar winds (Only in the Arctic) Over oceanIR: above 700hPa WV: above 550hPa Over land IR,WV: above 400hPa. • Data thinning 150km(horizontal) 100hPa(vertical) • The data in the Antarctic have large bias. That degraded the forecast scores in another experiment. EMC Sack Lunch Seminar

  34. Impacts on Analysis • Mean field • Temperature：Rise in the lower troposphere and fall in the upper troposphere ( about 0.5 degree in zonal mean) • 500hPa height:Increase over ocean and decrease over land, especially over Siberia area. • Comparison with Radiosonde observation(RAOB) • Analysis and first guess in the TEST became close to RAOB, especially over Siberia • Improvements on background bring better forecasts. EMC Sack Lunch Seminar

  35. Zonal mean difference of Temperature Monthly mean for July 2003 Monthly mean for January 2004 • Rise in the lower troposphere and fall in the upper troposphere ( about 0.5 degrees in zonal mean) hPa EMC Sack Lunch Seminar

  36. Impacts on Analysis • Mean field • Temperature：Rise in the lower troposphere and fall in the upper troposphere ( about 0.5 degree in zonal mean) • 500hPa height:Increase over ocean and decrease over land, especially over Siberia area. • Comparison with Radiosonde observation(RAOB) • Analysis and first guess in the TEST became close to RAOB, especially over Siberia. • Improvements on background bring better forecasts. EMC Sack Lunch Seminar

  37. TEST first guess against RAOB CNTL first guess against RAOB Change of 500hPa Z Analysis (TEST-CNTL) • Monthly mean error for July 2003 (m) (m) (m) (m) First guess (TEST-CNTL) Analysis and first guess in the TEST became close to RAOB, especially over Siberia. EMC Sack Lunch Seminar

  38. Impacts on Forecast • Anomaly Correlation and RMSE at 500hPa • Large improvement for both seasons in the N.H. • Neutral for the Tropics and the S.H. (no MODIS assimilation in these regions in this test.) • Change in the Arctic spread to the lower latitudes. • RMS forecast error of wind vectors were reduced. • Especially, improvements at 500hPa was remarkable. ( 500hPa was the level with maximum data number) • Improvements on typhoon track prediction • Small, but positive impacts were found at the later stage in the forecasts. EMC Sack Lunch Seminar

  39. AnomalyCorrelation of 500hPa height MODIS polar winds assimilation in the Arctic TEST:With MODIS CNTL:Without MODIS Large improvements were found for the forecast score. EMC Sack Lunch Seminar

  40. Impacts on Forecast by MODIS polar winds • RMSE at 500hPa • Large improvement for both seasons in the polar region • Change in the polar regions spread to the lower latitudes. RMS forecast error difference for 500hPa Z TEST minus CNTL • Monthly mean difference for July 2003 1day forecast 3day forecast 5day forecast Positive Impacts of MODIS (negative difference) spread to mid-latitudes with procession of forecast. EMC Sack Lunch Seminar

  41. Impacts on Forecast • Anomaly Correlation and RMSE at 500hPa • Large improvement for both seasons by 9-day forecasts • Neutral for the tropics and the S.H. (no MODIS assimilation) • Change in the Arctic spread to the lower latitudes. • RMS forecast error of wind vectors were reduced. • Especially, improvements at 500hPa were remarkable. ( 500hPa was the level with maximum data number) • Improvements on typhoon track prediction • Small, but positive impacts were found at the later stage in the forecasts. EMC Sack Lunch Seminar

  42. Zonal mean of RMSE of Wind Speed at 500hPa（5-day forecasts,January2004） Improvement Improvement EMC Sack Lunch Seminar

  43. Impacts on Forecast • Anomaly Correlation and RMSE at 500hPa • Large improvement for both seasons by 9-day forecasts • Neutral for the tropics and the S.H. (no MODIS assimilation) • Change in the Arctic spread to the lower latitudes. • RMS forecast error of wind vectors were reduced. • Especially, improvements at 500hPa was remarkable. ( 500hPa was the level with maximum data number) • Improvements on typhoon track prediction • Small, but positive impacts were found at the later stage in the forecasts. EMC Sack Lunch Seminar

  44. Mean positional error of typhoon track predictions • 22 events in July 2003 • Neutral or slightly positive at the later stage in the forecast time. EMC Sack Lunch Seminar

  45. Summary • The MODIS polar winds assimilation experiments were performed at JMA • Period:July 2003, January 2004 • Used data:Aqua/MODIS,Terra/MODIS in the Arctic • QC • Over land, above 400hPa for IR and WV • Over ocean, above 700hPa for IR and 550hPa for WV • Data thinning 150km(horizontal) 100hPa(vertical) • Results • Improvements on the analysis and first guess in the Arctic. • Large positive impacts on forecasts for both seasons. ( height, temperature, wind fields) • Improvements on the typhoon track prediction. Since 27 May 2004, operational use in the Arctic Since 16 Sep 2004, operational use in the Antarctic EMC Sack Lunch Seminar

  46. The change of Quality of MODIS polar winds Wind speed comparison between CIMSS and NESDIS After June 2004, both data became similar. Same algorithm for retrieval, and same first guess for height assignment. CIMSS changed the first guess for height assignment from NAVY model to GFS in June 2004. EMC Sack Lunch Seminar

  47. Next… EMC Sack Lunch Seminar

  48. Improvement of operational forecast score 12-month average Forecast score of JMA global model is rapidly improving. EMC Sack Lunch Seminar

  49. RMSE of 500hPa height against initial 5day forecast 1day forecast N.H. N.H. 1day forecast S.H. 5day forecast S.H. EMC Sack Lunch Seminar

  50. Conclusion • ATOVS level 1C radiance data are used in JMA global data assimilation system operationally. • MODIS polar winds data are used in JMA global data assimilation system operationally. • In virtue of these data and 4D-Var system, operational forecast score of JMA global model is improving rapidly. Filling data poor region(space) with satellite data make better analysis and better forecast. Next step: Effective data usage for 4D-Var. Intelligent data thinning and Quality control for 4D-Var time slot in the assimilation window. Use of new satellite data (AIRS, SSMI, AMSR-E, SSMIS, etc…) EMC Sack Lunch Seminar

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