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Radiance assimilation in JMA’s Meso-scale Analysis

Radiance assimilation in JMA’s Meso-scale Analysis. Masahiro Kazumori Izumi Okabe Japan Meteorological Agency. June 28-29, 2011. AMSR-E Science Team Meeting, Asheville, North Carolina, U.S.A. Outline. Introduction JMA Meso-scale Analysis Assimilation experiments

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Radiance assimilation in JMA’s Meso-scale Analysis

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  1. Radiance assimilation in JMA’sMeso-scale Analysis Masahiro Kazumori Izumi Okabe Japan Meteorological Agency June 28-29, 2011 AMSR-E Science Team Meeting, Asheville, North Carolina, U.S.A.

  2. Outline • Introduction • JMA Meso-scale Analysis • Assimilation experiments • Case study 1: Heavy precipitation in Baiu season • Case study 2: Typhoon • Summary and Plan

  3. Introduction • The main objective of JMA’s Meso-Scale Model (MSM) is to provide guidance for issuing warnings or very short-range forecasts of precipitation covering Japan and its surrounding areas. • JMA’s Meso-scale Analysis (4D-Var) requires a lot of observations to produce accurate initial condition for the forecast model. • Total column water vapor and Rain rate from AMSR-E, TMI, and Temperature profiles from ATOVS had been assimilated together with other observation data. • On Dec. 13, 2010, direct radiance assimilation was introduced in JMA operational Meso-scale Analysis as the replacement of the retrieval assimilation.

  4. JMA Meso-scale Analysis Japan is an island country surrounded by ocean. Moisture information over the ocean is a key for accurate precipitation forecasting. Retrieval assimilation of TCWV is out-of-date. Most operational NWP centers use observed radiances directly in data assimilation system. Direct radiance assimilation enable us to use the observations without any retrieval process and retrieval error contamination. Early use of satellite data into operational NWP is possible after the L1 data release. Fast radiative transfer model (e.g. RTTOV) is necessary for the forward and adjoint calculation in the variational data assimilation. Meso-Scale Model domain Horizontal res. 5km (3600x2880km) 50 vertical layers up to 22km 15-hours forecast from 00,06,12,18UTC initial 33-hours forecast from 03,09,15,21UTC Initial

  5. Data coverage in Meso-scale Analysis In Situ Observations 03UTC(Daytime) 21UTC (Night time) Available observation data depend on the analysis time.

  6. Remote Sensing Observations 03UTC(Daytime) 21UTC(Nighttime) Also available polar orbiting satellite data depend on the analysis time. 01 July, 2010

  7. Retrieval Assimilation Radiance assimilation Ground based GPS TCWV Addition of F-16,F17 SSMIS Tb F13 SSMI TCWV Change to F13 SSMI Tb Addition of F-16,F17 SSMIS Rain Rate Ground based GPS TCWV F13 SSMI Rain Rate F13 SSMI Rain Rate Rain Rate from ground-based Radar. Rain Rate from Radar 2009/07/22/00 UTC

  8. Bias correction for Tb • Scan bias correction • Biases dependent on scan position • Scan biases were corrected by fixed coefficient tables for each channels and sensors • Air-mass bias correction (VarBC) • In the JMA global DA system, the biases in O-B are corrected by variational bias correction scheme (VarBC). The biases are estimated by using a linear function with some predictors and those coefficients are optimized inside the 4D-Var analysis and updated every analysis cycle. • Predictors : Integrated weighted lapse rate, surface temperature, cloud liquid water, zenith angle. F-17 SSMIS 19V 22V 37V 92V [K] Red: Mean Bias, Green: Std, Blue: Data counts (after thinning)

  9. Configuration of Assimilation Experiments Test Control Microwave Imager AMSR-E, TMI Radiance,Rain Rate Microwave Sounder Radiance Microwave Imager (AMSR-E, TMI) TCWV, Rain Rate Microwave Sounder Temperature Microwave Imager SSMIS F16 F17 Radiance, Rain Rate Retrieval Assimilation (Same as operational as of Oct. 2010) Microwave Humidity Sounder(MHS, AMSU-B) Radiance MTSAT-1R IR Clear Sky Radiance Radiance Assimilation (addition of other available radiance data)

  10. MTSAT IR Case study 1 7/2-7/4 Total rainfall amount

  11. Comparison of data coverage Retrieval Assimilation (Control) Radiance Assimilation (Test )

  12. Radar obs. vs. MSM precipitation forecasts 3-hr accumulated rainfall Retrieval assimilation(Control) RA observation 6-hr forecast Weak rain in forecasts 12-hr forecast Valid time: 12JST 03 July, 2010 18-hr forecast

  13. Radar obs. vs. MSM precipitation forecasts 3-hr accumulated rainfall Radiance assimilation (Addition of F-16,17 SSMIS Imagers) RA observation 6-hr forecast Improvement in short-range precipitation forecast 12-hr forecast Valid time: 12JST 03 July, 2010 18-hr forecast

  14. The reason of the precipitation forecast improvement is the difference of analyzed TCWV field Retrieval assimilation Difference (Test-Control) [mm] [mm] Radiance assimilation Moisture flow from southwest around Kyushu area was strengthened in the radiance assimilation’s analysis [mm] 2010/07/02 21UTC

  15. Verification with MTSAT cloud image Observed MTSAT image (WV) Simulated MTSAT image (WV) Observation Retrieval assimilation Radiance assimilation MTSAT WV image contains moisture information in the upper troposphere. Simulated image from Test’s forecast field is close to real observation. Valid time: 03UTC 3 July, 2010, 6-hour forecast from 21UTC 2 July, 2010 initial time.

  16. Data coverage of newly added DMSP F16,17 SSMIS radiance Case study 2 Diff. of TCWV(Test-Cntl) F-16, F17 SSMIS radiance and rain rate data were newly added in the Test run.

  17. The first analysis Control’s analyzed TCWV Control’sTCWV Increment [mm] [mm] Test’s analyzed TCWV Test’s TCWV Increment New microwave imagers data enhanced the TCVW contrast.

  18. Simulated MTSAT image (IR) Observed MTSAT image (IR) Retrieval assimilation Radiance assimilation Separated feature is well represented in the analysis. Valid time: 09UTC 9 Aug., 2010 Simulation from 09UTC 9 Aug., 2010 (initial time)

  19. 3-hr precipitation forecast Retrieval assimilation Radiance assimilation Radar observation [mm/3hr] Clearly separated

  20. Summary and Plan • Atmospheric water vapor content is one of the fundamental amount in NWP model. The information provided by Microwave imagers is essential for the accurate forecasting of heavy precipitation and the typhoon. • Direct radiance assimilation showed large positive impactson the analyses and forecasts. Direct radiance assimilation enable us to use a lot of satellite data without retrieval process. And new data, DMSP F-16, F-17 SSMIS were incorporated in the analysis. • A number of MW-Imager data provide realistic moisture field in the analysis. • It is desirable to use well calibrated Microwave radiance data as much as possible. New Microwave imagers are • TMI Ver. 7 as a replacement of current Ver. 6 data • WindSat • F-18 SSMIS

  21. TMI Tb data in JMA’s NWP system • JMA assimilates TRMM Microwave Imager (TMI) observations for their information on humidity over the ocean in Global DA system. • Variational DA assumes no bias between observed Tb and model equivalent. Variational bias correction (VarBC) is appliedfor Tb. • A linear function is assumed to describe the bias by using some predictors. Coefficients are optimized in the analysis and used in the next analysis. However, the coefficients are determined as global constants in every analysis. It is difficult to correct local biases in the current VarBC scheme. Time evolution of coefficients TMI 19.35GHz V pol. Bias correction term is in the observation operator Coefficients: Predictors: p TCPW, TSRF, TSRF2, WSSRF, CLW Const. Coefficients are determined in JMA global analysis.

  22. Comparison of Ver. 7 and 6 TMI data • TMI data (Ver. 6) is erroneous because it assumes a fixed reflector temperaturein calibration. Time varying solar biases are reported in the comparison with ECMWF first guess (A. Geer 2010). • NASA plans to distribute Ver.7 TMI data. JMA obtains the sample data via JAXA. • An evaluation was performed to confirm the improved calibration. Ver. 7 TMI 19GHz V pol. Tb and the difference from Ver. 6 June 1, 2010 [K] [K]

  23. TMI Tb data in JMA’s NWP system • Solar biases observed in TMI Ver.6 Tb. TMI 19GHz V.pol Bias corrected O-B (observed Tb – background Tb), clear scene only 19V pol. MayJun.Jul.Aug.Sep. TMI V6 O-B Lat Local time [K] Data counts

  24. TMI Tb data in JMA’s NWP system • TMI Ver.7 Tb showed improved data quality. Solar biases are much reduced. 19V pol. MayJun.Jul.Aug.Sep. TMI V6 Lat Local time [K] TMI V7

  25. Time sequences of VarBC coefficients AMSR-E 19GHz V pol. Dotted : Coefficients for Ver.6 TMI Solid: Coefficients for Ver.7 TMI TMI 19GHz V pol. 47days F-16 SSMIS 19GHz V pol. Cold start F-17 SSMIS 19GHz V pol. TCPW TSRF TSRF2 WSSRF CLW Const. Coefficients’ change is reduced, 47 days gaps disappeared.

  26. Comparison of water vapor channel’s O-B biases in JMA NWP 21V TMI-V6 TMI-V7SSMIS16SSMIS17AMSR-E June 2010 Lat [K] Local time From RSS home page

  27. Final comments • Radiance assimilation of Microwave imagers was started in JMA’s Meso-scale Analysis in Dec. 13, 2010. • Direct radiance assimilation of Microwave imagers has large positive impacts in JMA NWP system. Microwave imager’s radiance data is necessary for accurate humidity analyses and precipitation forecasts for Japan. • As direct assimilation of radiance data in NWP is major trend, the Tb’s calibration accuracy is more important than before.Our NWP system has capability to detect the calibration problem. • NWP is expected as a powerful tool for Cal/Val process of GCOM-W1/AMSR2 and GPM/GMI.

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