radiance assimilation in jma’s meso-scale analysis
DESCRIPTION
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 - PowerPoint PPT PresentationTRANSCRIPT
Radiance assimilation in JMA’sMeso-scale Analysis
Masahiro Kazumori
Izumi Okabe
Japan Meteorological Agency
AMSR-E Science Team Meeting, Asheville, North Carolina, U.S.A.June 28-29, 2011
Outline• Introduction
– JMA Meso-scale Analysis
• Assimilation experiments– Case study 1: Heavy precipitation in Baiu season– Case study 2: Typhoon
• Summary and Plan
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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.
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JMA Meso-scale AnalysisJapan 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Retrieval assimilation of TCWV is out-of-date.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 domainHorizontal res. 5km (3600x2880km)
50 vertical layers up to 22km15-hours forecast
from 00,06,12,18UTC initial 33-hours forecast
from 03,09,15,21UTC Initial
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Data coverage in Meso-scale AnalysisIn Situ Observations
03UTC(Daytime)
Available observation data depend on the analysis time.
21UTC(Night time)
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Remote Sensing Observations
03UTC(Daytime)
01 July, 2010
21UTC(Nighttime)
Also available polar orbiting satellite data depend on the analysis time.
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2009/07/22/00 UTC
Addition of F-16,F17 SSMIS TbChange to F13 SSMI Tb
F13 SSMI TCWV Ground based GPS TCWV
Ground based GPS TCWV
Addition of F-16,F17 SSMIS Rain Rate
F13 SSMI Rain Rate Rain Rate from ground-based Radar.
F13 SSMI Rain RateRain Rate from Radar
Retrieval Assimilation Radiance assimilation
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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.
Red: Mean Bias, Green: Std, Blue: Data counts (after thinning)
F-17 SSMIS 19V 22V 37V 92V
[K]
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Configuration of Assimilation Experiments
Microwave Imager (AMSR-E, TMI)TCWV, Rain Rate
Microwave SounderTemperature
Microwave ImagerAMSR-E, TMI Radiance,
Rain RateMicrowave Sounder
Radiance
Microwave ImagerSSMIS F16 F17
Radiance, Rain Rate
Microwave Humidity Sounder(MHS, AMSU-
B)RadianceMTSAT-1R
IR Clear Sky Radiance
ControlTest
Retrieval Assimilation(Same as operational as of Oct. 2010)
Radiance Assimilation(addition of other available radiance data) 9
Radar obs. vs. MSM precipitation forecasts
6-hr forecast
12-hr forecast
18-hr forecast
Retrieval assimilation(Control)
RA observation
Valid time: 12JST 03 July, 2010
Weak rain in forecasts
3-hr accumulated rainfall
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Radar obs. vs. MSM precipitation forecasts
6-hr forecast
12-hr forecast
18-hr forecast
Radiance assimilation (Addition of F-16,17 SSMIS Imagers)
RA observation
Improvement in short-range precipitation forecast
3-hr accumulated rainfall
Valid time: 12JST 03 July, 201013
Retrieval assimilation
Radiance assimilation
Difference (Test-Control)
2010/07/02 21UTC
The reason of the precipitation forecast improvement is the difference of analyzed TCWV field
Moisture flow from southwest around Kyushu area was strengthened in the radiance assimilation’s analysis
[mm][mm]
[mm]
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Observation Retrieval assimilation
Radiance assimilation
Valid time: 03UTC 3 July, 2010, 6-hour forecast from 21UTC 2 July, 2010 initial time.
Simulated MTSAT image (WV)Observed MTSAT image (WV)
MTSAT WV image contains moisture information in the upper troposphere.
Simulated image from Test’s forecast field is close to real observation.
Verification with MTSAT cloud image
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Diff. of TCWV(Test-Cntl)
Data coverage of newly added DMSP F16,17 SSMIS radiance
Case study 2
F-16, F17 SSMIS radiance and rain rate data were newly added in the Test run.
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Test’s analyzed TCWV
Control’s TCWV IncrementControl’s analyzed TCWV
Test’s TCWV Increment
The first analysis
[mm] [mm]
New microwave imagers data enhanced the TCVW contrast.
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Valid time: 09UTC 9 Aug., 2010Simulation from 09UTC 9 Aug., 2010 (initial time)
Simulated MTSAT image (IR)Observed MTSAT image (IR)Retrieval assimilation
Radiance assimilation
Separated feature is well represented in the analysis.
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Radiance assimilation
Retrieval assimilation
Clearly separated
Radar observation
[mm/3hr]
3-hr precipitation forecast
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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 20
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 applied for 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.
xxβxz bphh~
y
Bias correction term is in the observation operator Coefficients: Predictors: p TCPW, TSRF, TSRF
2, WSSRF, CLW Const.
TMI 19.35GHz V pol.
Coefficients are determined in JMA global analysis.
Time evolution of coefficients
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Comparison of Ver. 7 and 6 TMI data• TMI data (Ver. 6) is erroneous because it assumes a fixed reflector temperature in
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]22
19V pol. May Jun. Jul. Aug. Sep.
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
TMI V6
Data counts
O-B
Local time
Lat
[K]
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TMI Tb data in JMA’s NWP system• TMI Ver.7 Tb showed improved data quality. Solar biases are
much reduced. 19V pol. May Jun. Jul. Aug. Sep. TMI V6
TMI V7
Local time
Lat
[K]
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Time sequences of VarBC coefficients
Cold start
TMI 19GHz V pol.
Dotted : Coefficients for Ver.6 TMISolid: Coefficients for Ver.7 TMI
47days
Coefficients’ change is reduced, 47 days gaps disappeared.
AMSR-E 19GHz V pol.
F-16 SSMIS 19GHz V pol.
F-17 SSMIS 19GHz V pol. TCPW
TSRF
TSRF2
WSSRF
CLW Const.
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21V TMI-V6 TMI-V7 SSMIS16 SSMIS17 AMSR-E
June 2010
[K]Local time
Lat
From RSS home page
Comparison of water vapor channel’s O-B biases in JMA NWP
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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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