radiance assimilation in jma s meso scale analysis
Download
Skip this Video
Download Presentation
Radiance assimilation in JMA’s Meso-scale Analysis

Loading in 2 Seconds...

play fullscreen
1 / 27

Radiance assimilation in JMA’s Meso-scale Analysis - PowerPoint PPT Presentation


  • 105 Views
  • Uploaded on

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

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about ' Radiance assimilation in JMA’s Meso-scale Analysis' - henry-mcgowan


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
radiance assimilation in jma s meso scale analysis

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.

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

data coverage in meso scale analysis
Data coverage in Meso-scale Analysis

In Situ Observations

03UTC(Daytime)

21UTC

(Night time)

Available observation data depend on the analysis time.

slide6

Remote Sensing Observations

03UTC(Daytime)

21UTC(Nighttime)

Also available polar orbiting satellite data depend on the analysis time.

01 July, 2010

slide7

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

slide8

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)

slide9

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)

case study 1

MTSAT IR

Case study 1

7/2-7/4

Total rainfall amount

comparison of data coverage
Comparison of data coverage

Retrieval Assimilation (Control)

Radiance Assimilation (Test )

radar obs vs msm precipitation forecasts
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

radar obs vs msm precipitation forecasts1
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

the reason of the precipitation forecast improvement is the difference of analyzed tcwv field
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

verification with mtsat cloud image
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.

slide16

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.

slide17

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.

slide18

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)

slide19

3-hr precipitation forecast

Retrieval assimilation

Radiance assimilation

Radar observation

[mm/3hr]

Clearly separated

summary and plan
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
tmi tb data in jma s nwp system
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.

comparison of ver 7 and 6 tmi data
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]

tmi tb data in jma s nwp system1
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

tmi tb data in jma s nwp system2
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

time sequences of varbc coefficients
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.

slide26

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

final comments
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.
ad