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Lars Peter Riishojgaard

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Preparation for operational assimilation MODIS winds in the DAO ... heights for July, 2002, for MODIS I run; NH (top left) and SH (bottom left) ... – PowerPoint PPT presentation

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Title: Lars Peter Riishojgaard


1
Preparation for operational assimilation MODIS
winds in the DAO
  • Lars Peter Riishojgaard
  • Yan-Qiu Zhu
  • Global Modeling and Assimilation Office
  • NASA Goddard Space Flight Center

2
Overview
  • Data assimilation at Goddard and the JCSDA
  • Characteristics of the MODIS winds
  • Results from pre-operational testing
  • Summary and outlook

3
GMAO
  • New Office at Goddard, formed via a merger of the
    DAO and NSIPP (NASA Seasonal to Interannual
    Prediction Project)
  • Head of Office Michele Rienecker
  • Modeling
  • New model targeted for 04 based on fv dynamical
    core, but with NWP-tuned physics
  • Analysis
  • Last PSAS-based system being frozen
  • Next system will be based on GSI developed at EMC

4
MODIS winds pilot period assimilation experiments
in the DAO
  • Control (all standard observations no MODIS
    winds)
  • MODIS winds used as is no filtering, no
    modification
  • Interactive height assignment with ?pmax150 hPa
  • Interactive height assignment with ?pmax75 hPa

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Preparation for operational assimilation of MODIS
winds
  • Extensive experimentation with near-real time
    winds provided by CIMSS starting 07/02/2002 with
    versions 1.3r6 and 1.4r1 of the fv-DAS
  • Main changes with respect to 1.2r5 (pilot period)
  • Increased weight given to ITOVS
  • Additional ITOVS data in polar areas
  • Modified background error covariance
  • Main metrics
  • Consistency of data delivery
  • Quality of MODIS winds
  • Contribution to forecast skill

8
MODIS experiments
  • Basic - MODIS winds used "as is"
  • Height adjustment - the heights of MODIS winds
    are adjusted by minimizing a cost function
  • Quality indicator-based selection only MODIS
    winds with qi larger than 0.80 are used
  • Retuned ?o error for MODIS wind is tuned using
    maximum likelihood technique
  • ECMWF filtering over land winds used above 400
    hPa over sea, IR winds above 700 hPa and WV
    winds above 550 hPa
  • DAOTOVS exclusion Interactive TOVS retrievals
    beyond 65S removed

9
Experimental results
  • Innovations (observation minus forecast
    residuals)
  • RAOB heights and winds
  • ITOVS heights
  • MODIS winds
  • Impact
  • Troposphere
  • Stratosphere
  • Forecast skill

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MODIS IR U, V innovations for NH control (solid),
MODIS Arctic
13
MODIS WV U, V innovations for NH control (solid),
MODIS Arctic
14
RAOB U, V innovations for control (solid), MODIS
Arctic
15
RAOB U, V innovations for control (solid), MODIS
SP
16
RAOB height innovations for control (solid),
MODIS Arctic region
17
RAOB height innovations for control (solid),
MODIS South Pole
18
ITOVS height innovations Arctic region
19
ITOVS height innovations Antarctic region
20
Mean analyzed 500 hPa geopotential heights for
July, 2002, for MODIS I run NH (top left) and
SH (bottom left) RHS shows difference fields
(MODIS minus control).
21
NCEP mean anlyzed 500 hPa heights, July 2002,
Anarctica
22
Control minus NCEP
MODIS minus NCEP
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Summary
  • MODIS winds complement other observations at the
    highest latitudes more so in the SH than in the
    NH due to the current data sparsity
  • Consistency of data delivery is acceptable
  • Based on independent verification and innovation
    statistics, the quality of the information is
    acceptable
  • Positive contribution to forecast skill, but not
    where one would expect it the most
  • Current version of fv-DAS is hostile to
    high-latitude wind information

31
Outlook
  • MODIS winds experiments with new GMAO
    assimilation system based on GSI (next-generation
    EMC analysis )
  • Impact
  • Background error covariance
  • Timeliness
  • MODIS winds from Aqua
  • ECMWF verification if possible
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