Lars Peter Riishojgaard

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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

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Overview

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

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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

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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

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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

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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
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MODIS WV U, V innovations for NH control (solid),
MODIS Arctic
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RAOB U, V innovations for control (solid), MODIS
Arctic
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RAOB U, V innovations for control (solid), MODIS
SP
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RAOB height innovations for control (solid),
MODIS Arctic region
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RAOB height innovations for control (solid),
MODIS South Pole
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ITOVS height innovations Arctic region
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ITOVS height innovations Antarctic region
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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).
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NCEP mean anlyzed 500 hPa heights, July 2002,
Anarctica
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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

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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