Water Vapor Radiance Assimilation in the NCEP Global Data Assimilation System

Water Vapor Radiance Assimilation in the NCEP Global Data

Assimilation System

James JungCooperative Institute for Meteorological Satellite Studies

[email protected]

In collaboration with

NCEP/EMC, NASA/GMAO, NESDIS/STAR, NESDIS/JPSS, CAWCR, etc.

mailto:[email protected]

12th Annual JCSDA Review

Outline

• Recent Projects Supersaturation removal Using q instead of RH for background error Addition to Baseline Observing System Experiments

• Current Work 2014 Global Data Assimilation System transition

• Future Projects Water Vapor Radiance Assimilation Addition to Baseline Observing System Experiments

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12th Annual JCSDA Review3

Water Vapor Assimilation


Background

• May 2012 version of the GDAS/GFS Hybrid (80 ensembles, T254) T574, NCEP operational resolution

• Summer and winter seasons• No changes to observations

Counts / fits differences due to atmosphere changes

• Forecast scores verified against own analysis

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BackgroundSupersaturation removal

• NCEP Ticket #346 Supersaturation removal experiments

• Subversion branch r29873 with updates

• T574 hybrid (~May 2012 version ported to JIBB)

• No changes to observations

• Perturbations derived from the ensembles used RH

• Namelist variable added (clip_supersaturation=.true.)

• Factqmax=50.0 (penalize minimization for generating supersaturation)

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Backgroundrelative humidity vs specific humidity

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• NCEP ticket #338 Use q instead of RH perturbations for the moisture component in the ensembles.

• Includes the changes from the supersaturation removal experiment.

• RH background error derived from ensemble specific humidityF RH perturbations are computed from q only (Removes

ΔT)

F From Daryl Kleist

F Namelist variable (q_hyb_ens=.true.)


Supersaturation counts before each outer loop from the control

SUPERSAT RH COUNT,RMS= 770051 0.118376SUPERSAT RH COUNT,RMS= 1494196 0.103566SUPERSAT RH COUNT,RMS= 1585750 0.101427

Supersaturation counts before each outer loop from the experiment

SUPERSAT RH COUNT,RMS= 0 0.00000SUPERSAT RH COUNT,RMS= 235489 0.266668E-01SUPERSAT RH COUNT,RMS= 160906 0.716671E-02

Supersaturation counts accumulate with each outer loop in the control. Counts and RMS are an order of magnitude higher in the control

Effect on supersaturation counts

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Latitude – Height Analysis DifferencesRelative Humidity

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About equally less relative humidity in both experiments

20120801 - 20120915 20130101 - 20130215


Latitude – Height Analysis DifferencesCloud Water

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About equally less cloud water in both experiments

20120801 - 20120915 20130101 - 20130215


Latitude – Height Analysis DifferencesGeopotential Height

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Lower heights in the enkf_q experiment

20120801 - 20120915 20130101 - 20130215


Latitude – Height Analysis DifferencesTemperature

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Significantly colder over both poles

20120801 - 20120915 20130101 - 20130215


Analysis DifferencesNear Surface Temperature

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Significantly colder over both poles

20120801 - 20120915 20130101 - 20130215

12th Annual JCSDA ReviewCourtesy A. Collard

North Pole Rawinsonde Comparisons

Black = control, green = experiment

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South Pole Rawinsonde Comparisons

Courtesy A. CollardBlack = control, green = experiment

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Analysis troposphere fit to rawinsondes

Experiment is generally cooler

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Control: solidExperiment: dash

Analysis: black6-hr guess: red


Analysis troposphere fit to rawinsondes

Experiment generally drier

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Analysis: black6-hr guess: red


12 & 36 hr forecast fit to rawinsondes

Experiment remains cooler in troposphere but drifting back to control

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12-hr: black36-hr: red


12 & 36 hr forecast fit to rawinsondes

Experiment remains drier

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12-hr: black36-hr: red


Anomaly Correlations500 hPa Northern Hemisphere

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20120801 - 20120915 20130101 - 20130215


Anomaly Correlations500 hPa Southern Hemisphere

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20120801 - 20120915 20130101 - 20130215


Tropical Wind Vector RMSE

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20120801 - 20120915 20130101 - 20130215


Summary

• Reduce supersaturation counts and RMS (wrt RH) by an order of magnitude

• Troposphere - drier / stratosphere – wetter

• Troposphere cooler, less drift in stratosphere temperature

• Less clouds at upper levels

• Convergence and penalty marginally worse (factqmax)

• Benchmarks / scores are mixed to postitive. ENKF_Q better than SUPERSAT.

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Addition to baseline Observing System

Experiments

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Background

• May 2012 version of the GDAS/GFS Hybrid (80 ensembles, T254) T574, NCEP operational resolution

• Summer and winter seasons

• Baseline is conventional data and GPS-RO

• Add single instruments ATMS (SNPP) AMSUA, MHS (NOAA-19) AIRS (Aqua)

• Verified against a control analysis with all operational data (including NOAA-19, SNPP, and Aqua)

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Latitude – HeightAnalysis Differences

All_Data SNPP-ATMS – All_Data

Aqua-AIRS – All_Data N19-AMSU/MHS – All_Data

Base – All_Data

Relative Humidity

00Z20120801 - 20120920



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Base – All_Data

Cloud Water

00Z20120801 - 20120920





Base – All_Data

Temperature

00Z20120801 - 20120920


500 hPa AC scores for 00Z 20120801-20120930

Base SNPP-ATMSAll_Data Aqua-AIRSN19-AMSU/MHS


Northern Hemisphere Southern Hemisphere

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1000 hPa AC scores for 00Z 20120801 – 20120930



Northern Hemisphere Southern Hemisphere

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Tropical Vector Wind RMSE for 00Z 20120801 - 20120930



200 hPa 850 hPa

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Hurricane Statistics20120801 - 20120915

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Summary(WRT the control)

• Increased cloud water in the tropics. Baseline has the most clouds

• Higher geopotential heights at upper levels. Baseline and SNPP-ATMS are the highest

• Greater RH in Southern Hemisphere upper troposphere Except for Aqua-AIRS

• Anomaly correlation scores: SNPP-ATMS and Aqua-AIRS are generally equal N19-AMSU/MHS is slightly lower

• Tropical wind vector RMSE: Aqua-AIRS is best (first 24 hours) Baseline worst throughout

• Aqua-AIRS generally best hurricane stats

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Future Projects

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Water Vapor Radiance Assimilation • Build from previous work. Control will including these namelist

changes: clip_supersaturation=.true. factqmax=50.0 q_hyb_ens=.true.

• Due to all of the recent changes in both the analysis and forecast model, a review of the QC procedures for MW and IR water vapor channels currently used by GDAS is in order. Adjust gross error check Adjust assimilation weights

• Review water vapor channel selection for AIRS, IASI, and CrIS. Remove AIRS stratospheric channels ( ~11) Add tropospheric channels for IASI and CrIS.

• Two season impact tests. Operations resolution (T1534?)

• Wiki page for progress updates. Ticket #394 Branch jung_wv_chans

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Observing System ExperimentsData Additions

• Control Lower resolution semi-Lagrangian (T670?) All available data

• Baseline Conventional data only (unless unstable)

• Experiments AIRS IASI CrIS

• If time permits ATMS SSMIS

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Documents

Water Vapor Radiance Assimilation in the NCEP Global Data Assimilation System