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Water Vapor Radiance Assimilation in the NCEP Global Data Assimilation System. James Jung Cooperative Institute for Meteorological Satellite Studies Jim.Jung@noaa.gov In collaboration with NCEP/EMC, NASA/GMAO, NESDIS/STAR, NESDIS/JPSS, CAWCR, etc. Outline. Recent Projects
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Water Vapor Radiance Assimilation in the NCEP Global Data Assimilation System James Jung Cooperative Institute for Meteorological Satellite Studies Jim.Jung@noaa.gov In collaboration with NCEP/EMC, NASA/GMAO, NESDIS/STAR, NESDIS/JPSS, CAWCR, etc.
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
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
Background Supersaturation 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)
Backgroundrelative humidity vs specific humidity • 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 humidity • RH perturbations are computed from q only (Removes ΔT) • From Daryl Kleist • Namelist variable (q_hyb_ens=.true.)
Effect on supersaturation counts Supersaturation counts before each outer loop from the control SUPERSAT RH COUNT,RMS= 770051 0.118376 SUPERSAT RH COUNT,RMS= 1494196 0.103566 SUPERSAT RH COUNT,RMS= 1585750 0.101427 Supersaturation counts before each outer loop from the experiment SUPERSAT RH COUNT,RMS= 0 0.00000 SUPERSAT RH COUNT,RMS= 235489 0.266668E-01 SUPERSAT 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
Latitude – Height Analysis DifferencesRelative Humidity 20120801 - 20120915 20130101 - 20130215 About equally less relative humidity in both experiments
Latitude – Height Analysis DifferencesCloud Water 20120801 - 20120915 20130101 - 20130215 About equally less cloud water in both experiments
Latitude – Height Analysis DifferencesGeopotential Height 20120801 - 20120915 20130101 - 20130215 Lower heights in the enkf_q experiment
Latitude – Height Analysis DifferencesTemperature 20120801 - 20120915 20130101 - 20130215 Significantly colder over both poles
Analysis DifferencesNear Surface Temperature 20120801 - 20120915 20130101 - 20130215 Significantly colder over both poles
North Pole Rawinsonde Comparisons Black = control, green = experiment Courtesy A. Collard
South Pole Rawinsonde Comparisons Black = control, green = experiment Courtesy A. Collard
Analysis troposphere fit to rawinsondes Control: solid Experiment: dash Analysis: black 6-hr guess: red Experiment is generally cooler
Analysis troposphere fit to rawinsondes Control: solid Experiment: dash Analysis: black 6-hr guess: red Experiment generally drier
12 & 36 hr forecast fit to rawinsondes Control: solid Experiment: dash 12-hr: black 36-hr: red Experiment remains cooler in troposphere but drifting back to control
12 & 36 hr forecast fit to rawinsondes Control: solid Experiment: dash 12-hr: black 36-hr: red Experiment remains drier
Anomaly Correlations500 hPa Northern Hemisphere 20120801 - 20120915 20130101 - 20130215
Anomaly Correlations500 hPa Southern Hemisphere 20120801 - 20120915 20130101 - 20130215
Tropical Wind Vector RMSE 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.
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)
Latitude – HeightAnalysis Differences All_Data SNPP-ATMS – All_Data Aqua-AIRS – All_Data N19-AMSU/MHS – All_Data Base – All_Data Relative Humidity 00Z 20120801 - 20120920
Latitude – HeightAnalysis Differences All_Data SNPP-ATMS – All_Data Aqua-AIRS – All_Data N19-AMSU/MHS – All_Data Base – All_Data Cloud Water 00Z 20120801 - 20120920
Latitude – HeightAnalysis Differences All_Data SNPP-ATMS – All_Data Aqua-AIRS – All_Data N19-AMSU/MHS – All_Data Base – All_Data Temperature 00Z 20120801 - 20120920
500 hPa AC scores for 00Z 20120801-20120930 Base SNPP-ATMS All_DataAqua-AIRS N19-AMSU/MHS Base SNPP-ATMS All_DataAqua-AIRS N19-AMSU/MHS Northern Hemisphere Southern Hemisphere
1000 hPa AC scores for 00Z 20120801 – 20120930 Base SNPP-ATMS All_DataAqua-AIRS N19-AMSU/MHS Base SNPP-ATMS All_DataAqua-AIRS N19-AMSU/MHS Northern Hemisphere Southern Hemisphere
Tropical Vector Wind RMSE for 00Z 20120801 - 20120930 200 hPa 850 hPa Base SNPP-ATMS All_DataAqua-AIRS N19-AMSU/MHS Base SNPP-ATMS All_DataAqua-AIRS N19-AMSU/MHS
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
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
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