Impact of Satellite Atmospheric Motion Vectors in the GMAO GEOS 5 Global Data Assimilation System
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1 Impact of Satellite Atmospheric Motion Vectors in the GMAO GEOS 5 Global Data Assimilation System update of results presented at the 5 th WMO Workshop on the Impact of Various Observing Systems on NWP, Sedona, AZ, May 2012 Ron Gelaro, Dagmar Merkova, Will McCarty, King Sheng Tai Global Modeling and Assimilation Office, NASA GSFC, USA with special thanks to Pat Pauley, Nancy Baker Naval Research Laboratory, USA 1
2 FNMOC and GMAO Observation Impact Monitoring Current Operations _main_od.html ms/fp/obs_impact/ Much larger relative impact of AMVs in Navy system 2
3 Why does FNMOC get such large impact from satellite winds? Use of more satellite winds from more sources? More effective treatment of satellite winds superobing? Assimilation of fewer satellite radiances? All of the above? Observation Counts per 6h (K) Notable observing system differences: Satwind AMSU A Hyps IR AllObs GMAO FNMOC Approximate average values for the year ending 15 May 2012 GMAO: 1.5x Radiances FNMOC: 4x Winds A simple first experiment: Assimilate NRL/FNMOC prepared satellite winds in the GMAO forecast system 3
4 GEOS 5 AMV Impact Experiments GEOS 5 Forecast System (reduced resolution) GEOS 5 AGCM + GSI analysis (~½ L72) 6 h assimilation cycle, 3DVar 5 day forecasts, adjoint based 24h obs impacts at 00z (dry energy norm, sfc 150 hpa) Experiments for two periods: Aug Sep 2010 and Dec Jan 2011 Control GMAO/NCEP operational data set (full monty) NRLAMV substitute all NRL Geo, MODIS, LeoGeo winds (super obs) NGMAO NRL Geo types/sources matching GMAO control (# radiances) NLoRAD NRLAMV but with thinned radiances (super obs, data NGMAO2 NGMAO but allow multiple sources redundancy) NRL obs error & QC used in all substitution experiments, no other retuning 4
5 Geo Wind Data Coverage and Density 10 Jan z Control Data Density Control Data Coverage NRLAMV Data Coverage NRLAMV Data Density noncimss CIMSS 5
6 Geo Wind Experimental Configurations Dec Jan 2011 Control NRLAMV NLoRAD (super obs, multiple sources) NGMAO (super obs) NGMAO2 6
7 Geo Wind Observation Counts (used) Global 10 Dec Jan z Control NRLAMV 65 80K obs/anal K obs/anal (No satwinds received on 18 Jan) 7
8 Data Assimilation Statistics for Satellite Winds 10 Dec Jan z Data Counts Departures Normalized Cost UsedPassiveRejected RMS(O B)RMS(O A) Jo(O B)/pJo(O A)/p Control UsedPassiveRejected RMS(O B)RMS(O A) Jo(O B)/pJo(O A)/p NRLAMV 8
9 Observation Impact Global Aug Sep 2010 Control NRLAMV NGMAO NLoRAD 9
10 Observation Impact Global Dec Jan 2011 Control NRLAMV NGMAO NLoRAD 10
11 Time Series of Satellite Wind Total Impact Global 10 Dec Jan z Total impact of NLR satwinds is roughly 2x that of control satwinds Dates with large satwind impact tend to coincide in both experiments Control NRLAMV 11
12 Gridded Vertically Summed Impact of Geo Winds Aug Sep 2010 Dec Jan 2011 Control NRLAMV 12
13 Relative Impacts of Selected AMV Types Dec Jan 2011 Global Data Count Global Total Impact WINDSAT MODIS IR MODIS WV METEOSAT WV METEOSAT IR/Vis LeoGeo JMA WV JMA IR/Vis GOES WV GOES IR/Vis ASCAT WINDSAT MODIS IR MODIS WV METEOSAT WV METEOSAT IR/Vis LeoGeo JMA WV JMA IR/Vis GOES WV GOES IR/Vis ASCAT Relative impacts of most AMV types correlate well with data counts Beneficial impact of MODIS winds in NRLAMV (versus non beneficial impact in control) requires other explanation.superobs? 13
14 Vertical Profiles of Geo Wind Impact Aug Sep 2010 Dec Jan 2011 Glo bal Tropics Global Tropics Control NRLAMV NGMAO NGMAO (MTSAT not plotted) NGMAO > Control suggests beneficial impact of super obs 14
15 Gridded Vertically Summed Impact of Geo Winds Aug Sep 2010 Dec Jan 2011 Control MTSAT not plotted NGMAO 15
16 Relative Impacts of Geo Wind Types Tropics Aug Sep 2010 Dec Jan 2011 X 16
17 Vertical Profiles of Geo Wind Impact Aug Sep 2010 Dec Jan 2011 Glo bal S.Hem Global S.Hem Control NRLAMV NLoRAD Evidence of larger AMV impact when fewer radiances assimilated, but mostly in S.Hem during summer 17
18 Relative Impacts of Geo Wind Types Southern Hemisphere Aug Sep 2010 Dec Jan
19 Conclusions NRL AMVs were crudely but successfully assimilated into the GMAO GEOS 5 data assimilation system Compared to the control run with GMAO (NCEP) AMVs, the assimilation of NRL AMVs provides substantially increased beneficial impact (ADJ), and also appears to improve forecast skill overall (OSE) The greater volume of the NRL AMVs (from multiple sources) appears to be the primary reason for their larger impact Use of super obs and reduction in radiance data increase AMV impacts but these effects are secondary and vary by season and location In general, robustness of the current observing system reduces the dominance/impact of any one data type (also see 5 th WMO Workshop on Obs Impact Impact on NWP). Subtleties of DAS response to observing system changes argue for continued use of both OSE and ADJ techniques where possible 19
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