Microwave Integrated Retrieval System (MIRS)

Transcription

1 Microwave Integrated Retrieva System (MIRS) Fuzhong Weng, Mitch Godberg, Mark Liu, Sid Boukabara, Raph Ferraro and Tony Reae Office of Research Appications NOAA/NESDIS The 14 th Internationa TOVS Study Conference, Beijing, China May 25-31, 25

2 NESDIS Pan on ATOVS System MIRS AMSU A/B(MHS) ATMS CMIS Integrated Vaidation System INFRARED Sounder/Imager HIRS, AIRS, IASI, CriS,HES AVHRR, MODIS, VIIRS,ABI

3 NESDIS ATOVS Pan Use MIRS as front end MIRS provides microwave-ony retrieva, and wi become an operationa product MIRS microwave retrieva is first guess for HIRS. HIRS processing software ca a function which returns the required microwave information from the MIRS. HIRS information is used to derive couds, OLR, ozone and for cear fovs improved temperature and moisture profies (for temperature improvement is in ower troposphere)

4 AIRS, CrIS, and IASI Continue deveopment of a singe software processing system for a three instrument. The processing system incorporates microwave information from AMSU and ATMS and imager information from MODIS, VIIRS and AVHRR. Use of MIRS is desirabe as front-end. (Microwave agorithms are embedded within AIRS processing system, - so software modues from MIRS wi need to be embedded)

5 Enhance Microwave Surface and Precipitation Products System (MSPPS) Performance Provide front-end retrievas for the robust first guess to infrared sounding system (HIRS, hyperspectra) MIRS Objectives AMSU Coud Liquid Water Path (3/24, 21) Profie temperature, water vapor and coud water from microwave instrument under a weather conditions Improve profiing ower troposphere by using more surface viewing channes in retrieva agorithm Weng et a. (Radio Science, 23) Microwave surface Emissivity at 37 GHz Integrate the state-of-the-art radiative transfer mode components from JCSDA into MIRS Prepare for future microwave sounding system (e.g. ATMS, CMIS, SSMIS, Geo- MW)

6 Microwave Products from NPOESS CMIS EDR Tite Atmospheric Vertica Moisture Profie (surf to 6mb) Sea Surface Winds (Speed) Soi Moisture Atmospheric Vertica Moisture Profie (6 to 1mb) Atmospheric Vertica Temperature Profie Coud Ice Water Path Coud Liquid Water Ice Surface Temperature Land Surface Temperature Precipitation Precipitabe Water Sea Ice Age and Sea Ice Edge Motion Sea Surface Temperature Sea Surface Winds (Direction) Tota Water Content Coud Base Height Fresh Water Ice Imagery Pressure Profie Snow Cover / Depth Surface Wind Stress Vegetation / Surface Type Category IA IA IA IIA IIA IIA IIA IIB IIB IIA IIA IIB IIA IIA IIA IIIB IIIB IIIB IIIB III B/A IIIB IIIB The highighted are operationa EDRS derived from POES AMSU

7 ER-2/DC-8 Measurements during TOGA/CORE (1/2) Weng and Grody (2, JAS)

8 ER-2/DC-8 Measurements during TOGA/CORE (2/2)

9 Couds Modify AMSU Weighting Function / /-.217 pressure ( hpa) pressure (hpa) coud weighting function Cear condition weighting function Incude two separated coud ayers at 61 and 84 hpa with.5 g m -3 iquid water.

10 Microwave Integrated Retrieva System Fowchart Fast scattering/poarimetric radiative transfer mode/jacobian for a atmospheric conditions Surface emissivity/refectivity modes (soi, vegetation, snow/sea ice, water) Fast variationa minimization agorithm NWP forecast outputs, cimatoogy, regressions as first guess Temperature, water vapor and coud and rain water profies Fexibe channe seection/sensor geometry and noise Liu and Weng (25, IEEE)

11 Cost Function: Agorithm Theoretica Basis (1/9) J = ( ) ( ) [ ] [ ] b T 1 b o T o x x B x x + I( x) I ( E+ F) I( x) I where x b is background vector x is state vector to be retrieved I is the radiance vector B is the error covariance matrix of background E is the observation error covariance matrix F is the radiative transfer mode error matrix

12 Agorithm Theoretica Basis (2/9) Retrievas are made at vertica pressure eves (.1 to surface, maximum eves of 42) Surface pressure from GDAS 6-hour forecasts Background state variabes from cimatoogy which is atitude-dependent First guess is from regression (coud be the same as background) No background information needed for coud water Bias correction for forward modes, residuas wi be observationa error covariance

13 Agorithm Theoretica Basis (3/9) JCSDA Community Radiative Transfer Mode Atmospheric State Vectors Surface State Vectors Aeroso and Coud Optica Mode Atmospheric Spectroscopy Mode Forward Radiative Transfer Schemes Surface Emissivity, Refectivity Modes Receiver and Antenna Transfer Functions Jacobian (Adjoint) Mode

14 Agorithm Theoretica Basis (4/9) Radiative Transfer Mode: di(, Ω) µ d = I(, Ω) + ω 4π 4π ' ' ' ωf M(, Ω, Ω ) I(, Ω ) dω + (1 ω) B + exp( / µ ) S 4π Radiative transfer scheme incuding four Stokes components is based on VDISORT (Weng, JQRST, 1992) Accuracies on various transfer probems incuding moecuar scattering, L13 aerosos and microwave poarimetry are discussed (Schuz et a., JQSRT, 1999, Weng, J. Eec&App., 22 ) Jacobians incuding coud iquid and ice water are derived using VDISORT soutions (Weng and Liu, JAS, 23) Surface emissivity and bi-directiona refectivity modes are integrated (Weng et a., JGR, 21)

15 Agorithm Theoretica Basis (5/9) Jacobians Incuding Scattering: Vector DIScrete Ordinate Radiative (VDISORT) Soution: Ψ E A Ξ Ξ A Ξ s s c A I ) / exp( ] [ )] ) ( ( ) ( ) ( ) ( [ ) ( ) ( )] ( exp[ ) ( µ π ϖ µ µ δ = + = F B B B m j N N j L k N N j j k k k x j x x j x } )] ( exp[ ){, ( ) ( } )) ( ) ( ( ){, ( ) ( c A K s s s K I µ µ δ µ µ = = = = = + = (Weng and Liu, JAS, 23)

16 Agorithm Theoretica Basis (6/9) Emissivity Mode: Surface Emissivity Spectra (θ=53 ) Open water two-scae roughness theory Sea ice Coherent refection Canopy Four ayer custering scattering Bare soi Coherent refection and surface roughness Snow/desert Random media Weng et a (21, JGR) Emissivity at H-Poarization Emissivity at V-Poarization Frequency (GHz) Surface Emissivity Spectra (θ=53 ) Frequency (GHz) Snow Canopy Bare Soi Wet Land Desert Ocean Snow Canopy Bare Soi Wet Land Desert Ocean

17 Snow Emissivity: Agorithm Theoretica Basis (7/9)

18 Agorithm Theoretica Basis (8/9)

19 Advanced Microwave Sounding Unit Fown on NOAA-15 (May 1998), NOAA-16 (Sept. 2) and NOAA-17 (June 22) sateites Contains 2 channes: AMSU-A 15 channes GHz AMSU-B 5 channes GHz 4-hour tempora samping: 13, 73, 13, 133, 193, 223 LST

20 Advanced Microwave Sounding Unit AMSU measurement at each sounding channe responds primariy to emitted radiation within a ayer, indicated by its weighting function The vertica resoution of sounding is dependent on the number of independent channe measurements Lower tropospheric channes are aso affected by the surface radiation which is highy variabe over and

21 Water Vapor and Coud Water Profies P ressure ( hpa) TPW=74.56 kg/m2 TPW=73.1 kg/m2 retrieved origina P ressure ( hpa) LWP=.416 kg/m2 LWP=.492 kg/m2 origina retrieved water vapor mixing ratio (kg/kg) coud water mixing ratio (kg/kg) Standard atmosphere and coudy ayers between 8 and 95 hpa Retrievas is based on simuated AMSU brightness temperatures at nadir

22 Verticay Integrated Water Vapor TPW (mm, 1dvar) Match-up TPW from radiosondes and AMSU retrieva in N=618, NOAA-16 bias=.12 mm, rms=2.28 mm TPW (mm, radiosonde) TPW (mm, 1dvar) TPW (mm, 1dvar) N=585, NOAA-15 bias=.28 mm, rms=2.7 mm TPW (mm, radiosonde) N=679, NOAA-17 bias=.7 mm, rms=2.52 mm TPW (mm, radiosonde)

23 TPW Vaidation (NOAA-17) TPW (GDAS, mm) rms=6.2 mm (15%) Ocean TPW (MSPPS, mm std=4.7mm (11%) Ocean, NOAA-17 TPW (retrived, mm std=3.3 mm (8%) Ocean, NOAA TPW (radiosonde, mm) TPW (radiosonde, mm) TPW (radiosonde, mm) In comparison to radiosonde measurements, MSPPS TPW product gives accuracy better than GDAS but is sighty biased. MIRS TPW is the best (no priori information).

24 TPW Vaidation (NOAA-15) TPW (GDAS, mm std=6.1 mm (15%) Ocean TPW (MSPPS, mm std=4.8mm (12%) Ocean, NOAA-15 TPW (retrived, mm std=3.5 mm (9%) Ocean, NOAA TPW (radiosonde, mm) TPW (radiosonde, mm) TPW (radiosonde, mm)

25 Retrieva Bias vs. Viewing Anges TPW bias (mm) Match-up TPW from radiosondes and AMSU retrieva in 22. Bias variation to viewing anges. Bias = radiosonde AMSU 4 NOAA-16, bias=radiosonde-retrieva dvar 1 MSPPS viewing ange TPW bias (mm) TPW bias (mm) 6 NOAA-15, bias = radiosonde - retrieva viewing ange (degree) 7 NOAA-17, bias-radiosonde-retrieva viewing ange (degree) 1 dvar MSPPS 1 dvar MSPPS

26 Temperature Vaidation (NOAA-17) Comparison of temperature to radiosondes ocean, NOAA-17 retrieved vs radiosondes temperature ocean, NOAA-17 P ressure (hp a) retrieved, cear GDAS P ressure ( hpa) cear + coudy + precipitation standard deviation (K) standard deviation (K) GDAS (1 x 1 deg) and radiosondes agree we. Couds degrade the retrieva accuracy sighty. Cear sampes = 357, cear+coud sampes = 51, cear+coud+precipitation=552

27 Temperature Vaidation (NOAA-17) retrieved vs radiosondes temperature ocean, NOAA-17 retrieved vs radiosondes temperature ocean, NOAA-15 P r essur e ( hpa) cear + coudy + precipitation P ressure ( hpa) cear + coudy + precipitation bias (K) bias (K)

28 Temperature Vaidation (NOAA-15) Comparison of temperature to radiosondes ocean, NOAA-15 retrieved vs radiosondes temperature ocean, NOAA-15 P ressure ( hpa) retrieved, cear GDAS P ressure ( hpa) cear + coudy + precipitation standard deviation (K) standard deviation (K) GDAS (1 x 1 deg) and radiosondes agree we. Couds degrade the retrieva accuracy sighty. Cear sampes = 278, cear+coud sampes = 386, cear+coud+precipitation=416

29 Temperature Accuracy (Nadir vs. off-nadir) Comparison of temperature to radiosonde ocean, NOAA-15 Comparison of temperature to radiosonde ocean, NOAA-17 P r essur e ( hpa) < 15 degree > 45 degree P ressure (hp a) < 15 degree > 45 degree standard deviation (K) standard deviation (K)

30 Vaidation for Water Vapor Profie retrieved vs radiosondes water vapor profie ocean, NOAA-17 retrieved vs radiosondes water vapor profie ocean, NOAA-17 P r e s s u r e ( h P a ) RMS water vapor (%) cear + coudy + precipitation P r e s s u r e ( h P a ) cear + coudy + precipitation bias water vapor (%)

31 Coud Liquid Water (MIRS vs. MSPPS)

32 Precipitation (MIRS vs. MSPPS)

33 Temperature at 85 hpa (MIRS vs. GDAS)

34 Temperature at 5 hpa (MIRS vs. GDAS)

35 Tota Precipitabe Water (MIRS vs. MSPPS)

36 Water Vapor at 5 hpa (MIRS vs. GDAS)

37 Water Vapor, Coud Water and Rain Rate TPW CLW Rain Rate IWP

38 Impacts of Forward Modes T(85hPa) Scattering T(85hPa) Emission ony T(2hPa) Scattering T(2hPa) Emission ony

39 Temperature Anomay Vertica cross section of temperature anomaies at 6: UTC 9/12/23. Left pane: west-east cross section aong 22EN, and right pane: south-north cross section aong 56EW for Hurricane Isabe t d d d S I M I I ) (1 ), ( ),, ( 4 ), ( ), ( ' 4 ' ' ω Ω Ω Ω Ω π ω Ω Ω µ π + + = With Coud/Precipitation Scattering

40 Temperature Anomay Without Coud/Precipitation Scattering Vertica cross section of temperature anomaies at 6: UTC 9/12/23. Left pane: west-east cross section aong 22EN, and right pane: south-north cross section aong 56EW for Hurricane Isabe di(, Ω) µ = I(, Ω) + (1 ω ) S d t

41 Improved 3DVAR Anaysis with AMSU Without AMSU Coudy Radiances With AMSU Coudy Radiances

42 Summary and Concusions Integrated uses of microwave imager and sounder data can significanty improve temperature profiing in ower troposphere Advanced radiative transfer modes incuding coud/precipitation scattering are vita for improving profiing capabiity in severe weather conditions such as hurricanes MIRS with bias corrections to radiative transfer modes produces improved performance from AMSU and makes the retrieva errors ess dependent on scan ange MIRS retrievas are being vaidated against variabe independent sources. Overa performances are very encouraging. The system is of great potentias for NPOESS ATMS, CMIS appications MIRS has a ot of room to improve and incorporate more variabes in the processing.

43 Open Issues Refine the retrievas for better regiona performance (e.g. high terrains, deserts, snow/sea ice cover, coast conditions) Bias corrections for water vapor sounding channes are highy required. Aternativey, the retrievas wi be aso tested with imb-adjusted AMSU measurements Investigate non-convergent behaviors over particuar regions (scattering RT mode vs. abnorma observations) in the ast retrieva process when AMSU-B water vapor sounding channes are incuded Test the system for SSMIS appications