1 Brief overview of the Joint Center for Satellite Data Assimilation (JCSDA) & Description of an All-Weather One-Dimensional Variational Assimilation/Retrieval System Sid Ahmed Boukabara MSFC/SPoRT Seminar, November 19th 2010

The Joint Center for Satellite Data Assimilation (JCSDA) Sid Ahmed Boukabara, Deputy Director, JCSDA and Lars Peter Riishojgaard, Director, JCSDA

NASA/Earth Science Division US Navy/Oceanographer and Navigator of the Navy and NRL NOAA/NESDISNOAA/NWS NOAA/OAR US Air Force/Director of Weather Mission: …to accelerate and improve the quantitative use of research and operational satellite data in weather, ocean, climate and environmental analysis and prediction models. Vision: An interagency partnership working to become a world leader in applying satellite data and research to operational goals in environmental analysis and prediction JCSDA Partners

JCSDA Executive Team Director (Riishojgaard) Deputy Director (Boukabara) Partner Associate Directors (Lord, Rienecker, Phoebus, Zapotocny) Management Oversight Board NOAA / NWS / NCEP (Uccellini) NASA/GSFC/Earth Sciences Division (Lee, acting) NOAA / NESDIS / STAR (Powell) NOAA / OAR (Atlas) Department of the Air Force / Air Force Director of Weather (Zettlemoyer) Department of the Navy / N84 and NRL (Chang, Curry) Agency Executives NASA, NOAA, Department of the Navy, and Department of the Air Force Advisory Panel Co-chairs: Jim Purdom, Tom Vonder Haar, CSU Science Steering Committee (Chair: Craig Bishop, NRL) JCSDA Management Structure

New JCSDA short-term goal: (adopted 03/2008)  “ Contribute to making the forecast skill of the operational NWP systems of the JCSDA partners internationally competitive by assimilating the largest possible number of satellite observations in the most effective way ”

JCSDA Science Priorities  Radiative Transfer Modeling (CRTM)  Preparation for assimilation of data from new instruments  Clouds and precipitation  Assimilation of land surface observations  Assimilation of ocean surface observations  Atmospheric composition; chemistry and aerosol Driving the activities of the Joint Center since 2001, approved by the Science Steering Committee Overarching goal: Help the operational services improve the quality of their prediction products via improved and accelerated use of satellite data and related research

JCSDA Mode of operation  Directed research  Carried out mainly by the partners  Mixture of new and leveraged funding  JCSDA plays coordinating role  Also accessible to external community (CIs)  External research  Historically implemented as a NOAA-administered FFO, open to the broader research community  Typically ~$1.5 M/year available => revolving portfolio of ~15 three-year projects  Extended to include contracts (administred by NASA)  Visiting Scientists  Open to all experts (global reach)  Main conditions: Have a host at one of the partners and work on a JCSDA- related activity  Results and progress from both directed and external work reported at annual JCSDA Science Workshop (most recent held on May 2010)

JCSDA Working Groups  Composed of working level scientists from (in principle) all JCSDA partners, plus additional members where appropriate  Tasked with sharing information and coordinating work where possible  Six WGs formed  CRTM  IR sounders  Microwave sensors  Ocean data assimilation  Atmospheric composition  Land data assimilation

Some of JCSDA Past Accomplishments  Common assimilation infrastructure (between NCEP/EMC, NASA/GMAO)  Community radiative transfer model  Common NOAA/NASA/AFWA land data assimilation system  Interfaces between JCSDA models and external researchers  Snow/sea ice emissivity model  MODIS polar winds  AIRS radiances assimilated  COSMIC data assimilation  Improved physically based SST analysis  Advanced satellite data systems such as DMSP (SSMIS), CHAMP GPS, WindSat tested for implementation  Data denial experiments completed for major data base components in support of system optimization NASA/GSFC/GMAO)

IASI Impact on Standard Verification Scores NH 500 hPa Height Anom. Cor August 2007 SH 500 hPa Height Anom. Cor. J. Jung IASI Control

ASCAT Impact Experiments with GFS

COSMIC: recent impact  AC scores (the higher the better) as a function of the forecast day for the 500 mb gph in Southern Hemisphere  40-day experiments:  expx (NO COSMIC)  cnt (operations - with COSMIC)  exp (updated RO assimilation code - with COSMIC) Many more observations Reduction of high and low level tropical winds error L. Cucurull

Challenges  US falling behind internationally in terms of NWP skill  Risk of falling further behind if no remedies and current readiness for upcoming missions is not improved

NOAA/NCEP vs. ECMWF skill over 20+ years

Potential Remedies  Bring resources to adequate levels (Human & IT)  Bring science up to standards (4DVAR, etc)  Better leveraging/coordination between partners  Get help from experts (Technology transfer) or better R2O

Potential Strategy for R2O Improvement (underway) JCSDA IT Infrastructure NASA NOAA Cooperative Institutes Research Institutions In general (Supported by grants, contracts, etc) Operational Centers (NCEP,FNMOC, etc) Navy All benefit from improvements being made in Central Testbed Tools to be (1) developed, (2) improved, (3) validated, (4) made portable and (5) modularized or (6) simply made available: -CRTM -GSI -Calibration tools, BUFR tools, -OSSE/OSE -Diagnostic Tools -Etc AFWA

Summary  JCSDA Recent refocus on NWP skill to address issue of underperforming US forecast skill  Multi-level efforts needed and underway:  Operational readiness for GOES-R, NPP/JPSS and other missions  Science improvements in Data Assimilation  Set Up of an IT infrastructure (O2R, OSSE/OSE, etc)  Coordination of efforts between JCSDA partners  Potential coordination with other programs? (GOES-R, SpoRT, HFIP, OSD/PSDI, Testbeds, etc) for a better leveraging of efforts/resources?  Continued need for interaction with outside research community

MiRS: A Physical Algorithm for Rain, Cloud, Ice, Atmospheric Sounding, and Surface Parameters Sid-Ahmed Boukabara, Kevin Garrett, Wanchun Chen, Flavio Iturbide-Sanchez, Chris Grassotti and Cezar Kongoli NOAA/NESDIS Camp Springs, Maryland, USA Description of an All-Weather One-Dimensional Variational Assimilation/Retrieval System MSFC/SPoRT Seminar, November 19th 2010

19 Contents All-Weather and All-Surface Applicability (or Cloudy/Rainy data assimilation & Surface Handling) 2Performance Assessment3 General Overview and Mathematical Basis 1Summary & Conclusion4

20 Retrieval Mathematical Basis Main Goal in ANY Retrieval System is to find a vector X with a maximum probability of being the source responsible for the measurements vector Y m Main Goal in ANY Retrieval System is to find a vector X: P(X|Y m ) is Max In plain words: Mathematically: Bayes Theorem (of Joint probabilities) =1

21 Mathematically: Core Retrieval Mathematical Basis Main Goal in ANY Retrieval System is to find a vector X with a maximum probability of being the source responsible for the measurements vector Y m Main Goal in ANY Retrieval System is to find a vector X: P(X|Y m ) is Max In plain words: Problem reduces to how to maximize: Probability PDF Assumed Gaussian around Background X 0 with a Covariance B Mathematically: Probability PDF Assumed Gaussian around Background Y(X) with a Covariance E                                                           Y(X) m Y 1 E T m Y 2 1 exp 0 XX 1 B T 0 XX 2 1 Maximizing                                                           Y(X) m Y 1 E T m Y 2 1 exp 0 XX 1 B T 0 XX 2 1 Is Equivalent to Minimizing Which amounts to Minimizing J(X) –also called COST FUNCTION – Same cost Function used in 1DVAR Data Assimilation System

22  Cost Function to Minimize:  To find the optimal solution, solve for:  Assuming Linearity  This leads to iterative solution: Cost Function Minimization More efficient (1 inversion) Preferred when nChan << nParams (MW) Jacobians & Radiance Simulation from Forward Operator: CRTM

23 Assumptions Made in Solution Derivation  The PDF of X is assumed Gaussian  Operator Y able to simulate measurements-like radiances  Errors of the model and the instrumental noise combined are assumed  (1) non-biased and  (2) Normally distributed.  Forward model assumed locally linear at each iteration.

24 Retrieval in Reduced Space (EOF Decomposition) Covariance matrix (geophysical space) Transf. Matrx (computed offline) Diagonal Matrix (used in reduced space retrieval)  All retrieval is done in EOF space, which allows:  Retrieval of profiles (T,Q, RR, etc): using a limited number of EOFs  More stable inversion: smaller matrix but also quasi-diagonal  Time saving: smaller matrix to invert  Mathematical Basis:  EOF decomposition (or Eigenvalue Decomposition) By projecting back and forth Cov Matrx, Jacobians and X

25 CRTM as the Forward Model  Have a fully-validated, externally maintained forward operator,  Unrivaled leverage (~4 FT working on CRTM at JCSDA plus a number of on-going funded projects with academia, industry to upgrade CRTM ). Funded by JCSDA  Have access to a model capable of producing not only radiances but also Jacobians  Long-term benefit: stay up to science art by benefiting from advances in CRTM modeling capabilities

26 MiRS General Overview Radiances Rapid Algorithms (Regression) Advanced Retrieval (1DVAR) Vertical Integration & Post-processing selection 1 st Guess MIRS Products Vertical Integration and Post-Processing 1DVAR Outputs Vertical Integration Post Processing (Algorithms) TPW RWP IWP CLW Core Products Temp. Profile Humidity Profile Emissivity Spectrum Skin Temperature Liq. Amount Prof Ice. Amount Prof Rain Amount Prof -Sea Ice Concentration -Snow Water Equivalent -Snow Pack Properties -Land Moisture/Wetness -Rain Rate -Snow Fall Rate -Wind Speed/Vector -Cloud Top -Cloud Thickness -Cloud phase

27 1D-Variational Retrieval/Assimilation MiRS Algorithm Measured Radiances Initial State Vector Solution Reached Forward Operator (CRTM) Simulated Radiances Comparison: Fit Within Noise Level ? Update State Vector New State Vector Yes No Jacobians Geophysical Covariance Matrix B Measurement & RTM Uncertainty Matrix E Geophysical Mean Background Climatology (Retrieval Mode)Forecast Field (1D-Assimilation Mode)

28 Parameters are Retrieved Simultaneously X is the solution F(X) Fits Y m within Noise levels X is a solution Necessary Condition (but not sufficient) If X is the set of parameters that impact the radiances Ym, and F the Fwd Operator If F(X) Does not Fit Y m within Noise X is not the solution All parameters are retrieved simultaneously to fit all radiances together Suggests it is not recommended to use independent algorithms for different parameters, since they don’t guarantee the fit to the radiances

29 Solution-Reaching: Convergence  Convergence is reached everywhere: all surfaces, all weather conditions including precipitating, icy conditions  A radiometric solution (whole state vector) is found even when precip/ice present. With CRTM physical constraints. Previous version (non convergence when precip/ice present) Current version

MiRS is applied to a number of microwave sensors, each time gaining robustness and improving validation for Future New Sensors The exact same executable, forward operator, covariance matrix used for all sensors Modular design Cumulative validation and consolidation of MiRS POES N18/N19  DMSP SSMIS F16/F18  AQUA AMSR-E  NPP/JPSS ATMS   : Applied Operationally  : Applied occasionally  : Tested in Simulation Metop-A  TRMM/GPM/ M-T TMI, GMI proxy, SAPHIR/MADRAS  Current & Planned Capabilities

31 Contents All-Weather and All-Surface Applicability (or Cloudy/Rainy data assimilation & Variational Handling of Surface) 2Performance Assessment3 General Overview and Mathematical Basis 1Summary & Conclusion4

32 All-Weather and All-Surfaces Upwelling Radiance Downwelling Radiance Surface-reflected Radiance Cloud-originating Radiance Surface-originating Radiance Scattering Effect Absorption Surface sensor Major Parameters for RT: Sensing Frequency Absorption and scattering properties of material Geometry of material/wavelength interaction Vertical Distribution Temperature of absorbing layers Pressure at which wavelength/absorber interaction occurs Amount of absorbent(s) Shape, diameter, phase, mixture of scatterers. Sounding Retrieval: Temperature Moisture  Instead of guessing and then removing the impact of cloud and rain and ice on TBs (very hard), MiRS approach is to account for cloud, rain and ice within its state vector.  It is highly non-linear way of using cloud/rain/ice-impacted radiances. To account for cloud, rain, ice, we add the following in the state vector: Cloud (non-precipitating) Liquid Precipitation Frozen precipitation To handle surface-sensitive channels, we add the following in the state vector: Skin temperature Surface emissivity (proxy parameter for all surface parameters)

33 Contents All-Weather and All-Surface Applicability (or Cloudy/Rainy data assimilation & Variational Handling of Surface) 2Performance Assessment3 General Overview and Mathematical Basis 1Summary & Conclusion4

34 MiRS List of Products Official ProductsProducts being investigated Vertical Integration and Post-Processing 1DVAR Outputs Vertical Integration Post Processing (Algorithms) TPW RWP IWP CLW Core Products Temp. Profile Humidity Profile Emissivity Spectrum Skin Temperature Liq. Amount Prof Ice. Amount Prof Rain Amount Prof -Sea Ice Concentration -Snow Water Equivalent -Snow Pack Properties -Land Moisture/Wetness -Rain Rate -Snow Fall Rate -Wind Speed/Vector -Cloud Top -Cloud Thickness -Cloud phase 1.Temperature profile 2.Moisture profile 3.TPW (global coverage) 4.Land Surface Temperature 5.Emissivity Spectrum 6.Surface Type (sea, land, snow, sea-ice) 7.Snow Water Equivalent (SWE) 8.Snow Cover Extent (SCE) 9.Sea Ice Concentration (SIC) 10.Cloud Liquid Water (CLW) 11.Ice Water Path (IWP) 12.Rain Water Path (RWP) 1.Cloud Profile 2.Rain Profile 3.Atmospheric Ice Profile 4.Snow Temperature (skin) 5.Sea Surface Temperature 6.Effective Snow grain size 7.Multi-Year (MY) Type SIC 8.First-Year (FY) Type SIC 9.Wind Speed 10.Soil Wetness Index The following section about performance assessment is a snapshot.

35 Temperature Profile Assessment (against ECMWF) N18 MIRS MIRS – ECMWF Diff Note: Retrieval is done over all surface backgrounds but also in all weather conditions (clear, cloudy, rainy, ice) ECMWF MIRS – ECMWF Diff Angle dependence taken care of very well, without any limb correction

36 Moisture Profile (against ECMWF) N18 MIRS Validation of WV done by comparing to: -GDAS -ECMWF -RAOB Assessment includes: - Angle dependence - Statistics profiles - Difference maps ECMWF Stdev Bias land Sea When assessing, keep in mind all ground truths (wrt GDAS, ECMWF, RAOB)

37 TPW Global Coverage Smooth transition over coasts Very similar features to GDAS MiRSGDAS MiRS TPW Retrieval (zoom over CONUS)

38 RainFall Rate Assessment Significantly Reduced False Alarms at the Sea-Ice Edges Significant reduction in Rain false alarm using MiRS, at surface transitions and edges MiRS Monthly composite (Metop-A) 1DVAR MSPPS Monthly composite (Metop-A) Heritage algorithm: based on physical regression

39 MiRS RR part of IPWG Intercomparison (N. America, S. America and Australia sites) Image taken from IPWG web site: credit to John Janowiak This is an independent assessment where comparisons of MiRS RR composites are made against radar and gauges data. Image taken from IPWG web site: credit to Daniel Villa No discontinuity at coasts (MiRS applies to both land and ocean)

Independent Validation (IPWG) 2/2  Monitor a running time series of statistics relative to rain gauges  Intercomparison with other PE algorithms and radar Caution: algorithms perfs depend on how many sensors are used

Global Variationally-based Inversion of Emissivity: Routine Assessment 41 MiRS inverts emissivities for all channels, including high-frequency (Inversion performed in EOF space) Emissivity is assessed by comparing it to analytically- inverted emissivity

Surface Emissivity Inter-Comparison 12/01/2007 – 02/28/2009 Frequency (GHz) Es MiRS N18 GDAS MiRS N18 minus GDAS Emissivity difference (MiRS-Analyt) Frequency (GHz) Ocean __ Sea Ice (Antartic) ___ Sea Ice (Arctic) ___ Sea Ice (First Year) ___ Desert __ Amazon __ Wet Land __ Snow __ Intercomparison between MiRS variational emissivities and analytical ones Differences within 2%. Larger diffs noticed for snow (~8%) & Arctic sea-ice (3%). Questions: Tskin used in analytical emiss from GDAS accurate enough? Is assumption of specularity valid for snow and sea-ice?

Case area after rain event CPC Figures courtesy CPC real-time 24-hour precipitation from 12Z , , and (from left to right) MiRS N18 retrieved emissivity at 31 GHz ascending node for , , and (from left to right) Day in October Es 19.35V channel 37.0 V channel Illustration of High Variability of Emissivity

44 MIRS Emissivity Response to Surface Moisture Variations –Case study-.  A significant storm system recorded for its wide-spread damage in human life and property  These storms hit the Midwest during May 5-7, 2007, as seen from MSPPS (top) and NEXRAD Radar (bottom) images 05/05/0705/06/0705/07/07 MSPPS NEXRAD

45 MIRS Emissivity Response to Surface Moisture Variations –MIRS Emissivity response  MIRS responds to surface wetness variations before (May 4), right after the storm (May 8) and later (May 10). Note the emissivity depression at 21 GHz and the inverted emissivity spectra on May 8,  Physically-consistent behavior noticed in the emissivity variation May 4, 2007 (before the event) May 8, 2007 (1 day after the event, no rain anymore) May 10, 2007 (3 days after event, emiss back to previous state) Emisivity at 23 GHzEmisivity at 89 GHzEmisivity Spectra ( GHz)

46 MiRS/N18 Sea-Ice Concentration Assessment Comparison with AMSR-E MiRS/N18 AMSR-E All MiRS surface parameters are obtained from emissivity, not TBs (so the validation of these products is an indirect validation of emissivity itself)

47 MiRS/F16 SSMIS Snow Cover Extent (SCE) Comparison with IMS & AMSR-E AMSRE F16 MIRSF16 NRL IMS False alarms Extensive snow cover Less Extensive snow cover All MiRS surface parameters are obtained from emissivity, not TBs (so the validation of these products is an indirect validation of emissivity itself)

48 Contents All-Weather and All-Surface Applicability (or Cloudy/Rainy data assimilation & Variational Handling of Surface) 2Performance Assessment3 General Overview and Mathematical Basis 1Summary & Conclusion4

49 Summary of Added Values  All physical approach & simultaneous retrieval  Consistent solution that fit the measurements (satisfying a necessary but often overlooked requirement!).  Applicability to all microwave sensors with same code  All-Weather Sounding  Temperature/Moisture sounding in rainy/cloudy conditions using an all-weather RT/Jacobians operator  Emissivity-Based Retrieval of surface paremeters  Higher accuracy of surface products by using Emissivity instead of Radiances (for Wind speed, Soil moisture, Snow, Ice concentration, etc)  Extended retrieval of TPW to land, sea-ice, snow, coasts, sea  Physical Retrieval of atmospheric rain, ice over ocean & land  System is a retrieval & assimilation system

50  Extension to new sensors: sounders/imagers (ATMS, GPM/GMI, Megha-Tropiques, etc)  Multi-Sensors Synergy  Take advantage of wider spectral coverage to fully characterize surface emissivity and therefore improve surface classification as well as retrieval of other parameters  Take advantage of multi-angle viewing geometries to more accurately sound temperature and moisture  Extension to other spectral Regions (IR).  Feasible since CRTM is valid in all spectral regions  Cloud/Rain/Ice Sounding  Retrieval of cloud and rain in profile form. Combination of sensors, could reduce ill-posed nature of the problem. Many by- products could result from the cloud profiling (cloud top, thickness, bottom, multi-layer nature, mixed phase information, etc)  Better geophysical background characterization Foreseen Scientific Advances

MiRS Extension to TRMM/TMI (GPM project) Example of retrieved rainfall rate from MiRS on TMI data at ~5 km resolution (left) compared to TRMM 2A12 (right) for MiRS has been extended to TRMM/TMI (work still in progress) Current issues being addressed: -Non-convergence -Coastal false alarm signal

Extension of MiRS to GPM/GMI (1/2) 52  GPM/GMI proxy data (simulated brightness temperatures) were generated to test MiRS algorithm.  Simulations performed using CRTM forward model and ECMWF geophysical inputs  Simulations over all surfaces  TRMM/TMI metadata used (for scanning geometry, angle, swath, time, etc) and also for emissivity  Simulations performed daily at NOAA.  Goal: Make sure the algorithm is ready on day-1 for GPM/GMI data (switch between proxy data flow and real data stream) Example: GMI simulated 36.5 GHz H-pol TB Current issue being addressed: Apparent pixel shift GMI is similar to TMI with additional high frequency channels (166 and 183 GHz) We look forward to using L1B data from GPM simulator (Matsui et al)

Extension of MiRS to GPM/GMI (2/2) 53 MiRS has been applied on the GPM/GMI Proxy data. All products are being assessed, including RR, Emissivity, TPW, etc Draft Results: Work is still in progress to optimize the emissivity covariance for GMI and TMI GMI 36.5 GHz H-pol GMI TPW

Current Limitations & Planned Improvements Sensor Applicability Current LimitationsPlanned Improvements Importance/ Difficulty All sensorsCurrent atmospheric covariance is a single covariance used globally Current effort aims at developing stratified covariances, by latitude and season Important (to improve warm season perfs) All sensorsRain Rate relationship (w 1DVAR hydrometeors) is also a single relationship, used globally Investigate the stratification of rainrate relationship by season/latitude Important (to improve warm season perfs) All sensorsVery low false alarm rate but Low detection Rate, especially for light rain, due to compensation of light rain signal by other parameters (such as WV) Make sure high frequency channels have a stronger weight in the Chi-Square computation Moderate TMI/GMIImportant coastal False Alarm Improve emissivity covariance (not mature yet for these sensors) Low

Future improvement: Stratification of Covariances Mid-latitude Profiles Tropical Profiles Rain Ice WRF Model Simulation  Differences in vertical structures of ice, cloud, rain  Differences in how temperature and moisture correlate to hydrometeors  Differences in how rainfall rate relate to integrated values of rain and ice (IWP, RWP)

Atmospheric Covariance Matrix New Atmospheric Background Covariance Matrix based on ECMWF 60, and WRF simulations over tropic oceans performed during SON season Cloud liquid, Rain and Ice water from WRF MiRS Current Atmospheric Background Covariance Matrix based on Global ECMWF 60, and tropic-ocean MM5 simulations Temperature, Water Vapor and CLW from ECMWF 60 Rain and Ice water from MM5 Temperature and Water Vapor from ECMWF 60 Noticeable Differences noticed in covariances, especially in hydrometeors. Impact assessment on RR performances in progress

Rainy (RPW>0.05mm) Land Surf. Emissivity Correlation Matrix from 5,000 scenes Oct Non-Precipitating Land Surface Emissivity Correlation Matrix from 53,000 scenes Oct Channel Freq. (MHz): 1 = 50.3 H 2 = 52.8 H 3 = 53.6 H 4 = 54.4 H 5 = 55.5 H 6 = RCP 7 = 59.4 RCP 8 = 150 H 9,10,11 = H 12,13 = H/V 14 = V 15,16 = 37 H/V 17,18 = V/H 19 = RCP = RCP Note: difference in color bar range SSMI/S Surface Emissivity Correlation Matrix Clear & Rainy Conditions over Land

58 Future MiRS Application: 2dVAR Geostationary Application  Using 5 GDAS analyses, a 24-hour time series was simulated using linear time-interpolation  CRTM used to simulate brightness temperatures  Regular 1DVAR applied on TBs (independent retrievals)  2DVAR applied (Red) 2DVAR 1DVAR Simulated Time-series 2dVAR 1dVAR 2dVAR 1dVAR

59 Future Challenges Assessment of Profiling in Active Areas Case of July 8 th 2005 Zoom in space (over the Hurricane Eye) and Time (within 2 hours) MHS footprint size at nadir is 15 Kms. But at this angles range (around 28 o ), the MHS footprint is around 30 Kms All these 4 Dropsondes were dropped within 45 minutes and are located within 10 kms from each other Temperature [K] Water Vapor [g/Kg] 700 mb DeltaQ=4g/Kg DeltaT=3K

More Information  Publications  S.A. Boukabara, F. Weng and Q. Liu, Passive Microwave Remote Sensing of Extreme Weather Events Using NOAA-18 AMSUA and MHS. IEEE Trans. on Geoscience and Remote Sensing, July Vol 45, (7),  S.A. Boukabara, F. Weng, Microwave Emissivity Retrieval over Ocean in All-Weather Conditions. Validation Using Airborne GPS-Dropsondes. IEEE Trans Geos Remote Sens, 46, , 2007  S.-A. Boukabara, K. Garrett, and W. Chen, “Global Coverage of Total Precipitable Water using a Microwave Variational Algorithm,” IEEE TGARS, vol. 48, Sept  F. Iturbide-Sanchez, S.-A. Boukabara, R. Chen, K. Garrett, C. Grassotti, W. Chen, and F. Weng, “Assessment of a Variational Inversion System for Rainfall Rate over Land and Water Surfaces,” Submitted IEEE TGARS, July  S.-A. Boukabara et al. “MiRS: An All-Weather 1DVAR Satellite Data Assimilation and Retrieval System,” Submitted IEEE TGARS, May  Website For More Information: MiRS is a community algorithm (available publicly), benefiting from community-driven improvements, suggestions, scrutiny and assessment.

61 BACKUP SLIDES

62 Qualitative check of the Cloudy/Rainy radiance handling MiRS Rain Water Path TRMM (2A12) Rain Rate Vertical Cross section A test case comparison with TRMM rain/ice product was conducted on 2010/02/02 -The rain events were not captured exactly at the same time (shift noticed) -A qualitative assessment was done on the vertical cross-section -MiRS produces T(p), Q(p), cloud, rain and ice profile -Purpose is to check if these products behave physically MiRS Moisture MiRS Temperature MiRS Rain/Ice Profiles TRMM Rain/Ice Profiles Cross-sections of both TRMM and MiRS products at 25 degrees North Notes: -Generally, consistent features between TRMM and MiRS (except for expected shift) - Ice is found on top of liquid rain -Transition between frozen and liquid is delineated by the freezing level determined from the temperature profile. -Moisture increases in and around the rain event - Suggests that these products are reasonably constrained within physical inversion Ice bottom Rain top Freezing level

Summary  MiRS is a variational algorithm (1DVAR) and can be applied to virtually any microwave sensor  MiRS uses CRTM as forward and jacobian operators  Retrieves sounding & surface parameters simultaneously, including hydrometeor profiles, rain rate & surface emissivity  Applicable over all surfaces (emissivity is part of the state vector), allowing a spot-by-spot variability of the surface emissivity.  Extensively assessed both internally and independently.  Applicability in all-weather conditions (including rainy)  Run operationally at NOAA for N18, N19, SSMIS F16, F18 and Metop-A, and being integrated for NPP/JPSS ATMS  MiRS is also currently being extended to support GPM (GMI) and Megha- Tropiques (MADRAS and SAPHIR)  Current enhancements to the algorithm expected to improve performances of hydrometeor retrievals for all sensors  We look forward to using GV data when they become available (plan to extend CRTM, and therefore MiRS to airborne setups) and GPM simulator.  Variational Emissivities from MiRS are available (all surfaces, for all frequencies) as well as corresponding covariances. MiRS is a community algorithm (available publicly), benefiting from community-driven improvements, suggestions, scrutiny and assessment.

64 TPW Global Coverage Smooth transition over coasts Very similar features to GDAS MiRSGDAS MiRS TPW Retrieval (zoom over CONUS)

65 MiRS Emissivity Assessment using target areas (ocean, desert, snow, young and old ice, wetland, Amazon) for: -Angle and spectral variations -Seasonal time series and Geographic distribution Time series: Julian Day Frequency (GHz) Angle Dependence Stable ocean emissivitiesSeasonally-varying Sea-Ice emissivities

66 Variational vs Analytical Emissivity (Land and Snow) 50.3 GHz Std Dev Bias Land Snow MiRS/N18 Analytical Difference (Varia.-Analy.)