1. Improvement of Cold Season Land Precipitation Retrievals Through The Use of Field Campaign Data and High Frequency Microwave Radiative Transfer Model IPWG 2008, Beijing October 14, 2008 Nai-Yu Wang 1, Ralph Ferraro 2 1 University of Maryland/ESSIC 2 NOAA/NESDIS

2. Outline Where are we? Where are we? High latitudes precipitation detection and retrieval challenges High latitudes precipitation detection and retrieval challenges Model chain approach –couple field campaign data with radiative transfer models Model chain approach –couple field campaign data with radiative transfer models C3VP radiative transfer model simulation C3VP radiative transfer model simulation Summary and future work Summary and future work

3. Where are we? NEXRAD at 12Z NOAA16 snowfall rate at about 11:40Z NOAA16 rain rate/ snowfall identifier at 11:40Z

4. AMSU Snowfall rate methodology Detect snowfall using 53 and 183 GHz Detect snowfall using 53 and 183 GHz Retrieve IWP using AMSU multiple frequency passive microwave and RTM under snow condition Retrieve IWP using AMSU multiple frequency passive microwave and RTM under snow condition Empirical equation connecting IWP with NEXRAD reflectivity Empirical equation connecting IWP with NEXRAD reflectivity Adopt existing reflectivity-snowfall rate equations Adopt existing reflectivity-snowfall rate equations Derive snowfall rate (water equivalent rate) from IWP Derive snowfall rate (water equivalent rate) from IWP Catch basic snowfall patterns Catch basic snowfall patterns Underestimate intense snowfall rate Underestimate intense snowfall rate

5. Precipitation at high latitude land? Challenges Passive microwave is sensitive to column-integrated quantities (column integrated water and ice path) and not sensitive to the quantity at a particular level Passive microwave is sensitive to column-integrated quantities (column integrated water and ice path) and not sensitive to the quantity at a particular level Emission signal weak, mainly from cloud liquid water and melting particles Emission signal weak, mainly from cloud liquid water and melting particles Scattering signal from non-spherical particles, scattering properties not well understood Scattering signal from non-spherical particles, scattering properties not well understood Land surface emissivity (including snow-covered ground) at high latitudes highly varying and not well described Land surface emissivity (including snow-covered ground) at high latitudes highly varying and not well described

6. Key Questions Can we detect light rain and snowfall (differentiate phase) over land from space? Can we detect light rain and snowfall (differentiate phase) over land from space? Passive microwave very difficult, Active and passive highly desirable Passive microwave very difficult, Active and passive highly desirable What characteristics of snow can we measure with multi- frequency active and passive microwave from space in the GPM era? What characteristics of snow can we measure with multi- frequency active and passive microwave from space in the GPM era? Microphysical properties of snow (size, density, shape, and number) to radiative properties (scattering and absorption) Microphysical properties of snow (size, density, shape, and number) to radiative properties (scattering and absorption) Radiative properties to microwave radiances and radar reflectivities Radiative properties to microwave radiances and radar reflectivities

7. Connecting the dots…… linking field campaign, radiative transfer model, and microwave observations linking field campaign, radiative transfer model, and microwave observations >> microphysics measurements of snow from aircraft, radar, and disdrometer radar, and disdrometer >> radiative properties scattering and absorption >> microwave radiances and radar reflectivities Field campaigns Field campaigns C3VP 2006-2007 C3VP 2006-2007 Finland 2009 Baltic campaign Finland 2009 Baltic campaign

8. Cold Season Field Campaign: C3VP

9. C3VP Instruments Source: Walt Petersen

10. C3VP: Example GMI-Radar Enhanced (GMI-RE) Snowfall algorithm Development Bayesian retrieval: Building the data base Multi-frequency forward modeling to simulate brightness temperatures given C3VP measured atmospheric state, observed and/or CRM-simulated hydrometeor profiles DONE! Simulated TB Radiative Transfer Model Observed TB AMSU, SSMIS Convair 1D/2D Snow DSD/density, etc King city radar Snow,contents,PSD, density Disdrometers Parsivel, 2DVD Clear air Radiosonde (T, q)   climo, satellite- retrieved, simulate = C3VP Observations = Existing/external Compare Refine Output Surface Emissivity Atmosphere Scattering + Emission input Environment Obs.Precipitation Observations ? input CRM/LSM Validate input

11. C3VP snow case Aircraft state parameters (e.g., temperature, humidity) and microphysics (water content and particle size distributions, habit, density) measurements Aircraft state parameters (e.g., temperature, humidity) and microphysics (water content and particle size distributions, habit, density) measurements Very small amount of liquid Large amount of ice January 22, 2006 UTC 5.999-6.397

12. C3VP aircraft snow DSD Exponential DSD N(D) = N0 exp(- D) is fitted to size spectra measurements (A. Heymsfield) Exponential DSD N(D) = N0 exp(- D) is fitted to size spectra measurements (A. Heymsfield)

13. Inputs for radiative transfer model Assume sphere; Mie calculations for backscattering (s bs ), scattering (s sca ), scattering angle (cos  ), and extinction cross sections (s ext ); Maxwell-Garnet mixing Assume sphere; Mie calculations for backscattering (s bs ), scattering (s sca ), scattering angle (cos  ), and extinction cross sections (s ext ); Maxwell-Garnet mixing Extinction coefficient (  ext ), single scattering albedo (  0), asymmetry factor g, and backscattering coefficient  bs are calculated by integrating over the size spectra N(D) Extinction coefficient (  ext ), single scattering albedo (  0), asymmetry factor g, and backscattering coefficient  bs are calculated by integrating over the size spectra N(D)

14. MHS simulation January 22 NOAA18 MHS Brightness temperature simulations using coincident C3VP field campaign data and radiative transfer solver SOI (University of Wisconsin); surface emissivity is derived from MHS brightness temperatures on January 24 under clear sky freq (GHz) freq (GHz) MHS (K) MHS (K) RT (K) RT (K) 89 89 236.50 236.50 247.28 247.28 157 157 241.41 241.41 242.69 242.69 183±1 242.57 242.57 231.27 231.27 183±3 250.43 250.43 216.47 216.47 190 190 255.79 255.79 238.09 238.09 Preliminary!!

15. Summary and Future Work Summary Summary High Latitudes, cold season, precipitation over land faces many challenges High Latitudes, cold season, precipitation over land faces many challenges Empirical retrievals has some success in identifying snowfall patterns but needs work on the snowfall intensity Empirical retrievals has some success in identifying snowfall patterns but needs work on the snowfall intensity Approach using field campaign measurements and radiative transfer models to relate the snow microphysics and to the radiative properties show promise Approach using field campaign measurements and radiative transfer models to relate the snow microphysics and to the radiative properties show promise On going and future work On going and future work Verify radiative transfer model calculations Verify radiative transfer model calculations Continue C3VP simulations with various microphysics parameterizations and single scattering calculations (e.g., DDA) Continue C3VP simulations with various microphysics parameterizations and single scattering calculations (e.g., DDA) Develop Bayesian snowfall retrieval Develop Bayesian snowfall retrieval

16. Backup slides

17. Radiometer Snow Observations Radiometer Snow Observations January 20, 2007, lake effect snow bands January 20, 2007, lake effect snow bands DMSP F-16 SSMIS overpass at 00:22 UTC DMSP F-16 SSMIS overpass at 00:22 UTC 91 GHz 150 GHz 183±7 GHz 183±3 GHz 183±1 GHz January 22, 2007, synoptic snow system January 22, 2007, synoptic snow system DMSP F-16 SSMIS overpass at 01:38 UTC DMSP F-16 SSMIS overpass at 01:38 UTC King city radar

18. Cold Season land Emissivity Example CNRM Microwave Monthly Emissivity Atlas (0.25 X 0.25 grids), January 2007 AMSU-A 89 GHz highly variable in space, time and satellite incidence angle Low incidence angle < = 40 o High incidence angle > 40 o Monthly mean valueStandard deviation

19. Atmosphere inputs for RT Modeling January 22, 2007 case Particle size distribution and bulk microphysics measurement Particle size distribution and bulk microphysics measurement 3-D PSD is derived using disdrometer (compute volume medium size) and C-band radar reflectivity  will compute vertical bulk microphysics such as liquid water content and ice water content, and compare with aircraft microphysics measurements to verify PSD and density assumptions 3-D PSD is derived using disdrometer (compute volume medium size) and C-band radar reflectivity  will compute vertical bulk microphysics such as liquid water content and ice water content, and compare with aircraft microphysics measurements to verify PSD and density assumptions Size distribution derived from radar/disdrometer Aircraft microphysics data