1. Developing Winter Precipitation Algorithm over Land from Satellite Microwave and C3VP Field Campaign Observations Fifth Workshop of the International Precipitation Working Group Hamburg, Germany October 13, 2010 Nai-Yu Wang, University of Maryland, ESSIC/CICS Kaushik Gopalan, University of Maryland, ESSIC/CICS Kaushik Gopalan, University of Maryland, ESSIC/CICS Ralph Ferraro, NOAA/NESDIS Ralph Ferraro, NOAA/NESDIS

2. Motivation Winter precipitation in the form of snow Winter precipitation in the form of snow The goal is to construct an a-priori database for Bayesian-type Satellite PMW snowfall retrieval algorithm in GPM era using GV field campaign data, in conjunction with existing satellite snowfall observations and cloud resolving model simulations The goal is to construct an a-priori database for Bayesian-type Satellite PMW snowfall retrieval algorithm in GPM era using GV field campaign data, in conjunction with existing satellite snowfall observations and cloud resolving model simulations Multi-frequency microwave brightness temperatures and radar reflectivites Multi-frequency microwave brightness temperatures and radar reflectivites Atmospheric and surface radiative properties including atmospheric scattering and absorption and surface emissivity Atmospheric and surface radiative properties including atmospheric scattering and absorption and surface emissivity Snow physical properties (e.g., size distribution, habit) from satellite radar, aircraft microphysics, cloud resolving model, and ground disdrometer Snow physical properties (e.g., size distribution, habit) from satellite radar, aircraft microphysics, cloud resolving model, and ground disdrometer

3. Outline MHS simulation using C3VP field campaign data MHS simulation using C3VP field campaign data Bayesian retrievals of ice water path and content from synthetic MHS brightness temperatures using C3VP cloud resolving model WRF/Goddard microphysics scheme vertical hydrometeor profiles and radiative transfer model for spherical snow particle Bayesian retrievals of ice water path and content from synthetic MHS brightness temperatures using C3VP cloud resolving model WRF/Goddard microphysics scheme vertical hydrometeor profiles and radiative transfer model for spherical snow particle CloudSat and MHS snow DSD estimation and reflectivity and brightness temperature simulations for non-spherical particles CloudSat and MHS snow DSD estimation and reflectivity and brightness temperature simulations for non-spherical particles

4. 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 Surface Emissivity Atmosphere Emission scattering Simulated TB Radiative Transfer Model Observed TB AMSU, SSMIS Convair 1D/2D Snow DSD/density, etc King city radar CloudSat Snow DSD? Clear air T,q profiles AMSUB/MHS TBs Cloud Resovling Model simulations

5. C3VP Cold Season Field Campaign

6. NOAA 18 AMSU/MHS TB Simulation with C3VP Aircraft Microphysics January 22, 2007 Synoptic-scale snow event, widespread light to moderate snowfall eff eff Frequency (GHz)MHS TB (K)Simulated TB(K) 89229.94209.96 157229.57216.36 183±1239.96243.57 183±3247.11244.96 190249.46234.76 Vertical structure of effective snow density calculated from aircraft size distribution measurements Surface emissivity calculated from precious clear air MHS TBs Radiative transfer model simulation of satellite TB using snow density estimates and sounding data MHS overpass at 06:45Z, aircraft flight at 06Z

7. Bayesian C3VP IWP Retrievals with WRF/Goddard Microphysics scheme at 06Z January 22, 2007 Marshall-Palmer distribution for rain, modified gamma distribution for cloud, exponential distribution with fixed intercepts for snow and grapple The density is 0.9 g cm − 3 for ice, 0.1 g cm − 3 for snow, and 0.4 g cm − 3 for graupel Mie theory is used Surface emissivity of 0.9 is assumed for all frequencies 48 hours of 1km simulations are used in the database, 32 classes of IWP and TBs using K- means clustering 06Z simulation + 1.5 K noise is used as synthetic satellite data

8. Bayesian C3VP IWC retrievals with WRF/Goddard Microphysics Scheme 06Z January 22, 2007 Same Database as IWP 6 layers of IWC Mie theory is used, assumed sphere for snow

9. Non-spherical Snow Particle Radiative Modeling from G. Liu (2004) Single scattering properties of ice particle as a function of frequency, temperature, Range of size and shape

10. CloudSat Case 1 February 12, 2008 Assume snow follows the Gamma size distribution N(D) = N 0 D m e -D slope (related to mass-weighted mean diameter D m ; D m =4+m) snow habit : Dendrite 0 cloud liquid water NOAA 18 MHS obs sim obs sim

11. CloudSat Case 2 February 10 2008 Assume snow follows the Gamma size distribution N(D) = N 0 D m e -D slope ( related to the mass-weighted mean diameter through D m =4+m) M= 4 in the simulation snow habit : Dendrite 0 cloud liquid water NOAA 18 MHS obs sim obs sim

12. ε = (TB – T up – T down  )/ (T s -T down )  Surface emissivity estimates for 89, 157 and 190 GHz TB: AMSUB/MHS brightness temperatures Tup, Tdown, and  atmospheric parameters for clear air calculated from GDAS Ts : surface temperature from GDAS Clear day Snowing day Feb 3, 2008Feb 4, 2008 Feb 12, 2008 What is the surface emissivity under snowing condition?

13. Summary and Future Work Simulate passive microwave high frequency brightness temperatures utilizing C3VP aircraft and sounding data, WRF/GCE cloud model simulations, CloudSat DSD and land surface emissivity Simulate passive microwave high frequency brightness temperatures utilizing C3VP aircraft and sounding data, WRF/GCE cloud model simulations, CloudSat DSD and land surface emissivity Water vapor sounding channel (183 and 190) simulations are fairly consistently with satellite observations; Window channels (89 and 157 GHz) are sensitive to surface emissivity Water vapor sounding channel (183 and 190) simulations are fairly consistently with satellite observations; Window channels (89 and 157 GHz) are sensitive to surface emissivity Will incorporate cloud liquid water in the CloudSat DSD retrievals and MHS TB simulations Will incorporate cloud liquid water in the CloudSat DSD retrievals and MHS TB simulations Explore the surface emissivity when it’s snowing. Before/after clear day? Average over a period of time? Explore the surface emissivity when it’s snowing. Before/after clear day? Average over a period of time? Explore how much snow signal is there in both sounding and wind channels, can we extract the snow signal amid the strong and noisy background (surface)? Explore how much snow signal is there in both sounding and wind channels, can we extract the snow signal amid the strong and noisy background (surface)? How well can we detect snowfall from passive microwave without coincident radar (radiometer only), and how well can we do the retrieval with the Bayesian-type algorithm? How well can we detect snowfall from passive microwave without coincident radar (radiometer only), and how well can we do the retrieval with the Bayesian-type algorithm?