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Improvement of Cold Season Land Precipitation Improvement of Cold Season Land Precipitation Retrievals Through The Use of Field Campaign Retrievals Through The Use of Field Campaign Data and High Frequency Microwave Radiative Data and High Frequency Microwave Radiative
Transfer ModelTransfer Model
IPWG 2008, BeijingIPWG 2008, BeijingOctober 14, 2008October 14, 2008
Nai-Yu WangNai-Yu Wang11, Ralph Ferraro, Ralph Ferraro22
1 1 University of Maryland/ESSICUniversity of Maryland/ESSIC2 NOAA/NESDIS 2 NOAA/NESDIS
OutlineOutline
• Where are we?Where are we?• High latitudes precipitation detection and High latitudes precipitation detection and
retrieval challengesretrieval challenges• Model chain approach –couple field campaign Model chain approach –couple field campaign
data with radiative transfer modelsdata with radiative transfer models• C3VP radiative transfer model simulationC3VP radiative transfer model simulation• Summary and future workSummary and future work
Where are we?Where are we?
NEXRAD at 12ZNEXRAD at 12Z
NOAA16 snowfall rate at NOAA16 snowfall rate at about 11:40Zabout 11:40Z
NOAA16 rain rate/ NOAA16 rain rate/ snowfall identifier at snowfall identifier at 11:40Z 11:40Z
AMSU Snowfall rate methodologyAMSU Snowfall rate methodology
• Detect snowfall using 53 and 183 GHzDetect snowfall using 53 and 183 GHz
• Retrieve IWP using AMSU multiple frequency passive Retrieve IWP using AMSU multiple frequency passive
microwave and RTM under snow condition microwave and RTM under snow condition
• Empirical equation connecting IWP with NEXRAD Empirical equation connecting IWP with NEXRAD
reflectivityreflectivity
• Adopt existing reflectivity-snowfall rate equationsAdopt existing reflectivity-snowfall rate equations
• Derive snowfall rate (water equivalent rate) from IWPDerive snowfall rate (water equivalent rate) from IWP
• Catch basic snowfall patternsCatch basic snowfall patterns
• Underestimate intense snowfall rateUnderestimate intense snowfall rate
Precipitation at high latitude land? Precipitation at high latitude land? ChallengesChallenges
• Passive microwave is sensitive to column-integrated Passive microwave is sensitive to column-integrated quantities (column integrated water and ice path) and not quantities (column integrated water and ice path) and not sensitive to the quantity at a particular levelsensitive to the quantity at a particular level
• Emission signal weak, mainly from cloud liquid water and Emission signal weak, mainly from cloud liquid water and melting particlesmelting particles
• Scattering signal from non-spherical particles, scattering Scattering signal from non-spherical particles, scattering properties not well understoodproperties not well understood
• Land surface emissivity (including snow-covered ground) Land surface emissivity (including snow-covered ground) at high latitudes highly varying and not well described at high latitudes highly varying and not well described
Key QuestionsKey Questions
• Can we detect light rain and snowfall (differentiate Can we detect light rain and snowfall (differentiate phase) over land from space? phase) over land from space?
• Passive microwave very difficult, Active and passive highly Passive microwave very difficult, Active and passive highly desirable desirable
• What characteristics of snow can we measure with multi-What characteristics of snow can we measure with multi-frequency active and passive microwave from space in frequency active and passive microwave from space in the GPM era?the GPM era?
• Microphysical properties of snow (size, density, Microphysical properties of snow (size, density, shape, and number) to radiative properties (scattering shape, and number) to radiative properties (scattering and absorption)and absorption)
• Radiative properties to microwave radiances and Radiative properties to microwave radiances and radar reflectivitiesradar reflectivities
Connecting the dots……Connecting the dots……
• linking field campaign, radiative transfer model, and linking field campaign, radiative transfer model, and microwave observationsmicrowave observations
>> >> microphysics measurements of snow from aircraft,microphysics measurements of snow from aircraft,
radar, and disdrometer radar, and disdrometer
>>>> radiative properties scattering and absorption radiative properties scattering and absorption
>> >> microwave radiances and radar reflectivitiesmicrowave radiances and radar reflectivities
• Field campaigns Field campaigns • C3VP 2006-2007 C3VP 2006-2007 • Finland 2009 Baltic campaignFinland 2009 Baltic campaign
Cold Season Field Campaign: C3VPCold Season Field Campaign: C3VP
C3VP InstrumentsC3VP Instruments
Source: Walt Petersen
C3VP: Example GMI-Radar Enhanced (GMI-RE) Snowfall algorithm DevelopmentBayesian 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 TBAMSU, SSMIS
Convair 1D/2DSnow DSD/density, etc
King city radarSnow,contents,PSD,
density
DisdrometersParsivel, 2DVD
Clear airRadiosonde
(T, q)
climo, satellite- retrieved, simulate
= C3VP Observations
= Existing/external
Compare
RefineOutput
Surface Emissivity
AtmosphereScattering+ Emission
input
Environment Obs. Precipitation Observations
?
input input
CRM/LSM Validateinput
input
C3VP snow caseC3VP snow case• Aircraft state parameters (e.g., temperature, humidity) Aircraft state parameters (e.g., temperature, humidity)
and microphysics (water content and particle size and microphysics (water content and particle size distributions, habit, density) measurementsdistributions, habit, density) measurements
Very small amount of liquidLarge amount of ice
January 22, 2006 UTC 5.999-6.397
C3VP aircraft snow DSDC3VP aircraft snow DSD
• Exponential DSD N(D) = N0 exp(- Exponential DSD N(D) = N0 exp(- D) is fitted to size D) is fitted to size spectra measurements (A. Heymsfield)spectra measurements (A. Heymsfield)
Inputs for radiative transfer modelInputs for radiative transfer model• Assume sphere; Mie calculations for backscattering (sAssume sphere; Mie calculations for backscattering (sbsbs), scattering (s), scattering (sscasca), ),
scattering angle (cosscattering angle (cos), and extinction cross sections (s), and extinction cross sections (sextext); Maxwell-Garnet ); Maxwell-Garnet
mixingmixing
• Extinction coefficient (Extinction coefficient (extext), single scattering albedo (), single scattering albedo (0), asymmetry factor g, 0), asymmetry factor g,
and backscattering coefficient and backscattering coefficient bs bs are calculated by integrating over the size are calculated by integrating over the size
spectra N(D)spectra N(D)
dDD N(D),cos,ss ,
4 g, , , 2
0 ext
scaextbs0ext bsss
MHS simulationMHS simulationJanuary 22 NOAA18 MHS Brightness temperature simulations using January 22 NOAA18 MHS Brightness temperature simulations using
coincident C3VP field campaign data and radiative transfer solver coincident C3VP field campaign data and radiative transfer solver SOI (University of Wisconsin); surface emissivity is derived from SOI (University of Wisconsin); surface emissivity is derived from MHS brightness temperatures on January 24 under clear skyMHS brightness temperatures on January 24 under clear sky
freq freq (GHz)(GHz)
MHS (K)MHS (K) RT (K)RT (K)
8989 236.50236.50 247.28247.28
157157 241.41241.41 242.69242.69
183183±1±1 242.57242.57 231.27231.27
183183±3±3 250.43250.43 216.47216.47
190190 255.79255.79 238.09238.09
Preliminary!!
Summary and Future WorkSummary and Future Work
SummarySummary• High Latitudes, cold season, precipitation over land faces many High Latitudes, cold season, precipitation over land faces many
challengeschallenges• Empirical retrievals has some success in identifying snowfall Empirical retrievals has some success in identifying snowfall
patterns but needs work on the snowfall intensitypatterns but needs work on the snowfall intensity• Approach using field campaign measurements and radiative transfer Approach using field campaign measurements and radiative transfer
models to relate the snow microphysics and to the radiative models to relate the snow microphysics and to the radiative properties show promise properties show promise
On going and future workOn going and future work• Verify radiative transfer model calculationsVerify radiative transfer model calculations• Continue C3VP simulations with various microphysics Continue C3VP simulations with various microphysics
parameterizations and single scattering calculations (e.g., DDA) parameterizations and single scattering calculations (e.g., DDA) • Develop Bayesian snowfall retrieval Develop Bayesian snowfall retrieval
Backup slidesBackup slides
Radiometer Snow ObservationsRadiometer Snow Observations January 20, 2007, lake effect snow bandsJanuary 20, 2007, lake effect snow bands
DMSP F-16 SSMIS overpass at 00:22 UTCDMSP F-16 SSMIS overpass at 00:22 UTC
91 GHz 150 GHz 18391 GHz 150 GHz 183±7 GHz 183±3 GHz 183±1 ±7 GHz 183±3 GHz 183±1
GHz GHz
January 22, 2007, synoptic snow systemJanuary 22, 2007, synoptic snow system DMSP F-16 SSMIS overpass at 01:38 UTCDMSP F-16 SSMIS overpass at 01:38 UTC
King city radar
Cold Season land Emissivity ExampleCold Season land Emissivity ExampleCNRM Microwave Monthly Emissivity Atlas (0.25 X 0.25 grids), CNRM Microwave Monthly Emissivity Atlas (0.25 X 0.25 grids),
January 2007 AMSU-A 89 GHz January 2007 AMSU-A 89 GHz highly variable in space, time and satellite incidence anglehighly variable in space, time and satellite incidence angle
Low incidence angle< = 40o
High incidence angle> 40o
Monthly mean value Standard deviation
Atmosphere inputs for RT Modeling Atmosphere inputs for RT Modeling January 22, 2007 caseJanuary 22, 2007 case
• Particle size distribution and bulk microphysics measurementParticle size distribution and bulk microphysics measurement• 3-D PSD is derived using disdrometer (compute volume medium 3-D PSD is derived using disdrometer (compute volume medium
size) and C-band radar reflectivitysize) and C-band radar reflectivity will compute vertical bulk will compute vertical bulk microphysics such as liquid water content and ice water content, microphysics such as liquid water content and ice water content, and compare with aircraft microphysics measurements to verify and compare with aircraft microphysics measurements to verify PSD and density assumptionsPSD and density assumptions
Size distribution derived from radar/disdrometer
Aircraft microphysics data