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