A Combined Radar/Radiometer Retrieval for Precipitation

IGARSS – Session 1.1Vancouver, Canada26 July, 2011

Christian Kummerow1, S. Joseph Munchak1,2

1 Dept. of Atmospheric ScienceColorado State University

2NASA/Goddard Space Flight Center

Existing Algorithms for TRMM

• Radiometer-only

– TMI 2A12, aka GPROF

• Radar-only

– PR 2A25

• Combined

– 2B31

TMI PR COM (2B31)

Kwajalein 7.9% 13.7% 5.7%Melbourne, FL 8.2% +4.1% +21.3%

Biases against Ground Radar (1999-2004)

Precipitation Radar 2A25

Reflectivity (Z) profileReflectivity (Z) profile

Z-R, Z-k relationshipZ-R, Z-k relationship

Consistent with SRT PIA? Consistent with SRT PIA?

Modify epsilon (rain DSD)Modify epsilon (rain DSD)

SRT reliable?SRT reliable?

Rain ProfileRain Profile

N

N

Y

Y

Surface reflectionSurface reflection

GPROF (TMI 2A12)

Observed Tb (Brightness temperature)

Observed Tb(Brightness temperature)

Profile DatabaseCRM (V6)

Profile DatabaseCRM (V6)

Bayesian matchingBayesian matching

Rain/No rainRain/No rain

Nonraining parametersNonraining parameters

Rain ParametersRain Parameters

Combined Algorithms

Reflectivity profileReflectivity profile

Assumptions: rain DSD, ice density. Cloud water

Assumptions: rain DSD, ice density. Cloud water

Consistent with SRT PIA and Tbs?

Consistent with SRT PIA and Tbs?

Modify assumptionsModify assumptions

Radiative TransferRadiative Transfer

Rain ProfileRain Profile

N

Y

Surface reflectionSurface reflection

Observed TbsObserved Tbs

Algorithm Philosophy

• Build on existing single-sensor methods

– Core is a radar profiling algorithm similar to 2A25

• Use internally consistent, interchangeable modules for scattering physics and radiative transfer

• Improve upon previous combined algorithms

– Identify key assumptions needed for microwave RT and use these as variable inputs to radar profiling algorithm

– Minimize errors over large scenes to overcome beam filling and field-of-view overlap problems

– Use all channels to maximize resolution and sensitivity to rain

Inversion Method

• Use variational optimal estimation (OE) to minimize cost function over scene:

observation term retrieval parameter term

What is included in the retrieval parameter term?

Modeling Microwave Tbs requires knowledge of:

Non-raining parameters:• Surface emission (SST, wind)

• Water vapor

• Cloud water

Rain parameters:• Precipitation ice

• Melting layer

• Cloud water

• Rain water

Retrieval of Non-Rain Parameters

Adapted from Kummerow and Elsaesser (2008)

Retrieval of Precipitation Parameters

Ice layer: contributes to scattering at 85 and 37 GHz

Melting layer: strongly contributes to emission and radar attenuation

Rain layer: contributes to emission and radar attenuation

Cloud water, water vapor: relatively weak sources of emission and attenuation

What drives ice scattering at a given PR reflectivity?

Increase IWP (Decrease D0)

Incr

ease

gra

upel

fra

ctio

n (d

ensi

ty)

Snow/graupel partitioning is fixed by height and rain type

Define retrieval parameter ε

ICE to

adjust exponential ice PSD:

D0=ε

ICEaZb

where a and b are fixed by species and rain type

Ice Retrieval

What drives emission/extinction in the rain layer at a given PR reflectivity?

• Assume gamma distribution with shape parameter μ=3

• Define retrieval parameter ε

DSD to

adjust rain DSD:

D0=ε

DSDaZb

– where a and b are fixed by rain type

Rain DSD Retrieval

Cloud water: location vs. amount

• Initial profiles: Cloud water is a fraction of rain water that depends on height and rain type

• Define retrieval parameter ε

CLW as a

multiplier for the total integrated amount of cloud water

Cloud Water Retrieval

What assumptions are necessary to model melting layer?

Use reflectivity peak to determine melt density

Use reflectivity profile to determine melt fraction

Use same DSD assumption as rain

Non-rain parameter retrieval

OE retrieval: cloud water/drizzleoutside raining area

OE retrieval: ice PSD

OE retrieval: rain DSD, cloud water

Retrieval Parameters:ε

DSD,ε

ICE,ε

CLW

Algorithm Flow

Back to the original question: Can a combined algorithm improve upon radar- or radiometer-only product biases in multiple locations?