Combining Space-based Active and Passive Microwave Observations to Improve Global Snowfall Estimates

Norman Wood

Colorado State UniversityFort Collins, Colorado

Acknowledgements: John Haynes, CSU CIRA; Peter Rodriguez and David Hudak, EC; Andy Heymsfeld, NCAR; Larry Bliven, NASA

GSFC; Gwo-Jong Huang, CSU

University of WisconsinMadison, Wisconsin

Tristan L'Ecuyer

Outline

Detecting snowfall from CloudSat

CloudSat's radar-only retrieval

Limitations of refectivity observations

a priori information

Retrieval performance characteristics

Constraints from PMW observations

CloudSat Observations

CloudSat and CPR Parameters94 GHz (3.2 mm, W-band)Inclination: 98 degrees

Vertical resolution: 485 mFootprint: 1.7 x 1.4 km

Sensitivity: -28 dBZ

Snow detection: near-surface refectivity, temperature profle, and a melting layer model

Ze(0): > -7 dBZe, snow certain> -15 dBZe, snow likely

(2C-PRECIP-COLUMN, J. Haynes)

Ze(z): Precip layer?T(z): Melting level heightfmelt at surface:

The refectivity-based retrieval problem is underconstrained

Optimal estimation relates Ze to P and incorporates prior information

A priori particle properties are developed from intensive ground-based observations

StratiformLake Efect

Observations: Snow size distributionSize-resolved fallspeed

X-band ZePrecip. rate

Results provide parameter distributions and constraints on scattering properties

Composite PDF, e.g. ln(gamma) vs ln(alpha)

DDA “habits” constructed usingm(D) and Ap(D)

Snowfall rates are produced from retrieved N0, lambda + a priori particle model

Measurements contribute primarily to determination of lambda

ForwardModelSensitivities

A-matrixDiagonals

lambda N0

Constraint provided by coincident PMW observations...

Simulations w/ homogeneousPrecipitation layer

- Snowstorm simulation witha cloud-resolving model + PMW simulator (W. Berg)

Liu & Curry, 1996

Tb depression vs IWP