1. Combining Space-based Active and
Passive Microwave Observations to
Improve Global Snowfall Estimates
Tristan L'Ecuyer Norman Wood
University of Wisconsin Colorado State University
Madison, Wisconsin Fort Collins, Colorado
Acknowledgements: John Haynes, CSU CIRA; Peter Rodriguez and
David Hudak, EC; Andy Heymsfeld, NCAR; Larry Bliven, NASA
GSFC; Gwo-Jong Huang, CSU

2. Outline
Detecting snowfall from CloudSat
CloudSat's radar-only retrieval
Limitations of refectivity observations
a priori information
Retrieval performance characteristics
Constraints from PMW observations

3. CloudSat Observations
CloudSat and CPR Parameters
94 GHz (3.2 mm, W-band)
Inclination: 98 degrees
Vertical resolution: 485 m
Footprint: 1.7 x 1.4 km
Sensitivity: -28 dBZ

4. Snow detection: near-surface refectivity,
temperature profle, and a melting layer
model
(2C-PRECIP-COLUMN, J. Haynes)
Ze(z): Precip layer?
T(z): Melting level height
fmelt at surface:
Ze(0): > -7 dBZe, snow certain
> -15 dBZe, snow likely

5. The refectivity-based retrieval problem is
underconstrained

6. Optimal estimation relates Ze to P and
incorporates prior information

7. A priori particle properties are
developed from intensive ground-based
observations
Stratiform
Lake Efect
Observations:
Snow size distribution
Size-resolved fallspeed
X-band Ze
Precip. rate

8. Results provide parameter distributions
and constraints on scattering properties
DDA “habits” constructed using Composite PDF, e.g.
m(D) and Ap(D) ln(gamma) vs ln(alpha)

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

10. Measurements contribute primarily to
determination of lambda
lambda N0
Forward
Model
Sensitivities
A-matrix
Diagonals

11. Constraint provided by coincident PMW
observations...
Tb depression vs IWP Simulations w/ homogeneous
Precipitation layer
Liu & Curry, 1996
- Snowstorm simulation with
a cloud-resolving model + PMW simulator (W. Berg)