a global rainfall validation strategy wesley berg, christian kummerow, and tristan l’ecuyer...

A Global Rainfall Validation StrategyA Global Rainfall Validation Strategy

Wesley Berg, Christian Kummerow, and Wesley Berg, Christian Kummerow, and Tristan L’EcuyerTristan L’Ecuyer

Colorado State UniversityColorado State University

• All existing satellite rainfall retrieval algorithms are significantly under constrained. This means that global assumptions are made which may or may not be applicable to many regions and/or times.

Satellite Rainfall BiasesMean DJF Rainfall (1987 – 1996)

Satellite Rainfall Biases(Bias Adjusted TRMM Retrievals)

Regional IR (GPI) Biases(TRMM VIRS-PR)

Regional TRMM Biases(TMI-PR)

• Regional differences in the structure and microphysical properties of rain systems occur as the result of differences between meteorological regimes.

East vs. West PacificStratiform/Convective Rain Profiles

East vs. West PacificRain Column Height

Storm Height (km)

• These regime-dependent changes in cloud structure and microphysical properties lead to systematic regional biases in the satellite retrieval algorithms due to global algorithm assumptions.

Algorithm Assumptions

IR Retrievals• Relationship of cloud-top temperature to surface rain rate is constant

Radiometer (ocean)• Freezing level is known• Shape of rain profile is independent of location• Horizontal inhomogeneity is constant

Radiometer (land)• Relationship of ice aloft to surface rainfall is constant• All rain has a scattering signature (no warm rain)

Radar• Drop Size Distribution

Passive Microwave RetrievalsColumn integrated water vs rainfall rate

Fre

ezin

g

Le

vel

Ra

in P

rofi

le

Surface Rainfall

0C

Surface Rainfall

0C

Surface Rainfall

0C

Surface Rainfall

0C

Passive Microwave RetrievalsRain Profile

Passive Microwave RetrievalsLiquid Water Column Height (Freezing Level)

TMI Freezing Level – PR Bright Band Height

Passive Microwave RetrievalsHorizontal Inhomogeneity

Rainfall Total (mm) Beam Filling Factor

Beam Filling Correction Factor(January – March, 2000)

Precipitation Radar RetrievalsRadar Rainfall for 40 dBZ (Battan, 1973)

Location Rainfall rate [mm/hr]

Canada 48.6

Hawaii 112.6

Midwest 50.2

Australia 10.2

Washington, DC 54.0

Massachusetts 254.0

Moscow 40.0

Poon, India 43.0

France 24.2

Franklin, NC 76.7

Precipitation Radar RetrievalsDrop Size Distribution

Epsilon Adjustment for Convective Rain(DJF 1997/98)

• While it is possible to “tune” an algorithm for a specific region, without continuous monitoring by a network of ground-based observations time-dependent changes in cloud morphology associated with rainfall regime changes can still result in biased estimates.

Regional Validation

• The determination of a “best” rainfall algorithm is highly dependent on both application as well as location.

• Depends on Application– Temporal sampling requirements– Spatial sampling requirements– Observations needed (e.g. latent heating)– Accuracy requirements (unbiased?)

• Depends on Region– Land or Ocean?

– Snow covered or desert? (no passive microwave)

– High Latitude?

Which is the Best Algorithm?

TRMM PR vs. TMI Coverage

• With the proper algorithm framework, local observations should contribute to optimizing quality of regional satellite retrievals.

Using Local Observations(with GPROF type algorithm)

Global Cloud Database

Satellite Observations Global Product

Regional Cloud Database

RegionalObservations

Regional Product

• High quality regional observations can be used to improve the global algorithm assumptions if the observations are properly grouped via satellite observables.

Using Local Observations(with GPROF type algorithm)

Global Cloud Database

Satellite Observations Global Product

Rain Classification

High Quality Observations

Regional Cloud Database

RegionalObservations

Regional Product

A Global Validation Scheme

• Classify rain using satellite observables (e.g. horizontal and vertical reflectivity structure)

• Use ground-based observations to determine mean and variability of satellite assumed values (such as DSD) for each rain class.

• Export constrained values globally using satellite observations.

• Use variability in constrained values to determine global uncertainties

X (km)

Y (

km

)

Near-Surface Z

Reflectivity (dBZ)

Mean and Max. Gradient Between Nearest Neighbors

Reflectivity (dBZ)

Hei

ght

(km

)

Height of Max Z

Near-Surface Z

Slopes (0-2, 2-4, 4-6, and 6-8 km)

Storm Top Height

Z

h

Max ZSurface Z

Surface Z

Z h6 km

TOA

Ratios :

Rain Classification Scheme

TRMM-LBAEasterly vs. Westerly Regimes

Category I

Category II

Mean

Reflectivity (dBZ)

Hei

gh

t (k

m)

ZDR (dB)

PD

F (

ZD

R)

Mean (dB) (%)0.85 0.3971.01 0.4940.71 0.1410.76 0.1391.34 0.4241.37 0.412

Easterly

Westerly

Summary

• All existing satellite rainfall retrieval algorithms are significantly under constrained. This means that global assumptions are made which may or may not be applicable to many regions and/or times.

• Changes in the structure and microphysics of rain systems between regions leads to systematic biases in the satellite retrieval algorithms.

• The determination of a “best” rainfall algorithm is highly dependent on both application as well as location.

• While it is possible to “tune” an algorithm for a specific region, without continuous monitoring by a network of ground-based observations time-dependent changes in cloud morphology such as those associated with El Niño can still result in biased estimates.

• With the proper algorithm framework, local observations contribute to improving quality of regional satellite retrievals. They may also feed back into the global retrieval if they are of sufficient quality.

• It appears possible to develop a methodology which constrains the algorithm assumptions (i.e. DSD) based on a detailed classification via the satellite observables.