Transcript Climate Trends PR/TMI Differences

A Global Rainfall Validation Strategy
Wesley Berg, Christian Kummerow, and
Tristan L’Ecuyer
Colorado 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 Biases
Mean 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 Pacific
Stratiform/Convective Rain Profiles
East vs. West Pacific
Rain 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 Retrievals
Freezing Level
Column integrated water vs rainfall rate
0C
0C
Rain Profile
Surface Rainfall
0C
Surface Rainfall
0C
Surface Rainfall
Surface Rainfall
Passive Microwave Retrievals
Rain Profile
Passive Microwave Retrievals
Liquid Water Column Height (Freezing Level)
TMI Freezing Level – PR Bright Band Height
Passive Microwave Retrievals
Horizontal Inhomogeneity
Beam Filling Correction Factor
(January – March, 2000)
Rainfall Total (mm)
Beam Filling Factor
Precipitation Radar Retrievals
Radar Rainfall for 40 dBZ (Battan, 1973)
Location
Canada
Rainfall rate [mm/hr]
48.6
Hawaii
Midwest
Australia
112.6
50.2
10.2
Washington, DC
Massachusetts
Moscow
Poon, India
54.0
254.0
40.0
43.0
France
Franklin, NC
24.2
76.7
Precipitation Radar Retrievals
Drop 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 timedependent 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.
Which is the Best Algorithm?
• 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?
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)
Satellite
Observations
Global Product
Global Cloud
Database
Regional
Observations
Regional Product
Regional Cloud
Database
• 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)
Satellite
Observations
Global Product
Global Cloud
Database
Rain
Classification
Regional
Observations
High Quality
Observations
Regional Product
Regional Cloud
Database
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
Rain Classification Scheme
Y (km)
Near-Surface Z

X (km)
Reflectivity (dBZ)
Slopes (0-2, 2-4,
4-6, and 6-8 km)
h
Height (km)
Storm Top
Height
Mean and Max.
Gradient Between
Nearest Neighbors
Z
TOA
Ratios :
Height of Max Z
Near-Surface Z
Reflectivity (dBZ)
 Z h
6 km
Surface Z
Max Z
Surface Z
TRMM-LBA
Easterly vs. Westerly Regimes
Category I
Category II
Height (km)
Mean
Mean (dB)  (%)
PDF (ZDR)
Reflectivity (dBZ)
0.85
1.01
0.71
0.76
1.34
1.37
Easterly
Westerly
ZDR (dB)
0.397
0.494
0.141
0.139
0.424
0.412
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.