# Presentation: 090143

## Passive Microwave Retrieval Algorithms

- C. Kummerow & H. Masunaga
- Colorado State University

- 4th International GPM Planning Meeting
- Greenbelt, MD
- 15-17 June, 2004

## Algorithm Goals

- •  Some sort of consensus is necessary among algorithm developers to keep a consistent physical basis among products.  This is often referred to as the “consensus algorithm”    but it is really just a framework, not a piece of code.

- •  Algorithm should have open architecture for examination by any member of the GPM constellation team.

- •  Algorithm should be both evolutionary (starting with TRMM TMI and incorporating sensors as they come along e.g. AMSR-E, AMSR, WindSat, ...)  and flexible in order to accommodate any radiometer.

## The Main pieces

- • Satellite Tb normalization
- • Surface background and rain probability assignment
- • Rainfall *a-priori* database generation
- • Error model (possibly a parallel algorithm)

## Satellite Tb normalization

- • Tc is intended to normalize inter-satellite differences needed to insure that rainfall differences are not due to sensor calibration.
- • Do by statistical comparisons of orbit overlap together with non/raining retrieval and radiative transfer to account for channel and view-angle differences.  Same radiative transfer to be used throughout.
- • Explore use of surface observation for absolute calibration/radiative transfer consistency in clear air.

## Surface background

- • Geographical database is simple but many surfaces are not constant in time (e.g. sea ice, snow over land, Aral Sea, frozen lakes)
- • For each lat/lon (1x1°) use GPM core to assign a surface type (e.g. Water; Vegetated land; Deserts; Ice; Snow; Undetermined)  A sfc. type is assigned if GPM core can fully characterize geophysical parameters needed to forward compute radiances. “Undetermined” otherwise.  Statistics are kept track for each grid box by season.   e.g. N. Atlantic grid box might say:  70% water, 24% ice, 6% undetermined.  If sensor cannot distinguish independently, it runs most probable surface (water) and assigns 70% confidence that appropriate data base was used.

## Rain probability assignment

- • *A-priori* data base will consist of raining and non-raining pixels.  No longer have to make rain/no rain decisions.  “truth” will be based upon DPR.  The better the sensor, the fewer ambiguous  probabilities (e.g. 50% rain probability) it will have

## Rainfall a-priori database

- • Use DPR, in conjunction with Cloud-Resolving-Models to construct 3-D rain scene.  Adjust as necessary to make consistent with GMI observations.

## TMI

## PR

## Slide 10

## With initial assumptions

## With updated assumptions

## Error Model

- • Have rain probability; rainfall uncertainty from Bayesian or variational approach; in some case, <100% confidence that correct data base was applied.
- • This all has to be merged with regionally and seasonally observed variations from core satellite that radiometers cannot detect to estimate potential biases
- • This has to be merged with error correlations lengths and times for aggregation into larger needed by some applications
- (e.g. data assimilation)
- • All this has to be done correctly in order to merge multiple satellites into a coherent product.

## Summary

- • Basic radiometer algorithm for GPM exists today (V6 of TRMM TMI, AMSR-E).  It is simply not very elegant when forced into arbitrary radiometer configurations.
- • New versions for TRMM and AMSR-E have GPM in mind and strive to become more consistent with multiple sensor architecture.
- • Error modeling and land background issues need more attention to establish physical link needed by consensus framework (today, there is no end-to-end error model and all surfaces except open ocean are “undetermined”)
- • A consensus algorithm group exists and is working on an ad-hoc basis to slowly move the field forward.
- • The group is international with only one requirement for membership:  Some knowledge of microwave rain retrievals.