Science Service System

Summary of Proposal OCE0005

TitlePrecipitation Measurement with TSX and other Space-borne Sensors
Investigator Weinman, James - University of Washington, Atmospheric Sciences Department
Team Members
Professor Marzano, Frank - University of Rome, Department of Electrical Engineering
Dr. Haddad, Ziad S. - Jet Propulsion Laboratory,
Dr. Lehner, Susanne - German Aerospace Center (DLR), Remote Sensing Technology Institute
Summary

OBJECTIVE:

We will develop techniques to measure precipitation from TSX data in combination with data from space-borne microwave radiometers and mesoscale model output. The retrieved precipitation will be validated against ground based and/or aircraft radar and space borne microwave radiometric data. Applications that benefit from the high spatial resolution of the TSX retrievals will be investigated as measurement opportunities occur. Derivation of precipitation occurring in frontal systems and hurricanes is of special significance.

BACKGROUND:

Because the Tropical Rainfall Measurement Mission (TRMM) program revealed the benefits of space-borne precipitation measurements, the NASA Global Precipitation Mission (GPM) and the European GPM (EGPM) will extend precipitation measurements to temperate latitudes after 2010. Unfortunately, TRMM may end operation in 2005. Obtaining precipitation measurements from TSX after 2006 will thus fill a critical gap.

METHOD:

1. We will first scan the archives of X-SAR images to acquire examples digital data from precipitating events. We have already identified cases of interest.

2. We will augment our one-layer NRCS analytical rain cloud model with a numerical model that accommodates liquid, melting and frozen hydrometeors. Those models will be used to compute DPPS as well. Sensitivity limits will also be addressed with these models.

3. The numerical model will accommodate irregularly shaped clouds obtained from CRMs provided by Professor Bretherton. Those CRMs will be used to build a data-base for insertion into a high resolution Bayesian retrieval algorithm. The models will also be used to compute microwave brightness temperatures so that data from SSM/I and AMSU-B can supplement the analysis of TSX data.

4. With the launch of TSX, we will acquire HH and VV polarization data to incorporate into the high resolution Bayesian model.

5. The retrieved precipitation distributions will be validated with measurements acquired from operational weather radars in the USA, Europe, Japan and from TRMM validation sites such as the radar at Kwajalein. In the case of hurricanes, we will employ data from NOAA research aircraft radars.

6. A high priority of our study will be the analysis of TSX data acquired over frontal systems and hurricanes. We cannot be more specific at this time because TSX operation will be determined primarily by the needs of other TSX investigators.

DATA REQUIREMENTS:

Our initial needs will be satisfied by a few (~5) case studies over operational weather radar sites in support of our algorithm development. We will also seek a few (~3) observations over hurricanes and frontal systems with intense convection.

DELIVERABLES:

Our precipitation retrieval algorithm will be described in scientific publications and at conferences relevant to the remote measurement of rainfall. Reports of progress will be presented at annual TSX science team meetings at DLR. The computer programs will be documented and made available to DLR personnel upon completion of their development.

BUDGET:

This proposal seeks funds to cover only those expenses that we will incur while the PI and other members of this team will be conducting our proposed study in Germany. We propose to open an account with DLR to cover those expenses. Our anticipated costs for this activitiy will be nearly constant over the 3 years, with a 3% inflation adjustment. Those costs will cover:

1) The cost of data: To be determined

2) Attendance at the annual TSX workshop: 2.500.

3) Transportation to DLR and compensation for 2 weeks of coordinated work with DLR personnel at DLR: 7.000.

COST:

Yr.1: 9.500

YR. 2: 9.800

YR. 3: 10.100

TOTAL: 29.400 + cost of data

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