John Kwiatkowski

Use of the Surface Reference Technique for Path Attenuation Estimates from the TRMM Precipitation Radar

Abstract Estimates of rain rate from the precipitation radar (PR) aboard the Tropical Rainfall Measuring Mission (TRMM) satellite require a means by which the radar signal attenuation can be corrected. One of the methods available is the surface reference technique in which the radar surface return in rain-free areas is used as a reference against which the path-integrated attenuation is obtained. Despite the simplicity of the basic concept, an assessment of the reliability of the technique is difficult because the statistical properties of the surface return depend not only on surface type (land/ocean) and incidence angle, but on the detailed nature of the surface scattering. In this paper, a formulation of the technique and a description of several surface reference datasets that are used in the operational algorithm are presented. Applications of the method to measurements from the PR suggest that it performs relatively well over the ocean in moderate to heavy rains. An indication of the reliability of...

A Hybrid Surface Reference Technique and Its Application to the TRMM Precipitation Radar

Abstract Satellite weather radars that operate at attenuating wavelengths require an estimate of path attenuation to reconstruct the range profile of rainfall. One such method is the surface reference technique (SRT), by which attenuation is estimated as the difference between the surface cross section outside the rain and the apparent surface cross section measured in rain. This and the Hitschfeld–Bordan method are used operationally to estimate rain rate using data from the precipitation radar (PR) aboard the Tropical Rainfall Measuring Mission (TRMM) satellite. To overcome some of the problems associated with the latest operational version of the SRT, a hybrid surface reference is defined that uses information from the along-track and cross-track variations of the surface cross sections in rain-free areas. Over ocean, this approach eliminates most of the discontinuities in the path-attenuation field. Self-consistency of the estimates is tested by processing the orbits backward as well as forward. Calcu...

Statistical Methods of Estimating Average Rainfall over Large Space–Timescales Using Data from the TRMM Precipitation Radar

Abstract Data from the Tropical Rainfall Measuring Mission (TRMM) precipitation radar represent the first global rain-rate dataset acquired by a spaceborne weather radar. Because the radar operates at an attenuating wavelength, one of the principal issues concerns the accuracy of the attenuation correction algorithms. One way to test these algorithms is by means of a statistical method in which the probability distribution of rain rates at the high end is inferred by measurements at the low to intermediate range and by the assumption that the rain rates are lognormally distributed. Investigation of this method and the area–time integral methods using a global dataset provides an indication of how well methods of this kind can be expected to perform over different space–timescales and climatological regions using the sparsely sampled TRMM radar data. Identification of statistical relationships among the rain parameters and an understanding of the rain-rate distribution as a function of time and space may h...

Surface Rain Rates from Tropical Rainfall Measuring Mission Satellite Algorithms

The Tropical Rainfall Measuring Mission (TRMM), jointly sponsored by the National Aeronautics and Space Administration (NASA) and Japan Aerospace Exploration Agency (JAXA, previously known as National Space Development Agency, or NASDA), is the first coordinated international satellite mission to monitor and study tropical and subtropical rain systems (Simpson et al., 1988). A detail description of the TRMM sensor package and a preliminary assessment of the sensor performance are given by Kummerow et al. (1998). The TRMM rain sensor package includes the first space-borne Precipitation Radar (TPR), a TRMM Microwave Imager (TMI) and a Visible and Infrared Scanner (VIRS). Rainfall estimates provided by the TRMM have found applications in climate analysis, data assimilation, water resource management, and decision support to agriculture and health issues.

Gridded hourly text products: a TRMM data reduction approach

The quantity of satellite observations available for the study of global precipitation is both a blessing and a curse. The sheer volume and complexity of many of the data products makes it difficult for many researchers to use. This paper describes one approach that TRMM used to reduce the volume and complexity of data.

Selected analyses of TRMM instantaneous rainfall data

Distributions of Tropical Rainfall Measuring Mission (TRMM) Version 5 (V5) and Version 6 (V6) data are presented. The fraction of pixels determined by the Precipitation Radar (PR) to be convective has increased by 5% from V5 to V6. In V6, PR contributes more rain in medium ( 4 to 20 mm/hr) but less in heavy ( >20 mm/hr) rainfall rates than the TRMM Microwave Imager (TMI). Joint probability distributions show the V6 TRMM Combined Instrument rainfall more strongly related to PR than to TMI.

Estimating TRMM spacecraft attitude errors using the precipitation radar

Results are presented from analysis of the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) data for the purpose of estimating satellite attitude information. Deviations from nominal roll are detected using the surface echo distances measured by the PR. This technique has been used for continued monitoring of attitude anomalies that occurred shortly after the TRMM satellite altitude increase and for historical data.

Tropical Rainfall Measuring Mission algorithm consistency studies

Results are presented from analysis of the Tropical Rainfall Measuring Mission (TRMM) data from two perspectives: instrument/algorithm consistency checks and comparisons of precipitation retrievals between instruments. The instruments specific to this study are the Precipitation Radar (PR) and the TRMM Microwave Imager (TMI).

Variations in the convective rain fraction observed by the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR)

Using a data clustering method, monthly statistics are calculated from rain rate estimates of the TRMM Precipitation Radar. The statistics show how the following variables change with the size of a rain event: convective rain rate, total rain rate, and convective rain fraction. On average, the convective rain fraction is found to be greatest for small mesoscale events covering 10/sup 3/ km/sup 2/, while rain rate increases monotonically with the size of the rain event.