Eyal Amitai

Ground Validation for the Tropical Rainfall Measuring Mission (TRMM)

Abstract An overview of the Tropical Rainfall Measuring Mission (TRMM) Ground Validation (GV) Program is presented. This ground validation (GV) program is based at NASA Goddard Space Flight Center in Greenbelt, Maryland, and is responsible for processing several TRMM science products for validating space-based rain estimates from the TRMM satellite. These products include gauge rain rates, and radar-estimated rain intensities, type, and accumulations, from four primary validation sites (Kwajalein Atoll, Republic of the Marshall Islands; Melbourne, Florida; Houston, Texas; and Darwin, Australia). Site descriptions of rain gauge networks and operational weather radar configurations are presented together with the unique processing methodologies employed within the Ground Validation System (GVS) software packages. Rainfall intensity estimates are derived using the Window Probability Matching Method (WPMM) and then integrated over specified time scales. Error statistics from both dependent and independent val...

The Window Probability Matching Method for Rainfall Measurements with Radar

Abstract A simplified probability matching method is introduced that relies on matching the unconditional probabilities of R and Ze, using data from a C-band radar and raingage network near Darwin, Australia. This is achieved by matching raingage intensifies to radar reflectivities taken only from small “windows” centered about the gauges in time and space. The windows must be small enough for the gauge to represent the rainfall depth within the radar window yet large enough to encompass the tinting and geometrical errors inherent to such coincident observations. The calculation of the Ze − R relation with the window probability marching method (WPMM) is quite straightforward, whereby the unconditional cumulative probabilities of Ze, and R, which are obtained from all of the windows, are matched. In practice Ze and R, having the same cumulative percentile, are related to each other. A relatively small sample size (about 600 mm for all gauges combined) is required to achieve a stable Ze − R relation with a...

Classification of rain regimes by the three-dimensional properties of reflectivity fields

Abstract An automated scheme to characterize precipitation echoes within small windows in the radar field is presented and applied to previously subjectively classified tropical rain cloud systirns near Darwin, Australia. The classification parameters are (a) Ee, effective efficiency, as determined by cloud-top and cloud-base water vaporsaturation mixing ratios; (b) BBF, brightband fraction, as determined by the fraction of the radar echo area in which the maximal reflectivity occurs within +1.5 km of the 0C isotherm level; and (c) ΔrZ, radial reflectivity gradients (dB km-1). These classification criteria were applied to tropical rain cloud systems near Darwin, Australia, and to winter convective rain cloud systems in Israel. Both sets of measurements were made with nearly identical networks of C-band radars and rain gauge networks. The results of the application of these objective classification criteria to several independently predetermined rain regimes in Darwin have shown that better organized rain ...

Improved Accuracy of Radar WPMM Estimated Rainfall upon Application of Objective Classification Criteria

Abstract Application of the window probability matching method to radar and rain gauge data that have been objectivelyclassified into different rain types resulted in distinctly different Ze-R relationships for the various classifications.The classification parameters, in addition to the range from the radar, are (a) the horizontal radial reflectivitygradients [dB km-1; (b) the cloud depth, as scaled by the effective efficiency; (c) the brightband fraction withinthe radar field window; and (d) the height of the freezing level. Combining physical parameters to identify thetype of precipitation and statistical relations most appropriate to the precipitation types results in considerableimprovement of both point and areal rainfall measurements. A limiting factor in the assessment ofthe improvedaccuracy is the inherent variance between the true rain intensity at the radar measured volume and the rainintensity at the mouth of the rain gauge. Therefore, a very dense rain gauge network is required to validate mo...

Systematic Variation of Observed Radar Reflectivity–Rainfall Rate Relations in the Tropics

Abstract The Tropical Rainfall Measuring Mission Global Validation Program provides a unique opportunity to compare radar datasets from different sites, because they are analyzed in a relatively uniform procedure. Monthly observed radar reflectivity–rainfall rate (Ze–R) relations for four different sites that are surrounded by tipping bucket gauge networks (Melbourne, Florida; Houston, Texas; Darwin, Australia; and Kwajalein Atoll, Republic of Marshall Islands) were derived. The radar and gauge data from all sites are controlled for quality using the same algorithms, which also include an automated procedure to filter unreliable rain gauge data upon comparison with radar data. The relations are generated by two different methods. The first method is based on using a power law Ze–R with a fixed exponent of 1.4, and the second is based on matching unconditional probabilities of rain rates as measured by the gauge to radar-observed reflectivities and is known as the window probability matching method (WPMM)....

Beamwidth Effects on Z-R Relations and Area-integrated Rainfall

Abstract The effective radar reflectivity Ze, measured by a radar is the convolution of the actual distribution of reflectivity with the beam radiation pattern. Because of the nonlinearity between Z and nun rate R, Ze gives a biased estimator of R whenever the reflectivity field is nonuniform. In the presence of sharp horizontal reflectivity gradients, the measured pattern of Ze, extends beyond the actual precipitation boundaries to produce false precipitation echoes. When integrated across the radar image of the storm, the false echo areas contribute to the sum to produce overestimates of the areal rainfall. As the range or beamwidth increases the ratio of measured to actual rainfall increases. Beyond some range, the normal decrease of reflectivity with height dominates and the measured rainfall underestimates the actual amount. The net effect is a Ze-R relationship that may differ largely from that which would be obtained from consideration of the drop-size distribution alone. The range dependence is al...

Radar rain field evaluation and possible use of its high temporal and spatial resolution for hydrological purposes

Abstract The purpose of this paper is to show how accurate radar-estimated rainfall, with good temporal and spatial resolution, can be used for hydrological purposes. A recent methodological advance in rainfall measurement using conventional weather radars has made it possible to account for much of the variation between the precipitation radar echo intensity and rain intensity. A method known as the window probability matching method (WPMM) was applied to radar measurements over several catchment areas in central Israel. Comparison of daily raingauge measurements with radar rainfall estimates demonstrated good agreement. The standard error of radar-estimated rainfall was only 7% for a storm with a total average accumulation of 328 mm. Several case studies are provided which demonstrate the advantage of having an accurate rain field for calculating excess rainfall for each of the area grid squares in the watershed. Storm excess rainfall for different time durations depends strongly not only on the area size, but also on the differences in the rainfall intensity sequences. Accurate radar rain fields can permit dynamic calculations to be made along the storm path.

Comparison of WPMM versus Regression for Evaluating Z-R Relationships

The accuracy of the estimation of Z‐Rrelationships is evaluated for the Window Probability Matching Method (WPMM) and regression methods. The evaluation is based on experiments of random subsampling of disdrometerobtained 1-min reflectivity Z and rain-rate R pairs. The simulation of the disparity between the radar and the rain gauge measurement volumes was done by 3-min time averaging of the reflectivity data. Geometrical mismatch and synchronization inaccuracies between the radar and rain gauges are simulated by desynchronization of dt minutes, that is, shifting the R and Z time series with respect to each other by dt minutes. The WPMM and bias-corrected regression methods have similar skill in estimating rainfall accumulation even when geometrical and synchronization errors are introduced. However, the WPMM has significant advantage in estimating the rain intensities when geometrical and synchronization errors are introduced to the radar‐gauge-measured Z‐Rpairs for simulating real-world radar and rain gauge comparisons. Regression-based Z‐Rrelationships tend to overestimate the low rain intensities and underestimate the high rain intensities with the crossover at the estimated median rain volume intensity. This trend becomes more severe with the increased desynchronization. This reduction of the dynamic range of R does not occur when using WPMM. Although rain gauge bias correction may render the overall rain accumulation insensitive to the power of the Z‐R law, its appropriate selection has a major effect on the partition of rainfall amounts between weak and strong intensities or the partition between convective and stratiform rainfall.

Evaluation of Radar Rainfall Products: Lessons Learned from the NASA TRMM Validation Program in Florida

Abstract Evaluation of the Tropical Rainfall Measuring Mission (TRMM) satellite observations is conducted through a comprehensive ground validation (GV) program. Since the launch of TRMM in late 1997, standardized instantaneous and monthly rainfall products are routinely generated using quality-controlled ground-based radar data adjusted to the gauge accumulations from four primary sites. As part of the NASA TRMM GV program, effort is being made to evaluate these GV products. This paper describes the product evaluation effort for the Melbourne, Florida, site. This effort allows us to evaluate the radar rainfall estimates, to improve the algorithms in order to develop better GV products for comparison with the satellite products, and to recognize the major limiting factors in evaluating the estimates that reflect current limitations in radar rainfall estimation. Lessons learned and suggested improvements from this 8-yr mission are summarized in the context of improving planning for future precipitation mis...

Relationships between Radar Properties at High Elevations and Surface Rain Rate: Potential Use for Spaceborne Rainfall Measurements

Abstract Ground-based radar data have been used to investigate the relationship between reflectivity at high elevations and surface rain rates. Such relations are useful for rainfall measurements by spaceborne radars at attenuating wavelength such as the 2.2-cm Precipitation Radar (PR) on board the Tropical Rainfall Measuring Mission (TRMM) satellite. In addition to attenuation, these relations are complicated by partial beamfilling, hail, and windshear sorting of particles, among others that affect the corresponding ground-based radar reflectivity–surface rain-rate relationships. TRMM-PR observations were simulated based on radar data from Darwin, Australia. The three-dimensional simulated data were classified by rain type according to several radar properties at high altitudes that are not seriously affected by attenuation. These properties are physical parameters relevant to the variations in the desired relationships. The resulting relationships are robust and permit the classification of near-surface...