Emad Habib

An analysis of small-scale rainfall variability in different climatic regimes

Abstract Statistical characteristics of spatial variability in rain fields at the scales below 5 km2 are discussed based on experimental data from five locations worldwide. The work is motivated by the practical needs of remote sensing applications using radar and satellite technologies, and by the scientific needs of rainfall modelling. Two types of rain-field characteristics are described: spatial correlations and conditional probabilities of occurrence. The accumulation intervals range from 5 min to 1 h. The analysed data samples were collected in Brazil, Florida, Iowa, Oklahoma, and on Guam.

Uncertainty Analysis of the TRMM Ground-Validation Radar-Rainfall Products: Application to the TEFLUN-B Field Campaign

Efforts to validate the Tropical Rainfall Measuring Mission (TRMM) space-based rainfall products have encountered many difficulties and challenges. Of particular concern is the quality of the ground-based radar products—the main tool for validation analysis. This issue is addressed by analyzing the uncertainty in the maps of rain rate provided by the ground-validation radar. To look closely at factors that contribute to the uncertain performance of the radar products, this study uses high-quality rainfall observations from several surface sensors deployed during the Texas and Florida Underflights (TEFLUN-B) field experiment in central Florida during the summer of 1998. A statistical analysis of the radar estimates is performed by comparison with a high-density rain gauge cluster. The approach followed in the current analysis accounts for the recognized effect of rainfall’s spatial variability in order to assess its contribution to radar differences from independent reference observations. The study provides uncertainty quantification of the radar estimates based on classification into light and heavy rain types. The methodology and the reported results should help in future studies that use radar-rainfall products to validate the various TRMM products, or in any other relevant hydrological applications.

Estimation of Rainfall Interstation Correlation

Abstract This study discusses questions of estimating correlation coefficient of point rainfall as observed at two measuring stations. The focus is on issues such as sensitivity to sample size, extreme rain events, and distribution of rainfall. The authors perform extensive analysis based on a two-point data-driven rainfall model that simulates the intermittence and extreme variability of rainfall using a bivariate mixed-lognormal distribution. The study examines the commonly used product–moment estimator along with an alternative transformation-based estimator. The results show a high level of bias and variance of the traditional correlation estimator, which are caused mostly by significant skewness levels that characterize rainfall observations. Application using data from a high-density cluster indicated the advantages of using the alternative estimator. The overall aim of the study is to draw the attention of researchers working with rainfall to some commonly disregarded issues when they seek accurate...

Analysis of radar-rainfall error characteristics and implications for streamflow simulation uncertainty

Abstract Due to the inherent indirect nature of radar-rainfall measurements, hydrologists have been interested in understanding the characteristics of radar-rainfall estimation errors and how they propagate through hydrological simulations. This study implements an observation-based empirical approach to analyse different characteristics of the total radar-rainfall estimation error such as overall and conditional bias, random error and spatio-temporal dependence. The implications of the radar error characteristics for streamflow simulations and the estimation of their uncertainty are examined using a physically-based distributed rainfall—runoff model. An empirical error model is used to generate several realizations of probable surface rainfall fields that reflect the identified characteristics of the radar error. These realizations are used to generate ensemble of streamflow predictions. The main conclusions are that: (a) radar errors have complex spatio-temporal characteristics that exhibit significant sampling and natural variations; (b) adjustment of overall and conditional radar biases results in the most significant improvements in runoff predictions; (c) radar random errors have non-negligible correlations both in time and space; and (d) the simulated runoff hydrographs are sensitive to the assumed degree of correlation in the radar errors fields. This study is an initial step toward developing more rigorous approaches for accounting for the effects of radar error on hydrological predictions.

Evaluation of the High-Resolution CMORPH Satellite Rainfall Product Using Dense Rain Gauge Observations and Radar-Based Estimates

AbstractThis study focuses on the evaluation of the NOAA–NCEP Climate Prediction Center (CPC) morphing technique (CMORPH) satellite-based rainfall product at fine space–time resolutions (1 h and 8 km). The evaluation was conducted during a 28-month period from 2004 to 2006 using a high-quality experimental rain gauge network in southern Louisiana, United States. The dense arrangement of rain gauges allowed for multiple gauges to be located within a single CMORPH pixel and provided a relatively reliable approximation of pixel-average surface rainfall. The results suggest that the CMORPH product has high detection skills: the probability of successful detection is ~80% for surface rain rates >2 mm h−1 and probability of false detection <3%. However, significant and alarming missed-rain and false-rain volumes of 21% and 22%, respectively, were reported. The CMORPH product has a negligible bias when assessed for the entire study period. On an event scale it has significant biases that exceed 100%. The fine-re...

Modeling Radar Rainfall Estimation Uncertainties: Random Error Model

Precipitation is a major input in hydrological models. Radar rainfall data compared with rain gauge measurements provide higher spatial and temporal resolutions. However, radar data obtained form reflectivity patterns are subject to various errors such as errors in reflectivity-rainfall Z-R relationships, variation in vertical profile of reflectivity, and spatial and temporal sampling among others. Characterization of such uncertainties in radar data and their effects on hydrologic simulations is a challenging issue. The superposition of random error of different sources is one of the main factors in uncertainty of radar estimates. One way to express these uncertainties is to stochastically generate random error fields and impose them on radar measurements in order to obtain an ensemble of radar rainfall estimates. In the present study, radar uncertainty is included in the Z-R relationship whereby radar estimates are perturbed with two error components: purely random error and an error component that is proportional to the magnitude of rainfall rates. Parameters of the model are estimated using the maximum likelihood method in order to account for heteroscedasticity in radar rainfall error estimates. An example implementation of this approached is presented to demonstrate the model performance. The results confirm that the model performs reasonably well in generating an ensemble of radar rainfall fields with similar stochastic characteristics and correlation structure to that of unperturbed radar estimates.

Raindrop Size Distribution Measurements in Tropical Cyclones

Characteristics of the raindrop size distribution in seven tropical cyclones have been studied through impact-type disdrometer measurements at three different sites during the 2004–06 Atlantic hurricane seasons. One of the cyclones has been observed at two different sites. High concentrations of small and/or midsize drops were observed in the presence or absence of large drops. Even in the presence of large drops, the maximum drop diameter rarely exceeded 4 mm. These characteristics of raindrop size distribution were observed in all stages of tropical cyclones, unless the storm was in the extratropical stage where the tropical cyclone and a midlatitude frontal system had merged. The presence of relatively high concentrations of large drops in extratropical cyclones resembled the size distribution in continental thunderstorms. The integral rain parameters of drop concentration, liquid water content, and rain rate at fixed reflectivity were therefore lower in extratropical cyclones than in tropical cyclones. In tropical cyclones, at a disdrometercalculated reflectivity of 40 dBZ, the number concentration was 700 100 drops m 3 , while the liquid water content and rain rate were 0.90 0.05 g m 3 and 18.5 0.5 mm h 1 , respectively. The mean mass diameter, on the other hand, was 1.67 0.3 mm. The comparison of raindrop size distributions between Atlantic tropical cyclones and storms that occurred in the central tropical Pacific island of Roi-Namur revealed that the number density is slightly shifted toward smaller drops, resulting in higher-integral rain parameters and lower mean mass and maximum drop diameters at the latter site. Considering parameterization of the raindrop size distribution in tropical cyclones, characteristics of the normalized gamma distribution parameters were examined with respect to reflectivity. The mean mass diameter increased rapidly with reflectivity, while the normalized intercept parameter had an increasing trend with reflectivity. The shape parameter, on the other hand, decreased in a reflectivity range from 10 to 20 dBZ and remained steady at higher reflectivities. Considering the repeatability of the characteristics of the raindrop size distribution, a second impact disdrometer that was located 5.3 km away from the primary site in Wallops Island, Virginia, had similar size spectra in selected tropical cyclones.

Accounting for Uncertainties of the TRMM Satellite Estimates

Recent advances in the field of remote sensing have led to an increase in available rainfall data on a regional and global scale. Several NASA sponsored satellite missions provide valuable precipitation data. However, the advantages of the data are limited by complications related to the indirect nature of satellite estimates. This study intends to develop a stochastic model for uncertainty analysis of satellite rainfall fields through simulating error fields and imposing them over satellite estimates. In order to examine reliability and performance of the presented model, ensembles of satellite estimates are simulated for a large area across the North and South Carolina. The generated ensembles are then compared with original satellite estimates with respect to statistical properties and spatial dependencies. The results show that the model can be used to describe the uncertainties associated to TRMM multi-satellite precipitation estimates. The presented model is validated using random sub-samples of the observations based on the bootstrap technique. The results indicate that the model performs reasonably well with different numbers of available rain gauges.

Climatology-Focused Evaluation of CMORPH and TMPA Satellite Rainfall Products over the Nile Basin

AbstractWith many operational satellite rainfall products being available for long periods, it is now possible to examine whether these products can reproduce climatologically known rainfall characteristics over large river basins that suffer from poor surface monitoring resources. Such assessment is a prerequisite for any further hydrologic applications that rely on these products. The current study evaluates two satellite rainfall products, the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis research-grade product (TMPA-3B42) and the Climate Prediction Center (CPC) morphing technique (CMORPH), over the domain of the Nile basin in eastern Africa. The large latitudinal extent of the basin, its complex topography, and its diverse land use result in widely contrasting regimes and distributions of annual and seasonal rainfall. The results suggest that the two products are fairly successful in reproducing some of the region-specific rainfall patterns across climatologically di...

Numerical simulation studies of rain gage data correction due to wind effect

Investigation of the correction of rain gage measurements due to the wind effect is described. The focus is on the effect of the temporal averaging scale on the estimation of the wind-induced error correction. Numerically derived correction formulae for a specific class of rain gage types, along with high temporal resolution measurements of rainfall and wind speed, are used to perform the study. The rainfall measurements are corrected on a variety of temporal scales ranging from 1 min to 1 month. The results showed the importance of applying the correction procedure at a short timescale, otherwise a significant overestimation of the wind-induced bias results. The wind-induced error is characterized by a nonlinear complex behavior dependent on wind speed, rainfall rate, and the microphysical rain structure quantified by a drop size distribution parameter. The estimated correction factors are found to be sensitive to the change of the drop size distribution. Finally, comparison with certain formulae reported in the literature showed a significant random scatter of the estimated correction if it is expressed only as a function of the wind speed and the drop size distribution characteristics are ignored.