Efforts in hydrometeorology are concentrated on finding the causes of the ground based radar uncertainty sources for rainfall estimation recently. The error sources are interactions between radar with atmosphere and topography. Radar singly often covers systematic error whereas atmospheric and topographic errors are relevant to location and precipitation pattern. This article reviews uncertainty sources on weather ground-based radar in order to rainfall estimation that have been discussed in nine main categories includes; the range effects, radar signal attenuation, beam blockage, vertical air motion and precipitation drift, ground clutter, anomalous propagation, vertical variability of the precipitation system, variability of the Z-R relationship and bright band. Topographic errors such as ground clutter and beam blockage primarily can be eliminated by attentive in site selection. Atmospheric errors such as vertical air motion and precipitation drift can filter or eliminated by calibration methods. Systematic errors include radar signal attenuation and anomalous propagation is the result of structural changes in the radar waves in space. Thus, this review is explored the characteristics of uncertainty error resources and a comprehensive appraisal of experimental procedures is provided.

Abstract-Ground-based radar is known as one of the most important systems for precipitation measurement at high spatial and temporal resolutions. Radar data are recorded in digital manner and readily ingested to any statistical analyses. These measurements are subjected to specific calibration to eliminate systematic errors as well as minimizing the random errors, respectively. Since statistical methods are based on mathematics, they offer more precise results and easy interpretation with lower data detail. Although they have challenge to interpret due to their mathematical structure, but the accuracy of the conclusions and the interpretation of the output are appropriate. This article reviews the advanced methods in using the calibration of ground-based radar for forecasting meteorological events include two aspects: statistical techniques and data mining. Statistical techniques refer to empirical analyses such as regression, while data mining includes the Artificial Neural Network (ANN), data Kriging, Nearest Neighbour (NN), Decision Tree (DT) and fuzzy logic. The results show that Kriging is more applicable for interpolation. Regression methods are simple to use and data mining based on Artificial Intelligence is very precise. Thus, this review explores the characteristics of the statistical parameters in the field of radar applications and shows which parameters give the best results for undefined cases.

Weather unipolar ground-based radar estimation can experience momentous changes by using other effective parameters such as distance from radar, altitudes and rainfall time duration that directly compromise the accuracy of the hydrometeorology applications. These radar measurements however, need to be calibrated for more accurate rainfall estimation. In addition to the radar-rainfall (Z-R) relationship, this is a pragmatic approach based on careful analyses of other parameters. This article introduces a new calibration approach using altitude parameters and time-stepwise processing of reflectivityrainfall (Z-R) rate relationship. This research leads to introduce a new effective parameter and generate two new empirical coefficients in radar-rainfall relationship. Two consecutive years unipolar ground-based radar data sets with 190 occurrences of rainfall from 43 stations in calibration window of 3 h; and the corresponding rainfall measured from registered rain gauges were used in this study. The results indicated that radar-rainfall relationship Z ¼ AR b is better improvised with altitudes effect (H) and empirical coefficient (c), such that Z ¼ AR b H c . It therefore is concluded that the use of other effective parameters (distance from radar, altitudes and rainfall time duration) leads to optimum accuracy of Z-R relationship.

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