| This study aims at providing a better understanding of the quantitative accuracy of the space-time distribution of rainfall and its characteristics derived from remote sensing. This will be beneficial to clearly understand the capability and limitations of remotely sensed data, to properly utilize the data, and to formulate more reliable remote sensing rainfall algorithms. The specific objectives of this study are: (1) to understand the implication of the uncertainties in the small-scale spatial statistics derived from ground-based radar; (2) to examine the uncertainties associated with space-derived rainfall estimates at a range of space-time scales; (3) to understand the implication of the uncertainties in the scaling properties of rainfall derived from the space-based radar; and (4) to investigate the scaling properties of rainfall across the entire tropics.; First, this study investigates the uncertainty in the small-scale (less than ∼20 km) spatial statistics of rainfall derived from ground-based radar products. Second, the mean-squared uncertainty in satellite-derived rainfall estimates, which arises due to temporal gaps in satellite observations, is investigated. Third, the probability distribution functions of these temporal sampling errors are sought by examining a number of distribution models. Fourth, using the radar-derived rainfall dataset over the Mississippi River Basin as the ground-truth, the total error in the Global Precipitation Climatology Project (GPCP) one-degree daily (1DD) rainfall estimates is quantified. Fifth, the ability of the TRMM precipitation radar (PR) to characterize the scaling characteristics of rainfall is assessed by comparing the derived results with those obtained from ground-based radar data. Results show that, using the TRMM ground validation sites as the ground-truth, the TRMM PR has the ability to characterize the scaling characteristics of rainfall, though the resulting parameters will differ to some degree. Finally, this study provides a global perspective of the scaling properties of rainfall based on the TRMM PR data in terms of its characteristics, external forcing, predictability, spatial/temporal modes of variability, and applicability. |
