Probabilistic radar-gauge merging by multivariate spatiotemporal techniques

Abstract The quality of quantitative precipitation estimation (QPE) is degraded by considerable discrepancies between radar and ground measurements, which are common due to inherent uncertainties between these two kinds of sensor systems. The causes include measurement errors and differences in sampling schemes. Nevertheless, the remaining discrepancies can be statistically modeled. A model describing detection probabilities of ground rainfall, systematic biases as well as the variance of residual discrepancies between radar and rain gauges is developed. These are modeled by means of multiple explanatory variables such as rain rate and distance from radar. The model is implemented by using nonparametric kernel methods and spatiotemporal Kriging interpolation. A key feature of the model is that for a given radar-derived rainfall field and explanatory variables, it determines probability distributions for the corresponding ground rainfall. Unbiased estimates for ground rainfall can be obtained from the expected values of the distributions. From such distributions, one can also obtain uncertainty estimates and exceedance probabilities that are important for hydrological applications. Performance of the model is assessed by cross-validation using hourly rainfall accumulations measured by the Finnish rain gauges and C-band dual polarization radars.