Abstract

AbstractPrecipitation estimates from Doppler weather radar (DWR) provide much better spatial resolution as compared to rain gauges and are therefore becoming more popular in hydrological applications. However, traditional estimates of precipitation from radar-measured reflectivity (e.g., Z=aRb) are deterministic and thus do not offer any information about the uncertainty associated with the estimate. However, the radar scans may contain significant errors that propagate to the rainfall estimates. This gives rise to the need for the probabilistic estimates of rainfall. This paper proposes a copula-based approach to obtain the joint cumulative distribution function (CDF) of reflectivity (Z) and precipitation (R) from which the conditional CDF of precipitation is determined. Three copulas are implemented, and the temporal and spatial transferability of each model is evaluated using different measures of performance. It is established that the precipitation estimates are better than those obtained from the fo...

Full Text
Paper version not known

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call