Transfer of Satellite Rainfall Uncertainty from Gauged to Ungauged Regions at Regional and Seasonal Time Scales

Abstract

Abstract Hydrologists and other users need to know the uncertainty of the satellite rainfall datasets across the range of time–space scales over the whole domain of the dataset. Here, “uncertainty” refers to the general concept of the “deviation” of an estimate from the reference (or ground truth) where the deviation may be defined in multiple ways. This uncertainty information can provide insight to the user on the realistic limits of utility, such as hydrologic predictability, which can be achieved with these satellite rainfall datasets. However, satellite rainfall uncertainty estimation requires ground validation (GV) precipitation data. On the other hand, satellite data will be most useful over regions that lack GV data, for example developing countries. This paper addresses the open issues for developing an appropriate uncertainty transfer scheme that can routinely estimate various uncertainty metrics across the globe by leveraging a combination of spatially dense GV data and temporally sparse surrogate (or proxy) GV data, such as the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar and the Global Precipitation Measurement (GPM) mission dual-frequency precipitation radar. The TRMM Multisatellite Precipitation Analysis (TMPA) products over the United States spanning a record of 6 yr are used as a representative example of satellite rainfall. It is shown that there exists a quantifiable spatial structure in the uncertainty of satellite data for spatial interpolation. Probabilistic analysis of sampling offered by the existing constellation of passive microwave sensors indicate that transfer of uncertainty for hydrologic applications may be effective at daily time scales or higher during the GPM era. Finally, a commonly used spatial interpolation technique (kriging), which leverages the spatial correlation of estimation uncertainty, is assessed at climatologic, seasonal, monthly, and weekly time scales. It is found that the effectiveness of kriging is sensitive to the type of uncertainty metric, time scale of transfer, and the density of GV data within the transfer domain. Transfer accuracy is lowest at weekly time scales with the error doubling from monthly to weekly. However, at very low GV data density (<20% of the domain), the transfer accuracy is too low to show any distinction as a function of the time scale of transfer.