Gridded Ensemble Precipitation and Temperature Estimates for the Contiguous United States

TLDR

Gridded precipitation and temperature products are inherently uncertain due to sparse observation networks, measurement representativeness, and errors, and these uncertainties are rarely quantified, limiting their use in advanced hydrologic modeling and data assimilation. This study creates the first daily, observation‑based ensemble of precipitation and temperature for 1980–2012 across the conterminous United States, northern Mexico, and southern Canada, with plans to extend it by addressing temporal correlation, adding data streams, and refining lapse‑rate choices. The ensemble is constructed from observations and uses ensemble variance to estimate precipitation and temperature uncertainty, enabling its application in hydrologic modeling and data assimilation. Verification shows the ensemble has good reliability and event discrimination, a slight wet bias for high‑threshold events, realistic precipitation statistics that improve land‑surface inputs, and streamflow skill comparable to other coarse‑resolution datasets.

Abstract

Abstract Gridded precipitation and temperature products are inherently uncertain because of myriad factors, including interpolation from a sparse observation network, measurement representativeness, and measurement errors. Generally uncertainty is not explicitly accounted for in gridded products of precipitation or temperature; if it is represented, it is often included in an ad hoc manner. A lack of quantitative uncertainty estimates for hydrometeorological forcing fields limits the application of advanced data assimilation systems and other tools in land surface and hydrologic modeling. This study develops a gridded, observation-based ensemble of precipitation and temperature at a daily increment for the period 1980–2012 for the conterminous United States, northern Mexico, and southern Canada. This allows for the estimation of precipitation and temperature uncertainty in hydrologic modeling and data assimilation through the use of the ensemble variance. Statistical verification of the ensemble indicates that it has generally good reliability and discrimination of events of various magnitudes but has a slight wet bias for high threshold events (>50 mm). The ensemble mean is similar to other widely used hydrometeorological datasets but with some important differences. The ensemble product produces a more realistic occurrence of precipitation statistics (wet day fraction), which impacts the empirical derivation of other fields used in land surface and hydrologic modeling. In terms of applications, skill in simulations of streamflow in 671 headwater basins is similar to other coarse-resolution datasets. This is the first version, and future work will address temporal correlation of precipitation anomalies, inclusion of other data streams, and examination of topographic lapse rate choices.