Abstract
Precipitation is a primary input for hydrologic, agricultural, and engineering models, so making accurate estimates of it across the landscape is critically important. While the distribution of in-situ measurements of precipitation can lead to challenges in spatial interpolation, gridded precipitation information is designed to produce a full coverage product. In this study, we compare daily precipitation accumulations from the ERA5 Global Reanalysis (hereafter ERA5) and the US Global Historical Climate Network (hereafter GHCN) across the northeastern United States. We find that both the distance from the Atlantic Coast and elevation difference between ERA5 estimates and GHCN observations affect precipitation relationships between the two datasets. ERA5 has less precipitation along the coast than GHCN observations but more precipitation inland. Elevation differences between ERA5 and GHCN observations are positively correlated with precipitation differences. Isolated GHCN stations on mountain peaks, with elevations well above the ERA5 model grid elevation, have much higher precipitation. Summer months (June, July, and August) have slightly less precipitation in ERA5 than GHCN observations, perhaps due to the ERA5 convective parameterization scheme. The heavy precipitation accumulation above the 90th, 95th, and 99th percentile thresholds are very similar for ERA5 and the GHCN. We find that daily precipitation in the ERA5 dataset is comparable to GHCN observations in the northeastern United States and its gridded spatial continuity has advantages over in-situ point precipitation measurements for regional modeling applications.
Highlights
Accurate representations of precipitation across a landscape are important in the design of various engineering systems, as well as in the modeling of meteorological, hydrologic, and agricultural systems
Dataset [3], Meteorological Forcing Dataset [4,5], the Parameter-elevation Regressions on Independent Slopes Model (PRISM; gridded precipitation estimates adjusted by physiographic factors) [6] and the ERA5 Global Reanalysis to address the challenges posed by in-situ observations
This is not surprising given that the ERA5 grid boxes are a distributed measure of precipitation over the full quarter-degree grid box, whereas the actual location of the GHCN precipitation gauges on these two mountain peaks were 949 m and 678 m above their respective mean grid box elevations
Summary
Accurate representations of precipitation across a landscape are important in the design of various engineering systems, as well as in the modeling of meteorological, hydrologic, and agricultural systems. This is especially true for the northeastern United States (hereafter Northeast). Dataset (spatial interpolation) [3], Meteorological Forcing Dataset (observation-based land surface forcings, derived surface fluxes and state variables) [4,5], the Parameter-elevation Regressions on Independent Slopes Model (PRISM; gridded precipitation estimates adjusted by physiographic factors) [6] and the ERA5 Global Reanalysis (hereafter ERA5; [7]) to address the challenges posed by in-situ observations. ERA5 is the most recent global climate reanalysis from the European Centre for Medium-Range Weather Forecasts (ECMWF) and represents an improvement over its predecessor
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