Meteorological Driving Data Sets for the U.S. Midwest and Great Lakes Region Incorporating Precipitation Gauge Undercatch Corrections

Precipitation (P) gauge undercatch (PUC) is an important source of error when using observed meteorological datasets for hydrologic modeling studies in regions with cold and windy winters. Preliminary simulations using the Variable Infiltration Capacity (VIC) hydrological model forced with different meteorological datasets showed significant underprediction of simulated streamflow throughout the domain. A new hybrid gridded meteorological dataset at 1/16th degree resolution based on observed station data was assembled over the U.S. Midwest and Great Lakes region from 1915-2021 at daily time step. Correction of primary station data using existing techniques is generally difficult or infeasible in the U.S. due to missing station meta-data and lack of local wind speed (WS) measurements. We tested several different post-processing adjustment techniques using regridded WS obtained from NCAR Reanalysis. The most effective approach corrected rain or mixed P using WS alone, and P as snow using a regressed snow-to-P ratio from a group of high-quality reference stations (to account for different and generally unknown snow measurement techniques). The PUC-corrected gridded products were validated against high-quality shielded stations, and corrected GHCN stations with in-situ WS, showing good overall agreement. Validation was also done over 40 river basins using comparisons between observed monthly streamflow and VIC model simulations forced by datasets with and without PUC corrections. The new dataset produced improvements in streamflow simulations in at least 80% of the streamflow locations for three validation metrics (R², Nash Sutcliff efficiency, bias in the mean), demonstrating its value for hydrometeorological studies in the greater Midwest region.