1100 CONUS-Scale Evaluation of NLDAS-2 Precipitation Estimates used in the Retrospective Implementation of the National Water Model of U.S. National Weather Service

Wednesday, 25 January 2017
4E (Washington State Convention Center )
Arezoo Rafieeinasab, NCAR, Boulder, CO; and D. J. Gochis, Y. Zhang, A. Dugger, J. McCreight, L. karsten, Y. Liu, and B. Cosgrove

The quality of hydro-meteorological forcing, particularly precipitation, is critical for the performance of hydrological models and streamflow simulation/prediction. It is important to first assess the quality of precipitation estimates before attributing errors or biases in model simulations to model physics or parameters and before trying to correct errors through extensive calibration. As part of the operational implementation of the WRF-Hydro model at the National Water Center, hereafter referred to as the National Water Model (NWM), NLDAS-2 climate data are used to drive the spin-up and long-term retrospective model runs. To inform NWM benchmarking and evaluation, as well as evaluation of other models using this rich NLDAS-2 dataset, this work presents an evaluation of the NLDAS-2 precipitation over the contiguous U.S. for 2011-2015 using multiple observational data sets.

Native NLDAS-2 precipitation data at 1/8 degree resolution are regridded to the spatial resolution of the NWM model at 1 km. The observational data sets used for comparison are the hourly Hydrometeorological Automated Data System (HADS), as well as a blended StageIV-StageII product. The purpose of blending the StageIV/II data is to ensure a better representation of precipitation over the western part of the U.S. where StageIV data are generally poor. The evaluation is carried out at a point-grid scale for HADS and at a grid-grid scale for the blended StageIV/StageII product.  We also assess the NLDAS-2 data accuracy at different spatial aggregations, such as the US Geological Survey hydrologic units (HUC) at various levels from HUC12 to HUC2 as well as at the ecoregion scale. These spatial aggregation units permit direct comparison to performance metrics suitable for evaluation of streamflow, snowpack evolution, and other integrated hydrologic fluxes.

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