NCEP Unified Land Data Assimilation System (NULDAS): Surface Meteorological Forcing Generation

The NCEP North American (operational in August 2014) and Global (operational in July 2011) Land Data Assimilation Systems (NLDAS & GLDAS) are being integrated to form a NCEP Unified Land Data Assimilation System (NULDAS) to achieve simplifying the NCEP operational systems. The NULDAS is not a simple combination of NLDAS and GLDAS but a new development using NLDAS and GLDAS methodologies. The NULDAS is a LIS-based and stand-alone 0.04-degree global LDAS system with soil moisture and snowpack data assimilation. In NULDAS, generation of optimal surface meteorological forcing is a critical step. We use the near-future implemented NCEP GLDAS v2.3 (expected to become operational in the spring of 2019) surface forcing dataset as a backbone by incorporating CPC gauge-based global 0.125-degree daily precipitation with 0.1-degree 3-hourly Multi-Source Weighted-Ensemble Precipitation (MSWEP) developed by Princeton University and 0.25-degree hourly GLDAS precipitation, bias-corrected GLDAS air temperature using CPC monthly mean daily maximum and minimum air temperature, and bias-corrected downward shortwave and longwave radiation using GEWEX/SRB 0.5-degree 3-hourly all-sky radiation product to produce a 0.04 degree hourly global surface meteorological forcing data set. These data include hourly precipitation, 2-m air temperature and specific humidity, 10-m wind speed, and downward shortwave and longwave radiation for the retrospective period from 1 January 1979 to 31 December 2017.

This presentation summarizes the NULDAS surface meteorological forcing generation procedure, evaluation and validation of the produced forcing data against NLDAS, GLDAS, gauge-based observations, and satellite retrievals. The assessment is performed at various time scales (hourly, daily, and monthly) and various spatial scales from grids, watershed, countries, continents and the globe. This dataset is the first global 0.04-degree hourly surface meteorological forcing dataset that can drive land surface, hydrological, ecological, and other models. In particular, it will be used as a key component in the NULDAS system to drive multiple land surface models to produce water and energy fluxes, and state variables to support high-resolution operational drought monitoring and prediction, water resource management, and to provide optimal initial conditions for use in coupled weather and sub-seasonal-to-seasonal (S2S) models to enhance short-range weather and long-term S2S forecast skill.