Assimilating Soil Moisture and Snow Products for Improved Drought Monitoring with the North American Land Data Assimilation System (NLDAS)

Background and Objectives

The North American Land Data Assimilation System (NLDAS) has a long successful history of producing surface meteorology and precipitation datasets used as forcing for land-surface models (LSMs) to produce soil moisture, snow cover, and runoff/streamflow products. These products have been used in numerous applications for researchers both within GAPP & CPPA as well as in other communities. Real-time NLDAS products are used for drought monitoring and as initial conditions for a drought forecast system. Currently, remotely-sensed estimates of land-surface states such as soil moisture and snowpack are not assimilated into NLDAS. Therefore, the primary objective of this proposal is to support the routine assimilation of remotely-sensed soil moisture, snow-covered area (SCA) and snow water-equivalent (SWE) in NLDAS. We believe that assimilating satellite products into NLDAS will not only produce improved soil moisture profiles and snowpack states to better represent evolving conditions, but will directly improve the monitoring of drought. To accomplish our objective, we propose to implement the current NLDAS system within the Land Information System (LIS) architecture, which will allow multiple LSMs to assimilate soil moisture, SCA and SWE. Additionally, we will implement the Catchment LSM (a successor to Mosaic) as well as the latest versions of Noah, SAC, and VIC.

Brief Summary of Work to be Completed

The proposed work will include the following Tasks: 1) Benchmark and extend NLDAS data production within the latest LIS architecture, including implementation of Catchment and the latest version of Noah, in addition to (re-)producing NLDAS outputs for the 1979-present retrospective period; 2) Assimilating AMSR-E soil moisture and SWE products and MODIS SCA products into the NLDAS system for better diagnosis of drought and improvement of initial land conditions in the NLDAS drought forecast system at NOAA/EMC; and 3) Evaluate NLDAS output products and drought monitoring skill both with and without assimilation.

Relevance to Program Announcement Research Area

The proposed work will be primarily relevant to the MAPP theme #2 – Develop an Integrated Drought Prediction Capability. The improvements to the NLDAS drought monitoring and data products for the drought forecast system will be made using multiple data sources and models to objectively evaluate model and data upgrades to soil moisture, hydrology, and vegetation. The capability to simultaneously execute multi-model ensembles and assimilate soil moisture and snow products using the existing capabilities of LIS at NCEP will represent a significant advance over the current state-of-the-art in drought assessment and prediction.