Task 245

Develop Satellite Snow Data Assimilation Capabilities into the NASA Land Information System (LIS) to Support NWS Hydrologic Applications

Principal Investigator(s):

Y. Liu


C. Peters-Lidard

Last Updated:

October 26, 2012 15:26:10

Description of Problem

Accurate snow estimation during snow accumulation and melting periods is essential for various hydrologic and water resources applications in snow-impacted areas. However, estimates of snow water equivalent (SWE) from current land surface models typically contain errors due to poor model physics in representing the snow processes and other sources of uncertainties. Satellite-derived snow products, albeit subject to errors themselves, hold great potential for improving snow estimates if properly assimilated into the models. This project represents a collaborative effort between NASA GSFC and the National Operational Hydrologic Remote Sensing Center (NOHRSC) of the National Weather Service, which aims to develop snow data assimilation (DA) capabilities in LIS to integrate MODIS fraction snow cover area (fSCA) products into several land surface models to improve snow prediction over the Alaska and Afghanistan domains. The developed snow DA capability will be transferred to NOHRSC to support their hydrologic operations.

Scientific Objectives and Approach

1. Learn and test the LIS software to develop sufficient familiarity and knowledge about the system. Gain thorough understanding of the structure of data assimilation in LIS.

2. As a baseline, make open loop runs in LIS for the Alaska and Afghanistan domains using NOAH2.7.1 and 3.2, CLM 2 and 4, as well as SNOW-17 models and GDAS forcing ( with and without topographic correction).

3. Assimilate MODIS fSCA products into these models using direct insertion (DI) and ensemble Kalman filtering (EnKF) techniques.

4. Evaluate the DA results against open loop simulations and ground-based snow observations. Summarize the findings in presentations and journal paper(s).

5. Deliver the code to NOHRSC to support hydrologic applications.


The main effort in the first 1.5 months involves learning and testing the LIS soft, including setting up the accounts with NASA NCCS required for running LIS and learning how to use the computing system at NASA (Step 1). Work on making open loop runs of the LSMs (Step 2) has begun.

The figure below presents a map of surface soil moisture over CONUS computed by the NOAH2.7.1 model using LIS and GDAS forcing, as a result from LIS test runs.

Task Figures

Fig. 1 – Average surface soil moisture content (kg/m2) for the period of Oct 29~Nov 10, 2002, as modeled by the NOAH 2.7.1 model using LIS.