Operational Snow Data Assimilation at the U.S. Air Force 557th Weather Wing Using the NASA Land Information System

Accurate estimates of snow conditions are important to many aspects of water security. This presentation describes the operational assimilation of snow conditions within the US Air Force (USAF) 557^th Weather Wing (WW)’s global land data assimilation enabled by the NASA Land Information System (LIS). The land surface model (LSM) estimates of snow conditions in the USAF LIS configuration are constrained by estimates from the Snow Depth Analysis Model (SNODEP) using LIS data assimilation (DA) capabilities. SNODEP is a daily global snow depth analysis and is estimated by using passive microwave satellite data (i.e., SSMIS) and available observations (i.e., synoptic and METAR surface reports) of snow depth. To improve the quality of the snow analysis from the USAF LIS, a number of enhancements are being implemented. First, the components and the blending algorithms within SNODEP are being updated to improve the quality of retrieved snow products. Second, the USAF LIS configuration will employ advanced data assimilation methods for ingesting SNODEP. The legacy operational configuration at USAF employs a direct insertion (DI) method to incorporate SNODEP. This is being replaced with a one-dimensional ensemble Kalman Filter (EnKF), allowing the flexible incorporation of model and observational errors in the analysis. The results of SNODEP assimilation into the Noah LSM with multi-parameterization (Noah-MP) options, version 3.6, within LIS will be described in this presentation. The Noah-MP LSM contains a multi-layer snowpack and not just a simple single-layer representation. We will describe how we address the multi-layer snowpack interface in consideration of the thermodynamic state of the snow layers. Evaluation of the analysis will be presented by comparing to a large suite of reference datasets, including both in-situ, reanalysis and remote sensing estimates.