Assimilation of MODIS snow cover through the Data Assimilation Research Testbed and the Community Land Model version 4. Issue 12 (18th June 2014) - Zhang, Yong‐Fei; Hoar, Tim J.; Yang, Zong‐Liang; Anderson, Jeffrey L.; Toure, Ally M.; Rodell, Matthew
Abstract To improve snowpack estimates in Community Land Model version 4 (CLM4), the Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover fraction (SCF) was assimilated into the Community Land Model version 4 (CLM4) via the Data Assimilation Research Testbed (DART). The interface between CLM4 and DART is a flexible, extensible approach to land surface data assimilation. This data assimilation system has a large ensemble (80‐member) atmospheric forcing that facilitates ensemble‐based land data assimilation. We use 40 randomly chosen forcing members to drive 40 CLM members as a compromise between computational cost and the data assimilation performance. The localization distance, a parameter in DART, was tuned to optimize the data assimilation performance at the global scale. Snow water equivalent (SWE) and snow depth are adjusted via the ensemble adjustment Kalman filter, particularly in regions with large SCF variability. The root‐mean‐square error of the forecast SCF against MODIS SCF is largely reduced. In DJF (December‐January‐February), the discrepancy between MODIS and CLM4 is broadly ameliorated in the lower‐middle latitudes (23°–45°N). Only minimal modifications are made in the higher‐middle (45°–66°N) and high latitudes, part of which is due to the agreement between model and observation when snow cover is nearly 100%. In some regions it also reveals that CLM4‐modeled snow cover lacks heterogeneous features compared to MODIS. In MAM (March‐April‐May), adjustments to snow move poleward mainly due to the northward movement of the snowline (i.e., where largest SCF uncertainty is and SCF assimilation has the greatest impact). The effectiveness of data assimilation also varies with vegetation types, with mixed performance over forest regions and consistently good performance over grass, which can partly be explained by the linearity of the relationship between SCF and SWE in the model ensembles. The updated snow depth was compared to the Canadian Meteorological Center (CMC) data. Differences between CMC and CLM4 are generally reduced in densely monitored regions.