Assimilation of Satellite-Based Snow Cover and Freeze/Thaw Observations Over High Mountain Asia

TLDR

The study aims to assess hyper‑resolution (1 km) land data assimilation in High Mountain Asia using the NASA Land Information System and the Noah‑MP model driven by MERRA‑2 boundary conditions. Two DA experiments were performed: assimilating MOD10A1 snow‑cover data and NASA MEaSUREs freeze/thaw data, and their performance was evaluated against satellite snow‑water‑equivalent products and ground‑based snow‑depth observations. The assimilations yielded modest but statistically significant improvements in snow‑depth fit at most stations, reduced land‑surface temperature errors by up to 0.58 K, and lowered soil‑temperature bias and RMSE by 9–10 % in the top layers, though freeze/thaw DA showed no significant skill for mid‑low altitude surface temperatures, indicating potential benefits that require more high‑altitude observations.

Abstract

Towards qualifying hydrologic changes in the High Mountain Asia (HMA) region, this study explores the use of a hyper-resolution (1 km) land data assimilation (DA) framework developed within the NASA Land Information System using the Noah Multi-parameterization Land Surface Model (Noah-MP) forced by the meteorological boundary conditions from Modern-Era Retrospective analysis for Research and Applications, Version 2 data. Two different sets of DA experiments are conducted: 1) the assimilation of satellite-derived snow cover map (MOD10A1), and 2) the assimilation of NASA MEaSUREs landscape freeze/thaw product from 2007 to 2008. The performance of the snow cover assimilation is evaluated via comparisons with available remote sensing based snow water equivalent product and ground-based snow depth measurements. For example, in the comparison against ground-based snow depth measurements, the majority of the stations (13 out of 14) shows slightly improved goodness-of-fit statistics as a result of the snow DA, but only four are statistically significant. In addition, comparisons to the satellite-based land surface temperature products (MOD11A1 and MYD11A1) show that freeze/thaw DA yields improvements (at certain grid cells) of up to 0.58 K in the root-mean-square error (RMSE) and 0.77 K in the absolute bias (relative to model-only simulations). In the comparison against three ground-based soil temperature measurements along the Himalayas, the bias and the RMSE in the 0 - 10 cm soil temperature are reduced (on average) by 10% and 7%, respectively. The improvements in the top-layer of soil estimates also propagate through the deeper soil layers, where the bias and the RMSE in the 10 cm - 40 cm soil temperature are reduced (on average) by 9% and 6%, respectively. However, no statistically significant skill differences are observed for the freeze/thaw DA system in the comparisons against ground-based surface temperature measurements at mid-to-low altitude. Therefore, the two proposed DA schemes show the potential of improving the predictability of snow mass, surface temperature, and soil temperature states across HMA, but more ground-based measurements are still required, especially at high-altitudes, in order to document a more statistically significant improvement as a result of the two DA schemes.