Kumar

Project Summary

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The project utilizes the capabilities of the NASA Land Information System (LIS) and the NASA Unified Weather Research and Forecasting (NU-WRF) systems for quantifying hydrologic and cryospheric changes and the primary land-atmosphere drivers of such changes over the High Mountain Asia (HMA) region. Using the modeling and data assimilation capabilities in LIS, the project will develop characterization of the changes in snow and glaciers in the last 30+ years over HMA by incorporating information from satellite measurements with land surface model simulations through a comprehensive land reanalysis. The NU-WRF system will be used to quantify and understand the impact of changes in snow and glaciers on elevation dependent warming and land-atmosphere feedbacks. The model outputs and forecasts produced through the project will contribute significantly towards the GMELT effort through the characterization of water resource vulnerabilities and risks in the HMA region.

Relevance. The proposed work is highly relevant with respect to a number of several areas identified by the NRA, including (1) modeling of snow and glacier processes using advanced land surface models at high spatial resolution, (2) the use of information from satellite remote sensing observations and computational tools such as machine learning to inform and improve model simulations through data assimilation, (3) development of data sets to understand the causality of change through the determination of specific processes involved and (4) the use of high resolution meteorological models for HMA to understand the impact of elevation dependent warming and the land-atmosphere feedbacks that drive such changes.

Approach. Combining information from satellite and remote sensing platforms with model simulations provides an effective way to develop spatially and temporally continuous estimates of changes in land surface snow and glaciers over HMA. As part of this work, we will establish a high resolution (1km) modeling environment over HMA with the best available, downscaled meteorology conditions and state-of-the-art land surface model. The data assimilation environment in LIS will be augmented with machine learning tools to enable the effective utilization of information from a range of optical, thermal and passive microwave remote sensing measurements and will be used to develop a multidecadal land reanalysis. Fully coupled seasonal forecast simulations initialized from this reanalysis will be used to study the mechanism and magnitude of land atmosphere feedbacks related to surface states of snow, ice, vegetation and soil moisture.