A High-Resolution Land Data Assimilation System for High Mountain Asia

The overall goal of this project is to study surface flux, snow/ice storage, and water balance changes in HMA and investigate the causality of these changes at the regional to local scale. To this end, we are developing a high-resolution 1-km High Mountain Asia (HMA)-Land Data Assimilation System (LDAS) 2000- current terrestrial reanalysis using the Noah-MP model, forced by physically downscaled surface meteorology, parameterized by remotely sensed topography and vegetation, and constrained by remotely sensed snow, temperature, and glacier observations.

Specific objectives to achieve the project goal are:

  1. to develop a hourly, 1-km high-resolution forcing land surface weather boundary condition dataset (near- surface air temperature and humidity, wind speed and direction, incident longwave and shortwave radiation, pressure and precipitation) over 4.3 million km2 HMA for 2000-current; 

  2. to study the spatial (both horizontally and vertically) and temporal variability of hyper-resolution key land surface states and fluxes, such as temperature, soil moisture, snow, ice, evaporation and runoff, over the HMA domain, produced by assimilating NASA satellite-based products into a Land Data Assimilation System, forced with a hyper-resolution land surface weather boundary condition dataset;
  3. to assess the utility of assimilating remote sensing data, such as snow, freeze/thaw condition, and glacier observations; 

  4. to investigate the causality of changes in land states and fluxes and snow and ice storage at the regional to local scale by analyzing the hyper-resolution system output; 

  5. to calibrate and validate the high-resolution output using ground-based (including citizen science data), airborne, and satellite observations.

Funded by NASA-ROSES 2016 Understanding Changes in High Mountain Asia

Team: Dr. Houser (PI); Dr. Maggioni (co-I); Dr. Mei (Postdoctoral Fellow); Dr. Xue (Postdoctoral Fellow)

Performance Period: September 2016 – August 2019