<h2>Improving Near-Surface Hydroclimate Estimation through Snow Physics/Dynamics
    Refinement in a Global Climate Model</h2>
    <p ><strong>Researcher:</strong> Kazuyuki Saito<br />
      <strong>Funding Source: </strong>JAXA<br />
      <strong>Collaborators:</strong> M. Hori (JAXA)</p>
    <p > The goal is to estimate near-surface hydroclimate with higher plausibility to provide the background
      and preconditioning information for forest fire.</p>
    <p > The approach is to focus on the snow scheme in a global climate model as a pivotal process,
      and improve it by use of the in situ and satellite snow observations. I will use the improved scheme
      for near-surface hydroclimate estimation.</p>
    <p > Anticipated results are an improved snow scheme that can cover tundra and taiga zones, and better
      simulation/estimation of background hydroclimate states for assessment of the forest fire hazard.</p>
    <p> This research will result in improvement and validation of the snow process and related physical
      property values (e.g., albedo, water content, density) in the physical terrestrial schemes.</p>