Ongoing Research Projects

Huang Research Group/Ongoing Research Projects
  1. Polar Radiant Energy in the Far InfraRed Experiment (PREFIRE) (Primary Sponsor: NASA)
    This is a recently EV-I mission selected by NASA. Our group will provide algorithms for two critical L-2 data products: spectral flux and surface spectral emissivity inferred from the L-1 radiometric measurements.
  2. Libera: the next-generation earth radiation budget measurement from space (Primary Sponsor: NASA)
    This is the first Earth Venture-Continuity Class mission selected by NASA. We are responsible for developing the algorithm for the longwave split-channel flux and demonstrate its usage in climate studies.
  3. Spectral flux from multiple years of Aqua and SUOMI-NPP measurements: derivation, validation, and application in climate studies (Primary Sponsor: NASA)
    This is primarily a continuity project based on our previous studies on deriving spectral flux from hyperspectral radiance measurements. The goal is to use SUMO-NPP measurements to derive spectral flux and then to produce a multi-decade time series of spectral flux for climate studies.
  4. Incorporate more realistic surface-atmosphere radiative coupling in E3SM (Primary Sponsor: DoE)
    This is primarily a modeling project to revamp the longwave radiation scheme in the global climate model for a more faithful representation of surface-atmosphere radiative coupling and of the longwave ice cloud optics, especially for the treatments in the far IR.
  5. On the use of spectral observations and derived products in CERES EBAF data productions (Primary Sponsor: NASA)
    This project is to provide spectral diagnostics for the assistance of CERES EBAF data productions, especially for the EBAF SARB (surface-atmosphere radiation budget) data product.
  6. Solar forecasting and the impact of solar farms on the environment and climate
    This project intersects solar energy and weather/climate studies. My group has used long-term satellite observations to study the impact of solar farms on surface radiation budget (link) and used a machine-learning technique to help solar irradiance forecasting (link). We are continuing on both lines of research