Remote Sensing (Atmosphere-focused)

Atmospheric Science

1. 1. Radiative transfer fundamentals

   Radiative transfer fundamentals: absorption/scattering, emissivity, SRF concept

   absorptionscatteringemissivityspectral response function

   Open AI study plan →
2. 2. Passive vs active sensing

   Passive vs active sensing: IR/MW vs radar/lidar; what each measures and limitations

   passive sensingactive sensingIRmicrowaveradarlidar

   Open AI study plan →
3. 3. Pre-processing

   Pre-processing: geolocation, resampling, cloud masking, atmospheric correction

   geolocationresamplingcloud maskingatmospheric correction

   Open AI study plan →
4. 4. Retrieval methods

   Retrieval methods: regression/ML vs physical retrievals; uncertainty quantification

   regression retrievalsmachine learning retrievalsphysical retrievalsuncertainty quantification

   Open AI study plan →
5. 5. Data fusion

   Data fusion: multi-sensor synergy (e.g., MW+IR, GEO+LEO), reanalysis integration

   multi-sensor synergyMW+IRGEO+LEOreanalysis integration

   Open AI study plan →
6. 6. Applications

   Applications: aerosols, precipitation, land–atmosphere interaction, climate trends

   aerosolsprecipitationland-atmosphere interactionclimate trends

   Open AI study plan →