Developing high-resolution multidecadal satellite remote sensing-based snow lifecycle reanalysis products over the Northern Hemisphere
Description
A deep learning model is being trained using a subset of meteorological forcings and remote sensing observations of snow cover to reconstruct seasonal snow water content globally. The relatively small quantity and location-independent model inputs mean that the model is transferrable globally with little computational cost.
Detailed example
Global, daily, and 1km-resolution SWE, and SWE uncertainty
AI / analytics pattern
Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relationships.
Automation level / stage
a) Pre-deployment – The use case is in a development or acquisition status.
Expected benefit
Deep learning model outputs are being assimilated with NASA process based hydrologic models. This will provide the capability to bias correct for common model errors that are driving snow-fed hydrologic biases in operationally used NASA products.
Controls / human review
ATO: Not reported; PIA: Not published