Decomposing Satellite-Based Classification Uncertainties in Large Earth
Science Datasets
Abstract
Collection of increasingly voluminous multi-spectral data from multiple
instruments with high spatial resolution has posed both an opportunity
and a challenge for maximizing their utilization, analysis, and impact.
Obtaining accurate estimates of precipitation globally with high
temporal resolution is crucial for assessing multi-scale hydrologic
impacts and providing a constraint for development of numerical models
of the atmosphere that provide weather and climate predictions.
Precipitation type classification plays an important role in
constraining both the inverse problem in satellite precipitation
retrievals and latent heat transfer within weather prediction
simulations. Precipitation type, however, is often reported
deterministically, without uncertainty attached to an estimate. Machine
learning techniques are capable of extracting content of interest from
large datasets and accurately retrieving discrete and continuous
properties of physical systems, but with limited insights to the
retrieval components–such as errors and the physical relationship
between the observed and retrieved properties. To address this
shortcoming, we perform precipitation type classification to introduce a
novel tool for decomposing errors of satellite-retrieved products. We
use Bayesian neural networks to map Global Precipitation Measurement
mission Microwave Imager observations to Dual-frequency Precipitation
Radar-derived precipitation type, which perform comparably to
deterministic models, but with the added benefit of providing well
calibrated uncertainties. Through uncertainty decomposition, we
demonstrate well calibrated uncertainties as useful for making decisions
concerning high uncertainty predictions, model selection, targeted data
analysis, and data collection and processing. Additionally, our Bayesian
models enable mathematical confirmation of a data distribution change as
the cause for an unacceptable decline in model accuracy.