The paper proposes using alternative loss functions beyond mean squared error (MSE) for geoscience image regression tasks with neural networks. The limitations of MSE for capturing spatial correlation and overall distribution are discussed. Loss functions incorporating earth mover’s distance, Pearson Correlation Coefficient, and selective input-driven weighting are presented. Experiments applying these to a passive microwave brightness temperature emulation task demonstrate improved performance over MSE baseline for preserving spatial patterns and distributions. The input-driven loss function utilizing the solar angle showed the greatest improvement in mean absolute error. Overall, the results demonstrate the importance of selecting appropriate loss functions in geoscience regression models to better capture physical properties.

Reconstructing spatially continuous 2-D fields out of their individually derived building blocks typically introduces artifacts that decrease the overall perceptual quality of the field. Machine learning (ML) applications encounter such a challenge when patching a U-net-like architecture output. Numerous techniques have been developed to mitigate this problem. Yet, few are informed scalable solutions. The present work manages the stitching of Unet-inferred images using Bayesian deep learning (BDL) probabilistic output. The ability to preserve a Bayesian prediction’s variance while effectively reducing the artifacts within a patched scene is presented through an example of predicting a field related to atmospheric radiance. In areas of high variance, adjacent patches of inferred atmospheric radiances may significantly vary in magnitude, leading to large undesirable spatial gradients in the combined (patched) product. Multiple weighted aggregation strategies and weighting schema are surveyed to investigate how to efficiently decrease artificial gradients in large images constructed by stitching several small predictions while maintaining naturally occurring gradients expected to appear in the mosaiced image. Structural similarity (SSIM) Index and visual information fidelity (VIF) are used to evaluate the perceptual quality of the resultant images and confirm the successful employment of Bayesian U-nets with well-calibrated uncertainty, yielding geospatial images with fewer artifacts than naive methods. Log-linear pooling (LLP) proved to be the optimal aggregation strategy tested for fusing patch uncertainties by retaining per-pixel Gaussian distributions and scaling uncertainties in a principled manner to maintain calibration across the spatial map.

Passive microwave (PMW) satellite data provides numerous benefits for forecasting tropical cyclone (TC) track and intensity when available, but due to the low-earth orbits of PMW sensors, several days may pass before a PMW swath fully captures a TC center, which is insufficient for operational needs. This study uses Bayesian deep learning to "fill in the gaps" of PMW data and explore the potential for deriving a high spatio-temporal "full-disk" synthetic PMW brightness temperature (TB) product and its uncertainty from geostationary infrared observations. Current results indicate that synthetic product uncertainty may be particularly beneficial for TC forecasters, and can be used to enhance the visibility of shallow low-level convection, environmental moisture, and nascent TC eyes that are thinly obscured.

The data abundance that characterizes many remote sensing problems can be as difficult to deal with as data sparsity. Bayesian Deep Learning (BDL) is a tool that can help remote sensing researchers with this challenge. A classification problem and a regression problem are presented as examples of how the quantified uncertainty from BDL can be coupled with uncertainty decomposition to make more informed decisions during remote sensing research involving deep learning.

Precipitation is one of the primary drivers of earth’s water and energy cycles. While its role in the climate system is generally well-understood, our ability to accurately capture the spatio-temporal distribution of precipitation is limited. Satellite-borne passive microwave (PMW) radiometers and the associated research on their precipitation retrievals provide a highly accurate understanding of global total precipitation. Still, these precipitation retrievals are prone to large systematic errors at all spatial and temporal scales given their inability to accurately relate changes in precipitation intensity to the changes in radiometric signatures caused by variability in cloud system structure. Central to generating estimates of precipitation is proper classification of precipitation type, that is, convective or stratiform. This work presents a study on using a Bayesian deep learning (BDL) to help mitigate this problem by accurately classifying precipitation type and providing uncertainty in the classification. Specifically, it adopts a Bayesian form of Residual Networks (ResNet) architectures to extract the information from PMW observations vectors and identify these structural differences of cloud systems while providing, per pixel, classification uncertainty estimates. Benchmarked ResNet architectures in a deterministic configuration achieve accuracies above 86% on the classification task for precipitation type, while a Bayesian configuration of the same architectures reaches accuracy above 90%. Most importantly, uncertainty estimates are predicted by the Bayesian configuration to accompany each individual output value. This allows end-users to use them to calibrate the output by filtering the least certain predictions. The performance of the classifier, therefore, can be further improved depending on the application needs.

Collection of increasingly voluminous multispectral 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 multiscale 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.