Many statistical downscaling (SD) methods require observational inputs and expert knowledge, and thus cannot be generalized well across different regions. Convolutional Neural Networks (CNNs) are deep learning models that have generalization abilities for various applications. In this research, we modify UNet, a semantic-segmentation CNN, and apply it to the downscaling of daily maximum/minimum 2-m temperature (TMAX/TMIN) over the western continental US from 0.25-degree to 4-km grid spacings. We select high resolution (HR) elevation, low resolution (LR) elevation and LR TMAX/TMIN as inputs, train UNet using Parameter-Elevation Regressions on Independent Slopes Model (PRISM) data over the south- and centralwestern US from 2015 to 2018, and test it independently over both the training domains and the northwestern US from 2018 to 2019. We found the original UNet cannot generate enough fine-grained spatial details when transferred to the new northwestern US domain. In response, we modified the original UNet by assigning an extra HR elevation output branch/loss function and training the modified UNet to reproduce both the supervised HR TMAX/TMIN and the unsupervised HR elevation. This improvement is named “UNet-AE”. UNet-AE supports semi-supervised model fine-tuning for unseen domains and showed better grid-point-level performance with more than 10% mean absolute error (MAE) reduction compared to the original UNet. Based on its performance relative to the 4-km PRISM, UNet-AE is a good option to provide generalizable downscaling for regions that are under-represented by observations.

Statistical downscaling (SD) derives localized information from larger scale numerical models. Convolutional Neural Networks (CNNs) have learning and generalization abilities that can enhance the downscaling of gridded data (Part I of this study experimented with 2-m temperature). In this research, we adapt a semantic-segmentation CNN, called UNet, to the downscaling of daily precipitation in western North America, from the low resolution (LR) of 0.25-degree to the high resolution (HR) of 4-km grid spacings. We select LR precipitation, HR precipitation climatology and elevation as inputs, train UNet over the subsetted south- and central-western US using Parameter-Elevation Regressions on Independent Slopes Model (PRISM) data from 2015 to 2018, and test it independently in all available domains from 2018 to 2019. We proposed an improved version of UNet, that we call “Nest-UNet”, by adding deep-layer aggregation and nested skip connections. Both the original UNet and Nest-UNet show generalization ability across different regions, and outperform the SD baseline (bias-correction spatial disaggregation) with lower downscaling error and more accurate fine-grained textures. Nest-UNet also shares the highest amount of information with station observations and PRISM, indicating a good ability to reduce the uncertainty of HR downscaling targets.

We present a novel approach for the automated quality control (QC) of precipitation for a sparse station observation network within the complex terrain of British Columbia, Canada. Our QC approach uses Convolutional Neural Networks (CNNs) to classify bad observation values, incorporating a multi-classifier ensemble to achieve better QC performance. We train CNNs using human QC’d labels from 2016 to 2017 with gridded precipitation and elevation analyses as inputs. Based on the classification evaluation metrics, our QC approach shows reliable and robust performance across different geographical environments (e.g., coastal and inland mountains), with 0.927 Area Under Curve (AUC) and type I/type II error lower than 15%. Based on the saliency-map-based interpretation studies, we explain the success of CNN-based QC by showing that it can capture the precipitation patterns around, and upstream of the station locations. This automated QC approach is an option for eliminating bad observations for various applications, including the pre-processing of training datasets for machine learning. It can be used in conjunction with human QC to improve upon what could be accomplished with either method alone.

An ensemble precipitation forecast post-processing method is proposed by hybridizing the Analog Ensemble (AnEn), Minimum Divergence Schaake Shuffle (MDSS), and Convolutional Neural Network (CNN) methods. This AnEn-CNN hybrid takes the ensemble mean of Global Ensemble Forecast System (GEFS) 3-hourly precipitation forecasts as input and produces bias-corrected, probabilistically calibrated, and physically realistic gridded precipitation forecast sequences out to 7-days. The AnEn-CNN hybrid post-processing is trained on the European Centre for Medium-Range Weather Forecasts Reanalysis version 5 (ERA5), and verified against station observations across British Columbia (BC), Canada, from 2017 to 2019. The AnEn-CNN hybrid produces more skillful forecasts than a quantile-mapped GEFS baseline and other conventional AnEn methods, with a roughly 10% increase in Continuous Ranked Probability Skill Score. Further, it outperforms other AnEn methods by 0-60% in terms of Brier Skill Score (BSS) for heavy precipitation periods across disparate hydrological regions. Longer forecast lead times exhibit larger performance gains. Verification against 7-day accumulated precipitation totals for heavy precipitation periods also demonstrates that precipitation sequences are realistically reconstructed. Case studies further show that the AnEn-CNN hybrid scheme produces more realistic spatial precipitation patterns and precipitation intensity spectra. This work pioneers the combination of conventional statistical post-processing and neural networks, and is one of only a few studies pertaining to precipitation ensemble post-processing in BC.

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