Super-resolution involves synthetically increasing the resolution of gridded data beyond its native resolution. Typically, this is done using interpolation schemes, which estimate sub-grid scale values from neighboring data, and perform the same operation everywhere regardless of the large-scale context, or by requiring a network of radars with overlapping fields of view. Recently, significant progress has been made in single image super resolution using convolutional neural networks. Conceptually, a neural network may be able to learn relations between large scale precipitation features and the associated sub-pixel scale variability and outperform interpolation schemes. Here, we use a deep convolutional neural network to artificially enhance the resolution of NEXRAD PPI scans. The model is trained on 6-months of reflectivity observations from the Langley Hill WA (KLGX) radar, and we find that it substantially outperforms common interpolation schemes for x4 and x8 resolution increases based on several objective error and perceptual quality metrics.

A network of sonic anemometers was deployed over gentle terrain in north-eastern Colorado, USA to observe and characterize local nocturnal circulations. Our study focuses on a small valley about 270 m wide and 12 m deep with a down-valley slope of 2-3 %. The measurements include 19 stations with sonic anemometers at 1 m and a 20-m tower that includes six sonic anemometers in the lowest 5 m. Shallow cold pools and drainage down the valley develop for weak ambient flow and relatively clear skies. However, transient modes constantly modulate or intermittently eliminate the cold pool, which makes extraction and analysis of the horizontal structure of the cold pool difficult with traditional analysis methods. Singular value decomposition successfully isolates the effects of large-scale flow from local down-valley cold-air drainage within the cold pool in spite of the intermittent nature of this local flow. Shortcomings of the method are noted.

Abstract. Missing and low-quality data regions are a frequent problem for weather radars. They stem from a variety of sources: beam blockage, instrument failure, near-ground blind zones, and many others. Filling in missing data regions is often useful for estimating local atmospheric properties and the application of high-level data processing schemes without the need for preprocessing and error-handling steps – feature detection and tracking, for instance. Interpolation schemes are typically used for this task, though they tend to produce unrealistically spatially smoothed results that are not representative of the atmospheric turbulence and variability that are usually resolved by weather radars. Recently, generative adversarial networks (GANs) have achieved impressive results in the area of photo inpainting. Here, they are demonstrated as a tool for infilling radar missing data regions. These neural networks are capable of extending large-scale cloud and precipitation features that border missing data regions into the regions while hallucinating plausible small-scale variability. In other words, they can inpaint missing data with accurate large-scale features and plausible local small-scale features. This method is demonstrated on a scanning C-band and vertically pointing Ka-band radar that were deployed as part of the Cloud Aerosol and Complex Terrain Interactions (CACTI) field campaign. Three missing data scenarios are explored: infilling low-level blind zones and short outage periods for the Ka-band radar and infilling beam blockage areas for the C-band radar. Two deep-learning-based approaches are tested, a convolutional neural network (CNN) and a GAN that optimize pixel-level error or combined pixel-level error and adversarial loss respectively. Both deep-learning approaches significantly outperform traditional inpainting schemes under several pixel-level and perceptual quality metrics.

Linear temporal trends in cloud fraction over the extratropical oceans, observed by NASA's Multi-angle Imaging SpectroRadiometer (MISR) during the period from 2000 to 2013, are examined in the context of coincident European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis data using a maximum covariance analysis. Changes in specific cloud types defined with respect to cloud-top height and cloud optical depth are related to trends in reanalysis variables. A pattern of reduced high-altitude optically thick cloud and increased low-altitude cloud of moderate optical depth is found to be associated with increased temperatures, geopotential heights, and anti-cyclonic flow over the extratropical oceans. These and other trends in cloud occurrence are shown to be correlated with changes in the El Niño-Southern Oscillation (ENSO), the Pacific Decadal Oscillation (PDO), the North Pacific index (NPI), and the Southern Annular Mode (SAM).