Physics-based Neural Super-resolution: Training high resolution models using low resolution data
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Upper air sounding are used to examine a cold front of average intensity. Vertical cross sections of potential temperature and wind, and horizontal analyses were compared and adjusted for consistency. These analyses were then used to study the evolution of the front, found to consist of a complex system of fronts occurring at all levels of the troposphere. Low level fronts were strongest at the surface and rapidly weakened with height. Fronts in the midddle troposphere were much more intense. The warm air ahead of the fronts was nearly barotropic, while the cold air behind was baroclinic through deep layers. A deep mixed layer was observed to grow in this cold air.
There are no author-identified significant results in this report.
The formulation of generalized models tracing the geometry and intensity of the synchrotron emission from precessing, twin, relativistic jets as projected on the plane of the sky is presented. It is shown that neither the shape of the image nor its relative intensities are altered by including the effects of a cosmological redshift and a relative velocity between the source and observer. The models are fitted to the available data for several quasars and radio galaxies and demonstrate the plausibility of the phenomenon. Probable selection effects are considered and diagnostics given for recognizing objects showing this behavior. In the radio galaxies considered, velocities up to about 0.2c and precession periods of 1,000,000 yr are deduced. In the QSOs investigated, velocities of 0.7c and greater are found and periods of order 10,000 yr. In some cases precession cone angles increase with time. Consequences in terms of lifetimes of QSO behavior and binary supermassive objects are discussed.
Data collected over the Sleeping Bear Sand Dunes Test Site and the Saginaw Forest Test Site (Michigan) with the JPL Airborne Imaging Spectrometer and the Collins' Airborne Spectroradiometer are being used for forest species identification. The linear discriminant function has provided higher identification accuracies than have principal components analyses. Highest identification accuracies are obtained in the 450 to 520 nm spectral region. Spectral bands near 1,300, 1,685 and 2,220 nm appear to be important, also.
Between 1979 and 1992 we took some 120,000 measurements of atmospheric methane at Cape Meares on the Oregon coast. The site is representative of methane concentrations in the northern latitudes (from 30 deg N to 90 deg N). The average concentration during the experiment was 1698 parts per billion by volume (ppbv). Methane concentration increased by 190 ppbv (or 11.9 percent) during the 13-year span of the experiment. The rate of increase was about 20 +/- 4 ppbv/yr in the first 2 yr and 10 +/- 2 ppbv/yr in the last 2 yr of the experiment, suggesting a substantial decline in the trend at northern middle and high latitudes. Prominent seasonal cycles were observed. During the year, the concentration stays more or less constant until May and then starts falling, reaching lowest levels in July and August, then rises rapidly to nearly maximum concentrations in October. Interannual variations with small amplitudes of 2-3 ppbv occur with periods of 1.4 and 6.5 yr.
This is the first in a series of gamma-ray burst spectroscopy catalogs from the Burst And Transient Source Experiment (BATSE) on the Compton Gamma Ray Abstract: Observatory, each covering a different aspect of burst phenomenology. In this paper, we present time-sequences of spectral fit parameters for 156 bursts selected either for their high peak flux or fluence.
The effects of horizontal and vertical data resolution, data density, data location, different objective analysis algorithms, and measurement error on mesoscale-forecast accuracy are studied with observing-system simulation experiments. Domain-averaged errors are shown to generally decrease with time. It is found that the vertical distribution of error growth depends on the initial vertical distribution of the error itself. Larger gravity-inertia wave noise is produced in forecasts with coarser vertical data resolution. The use of a low vertical resolution observing system with three data levels leads to more forecast errors than moderate and high vertical resolution observing systems with 8 and 14 data levels. Also, with poor vertical resolution in soundings, the initial and forecast errors are not affected by the horizontal data resolution.
High Spectral Resolution Lidar Data 5-Second Resolution
Arctic low clouds influence the evolution of the Arctic system through their effects on radiative fluxes, boundary layer mixing, stability, turbulence, humidity, and precipitation. Unfortunately, atmospheric models and retrospective analysis (reanalysis) products struggle to accurately simulate the occurrence and properties of low clouds in the Arctic. To better understand the magnitude and possible causes of this problem, we analyze the disagreements between the Arctic System Reanalysis version 2 (ASR) and data taken during the September 2014 Arctic Radiation-IceBridge Sea and Ice Experiment (ARISE) airborne campaign conducted over the Beaufort Sea. Previous analysis of grid scale-averaged meteorological variables revealed a lack of cloud water produced in ASR, related to warm/dry biases, a failure to produce sufficient cloud water in conditions that are observed to be favorable for large cloud water values, and sampling issues with the ARISE flight paths. However, ARISE collected meteorological observations at one second intervals, offering spatial data resolution comparable to the subgrid scale of ASR. Subgrid scale properties and processes must be parameterized in conventional atmospheric models and reanalyses such as ASR, and often rely on assumed distributions of meteorological variables about the grid box mean state. To enable an examination of the realism of ASR’s subgrid scale properties, we use high resolution data from ARISE determine the “subgrid scale” distributions of cloud water and thermodynamic variables in the real atmosphere. Preliminary results show that the subgrid scale distribution of thermodynamic variables about the grid scale mean resembles the triangular distribution assumed in many bulk microphysical parameterization schemes, but also with tails resembling Gaussian distributions. The main goal is to understand the partitioning of the total available atmospheric water into water vapor and cloud water. Models typically covert some percentage of total water in a grid box to cloud water based on the estimated amount of supersaturation from the assumed vapor distribution. The ARISE measurements can reveal the relationship between subgrid scale supersaturation and the partition between cloud water and water vapor.
A linear mixing model typically applied to high resolution data such as Airborne Visible/Infrared Imaging Spectrometer, Thematic Mapper, and Multispectral Scanner System is applied to the NOAA Advanced Very High Resolution Radiometer coarse resolution satellite data. The reflective portion extracted from the middle IR channel 3 (3.55 - 3.93 microns) is used with channels 1 (0.58 - 0.68 microns) and 2 (0.725 - 1.1 microns) to run the Constrained Least Squares model to generate fraction images for an area in the west central region of Brazil. The derived fraction images are compared with an unsupervised classification and the fraction images derived from Landsat TM data acquired in the same day. In addition, the relationship betweeen these fraction images and the well known NDVI images are presented. The results show the great potential of the unmixing techniques for applying to coarse resolution data for global studies.
In this work, we present the first application of a fast super resolution convolutional neural network (FSRCNN) based approach for downscaling earth system model (ESM) simulations. Unlike other SR approaches, FSRCNN uses the same input feature dimensions as the low resolution input. This allows it to have smaller convolution layers, avoiding over-smoothing, and reducing computational costs. We adapt the FSRCNN to improve reconstruction on ESM data, we term the FSRCNN-ESM. We use high-resolution (~0.25°) monthly averaged model output of five surface variables over North America from the US Department of Energy's Energy Exascale Earth System Model's control simulation. These high-resolution and corresponding coarsened low-resolution (~1°) pairs of images are used to train the FSRCNN-ESM and evaluate its use as a downscaling approach. We find that FSRCNN-ESM outperforms FSRCNN and other super-resolution methods in reconstructing high resolution images producing finer spatial scale features with better accuracy for surface temperature, surface radiative fluxes, and precipitation.
Data was derived using daily runoff from models participating in the Coupled Model Intercomparison Project, version 6 (CMIP6) that have been routed through the Model for Scale Adaptive River Transport (MOSART) routing model to represent streamflow at individual streamflow gage locations. Data from 750 streamflow gage locations are represented, which are indexed by gage id according to the metadata.csv file. Streamflow gage location sites were based on gage records downloaded from the United States Geological Survey (USGS), National Water Data (HYDAT) Archive from Canada, and the State Hydrological Institute of Russia. Data are available from 11 Earth System Models (ESMs) in total at a daily timestep with a duration of 1920-2099 (1850-2014 for E3SM only). The spatial domain of the modeled data is 0.5 degree resolution. Earth System Models and Units of mean daily streamflow are presented in cubic meters per second (cms). Data files consist of 11 Earth System Model mean daily streamflow output per gage location in .csv format, 1 metadata file in .csv format and 1 Readme file in .docx format (13 files in total). These data were used to benchmark CMIP6 modeled representations of streamflow against gage records.
Multi-resolution methods such as Adaptive Mesh Refinement (AMR) can enhance storage efficiency for HPC applications generating vast volumes of data. However, their applicability is limited and cannot be universally deployed across all applications. Furthermore, integrating lossy compression with multi-resolution techniques to further boost storage efficiency encounters significant barriers. To this end, we introduce an innovative workflow that facilitates high-quality multi-resolution data compression for both uniform and AMR simulations. Initially, to extend the usability of multi-resolution techniques, our workflow employs a compression-oriented Region of Interest (ROI) extraction method, transforming uniform data into a multi-resolution format. Subsequently, to bridge the gap between multi-resolution techniques and lossy compressors, we optimize three distinct compressors, ensuring their optimal performance on multi-resolution data. These optimizations can improve the compression ratio of SOTA approaches by up to 3.3× under the same data quality loss. Lastly, we incorporate an advanced uncertainty visualization method into our workflow to understand the potential impacts of lossy compression. Experimental evaluation demonstrates that our workflow achieves significant compression quality improvements.
High spectral resolution (about 0.06 A) solar data for an active region and a large two-ribbon flare degraded to the typical resolution of low-dispersion (about 5 A) spectra of the IUE satellite are presented. This clearly shows the amount of detail yet to be acquired in stellar spectra and indicates the main spectral regions where line blending is a problem.
MR-CDF is a system for managing multi-resolution scientific data sets. It is an extension of the popular CDF (Common Data Format) system. MR-CDF provides a simple functional interface to client programs for storage and retrieval of data. Data is stored so that low resolution versions of the data can be provided quickly. Higher resolutions are also available, but not as quickly. By managing data with MR-CDF, an application can be relieved of the low-level details of data management, and can easily trade data resolution for improved access time.
Medium resolution radiometric data from Nimbus 2