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Atmospheric gravity waves (GWs) span a broad range of length scales. As a result, the un‐resolved and under‐resolved GWs have to be represented using a sub‐grid scale (SGS) parameterization in general circulation models (GCMs). In recent years, machine learning (ML) techniques have emerged as novel methods for SGS modeling of climate processes. In the widely used approach of supervised (offline) learning, the true representation of the SGS terms have to be properly extracted from high‐fidelity data (e.g., GW‐resolving simulations). However, this is a non‐trivial task, and the quality of the ML‐based parameterization significantly hinges on the quality of these SGS terms. Here, we compare three methods to extract 3D GW fluxes and the resulting drag (Gravity Wave Drag [GWD]) from high‐resolution simulations: Helmholtz decomposition, and spatial filtering to compute the Reynolds stress and the full SGS stress. In addition to previous studies that focused only on vertical fluxes by GWs, we also quantify the SGS GWD due to lateral momentum fluxes. We build and utilize a library of tropical high‐resolution (Δx = 3 km) simulations using weather research and forecasting model. Results show that the SGS lateral momentum fluxes could have a significant contribution to the total GWD. Moreover, when estimating GWD due to lateral effects, interactions between the SGS and the resolved large‐scale flow need to be considered. The sensitivity of the results to different filter type and length scale (dependent on GCM resolution) is also explored to inform the scale‐awareness in the development of data‐driven parameterizations.

 

The document includes a summary of the AGET project background including the process of forming the Executive Advisory Board. In addition, a diagram of the curriculum structure is provided to demonstrate use of modular and stackable credentials. Semi-structured interviews were used to identify and classify lessons learned and results from these semi-structured interviews with AGET team members and project collaborators are provided. Lastly, teaching resources include samples of course syllabi, surveying and geomatics educational materials, and GIS lab exercises. 

Abstract Based on 20-day control forecasts by the 9-km Integrated Forecasting System (IFS) at the European Centre for Medium-Range Weather Forecasts (ECMWF) for selected periods of summer and winter events, this study investigates global distributions of gravity wave momentum fluxes resolved by the highest-resolution-ever global operational numerical weather prediction model. Two supplementary datasets, including 18-km ECMWF IFS experiments and the 30-km ERA5, are included for comparison. In the stratosphere, there is a clear dominance of westward momentum fluxes over the winter extratropics with strong baroclinic instability, while eastward momentum fluxes are found in the summer tropics. However, meridional momentum fluxes, locally as important as the above zonal counterpart, show different behaviors of global distribution characteristics, with northward and southward momentum fluxes alternating with each other especially at lower altitudes. Both events illustrate conclusive evidence that stronger stratospheric fluxes are found in the ECMWF forecast with finer resolution, and that ERA5 datasets have the weakest signals in general, regardless of whether regridding is applied. In the troposphere, probability distributions of vertical motion perturbations are highly asymmetric with more strong positive signals especially over latitudes covering heavy rainfall, likely caused by convective forcing. With the aid of precipitation accumulation, a simple filtering method is proposed in an attempt to eliminate those tropospheric asymmetries by convective forcing, before calculating tropospheric wave-induced fluxes. Furthermore, this research demonstrates promising findings that the proposed filtering method could help in reducing the potential uncertainties with respect to estimating tropospheric wave-induced fluxes. Finally, absolute momentum flux distributions with proposed approaches are presented, for further assessment in the future. 

The National Science Foundation (NSF) awarded a three-year, $609,739 grant (#1700568) to the University of North Georgia’s Lewis F. Rogers Institute for Environmental and Spatial Analysis (IESA) for a project entitled, "Applying Geospatial and Engineering Technology (AGET). A goal of the project was to meet the demand for highly skilled and educated technicians in the burgeoning field of geospatial and environmental technologies to prepare them for careers in fields such as hydrology, land-use planning, flood-plain mapping, environmental protection, land surveying, precision farming and water resource management. Courses developed led to a new associate of science degree in Environmental, Earth & World Studies, Spatial Science & Engineering plus an undergraduate Land Surveying Certificate. Associated courses build progressive steps in understanding engineering, hydrology, CAD, surveying, GST and applied environmental skills via directed emphasis areas for specific science and engineering careers. These stackable courses and credentials may also articulate with baccalaureate programs to meet workforce needs at multiple levels. Courses developed included Physical Environmental Science, Environmental Management & Sustainability, Surveying I and II, Legal Aspects of Surveying, and Professional Practice of Surveying. In addition to introductory hydrologic concepts in these courses, a newly planned undergraduate certificate in hydrology is planned to meet workforce requirements or licensing benchmarks for environmental scientists and professional land surveyors. 

Students engage with technical geospatial methods while learning essential water resources concepts.