This project will utilize observations from uncrewed aerial systems (UAS) to gain a better understanding, as well as new perspectives, of weather and water over complex terrain. These observations, which include soil moisture, snow cover, snow reflectivity, vegetation, as well as lower atmospheric properties, will aid in development and advancement of weather and water forecasting…
By fusing high spatial resolution data from optical and Synthetic Aperture Radar (SAR) sensors obtained from both satellite and Unmanned Aerial Vehicles (UAVs) remote sensing, this project aims to develop an innovative methodology based on machine learning techniques for rapid delineation of flood boundary and water depth measurements. These measurements will provide floodplain topography data…
Accurate forecasting of blizzard occurrence, intensity, and extent is key to improving response to anticipated blizzard conditions. This project aims to use new particle imagers and laser ceilometers to measure and detect various snow properties to aid in improving blizzard and winter precipitation forecasting, as well as contribute to model verification, to reduce adverse outcomes…
Through the deployment of affordable, automated, profiling, and high-temporal-resolution vertically pointing radars, snow particle imagers, and boundary layer sensors, this project will examine the characteristics of different snow regimes to demonstrate the utility of snow microphysical and ambient environmental characteristics observations in providing meaningful information for improved winter weather situational awareness and forecasting.
This project utilizes the innovative capabilities of WindBorne Systems’ Global Sounding Balloons (GSBs) to fill critical data gaps. To test this technology for operational use, multiple observation campaigns will be conducted, including over the Northern Pacific Ocean to collect data on atmospheric river formation and over the Atlantic Ocean to collect environmental data surrounding tropical…
This project will develop a new snow cover monitoring system, as well as enhanced daily snow products. The outcomes of this work will contribute to reanalysis products and will benefit operational activities with the near real-time snow cover data.
With a focus on upgrading the quality control algorithm for the Multi-Radar Multi-Sensor (MRMS) system, this project aims to allow the MRMS system to more effectively identify and filter non-meteorological artifacts from the radar data. This will eliminate these non-meteorological artifacts from transferring into final radar data products that serve operational end users across aviation,…
This project will develop a non-hydrometeor subclass for classifying sea clutter, i.e. unwanted radar returns from ocean waves, within the HCA. The ability to classify and remove sea clutter will enable Air Traffic Control safer decision making, improve radar data assimilated into forecast models, and assist NWS forecasters in more confidence discerning coastal weather in…
This project will build and deploy the uncrewed aerial system (UAS) platform, University of Oklahoma (OU) CopterSonde, in the central United States to collect vertical, atmospheric profiles before and during hazardous weather events, specifically severe weather. Results of this work will illustrate the benefits that UAS observations have on forecasting and numerical weather prediction of…
Utilizing a network of near-surface observing systems, this project aims to develop a better understanding of the spatial variability and movement of urban heat within and around small cities. The datasets provided by this work will aid in improving urban heat forecasts, urban planning, and risk management.