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Articles 1 - 5 of 5
Full-Text Articles in Meteorology
Relative Importance Of Radar Variables For Nowcasting Heavy Rainfall: A Machine Learning Approach, Yi Victor Wang, Seung Hee Kim, Geunsu Lyu, Choeng-Lyong Lee, Gyuwon Lee, Ki-Hong Min, Menas C. Kafatos
Relative Importance Of Radar Variables For Nowcasting Heavy Rainfall: A Machine Learning Approach, Yi Victor Wang, Seung Hee Kim, Geunsu Lyu, Choeng-Lyong Lee, Gyuwon Lee, Ki-Hong Min, Menas C. Kafatos
Institute for ECHO Articles and Research
Highly short-term forecasting, or nowcasting, of heavy rainfall due to rapidly evolving mesoscale convective systems (MCSs) is particularly challenging for traditional numerical weather prediction models. To overcome such a challenge, a growing number of studies have shown significant advantages of using machine learning (ML) modeling techniques with remote sensing data, especially weather radar data, for high-resolution rainfall nowcasting. To improve ML model performance, it is essential first and foremost to quantify the importance of radar variables and identify pertinent predictors of rainfall that can also be associated with domain knowledge. In this study, a set of MCS types consisting of …
Comparing Past And Future Drought And Surplus Periods In The Colorado River Basin, Rama Bedri
Comparing Past And Future Drought And Surplus Periods In The Colorado River Basin, Rama Bedri
Student Scholar Symposium Abstracts and Posters
The Colorado River Basin is crucial to the Western United States, providing water for seven states and Mexico. Historical and future periods of drought and surplus are analyzed in 17 Colorado River stations. Unimpaired streamflow data are evaluated from the U.S. Geological Survey, Bureau of Reclamation, and Coupled Modeled Intercomparison Projection 5 from 1950-2099. Future projections are based on eight climate scenarios. Four climate models (HadGEM2-ES, CNRM-CM5, CanESM2, MI-ROC5) are observed at Representative Concentration Pathways (RCP) 4.5 and 8.5 emission scenarios. Furthermore, the ensemble water year means of the four models are analyzed. The durations of drought or surplus, magnitudes, …
A Demonstration Of A Simple Methodology Of Flood Prediction For A Coastal City Under Threat Of Sea Level Rise: The Case Of Norfolk, Va, Usa, Tal Ezer
CCPO Publications
Many coastal cities around the world are at risk of increased flooding due to sea level rise (SLR), so here a simple flood prediction method is demonstrated for one city at risk, Norfolk, VA, on the U.S. East Coast. The probability of future flooding is estimated by extending observed hourly water level for 1927–2021 into hourly estimates until 2100. Unlike most other flood prediction methods, the approach here does not use any predetermined probability distribution function of extreme events, and instead a random sampling of past data represents tides and storm surges. The probability of flooding for 3 different flood …
Impacts Of Physical Parameterization Schemes And Soil Moisture Initialization On Boundary Layer Evolution In The Weather Research And Forecasting (Wrf) Model, Grace Cutting
Department of Earth and Atmospheric Sciences: Dissertations, Theses, and Student Research
Numerical weather prediction (NWP) models have become a necessary addition to the atmospheric research community over the last several decades, and atmospheric modeling has been used internationally for numerous operational and research purposes. NWP models contain a vast number of combinations of physical and dynamical parameterization schemes; however, they are not always accurate in forecasting weather phenomena at a particular location, as different combinations of parameterization schemes represent differing conditions. Weather Research and Forecasting (WRF) model simulations were run to explore which of the commonly used planetary boundary layer (PBL) schemes best represented upper-air data (as well as PBL evolution) …
Using Remote And In Situ Observations From Torus To Investigate A Preexisting Airmass Boundary And Its Influence On A Tornadic Supercell On 28 May 2019, Kristen Axon
Department of Earth and Atmospheric Sciences: Dissertations, Theses, and Student Research
During the 2019 field phase of Targeted Observation by Radars and UAS of Supercells (TORUS), a preexisting airmass boundary was sampled on 28 May 2019 in north-central Kansas in close proximity to a tornadic supercell. This work hypothesized that the preexisting airmass boundary was associated with a mesoscale air mass with high theta-E (MAHTE) that favorably interacted with the tornadic supercell to increase the likelihood of tornadogenesis. Observations from TORUS including mobile mesonets, unoccupied aerial vehicles, soundings, and ground-based mobile radar were used along with GOES-16 visible satellite imagery, Kansas mesonet surface stations, and KUEX WSR-88D data to investigate this …