Impact of assimilation of New York State Mesonet doppler wind lidar on high impact weather predictions in New York State
Kay, J., Weckwerth, T.. (2024). Impact of assimilation of New York State Mesonet doppler wind lidar on high impact weather predictions in New York State. Atmosphere, doi:https://doi.org/10.14191/Atmos.2024.34.4.481
| Title | Impact of assimilation of New York State Mesonet doppler wind lidar on high impact weather predictions in New York State |
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| Genre | Article |
| Author(s) | Junkyung Kay, Tammy Weckwerth |
| Abstract | The New York State (NYS) Mesonet consists of 126 surface weather stations across the state with 17 of the sites also instrumented with active and passive profiler systems. The NYS Mesonet (NYSM) is the first and only state-run network in the USA, that includes a combination of surface stations, Doppler wind lidars (DWL) and thermodynamic profiles from Microwave Radiometers (MWR). NYSM's continuous and extensive observations from the surface to the lower atmosphere have a wide range of applications in air quality and human health, forecasting of severe storms, and predicting renewable energy production. This study provides results of assimilating the NYSM surface station data and the DWL wind profiles. The impact of NYSM observations on predictive skill is evaluated for one tornadic supercell case that has large uncertainties in analysis with respect to low-level temperature, moisture, and wind variability. Compared to forecasts assimilating solely conventional observations except NYSM, the additional assimilation of NYSM observations effectively corrects the cold and dry biases in central New York State, resulting in a more accurate representation of surface conditions. Notably, the assimilation of NYSM DWL wind profiles improves the prediction of the location and intensity of convective systems, thereby creating an environment that increases the likelihood of supercell and tornado formation. |
| Publication Title | Atmosphere |
| Publication Date | Dec 1, 2024 |
| Publisher's Version of Record | https://doi.org/10.14191/Atmos.2024.34.4.481 |
| OpenSky Citable URL | https://n2t.net/ark:/85065/d7f76hwq |
| OpenSky Listing | View on OpenSky |
| EOL Affiliations | RSF |