Established in 2024 by the Atmospheric Radiation Measurement (ARM) user facility, the Bankhead National Forest (BNF) atmospheric observatory is designed to advance the understanding of land-atmospheric interactions within northwest Alabama. One of BNF's primary science motivations is to characterize the influence of the local environment on the convective cloud lifecycle, which includes how mesoscale temperature, moisture, and precipitation perturbations control subsequent onset and shallow to deeper transitions of convective clouds. Consequently, the BNF site includes complementary precipitation observations from a scanning C-Band dual-polarimetric radar (CSAPR-2) to provide observations at a high spatiotemporal resolution towards spatial precipitation estimates across this region. Empirical rainfall relationships based on the equivalent radar reflectivity factor, specific differential phase, and specific attenuation are applied to the quality controlled CSAPR-2 observations to estimate precipitation. Using the Python-ARM Radar Toolkit (Py-ART), radar columns have been extracted above various in-situ BNF and partner anchor locations within the region to directly compare derived precipitation observations with rain gauges and laser disdrometers (RadCLss). While the RadCLss product has been released at BNF for the CSAPR-2 and X-band Scanning Cloud Radar (X-SACR) radars, a robust evaluation of quantitative precipitation estimates (QPE) over the region is needed. Utilizing supervised machine learning (i.e, XG Boost), a blended best estimate QPE will be determined for the region utilizing the various empirical relationships within RadClss.
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Job Type
Full-time
Career Level
Intern
Education Level
No Education Listed