Snowfall Rates from Satellite Data Help Weather Forecasters

Ferraro, Ralph; Meng, Huan; Zavodsky, Bradley; Kusselson, Sheldon J.; Kann, Deirdre M.; Guyer, Brian; Jacobs, Aaron; Perfater, Sarah; Folmer, Michael J.; Dong, Jun; Kongoli, Cezar; Yan, Banghua; Wang, Naiyu; Zhao, Limin

doi:10.1029/2018eo096715

Cited by 8 publications

(1 citation statement)

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“…Currently, ATMS Sensor Data Record (SDR) data have contributed significantly to Numerical Weather Prediction (NWP) Systems for generating short to medium range (1-10 days) weather forecasts (Kelly & Thépaut, 2017;Tong et al, 2017;Zhu et al, 2017;Zou et al, 2013). The data are also widely used by various satellite-based algorithms to retrieve environmental Earth atmospheric and surface (Boukabara et al, 2011(Boukabara et al, , 2013Ferraro et al, 2018;Meng et al, 2017;Tian & Zou, 2016;You et al, 2016;Zhu et al, 2002;Zhu & Weng, 2013).…”

Section: Introductionmentioning

confidence: 99%

Gap Filling of Advanced Technology Microwave Sounder Data as Applied to Hurricane Warm Core Animations

Yan

Liang

Porter

et al. 2020

Earth and Space Science

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As a microwave radiometer seeing through clouds, Advanced Technology Microwave Sounder (ATMS) observations play a critical role in visually monitoring hurricane warm core structures. However, the presence of orbit gaps within the ATMS observations at low latitudes regions, where hurricanes frequently develop, raises concerns in monitoring hurricanes' spatial variability. To resolve this issue, this study generates gap-filled ATMS brightness temperature data by using the smoothing algorithm of Penalized Least Square Discrete Cosine Transform. The accuracies of the missing-filled brightness temperatures for temperature sounding channels are approximately within 1 K. Furthermore, the gap-filled brightness temperature data from channels 5-12 are utilized to establish a three-dimensional Hurricane Warm Core Animation System (HWCAS) in near real time (NRT), which helps to visually observe realistic warm core structures of a hurricane system. The information of hurricane warm core over open oceans and coastal areas is derived using a combination of three new regression-based atmospheric temperature retrieval algorithms, with the averaged error typically within ±1 K at the vertical levels a warm core could occur. Each animation consists of 97 two-dimensional atmospheric temperature anomaly images at different cross-sections through hurricane core regions. The retrieved maximum temperature anomalies show well the formation, intensification, weakening, reintensification, and dissipation stages of Hurricane Florence that are similar to those from the European Centre for Medium-Range Weather Forecasts (ECMWF) analysis. They also show similar weakening and reintensifying stages to those of the maximum sustained winds by the best track data, albeit with some temporal lead/lag. With its strength in NRT hurricane monitoring, the HWCAS demonstrate its great potential in providing meteorologists with timely information of temperature anomaly fields in the inner-core regions of a hurricane.

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