Abstract

Abstract. The ability to detect convective regions and to add latent heating to drive convection is one of the most important additions to short-term forecast models such as National Oceanic and Atmospheric Administration's (NOAA's) High-Resolution Rapid Refresh (HRRR) model. Since radars are most directly related to precipitation and are available in high temporal resolution, their data are often used for both detecting convection and estimating latent heating. However, radar data are limited to land areas, largely in developed nations, and early convection is not detectable from radars until drops become large enough to produce significant echoes. Visible and infrared sensors on a geostationary satellite can provide data that are more sensitive to small droplets, but they also have shortcomings: their information is almost exclusively from the cloud top. Relatively new geostationary satellites, Geostationary Operational Environmental Satellite-16 and Satellite-17 (GOES-16 and GOES-17), along with Himawari-8, can make up for this lack of vertical information through the use of very high spatial and temporal resolutions, allowing better observation of bubbling features on convective cloud tops. This study develops two algorithms to detect convection at vertically growing clouds and mature convective clouds using 1 min GOES-16 Advanced Baseline Imager (ABI) data. Two case studies are used to explain the two methods, followed by results applied to 1 month of data over the contiguous United States. Vertically growing clouds in early stages are detected using decreases in brightness temperatures over 10 min. For mature convective clouds which no longer show much of a decrease in brightness temperature, the lumpy texture from rapid development can be observed using 1 min high spatial resolution reflectance data. The detection skills of the two methods are validated against Multi-Radar/Multi-Sensor System (MRMS), a ground-based radar product. With the contingency table, results applying both methods to 1-month data show a relatively low false alarm rate of 14.4 % but missed 54.7 % of convective clouds detected by the radar product. These convective clouds were missed largely due to less lumpy texture, which is mostly caused by optically thick cloud shields above.

Highlights

  • While weather forecast models have improved tremendously throughout the decades (Bauer et al, 2015), local-scale phenomena such as convection remain challenging (Yano et al, 2018)

  • In some forecast models such as the High-Resolution Rapid Refresh (HRRR) model in the United States, latent heating is added, along with precipitation affected radiances, to adjust model dynamics to correspond to the observed convection (Benjamin et al, 2016)

  • This study explores whether high-temporalresolution data from recent operational geostationary satellite measurements from the Geostationary Operational Environmental Satellites (GOES) R Series can provide similar information to radar for the location of convection so that it can be used for initializing forecast models over regions without ground-based radar

Read more

Summary

Introduction

While weather forecast models have improved tremendously throughout the decades (Bauer et al, 2015), local-scale phenomena such as convection remain challenging (Yano et al, 2018). Precipitation is especially hard to predict as numerical models struggle with initiating convection in the right location and at the right intensity. To address this issue in short-term predictions, many models assimilate all-sky radiances and precipitation-related products where available (Benjamin et al, 2016; Bonavita et al, 2017; Geer et al, 2017; Gustafsson et al, 2018; Jones et al, 2016; Migliorini et al, 2018; Scheck et al, 2020). In order to correctly detect convective regions and add heat-

Methods
Results
Conclusion

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.