Editorial Type:
Article
Article Type:
Research Article

Probabilistic 0–1-h Convective Initiation Nowcasts that Combine Geostationary Satellite Observations and Numerical Weather Prediction Model Data

John R. Mecikalski Atmospheric Science Department, University of Alabama in Huntsville, Huntsville, Alabama

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John K. Williams National Center for Atmospheric Research,* Boulder, Colorado

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Christopher P. Jewett Atmospheric Science Department, University of Alabama in Huntsville, Huntsville, Alabama

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David Ahijevych National Center for Atmospheric Research, Boulder, Colorado

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Anita LeRoy Atmospheric Science Department, University of Alabama in Huntsville, Huntsville, Alabama

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John R. Walker Atmospheric Science Department, University of Alabama in Huntsville, Huntsville, Alabama

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Print Publication:
01 May 2015
DOI:
https://doi.org/10.1175/JAMC-D-14-0129.1
Page(s):
1039–1059
Restricted access

Abstract

The Geostationary Operational Environmental Satellite (GOES)-R convective initiation (CI) algorithm predicts CI in real time over the next 0–60 min. While GOES-R CI has been very successful in tracking nascent clouds and obtaining cloud-top growth and height characteristics relevant to CI in an object-tracking framework, its performance has been hindered by elevated false-alarm rates, and it has not optimally combined satellite observations with other valuable data sources. Presented here are two statistical learning approaches that incorporate numerical weather prediction (NWP) input within the established GOES-R CI framework to produce probabilistic forecasts: logistic regression (LR) and an artificial-intelligence approach known as random forest (RF). Both of these techniques are used to build models that are based on an extensive database of CI events and nonevents and are evaluated via cross validation and on independent case studies. With the proper choice of probability thresholds, both the LR and RF techniques incorporating NWP data produce substantially fewer false alarms than when only GOES data are used. The NWP information identifies environmental conditions (as favorable or unfavorable) for the development of convective storms and improves the skill of the CI nowcasts that operate on GOES-based cloud objects, as compared with when the satellite IR fields are used alone. The LR procedure performs slightly better overall when 14 skill measures are used to quantify the results and notably better on independent case study days.

The National Center for Atmospheric Research is sponsored by the National Science Foundation.

Corresponding author address: John R. Mecikalski, Atmospheric Science Department, University of Alabama in Huntsville, National Space Science and Technology Center, 320 Sparkman Drive, Huntsville, AL 35805-1912. E-mail: johnm@nsstc.uah.edu

Abstract

The Geostationary Operational Environmental Satellite (GOES)-R convective initiation (CI) algorithm predicts CI in real time over the next 0–60 min. While GOES-R CI has been very successful in tracking nascent clouds and obtaining cloud-top growth and height characteristics relevant to CI in an object-tracking framework, its performance has been hindered by elevated false-alarm rates, and it has not optimally combined satellite observations with other valuable data sources. Presented here are two statistical learning approaches that incorporate numerical weather prediction (NWP) input within the established GOES-R CI framework to produce probabilistic forecasts: logistic regression (LR) and an artificial-intelligence approach known as random forest (RF). Both of these techniques are used to build models that are based on an extensive database of CI events and nonevents and are evaluated via cross validation and on independent case studies. With the proper choice of probability thresholds, both the LR and RF techniques incorporating NWP data produce substantially fewer false alarms than when only GOES data are used. The NWP information identifies environmental conditions (as favorable or unfavorable) for the development of convective storms and improves the skill of the CI nowcasts that operate on GOES-based cloud objects, as compared with when the satellite IR fields are used alone. The LR procedure performs slightly better overall when 14 skill measures are used to quantify the results and notably better on independent case study days.

The National Center for Atmospheric Research is sponsored by the National Science Foundation.

Corresponding author address: John R. Mecikalski, Atmospheric Science Department, University of Alabama in Huntsville, National Space Science and Technology Center, 320 Sparkman Drive, Huntsville, AL 35805-1912. E-mail: johnm@nsstc.uah.edu
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Journal of Applied Meteorology and Climatology

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