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

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.

Detection of deterministic and probabilistic convection initiation using Himawari-8 Advanced Himawari Imager data

“…They validated the performance of the probabilistic algorithms for CI cases in the United States, resulting in a FAR of 10-18 %, which is much lower than the existing deterministic CI detection algorithms for GOES (FAR ∼ 48-60 %;Walker et al, 2012). However, Mecikalski et al (2015) used fewer satellite-based interest fields than the GOES-R CI algorithm due to the limited number of spectral channels of GOES, which implies that it is unknown whether such probabilistic approaches would work for Himawari-8 AHI as well. In this study, CI detection algorithms for Himawari-8 AHI are developed and validated over the Korean Peninsula in eastern Asia.…”

“…The spectral characteristics of Himawari-8 AHI are comparable to the Advanced Baseline Imager (ABI) integrated into the Geostationary Operational Environmental Satellite-R series (GOES-R) satellites (Schmit et al, 2005), which are a series of geostationary satellites, the first of which will be launched in November 2016 and operated by the National Oceanic and Atmospheric Administration (NOAA). The University of Alabama in Huntsville with NOAA has developed a CI detection algorithm for GOES-R using 12 interest fields designed for the spectral bands of ABI (Walker and Mecikalski, 2011;Walker et al, 2012;Mecikalski et al, 2015). The interest fields of the GOES-R CI algorithm can be directly adopted for Himawari-8 AHI.…”

“…Such deterministic approaches might provide incorrect classification results for unsampled pixels (or objects), especially around the boundaries of CI, increasing the FAR of predictions. Probabilistic approaches produce significantly lower FAR than the deterministic ones through the selection of an appropriate probability threshold (Mecikalski et al, 2015). Mecikalski et al (2015) developed CI nowcasting algorithms by combining the interest fields derived from GOES and numerical weather prediction (NWP) model data based on probabilistic approaches.…”

“…Probabilistic approaches produce significantly lower FAR than the deterministic ones through the selection of an appropriate probability threshold (Mecikalski et al, 2015). Mecikalski et al (2015) developed CI nowcasting algorithms by combining the interest fields derived from GOES and numerical weather prediction (NWP) model data based on probabilistic approaches. They validated the performance of the probabilistic algorithms for CI cases in the United States, resulting in a FAR of 10-18 %, which is much lower than the existing deterministic CI detection algorithms for GOES (FAR ∼ 48-60 %;Walker et al, 2012).…”

Impacts of assimilating all or GOES-like AHI infrared channels radiances on QPFs over Eastern China

“…In the past two decades, the United States geostationary operational environmental satellite program (GOES) observations were not only applied to nowcasting (Velden, 1996;Velden et al, 1997Velden et al, , 1998Mecikalski and Bedka, 2006;Mecikalski et al, 2015;Gravelle et al, 2016), but also extended to regional numerical weather prediction. For example, positive impacts of the GOES radiance assimilation on regional weather forecasts were demonstrated by Köpken et al (2004) and Okamoto (2013) for the Meteosat Visible and Infrared Imager (MVIRI); Szyndel et al (2005), Guedj et al (2011) and Stengel et al (2009) for the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on board Meteosat-8, Zou et al (2011Zou et al ( , 2015 and Qin et al (2013) for the GOES imagers on board the United States On 7 October 2014, the Japan Meteorological Agency (JMA) successfully launched a Geostationary Meteorological Satellite (GMS) -Himawari-8.…”