An Empirical Model for Assessing the Severe Weather Potential of Developing Convection

Abstract

The formation and maintenance of thunderstorms that produce large hail, strong winds, and tornadoes are often difficult to forecast due to their rapid evolution and complex interactions with environmental features that are challenging to observe. Given inherent uncertainties in storm development, it is intuitive to predict severe storms in a probabilistic manner. This paper presents such an approach to forecasting severe thunderstorms and their associated hazards, fusing together data from several sources as input into a statistical model. Mesoscale numerical weather prediction (NWP) models have been developed in part to forecast environments favorable to severe storm development. Geostationary satellites, such as the Geostationary Operational Environmental Satellite (GOES) series, maintain a frequently updating view of growing cumulus clouds over the contiguous United States to provide temporal trends in developing convection to forecasters. The Next Generation Weather Radar (NEXRAD) network delivers repeated scans of hydrometeors inside storms, monitoring the intensification of hydrometeor size and extent, as well as hydrometeor motion. Forecasters utilize NWP models, and GOES and NEXRAD data, at different stages of the forecast of severe storms, and the model described in this paper exploits data from each in an attempt to predict severe hazards in a more accurate and timely manner while providing uncertainty information to the forecaster. A preliminary evaluation of the model demonstrates good skill in the forecast of storms, and also displays the potential to increase lead time on severe hazards, as measured relative to the issuance times of National Weather Service (NWS) severe thunderstorm and tornado warnings and occurrence times of severe events in local storm reports.

Rapid Refresh Information of Significant Events: Preparing Users for the Next Generation of Geostationary Operational Satellites

“…In fact, researchers have shown how the GOES rapid scan information could be used to increase lead time before severe weather (Bedka et al 2015). Of course the most complete information results from combining information provided by geostationary, polar orbiting, and other sources such as from radars (Cintineo et al 2014). One challenge for the GOES-R era will be to effectively use the rapid scan, multispectral imagery and derived products, along with other measurements, to best monitor the earth-atmosphere system in order to enhance the timeliness of forecasts and warnings for a wide range of environmental phenomena that impact human activities.…”

“…GOES-14 SRSOR data provided a compelling look at the convective development at 1-min intervals. The cloud-top cooling (CTC) product developed for GOES/ GOES-R at the University of Wisconsin (UW) provides an estimate of the cooling rate of the cloud tops to give better situational awareness (Dworak et al 2012;Sieglaff et al 2013Sieglaff et al , 2014Cintineo et al 2013). Figure 6 shows the GOES-14 visible band and CTC product on 21 August 2013.…”

“…Additionally, the ability to scan in intervals as small as 1 min may be necessary to more accurately diagnose physical processes within deep convection. Cintineo et al (2013) demonstrated using GOES-14 SRSOR data that infrared cloud-top brightness temperatures can cool substantially within minutes and even 5-min scanning intervals can underrepresent the actual vertical growth and associated cloud-top cooling.…”

Spectrally Enhanced Cloud Objects—A generalized framework for automated detection of volcanic ash and dust clouds using passive satellite measurements: 1. Multispectral analysis

“…In an effort to sufficiently capture the complicated multispectral relationships over a wide range of conditions and reduce the many pieces of spectral information into a single objective metric, a Bayesian approach is utilized. Bayesian approaches have been successfully applied to several satellite‐based classification problems [ Uddstrom et al ., ; Merchant et al ., ; Heidinger et al ., ; Kossin and Sitkowski , ; Cintineo et al ., ; Mackie and Watson , ]. As discussed by Kossin and Sitkowski [] and Heidinger et al .…”

Environmental Analysis of GOES-R Proving Ground Convection-Initiation Forecasting Algorithms

“…The sample covariance matrix and sample mean matrix are derived from the data collected, and new data stored in x o are used from different case studies. The assignment of these probabilities is acknowledged to be heuristic; because these products are evolving to probability-based systems (Cintineo et al 2014), however, the assignment is meant to provide future users with examples on how to properly use the QDA database. Costs can be adjusted by future users to weight the discriminant function toward assigning a specific group if necessary.…”