The weather radar–based object-oriented convective storm tracking is a standard approach for analyzing and nowcasting convective storms. However, the majority of current storm-tracking algorithms provide nowcasts only in a deterministic fashion with limited ability to estimate the related uncertainties.
This paper proposes a method for probabilistic nowcasting of convective storms that addresses the issue of uncertainty of nowcasts. The approach first utilizes a two-dimensional radar-based storm identification and tracking algorithm in conjunction with the Kalman filtering of noisy measurements of storm centroid with the continuous white noise acceleration model. The resulting smoothed estimates of storm centroid and velocity components and their error covariance values are then applied to nowcast the probability of storm occurrence.
To verify the approach, 20–60-min nowcasts were computed every 5 min using composite weather radar data in Finland including approximately 22 000 tracked storms. The verification shows that the algorithm is applicable in both deterministic and probabilistic manner. Moreover, the forecast probabilities are consistent with observed frequencies of the storms, especially with 20- and 30-min nowcasts. The accuracy of the probabilistic nowcasts was evaluated through the Brier skill score with respect to the deterministic nowcasts and nowcasts based on observation persistence and sample climatology. The results show that the proposed nowcasting method has an improved accuracy over all of the reference forecast types.
Convective storms cause several types of damage, including economic and ecological losses, every year. This paper focuses on an automatic hazard-level determination of convective storms based on a largely unused information source: real-time emergency report data. In addition to the location of the report, the emergency response centers classify cases into different emergency types and deliver a free-form verbal description of the incident for online use. This study uses archived weather-related emergency reports to determine hazard levels for convective storms detected by the weather radar. To develop an algorithm for estimating the hazard level of convective storms, a weather radar–databased convective storm-tracking algorithm was applied with a method that links reported emergency events to individually tracked convective storms. Based on the relationship between each convective storm track and an emergency report, the algorithm determines the hazard level of the storms automatically. Moreover, the developed algorithm takes into account the population density at the location of the report because, in densely populated areas, the flow of emergency reports is more intense. The proposed algorithm with case studies shows the potential use of real-time emergency call data in operational severe weather nowcasting and warning tools. This study demonstrates that supplementing storms with emergency information is advantageous, especially with long-lasting storms such as supercell storms or mesoscale convective systems.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.