A statistical scheme to forecast the daily lightning threat over southern Africa using the Unified Model

Cloud-to-ground lightning data from the Southern Africa Lightning Detection Network and numerical weather prediction model parameters from the Unified Model are used to develop a lightning threat index (LTI) for South Africa. The aim is to predict lightning for austral summer days (September to February) by means of a statistical approach. The austral summer months are divided into spring and summer seasons and analysed separately. Stepwise logistic regression techniques are used to select the most appropriate model parameters to predict lightning. These parameters are then utilized in a rare-event logistic regression analysis to produce equations for the LTI that predicts the probability of the occurrence of lightning. Results show that LTI forecasts have a high sensitivity and specificity for spring and summer. The LTI is less reliable during spring, since it over-forecasts the occurrence of lightning. However, during summer, the LTI forecast is reliable, only slightly over-forecasting lightning activity. The LTI produces sharp forecasts during spring and summer. These results show that the LTI will be useful early in the morning in areas where lightning can be expected during the day.http://www.elsevier.com/locate/atmos2018-09-15Geography, Geoinformatics and Meteorolog

Evaluating South African Weather Service information on Idai tropical cyclone and KwaZulu-Natal flood events

Severe weather events associated with strong winds and flooding can cause fatalities, injuries and damage to property. Detailed and accurate weather forecasts that are issued and communicated timeously, and actioned upon, can reduce the impact of these events. The responsibility to provide such forecasts usually lies with government departments or state-owned entities; in South Africa that responsibility lies with the South African Weather Service (SAWS). SAWS is also a regional specialised meteorological centre and therefore provides weather information to meteorological services within the Southern African Development Community (SADC). We evaluated SAWS weather information using near real-time observations and models on the nowcasting to short-range forecasting timescales during two extreme events. These are the Idai tropical cyclone in March 2019 which impacted Mozambique, Zimbabwe and Malawi resulting in over 1000 deaths, and the floods over the KwaZulu-Natal (KZN) province in April 2019 that caused over 70 deaths. Our results show that weather models gave an indication of these systems in advance, with warnings issued at least 2 days in advance in the case of Idai and 1 day in advance for the KZN floods. Nowcasting systems were also in place for detailed warnings to be provided as events progressed. Shortcomings in model simulations were shown, in particular on locating the KZN flood event properly and over/-underestimation of the event. The impacts experienced during the two events indicate that more needs to be done to increase weather awareness, and build disaster risk management systems, including disaster preparedness and risk reduction.Significance: This paper is relevant for all South Africans and the SADC region at large because it provides information on: the weather forecasting processes followed at the South African Weather Service, available early warning products in South Africa and for the SADC region made possible through the public purse, the performance of nowcasting and modelling systems in the case of predicting two extreme weather events that had adverse impacts on southern African society, and the dissemination of warnings of future extreme weather events

New WMO certified megaflash lightning extremes for flash distance (768 km) and duration (17.01 seconds) recorded from space

Initial global extremes in lightning duration and horizontal distance were established in 2017 (Lang et al. 2017) by an international panel of atmospheric lightning scientists and engineers assembled by the WMO. The subsequent launch of NOAA’s latest GOES-16/17 satellites with their Geostationary Lightning Mappers (GLMs) enabled extreme lightning to be monitored continuously over the western hemisphere up to 55° latitude for the first time. As a result, the former lightning extremes were more than doubled in 2019 to 709 km for distance and 16.730 s for duration (Peterson et al. 2020). Continued detection and analysis of lightning “megaflashes” (Sequin, 2021) has now revealed two flashes that even exceed those 2019 records. As part of the ongoing work of the WMO in detection and documentation of global weather extremes (e.g., El Fadli et al. 2013; Merlone et al. 2010), an international WMO evaluation committee was created to critically adjudicate these two GLM megaflash cases as new records for extreme lightning.We thank S. A. Rutledge and two other reviewers for their valuable comments. M. J. Peterson was supported by the U.S. Department of Energy through the Los Alamos National Laboratory (LANL) Laboratory Directed Research and Development (LDRD) program under project number 20200529ECR. Los Alamos National Laboratory is operated by Triad National Security, LLC, for the National Nuclear Security Administration of U.S. Department of Energy (Contract 89233218CNA000001). T. Logan supported by a NOAA Grant NA16OAR4320115 “Lightning Mapper Array Operation in Oklahoma and the Texas Gulf Coast Region to Aid Preparation for the GOES-R GLM.” I. Kolmasova was supported by GACR Grant 20-09671. S. D. Zhang was supported by a NOAA Grant NNH19ZDA001N-ESROGSS. The participation of J. Montanya in this work is supported by research Grant ESP2017-86263-C4-2-R funded by MCIN/AEI/10.13039/501100011033 and by “ERDF A way of making Europe,” by the “European Union”; and Grants PID2019-109269RB-C42 funded by MCIN/AEI/10.13039/501100011033.Peer ReviewedPostprint (author's final draft

Evaluating South African weather service information on Idai tropical cyclone and KwaZulu- Natal flood events

Severe weather events associated with strong winds and flooding can cause fatalities, injuries and damage to property. Detailed and accurate weather forecasts that are issued and communicated timeously, and actioned upon, can reduce the impact of these events. The responsibility to provide such forecasts usually lies with government departments or state-owned entities; in South Africa that responsibility lies with the South African Weather Service (SAWS). SAWS is also a regional specialised meteorological centre and therefore provides weather information to meteorological services within the Southern African Development Community (SADC). We evaluated SAWS weather information using near real-time observations and models on the nowcasting to short-range forecasting timescales during two extreme events. These are the Idai tropical cyclone in March 2019 which impacted Mozambique, Zimbabwe and Malawi resulting in over 1000 deaths, and the floods over the KwaZulu-Natal (KZN) province in April 2019 that caused over 70 deaths. Our results show that weather models gave an indication of these systems in advance, with warnings issued at least 2 days in advance in the case of Idai and 1 day in advance for the KZN floods. Nowcasting systems were also in place for detailed warnings to be provided as events progressed. Shortcomings in model simulations were shown, in particular on locating the KZN flood event properly and over/underestimation of the event. The impacts experienced during the two events indicate that more needs to be done to increase weather awareness, and build disaster risk management systems, including disaster preparedness and risk reduction. Significance: This paper is relevant for all South Africans and the SADC region at large because it provides information on: • the weather forecasting processes followed at the South African Weather Service, • available early warning products in South Africa and for the SADC region made possible through the public purse, • the performance of nowcasting and modelling systems in the case of predicting two extreme weather events that had adverse impacts on southern African society, and • the dissemination of warnings of future extreme weather events.The Climate Research for Development (CR4D) Postdoctoral Fellowship CR4D-19-11 implemented by the African Academy of Sciences (AAS) in partnership with the United Kingdom’s Department for International Development (DfID) Weather and Climate Information Services for Africa (WISER) programme and the African Climate Policy Center (ACPC) of the United Nations Economic Commission for Africa (UNECA).http://www.sajs.co.zaam2022Geography, Geoinformatics and Meteorolog

Nowcasting for Africa: advances, potential and value

The high frequency of intense convective storms means there is a great demand to improve predictions of high-impact weather across Africa. The low skill of numerical weather prediction over Africa, even for short lead times highlights the need to deliver nowcasting based on satellite data. The Global Challenges Research Fund African SWIFT (Science for Weather Information and Forecasting Techniques) project is working to improve the nowcasting of African convective systems and so the ability to provide timely warnings

A lightning threat index for South Africa using numerical weather prediction data

Lightning is a phenomenon that can cause death or injury to humans and animals, damage to infrastructures, and can be a hazard to various sectors like the aviation and forestry industries. There is a need for prediction techniques to ensure the protection of people and property. In this dissertation, a new lightning threat index (LTI) is proposed for southern Africa. The aim of the LTI is to identify the areas where lightning is likely to occur during the day. Before the LTI could be developed, it was necessary to identify candidate model predictors capable of predicting the occurrence of lightning. In total 25 predictors were selected from literature that showed promising results to forecast the occurrence of lightning. The selected predictors are different variations from the following six groups of parameters; convective available potential energy, lifted index, precipitable water, equivalent potential temperature, relative humidity and air temperature. This study identifies the parameter from each of the six groups capable of predicting the occurrence of lightning over southern Africa the best during spring and summer by means of stepwise logistic regression techniques. The six parameters identified in this study for spring are; the most unstable convective available potential energy in the 1 - 6 km above ground level range, surface lifted index, mean precipitable water in the 850 to 300 hPa layer, minimum relative humidity in the 3-6 km above ground level layer, equivalent potential temperature lapse rate between 700 and 500 hPa and mean temperature in the 850 700 hPa layer. During summer, the same parameters were identified, except that the average relative humidity in the 3-6 km above ground level layer and equivalent potential temperature lapse rate between 850 and 400 hPa were identified. After the most appropriate parameters, capable of predicting the occurrence of lightning, were identified, the development of the new LTI could commence. Since the goal was to develop a single index that utilises the different model predictors to forecast the binary outcome of lightning occurrence (yes or no), attention was given to binary logistic regression techniques. In this study a rare-event binary logistic regression technique is used to develop equations for the LTI that utilise NWP model output early in the morning to provide a probability forecast of where lightning is expected to occur during the day between 07:00 and 21:00 UTC. The new LTI is evaluated over an entire independent spring and summer season. Results show that the LTI forecasts have a high sensitivity and specificity for both the spring and summer seasons. The LTI is not so reliable during the spring season, since it over-forecasts the occurrence of lightning, but during the summer season, the LTI forecast is reliable, only slightly over-forecasting the lightning activity. The LTI produces sharp forecasts during both the spring and summer seasons. The LTI will be a useful tool to operational weather forecasters or sectors interested in lightning forecasts, to provide guidance early in the morning on the areas of interest where lightning can be expected during the day, and can ultimately contribute to society by aiding with timely warnings of lightning or thunderstorms to protect humans, animals and property.Dissertation (MSc)--University of Pretoria, 2016.tm2016Geography, Geoinformatics and MeteorologyMScUnrestricte

Using satellite data to identify and track intense thunderstorms in South and southern Africa

To issue warnings of thunderstorms, which have the potential for severe weather elements such as heavy rainfall and hail, is a task of all weather services. In data sparse regions, where there is no or limited access to expensive observation systems, satellite data can provide very useful information for this purpose. The Nowcasting Satellite Application Facility in Europe developed software to identify and track rapidly developing thunderstorms (RDT) using data from the geostationary Meteosat Second Generation satellite. The software was installed in South Africa and tested over the South African as well as the southern African domain. The RDT product was validated by means of 20 case studies. Over the South African region, validation was done by means of visual comparison to radar images as well as in a quantitative manner against the occurrence of lightning. Visual comparisons between the RDT product and images from satellite data as well as the occurrence of heavier rainfall were done over areas outside South Africa. Good correlations were found between the identified storms and the occurrence of lightning over South Africa. Visual comparisons indicated that the RDT software can be useful over the southern African domain, where lightning and radar data are not available. Very encouraging results were obtained in the 20 case studies. The RDT software can be a valuable tool for general and aviation forecasters to warn the public of pending severe weather, especially in areas where other data sources are absent or not adequate

Using satellite data to identify and track intense thunderstorms in South and southern Africa

To issue warnings of thunderstorms, which have the potential for severe weather elements such as heavy rainfall and hail, is a task of all weather services. In data sparse regions, where there is no or limited access to expensive observation systems, satellite data can provide very useful information for this purpose. The Nowcasting Satellite Application Facility in Europe developed software to identify and track rapidly developing thunderstorms (RDT) using data from the geostationary Meteosat Second Generation satellite. The software was installed in South Africa and tested over the South African as well as the southern African domain. The RDT product was validated by means of 20 case studies. Over the South African region, validation was done by means of visual comparison to radar images as well as in a quantitative manner against the occurrence of lightning. Visual comparisons between the RDT product and images from satellite data as well as the occurrence of heavier rainfall were done over areas outside South Africa. Good correlations were found between the identified storms and the occurrence of lightning over South Africa. Visual comparisons indicated that the RDT software can be useful over the southern African domain, where lightning and radar data are not available. Very encouraging results were obtained in the 20 case studies. The RDT software can be a valuable tool for general and aviation forecasters to warn the public of pending severe weather, especially in areas where other data sources are absent or not adequate

Synoptic structure of a sub-daily extreme precipitation and flood event in Thohoyandou, north-eastern South Africa

An extreme sub-daily precipitation event produced about 300 mm of rainfall in less than 4 h overnight from 13–14 February 2019 resulting in high floods in Thohoyandou, a small town northeast of South Africa. We employed station, radar, satellite and reanalysis datasets to investigate the rainfall, circulation and thermodynamic fields and understand the meteorological structure of the extreme event via a multiscale analysis. The large-scale synoptic environment was characterized by a mid-tropospheric tropical-temperate trough and attendant cloud band coupled to a surface high ridging over the southeast coast of the country. We found that whilst heavy rainfall (>50 mm/24 h) was widespread ahead of the upper trough, extreme amounts (∼100 mm/h) were localized due to a cloudburst. A small perturbation to the favorable large scale mid-tropospheric environment also contributed to localized heavy rainfall. The south-north pressure gradient was steepened by a surface low over southern Mozambique resulting in enhanced moisture fluxes deriving from the southwest Indian Ocean. The interaction of prevailing surface winds and a low-level jet with the steep topography of the adjacent Soutpansberg Mountain Range enhanced low-level convergence and lifting in the area. We also show that the highest rainfalls were uphill of the location of flooding which was contained in a poorly drained valley. Whereas the Unified Model forecasts appeared accurate for the large-scale pattern of heavy rainfall in the area, the rainfall peak was generally underestimated, whilst the timing of extreme rainfall was delayed in the 18Z simulation, which is used by forecasters operationally. Our findings contribute to understanding the occurrence of extreme weather events over northeastern South Africa and also how models treat them, towards natural disaster risk reduction

Corrigendum: Evaluating South African Weather Service information on Idai tropical cyclone and KwaZulu-Natal flood events

Original article: https://doi.org/10.17159/sajs.2021/7911 The authors’ employment at the South African Weather Service (SAWS) was inadvertently omitted from the Competing Interests statement in the original article. All but one of the authors are employees of SAWS and were involved in the forecasting process or were in the section responsible for observations during the forecasting and observations of the weather events reported on