WEATHERSMARTWEATHERSMART Scienfic meteorological and climatological news from the South African Weather Service FEBRUARYFEBRUARY 20202020 • Predicng malaria cases... • New annual climate updates • Assessment of global horizontal irradiance in SA • Analysis of a mid-winter cape storm • Anvil and turbulence • What to do when thunderstorms are all around you • Reposioning for the fourth industrial revoluon... WEATHERSMARTWEATHERSMART Scienfic meteorological and climatological news from the South African Weather Service Impact-Based Severe Weather Warning System WHAT IS IMPACT-BASED FORECASTING? Severe weather is a regular occurrence across South Africa which often negatively affects humans. Due to the vast distribution of vulnerabilities across the country, the same weather hazard can result in different impacts in two areas, depending on the specific vulnerability of the area. Impact-Based warnings combine the level of impact the hazardous weather conditions expected with the level of likelihood of those impacts taking place Moving from : To : What the weather will be: What the weather will do: (Meteorological thresholds) (Impact Warnings) - 50mm in 24 hours - Roads flooded - 35 knot winds - Communities cut off Date of issue: FEBRUARY 2020 February 2020 FEBRUARY 2020 Frequency: 6 Monthly Publisher: South African Weather Service ISSN: 2414-8644 Address: Editorial team: Eco Glades Block 1B, Eco Park, Hannelee Doubell (Compiler and editor) Corner Olievenhoutbosch and Musiiwa Denga (Assistant editor) Ribbon Grass Streets, Nokuthula Dhlamini (Assistant editor) Centurion, 0157 Table of Contents Page FOREWORD BY THE ACTING CHIEF EXECUTIVE OFFICER 2 PREDICTING MALARIA CASES USING REMOTELY SENSED 3 ENVIRONMENTAL VARIABLES IN NKOMAZI, SOUTH AFRICA – by Abiodun Morakinyo Adeola, Joel Ondego Botai, Christina M. Botai NEW ANNUAL CLIMATE UPDATES 8 – by Andries Kruger, Musa Mkhwanazi, Sandile Ngwenya & Sifiso Mbatha ASSESSMENT OF GLOBAL HORIZONTAL IRRADIANCE IN SOUTH AFRICA 14 – by Brighton Mabasa, Henerica Tazvinga, Nosipho Zwane, Joel Botai and Lucky Ntsangwane ANALYSIS OF A MID-WINTER CAPE STORM , 21 JUNE 2019 24 – by Kate Turner and Stella Nake ANVIL AND TURBULENCE 31 – by Tumi Phatudi, Oscar Shiviti and Xolile Jele WHAT TO DO WHEN THUNDERSTORMS ARE ALL AROUND YOU 33 – by Elani Heyneke REPOSITIONING FOR THE FOURTH INDUSTRIAL REVOLUTION (4IR) ERA 35 – by Alex Malapane MEET THE AUTHORS 38 WeatherSMART News February FOREWORD AND EDITORIAL COMMENT – by the Acng Chief Execuve Officer Welcome to the WeatherSMART news edion of February 2020. The arcle on new climate updates and addions reflects on As we have entered a new decade, weather and climate science what to expect from end March 2020 on the website of the keeps on evolving and research allows us to obtain new insights. South African Weather Service. Several addions to our informaon will be added to these updated publicaons. As part of the strategy of the South African Weather Service, relevant research is done to enhance our body of knowledge, This edion also reflects on the South African Weather Service which is used in our efforts to combat the impact of climate radiometric network consisng of 13 staons collects data change, hazardous and severe weather and environmental which enable quanficaon of the potenal contribuon of the threats. solar energy resource, improvement and validaon of satellites as well as the development and verificaon of empirical models. This edion covers the scienfic weather news relang to Quality control assessment of data was conducted and our data malaria research in Nkomasi, South Africa; new annual climate were classified as good data. updates; and an assessment of global horizontal irradiance in South Africa. This edion also contains an in-depth discussion of the 21 June 2019 mid-winter storm – which could have caught the public off The importance of weather and health cannot be overempha- guard due to other maers requiring their aenon. Anvil and sised, and the research on predicng malaria cases using turbulence, their complexies and possible dangers are remotely sensed variables is indeed ground breaking. We are discussed and an important awareness raising arcle addresses now able to get a much clearer picture on possible weather- what needs to be done when thunderstorms occur. This arcle is related health threats. especially important in light of the dangers of lightning associated with thunderstorms. As the fourth industrial revoluon era is upon us, the way forward for organisaons to adapt to this new era is shortly addressed. The South African Weather Service produces a wealth of useful informaon which can be used by weather and climate- sensive industries as well as the general public. Our informaon is only useful when it is acted upon, and the aim of this newsleer is to provide relevant informaon to all interested cizens, sciensts and weather enthusiasts. Dr Jonas Mphepya Acng Chief Execuve Officer WeatherSMART News February PREDICTING MALARIA CASES USING REMOTELY SENSED ENVIRONMENTAL VARIABLES IN NKOMAZI, SOUTH AFRICA (EXTRACT) – by Abiodun Morakinyo Adeola, Joel Ondego Botai, Chrisna M. Botai Extract from the full arcle which was published in Geospaal Journal, 2019. Abstract Naonal Department of Health, 2011) and have recently There has been a conspicuous increase in malaria cases since witnessed a surge in malaria morbidity and mortality (STATS SA, 2016/2017 over the three malaria-endemic provinces of South 2017). The disease is markedly seasonal, with varying intensity Africa. This increase has been linked to climac and of transmission due to environmental and climac factors such environmental factors. In the absence of adequate tradional as rainfall, temperature, elevaon, and humidity favouring the environmental/climac data covering ideal spaal and temporal development of the vector and parasite (Ngomane and de Jager, extent for a reliable warning system, remotely sensed data are 2012). The transmission is highest during the wet summer useful for the invesgaon of the relaonship with, and the months (September to May), and peak transmission occurs in predicon of, malaria cases. Monthly environmental variables January/February (Ngomane and de Jager, 2012). The such as the normalised difference vegetaon index (NDVI), the Plasmodium falciparum parasite accounts for about 95% of the enhanced vegetaon index (EVI), the normalised difference total malaria infecons in South Africa and the mosquito water index (NDWI), the land surface temperature for night Anopheles arabiensis is the major local vector (Govere et al., (LSTN) and day (LSTD), and rainfall were derived and evaluated 2007). using seasonal auto-regressive integrated moving average Rainfall has both a direct and indirect relaonship with its (SARIMA) models with different lag periods. Predicons were incidence by creang suitable breeding habitats for the vector. made for the last 56 months of the me series and were However, excessive rainfall can also have a negave impact on compared to the observed malaria cases from January 2013 to the mosquitoes by washing away the larvae (Teklehaimanot et August 2017. All these factors were found to be stascally al., 2004). Temperature impacts survival of both mosquito and significant in predicng malaria transmission at a 2-months lag parasite. Larval development takes about 47 days at 16°C, while period except for LSTD which impact the number of malaria parasite development ceases at temperatures of <15°C and the cases negavely. Rainfall showed the highest associaon at the organism dies at temperatures >40°C (Weiss et al., 2014). two-month lag me (r=0.74; P<0.001), followed by EVI (r=0.69; Vegetaon provides a resng habitat for adult Anopheles and P<0.001), NDVI (r=0.65; P<0.001), NDWI (r=0.63; P<0.001) and also serves as an indicator of the availability of moisture LSTN (r=0.60; P<0.001). SARIMA without environmental (Machault et al., 2011; Sarfraz et al., 2014). Elevaon is variables had an adjusted R² of 0.41, while SARIMA with total associated with the flight range of the vector as the proporon of moving mosquitoes declines exponenally with distance and monthly rainfall, EVI, NDVI, NDWI and LSTN were able to explain height from the breeding habitat (Thomas et al., 2013). about 65% of the variaon in malaria cases. The predicon indicated a general increase in malaria cases, predicng about Environmental as well as climac variables derived from Earth- 711 against 648 observed malaria cases. The development of a observing satellites, for instance: land surface temperature predicve early warning system is imperave for effecve (LST), the enhanced vegetaon index (EVI), the normalised malaria control, prevenon of outbreaks and its subsequent difference vegetaon index (NDVI), the normalised difference eliminaon in the region. water index (NDWI) and rainfall esmates have been used to idenfy mosquito breeding habitats (Julie et al., 2010), to Key words: Malaria; Environmental; Climac; Remote sensing; predict outbreaks (Hay et al., 1998; Adimi et al., 2010) and for SARIMA; Predicon. the development of malaria EWSs (Ceccato et al., 2005; Midekisa et al., 2012). These studies have led to a significant INTRODUCTION contribuon to malaria control in various regions; however a large percentage of malaria studies in South Africa are based on Malaria is a major public health problem in the north-eastern the use of convenonal climate data (Kleinschmidt et al., 2001; part of South Africa, where it affects about 5 million of the Craig et al., 2004; Silal et al., 2014) rather than RS despite its populaon. The endemic regions are in the provinces of great potenal. Hence, this study aims at determining the Mpumalanga, Limpopo, and KwaZulu-Natal (South Africa relaonships between malaria cases and remotely sensed WeatherSMART News February climac and environmental variables; and to develop a The area covers a total area of 3255, 67 km2, represenng 4.1% modeling framework for predicng malaria cases based on and 23% of the total land area of Mpumalanga Province and remotely sensed variables. Ehlanzeni District, respecvely (Figure 1). The municipality had a total populaon of 277 864 in 1996 that has increased to 410 MATERIALS AND METHODS 907 in 2016 (STATS SA, 2016).