Subseasonal deterministic prediction skill of low-level geopotential height affecting southern Africa

The NCEP CFSv2 and ECMWF hindcasts are used to explore the deterministic subseasonal predictability of the 850-hPa circulation of a large domain over the Atlantic and Indian Oceans that is relevant to the weather and climate of the southern African region. For NCEP CFSv2, 12 years of hindcasts, starting on 1 January 1999 and initialized daily for four ensemble members up to 31 December 2010 are verified against ERA-Interim reanalysis data. For ECMWF, 20 years of hindcasts (1995–2014), initialized once a month for all the months of the year are employed in a parallel analysis to investigate the predictability of the 850-hPa circulation. The ensemble mean for 7-day moving averages is used to assess the prediction skill for all the start dates in each month of the year, with a focus on the start dates in each month that are representative of the week-3 and week-4 hindcasts. The correlation between the anomaly patterns over the study domain shows skill over persistence up into the week-3 hindcasts for some months. The spatial distribution of the correlation between the anomaly patterns show skill over persistence to notably reduce over the domain by week 3. A prominent area where prediction skill survives the longest, occur over central South America and the adjacent Atlantic Ocean.NRFhttps://journals.ametsoc.org/view/journals/wefo/wefo-overview.xmldm2022Geography, Geoinformatics and Meteorolog

A snow forecasting decision tree for significant snowfall over the interior of South Africa

Snowfall occurs every winter over the mountains of South Africa but is rare over the highly populated metropolises over the interior of South Africa. When snowfall does occur over highly populated areas, it causes widespread disruption to infrastructure and even loss of life. Because of the rarity of snow over the interior of South Africa, inexperienced weather forecasters often miss these events. We propose a five-step snow forecasting decision tree in which all five criteria must be met to forecast snowfall. The decision tree comprises physical attributes that are necessary for snowfall to occur. The first step recognises the synoptic circulation patterns associated with snow and the second step detects whether precipitation is likely in an area. The remaining steps all deal with identifying the presence of a snowflake in a cloud and determining that the snowflake will not melt on the way to the ground. The decision tree is especially useful to forecast the very rare snow events that develop from relatively dry and warmer surface conditions. We propose operational implementation of the decision tree in the weather forecasting offices of South Africa, as it is foreseen that this approach could significantly contribute to accurately forecasting snow over the interior of South Africa. SIGNIFICANCE : • A method for forecasting disruptive snowfall is provided. It is envisaged that this method will contribute to the improved forecasting of these severe weather events over South Africa. • Weather systems responsible for snowfall are documented and the cloud microphysical aspects important for the growth and melting of a snowflake are discussed. • Forecasting methods are proposed for the very rare events when snow occurs over the interior of South Africa when the air is relatively dry and somewhat warmer.This paper emanates from the work that J.H.S. conducted to obtain his MSc at the University of Pretoria under the supervision of L.D.South African Weather Service; University of Pretoriahttp://www.sajs.co.za/am2016Geography, Geoinformatics and Meteorolog

A snow forecasting decision tree for significant snowfall over the interior of South Africa

Snowfall occurs every winter over the mountains of South Africa but is rare over the highly populated metropolises over the interior of South Africa. When snowfall does occur over highly populated areas, it causes widespread disruption to infrastructure and even loss of life. Because of the rarity of snow over the interior of South Africa, inexperienced weather forecasters often miss these events. We propose a five-step snow forecasting decision tree in which all five criteria must be met to forecast snowfall. The decision tree comprises physical attributes that are necessary for snowfall to occur. The first step recognises the synoptic circulation patterns associated with snow and the second step detects whether precipitation is likely in an area. The remaining steps all deal with identifying the presence of a snowflake in a cloud and determining that the snowflake will not melt on the way to the ground. The decision tree is especially useful to forecast the very rare snow events that develop from relatively dry and warmer surface conditions. We propose operational implementation of the decision tree in the weather forecasting offices of South Africa, as it is foreseen that this approach could significantly contribute to accurately forecasting snow over the interior of South Africa. SIGNIFICANCE : • A method for forecasting disruptive snowfall is provided. It is envisaged that this method will contribute to the improved forecasting of these severe weather events over South Africa. • Weather systems responsible for snowfall are documented and the cloud microphysical aspects important for the growth and melting of a snowflake are discussed. • Forecasting methods are proposed for the very rare events when snow occurs over the interior of South Africa when the air is relatively dry and somewhat warmer.This paper emanates from the work that J.H.S. conducted to obtain his MSc at the University of Pretoria under the supervision of L.D.South African Weather Service; University of Pretoriahttp://www.sajs.co.za/am2016Geography, Geoinformatics and Meteorolog

Using SPI and SPEI for baseline probabilities and seasonal drought prediction in two agricultural regions of the Western Cape, South Africa

Drought is one of the most hazardous natural disasters in terms of the number of people directly affected. An important characteristic of drought is the prolonged absence of rainfall relative to the long-term average. The intrinsic persistence of drought conditions continuing from one month to the next can be utilized for drought monitoring and early warning systems. This study sought to better understand drought probabilities and baselines for two agriculturally important rainfall regions in the Western Cape, South Africa – one with a distinct rainfall season and one which receives year-round rainfall. The drought indices, Standardised Precipitation and Evapotranspiration Index (SPEI) and Standardised Precipitation Index (SPI), were assessed to obtain predictive information and establish a set of baseline probabilities for drought. Two sets of synthetic time-series data were used (one where seasonality was retained and one where seasonality was removed), along with observed data of monthly rainfall and minimum and maximum temperature. Based on the inherent persistence characteristics, autocorrelation was used to obtain a probability density function of the future state of the various SPI start and lead times. Optimal persistence was also established. The validity of the methodology was then examined by application to the recent Cape Town drought (2015–2018). Results showed potential for this methodology to be applied in drought early warning systems and decision support tools for the province.https://www.watersa.netGeography, Geoinformatics and Meteorolog

Literature survey of subseasonal‐to‐seasonal predictions in the southern hemisphere

Abstract Subseasonal‐to‐seasonal (S2S) prediction has gained momentum in the recent past as a need for predictions between the weather forecasting timescale and seasonal timescale exists. The availability of S2S databases makes prediction and predictability studies possible over all the regions of the globe. Most S2S studies are, however, relevant to the northern hemisphere. In this review, the S2S literature relevant to the southern hemisphere (SH) are presented. Predictive skill, sources of predictability, and the application of S2S predictions are discussed. Indications from the subseasonal predictability studies for the SH regions suggest that predictive skill is limited to 2 weeks in general, particularly for temperature and rainfall, which are the variables most frequently investigated. However, temperature has enhanced skill compared to rainfall. More S2S prediction studies that include the quantification of the sources of predictability and the identification of windows of opportunity need to be conducted for the SH, particularly for the southern African region. The African continent is vulnerable to weather‐ and climate‐related disasters, and S2S forecasts can assist in alleviating the risk of such disasters

Probabilistic skill of statistically downscaled ECMWF S2S forecasts of maximum and minimum temperatures for weeks 1–4 over South Africa

Abstract The probabilistic forecast skill level of statistically downscaled European Centre for Medium‐Range Weather Forecasts (ECMWF) subseasonal‐to‐seasonal (S2S) forecasts is determined in predicting maximum and minimum temperatures for weeks 1–4 lead times during 20‐year December–January–February (DJF) seasons from 2001 to 2020 over South Africa. Skilful S2S forecasts are vital in assisting decision‐makers in the development of contingency planning for any eventualities that may arise because of weather and climate phenomena. Extreme high‐ and low‐temperature events over a prolonged period can lead to hyperthermia and hypothermia, respectively, and can lead to loss of life. The results from the relative operating characteristic (ROC) and reliability diagrams indicate that the ECMWF S2S model has skill in predicting maximum temperature up to week 3 ahead, particularly over the central and eastern parts of South Africa. The ROC scores indicate that the model has skill in predicting minimum temperature up to week 4 ahead for the above‐normal category, particularly over the central and eastern parts of South Africa. Reliability diagrams indicate that the model has a tendency of overestimating the below‐normal category when predicting both maximum and minimum temperatures for weeks 1–4 lead times over South Africa. Furthermore, canonical correlation analysis (CCA) pattern analysis suggests that when there are anomalously positive and negative predicted 850‐hPa geopotential heights located over South Africa, there are anomalously hot and cold conditions during the DJF seasons over most parts of South Africa, respectively. These results suggests that statistical downscaling of model forecasts can improve forecast skill. Moreover, the results suggest that there is potential for S2S predictions in South Africa, and as such, S2S prediction system for maximum and minimum temperatures can be developed

A snow forecasting decision tree for significant snowfall over the interior of South Africa

This paper emanates from the work that J.H.S. conducted to obtain his MSc at the University of Pretoria under the supervision of L.D.Snowfall occurs every winter over the mountains of South Africa but is rare over the highly populated metropolises over the interior of South Africa. When snowfall does occur over highly populated areas, it causes widespread disruption to infrastructure and even loss of life. Because of the rarity of snow over the interior of South Africa, inexperienced weather forecasters often miss these events. We propose a five-step snow forecasting decision tree in which all five criteria must be met to forecast snowfall. The decision tree comprises physical attributes that are necessary for snowfall to occur. The first step recognises the synoptic circulation patterns associated with snow and the second step detects whether precipitation is likely in an area. The remaining steps all deal with identifying the presence of a snowflake in a cloud and determining that the snowflake will not melt on the way to the ground. The decision tree is especially useful to forecast the very rare snow events that develop from relatively dry and warmer surface conditions. We propose operational implementation of the decision tree in the weather forecasting offices of South Africa, as it is foreseen that this approach could significantly contribute to accurately forecasting snow over the interior of South Africa. SIGNIFICANCE : • A method for forecasting disruptive snowfall is provided. It is envisaged that this method will contribute to the improved forecasting of these severe weather events over South Africa. • Weather systems responsible for snowfall are documented and the cloud microphysical aspects important for the growth and melting of a snowflake are discussed. • Forecasting methods are proposed for the very rare events when snow occurs over the interior of South Africa when the air is relatively dry and somewhat warmer.South African Weather Service; University of Pretoriahttp://www.sajs.co.za/am2016Geography, Geoinformatics and Meteorolog

Modelling potential climate change impacts on sediment yield in the Tsitsa river catchment, South Africa

The effects of climate change on water resources could be numerous and widespread, affecting water quality and water security across the globe. Variations in rainfall erosivity and temporal patterns, along with changes in biomass and land use, are some of the impacts climate change is projected to have on soil erosion. Sedimentation of watercourses and reservoirs, especially in water-stressed regions such as sub-Saharan Africa, may hamper climate change resilience. Modelling sediment yield under various climate change scenarios is vital to develop mitigation strategies which offset the negative effects of erosion and ensure infrastructure remains sustainable under future climate change. This study investigated the relative change in sediment yield with projected climate change using the Soil and Water Assessment Tool (SWAT) for a rural catchment in South Africa for the period 2015–2100. Data from six downscaled Coupled Global Climate Models (CGCM) were divided into three shorter time periods, namely, 2015–2034, 2045–2064 and 2081–2100. Results were then compared with a control scenario using observed data for the period 2002–2017. The results show that, if left unmanaged, climate change will likely lead to greater sediment yield, of up to 10% more per annum. Peak sediment yield will also increase almost three-fold throughout the century. The study shows that projected climate change will have multiple negative effects on soil erosion and emphasised the need for changes in climate to be considered when embarking on water resource developments.Agricultural Research Councilhttps://www.ajol.info/index.php/wsaGeography, Geoinformatics and Meteorolog