scholarly journals Severe weather over the Highveld of South Africa during November 2016

Water SA ◽  
2018 ◽  
Vol 44 (1 January) ◽  
Author(s):  
Lee-ann Simpson ◽  
Liesl L Dyson
Keyword(s):  
South Africa ◽  
Prediction Models ◽  
Weather Forecast ◽  
Severe Weather ◽  
Severe Thunderstorm ◽  
Weather Events ◽  
Circulation Patterns ◽  
Conditional Instability ◽  
Synoptic Circulation

November months are notorious for severe weather over the Highveld of South Africa. November 2016 was no exception and a large number of severe events occurred. Very heavy rainfall, large hail and tornadoes were reported. The aim of this paper is to compare the synoptic circulation of November 2016 with the long-term mean November circulation and to investigate some sounding derived parameters. Furthermore, a few of the severe weather events are described in detail. The surface temperatures and dewpoint temperatures were found to be higher than normal resulting in increased conditional instability over the Highveld. Low-level moisture originated over the warm Mozambique Channel and the 500 hPa temperature trough was located favourably over the Highveld; further east than normal. The combination of these factors and weak steering winds resulted in flash flooding on the 9th while favourable wind shear conditions caused the development of a tornado on 15 November. The favourable circulation patterns and moisture gave rise to an atmosphere in which severe weather was a possibility, and the awareness of such factors is used as one of many tools when considering the severe weather forecast. The consideration of the daily variables derived from sounding data were good precursors for the prediction of severe thunderstorm development over the Highveld during November 2016. It is recommended that an operational meteorologist incorporates upper air sounding data into the forecasting process and not to rely on numerical prediction models exclusively.

scholarly journals A Total Lightning Trending Algorithm to Identify Severe Thunderstorms

2010 ◽  
Vol 27 (1) ◽  
pp. 3-22 ◽  
Author(s):  
Patrick N. Gatlin ◽  
Steven J. Goodman
Keyword(s):  
Severe Weather ◽  
Lightning Flash ◽  
Severe Thunderstorm ◽  
Critical Success ◽  
Severe Thunderstorms ◽  
Weather Events ◽  
Total Lightning ◽  
Jump Algorithm ◽  
Early Indication ◽  
Critical Success Index

Abstract An algorithm that provides an early indication of impending severe weather from observed trends in thunderstorm total lightning flash rates has been developed. The algorithm framework has been tested on 20 thunderstorms, including 1 nonsevere storm, which occurred over the course of six separate days during the spring months of 2002 and 2003. The identified surges in lightning rate (or jumps) are compared against 110 documented severe weather events produced by these thunderstorms as they moved across portions of northern Alabama and southern Tennessee. Lightning jumps precede 90% of these severe weather events, with as much as a 27-min advance notification of impending severe weather on the ground. However, 37% of lightning jumps are not followed by severe weather reports. Various configurations of the algorithm are tested, and the highest critical success index attained is 0.49. Results suggest that this lightning jump algorithm may be a useful operational diagnostic tool for severe thunderstorm potential.

scholarly journals Forecasting Intense Cut-Off Lows in South Africa Using the 4.4 km Unified Model

Climate ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 129
Author(s):  
Tshimbiluni Percy Muofhe ◽  
Hector Chikoore ◽  
Mary-Jane Morongwa Bopape ◽  
Nthaduleni Samuel Nethengwe ◽  
Thando Ndarana ◽  
...  
Keyword(s):  
South Africa ◽  
South African ◽  
Weather Forecasting ◽  
Daily Rainfall ◽  
Heavy Precipitation ◽  
Unified Model ◽  
Severe Weather ◽  
Economic Activities ◽  
Circulation Patterns ◽  
Extreme Cold

Mid-tropospheric cut-off low (COL) pressure systems are linked to severe weather, heavy rainfall and extreme cold conditions over South Africa. They occur during all the above and often result in floods and snowfalls during the winter months, disrupting economic activities and causing extensive damage to infrastructure. This paper examines the evolution and circulation patterns associated with cases of severe COLs over South Africa. We evaluate the performance of the 4.4 km Unified Model (UM) which is currently used operationally by the South African Weather Service (SAWS) to simulate daily rainfall. Circulation variables and precipitation simulated by the UM were compared against European Centre for Medium-Range Weather Forecast’s (ECMWF’s) ERA Interim re-analyses and GPM precipitation at 24-hour timesteps. We present five recent severe COLs, which occurred between 2016 and 2019, that had high impact and found a higher model skill when simulating heavy precipitation during the initial stages than the dissipating stages of the systems. A key finding was that the UM simulated the precipitation differently during the different stages of development and location of the systems. This is mainly due to inaccurate placing of COL centers. Understanding the performance and limitations of the UM model in simulating COL characteristics can benefit severe weather forecasting and contribute to disaster risk reduction in South Africa.

Colorful Language: Investigating Public Interpretation of the Storm Prediction Center Convective Outlook

2021 ◽  
Author(s):  
Sean Ernst ◽  
Joe Ripberger ◽  
Makenzie J. Krocak ◽  
Hank Jenkins-Smith ◽  
Carol Silva
Keyword(s):  
Statistical Tests ◽  
Weather Forecast ◽  
Severe Weather ◽  
Logistic Regression Models ◽  
Public Interpretation ◽  
Severe Thunderstorms ◽  
Weather Events ◽  
The Us ◽  
Storm Prediction ◽  
Future Work

AbstractAlthough severe weather forecast products, such as the Storm Prediction Center (SPC) convective outlook, are much more accurate than climatology at day-to-week time scales, tornadoes and severe thunderstorms claim dozens of lives and cause billions of dollars in damage every year. While the accuracy of this outlook has been well documented, less work has been done to explore the comprehension of the product by non-expert users like the general public. This study seeks to fill this key knowledge gap by collecting data from a representative survey of U.S. adults in the lower 48 states about their use and interpretation of the SPC convective outlook. Participants in this study were asked to rank the words and colors used in the outlook from least to greatest risk, and their answers were compared through visualizations and statistical tests across multiple demographics. Results show that the US public ranks the outlook colors similarly to their ordering in the outlook but switch the positions of several of the outlook words as compared to the operational product. Logistic regression models also reveal that more numerate individuals more correctly rank the SPC outlook words and colors. These findings suggest that the words used in the convective outlook may confuse non-expert users, and that future work should continue to use input from public surveys to test potential improvements in the choice of outlook words. Using more easily understood words may help to increase the outlook’s decision support value and potentially reduce the harm caused by severe weather events.

Ensuring long-term resilience of Highways England's geotechnical assets to severe weather events

2016 ◽  
Author(s):  
O. Pritchard ◽  
S. Carluccio ◽  
J. Mian ◽  
D. Patterson
Keyword(s):  
Severe Weather ◽  
Weather Events

An Analysis of Subdaily Severe Thunderstorm Probabilities for the United States

2020 ◽  
Vol 35 (1) ◽  
pp. 107-112 ◽  
Author(s):  
Makenzie J. Krocak ◽  
Harold E. Brooks
Keyword(s):  
United States ◽  
Spatial Scales ◽  
The United States ◽  
Severe Weather ◽  
Severe Thunderstorm ◽  
Probabilistic Information ◽  
Weather Events ◽  
Single Location ◽  
Spatiotemporal Scales ◽  
Definition Of

Abstract One of the challenges of providing probabilistic information on a multitude of spatiotemporal scales is ensuring that information is both accurate and useful to decision-makers. Focusing on larger spatiotemporal scales (i.e., from convective outlook to weather watch scales), historical severe weather reports are analyzed to begin to understand the spatiotemporal scales that hazardous weather events are contained within. Reports from the Storm Prediction Center’s report archive are placed onto grids of differing spatial scales and then split into 24-h convective outlook days (1200–1200 UTC). These grids are then analyzed temporally to assess over what fraction of the day a single location would generally experience severe weather events. Different combinations of temporal and spatial scales are tested to determine how the reference class (or the choice of what scales to use) alters the probabilities of severe weather events. Results indicate that at any given point in the United States on any given day, more than 95% of the daily reports within 40 km of the point occur in a 4-h period. Therefore, the SPC 24-h convective outlook probabilities can be interpreted as 4-h convective outlook probabilities without a significant change in meaning. Additionally, probabilities and threat periods are analyzed at each location and different times of year. These results indicate little variability in the duration of severe weather events, which allows for a consistent definition of an “event” for all locations in the continental United States.

scholarly journals Initial results of the project SINOPTICA (Satellite-borne and IN-situ Observations to Predict The Initiation of Convection for ATM) 

2021 ◽  
Author(s):  
Laura Esbri ◽  
Maria Carmen Llasat ◽  
Tomeu Rigo ◽  
Massimo Milelli ◽  
Vincenzo Mazzarella ◽  
...  
Keyword(s):  
Weather Prediction ◽  
Wrf Model ◽  
Remote Sensing Data ◽  
Weather Forecast ◽  
Relevant Information ◽  
Air Traffic ◽  
Severe Weather ◽  
Radar Images ◽  
Weather Events ◽  
Adverse Weather

<p>In the framework of the SINOPTICA project (EU H2020 SESAR, 2020 – 2022), different meteorological forecasting techniques are being tested to better nowcast severe weather events affecting Air Traffic Management (ATM) operations. Short-range severe weather forecasts with very high spatial resolution will be obtained starting from radar images, through an application of nowcasting techniques combined with Numerical Weather Prediction (NWP) model with data assimilation. The final goal is to integrate compact nowcast information into an Arrival Manager to support Air Traffic Controllers (ATCO) when sequencing and guiding approaching aircraft even in adverse weather situations. The guidance-support system will enable the visualization of dynamic weather information on the radar display of the controller, and the 4D-trajectory calculation for diversion coordination around severe weather areas. This meteorological information must be compact and concise to not interfere with other relevant information on the radar display of the controller.</p><p>Three severe weather events impacting different Italian airports have been selected for a preliminary radar analysis. Some products are considered for obtaining the best radar approach to characterize the severity of the events for ATM interests. Combining the Vertical Integrated Liquid and the Echo Top Maximum products, hazard thresholds are defined for different domains around the airports. The Weather Research and Forecasting (WRF) model has been used to simulate the formation and development of the aforementioned convective events. In order to produce a more accurate very short-term weather forecast (nowcasting), remote sensing data (e.g. radar, GNSS) and conventional observations are assimilated by using a cycling three-dimensional variational technique. This contribution presents some preliminary results on the progress of the project.</p>

scholarly journals Internet of Things Based Weather Forecast Monitoring System

2018 ◽  
Vol 9 (3) ◽  
pp. 555 ◽  
Author(s):  
Atul Kulkarni ◽  
Debajyoti Mukhopadhyay
Keyword(s):  
Weather Forecasting ◽  
Weather Forecast ◽  
Severe Weather ◽  
Weather Data ◽  
Partial Explanation ◽  
Significant Function ◽  
Weather Events ◽  
Weather Stations ◽  
Communication Methods ◽  
Cost Components

<p>Weather forecasting is a significant function in meteorology and has been one of the most systematically challenging troubles around the world.This scheme deals with the structure of a weather display method using small cost components so that any electronics hobbyist can construct it. As a replacement for using sensors to collect the weather data, the development gets the information from weather stations placed around the world through a global weather data supplier. Severe weather phenomena challengedifficult weather forecast approach with the partial explanation. Weather events have numerous parameters that are not possible to detail and compute. Growing on communication methods enables weather predictsspecialist systems to combine and share possessions and thus hybrid systems have emerged. Still, though these improvements on climate predict, these expert systems can’t be entirely reliable while weather forecast is central problem.</p>

scholarly journals Detection of Severe Weather Events in a High-Resolution Ensemble Prediction System Using the Extreme Forecast Index (EFI) and Shift of Tails (SOT)

2018 ◽  
Vol 33 (4) ◽  
pp. 901-908
Author(s):  
Laure Raynaud ◽  
Benoît Touzé ◽  
Philippe Arbogast
Keyword(s):  
Reference Model ◽  
Weather Forecast ◽  
Ensemble Forecast ◽  
Severe Weather ◽  
Ensemble Prediction ◽  
False Alarms ◽  
Prediction System ◽  
Ensemble Prediction System ◽  
Wind Gusts ◽  
Weather Events

Abstract The extreme forecast index (EFI) and shift of tails (SOT) are commonly used to compare an ensemble forecast to a reference model climatology, in order to measure the severity of the current weather forecast. In this study, the feasibility and the relevance of EFI and SOT computations are examined within the convection-permitting Application of Research to Operations at Mesoscale (AROME-France) ensemble prediction system (EPS). First, different climate configurations are proposed and discussed, in order to overcome the small size of the ensemble and the short climate sampling length. Subjective and objective evaluations of EFI and SOT for wind gusts and precipitation forecasts are then presented. It is shown that these indices can provide relevant early warnings and, based on a trade-off between hits and false alarms, optimal EFI thresholds can be determined for decision-making.

Precipitation Nowcasting using Deep Neural Network

2020 ◽  
Author(s):  
Mohamed Chafik Bakey ◽  
Mathieu Serrurier
Keyword(s):  
Traffic Management ◽  
Prediction Models ◽  
Short Term Memory ◽  
Weather Prediction ◽  
Weather Forecast ◽  
Proposed Model ◽  
Anticipation Time ◽  
Memory Network ◽  
Convolutional Encoder

&lt;p&gt;Precipitation nowcasting is the prediction of the future precipitation rate in a given geographical region with an anticipation time of a few hours at most. It is of great importance for weather forecast users, for activitites ranging from outdoor activities and sports competitions to airport traffic management. In contrast to long-term precipitation forecasts which are traditionally obtained from numerical weather prediction models, precipitation nowcasting needs to be very fast. It is therefore more challenging to obtain because of this time constraint. Recently, many machine learning based methods had been proposed. In this work, we develop an original deep learning approach. We formulate precipitation nowcasting issue as a video prediction problem where both input and prediction target are image sequences. The proposed model combines a Long Short-Term Memory network (LSTM) with a convolutional encoder-decoder network (U-net). Experiments show that our method captures spatiotemporal correlations and yields meaningful forecasts&lt;/p&gt;

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

2016 ◽  
Vol Volume 112 (Number 9/10) ◽  
Author(s):  
Jan Hendrik Stander ◽  
Liesl Dyson ◽  
Christien J. Engelbrecht ◽  
◽  
◽  
...  
Keyword(s):  
South Africa ◽  
Decision Tree ◽  
Weather Forecasting ◽  
Second Step ◽  
Loss Of Life ◽  
Surface Conditions ◽  
Circulation Patterns ◽  
Weather Forecasters ◽  
Physical Attributes ◽  
Synoptic Circulation

Abstract 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.

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