Generating weather pattern definitions over <scp>South Africa</scp> suitable for future use in impact‐orientated medium‐range forecasting

Ireland, Lewis G.; Robbins, Joanne; Neal, Robert; Barciela, Rosa; Gilbert, Rebecca

doi:10.1002/joc.8396

Cited by 3 publications

(2 citation statements)

References 55 publications

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“…Finally, the seamless blended multi-model ensemble approach to probabilistic weather pattern forecasts presented here would also be appropriate for similar products under development in other parts of the world, such as India (Neal et al, 2020(Neal et al, , 2022, Southeast Asia (Howard et al, 2022) and South Africa (Ireland et al, 2024).…”

Section: Discussionmentioning

confidence: 99%

A seamless blended multi‐model ensemble approach to probabilistic medium‐range weather pattern forecasts over the UK

Neal,

Robbins,

Crocker

et al. 2024

Meteorological Applications

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This paper describes a new seamless blended multi‐model ensemble configuration of an existing probabilistic medium‐ to extended‐range weather pattern forecasting tool (called Decider) run operationally at the Met Office. In its initial configuration, the tool calculated and presented probabilistic weather pattern forecast information for five individual ensemble forecasting systems, which varied in terms of their number of ensemble members, horizontal resolution, update frequencies and forecast lead time. This resulted in multiple forecasts for the same validity time which varied in terms of forecast skill depending on the lead time in question. This presented challenges for end‐users (e.g., operational meteorologists) in terms of knowing which forecast output is best to use and at which lead time, as well as knowing what to do in situations where forecasts varied between ensembles. To get around these challenges, a new seamless blended multi‐model ensemble configuration has been implemented operationally, comprising of output from five separate ensembles, and provides a single best forecast from day one out to day 45. Objective verification for a set of eight weather pattern groups covering forecasts initialized over a 6‐year period (2017–2022) shows that the seamless blended multi‐model ensemble forecasts are at least as good as, if not better than the best performing individual model.

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Section: Discussionmentioning

confidence: 99%

A seamless blended multi‐model ensemble approach to probabilistic medium‐range weather pattern forecasts over the UK

Neal,

Robbins,

Crocker

et al. 2024

Meteorological Applications

Self Cite

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“…The exploitation of STDBs can provide valuable knowledge, for instance, in the context of road traffic control and monitoring [5], weather analysis [6], and location-based sociological behavior in social networks [7]. However, as stated above, traditional data mining techniques cannot be directly applied to STDBs, which complicates not only data exploitation but also processing times.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Probabilistic Algorithm to Detect Periodic Patterns in Spatio-Temporal Datasets

Gutiérrez-Soto,

Galdames,

Palomino

2024

BDCC

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Deriving insight from data is a challenging task for researchers and practitioners, especially when working on spatio-temporal domains. If pattern searching is involved, the complications introduced by temporal data dimensions create additional obstacles, as traditional data mining techniques are insufficient to address spatio-temporal databases (STDBs). We hereby present a new algorithm, which we refer to as F1/FP, and can be described as a probabilistic version of the Minus-F1 algorithm to look for periodic patterns. To the best of our knowledge, no previous work has compared the most cited algorithms in the literature to look for periodic patterns—namely, Apriori, MS-Apriori, FP-Growth, Max-Subpattern, and PPA. Thus, we have carried out such comparisons and then evaluated our algorithm empirically using two datasets, showcasing its ability to handle different types of periodicity and data distributions. By conducting such a comprehensive comparative analysis, we have demonstrated that our newly proposed algorithm has a smaller complexity than the existing alternatives and speeds up the performance regardless of the size of the dataset. We expect our work to contribute greatly to the mining of astronomical data and the permanently growing online streams derived from social media.

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High aerospora levels and associated atmospheric circulation patterns: Pretoria, South Africa

Roffe,

Ajikah,

John

et al. 2024

Int J Biometeorol

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At high exposure levels, airborne pollen grains and fungal spores (termed aerospora hereafter), can trigger severe allergic respiratory diseases. For South Africa’s administrative capital Pretoria, which boasts dense vegetation within a large urban forest, it is valuable from a health perspective to understand daily atmospheric circulation patterns associated with high aerospora levels. Therefore, we utilised a daily aerospora grain count dataset collected in Pretoria from 08/2019–02/2023 to investigate atmospheric circulation patterns (derived from ERA5 reanalysis sea level pressure [SLP] and 500 hPa geopotential height [zg500] fields) associated with high-risk aerospora levels (aerospora grain count > 90th percentile). Concentrated during October-May, there were 128 high-risk days, with 69.6% of days occurring in November, February and April. Although generally above-average mid-tropospheric subsidence levels prevailed over Pretoria during high-risk days, no single distinct atmospheric circulation pattern was associated with these high-risk days. Therefore, using Principal Component Analysis, we classified 14 Circulation Weather Types (CWTs) for October-May months between 08/2019–02/2023 to assess which CWTs most frequently occurred during high-risk days. Three CWTs had a statistically significant proportion of high-risk days – collectively they occurred during 37.1% of days studied, yet accounted for 45.3% of high-risk days. Among these CWTs, two CWTs were similarly associated with surface and mid-tropospheric high-pressure conditions, while the third was associated with a surface and mid-tropospheric trough. By comparing our CWT classification to daily synoptic charts (from the South African Weather Service), our classification can be used to identify days with potentially high allergenicity risk over Pretoria.

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Generating weather pattern definitions over South Africa suitable for future use in impact‐orientated medium‐range forecasting

Cited by 3 publications

References 55 publications

A seamless blended multi‐model ensemble approach to probabilistic medium‐range weather pattern forecasts over the UK

A seamless blended multi‐model ensemble approach to probabilistic medium‐range weather pattern forecasts over the UK

An Efficient Probabilistic Algorithm to Detect Periodic Patterns in Spatio-Temporal Datasets

High aerospora levels and associated atmospheric circulation patterns: Pretoria, South Africa

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