Biometeorological Conditions during the August 2015 Mega-Heat Wave and the Summer 2010 Mega-Heat Wave in Ukraine

The human-biometeorological conditions in Ukraine during two mega-heat waves were analyzed. The evaluation is based on physiologically equivalent temperature (PET). The calculation of PET is performed utilizing the RayMan model. The results revealed these two mega-heat waves produced strenuous human-biometeorological conditions on the territory of Ukraine. During the summer 2010 mega-heat wave, strong and extreme heat stress prevailed at about midday at the stations where this atmospheric phenomenon was observed. The mega-heat wave of August 2015 was characterized by a lower heat load. The diurnal variation of PET values during the researched mega-HW was similar to that of the diurnal variation of air temperature with minimum values in the early morning and maximum values in the afternoon. On the territory where mega-heat waves were observed, the number of days during which heat stress occurred for 9 h amounted to 97.6% for the period from 31 July to 12 August 2010 and 77.1% for the mega-heat wave of August 2015.

Potential of Multiway PLS (N-PLS) Regression Method to Analyse Time-Series of Multispectral Images: A Case Study in Agriculture

Recent literature reflects the substantial progress in combining spatial, temporal and spectral capacities for remote sensing applications. As a result, new issues are arising, such as the need for methodologies that can process simultaneously the different dimensions of satellite information. This paper presents PLS regression extended to three-way data in order to integrate multiwavelengths as variables measured at several dates (time-series) and locations with Sentinel-2 at a regional scale. Considering that the multi-collinearity problem is present in remote sensing time-series to estimate one response variable and that the dataset is multidimensional, a multiway partial least squares (N-PLS) regression approach may be relevant to relate image information to ground variables of interest. N-PLS is an extension of the ordinary PLS regression algorithm where the bilinear model of predictors is replaced by a multilinear model. This paper presents a case study within the context of agriculture, conducted on a time-series of Sentinel-2 images covering regional scale scenes of southern France impacted by the heat wave episode that occurred on 28 June 2019. The model has been developed based on available heat wave impact data for 107 vineyard blocks in the Languedoc-Roussillon region and multispectral time-series predictor data for the period May to August 2019. The results validated the effectiveness of the proposed N-PLS method in estimating yield loss from spectral and temporal attributes. The performance of the model was evaluated by the R2 obtained on the prediction set (0.661), and the root mean square of error (RMSE), which was 10.7%. Limitations of the approach when dealing with time-series of large-scale images which represent a source of challenges are discussed; however, the N–PLS regression seems to be a suitable choice for analysing complex multispectral imagery data with different spectral domains and with a clear temporal evolution, such as an extreme weather event.

The role of heat wave events on the occurrence and persistence of thermal stratification in the southern North Sea

Extremes in temperatures not only directly affect the marine environment and ecosystems but also have indirect impacts on hydrodynamics and marine life. The role of heat wave events responsible for the occurrence and persistence of thermal stratification was analysed using a fully coupled hydrodynamic and wave model within the framework of the Geesthacht Coupled cOAstal model SysTem (GCOAST) for the North Sea. The model results were assessed against satellite reprocessed data and in situ observations from field campaigns and fixed MARNET stations. To quantify the degree of stratification, a potential energy anomaly over the water column was calculated. A linear correlation existed between the air temperatures and the potential energy anomaly in the North Sea excluding the Norwegian Trench and the area south of 54° N latitude. Contrary to the northern part of the North Sea, where the water column is stratified in the warming season each year, the southern North Sea is seasonally stratified in years when a heatwave occurs. The influences of heatwaves on the occurrence of summer stratifications in the southern North Sea are mainly in the form of two aspects, i.e., a rapid rise in sea surface temperature at the early stage of the heatwave period and a relatively higher water temperature during summer than the multiyear mean. Another factor that enhances the thermal stratification in summer is the memory of the water column to cold spells earlier in the year. Differences between the seasonally stratified northern North Sea and the heatwave-induced stratified southern North Sea were attributed to changes in water depth.

Analysis of Ocean-Atmospheric features associated with extreme temperature variation over east coast of India-A special emphasis to Orissa heat waves of 1998 and 2005

During the decade of 1998-2007, both Orissa and Andhra Pradesh at east Coast of India have been affected by heat waves more frequently and more severely causing very high damages to human lives. The most severe heat wave years for the region in the recent past are summer of 1998 over Orissa and 2003 over Andhra Pradesh when 2,042 and nearly 3054 people lost their lives respectively. In summer of 2005, though severe heat wave conditions were experienced for some days over Orissa and adjoining east coasts, the damages were not high as before. In view of such extreme temperature events have been regularly affected the region during the period where their normal frequency is low, analyses of their long period temperature data and study of their relationship with various regional and global ocean-atmospheric features are very much necessary, to find possible causes and then use them in forecasting. In the present study, an attempt has been made to analyze various temperature time series as available, varying from large domain to small domain, e.g., all India temperature, east coast of India temperature etc., to understand whether years which had recorded extreme temperatures in these larger domains have any relationship with that occurred over its very smaller domain, e.g., Orissa from station data, of which later is a part. To understand the relation between the magnitude of heat indices and loss to total human lives it caused during respective whole periods of heat waves, different heat indices, viz., general heat indices, Thom’s discomfort and Webb’s comfort indices have been computed during these extreme years over Orissa and Andhra Pradesh states and compared with total heat wave related human deaths over the respective states for the corresponding years. In addition to various heat indices, various Ocean-atmospheric characteristics, e.g., monthly SST over Bay of Bengal, day-to-day synoptic flow pattern, recurving Cyclonic Storms which strengthen low-level westerly and prohibit onset of Sea breeze over the coastal stations in the region causing persistent of heat waves, have also been critically analyzed both spatially and temporally to find role of these features in such occurrences. Their statistical lag correlations if any with ensuing temperature rise have been tested to explore the possibility of using them in forecasting these events much in advance.

A Review of Heat Stress Policies in the Context of Climate Change and Its Impacts on Outdoor Workers

Record-breaking summer heat events are increasing in frequency in Zimbabwe and 2016 was a particularly hot year with the country experiencing its worst heat wave event in decades. Currently, Zimbabwe has no coordinated public health response to deal with heat wave events and no specific data on heat-related morbidity and mortality. The country has no legislation for protecting workers against environmental heat exposure, particularly those most vulnerable who are employed in the informal sector. These workers are also at risk due to their outdoor work environments. The article outlines the state of climate and heat stresses in Zimbabwe, as benchmarked against other African countries and France. It further summarizes outdoor workers' susceptibility to heat exposure and the need for the Zimbabwean Government to develop policies to ensure the health and safety of an increasing population of outdoor workers in Zimbabwe.

A Review of Heat Stress Policies in the Context of Climate Change and Its Impacts on Outdoor Workers

Record-breaking summer heat events are increasing in frequency in Zimbabwe and 2016 was a particularly hot year with the country experiencing its worst heat wave event in decades. Currently, Zimbabwe has no coordinated public health response to deal with heat wave events and no specific data on heat-related morbidity and mortality. The country has no legislation for protecting workers against environmental heat exposure, particularly those most vulnerable who are employed in the informal sector. These workers are also at risk due to their outdoor work environments. The article outlines the state of climate and heat stresses in Zimbabwe, as benchmarked against other African countries and France. It further summarizes outdoor workers' susceptibility to heat exposure and the need for the Zimbabwean Government to develop policies to ensure the health and safety of an increasing population of outdoor workers in Zimbabwe.

Synergies between Urban Heat Island and Urban Heat Wave Effects in 9 Global Mega–Regions from 2003 to 2020

Global urbanization significantly impacts the thermal environment in urban areas, yet urban heat island (UHI) and urban heat wave (UHW) studies at the mega–region scale have been rare, and the impact study of urbanization is still lacking. In this study, the MODIS land surface temperature (LST) product was used to depict the UHI and UHW in nine mega–regions globally between 2003 and 2020. The absolute and percentile–based UHW thresholds were adopted for both daily and three–day windows to analyze heat wave frequency, and UHW magnitude as well as frequency were compared with UHI variability. Results showed that a 10% increase in urban built-up density led to a 0.20 °C to 0.95 °C increase in LST, a 0.59% to 7.17% increase in hot day frequency, as well as a 0.08% to 0.95% increase in heat wave number. Meanwhile, a 1 °C increase in UHI intensity (the LST differences between the built-up and Non-built-up areas) led to a 2.04% to 92.15% increase in hot day frequency, where daytime LST exceeds 35 °C and nighttime LST exceeds 25 °C, as well as a 3.30% to 33.67% increase in heat wave number, which is defined as at least three consecutive days when daily maximum temperature exceeds the climatological threshold. In addition, the increasing rates of UHW magnitudes were much faster than the expansion rates of built-up areas. In the mega–regions of Boston, Tokyo, São Paulo, and Mexico City in particular, the increasing rates of UHW hotspot magnitudes were over 2 times larger than those of built-up areas. This indicated that the high temperature extremes, represented by the increase in UHW frequency and magnitudes, were concurrent with an increase in UHI under the context of climate change. This study may be beneficial for future research of the underlying physical mechanisms on urban heat environment at the mega–region scale.