Cold Nights Research Articles

СЕЗОННЫЕ ОСОБЕННОСТИ ПСИХОФИЗИОЛОГИЧЕСКИХ ПАРАМЕТРОВ СТУДЕНТОВ НА СЕВЕРО-ВОСТОКЕ РОССИИ

The study of the mechanisms of adaptation of the organism to extreme environmental factors is one of the main issues of physiology, which does not lose its relevance. Specific photoperiodism, characteristic of different seasons of the year in the North, can affect not only the physiological, but also the psychoemotional state of the organism. In order to identify evolutionarily formed chronophysiological mechanisms of human adaptation to extreme conditions of the North, a longitudinal study was conducted in different seasons (autumn, winter, spring and summer) of the features of psychoemotional instability, anxiety (situational and personal anxiety), regulatory restructuring of the heart rhythm of the organism of young practically healthy males of indigenous nationality. The object of the longitudinal study were young Yakut men (MY), volunteers, students of the Medical Institute of the North-Eastern Federal University named after M.K. Ammosov (n = 26). The average age was 19.1±2.2 years. The high psycho-emotional stress of young Yakut men in winter revealed by the study is probably an adaptation mechanism that determines high functional activity and stability of the nervous system during the period of combined exposure to cold and polar night. Intrasystemic activation of the cardiovascular system of young Yakut men against the background of high overall power of vegetative regulation (3476.15 ms2) and normal value of the stress index (119.92 c.u.) in winter contributes to the mobilization of the adaptive mechanisms of the human body, which is reflected in the growth of the adaptive-restorative potential in extreme conditions and higher indicators of this potential in people with a high level of psycho-emotional stress.

A winter cold nights pattern in the Northern Hemisphere lands: Circum–hemisphere teleconnection of extreme cold events

A winter cold nights pattern in the Northern Hemisphere lands: Circum–hemisphere teleconnection of extreme cold events

Spatial distribution patterns and hotspots of extreme agro-climatic resources in the Horro Guduru Wollega Zone, Northwestern Ethiopia.

Extreme temperatures and rainfall influence crop yields, soil health, and natural ecosystems. This study examined the extent of extreme agro-climatic factors in Northwestern Ethiopia, with a focus on identifying vulnerability hotspots. Rainfall and temperature data from 1982 to 2022 were collected from eight meteorological stations of the Ethiopian Meteorological Institute, and missing values and outliers were corrected using imputation and Z-scores. ClimPact2 software extracted agro-climatic indicators, and trend analyses were performed using the Mann-Kendall test and Sen's slope. Consecutive dry days (CDD) ranged from 27 in Fincha'a to 57 in Obora, with Obora showing an annual increase of 2.033days. Consecutive wet days (CWD) varied from 12 in Obora to 138 in Fincha'a. A positive trend in the warmest maximum temperatures (TXx) and a negative trend in the cold night index (TN10P) were observed. The Amuru District recorded the highest vulnerability index at 61, with most districts ranging from 42 to 60. These variations may significantly affect agriculture and water management in the region, necessitating the adoption of heat-tolerant crops and improved irrigation practices to enhance climate resilience.

Evaluating potential power output of terrestrial thermoradiative diodes with atmospheric modeling

A thermoradiative diode is a device that can generate power through thermal emission from the warm Earth to the cold night sky. Accurate assessment of the potential power output requires knowledge of the downwelling radiation from the atmosphere. Here, accurate modeling of this radiation is used alongside a detailed balance model of a diode at the Earth's surface temperature to evaluate its performance under nine different atmospheric conditions. In the radiative limit, these conditions yield power densities between 0.34 and 6.5W.m-2, with optimal bandgaps near 0.094 eV. Restricting the angles of emission and absorption to less than a full hemisphere can marginally increase the power output. Accounting for non-radiative processes, we suggest that if a 0.094 eV device would have radiative efficiencies more than two orders of magnitude lower than a diode with a bandgap near 0.25 eV, the higher bandgap material is preferred.

Artificial intelligence reveals past climate extremes by reconstructing historical records

The understanding of recent climate extremes and the characterization of climate risk require examining these extremes within a historical context. However, the existing datasets of observed extremes generally exhibit spatial gaps and inaccuracies due to inadequate spatial extrapolation. This problem arises from traditional statistical methods used to account for the lack of measurements, particularly prevalent before the mid-20th century. In this work, we use artificial intelligence to reconstruct observations of European climate extremes (warm and cold days and nights) by leveraging Earth system model data from CMIP6 through transfer learning. Our method surpasses conventional statistical techniques and diffusion models, showcasing its ability to reconstruct past extreme events and reveal spatial trends across an extensive time span (1901-2018) that is not covered by most reanalysis datasets. Providing our dataset to the climate community will improve the characterization of climate extremes, resulting in better risk management and policies.

Dynamical downscaling and data assimilation for a cold-air outbreak in the European Alps during the Year Without a Summer of 1816

Abstract. The “Year Without a Summer” in 1816 was characterized by extraordinarily cold and wet periods in central Europe, and it was associated with severe crop failures, famine, and socio-economic disruptions. From a modern perspective, and beyond its tragic consequences, the summer of 1816 represents a rare opportunity to analyze the adverse weather (and its impacts) after a major volcanic eruption. However, given the distant past, obtaining the high-resolution data needed for such studies is a challenge. In our approach, we use dynamical downscaling, in combination with 3D variational data assimilation of early instrumental observations, for assessing a cold-air outbreak in early June 1816. We find that the cold spell is well represented in the coarse-resolution 20th Century Reanalysis product which is used for initializing the regional Weather Research and Forecasting Model. Our downscaling simulations (including a 19th century land use scheme) reproduce and explain meteorological processes well at regional to local scales, such as a foehn wind situation over the Alps with much lower temperatures on its northern side. Simulated weather variables, such as cloud cover or rainy days, are simulated in good agreement with (eye) observations and (independent) measurements, with small differences between the simulations with and without data assimilation. However, validations with partly independent station data show that simulations with assimilated pressure and temperature measurements are closer to the observations, e.g., regarding temperatures during the coldest night, for which snowfall as low as the Swiss Plateau was reported, followed by a rapid pressure increase thereafter. General improvements from data assimilation are also evident in simple quantitative analyses of temperature and pressure. In turn, data assimilation requires careful selection, preprocessing, and bias-adjustment of the underlying observations. Our findings underline the great value of digitizing efforts of early instrumental data and provide novel opportunities to learn from extreme weather and climate events as far back as 200 years or more.

Analyzing the Spatiotemporal Changes in Climatic Extremes in Cold and Mountainous Environment: Insights from the Himalayan Mountains of Pakistan

This study assessed the past changes in extreme precipitation and temperature events across the Himalayan Mountains of Pakistan. This cold and mountainous environmental region has witnessed a significant increase in climate-related disasters over the past few decades. Spatiotemporal changes in extreme temperature and precipitation events were analyzed using 24 indices developed by the Expert Team on Climate Change Detection and Indices (ETCCDI). For this study, in situ data of 16 national meteorological stations were obtained from the Pakistan Meteorological Department (PMD) for the past three decades (1991–2020). The significance of the trends was assessed using the modified Mann–Kendall (MMK) test, and the Theil–Sen (TS) slope estimator was used to estimate the slope of the trend. The results showed that there has been a consistent decline in the total precipitation amount across the Himalayan Mountains of Pakistan. The trend exhibited a decrease in the annual average precipitation at a rate of −6.56 mm/year. Simultaneously, there was an increasing trend in the annual average minimum and maximum temperatures at rates of 0.02 °C/year and 0.07 °C/year, respectively. The frequencies of consecutive wet days (CWDs) and maximum 5-day precipitation (RX5day) have decreased significantly, with decreasing rates of −0.40 days/year and −1.18 mm/year, respectively. The amount of precipitation during very wet days (R95p) and extremely wet days was decreased by −19.20 and −13.60 mm/decade, respectively. The warm spell duration (WSDI) and the frequency of warm days (TX90p) across the Himalayan Range both increased by 1.5 and 1.4 days/decade. The number of cold days (TX10p) and cold nights (TN10p) decreased by 2.9 and 3.4 days/decade. The average temperature of the hottest nights (TXn) and the diurnal temperature range (DTR) were increased by 0.10 and 0.30 °C/decade. The results indicated an increasing tendency of dry and warm weather in the Himalayan region of Pakistan, which could have adverse consequences for water resources, agriculture, and disaster management in the country. Therefore, it is essential to prioritize the implementation of localized adaptation techniques in order to enhance sustainable climate resilience and effectively address the emerging climate challenges faced by these mountainous regions.

Comparison of 2-m surface temperature data between reanalysis and observations over the Arabian Peninsula

The 2-m temperature data is a significant indicator for studying the weather extremes and the exchange of water and energy fluxes between the surface and atmosphere. This study compared three reanalysis datasets, i.e., ERA5, ERA5-Land, and MERRA-2, with observations from in-situ sources from 1990 to 2022 using various statistical error metrics and extreme temperature indices over the Arabian Peninsula (AP) region. We selected these reanalysis datasets due to the continuous improvements and higher spatiotemporal output better capturing the temperature variability. The spatiotemporal climatology shows lower temperatures in winter (<15 °C) and maximum in summer (>35 °C); however, the reanalysis data show more deviations in temperature during the cold season than in the warm season. The reanalysis data underestimated the frequency of the cold (<10 °C) and hot (>30 °C) days across the four regions, except ERA5-Land, which closely followed observed data. The strength of the correlation shows better performance (>0.90) in the cold extremes (cold nights and cold days) frequency than the hot extremes. On an interannual scale, reanalysis products exhibit strong correlations (>0.90) with in-situ data across most regions, particularly in winter and autumn, moderate in spring, and weaker in summer. The reanalysis data shows negative biases in the inland regions and positive biases in the coastal areas with consistent root mean square differences (RMSD) spatiotemporally. The differences in performance are due to the topography and poor representation of the energy fluxes, especially in MERRA-2 as well as missing data in observations. This study recommends ERA5-Land as the first choice for extreme weather simulations in the region, followed by MERRA-2 and ERA5 on the same scale, but proper attention is needed when using reanalysis data for cold and hot extremes.

Impact of hot and cold nights on pneumonia hospitalisations in children under five years: Evidence from low-, middle-, and high-income countries

BackgroundStudies have shown that abnormal temperature at night is a risk factor for respiratory health. However, there is limited evidence on the impact of hot and cold nights on cause-specific diseases such as pneumonia, which is a leading cause of morbidity and mortality in children. MethodsWe collected daily data on pneumonia hospitalisations in children under five years from 2011 to 2017 in three low-, middle- and high-income countries (Bangladesh, China, and Australia). The intensity of hot and cold nights was measured by excess temperature. A space–time-stratified case-crossover analysis was used to estimate the association between hot and cold nights and childhood pneumonia hospitalisations. We further estimated the fraction of childhood pneumonia hospitalisations attributable to hot and cold nights. ResultsBoth hot and cold nights were associated with an increased risk of hospitalisations for childhood pneumonia in low-, middle-, and high-income countries, with a greater disease burden from hot nights. Specifically, the fraction of childhood pneumonia attributable to hot nights was the largest in Australia [21.2%, 95% confidence interval (CI): 11.8%-28.1%], followed by Bangladesh (15.2%, 95% CI: 4.1%–23.8%) and China (2.7%, 95% CI: 0.4%-4.7%). Additionally, the fraction of childhood pneumonia attributable to cold nights was 1.3% (95% CI: 0.4%-2.0%) in Bangladesh and 0.4% (95% CI: 0.1%-0.7%) in China. ConclusionThis multi-country study suggests that hot and cold nights are not only associated with a higher risk of pneumonia hospitalisations in children but also responsible for substantial fraction of hospitalisations, with a greater impact from hot nights.

Climate Change Threats to UNESCO-Designated World Heritage Sites: Empirical Evidence from Konso Cultural Landscape, Ethiopia

The purpose of this study was to investigate temperature and rainfall variations and their effects on the UNESCO World Heritage Sites of Konso cultural landscape, Ethiopia, using dense merged satellite–gauge-station rainfall data (1981–2020) with a spatial resolution of 4 km-by-4 km and observed maximum and min temperature data (1987–2020), together with qualitative data gathered from cultural leaders, local administrators and religious leaders. The Climate Data tool (CDT) software version 8 was used for rainfall- and temperature-data analysis. The results showed that the north and northeastern regions of Konso had significant increases in rainfall. However, it was highly variable and erratic, resulting in extreme droughts and floods. The study confirmed that there were significant (p &lt; 0.05) increasing trends in the number of days with heavy rainfall, very-heavy rainfall days, and annual total wet-day rainfall (R10 mm, 20 mm, and PRCPTOT). The highest daily minimum temperature, lowest and highest daily maximum-temperature number of warm days and nights, and number of cold days and nights all showed significant rising trends. The increasing trends in rainfall and temperature extremes have resulted in flooding and warming of the study area, respectively. These have led to the destruction of terraces, soil erosion, loss of life and damage of properties, loss of grasses, food insecurity, migration, loss of biodiversity, and commodification of stones. The continuous decline in farmland productivity is affecting the livelihood and traditional ceremonies of the Konso people, which are helpful for the transfer of traditional resource-management knowledge to the next generation. It is therefore necessary to implement local-scale climate change adaptation and mitigation strategies in order to safeguard the Konso cultural landscapes as a worldwide cultural asset and to bolster the resilience of smallholder farmers.

Near future variations in temperature extremes in northeastern Iran under CMIP6 projections.

Extreme air temperatures which are of significance in plant growth are influenced by climate change. The aim of this study was to assess the climate change effects on temperature extreme indices (TEIs) in northeastern Iran based on the CMIP6 projections. For this purpose, five extreme indices including maximum of maximum temperature (TXx), minimum of minimum temperature (TNn), cold nights (TN10p), warm nights (TN90p), and summer days (SU25) which are effective on plant growth were evaluated. The projections of the three Earth system models including ACCESS-CM2, MIROC6, and MRI-ESM2-0 were assessed under the three scenarios of SSP1-2.6, SSP2-4.5, and SSP5-8.5 for the period 2026-2050 compared to the historical period (1989-2014). The models' projections were evaluated by statistical tests on the changes in the average and trend of data. Results showed that the MRI-ESM2-0 model revealed the best efficiency compared to the other models. The projections of all models and scenarios indicated a significant increase in the average of TXx and SU25 indices over the study area at the confidence level of 95% by 1.6 to 2.4°C, as well as 15 to 18days under the three scenarios. Also, the results exhibited a significant increasing trend in TN90p and a decreasing one in TN10p overall province atconfidence level of 95%. These changes will lead to an increase in evapotranspiration of the plants and an increase in agricultural water demand, and as a result, a decrease in the yield of some main products like wheat and saffron which are the main products in the study area.

A comparison of climate drivers’ impacts on silage maize yield shock in Germany

Extreme weather events have become more frequent and severe with ongoing climate change, with a huge implication for the agricultural sector and detrimental effects on crop yield. In this study, we compare several combinations of climate indices and utilized the Least Absolute Shrinkage and Selection Operator (LASSO) to explain the probabilities of substantial drops in silage maize yield (here defined as “yield shock” by using a 15th percentile as threshold) in Germany between 1999 and 2020. We compare the variable importance and the predictability skill of six combinations of climate indices using the Matthews Correlation Coefficient (MCC). Finally, we delve into year-to-year predictions by comparing them against the historical series and examining the variables contributing to high and low predicted yield shock probabilities. We find that cold conditions during April and hot and/or dry conditions during July increase the chance of silage maize yield shock. Moreover, a combination of simple variables (e.g. total precipitation) and complex variables (e.g. cumulative cold under cold nights) enhances predictive accuracy. Lastly, we find that the years with higher predicted yield shock probabilities are characterized mainly by relatively hotter and drier conditions during July compared to years with lower yield shock probabilities. Our findings enhance our understanding of how weather impacts maize crop yield shocks and underscore the importance of considering complex variables and using effective selection methods, particularly when addressing climate-related events.

Potential processes leading to winter reddening of young Douglas-fir Pseudotsuga menziesii Mirb. Franco. in Europe

Key messageWinter reddening of young Douglas-fir (Pseudotsuga menziesii Mirb. Franco), triggered by large thermal fluctuations in late winter, is a critical problem for European forestry. A literature review identified certain climatic conditions that are characteristic of ‘reddening’ years, including warm daily temperatures, high daily temperature amplitude, low relative humidity, moderate wind speeds, as well as the occurrence of freeze-thaw cycles with cold night temperatures.By describing the triggering environmental and stand factors, we propose three hypotheses for the physiological processes leading to winter reddening, namely (i) hydraulic failure due to winter drought stress, (ii) photo-oxidative stress in shade-acclimated trees, and (iii) early cold deacclimation during warm periods.i) Low soil temperature, by reducing root water uptake, combined with anticyclonic conditions, by increasing water losses, can induce hydraulic failure in the xylem. Hydraulic failure may be further accelerated by night frosts.ii) Winter reddening can occur when low temperature and high irradiance coincide, disrupting photostasis. Overwhelming of winter photo-protection may lead to photodamage and subsequent reddening.iii) Warm periods, by inducing cold deacclimation, make trees susceptible to frost damage.Finally, the three processes may interact under atypical anticyclonic conditions in late winter (e.g. cold or dry soils, warm days, high irradiance and/or freezing nights). Indeed, trees under water stress would develop a higher sensitivity to freezing night and photooxidative stress. We therefore proposed mitigation actions to avoid exposing trees to stressful conditions based on e.g. stand characteristics, understorey vegetation and planting.

High thermal quality rookeries facilitate high thermoregulatory accuracy in pregnant female rattlesnakes

Temperature is a primary factor influencing organismal development, and the fluctuating daily and seasonal thermal regimes of temperate climates may challenge the ability of viviparous reptiles to optimize body temperatures during gestation. Testing how viviparous reptiles navigate highly variable thermal conditions (e.g., relatively cold nights and/or highly fluctuating temperatures) is a powerful way to understand how they use microhabitats for thermoregulatory benefits. We assessed the thermal ecology of pregnant and non-pregnant female Prairie Rattlesnakes (Crotalus viridis) inhabiting a high-elevation, montane shrubland in northwest Colorado throughout their short summer active season, addressing the thermal consequences of microhabitat selection with a focus on thermoregulation of pregnant females at communal rookery sites. We deployed operative temperature models to collect data on the thermal quality of microhabitats used by the snakes, and calculated thermoregulatory accuracy of the snakes by comparing their field-active body temperatures with preferred body temperatures of snakes placed in a thermal gradient. Pregnant females inhabited rocky, hilltop rookeries that had higher thermal quality due to higher and less variable nighttime temperatures compared to microhabitats in the surrounding prairie. Pregnant females therefore thermoregulated more accurately than non-pregnant females. The difference was most pronounced during the night, when pregnant females at rookeries maintained higher body temperatures than non-pregnant snakes in the prairie. Our results support the hypothesis that one major reason female rattlesnakes at high latitudes and/or high elevations forgo migration and gestate at communal, rocky, hilltop rookeries is that, relative to prairie microhabitats, they provide better conditions for thermoregulation during pregnancy.

Assessing the impacts of temperature extremes on agriculture yield and projecting future extremes using machine learning and deep learning approaches with CMIP6 data

Climate change, particularly extreme weather events, has significantly affected various sectors, including agriculture, human health, water resources, sea levels, and ecosystems. It is anticipated that the intensity, duration, and frequency of these extremes will escalate in the future. This study aims to discover the association between temperature extremes and agricultural yield and to project these extremes using machine learning (ML) and deep learning (DL) models with CMIP6 (Coupled Model Intercomparison Project Phase 6) data under two SSPs (Shared Socioeconomic Pathways). A bi-wavelet coherence technique is employed to investigate the association, providing detailed information in both the frequency and time domains for the period of 1980–2014. Various ML and DL models are trained and tested for the periods of 1985–2004 and 2005–2014, respectively, with gradient boosting machine chosen for projecting temperature extremes based on its superior performance. Mann-Kendall test is used for trend analysis in the projected temperature extremes. The results indicate strong negative and positive associations between TN10p (Cold nights) and TN90p (Warm nights), respectively, with wheat production. Additionally, there is a long-term negative association of CSDI (Cold Spell Duration Indicator) and strong positive association of WSDI (Warm Spell Duration Indicator) with rice yield. Projected results show an increase and decrease under SSP2-4.5 and SSP5-8.5, respectively, in DTR (Diurnal Temperature Range) at most stations. TN10p will increase in the future at most stations, with exceptions such as Muree station where it decreases during 2025–2049 and then increases under both SSPs. Projections show that TXn (annual or monthly minimum value of daily maximum temp) will increase in the future, with Muree station exhibiting the lowest value close to zero, while the average maximum value is around 20 °C at Khanpur station. Trend analysis reveals significantly increasing trend in TR20 (Tropical nights) and decreasing trend in CSDI in future durations under both SSPs. These findings hold implications for policymakers and stakeholders in various departments, including agriculture, health, and water resources management.

Quantifying future climate extreme indices: implications for sustainable urban development in West Africa, with a focus on the greater Accra region

Climate change leading to Climate extremes in the twenty-first century is more evident in megacities across the world, especially in West Africa. The Greater Accra region is one of the most populated regions in West Africa. As a result, the region has become more susceptible to climate extremes such as floods, heatwaves, and droughts. The study employed the Coupled Model Intercomparison Project 6 models in simulating climate extreme indices under the Shared Socioeconomic Pathway scenarios (SSPs) over West Africa between 1979 and 2059 as exemplified by the Greater Accra region. The study observed a generally weak drought in the historical period and expected to intensify especially under SSP585 in Greater Accra. For instance, continuous dry days (CDD) reveal an increasing trend under the SSPs. Similarly, the overall projected trend of CDD over West Africa reveals an increase signifying a more frequent and longer drought in the future. The flood indices revealed a surge in the intensity and duration of extreme precipitation events under the SSPs in the region. For instance, R99pTOT and Rx5days are expected to significantly increase under the SSPs with intensification under the SSP245, SSP370, and SSP585. A similar trend has been projected across West Africa, especially along the Guinean coast. The study foresees a gradual and intensifying rise in heatwave indices over the Greater Accra region. The warming and cooling indices reveal an increasing and decreasing trend respectively in the historical period as well as under the SSPs particularly within urban centers like Accra and Tema. Most West African countries are projected to observe more frequent warm days and nights with cold nights and days becoming less frequent. Expected effects of future climate extreme indices pose potential threats to the water, food, and energy systems as well as trigger recurrent floods and droughts over Greater Accra. The findings of the study are expected to inform climate policies and the nationally determined contribution of the Paris Agreement as well as address the sustainable development goal 11 (Sustainable cities) and 13 (Climate action) in West Africa.

Research on the Value and Language Features of Chinese Language and Literature Texts Based on Text Mining Technology

The linguistic characteristics of a literary work are the way of thinking embodied in the author’s use of language. From the textual value of Chinese language literature, this paper analyzes the spiritual connotation of Chinese language literature from two dimensions: reading and education. Based on the web crawler technology, we obtain the text data of Chinese language literature from three writers, Bajin, Yu Zheng and Qiong Yao, preprocess the data through data cleaning, Chinese word segmentation, de-duplication, etc., and extract the feature values of the text by using the TF-IDF algorithm. Then the text documents are mapped onto vectors using the VSM model, and the parameters of the LDA topic model are estimated by the Gibbs sampling algorithm in order to better obtain the topic changes of the Chinese language literature texts. This paper carries out linguistic feature verification from the lexical and similarity features of Chinese language literary texts. It is found that the difference in lexical density between Ba Jin’s Cold Night and Resting Garden is only 2.1 percentage points, and the frequency of the verb “to say” is 1,213 times and 735 times respectively. The average sentence lengths of Yu Zheng and Qiong Yao fluctuate within the range of [18.49,34.27], and Qiong Yao’s works have a higher thematic concentration than Zheng Zheng’s works. Analyzing the linguistic features of Chinese language literary texts based on text mining techniques helps to understand the authors’ language usage methods and helps to promote innovative expression paths in literary texts.

Study of Cold Wave and Cold Stress in the Four Metropolitan Cities of India for the Period 1985–2020

Cold waves, cold nights and warm nights are major threats to human beings during winter due to climate change in different parts of India. The analysis of these has been studied for four major metropolitan cities (Chennai, Mumbai, Kolkata, and Delhi) of India during the period 1985–2020. The authors have used the 90th and 10th percentile threshold to identify the cold nights, warm nights and cold waves during the winter season. The degree of discomfort and cold stress category are identified using the Humidity Index (HD), and the Universal Thermal Climate Index (UTCI). The results indicate that the cold night event in Mumbai is ~0.36% higher than both Kolkata and Chennai cities but it is ~0.42% higher than Delhi. The number of cold wave events in Delhi is 53.5% higher than in Kolkata during the period of the study. It is also observed from the study of UTCI that the possibility of slight cold stress in the Delhi region during cold nights is 59.36% more than in other metropolitan cities. The study indicates that in the winter season, the climate of Delhi is more dynamic but for Kolkata it is relatively less dynamic.

Early frost detection in wheat using machine learning from vertical temperature distributions

Frost damage significantly reduces global wheat production. Temperature development in wheat crops is a complex and dynamic process. During frost events, a vertical temperature gradient develops from soil to canopy due to the heat loss from the soil and canopy boundary. Understanding these temperature gradients is essential for improving frost management strategies in wheat crops. We hypothesise that the relationship between the temperatures of the canopy, plant and ground can be an early indicator of frost. We collected infrared thermal (IRT) images from field-grown wheat crops and extracted the temperatures from the canopy, plant and ground layers. We analysed these temperatures and applied four machine learning (ML) models to detect coldness scales leading to frost nights with different degrees of severity. We implemented a gated recurrent unit, convolutional neural network, random forest and support vector machines to evaluate the classification. Our study shows that in these three layers, temperatures have a relationship that can be used to determine frost early. The patterns of these three temperatures on a frost night differ from a cold no-frost winter night. On a no-frost night we observed that the canopy is the coldest, plant is warm, and the soil is warmest, and these three temperatures did not converge. On the other hand, on a frost night, before the frost event, the canopy and plant temperatures converged as the cold air penetrated through the canopy. These patterns in temperature distribution were translated into an ML problem to detect frost early. We classified coldness scales based on the temperatures conducive to frost formation of a certain severity degree. Our results show that the ML models can determine the coldness scales automatically with 93%–98% accuracy across the four models. The study presents a strong foundation for the development of early frost detection systems.

Hydraulic Analysis and Cold-Air Pool Interaction of the Raco Gap Wind

Abstract Raco is a local wind occurring in central Chile where the Maipo River Canyon exits into the Santiago valley. The intensification of the easterly down-canyon flow starts at any time during some cold season nights, accompanied by increases in temperature and drops in humidity. The hypothesis of the raco being a gap wind controlled by the narrowest section in the 12-km canyon exit corridor is tested with data from two events in July 2018 and July 2019. The data are analyzed in the framework of hydraulic theory, and a subcritical-to-supercritical transition is documented to occur at the narrows of the gap where the Froude number is close to unity, confirmed by radiosondes launched in the narrows in 2019. For the raco flow, the sum of potential and kinetic energy is conserved upstream of the narrows, while the acceleration occurring farther downstream loses a large fraction of energy to frictional dissipation. The raco events occur under the influence of regional subsidence, but a differential nocturnal warming of the in-canyon air mass is responsible for a pressure gradient driving the raco. In the 2019 case, a ceilometer mounted on an instrumented pickup truck documented the structure and movement of the interface between the raco air and the cold-air pool (CAP) existing over the valley to the west. Together with a radiosonde launched near the CAP–raco surface front, the observations reveal the intense shear-driven mixing taking place at the interface and the factors supporting the establishment of a stationary front.