Local Weather Changes Research Articles

Assessing systematic biases in farmers’ local weather change perceptions

Scientific data concerning climate change are critical for designing mitigation and adaptation strategies. Equally important is how stakeholders perceive climate change because perceptions influence decision-making. In this paper, we employ spatially-delineated primary surveys to evaluate weather perception biases among corn and soybean farmers located on western frontier of the U.S. Corn Belt where substantial loss of grassland has been documented. We characterize farmers’ perception biases by measuring the gap between survey-based perception reports for three distinct weather indicators (i.e., temperature, precipitation and drought) and corresponding meteorological evidence. About 70% farmers in our sample misperceive past weather changes. Three-fourths of these misperceiving farmers over-estimate local temperatures and drought frequency and 40% of them under-estimate precipitation trends relative to past records. We further find evidence that farmers’ weather change perceptions are systematically biased in a manner that would justify past land use decisions. Particularly, higher cropping incidence on previously protected grasslands effected more farmers to under-perceive drier conditions and over-perceive wetter conditions. Our investigation of perception biases across distinct weather indicators with a reference to past economic decisions enriches the understanding of climate change perceptions and related policies.

Application and simulation of IEC water chillers for summer free cooling in data centers

Free cooling is regarded as the most effective way to reduce the energy consumption of the cooling system in data centers. This study proposes a novel water-medium indirect evaporative cooling (IEC) chiller used in a data center to achieve free cooling throughout the year as an alternative to electrical chillers. Field tests and numerical modeling of the IEC water chillers are both reported to evaluate the performance and efficiency. This paper first-time reports on the application of IEC water chillers to deliver year-round free cooling for a data center in Changji, China. The wet-bulb efficiency of the tested chiller is measured as 120% to 152.8% and the dew-point efficiency is 27% to 62.7%. A numerical model is established to verify the operating conditions, and some common operational optimizations are pointed out in this test for energy savings. PUE can be reduced to 1.212 after the optimization. More future performance predictions of the IEC water chillers are studied when the data center load rate increases and when the local weather changes in a year to discuss the year-round availability. For further study, the performance of IEC water chillers in different climate regions is also forecasted. The results indicate that it’s an effective way for IEC to achieve free cooling in regions where natural cold resources are abundant. In this study, the field test performance and prediction of IEC water chillers are provided as a typical and practical application as the natural cooling source in data centers in hot and dry areas.

Artificial intelligence‐driven sustainability: Enhancing carbon capture for sustainable development goals– A review

AbstractArtificial intelligence (AI) and environmental points are equally important components within the response to local weather change. Therefore, based on the efforts of reducing carbon emissions more efficiently and effectively, this study tries to focus on AI integration with carbon capture technology. The urgency of tackling climate change means we need more advanced carbon capture, and this is an area where AI can make a huge impact in how these technologies are operated and managed. It will minimize manufacturing emissions and improve both resource efficiency as well as our planet's environmental footprint by turning waste into something of value again. Artificial intelligence could be leveraged to analyze huge data sets from carbon capture plants, searching for optimal system settings and more efficient ways of identifying patterns in the available information at a larger scale than currently possible. In addition, AI incorporated sensors and monitoring mechanisms in the supply chain can identify any operational failure at reception itself allowing for timely action to protect those areas. Artificial intelligence also helps generative design for carbon capture materials, which allows researchers to explore new types of carbon‐absorbing material, including metal–organic frameworks and polymeric materials that are important in industrial CO2, such as moisture. In addition, it increases the accuracy of reservoir simulations and controls CO2 injection systems for storage or enhanced oil recovery. Through applying AI algorithms on reservoir geology, production performance and real‐time data this study would like to facilitate the optimization of injection processes as well as minimize CO2 emissions while assuring a maximum efficiency. Artificial intelligence integrates with renewable‐based carbon capture efforts that can be employed by AI‐driven smart grid systems to improve carbon capture methods.

Pengamatan Cuaca Lokal secara Multi Node dengan Internet of Things dan Django Framework

Weather is one of the challenges that humans must experience in their activities. Like the MSMEs of sand crackers in Kediri Regency. This MSME uses sunlight as a means of drying products. What often becomes a problem is unpredictable weather conditions which reduce productivity and quality. Therefore, a real-time local weather observation system is needed to anticipate sudden weather changes. In this research, an IoT-based local weather condition measurement tool will be connected to a system built using the Django framework. This system and tools were tested for eight days. So it was concluded that the system was successfully built with data collection accuracy of 96.31%. Measurements are carried out every 5 minutes or a time frame of 5 minutes. In addition, this system supports observations in several places at once. This multiple node concept is used to detect local weather changes in the surrounding area. So it is not concentrated in the MSME area.

A new optimal PV installation angle model in high-latitude cold regions based on historical weather big data

PV technologies are regarded as one of the most promising renewable options for the transition towards Net Zero. Despite the rapid development of PV systems in recent years, achieving the necessary goals requires more than a threefold increase in annual capacity deployment by 2030. However, current PV systems often fall short of optimal performance due to improper installation angles. In high-latitude cold regions, the actual PV generation capacity is frequently overestimated due to the omission of snow conditions. This study introduces a novel model designed for high-latitude regions to predict local optimal PV installation angle that maximizes PV power generation, utilizing historical weather big data, including snowfall and melting effects. A case study is presented within a Swedish context to demonstrate the implementation of these methods. The results highlight the crucial role snow conditions play in determining PV performance, resulting in an average reduction of 14.7% in annual PV power generation. Optimal installation angle could yield approximately a 4.8% improvement compared to common installation angles. The study also explores the application of snow removal agents, which could potentially increase PV generation by 0.1–2.3%. Additionally, the new PV installation angle successfully captures the impact of the local weather changes on PV power generation, potentially serving as a bridge between climate change adaptation and future PV power generation endeavors.

Analysis of marine heatwaves and biogeochemistry in the Northern Arabian Sea

Marine heatwaves (MHWs) are abnormal Sea Surface Temperature (SST) conditions in which temperature exceeds the 90th percentile threshold limit. It has significant impacts on marine biodiversity causing large-scale coral bleaching, fish deaths or migration, and local weather changes. Analyzing MHWs and their causative mechanism can help to understand the notable influence on biodiversity. In this paper, we have investigated the MHW events in the Arabian Sea (AS) for the period 1982 to 2021. Our study reveals that an average of two MHW events occur every year with an increasing linear trend of 1.22 MHW events per decade in the northern AS region. In addition, we found a category-II MHW event that lasted about 134 days in the year 2020. This northern AS MHW event has a maximum, mean, and cumulative intensity of about 2.76 °C, 1.94 °C, and 259.35 °C-days (above the climatological mean), respectively, from 9thJune 2020 to 20thOctober 2020. According to our analysis, the western AS showed relatively higher intensity than the eastern part, especially near the coasts. The entire AS showed an increasing trend of MHW. We found that the decrease in wind velocity, increase of net downward heat flux, and lowering of surface ocean current is the main cause of the 2020 northern AS MHW event. Many of the biogeochemistry parameters, namely, chlorophyll (-16.2255%), net primary production (-25.5948%), dissolved oxygen (-0.7323%), nitrate (-23.9284%), and phosphate (-49.2542%) show the lowest values during the 2020 MHW event period except for silicate (13.9649%) for their corresponding climatological mean. Decreasing trends are also observed for chlorophyll (-0.0020 mg/m3-year), net primary production (-0.1747 mg/m3-year), oxygen (-0.1079 mmol/m3-year), nitrate (-0.0080 mmol/m3-year) and phosphate (-0.0076 mmol/m3-year) with increasing MHW days except for silicate that shows an increasing trend of 0.0018 mmol/m3-year with increasing MHW days with time.Plain Language SummaryForty years of SST data is used to find and analyze the most intense MHW events in the Arabian Sea (AS). We calculated its characteristics such as maximum, mean, cumulative intensity, and duration to understand and further analyze these MHW events. We have analyzed the time series of the number of MHW events per year from 1982 to 2021 alongside the trend to understand and predict the future MHW events in the northern AS region. We generated spatial plots of different MHW parameters over the entire AS and tried to understand their behavior. We compared changes in six marine biogeochemistry parameters spatially and temporally with the yearly MHW days and observed their relationship.

Using Protection Motivation Theory to examine information-seeking behaviors on climate change

Many earlier studies concluded that exposure to changes in local weather or extreme weather events prompt public interest in climate change, and in turn raise support for mitigation policies. However, these findings do not square with observations of record-breaking temperatures, and decades of failure to reduce greenhouse gas emissions. To address this conundrum, we use Protection Motivation Theory to form hypotheses on the specific type of climate change-related information that individuals seek during periods of extreme local weather. Using daily-level internet search engine data from Chinese cities, we find that residents are purposeful and rational in seeking information on climate change. Specifically, when faced with high or abnormal temperatures, they are much more likely to seek information to appraise their susceptibility to climate change threats, and evaluate coping responses. On the other hand, due to the lack of direct benefits, they do not seek out information on climate mitigation behaviors. In contrast to earlier studies, our findings suggest that it is unlikely that extreme weather events will prompt support for climate mitigation actions. Instead, as worldwide weather becomes more extreme and unpredictable, it is likely that public’s attention will shift in the direction of adaptation measures.

Radiation and Temperature of a Tropical Grassland in Summer Times: Experimental Observations

The surface texture of urbanized regions is altered by the replacement of natural vegetated surfaces with hardened pavement surfaces, which have been described as a heat source for the formation of urban heat islands. Grasslands may store rainfall in their roots and leaves for later cooling, but this has received little attention. This study investigated the radiant flux and temperature of a tropical grassland throughout the summer in order to understand the albedo, long-wave radiation, short-wave radiation, and surface temperature of the grassland over 10 days. The grassland had an albedo of 0.13, which did not fluctuate during the day compared to the albedo of other surfaces in metropolitan areas. Even if the local weather changes considerably, this albedo does not alter significantly. The surface temperature and the air temperature above the grassland increase linearly with the upwelling reflectance, incident solar radiation, and upwelling long-wave radiation. These two temperatures do not correspond with downwelling long-wave radiation, which is influenced by cloud cover in the sky. However, the peaks of these temperatures lag behind the incident shortwave radiation and net radiation that reaches the grassland surface. The finding that the thermal properties of grasslands could be harnessed to reduce the heat absorbed by urban surfaces provides valuable insights into the grasslands’ potential to mitigate the impacts of urbanization on temperature.

Mapping and identification of ice-sheet and glacier features using optical and SAR data in parts of central Dronning Maud Land (cDML), East Antarctica

High-resolution satellite data is used to extract ice-sheet features in Princess-Astrid Coast of Queen Maud Land, in Droning Maud Land between 70° and 72°S and 5°-20°E, East Antarctica. Their mapping and monitoring is cumbersome and time-consuming using traditional survey methods. Therefore, to achieve desirable results, RS based methods were employed. The main ice-sheet and glacial features mapped are BlueIce-Area (BIA), melt water channels, glacier radar zones, crevasse, nunataks, moraine, supraglacial lakes, and wind scoop. Support-vector-machine (SVM) and supervised classification algorithm were applied over Landsat-8, RISAT-1 and Sentinel-1 data to classify these features. Surface patterns of snow and clouds were found in the continental ice-sheet, which becomes an obstacle to clearly extract sub-surface icesheet features in optical data. Therefore, to improve mapping of snow and ice features, high-resolution microwave data was also used. By comparing optical and microwave data, it was found that more sub-surface features could be mapped using microwave data. Further, RS was used to map the temporal change in the glacial features using two-year images for the austral summer of 2016 and 2017. It was found that diurnal as-well-as seasonal changes in surface ice features occur due to changes in local weather and can be mapped using temporal RS data.

Exposure-lag-response associations between weather conditions and ankylosing spondylitis: a time series study

BackgroundPatients with ankylosing spondylitis (AS) have reported that their pain becomes worse when the local weather changes. However, there is limited evidence verifying the short-term associations between meteorological factors and outpatient visits for patients with AS. Therefore, this study evaluates this possible association.MethodsMeteorological data and data on daily AS outpatient visits to a general hospital in Hefei, China, from 2014 to 2019 were collected and analysed. Distributed lag nonlinear models and Poisson regression models were employed to determine the association between weather conditions and outpatient visits; the results were also stratified by gender and age.ResultsHigh relative humidity is significantly associated with all patient visits in lag 1 (RR = 1.113, 95% CI 1.021 to 1.213) and lag 7 days (RR = 1.115, 95% CI 1.014 to 1.227). A low relative risk to the nadir is observed in lag 4 days (RR = 0.920, 95% CI 0.862 to 0.983). Male and young patients (< 65 years) are more vulnerable to damp weather, and elderly people (≥ 65 years) are significantly affected by high temperatures in lag 7 days (RR = 3.004, 95% CI 1.201 to 7.510).ConclusionsOur findings suggest a potential relationship between exposure to weather conditions and increased risk of AS outpatient visits. These results can aid hospitals in preparing for and managing hospital visits by AS patients when the local weather conditions change.

Modeling Daily Temperatures Via a Phenology-Based Annual Temperature Cycle Model

High spatiotemporal resolution land surface temperature (LST) plays an important role in various environment applications. However, the limitation of thermal infrared sensors and the effect of clouds and other atmospheric conditions result in discontinuous daily thermal observations of the Moderate Resolution Imaging Spectroradiometer (MODIS). Annual temperature cycle (ATC) models can help to supply daily continuous LSTs via limited observations, but these ATC models seldom consider the disturbance of weather conditions or the land cover change. On the other hand, spatial interpolation techniques also limit in implementation when available data in one day or several days are not able to obtain enough spatiotemporal information for LST reconstruction. The objective of this study is to propose a phenology-based ATC model (termed PATC), which takes the phenology change and local weather change into account, to reconstruct daily unscanned LSTs at an annual scale. Daily MODIS LSTs collected in 2015 were utilized to analyze the performance of PATC compared with other ATC models. Results show that PATC improved the accuracy by 1.6 and 0.5 K compared to the classic ATC model in the daytime and nighttime, respectively. Compared to the enhanced ATC model, PATC also shows better performance with higher accuracies, especially during the growing season of vegetation in the daytime. Future research may focus on an incorporation with Landsat observations and diurnal temperature cycle models to implement LST reconstruction at a diurnal scale.

Mumps prediction based on improved support vector machine

The prevention of d isease, has the important guiding significance to safeguard the health of the people. In order to improve the accuracy rate of mumps disease forecast of Yinchuan City, Ning xia, joined the combined kernel function idea based on regression prediction model of the traditional Support Vector Machine, the polynomial and radial basis kernel function combination instead of the traditional single kernel function model, proposed a combination of kernel function of support vector machine algorithm, with mumps Ning xia disease for the practice of Yinchuan City, to conduct research and forecast for the pathogenesis of rules combined with the local weather changes. The meteorological data and the number of diseases using correlation analysis method for processing, select the factor related coefficient ordering large from, then these factors should be selected using a single kernel and mixed kernel function for processing, training and prediction using support vector machine. The experimental results show that the mixed kernel function of Support vector machine prediction model results than the single kernel function regression is better

Numerical Study on the Influence of Weir Construction on Near-Surface Atmospheric Conditions

Changes in land use/land cover (LULC) have an impact on weather and climate. Gumi Weir in the Nakdong River Basin of South Korea was built and led to changes of LULC in the river in which the width of the river was extended by 300 m on average from land to water. In this study, in order to analyze the effect of the LULC change on weather variables, the Large Eddy Simulation of Weather Research and Forecasting model was used based on the LULC before and after weir construction. The expanded river width increased the latent heat flux (LHF) and mixing ratio (q). The changes in the sensible heat flux (SHF) subsequently influenced the changes in the air temperature (Ta) around the river, where the LULC was directly changed. The distance of affected area due to the LULC change was investigated using a paired t-test. The results of the paired t-test revealed that an increase in the Nakdong river width led to a noticeable change in Ta and q with the mean distance of affected area up to 1100 m from the river shores. The results of this study could offer an understanding of the effects of small weir construction on local weather changes.

Warmer April Temperatures on Breeding Grounds Promote Earlier Nesting in a Long-Distance Migratory Bird, the Prothonotary Warbler

Global climate change and warming are altering hemispheric and local weather patterns. Altered weather patterns have great potential to affect the phenology of life history events, such as the initiation of breeding in organisms that reproduce seasonally. Long-distance migratory birds may be particularly challenged by changes in local weather on breeding grounds because they arrive from distant locations and must commence breeding when conditions are appropriate. Here we explore the effects of local temperature on first egg dates and annual productivity in a long-distance Neotropical migratory songbird, the prothonotary warblerProtonotaria citrea. We present results from a 20-year (1994 to 2013) study documenting the detailed nesting activities of a color-marked population (average of 155 individual females each year) of warblers in southern Illinois, United States. The warblers typically arrive in April and start breeding in late April and May in our study system. We tested for an effect of local average April daily temperature and female age on first egg dates, total number of offspring produced per female, and the probability of fledging two broods. We found that warmer April temperatures promoted earlier first egg dates and higher average annual productivity in the warblers. On average, older females had earlier first egg dates than 1-year-old females, but both age groups responded similarly to local April temperatures. The reproductive gains associated with earlier first egg dates in warmer years stemmed from an increased probability of successfully fledging two broods, suggesting that earlier first egg dates do not currently create a mismatch with food (insect) resources. Earliest arrival dates of warblers to the region of our study system were not affected by local April temperatures, suggesting that females vary their first egg date based on conditions they experience/assess after their arrival. Whereas these birds currently adjust the timing of their breeding and actually produce more offspring in warmer years, continued global warming may eventually upset the current balance between arrival dates, food resources, and the commencement of nesting.

Association between weather and Dall's sheep Ovis dalli dalli harvest success in Alaska

Although extensive research has been conducted on a variety of factors that influence wildlife harvest rates, few studies have quantified the impact of weather on harvest success. As global warming continues to contribute to unprecedented changes in local weather regimes, particularly in arctic and alpine ecosystems, understanding how these changes impact human–wildlife interactions will become increasingly important and relevant for wildlife managers. Therefore, we used a long-term dataset (1999–2015) on Dall's sheep Ovis dalli dalli, an alpine species in Alaska, USA, as a case study to explore how changes in local daily weather has affected hunter harvest success. We used generalized linear mixed models to estimate relationships between daily harvest count and weather variables using three separate models; all hunters, resident hunters and non-resident hunters. Weather variables included daily mean relative humidity, precipitation, air temperature and wind speed. For our model including all hunters, which excluded wind, we estimated that a mean increase in relative humidity, precipitation and temperature from one day to the next resulted in an 11.7, 4.3 and 2.9% decrease in daily harvest, respectively. The effect of relative humidity influenced harvest count two to three times more than all other weather variables across models. This study contributes to a limited body of knowledge on quantifying the impact of weather on harvest success and about how changes in weather affect hunter and wildlife behavior. Advancing knowledge on how weather influences variation in harvest may facilitate effective strategies for adapting hunting regulations to meet harvest and population goals.

Mathematical models for determining the boundaries of forest areas unstable to the appearance of insects using satellite data (MODIS)

The proposed approach allows us to evaluate the response of forest stands to local weather changes and resistance to insect attack without using data from weather stations, sometimes located far from experimental forest areas. All raw data were obtained during the season using remote sensing data (MODIS / AQUA). The ability to pre-determine decrease and loss of forest stands resistance to possible insect attacks can be extremely important for solving the tasks of forest-entomological monitoring and calculating the risk of forest insect’s outbreaks.

Examining the Effects of Hydropower Station Construction on the Surface Temperature of the Jinsha River Dry-Hot Valley at Different Seasons

On the completion of a large-scale hydropower station, the change of the water area can cause a corresponding change of local weather. To examine such changes, this paper analyzed the effect of the reservoir in the head area of the Xiluodu hydropower station based on the temperature data of MODIS MYD11A2. The temperature differences (TD) between various locations in the study area and the reservoir were calculated to explore the TD in different seasons. The reservoir effect change intensity (RECI) was established to explore the impact of the reservoir on local weather changes in different flood seasons. The combination of the TD and RECI was applied to explore the role of the hydropower station in regulating the temperature of the surrounding reservoir. The results showed the following: (1) after hydropower station construction (HSC), the TD in the valleys decreased and the TD in the dry season was lower than that in the wet season; (2) the RECI had different distribution characteristics in different flood seasons of the reservoir, and the RECI was stronger in the wet season than that in the dry season; and (3) unlike in the plains, cooling and warming effects existed simultaneously in different parts of the mountains.

Generalized Byram's formula for arbitrary fire front geometries

The expression proposed a half century ago by Byram allowing the estimation of wild land fire front heat flux was extensively used in fire research and by firefighters. However, this formula does not account for wild land fuel heterogeneity and local weather changes. Its validity is limited to stationary spreads, and fails in describing the behavior of curved fronts and non-spreading fires. A new formula is derived to generalize heat flux estimation to any fire front with arbitrary geometry. Heat flux is found to depend on the front local radius as well as its convexity/concavity. The front curvature induces also a quadratic dependence on the rate of spread. The derived equation is validated by numerical simulations using the small world network model for line and curved fronts. Extensions of generalized Byram formula including fractal dimension of the front and wind speed effect are also proposed.

Earlier breeding, lower success: does the spatial scale of climatic conditions matter in a migratory passerine bird?

Following over 20 years of research on the climatic effects on biodiversity we now have strong evidence that climate change affects phenology, fitness, and distribution ranges of different taxa, including birds. Bird phenology likely responds to changes in local weather. It is also affected by climatic year‐to‐year variations on larger scales. Although such scale‐related effects are common in ecology, most studies analyzing the effects of climate change were accomplished using climatic information on a single spatial scale. In this study, we aimed at determining the scale‐dependent sensitivity of breeding phenology and success to climate change in a migratory passerine bird, the barn swallow (Hirundo rustica). For both annual broods, we investigated effects of local weather (local scale) and the North Atlantic Oscillation (NAO, large scale) on the timing of breeding and breeding success. Consistent with previous studies in migratory birds we found that barn swallows in Eastern Germany bred progressively earlier. At the same time, they showed reduced breeding success over time in response to recent climatic changes. Responses to climatic variation were observed on both local and large climatic scales, but they differed with respect to the ecological process considered. Specifically, we found that the timing of breeding was primarily influenced by large‐scale NAO variations and to a lesser extent by local weather on the breeding grounds. Conversely, climatic conditions on the local scale affected breeding success, exclusively. The observed decrease in breeding success over years is likely a consequence of scale‐related mismatches between climatic conditions during different breeding phases. This provides further evidence that a species' response of earlier breeding may not be enough to cope with climate change. Our results emphasize the importance of considering the response of ecological processes along different climatic scales in order to better understand the complexity of climate change effects on biodiversity.

Changing local weather and adaptation in two coastal villages in Bangladesh

Coastal Bangladesh is experiencing the effects of weather and climate-related stressors. The paper reports on the ways in which members of nine occupational groups in two coastal villages in South-west Bangladesh understand and respond differentially to recent changes in local weather and weather-related events and processes. Their understanding is grounded in local experiences and varied livelihood options, which are a complex interaction between people's social and spatial locations and local weather and non-weather events and processes. Their priorities are to protect livelihoods through resilience-building actions, which address proximate causes of vulnerability (improving coastal embankments, rehabilitating and strengthening homesteads, protecting agricultural land). Government support is limited to traditional development interventions that assist some more than others and often disconnected from local people's priorities. Most adaptation actions are practical measures within the prevailing politico-legal order. There is some evidence of more rights-based cooperative action between a national non-governmental organisation and local farmers that challenged aspects of that order.