Weather Types Research Articles

Assessing the potential of compound extreme storm surge and precipitation along China's coastline

The occurrence of extreme storm surges and precipitation simultaneously or successively can lead to compound flooding. The interaction between extreme storm surges and precipitation holds significant implications for understanding the potential contributing to compound flood risk in coastal areas. This study examines the likelihood of joint occurrence for compound extreme storm surges and precipitation along the China's coastline using observations and model data sets based on tail dependence. We assess the complete characteristics of the tail dependence from observations at the spatio-temporal scale. Subsequently, we perform a principal component analysis to classify the compound flood into 6 synoptic patterns based on the mean sea level pressure data in two typical points (Xiamen and Shijiusuo). We analyze the structure dependence of both observed and simulated surge data and compare the dependence between the historical and the future tail dependence. The result shows that the Yellow Sea and East China Sea exhibit higher dependence compared to the Bohai Sea and South China Sea. The southeastern sea of China has more significant seasonal variation in dependence relative to the northern sea of China. The result indicates that the dominant weather type in Xiamen is associated with low sea pressure and high land pressure, while the type in Shijiusuo is located at the southern edge of a low-pressure center. Projected probabilities of future compound events (2015–2050) have shown substantial increases of 23.9%, 25.38%, 58.21%, and 119.47% over the current period (1979–2014), according to climate models CMCC-CM2-VHR4, GFDL-CMC192-SST, ECEarth3P-HR, and HadGEM3-GC31-HM, respectively. These findings emphasize the correlation between extreme precipitation and storm surges, contributing to a deeper understanding of the compound flood and promoting disaster prevention and control.

Sensitivity analysis and precursor emission sources reduction strategies of O3 for different pollution weather types based on the GRAPES-CUACE adjoint model

Sensitivity analysis and precursor emission sources reduction strategies of O3 for different pollution weather types based on the GRAPES-CUACE adjoint model

Weather Translation via Weather-Cue Transferring.

In this article, the weather translation task is proposed, which aims to transfer the weather type of the image from one category to another. Weather translation is a complicated image weather editing task that changes the weather cue of an image across multiple weather types, and it is related to image restoration, image editing, and photographic style transfer tasks. Although lots of approaches have been developed for traditional image translation and restoration tasks, only few of them are capable of handling the multicategory weather types problem with a single network due to the rich categories and highly complicated semantic structures of weather images. Especially, it is difficult to change the weather cue while preserving the weather-invariant area. To solve these issues, we developed a weather-cue guided multidomain translation approach based on StarGAN v2, termed WeatherGAN. In the proposed model, the core generator is redesigned to transfer the weather cue according to the target weather type. The weather segmentation module is first introduced to acquire the weather semantic structure of images in a weakly supervised multitask manner. In addition, a weather clues module is presented to reprocess the weather segmentation into a weather-specific clues map, which identifies the weather-invariant and weather-cue areas clearly. Extensive studies and evaluations show that our approach outperforms the state of the art. The data and source code will be publicly available soon after the manuscript is accepted.

Using random forest to improve EMEP4PL model estimates of daily PM2.5 in Poland

Long-term exposure to poor air quality is responsible for many diseases and increased mortality worldwide. European Environmental Agency reports that Poland is one of the most polluted countries in Europe due to high emissions associated with large coal and wood consumption and specific weather conditions. Exceedances of WHO-recommended PM2.5 thresholds are still common in Poland, so further action is needed to protect the health of the population. Atmospheric chemical transport models (CTMs) provide information on air quality to the public and are used to regulate air pollutant emissions. However, uncertainties associated with CTMs, related to e.g. physical/chemical processes and input data quality often lead to underestimation of pollutant concentrations, especially for PM2.5, and limits the applicability of CTMs e.g. for health impact studies.A hybrid approach combining the EMEP4PL chemical transport model for Poland with the Random Forest (RF) machine learning algorithm was applied to address the limitations of CTM and reduce its underestimation. We used EMEP4PL-modelled PM2.5 concentrations for period 2016–2019 as a predictor and measured daily PM2.5 from 71 monitoring stations in Poland as a dependent variable in three RF scenarios, which differed in terms of the selected predictors. The impact of different additional variables for the area was revealed, including population and emission data, dominant type of land use, Weather and Research Forecast (WRF) meteorological parameters, and temporal patterns of PM2.5 concentrations across the years. The models were evaluated in a random 5-fold and spatial leave-one-station-out cross-validations (LOSOCV), as well as for an independent test set. Our final model achieved a test set R2 of 0.71, compared to 0.38 for EMEP4PL, along with a reduction in negative bias (0.25 μg m−3 for the final RF, compared to −11 μg m−3 for EMEP4PL) and an improved ability to detect severe PM2.5 episodes. Enhanced coefficients of determination were observed in all seasons and at all monitoring sites included in the study, for both types of cross-validation and for the test set. We estimated the contribution of each group of variables separately and discovered, that the most impactful predictors for the area of Poland included temporal patterns of PM2.5 calculated based on the averages of EMEP4PL outcome (such as day of the year, week number, etc.) and meteorological modelled factors related to temperature, planetary boundary layer height, wind speed, and atmospheric pressure. The developed approach provides a basis for improved spatiotemporal estimates and forecasting of PM2.5 in the region, which is an important step toward better understanding the impact of air pollution on the local population well-being.

Robust autonomous vehicle control by leveraging multi-stage MPC and quantized CNN in HIL Framework

This study presents a novel path planning and path-following system designed explicitly for real-world driving conditions in autonomous vehicles. The pipeline considers uncertainties in the dynamic parameters of the vehicle, which are common in real-world scenarios. To address these uncertainties, a robust multi-stage model predictive control approach is developed to generate optimal control outputs. A customized convolutional neural network, Nano-FastSCNN, quantized to half-precision, is employed for accurately segmenting left and right lane boundaries. To ensure that our visual planner is robust against adverse weather conditions, we have rigorously evaluated its generalization capabilities under challenging weather types. The centerline of these lanes is then selected as the desired path and transformed into the camera reference frame by leveraging the camera projection matrix. The controller utilizes this reference trajectory to guide the vehicle along the desired trajectory. To demonstrate real-time processing capabilities, the entire pipeline, including the vision system module and controller, is implemented in a hardware-in-the-loop setup utilizing three NVIDIA Jetson devices to demonstrate the scalability of our architecture. Comprehensive evaluations demonstrate that the pipeline achieves an inference speed of 15.6 FPS, even on the less powerful NVIDIA Jetson Nano. Moreover, the system successfully alleviates the detrimental effects of uncertainties and reliably follows the desired path.

Experimental investigation of heat transfer rate for building roof with corrugated sheets using phase change material (PCM)

Research on cost-effective and efficient technology that can hold enormous amounts of heat or cold in a specific volume has been ongoing for a while. Due to its isothermal behaviour throughout the phase change process and high energy storage density, latent heat storage in phase change materials (PCM) is highly appealing. The incorporation of latent heat of storage in building materials considerably aids in the energy conservation of structures, which is a major role of thermal storage. By reducing the frequency of internal air temperature swings and keeping the indoor air temperature closer to the ideal temperature for longer periods of time, increasing the building's thermal storage capacity can improve human comfort. Nonetheless, a phase change material can be chosen to accommodate every type of weather in a certain area.

The effect of different extreme weather events on attitudes toward climate change.

Can exposure to extreme weather change political opinion and preferences about climate change? There is a growing literature on both the effects of extreme weather events and the factors explaining attitudes toward global warming, though there remains no clear consensus about whether being exposed to extreme weather influences public opinion about climate change. We contribute to this literature by studying the impact of a variety of extreme weather events associated with climate variability, including severe storms, floods, fires, and hurricanes, on attitudes toward climate change. Specifically, we use a three-wave panel survey and a dynamic difference-in-differences design to analyze public opinion data at the individual level in the US. We find that exposure to only one extreme weather type-fires-has a small but significant effect on acknowledging the existence of climate change and supporting the need for action. However, that impact quickly vanishes, and other types of extreme weather do not appear to have any effect on opinion.

Non-Destructive Assessment of Stone Heritage Weathering Types Based on Machine Learning Method Using Hyperspectral Data

Abstract. Stone cultural heritage is exposed to various environments, resulting in a diverse range of weathering types. The identification of these weathering types is vital for targeted conservation efforts. In this paper, a weathering type classification method based on hyperspectral imaging technology is proposed. Firstly, fresh sandstones are collected from Yungang Grottoes to carry out the simulated weathering experiments, including freeze-thaw cycles and wet-dry cycles with acid, alkali and salt solutions. Subsequently, the hyperspectral imaging system was used to collect the visible-near-infrared (VNIR) and short-wave infrared (SWIR) images of the sandstone samples with different weathering types and degrees. The surface spectral reflectance of sandstone samples with different weathering types were used as training data, with weathering types serving as the labels. Support vector machine (SVM), K-nearest neighbour (KNN), linear discriminant analysis (LDA) and random forest (RF) were used to establish weathering type classification models. The results show that the SVM model and LDA model based on both VNIR and SWIR spectra exhibit outstanding performance, with a best accuracy of 0.994. The framework proposed in this paper facilitates rapid and non-contact assessment of the weathering types of the superficial layers of stone cultural heritage, thereby supporting more targeted conservation work.

Creating synthetic energy meter data using conditional diffusion and building metadata

Advances in machine learning and increased computational power have driven progress in energy-related research. However, limited access to private energy data from buildings hinders traditional regression models relying on historical data. While generative models offer a solution, previous studies have primarily focused on short-term generation periods (e.g., daily profiles) and a limited number of meters. Thus, the study proposes a conditional diffusion model for generating high-quality synthetic energy data using relevant metadata. Using a dataset comprising 1,828 power meters from various buildings and countries, this model is compared with traditional methods like Conditional Generative Adversarial Networks (CGAN) and Conditional Variational Auto-Encoders (CVAE). It explicitly handles long-term annual consumption profiles, harnessing metadata such as location, weather, building, and meter type to produce coherent synthetic data that closely resembles real-world energy consumption patterns. The results demonstrate the proposed diffusion model's superior performance, with a 36% reduction in Fréchet Inception Distance (FID) score and a 13% decrease in Kullback-Leibler divergence (KL divergence) compared to the following best method. The proposed method successfully generates high-quality energy data through metadata, and its code will be open-sourced, establishing a foundation for a broader array of energy data generation models in the future.

Powerless in the storm: Severe weather-driven power outages in New York State, 2017–2020

The vulnerability of the power grid to severe weather events is a critical issue as climate change is expected to increase extreme events, which can damage components of the power grid and/or lessen electrical power supply, resulting in power outages. However, largely due to an absence of granular spatiotemporal outage data, we lack a robust understanding of how severe weather-driven outages, their community impacts, and their durations distribute across space and socioeconomic vulnerability. Here, we pair hourly power outage data in electrical power operating localities (n = 1865) throughout NYS with urbanicity, CDC Social Vulnerability Index, and hourly weather (temperature, precipitation, wind speed, lightning strike, snowfall) data. We used these data to characterize the impact of extreme weather events on power outages from 2017–2020, while considering neighborhood vulnerability factors. Specifically, we assess (a) the lagged effect of severe weather on power outages, (b) common combinations of severe weather types contributing to outages, (c) the spatial distribution of the severe weather-driven outages, and (d) disparities in severe weather-driven outages by degree of community social vulnerability. We found that across NYS, 39.9% of all outages co-occurred with severe weather. However, certain regions, including eastern Queens, upper Manhattan and the Bronx of NYC, the Hudson Valley, and Adirondack regions were more burdened with severe weather-driven outages. Using targeted maximum likelihood estimation, we found that the frequency of heat-, precipitation-, and wind-driven outages disproportionately impacted vulnerable communities in NYC. When comparing durations of outages, we found that in rural regions, precipitation- and snow-driven outages lasted the longest in vulnerable communities. Under a shifting climate, anticipated increases in power outages will differentially burden communities due to regional heterogeneity in severe weather event severity, grid preparedness, and population socioeconomic profiles/vulnerabilities. As such, policymakers must consider these characteristics to inform equitable grid management and improvements.

On the Importance of Regime-Specific Evaluations for Numerical Weather Prediction Models as Demonstrated Using the High-Resolution Rapid Refresh (HRRR) Model

Abstract The scientific literature has many studies evaluating numerical weather prediction (NWP) models. However, many of those studies averaged across a myriad of different atmospheric conditions and surface forcings that can obfuscate the atmospheric conditions when NWP models perform well versus when they perform inadequately. To help isolate these different weather conditions, we used observations from the U.S. Climate Reference Network (USCRN) obtained between 1 January and 31 December 2021 to distinguish among different near-surface atmospheric conditions [i.e., different near-surface heating rates (), incoming shortwave radiation (SWd) regimes, and 5-cm soil moisture (SM05)] to evaluate the High-Resolution Rapid Refresh (HRRR) Model, which is a 3-km model used for operational weather forecasting in the United States. On days with small (large) , we found afternoon T biases of about 2°C (−1°C) and afternoon SWd biases of up to 170 W m−2 (100 W m−2), but negligible impacts on SM05 biases. On days with small (large) SWd, we found daytime temperature biases of about 3°C (−2.5°C) and daytime SWd biases of up to 190 W m−2 (80 W m−2). Whereas different SM05 had little impact on T and SWd biases, dry (wet) conditions had positive (negative) SM05 biases. We argue that the proper evaluation of weather forecasting models requires careful consideration of different near-surface atmospheric conditions and is critical to better identify model deficiencies in order to support improvements to the parameterization schemes used therein. A similar, regime-specific verification approach may also be used to help evaluate other geophysical models. Significance Statement Improving weather forecasting models requires careful evaluations against high-quality observations. We used observations from the U.S. Climate Reference Network (USCRN) and found that the performance of the High-Resolution Rapid Refresh (HRRR) Model varies as a function of differences in near-surface heating and solar radiation. This finding indicates that model evaluations need to be conducted under varying near-surface weather conditions rather than averaging across multiple weather types. This new approach will allow for model developers to better identify model deficiencies and is a useful step to helping improve weather forecasts.

Agricultural Crop Recommendation System using Machine Learning

The main goal of these system is to achieve maximum yield and this can be achieved only with proper selection of crop. Selection of crop yield is maximized by considering the pro- portion of nutrients present in the soil. In this system, the farmer / beginner will classify and predict the crop cultivation based on their weather, monsoon and soil type along with their pH level. For classification we have used the Random Forest algorithm to choose the crop

Benefits of applying hourly resolution in the assessment of the climate aptitude to manage tourist activities in arid regions

The availability of reliable information on local climatic-tourism conditions is a growing need due to the influence it exerts on the quality of the organizational strategy of tourist destination’s, and travel experience. Evaluations of the tourism potential of the climate have been carried out on a daily or monthly resolution, thus limiting the collection of detailed information that makes it possible to fine-tune tourism management and operational decision-making on an intraday scale. This research is the first case study to analyse the climatic suitability for nature tourism, using the weather types method at hourly resolution. The study applies to arid tourist destinations in Isfahan province (Iran). The detailed resolution has made it possible to identify the time slots favourable to the development of nature tourism in those periods of the year recognized as critical in the daily resolution analyses. In the same way, the hourly resolution has also identified critical bands in those periods indicated as favourable in the evaluations to daily resolution. The hourly resolution provides detailed information that can allow tourists and also tourism managers to establish intraday adaptation strategies that make it possible to develop the activity even in places with extreme climates.

DWT-BILSTM-based models for day-ahead hourly global horizontal solar irradiance forecasting

Accurate forecasting of electricity generation from renewable energy sources is crucial for the operation, planning and management of smart grids. For reliable planning and operation of photovoltaic (PV) systems in grid-connected or islanded utilities, an hourly day-ahead forecast of PV output is critical. The forecast of PV power can be done indirectly by estimating solar irradiance. For forecasting day-ahead hourly global horizontal irradiance (GHI), two forecasting models with different multivariate inputs are proposed in this paper, and the results are compared. These models use a hybrid algorithm of discrete wavelet decomposition and bidirectional long short-term memory (BILSTM). The inputs of the first model contain GHI and weather type data. The other model allows for observation of the effect of meteorological values including GHI, temperature, humidity, wind speed, and weather type data. The forecasting performance of deep learning algorithms which contain recurrent neural network (RNN), long short-term memory (LSTM), and BILSTM algorithms for day ahead hourly solar irradiance forecasting problems are also compared. To evaluate the performance of proposed models, two datasets are used for Model 1 and one dataset is used for Model 2. An experiment is also done to demonstrate that the proposed Model 1 is applicable in datasets collected in the vicinity of the city of Trabzon. On the other hand, BILSTM algorithm outperforms RNN and LSTM algorithms. It is seen that the test successes of both proposed models are better than the results given in the literature.

From Performance Measurements to Molecular Level Characterization: Exploring the Differences between Ultraviolet and Damp Heat Weathering of Photovoltaics Modules

The degradation behaviors of the encapsulant and the imbedded additives significantly determine the reliability of solar modules. Nevertheless, a link between the degradation of the encapsulant, including the additive interactions, and the longevity of the overall module is rarely established until now. Herein, mini‐modules containing ethylene‐vinyl acetate copolymer (EVA) as encapsulant are subject to damp heat (DH) or ultraviolet (UV) weathering based on IEC 61215. Macroscopically, the degradation under both weathering types characterized by I–V measurements and electroluminescence (EL) measurements is diverging in dependence on the used stressor. Using electron paramagnetic resonance and orbitrap mass spectrometry, it is shown that deacetylation of the EVA occurs significantly for both types of weathering. In the case of DH, however, the mechanism of action of the UV stabilizer is hindered, so that strong encapsulant degradation is observed despite a lower energy input in comparison with UV. Furthermore, the produced acetic acid under DH weathering leads to the observed reduction in EL, an increase in series resistance, and, a reduction of the performance of the modules. The work carried out shows that the degradation of the solar modules is strongly dependent on the behavior of the UV stabilizer.

The total factor characteristics evaluation of photovoltaic power by coarse-fine-grained method

The study of photovoltaic(PV) power uncertainty is of great significance to promote the collaborative optimization of source-grid-load-storage. On the one hand, using a single probability distribution model to describe the fluctuation, intermittency and randomness of PV power is limited and controversial; On the other hand, qualitative analysis oriented to only one or two of them is characterized with poor interpretability and one-sidedness. So that, a evaluation framework for the total factor characteristics named "dual-DMU" is established corresponding to the discrete, mutation and unordered phenomenon of PV power, and a coarse-fine-grained evaluation method is proposed, which can realize the comprehensive description and all-sided evaluation of PV power. Combined with the historical data of a rooftop PV power plant, the analysis reveals the variability of total factor characteristics in different seasons, weather types, and typical time periods, and the methodology can provide a theoretical basis and decision-making support for the construction of PV power forecasting models, energy storage capacity optimization, and adjustable load resource allocation.

Integrated GIS-based and water quality index for evaluation of groundwater quality in the coastal slum settlements of Lagos, Nigeria

This study aimed to identify drinking water suitability through a statistical and water quality index (WQI) approach in coastal slum settlements in Lagos, Nigeria. A total of 250 borehole groundwater samples collected for drinking purposes during the wet (125) and dry (125) seasons were analysed for their physical and chemical properties from 2019 to 2020. The physical and chemical characterisation of the region was conducted based on the correlation matrix, scree plot, PCA, Q-mode HCA, and ROC curve to identify the types of weathering, the influence of rock on precipitation and evaporation, and types of reactions that affect groundwater composition. The analytical mean concentration results revealed that SO4, Cl, TH, NO3, TDS, and PO4 surpassed the USEPA and WHO permissible limits in both seasons. The WQI results demonstrate that 7.6% and 6.7% of water samples fall into the excellent category, while 12.4% and 11.5% are moderately tolerable. In contrast, for both seasons, >100% of the samples fell into the unsuitable drinking category across SA1-SA5, SB1-SB5, SC1-SC5, SD1−SD5, and SE1−SE5. Groundwater quality mainly deteriorates due to anthropogenic activities and climatic variability. The spatial variation map indicates that groundwater quality is primarily affected during the wet season and, to a lesser extent, in a few slums during the dry season. Seawater influx in coastline areas has been identified as the main factor threatening the quality and availability of groundwater resources. As a result, the groundwater quality is mostly poor to unsuitable, and this study confirms its unsuitability for drinking. Hence, this study serves as a guide for effective action in sustainable groundwater management.

Physical Activity Dependence on Relative Temperature and Humidity Characteristics in a Young, Insufficiently Active Population: A Weather Typing Analysis.

Physical activity (PA) is an important contributor to one's physical and mental health both acutely and across the lifespan. Much research has done on the ambient environment's impact on PA; however, these studies have used absolute values of atmospheric measures such as temperature and humidity, which vary spatiotemporally and make comparisons between studies which differ in location or time of year difficult to square with one another. Here, we employ the Global Weather Type Classification, Version 2, to determine the combined impact of temperature and humidity on PA in a sample of insufficiently active young adults. We conducted secondary analyses of data from a single-group behavioral intervention trial that varied the number of digital messages sent daily. Young adults (n = 81) wore Fitbit Versa smartwatches for a 6-month period sometime between April 2019 and July 2020, and location was tracked using a custom smartphone application. Mixed linear models indicated that, across 8179 person-days, PA was significantly lower on days with humid conditions and significantly higher on warm dry days, though the latter relationship was no longer significant when controlling for timing in relation to the COVID-19 pandemic declaration. Demographic factors did not affect the relationship between weather and PA. Results are a first step in providing additional guidance for encouraging PA in insufficiently active individuals given forecasted daily weather conditions. Future work should examine seasonal variability in the weather type-PA relationship without the influence of a world-altering event influencing results.

Some Geomorphological Implications of Chemical Weathering in Sequences of Sedimentary Rocks in the Kirkuk Structure

This study was based on a review of a group of geomorphological features and characteristics within the Kirkuk anticline in the Low Folded Zone within the Unstable Shelf of Iraq. As well as identifying the role of the chemistry of the rocks that form the geological formations in the studied area and explaining the role of chemical weathering processes in shaping the geomorphological units. Each of these geomorphological units includes several landforms with different lithologies. These shapes were produced as a result of weathering, erosion, and sedimentation processes associated with structural, compositional, rock, and climatic factors. Literature. The exposed rocks in the sedimentation basin, from which different formations are formed, showed variation in their resistance to chemical and physical weathering processes. It has been noted that the chemical weathering of gypsum rocks is of the type of incipient weathering and that of limestone is of the type of incipient to moderate weathering. For the sandstones, they are of the moderate weathering type, while it is noted that the chemical weathering of the claystone and marl is of the extreme weathering type. This demonstrates the important role played by the type of rocks and their chemical composition in highlighting the role of various geomorphological processes in determining the nature of land units and forming the geomorphological features, in addition to the influence of various geological processes and the presence of structural units such as joints, fractures, and faults, as well as the influence of slope and various climatic factors.

Predicting the type of road accidents based on air temperature in Iran: A case study of roads in Qazvin province

This study investigates the relationship between ambient temperature, weather conditions, and types of road accidents in Qazvin province, Iran. The research addresses a significant societal challenge of road accidents, particularly in developing countries like Iran. The objectives are to analyze the correlation between temperature and accident types and to develop a predictive model using data mining techniques. The study employs a quantitative approach, analyzing over 15,000 accident records from 2010 to 2020. The findings reveal a connection between the temperature variable and the type of road accidents as well as weather conditions. Additionally, data mining analysis identifies a predictable pattern among temperature variables, types of road accidents, and weather conditions. Implications of the study underscore the importance of considering temperature and weather conditions as secondary factors influencing accidents. The predictive model can aid decision-makers in formulating effective strategies to reduce accidents. Understanding the relationship between temperature, weather, and accident types enables the design of targeted interventions to enhance road safety. This research contributes valuable insights to accident reduction efforts and emphasizes the significance of addressing environmental variables in road safety planning and policy-making.Moreover, the results of the data mining pattern analysis indicate that car overturning accidents in various weather conditions are the primary type of accidents, followed by chain accidents. However, the types of accidents vary based on different weather conditions and temperatures. The study highlights the intricate connection between weather conditions, temperature, and types of road accidents. By utilizing data mining techniques, the research provides a predictive model for accident patterns, offering valuable insights to enhance road safety strategies.