Tag: Climate Change Downscaling Techniques

Abstract

Extensive research has been diligently conducted on wind energy technologies in response to pressing global environmental challenges and the growing demand for energy. Accurate wind speed predictions are crucial for the effective integration of large wind power systems. This study presents a novel and hybrid framework called ICEEMDAN-Informer-GWO, which combines three components to enhance the accuracy of wind speed predictions. The improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) component improves the decomposition of wind speed data, the Informer model provides computationally efficient wind speed predictions, and the grey wolf optimisation (GWO) algorithm optimises the parameters of the Informer model to achieve superior performance. Three different sets of wind speed prediction (WSP) models and wind farm data from Block Island, Gulf Coast, and Garden City are used to thoroughly assess the proposed hybrid framework. This evaluation focusses on WSP for three specific time horizons: 5 minutes, 30 minutes, and 1 hour ahead. The results obtained from the three conducted experiments conclusively demonstrate that the proposed hybrid framework exhibits superior performance, leading to statistically significant improvements across all three time horizons.

Abstract

In the current study, three optimistic (SSP1-2.6), medium (SSP2-4.5), and pessimistic (SSP5-8.5) scenarios were used to examine changes in precipitation based on the sixth phase of Coupled Model Intercomparison Project (CMIP6) in the Gorganrood watershed over two time periods: the near future (2021–2060) and the far future (2061–2100). To do this, the rainfall of 27 meteorological stations was studied. Using the RClimdex software in the R software, precipitation extreme indicators (11 indicators) were determined for different scenarios and periods, and Mann-Kendall (MK) and Sen’s estimator tests were then used to detect the trend. The results showed that in the near future under SSP1-2.6, the indicators of consecutive dry days (CDD) and consecutive wet days (CWD) have a significant downward and upward trend, respectively. While in the SSP5-8.5, the indicators of maximum five-day rainfall (RX1day), CDD, number of very wet days (R95p) and total wet day precipitation (PRCPTOT) have a significant downward trend in some stations. Similarly, in the far future, in the SSP5-8.5, the trend of rainfall indicators is insignificant compared to the near future, but still a significant decreasing trend can be seen in R95p, R99p, and PRCPTOT. Z score index (ZI) values ​​in both future periods showed that drought peaks occurred in the optimistic scenario and drought peaks occurred in the pessimistic scenario, and almost normal conditions prevailed in the intermediate scenario. The results can be effective in policies to deal with global warming and climate change.

Graphical Abstract

Abstract

The coarse spatial resolution of the Gravity Recovery and Climate Experiment (GRACE) dataset has limited its application in local water resource management and accounting. Despite efforts to improve GRACE spatial resolution, achieving high resolution downscaled grids that correspond to local hydrological behaviour and patterns is still limited. To overcome this issue, we propose a novel statistical downscaling approach to improve the spatial resolution of GRACE-terrestrial water storage changes (ΔTWS) using precipitation, evapotranspiration (ET), and runoff data from the Australian Water Outlook. These water budget components drive changes in the GRACE water column in much of the global land area. Here, the GRACE dataset is downscaled from the original resolution of 1.0° × 1.0° to 0.05° × 0.05° over a large hydro-geologic basin in northern Australia (the Cambrian Limestone Aquifer—CLA), capturing sub- grid heterogeneity in ΔTWS of the region. The downscaled results are validated using data from 12 in-situ groundwater monitoring stations and water budget estimates of the CLA’s land water storage changes from April 2002 to June 2017. The change in water storage over time (ds/dt) estimated from the water budget model was weakly correlated (r = 0.34) with the downscaled GRACE ΔTWS. The weak relationship was attributed to the possible uncertainties inherent in the ET datasets used in the water budget, particularly during the summer months. Our proposed methodology provides an opportunity to improve freshwater reporting using GRACE and enhances the feasibility of downscaling efforts for other hydrological data to strengthen local-scale applications.

Abstract

The growing need for energy from renewable sources, along with the unpredictable nature of wind power, has necessitated the development of efficient Wind Power Forecasting (WPF) algorithms. This study addresses the pressing issue of enhancing WPF algorithms in response to the growing demand for renewable energy and the inherent unpredictability of wind power. Over seven years from 2016 to 2023, conducted an exhaustive analysis of 92 research papers, focusing on the integration of Artificial Intelligence (AI) technologies to develop a robust WPF system. The study employs various AI approaches, including Deep Learning (DL), Machine Learning (ML), and neural networks, to predict wind energy generation with higher precision. Our main findings highlight a significant improvement in prediction accuracy, with the AI-based WPF system outperforming traditional methods by an average of 15%, based on a cross-validation of historical data. The integration of AI enables real-time adaptation to changing weather patterns, resulting in a 20% increase in responsiveness compared to conventional forecasting. Moreover, the proposed system optimizes resource allocation, leading to a 10% increase in energy efficiency and improved grid integration. Our results underscore the potential of AI in revolutionizing WPF, offering tangible advancements in accuracy, responsiveness, and resource efficiency. These findings advocate for the widespread adoption of AI-driven WPF systems to enhance the reliability and performance of renewable energy systems, contributing significantly to the global transition towards sustainable energy.

Abstract

Skin exposome encapsulates all internal and environmental exposures that affect skin health. Of these, photo-pollution refers to the combined effect on human skin of the simultaneous exposure to solar radiation (especially UV) and air pollution. Providing personalised photo-pollution exposure warnings and dose monitoring to an individual through a smartphone app could help in reducing skin ageing and degradation as well as in managing skin conditions (for example Atopic Dermatitis). However, accurate monitoring is challenging without a potentially expensive or cumbersome sensor device. In this work we present an innovative satellite-based air pollutant monitoring software service, ExpoPol, developed by siHealth Ltd. ExpoPol synthesises several inputs including live satellite imagery in real-time into an artificial intelligence (AI) model to provide assessment of the exposure of a smartphone user to relevant air pollutants, such as nitrogen oxides (NO_x), poly-aromatic hydrocarbons (PAH) and ozone (O₃). When combined with siHealth’s patented technology HappySun® for solar radiation monitoring, ExpoPol can effectively provide a sensor-less personal skin photo-pollution dosimetry. By downscaling satellite data using local geographic data, ExpoPol is capable of monitoring pollutants with street-level resolution and global coverage in near real-time. We evaluate the accuracy of ExpoPol against ground-station monitoring data for three pollutants across three continental regions (Europe, Asia, North America) and find R² values of 0.62, 0.65, 0.74 for PM₁₀, PM_2.5, NO₂ respectively. ExpoPol is shown to be significantly more accurate than a state-of-the-art global atmospheric forecasting system (CAMS) over the same ground-station dataset and provide data on much finer spatial resolutions. The presented system can support the real-time automatic assessment of the user’s skin exposome, anywhere and anytime. This paves the way for the development of mobile applications empowering users and clinicians to make informed decisions about skin health, or assisting dermocosmetic manufacturers in the creation of personalised products for personal care (e.g., skin ageing prevention or hair care).

Abstract

High temperature warning indicators play a pivotal role in meteorological departments, serving as crucial criteria for issuing warnings that guide both social production and daily life. Despite their importance, limited studies have explored the relationship between different global warming levels and changes in high temperature warning indicators. In this study, we analyze data from 2,419 meteorological stations over China and utilize the Coupled Model Intercomparison Project Phase 6 (CMIP6) models to examine historical changes in high temperature warning indicators used by the China Meteorological Administration. We evaluate model performance and estimate future changes in these indicators using an annual cycle bias correction method. The results indicate that since 1961, the number of high temperature days (TX35d and TX40d) and length of season (TX40d and TX40l) with daily maximum temperature reaching or exceeding 35°C and 40°C have increased over China. The intensity of high temperatures (TXx) has strengthened and the geographical extent affected by high temperatures has expanded. In 2022, the occurrence of 40°C high temperatures surges, with Eastern China experiencing a two-day increase in TX40d and an extended seasonal length in TX40l by over five days. While CMIP6 models have underestimated the high temperature indictors associated with 35°C during historical periods, notable difference is not observed between the models and observations for TX40d and TX40l, given their rare occurrence. However, future projections, after bias correction, indicate that the increasing trends for 35°C and 40°C high temperature days and length of season become more pronounced than the raw projection, suggesting a more severe increase than that anticipated originally. As global warming intensifies, the high temperature days and length of season are projected to increase non-linearly, while the intensity of high temperatures is expected to increase linearly. For every 1°C increase in global temperature, the intensity is projected to rise by approximately 1.4°C. The impact of high temperatures is expanding, with the major hotspot for China located in the eastern and northwestern regions. Under 5°C global warming, certain regions in China may experience prolonged extreme high temperatures. For instance, 40°C high temperature days in areas like North China and the Yangtze River Basin could increase by about 32 d, and the length of season could extend by approximately 100 d.

Abstract

High temperature warning indicators play a pivotal role in meteorological departments, serving as crucial criteria for issuing warnings that guide both social production and daily life. Despite their importance, limited studies have explored the relationship between different global warming levels and changes in high temperature warning indicators. In this study, we analyze data from 2,419 meteorological stations over China and utilize the Coupled Model Intercomparison Project Phase 6 (CMIP6) models to examine historical changes in high temperature warning indicators used by the China Meteorological Administration. We evaluate model performance and estimate future changes in these indicators using an annual cycle bias correction method. The results indicate that since 1961, the number of high temperature days (TX35d and TX40d) and length of season (TX40d and TX40l) with daily maximum temperature reaching or exceeding 35°C and 40°C have increased over China. The intensity of high temperatures (TXx) has strengthened and the geographical extent affected by high temperatures has expanded. In 2022, the occurrence of 40°C high temperatures surges, with Eastern China experiencing a two-day increase in TX40d and an extended seasonal length in TX40l by over five days. While CMIP6 models have underestimated the high temperature indictors associated with 35°C during historical periods, notable difference is not observed between the models and observations for TX40d and TX40l, given their rare occurrence. However, future projections, after bias correction, indicate that the increasing trends for 35°C and 40°C high temperature days and length of season become more pronounced than the raw projection, suggesting a more severe increase than that anticipated originally. As global warming intensifies, the high temperature days and length of season are projected to increase non-linearly, while the intensity of high temperatures is expected to increase linearly. For every 1°C increase in global temperature, the intensity is projected to rise by approximately 1.4°C. The impact of high temperatures is expanding, with the major hotspot for China located in the eastern and northwestern regions. Under 5°C global warming, certain regions in China may experience prolonged extreme high temperatures. For instance, 40°C high temperature days in areas like North China and the Yangtze River Basin could increase by about 32 d, and the length of season could extend by approximately 100 d.

Abstract

In recent years climate prediction has obtained more attention to mitigate the impact of natural disasters caused by climatic variability. Efficient and effective climate prediction helps palliate negative consequences and allows favourable conditions for managing the resources optimally through proper planning. Due to the environmental, geopolitical and economic consequences, forecasting of atmospheric and oceanic parameters still results in a challenging task. An efficient prediction technique named Sea Lion Autoregressive Deer Hunting Optimization-based Deep Recurrent Neural Network (SLArDHO-based Deep RNN) is developed in this research to predict the oceanic and atmospheric parameters. The extraction of technical indicators makes the devised method create optimal and accurate prediction outcomes by employing the deep learning framework. The classifier uses more training samples and this can be generated by augmenting the data samples using the oversampling method. The atmospheric and the oceanic parameters are considered for the prediction strategy using the Deep RNN classifier. Here, the weights of the Deep RNN classifier are optimally tuned by the SLArDHO algorithm to find the best value based on the fitness function. The devised method obtains minimum mean squared error (MSE), root mean square error (RMSE), mean absolute error (MAE) of 0.020, 0.142, and 0.029 for the All India Rainfall Index (AIRI) dataset.

Abstract

Aerosol optical depth (AOD) serves as a crucial indicator for assessing regional air quality. To address regional and urban pollution issues, there is a requirement for high-resolution AOD products, as the existing data is of very coarse resolution. To address this issue, we retrieved high-resolution AOD over Kanpur (26.4499°N, 80.3319°E), located in the Indo-Gangetic Plain (IGP) region using Landsat 8 imageries and implemented the algorithm SEMARA, which combines SARA (Simplified Aerosol Retrieval Algorithm) and SREM (Simplified and Robust Surface Reflectance Estimation). Our approach leveraged the green band of the Landsat 8, resulting in an impressive spatial resolution of 30 m of AOD and rigorously validated with available AERONET observations. The retrieved AOD is in good agreement with high correlation coefficients (r) of 0.997, a low root mean squared error of 0.035, and root mean bias of − 4.91%. We evaluated the retrieved AOD with downscaled MODIS (MCD19A2) AOD products across various land classes for cropped and harvested period of agriculture cycle over the study region. It is noticed that over the built-up region of Kanpur, the SEMARA algorithm exhibits a stronger correlation with the MODIS AOD product compared to vegetation, barren areas and water bodies. The SEMARA approach proved to be more effective for AOD retrieval over the barren and built-up land categories for harvested period compared with the cropping period. This study offers a first comparative examination of SEMARA-retrieved high-resolution AOD and MODIS AOD product over a station of IGP.

Abstract

Aerosol optical depth (AOD) serves as a crucial indicator for assessing regional air quality. To address regional and urban pollution issues, there is a requirement for high-resolution AOD products, as the existing data is of very coarse resolution. To address this issue, we retrieved high-resolution AOD over Kanpur (26.4499°N, 80.3319°E), located in the Indo-Gangetic Plain (IGP) region using Landsat 8 imageries and implemented the algorithm SEMARA, which combines SARA (Simplified Aerosol Retrieval Algorithm) and SREM (Simplified and Robust Surface Reflectance Estimation). Our approach leveraged the green band of the Landsat 8, resulting in an impressive spatial resolution of 30 m of AOD and rigorously validated with available AERONET observations. The retrieved AOD is in good agreement with high correlation coefficients (r) of 0.997, a low root mean squared error of 0.035, and root mean bias of − 4.91%. We evaluated the retrieved AOD with downscaled MODIS (MCD19A2) AOD products across various land classes for cropped and harvested period of agriculture cycle over the study region. It is noticed that over the built-up region of Kanpur, the SEMARA algorithm exhibits a stronger correlation with the MODIS AOD product compared to vegetation, barren areas and water bodies. The SEMARA approach proved to be more effective for AOD retrieval over the barren and built-up land categories for harvested period compared with the cropping period. This study offers a first comparative examination of SEMARA-retrieved high-resolution AOD and MODIS AOD product over a station of IGP.