Tag: Climate Change Downscaling Techniques

Abstract

Deep learning technologies have significantly advanced the field of single-image super-resolution (SISR), yet existing methods often prioritize peak signal-to-noise ratio (PSNR) over visual quality and realism. In this study, we propose NeXtSRGAN, which integrates a ConvNeXt-based discriminator to overcome these limitations and achieve more realistic and high-quality super-resolution (SR) images. NeXtSRGAN enhances image realism through its novel discriminator structure and generator residual block design, leveraging a relativistic discriminator and residual scaling. Experimental results on benchmark datasets demonstrate that NeXtSRGAN surpasses existing methods, including enhanced SRGAN (ESRGAN), with an average PSNR improvement of over 1.58 dB and an SSIM enhancement of over 0.05. Notably, NeXtSRGAN exhibits exceptional performance in facial image SR, as confirmed by the KID-F metric. By focusing on perceptual quality rather than solely PSNR, NeXtSRGAN sets a new standard for image restoration and holds promise for applications in various domains, such as video surveillance, medical imaging, and satellite photos. This code is available at https://github.com/PomKlementieff/NeXtSRGAN.

Abstract

Climate change has become an emerging topic, leading to widespread damage. However, when considering climate, attention is drawn to various scales, and urban microclimate has emerged as a trending subject due to its direct relevance to human living environments. Among the microclimatic factors, temperature and precipitation are utilized in order to identify trends. The identification of changes in precipitation and temperature from ground stations poses difficulties due to the lack of well-distributed stations; thus, satellite-based products are gaining popularity. The satellite products were validated against ground data, following which time-series and spatial analyses were conducted. The rainfall anomaly index, seasonality index, heat wave magnitude index, and mean temperature differ in the Colombo Metropolitan Area compared to the entire country. Each index is calculated decadal-wise to identify trends. By utilizing four climate indices, the analysis endeavors to investigate the microclimate identification in Colombo Metropolitan Area compared to its surrounding areas such as the Western Province and the entire country. This study aids local authorities in mitigating climate change by enhancing city resilience. These findings underscore the importance of understanding and addressing the impacts of climate change on temperature extremes to mitigate potential adverse effects on human activities and the environment. Understanding the specific reasons for spatial changes in rainfall anomalies often necessitates extensive climate modeling and data analysis.

Abstract

Precipitation prediction in mountainous regions is one of the most challenging topics in numerical weather prediction (NWP) models. This study aims to compare two types of NWP models: the General Circulation Model–Global Forecast System (GCM–GFS) and the mesoscale Weather Research and Forecasting (WRF). The comparison is based on various early lead-times (1, 3, and 5 days) in precipitation prediction and, consequently, flood forecasting in the northern parts of the Zagros Mountains, Iran. For this purpose, five observational flood events were selected in the region. To optimize the WRF model’s configuration, twelve setups were tested by combining microphysical and planetary boundary layer schemes. The Morrison and YSU schemes demonstrated superior performance in precipitation prediction. Comparative analysis of WRF and GFS model outputs revealed WRF’s better performance in point analysis using the nearest-neighbors method, while GFS exhibited greater reliability for mean areal precipitation. Subsequently, flood simulation was performed using the HEC-HMS model. Precipitation predicted by the GFS and WRF models was introduced to the HEC-HMS model in three early lead-times for flood forecast in all three domains of 3, 9, and 27 km. The results showed that in both the precipitation forecast and flood hydrographs produced by the HEC-HMS model, in most cases, the forecasting performance decreased with increasing early lead-time. Overall, based on the results of this study, the third domain of the mesoscale WRF model did not demonstrate significant added value over GFS outputs in most events. This underscores the necessity of focusing on reducing uncertainties and applying bias correction to the model outputs before their use in hydrological simulations, particularly in regions with complex topography.

Abstract

Sea surface temperature (SST) is a crucial factor in ocean analysis, playing a vital role in understanding oceanic dynamics. The changes in SST have significant implications for global warming or climate change, as seen in its potential for extreme weather events like droughts and floods. Tropical cyclone (TC) development is significantly influenced by SST, which serves as a key driver of their growth and strengthening, and its fluctuations are closely tied to the presence and intensity of these storms. Short and medium-range SST forecasting is vital since it can aid in detecting these extreme events and reduce the losses they generate through timely warnings and preventive measures. The usage of realistic forecasts of SST in high-resolution TC models will improve the estimation of its lifetime, intensity, and rainfall. Accuracy in SST forecasts has a tremendous economic and social impact. The forecasted SST can also help tackle the problem of gaps in satellite SST data. With the large-scale availability of high-resolution satellite data, data-driven techniques are gaining popularity and are being used for the short-range forecasting of SST. The study provides a detailed analysis of various machine learning and deep learning techniques for predicting SST with a lead time of 1–7 days in the Arabian Sea, Bay of Bengal, and South Indian Ocean regions. Furthermore, it recommends a suitable approach for operational use based on a thorough statistical evaluation of these forecasting methods.

Abstract

The Japanese Global Observing SATellite for Greenhouse gases and Water cycle (GOSAT-GW) will be an Earth-observing satellite to conduct global observations of atmospheric carbon dioxide (CO₂), methane (CH₄), and nitrogen dioxide (NO₂) simultaneously from a single platform. GOSAT-GW is the third satellite in the series of the currently operating Greenhouse gases Observing SATellite (GOSAT) and GOSAT-2. It will carry two sensors, the Total Anthropogenic and Natural emissions mapping SpectrOmeter-3 (TANSO-3) and the Advanced Microwave Scanning Radiometer 3 (AMSR3), with the latter dedicated to the observation of physical parameters related to the water cycle. TANSO-3 is a high-resolution grating spectrometer designed to measure reflected sunlight in the visible to short-wave infrared spectral ranges. It aims to retrieve the column-averaged dry-air mole fractions of CO₂ and CH₄ (denoted as XCO₂ and XCH₄, respectively), as well as the vertical column density of tropospheric NO₂. The TANSO-3 sensor onboard GOSAT-GW will utilize the wavelength bands of 0.45, 0.76, and 1.61 µm for NO₂, O₂, and CO₂ and CH₄ retrievals, respectively. GOSAT-GW will fly in a sun-synchronous orbit with a local overpass time of approximately 13:30 and a 3-day ground-track repeat cycle. The TANSO-3 sensor has two observation modes in the push-broom operation: Wide Mode, which provides globally covered maps with a 10-km spatial resolution within 3 days, and Focus Mode, which provides snapshot maps over targeted areas with a high spatial resolution of 1–3 km. The objectives of the GOSAT-GW mission include (1) monitoring atmospheric global-mean concentrations of greenhouse gasses (GHGs), (2) verifying national anthropogenic GHG emissions inventories, and (3) detecting GHG emissions from large sources, such as megacities and power plants. A comprehensive validation exercise will be conducted to ensure that the sensor products’ quality meets the required precision to achieve the above objectives. With a projected operational lifetime of seven years, GOSAT-GW will provide vital space-based constraints on both anthropogenic and natural GHG emissions. These measurements will contribute significantly to climate change mitigation efforts, particularly by supporting the Global Stocktake (GST) mechanism, a key element of the Paris Agreement.

Abstract

Climate change affects the hydrological cycle, leading to extreme events such as droughts and floods. Projection of climate change is necessary to understand the variability of future climate parameters for mitigating the impacts of climate change. The research aims to project the future precipitation over Amaravathi River Basin (ARB), Tamil Nadu, India considering as MME (Multi-Model Ensemble) CMIP6 (Coupled Model Inter comparison Project Phase-6) GCMs (General Circulation Models). The uncertainties and biases in the MME CMIP6 GCM precipitation were corrected and projected using the Empirical Quantile Mapping (EQM) employing the individual multiple Machine Learning (ML) and integrating algorithms through Stacking Regression (SR). Multiple machine learning algorithms used for bias-correction are Linear Regression (LR), Decision-Tree (DT) Regression, Random Forest (RF) Regression, Support-Vector Machine (SVM) Regression and Multi-Layer Perceptron (MLP) Regression with HyperParameter Tuning (HPT). Each machine learning algorithm with optimized hyperparameter was integrated into the SR to improve the model performance. The proposed SR showed better than the individual algorithms, with a RMSE (Root Mean Square Error) ranging from 37.14 to 66.28. The SR-based precipitation projection changes were analyzed as three periods: 2025–2050 (2040, near-future year), 2051–2075 (2065, mid-future year) and 2076–2100 (2090, far-future year) under SSP (Shared Socioeconomic Pathway) 1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 emission scenarios. The projected annual precipitation variations are in the range of 0.81–67.33% under the SSP1, followed by -4.51–72.13% (SSP2), -1.62–60.84% (SSP3) and − 0.71–65.75% under the SSP5 over the ARB. The precipitation was projected to be higher in magnitude in the southeast and lesser magnitude in the top northern part of ARB. The projection findings will be helpful in formulating strategies for addressing the climate impact and achieving the Sustainable Development Goal (SDG 13: Climate Action).

Abstract

In this study, the ET in the Dawen River Basin for the year 2021 was estimated using the SEBS model, combined with meteorological data and Landsat 8 remote sensing images. By analyzing representative remote sensing images from spring, summer, autumn, and winter, the temporal and spatial distribution characteristics of ET in the region, as well as its patterns of change, were studied. The results indicate that ET in the study area exhibits a distinct seasonal variation, with the highest levels occurring in summer, followed by fall, spring, and winter. ET peaked in July and gradually decreased to its lowest point in January. Although different land cover types showed similar seasonal variation in ET, the differences were particularly pronounced in summer, with the highest ET observed in water bodies and the lowest in built-up areas. A one-way analysis revealed that elevation was the most significant factor influencing ET, with an explanatory power of 0.272, followed by mean annual temperature and land type. This study provides a scientific basis for optimizing water resource management in the Dawen River Basin and offers new insights into the spatial and temporal dynamics of ET and its driving mechanisms in the region.

Abstract

Mapping species richness is a key goal of conservation research, but low data resolution and limited survey data make it challenging to accurately assess distribution patterns. In this study, the random forest (RF) and geographical random forest (GRF) models were used to construct a model of relationships between environmental factors and species richness, and high-resolution environmental data was used to downscale amphibian species distribution maps. The derived multi-scale species richness maps of 10 km, 5 km, and 1 km, revealed that the factors influencing the distribution of species richness and the locations of species richness hotspots vary with spatial scale. GRF outperformed GF in species richness map downscaling, with R² above 97% and RMSE between 0.98 and 1.29. GRF analysis shows that the spatial distribution of environmental factors affecting species distribution varies greatly, and precipitation dominates the distribution of most regions. This study suggests that machine learning algorithms can be used to downscale species richness maps. The multiscale species richness distribution map demonstrates the sensitivity of species richness patterns to spatial scales, which is crucial for macro-ecological analysis and identifying priority conservation areas. This information should be taken into account in future conservation planning.

Abstract

This study investigates the influence of high-resolution CMIP6 10-meter surface wind fields on wave climate dynamics in the South-East Australian region. We nest a regional unstructured grid spectral wave climate model within a global state-of-the-art spectral wave climate model to conduct our modelling experiments. The primary objective is to compare four distinct dynamical downscaling approaches of a single GCM product: CMIP, AMIP, HighResMIP, and a CORDEX downscaled ocean surface wind speed product corrected for SST and sea ice bias and variance. Of particular interest is the comparative performance between HighResMIP’s 25 km spatial resolution wind speed forcing and CORDEX’s 10 km resolution wind speed downscaling approach in replicating wind-wave climate extremes, as these products are currently the most appealing to downscaling wind wave climate extremes at the regional level. Our findings emphasize the critical importance of climate model wind-forcing downscaling for ensemble statistics of future regional extreme wave climate projections, which go beyond the sole impact of spatial resolution. Through detailed analysis, we describe the characteristics of each climate model’s downscaled wind speed input that impacts wind wave climate extremes in a region characterized by diverse wind wave climate conditions, ranging from local wind sea to swell conditions. These insights are valuable for estimating both past and future projected coastal flooding and erosion patterns and hold relevance for coastal risk assessment studies.

Abstract

Energy supply and consumption account for approximately 75% of global greenhouse gas emissions. Advances in semiconductor and power electronics technologies are required to integrate renewable energy into grids, electrify transport and the heating and cooling of buildings, and increase the efficiency of electricity conversion. This Review outlines the opportunities for carbon neutrality in the energy sector enabled by synergistic advances in wide-bandgap (WBG) semiconductors and power electronics. First, we present advances in WBG power devices, converter circuits and power electronics applications and their implications. For example, WBG materials have a high critical electric field and thermal stability; therefore, WBG devices can operate at higher temperatures and frequencies than silicon devices, enabling higher efficiency and reducing the number of passive components and cooling systems required in converter circuits. We then discuss advances in renewable energy systems, electric vehicles, data centres and heat pumps enabled by WBG devices, and their potential to reduce carbon emissions through electrification and increased energy conversion efficiency. We also consider the implications of the carbon footprint of WBG device manufacturing being larger than that of silicon manufacturing. Finally, we discuss research gaps that must be addressed to realize the potential of WBG semiconductors and power electronics for carbon neutrality.