Meteorological Applications最新文献

Heat waves harm human health and adversely impact the natural environment and society, especially in urban regions. Understanding the differences between heat waves in urban agglomerations and their driving mechanisms is essential for sustainable development. In this study, we investigate the spatiotemporal distribution of summertime heat waves and their association with sea surface temperature modes in two of China's most densely populated urban areas: the Beijing–Tianjin–Hebei (BTH) and the Yangtze River Economic Belt (YREB). The results indicate an increase in the frequency of heat waves for BTH and YREB by 0.02 times a⁻¹ and 0.1 times a⁻¹ and duration by 0.09d a⁻¹ and 0.48d a⁻¹, respectively. Regarding spatial distribution, the duration and frequency of BTH heat waves gradually decreased from northeast to southwest. In contrast, the heat waves in YREB were concentrated in the upper and parts of the lower reaches. The Atlantic Multidecadal Oscillation significantly influences heat waves in both the BTH and YREB regions. Nevertheless, the Pacific Decadal Oscillation, Indian Ocean Basin-Wide Index, and Cold-tongue ENSO Index primarily impact heat waves in the YREB region, with limited influence observed in the BTH region. This study provides a scientific basis for accurately identifying heat waves and understanding their changes, assisting decision-makers in formulating mitigation, adaptation strategies, and disaster prevention policies related to heat-induced consequences.

Precipitation, essential for the water cycle and key to surface runoff and groundwater, causes floods and droughts when unevenly distributed. Understanding the variations in precipitation across China is vital for managing water resources and preventing weather-related disasters. In this study, we analyzed the spatial–temporal variations in rainfall amounts and the number of rainy days across different levels in China using daily precipitation data during 1960–2019. We found a nonsignificant increase in annual total precipitation (ATP), but a significant decline in the number of days with ATP during this period. This shift suggests that precipitation is becoming more concentrated in fewer days, potentially due to an increase in the frequency of heavy rain (25 ≤ p < 50 mm/day, L₃), rainstorm (50 ≤ p < 100 mm/day, L₄), and heavy rainstorm (p > 100 mm/day, L₅). The amount and frequency of precipitation in light rain (0.1 ≤ p < 10 mm/day, L₁) and moderate rain (10 ≤ p < 25 mm/day, L₂) exhibited a decreasing trend during this period, whereas the patterns for L₃, L₄, and L₅ demonstrated an increasing trend. Notably, the decrease in the number of days with L₁ and L₂ precipitation was relatively minor compared with the substantial increase in the number of days experiencing L₃, L₄, and L₅ precipitation. Despite L₁ precipitation making up only 24.9% of China's ATP, it accounts for 78.6% of total precipitation days. This underscores the important role played by L₁ precipitation events in determining the overall frequency of precipitation occurrences in China. Significant regional disparities are observed in both precipitation amounts and the number of precipitation days across different precipitation levels. Furthermore, large-scale climate indices have consistently affected China's precipitation patterns since 1960, impacting not just the current year but possibly extending into the subsequent year.

Increasing global temperatures have triggered several environmental and ecological challenges. Recurring droughts across the globe are an adverse consequence of global warming. In this research, a new drought forecasting index—the Multimodal Forecastable Standardized Precipitation Evapotranspiration Index (MFSPEI)—has been suggested using projections from multiple climate models. The MFSPEI methodology is primarily based on the first component of the Forecastable Component Analysis (FCA) and the Standardized Precipitation Evapotranspiration Index (SPEI). For application purposes, the time series data of SPEI from 10 climatic models endorsed by the Coupled Model Intercomparison Project phase 6 (CMIP-6) at 50 random locations over the region of the Tibetan Plateau (TP) have been considered. The outcomes show that the first component of FCA captures a sufficient amount of variation while maintaining high forecastability in all the selected grid points and the chosen prominent timescales of drought monitoring indices. To assess the predictive performance of the proposed index (MFSPEI), comparison matrices of artificial neural network (ANN) models were identified. During the training and testing phases, the forecast efficiency of the developed indicator (MFSPEI) proved superior to that of the individual SPEI. The numerical assessment indicates that the deviations and difficulties in interpreting SPEI data from individual climate models can be addressed more effectively with the proposed indicator. Therefore, MFSPEI effectively reinforces drought predictions for drought preparedness and management in the context of multiple climate model projections.

The study aimed to assess the impact of climate change on the crop suitability index (CSI) of selected staple crops for current (1981–2010) and future (2021–2050 and 2051–2080) climates across Africa. Precipitation and mean temperature data from gridded observations, and 10 Global Climate Models (GCMs) were utilized to calculate the CSI for maize, soybean, wheat, plantain, cassava, rice, millet, sorghum, and yam. The Ecocrop model implemented in R, utilizing the FAO-Ecocrop database alongside climatic variables for different climatic zones across the continent, was employed to compute the CSI. The results indicate that all crops, except rain-fed rice, are suitable in parts of West and Central African regions, with wheat being inclusive in some parts of the Guinea Coast. The northern, eastern, and southern African regions are identified as the least suitable for any crop production based on the balance between the base climate parameters over the historical period. Analysis over this historical period reveals an increasing trend for major crops in most regions, except for wheat crop production, which demonstrates a decreasing trend in most areas. Projection analysis reveals that the Sahel region is expected to be the most affected by climate change, with a significant reduction in the suitability index for most crops. Conversely, the Southeastern Africa and the Guinea Coast regions are likely to be the least affected, as the suitability index increases for the considered crops. This analysis provides crucial information for effective agricultural planning and resource allocation, optimizing land use by identifying crops aligned with prevailing environmental conditions, including soil type, climate, and water availability. Such information enhances the understanding of crop suitability, contributing to improved agricultural productivity and sustainability.

Climate and land-use/land-cover (LULC) change each threaten the health of rivers. Rising temperatures, changes in rainfall and runoff, and other perturbations, will all impact rivers' physical, biological, and chemical characteristics over the next century. While scientists and policymakers have increasing access to climate and LULC forecasts, the implications of each for outcomes of interest have been difficult to quantify. This is partially because climate and LULC perturb ecological outcomes via incompletely understood site-specific, interacting, and nonlinear mechanisms that are not well suited to analysis using classical statistical methods. This creates uncertainties over the benefits of local-level interventions such as green infrastructure investments and urban densification, and limits how forecasts can be used to inform decision-making. Here, we demonstrate how machine learning can be used to quantify the relative contributions of LULC and climate drivers to impacts on riverine health as measured by taxonomic richness of the macroinvertebrate orders Ephemeroptera, Plecoptera, and Trichoptera (EPT). We develop a cross-validated Random Forest (RF) model to link EPT taxa richness to meteorological, water quality, hydrologic, and LULC variables in watersheds in New Hampshire and Vermont, USA. Prospective climate and LULC scenarios are used to generate predictions of these variables and of EPT taxa richness trends through the year 2099. The model structure is mechanistically interpretable and performs well on test data (R² ~ 0.4). Impacts on EPT taxa richness are driven by local LULC policy such as increased suburbanization. Future trends are likely to be exacerbated by climate change, although warming conditions suggest possible increases in springtime EPT taxa richness. Overall, this analysis highlights (1) the impact of local LULC decisions on riverine health in the context of a changing climate, and (2) the role machine learning methods can play in developing models that disentangle interacting physical mechanisms to advance decision support.

Latent heat flux (LE) is a measure of the water exchange between Earth's surface and atmosphere, also known as evapotranspiration. It is a fundamental component in the Earth's energy budget and hydrological cycle and plays an important role in regulating the weather and climate. Moderate Resolution Imaging Spectroradiometer (MODIS) offers a gap-filled biophysical product for LE at 8-day temporal and 500-meter spatial resolutions. Nonetheless, validation against the in situ eddy covariance measurement reveals significant errors in MODIS LE estimation. Our study integrates ground-measured, reanalysis and satellite data to predict LE by leveraging the advantage of the data-driven method. The study draws upon flux data derived from the AsiaFlux database, alongside reanalysis datasets from the Indian Monsoon Data Assimilation and Analysis (IMDAA) and the European Centre for Medium-Range Weather Forecasts (ERA5) products, as well as biophysical measurements from the MODIS satellite. An analysis of the annual water budget, based on ERA5 precipitation data, highlights net positive water balances across the study sites. By harnessing diverse datasets, we employ various machine learning regression algorithms. We find the support vector regression superior to linear, lasso, random forest, adaptive boosting and gradient boosting algorithms. This study highlights the robustness of support vector regression and accentuates the impact of climatic and environmental conditions on model performance, ultimately contributing to more precise predictions of latent heat flux.

This study seeks to understand the meteorological mechanisms that caused widespread and heavy rainfall from 6 to 14 February 2023, over southern Mozambique and the eastern and northeastern areas in South Africa, including Limpopo Province, Mpumalanga Province and northern KwaZulu-Natal, by examining different outputs from reanalysis datasets. The heavy rainfall had a substantial hydrological impact, leading to significant flooding and disruptions. This research revealed that a slow-moving cutoff low (COL) system remained over the central parts of South Africa, triggering extensive and heavy rainfall mostly over the northeastern and eastern provinces. The outcomes from the reanalysis datasets display the influence of the weather system and the interaction between an initiated westerly wave, which converted into a near-stationary upper-air cold core upper air COL system, and the easterly wind wave associated with the South Indian Ocean Convergence Zone (SICZ), bringing significant warm humid air from the Indian Ocean into the study area. This study revealed an abnormal structural pattern in the wind vectors, low-pressure trough, upper and mid-tropospheric westerly flows and humidity compared with the long-term climate normal values over Mozambique and the northeastern and eastern regions of southern Africa. This event is exciting from a meteorological perspective due to its intensity and duration, the involvement of cyclonic activity and its implications for understanding the impacts of climate change on weather patterns in southern Africa. The heavy rainfall had a substantial hydrological impact, leading to significant flooding and disruptions, providing valuable data for improving forecasting models and disaster preparedness strategies and underscoring the importance of enhancing climate resilience in regions prone to extreme weather.

It is vital to analyze extreme wind speed in marine engineering designs. However, due to the lack of observational data, it is impossible to establish the measured long-term wind speed series. This study simulates the annual hourly wind field of every tropical cyclone (TC) with a resolution of 5 km in the Northwest Pacific (NWP) from 1981 to 2020. On this basis, combined with the sea surface wind speed data observed by the satellites and the ships, the 40-year annual maximum wind speed series of NWP are established. The Gumbel, three-parameter Weibull (Weibull-3par), two-parameter Weibull (Weibull-2par), generalized extreme-value (GEV) distribution, and the two parameter estimation methods are used to estimate the extreme wind speeds with different return periods (RPs) at four typical locations in the NWP. Meanwhile, the effects of different extreme-value distributions and different parameter estimation methods on the estimation results are discussed. Subsequently, the best distribution and parameter estimation method for each grid in the NWP are determined by the goodness-of-fit test, and then the spatial distributions of extreme wind speeds with different RPs along with uncertainty estimates in the entire NWP are obtained. The results show that extreme wind speeds with RPs of 5, 25, 50, and 100 years in the east of Taiwan and Philippines can reach a maximum of 43.8, 60.8, 70.4, and 81.4 m s⁻¹, respectively.