Weather Types Research Articles

Atmospheric Drivers Associated with Extreme Snow Ablation and Discharge Events in the Susquehanna River Basin: A Climatology

Abstract Extreme snow ablation can greatly impact regional hydrology, affecting streamflow, soil moisture, and groundwater supplies. Relatively little is known about the climatology of extreme ablation events in the eastern United States, and the causal atmospheric forcing mechanisms behind such events. Studying the Susquehanna River basin over a 50-yr period, here we evaluate the variability of extreme ablation and river discharge events in conjunction with a synoptic classification and global-scale teleconnection pattern analysis. Results indicate that an average of 4.2 extreme ablation events occurred within the basin per year, where some 88% of those events resulted in an increase in river discharge when evaluated at a 3-day lag. Both extreme ablation and extreme discharge events occurred most frequently during instances of southerly synoptic-scale flow, accounting for 35.7% and 35.8% of events, respectively. However, extreme ablation was also regularly observed during high pressure overhead and rain-on-snow synoptic weather types. The largest magnitude of snow ablation per extreme event occurred during occasions of rain-on-snow, where a basinwide, areal-weighted 5.7 cm of snow depth was lost, approximately 23% larger than the average extreme event. Interannually, southerly flow synoptic weather types were more frequent during winter seasons when the Arctic and North Atlantic Oscillations were positively phased. Approximately 30% of the variance in rain-on-snow weather type frequency was explained by the Pacific–North American pattern. Evaluating the pathway of physical forcing mechanisms from regional events up through global patterns allows for improved understanding of the processes resulting in extreme ablation and discharge across the Susquehanna basin. Significance Statement The purpose of this study is to better understand how certain weather patterns are related to extreme snowmelt and streamflow events and what causes those weather patterns to vary with time. This is valuable information for informing hazard preparation and resource management within the basin. We found that weather patterns with southerly winds were the most frequent patterns responsible for extreme melt and streamflow, and those patterns occurred more often when the Arctic and North Atlantic Oscillations were in their “positive” configuration. Future work should consider the potential for these patterns, and related impacts, to change over time.

Estimating Full Longwave and Shortwave Radiative Transfer with Neural Networks of Varying Complexity

Abstract Radiative transfer (RT) is a crucial but computationally expensive process in numerical weather/climate prediction. We develop neural networks (NN) to emulate a common RT parameterization called the Rapid Radiative Transfer Model (RRTM), with the goal of creating a faster parameterization for the Global Forecast System (GFS) v16. In previous work we emulated a highly simplified version of the shortwave RRTM only—excluding many predictor variables, driven by Rapid Refresh forecasts interpolated to a consistent height grid, using only 30 sites in the Northern Hemisphere. In this work we emulate the full shortwave and longwave RRTM—with all predictor variables, driven by GFSv16 forecasts on the native pressure–sigma grid, using data from around the globe. We experiment with NNs of widely varying complexity, including the U-net++ and U-net3+ architectures and deeply supervised training, designed to ensure realistic and accurate structure in gridded predictions. We evaluate the optimal shortwave NN and optimal longwave NN in great detail—as a function of geographic location, cloud regime, and other weather types. Both NNs produce extremely reliable heating rates and fluxes. The shortwave NN has an overall RMSE/MAE/bias of 0.14/0.08/−0.002 K day−1 for heating rate and 6.3/4.3/−0.1 W m−2 for net flux. Analogous numbers for the longwave NN are 0.22/0.12/−0.0006 K day−1 and 1.07/0.76/+0.01 W m−2. Both NNs perform well in nearly all situations, and the shortwave (longwave) NN is 7510 (90) times faster than the RRTM. Both will soon be tested online in the GFSv16. Significance Statement Radiative transfer is an important process for weather and climate. Accurate radiative transfer models exist, such as the RRTM, but these models are computationally slow. We develop neural networks (NNs), a type of machine learning model that is often computationally fast after training, to mimic the RRTM. We wish to accelerate the RRTM by orders of magnitude without sacrificing much accuracy. We drive both the NNs and RRTM with data from the GFSv16, an operational weather model, using locations around the globe during all seasons. We show that the NNs are highly accurate and much faster than the RRTM, which suggests that the NNs could be used to solve radiative transfer inside the GFSv16.

On the potential use of weather types to describe the interannual variability of annual maximum discharge across the conterminous United States

AbstractWeather types or weather regimes represent the dominant modes of large‐scale atmospheric circulation patterns and have been used to understand and explain the physical mechanisms of different weather events. While there have been many studies that analyse the changes in extreme climate events through the lenses of weather typing, there is a lack of studies that attribute changes in flood extremes to changes in weather regimes. Here we examine the potential applicability of weather types as predictors of flood extremes. For 4535 streamgages across the conterminous United States, we employ a statistical attribution approach to model the seasonal and annual maximum discharge, utilizing five weather types with distinct synoptic features. Although there are regional patterns in the relationship between weather types and the major climate drivers of flooding, our results show that the frequency of weather types does not provide enough information to model the interannual variability in the magnitude of flood peaks across the conterminous United States.

Compounding Heatwave‐Extreme Rainfall Events Driven by Fronts, High Moisture, and Atmospheric Instability

AbstractHeatwaves have been shown to increase the likelihood and intensity of extreme rainfall occurring immediately afterward, potentially leading to increased flood risk. However, the exact mechanisms connecting heatwaves to extreme rainfall remain poorly understood. In this study, we use weather type data sets for Australia and Europe to identify weather patterns, including fronts, cyclones, and thunderstorm conditions, associated with heatwave terminations and following extreme rainfall events. We further analyze, using reanalysis data, how atmospheric instability and moisture availability change before and after the heatwave termination depending on whether the heatwave is followed by extreme rainfall, as well as the location of the heatwave. We find that most heatwaves terminate during thunderstorm and/or frontal conditions. Additionally, atmospheric instability and moisture availability increase several days before the heatwave termination; but only if heatwaves are followed by extreme rainfall. We also find that atmospheric instability and moisture after a heatwave are significantly higher than expected from climatology for the same time of the year, and that highest values of instability and moisture are associated with highest post‐heatwave rainfall intensities. We conclude that the joint presence of high atmospheric instability, moisture, as well as frontal systems are likely to explain why rainfall is generally more extreme and likely after heatwaves, as well as why this compound hazard is mainly found in the non‐arid mid and high latitudes. An improved understanding of the drivers of these compound events will help assess potential changing impacts in the future.

Klasifikasi Citra Cuaca Menggunakan Inception-V3 dan K-Nearest Neighbors

Weather imagery has a crucial role in various sectors, such as aviation, maritime and agriculture. Weather conditions have a big impact on activities in these fields and greatly influence operations. Classifying weather images can be done by analyzing weather image data, which can be used to predict the type of weather that may occur. The results of these weather predictions have significant value in daily decision making in these various sectors. One method for classifying weather images can be done by first extracting weather image features using Inception-V3 which is then calculated using the K-Nearest Neighbors method. This research uses 1748 weather images with 4 categories to carry out training which produces a model with Accuracy 91%, F1 91%, Recall 91%, Precision 91%, and uses 8 weather images with 4 categories to carry out testing which produces classifications with all correct values. every image.

The 10‐ to 30‐Day Intraseasonal Weather Process Associated With Wintertime PM2.5 Pollution in Eastern China

AbstractSevere PM2.5 pollution strikes winter eastern China, while its physical process associated with the atmospheric evolution from several days to weeks is not clear. This study uses weather types as intermediate agents between large‐scale atmospheric circulation and regional air pollution, to explore the whole circulation evolutionary chain related to wintertime PM2.5 pollution in eastern China. We extract objectively 6 weather types (WTs) for wintertime surface synoptic patterns in eastern China and analyze quantitatively the impacts of WTs on PM2.5 pollution. Then the preferred transitions between WTs are explored to explain potential weather process. We find a most frequently recurring circulation evolution corresponding to Mongolia high pressure that stretches southward to eastern China, referred as the WTs transition of YSH− > BLH− > MH, with PM2.5 increased in North China and dissipated as the high pressure moves southward. Further to establish intraseasonal connections between large‐scale circulation and regional synoptic patterns, several leading evolutionary chains are discovered among tropical Madden‐Julian Oscillation (MJO), extratropical teleconnections, and WTs of eastern China at an extended period. The positive or negative phase of teleconnections can indicate the WTs change 4–10 days in advance, while MJO further extends that indicative effect to 10–15 days. The intraseasonal oscillations of teleconnections such as conventional Eurasia, Scandinavia, Arctic Oscillation, Polar/Eurasia, Western Pacific and East Atlantic are associated with the transition of YSH− > BLH− > MH, and MJO may act as a precursor of teleconnection phases to extend effectively the prediction time for surface weather conditions and air pollution in eastern China.

Numerical simulation of atmospheric transmittance between heliostats and heat receiver in Tower-Type solar thermal power station

Based on the Simple Model for Atmospheric Transmission of Sunshine and meteorological observations, a simulation method for atmospheric transmittance between tower solar thermal power heliostats and heat receivers is developed. This method is verified by the vertical radiation observations in Tianjin, China. The results indicate remarkable differences between the simulated global horizontal irradiation (GHI) and the observations under different weather types, and the magnitude of the difference is mainly influenced by near-surface visibility and cloud cover. However, the ratios of the simulated GHI at the altitude of 40 m to that of 200 m are basically consistent with the observations, with deviations of less than 0.02 for more than 97 % of the moments, indicating this method is applicable to simulating the ratio of GHI at different heights. Results show a 20-m increase in altitude leads to an increase of 0.009 % in atmospheric transmittance within 200 m above the ground. Due to the influence of meteorology, the atmospheric transmittance shows obvious diurnal and seasonal variations. Assuming the vertical altitude of a heat receiver is 200 m and its horizontal distance from a heat receiver is 50 m, the average atmospheric transmittance between them is 94.72 %, and the transmittance drops to 76.49 % at the horizontal distance of 1000 m. This method estimates atmospheric transmittance between heliostats and heat receiver, and provides reference for the design of tower type solar thermal power station and the evaluation of the operation status.

Projection of extreme heat- and cold-related mortality in Sweden based on the spatial synoptic classification

BackgroundClimate change is projected to result in increased heat events and decreased cold events. This will substantially impact human health, particularly when compounded with demographic change. This study employed the Spatial Synoptic Classification (SSC) to categorize daily weather into one of seven types. Here we estimated future mortality due to extremely hot and cold weather types under different climate change scenarios for one southern (Stockholm) and one northern (Jämtland) Swedish region. MethodsTime-series Poisson regression with distributed lags was used to assess the relationship between extremely hot and cold weather events and daily deaths in the population above 65 years, with cumulative effects (6 days in summer, 28 days in winter), 1991 to 2014. A global climate model (MPI-M-MPI-ESM-LR) and two climate change scenarios (RCP 4.5 and 8.5) were used to project the occurrence of hot and cold days from 2031 to 2070. Place-specific projected mortality was calculated to derive attributable numbers and attributable fractions (AF) of heat- and cold-related deaths. ResultsIn Stockholm, for the RCP 4.5 scenario, the mean number of annual deaths attributed to heat increased from 48.7 (CI 32.2–64.2; AF = 0.68%) in 2031–2040 to 90.2 (56.7–120.5; AF = 0.97%) in 2061–2070, respectively. For RCP 8.5, heat-related deaths increased more drastically from 52.1 (33.6–69.7; AF = 0.72%) to 126.4 (68.7–175.8; AF = 1.36%) between the first and the last decade. Cold-related deaths slightly increased over the projected period in both scenarios. In Jämtland, projections showed a small decrease in cold-related deaths but no change in heat-related mortality. ConclusionsIn rural northern region of Sweden, a decrease of cold-related deaths represents the dominant trend. In urban southern locations, on the other hand, an increase of heat-related mortality is to be expected. With an increasing elderly population, heat-related mortality will outweigh cold-related mortality at least under the RCP 8.5 scenario, requiring societal adaptation measures.

Evaluation of Climate Suitability for Nature-Based Tourism (NBT) in Arid Regions of Isfahan Province (Iran)

This article applies the weather types method to assess the climate suitability for nature-based tourism (NBT) in the arid and hyper-arid climate zones of the province of Isfahan (Iran) based on bioclimatic criteria and the preferences of Iranian domestic tourists identified by means of a survey. To date, there are no climate potential assessments for the practice of nature tourism based on an analysis of climate preferences in the study area. According to the results, the distribution of favorable weather types in the study area between March and November during the period 1998–2017 showed that there is a low season in summer and two high seasons corresponding to autumn and spring. The highest frequencies of weather types conducive to NBT were recorded between the second half of September to the first half of November and between the second half of April until the end of May. The calendars resulting from application of the weather types method will serve as an efficient tool for providing tourists and the region’s main tourist stakeholders with information; in the case of the latter, they will be particularly useful for destination planning and activity scheduling.

Analysis of severe ozone-related human health and weather influence over China in 2019 based on a high-resolution dataset.

Ozone pollution in 2019 in China is particularly severe posing a tremendous threat to the health of Chinese inhabitants. In this study, we constructed a more reliable and accurate 1-km gridded dataset for 2019 with as many sites as possible using the inverse distance weight interpolation method to analyze spatiotemporal ozone pollution characteristics and health burden attributed to ozone exposure from the perspective of different diseases and weather influence. The accuracy of this new dataset is higher than other public datasets, with the coefficient of determination of 0.84 and root-mean-square error of 8.77 ppb through the validation of 300 external sites which were never used for establishing retrieval methods by the datasets mentioned-above. The averaged MDA8 (the daily maximum 8 h average) ozone concentrations over China was 43.5 ppb, and during April-July, 83.9% of total grids occurred peak-month ozone concentrations. Overall, the highest averaged exceedance days (60 days) and population-weighted ozone concentrations (55.0 ppb) both concentrated in central-eastern China including 9 provinces (only 11.4% of the national territory); meanwhile, all-cause premature deaths attributable to ozone exposure reached up to 142,000 (54.9% of national total deaths) with higher deaths for cardiovascular and respiratory, and the provincial per capita premature mortality was 0.27~0.44‰. The six most polluted weather types in the central-eastern China are in order as follows: westerly (SW and W), cyclonic, northerly, and southerly (NW, N, and S) types, which accounts for approximately 73.2% of health burden attributed to daily ozone exposure and poses the greatest public health risk with mean daily premature deaths ranging from 466 to 610. Our findings could provide an effective support for regional ozone pollution control and public health management in China.

Discriminant Analysis for Severe Storm Environments in South-Central Brazil

Abstract Severe storms produce hazardous weather phenomena, such as large hail, damaging winds, and tornadoes. However, relationships between convective parameters and confirmed severe weather occurrences are poorly quantified in south-central Brazil. This study explores severe weather reports and measurements from newly available datasets. Hail, damaging wind, and tornado reports are sourced from the PREVOTS project from June 2018 to December 2021, while measurements of convectively induced wind gusts from 1996 to 2019 are obtained from METAR reports and from Brazil’s operational network of automated weather stations. Proximal convective parameters were computed from ERA5 reanalysis for these reports and used to perform a discriminant analysis using mixed-layer CAPE and deep-layer shear (DLS). Compared to other regions, thermodynamic parameters associated with severe weather episodes exhibit lower magnitudes in south-central Brazil. DLS displays better performance in distinguishing different types of hazardous weather, but does not discriminate well between distinct severity levels. To address the sensitivity of the discriminant analysis to distinct environmental regimes and hazard types, five different discriminants are assessed. These include discriminants for any severe storm, severe hail only, severe wind gust only, and all environments but broken into “high” and “low” CAPE regimes. The best performance of the discriminant analysis is found for the “high” CAPE regime, followed by the severe wind regime. All discriminants demonstrate that DLS plays a more important role in conditioning Brazilian severe storm environments than other regions, confirming the need to ensure that parameters and discriminants are tuned to local severe weather conditions.

Summer upper-level jets modulate the response of South American climate to ENSO

The upper-level jet stream is a critical element of atmospheric circulation, driving synoptic systems and extreme weather events. This study analyzes the impact of upper-level jets on South American (SA) summer temperature and precipitation under different El Niño-Southern Oscillation (ENSO) phases. Using the ERA5 reanalysis dataset from 1979 to 2022, we perform a daily multiparametric characterization of the jet stream, considering its spatial and temporal discontinuities. Besides latitude and intensity, we find that the departure and number of branches of the subtropical jet (STJ) and the longitudinal extent of the Pacific branch of the polar front jet (PFJ) are needed for their description. An additional parameter is required to characterize the STJ due to its absence on around 40% of summer days over SA. Moreover, we observe distinct long-term changes in PFJ parameters across different ocean basins. Three synoptic weather types (WTs) of the upper-level zonal wind are identified: normal conditions, a prominent STJ pattern, and a PFJ-only pattern. The latter pattern is associated with anticyclonic anomalies at 500 hPa in the South Atlantic Ocean and an active SA Convergence Zone, which favors clear skies and warm (wet and cold) conditions in southern SA (Brazil). Consistently, the probability of experiencing warm spells in central Argentina is increased more than twofold. Finally, we detect that the temperature anomalies associated with the WTs are independent of the ENSO phase. However, ENSO modulates the frequency of the WTs: during La Niña (El Niño), the PFJ-only (prominent STJ) pattern is more common.

The effect of seasonality and weather conditions on human perception of the urban–rural transitional landscape

Landscape perception research into the impact of seasonally changing landscape characteristics with the simultaneous influence of the weather are rare. Therefore, eye-tracking metrics were calculated (fixation and saccades) for the whole tested landscape, while its areas of interest (AOIs) were established based on clustering methods. Moreover, the gaze pattern was analysed using the Voronoi cells method. To identify significant differences in landscape perception according to various weather and seasonality, nonparametric tests were applied. The significant influence of weather/seasonality and their synergistic influence is noticed. The results indicate a rather complex influence of the types of weather in warmer and cooler seasons. Regardless of the weather type, seasonal changes cause greater visual span and shorter fixations in the warmer season. The fixations and saccades are shorter in the warmer season in two AOI’s during positive weather, but are longer in negative weather during the colder season in most AOI’s. The main reasons for the influence of weather and seasonality on the visual perception include seasonal changes in the landscape, resulting in the appearance of the landscape from more urban to natural and vice versa (phenological changes) as well as lighting changes (due to seasonality and type of weather).

Traffic flow prediction under multiple adverse weather based on self-attention mechanism and deep learning models

To improve the accuracy of traffic flow prediction under adverse weather, a deep hybrid attention (DHA) model including the traffic and weather blocks is proposed. The traffic block introduces the convolutional neural network (CNN) and the gated recurrent unit (GRU) neural network to capture the spatio-temporal rules of the traffic flow data. To consider the impacts of adverse weather on traffic flow, the weather block is introduced. The weather block utilizes the convolutional long short-term memory (ConvLSTM) neural network to extract the relationship between the weather and traffic flow data. The self-attention mechanism is embedded in the two blocks. Four cases involving rainy, foggy and windy weather are used to verify the DHA model. The experiments reveal that: the DHA model shows the excellent performance under multiple adverse weather; different adverse weather has various impacts on the rules of traffic volume and speed; the prediction accuracy of each model reduces with the increase of the severe degree of each type of adverse weather.

The meteorological contrast index in the context of climate change and public health

In the context of climate change, extreme weather events and sudden shifts in weather patterns are becoming increasingly frequent. The atmosphere is considered a source of meteorological and climatic risks for human beings and living organisms. Numerous studies have examined the correlation between meteorological variables and human morbidity and mortality. However, only a few authors have investigated the impact of environmental changes on human health and, to our knowledge, there are no meteorological indices proposing a methodology for assessing changes in atmospheric conditions. Under the hypothesis that meteorological disruptions have an impact on human health, this article proposes a method to calculate a new index, the Meteorological Contrast Index (MCI), based on weather changes. This index takes into account three variables: i) categorization based on the type of atmospheric process, ii) changes in these categories over a specific time period, and iii) the level of stress associated with these changes, considering the severity of the transition from one category to another. If the predictive value of this index is proven for a specific meteorological variable and disease, it could be valuable in defining biometeorological early warning systems for the prevention and management of healthcare resources.•The Meteorological Contrast Index is the first index that proposes a method to assess changes in atmospheric conditions.•Atmospheric changes are a significant source of biometeorological distress, which can be quantitatively defined using the Meteorological Contrast Index.•Certain diseases are sensitive to the weather, and their incidence may increase under specific sequences of weather types.

Short‐term prediction of behind‐the‐meter PV power based on attention‐LSTM and transfer learning

AbstractDistributed photovoltaic (PV) systems often lack adequate measurements due to cost considerations, which makes it very difficult to predict them accurately. Here, an approach is proposed for behind‐the‐meter (BTM) PV power prediction using attention‐LSTM neural network and transfer learning. First, the weather is classified into four types based on the deviation ratio β. Second, the correlation analysis algorithm identifies the weather factors that contribute the most to PV power generation as GHI, DNI, humidity, and temperature. Then, attention‐LSTM pre‐trained model is constructed, in which the LSTM network fully extracts the temporal characteristics of PV power generation, while the attention mechanism enhances the attention to the important information in the input. Finally, a novel BTM PV short‐term power prediction method using transfer learning that the upper layer parameters of the pre‐trained model were frozen, and a small amount of DP‐NB data was used to fine‐tune the model. The proposed methodology has higher prediction accuracy compared with other benchmark methods under four weather types, and the time cost is saved by freezing the parameters of the first layer of the network by 37.2%.

STARC OSTEOARCH: An open access resource for recording and sharing human osteoarchaeological data

AbstractThis paper introduces an open access resource for recording and sharing macroscopic data on archaeological human skeletal remains: STARC OSTEOARCH. The resource was developed using a cloud collaboration service, Airtable, and it accommodates data collection on individual skeletons as well as disassociated commingled remains. The attributes covered include key information on demography (sex and age‐at‐death), taphonomy (zonation, anatomical preservation, weathering, fragmentation, and other types of post‐mortem alteration), diet (dental diseases and dental wear), mechanical stress (entheseal changes, osteoarthritis, Schmorl's nodes, and vertebral arthritis), biodistances (cranial metrics and nonmetrics and dental metrics and nonmetrics), and various pathological lesions. References and key descriptions of the categories per attribute have been incorporated in the resource to facilitate raw data sharing, which can be achieved using Airtable's sharing functionality. This initiative aims at promoting standard practices in the field and facilitating data collection and sharing.

О согласованности океанического переноса тепла через северные моря: баланс океанического тепла и его межгодовая изменчивость

The Atlantic Water is the main source of heat and salt in the Arctic. Properties of the Atlantic Water inflow regionally affect sea ice extent and deep water formation rate. The Atlantic Water heat transported into the Nordic Seas has a significant impact on the local climate and is investigated here along with its inter-annual variability. We use the ARMOR3D dataset, which is a collection of 3D monthly temperature, salinity and geostrophic velocities fields, derived from in situ and satellite data on a regular grid available since 1993. We compare the heat transport across seven zonal transects in the eastern part of the Nordic seas, from Svinøy section (65°N) to the Fram Strait (78.8°N). 
 The correlations of the interannual variations of the advective heat fluxes rapidly drop from Svinøy to Jan Mayen sections and between Bear Island and Sørkapp sections. This is a result of different tendencies over the latest decades in the southern and the northern parts of the study region, as well as of a differential damping of the observed periodicities along the Atlantic Water path on its way north (the amplitude of 5–6 year oscillations drops significantly faster than that of 2–3 year oscillations). 
 A certain link between the heat fluxes and the North Atlantic Oscillation (NAO), Arctic Oscillation (AO) and East Atlantic (EA) indices is observed only for the southern sections. On the other hand, the heat fluxes at all sections show a consistent increase during the dominance of western weather type W and a decrease – of meridional weather type C. The link is explained by the variations of the wind fields, favourable for the sea-level build-up (Ekman pumping) east of the branching of the Norwegian Current for type W and an opposite tendency for type C.

Changes in Mediterranean flood processes and seasonality

Abstract. Floods are a major natural hazard in the Mediterranean region, causing deaths and extensive damages. Recent studies have shown that intense rainfall events are becoming more extreme in this region but, paradoxically, without leading to an increase in the severity of floods. Consequently, it is important to understand how flood events are changing to explain this absence of trends in flood magnitude despite increased rainfall extremes. A database of 98 stations in southern France with an average record of 50 years of daily river discharge data between 1959 and 2021 was considered, together with a high-resolution reanalysis product providing precipitation and simulated soil moisture and a classification of weather patterns associated with rainfall events over France. Flood events, corresponding to an average occurrence of 1 event per year (5317 events in total), were extracted and classified into excess-rainfall, short-rainfall, and long-rainfall event types. Several flood event characteristics have been also analyzed: flood event durations, base flow contribution to floods, runoff coefficient, total and maximum event rainfall, and antecedent soil moisture. The evolution through time of these flood event characteristics and seasonality was analyzed. Results indicated that, in most basins, floods tend to occur earlier during the year, the mean flood date being, on average, advanced by 1 month between 1959–1990 and 1991–2021. This seasonal shift could be attributed to the increased frequency of southern-circulation weather types during spring and summer. An increase in total and extreme-event precipitation has been observed, associated with a decrease of antecedent soil moisture before rainfall events. The majority of flood events are associated with excess rainfall on saturated soils, but their relative proportion is decreasing over time, notably in spring, with a concurrent increased frequency of short rain floods. For most basins there is a positive correlation between antecedent soil moisture and flood event runoff coefficients that is remaining stable over time, with dryer soils producing less runoff and a lower contribution of base flow to floods. In a context of increasing aridity, this relationship is the likely cause of the absence of trends in flood magnitudes observed in this region and the change of event types. These changes in flood characteristics are quite homogeneous over the domain studied, suggesting that they are rather linked to the evolution of the regional climate than to catchment characteristics. Consequently, this study shows that even in the absence of trends, flood properties may change over time, and these changes need to be accounted for when analyzing the long-term evolution of flood hazards.

Spatial-temporal evaluation of PM2.5 concentration for health risk reduction strategy development in a basin with different weather patterns

Identifying the contribution of pollution sources to the atmospheric environment is critical to make an effective pollution reduction strategy for controlling the regional air quality. Spatial-temporal distribution of pollutant patterns is not only determined by the emission rate of a pollution source, but also closely related to the location of an emission source, regional topography, and wind field conditions. To understand the spatial pattern of pollutants under different weather conditions and topographical factors, this study evaluated the impact of large-scale stationary pollution sources in Taichung Basin located in Central Taiwan on regional air quality using The Air Pollution Model (TAPM), in which different weather types were taken into considerations. Results show that the present TAPM model could accurately simulate 44.0–95.0% of 5 actual synoptic weather conditions, including Outer-region circulation from tropical depression system, Weak northeast monsoon, Anticyclonic Outflow, The westward extension of Pacific Anticyclone, and South-deflected airflow. The research results revealed that weather types and regional wind fields dominated the pollutant transport patterns, where the westward extension of the Pacific Anticyclone and South-deflected Airflow could deteriorate the convection-diffusive conditions, causing high pollution events for Taichung Basin. Based on the present results, the air pollutants transport patterns under different synoptic weather conditions could be used as practical information by decision-makers to develop efficient air quality control strategies in the future.