Weather Types Research Articles

A hybrid regional climate downscaling for the southern Brazil coastal region

A hybrid method is applied to generate a high-resolution regional downscaling of atmospheric conditions to the southern coast of Brazil. The method consists of applying a principal component analysis to daily fields of the sea level pressure (SLP) data from the NCEP-CFSR reanalysis. A cluster analysis (K-means) is then applied to the 87 principal components that explained 95% of the variance of the time series. Daily atmospheric conditions were clustered into 36 weather types which represent the most predominant conditions observed in the study area. The estimated weather types were able to represent the major atmospheric systems affecting local climate, including the cyclones and anticyclones that are usually present in this region. Then, we applied the numerical Ocean-Land-Atmosphere Model (OLAM) to dynamically downscale the atmospheric condition that is closest to the centroid of each cluster. The model was set with a global grid and a refining approach with 6 km grid spacing over the coastal region of south Brazil. This approach allowed us to represent simultaneously the planetary waves and the local mesoscale systems, and their mutual interactions. The results provided new high-resolution atmospheric fields for the coastal region and showed that the model was capable of resolving the major local mesoscale features. The main advantage of applying such a method is in reducing the number of numerical simulations (lower computational cost) at the same time it represents the totality of the atmospheric conditions observed in the study area. The final results consist in detailed information of the local climate that can be related to injuries to the coastal area and thus is useful to support decision-makers.

Wind stilling ceased in the Iberian Peninsula since the 2000s

This study analyses quality controlled and homogenized near-surface wind speed series (SWS) at 87 meteorological stations distributed across Spain and Portugal for 1961–2019. Multidecadal variability analysis of both mean and gusts SWS confirms for the first time in the region the cessation of the stilling (decline of SWS) and a possible weak reversal phenomenon (increase in SWS) in the last decades, varying its onset from 1999 to 2018, depending on the season and variable. Different atmospheric circulation drivers are evaluated to explain the stilling and reversal phenomena. Among the chosen teleconnection indices, the Western Mediterranean Oscillation index (WeMOi) is the one that presents the highest (positive) correlation with SWS, although the correlation (negative) with the North Atlantic Oscillation index and the Mediterranean Oscillation index in winter is also statistically significant (p < 0.05). Results show that the interannual variability of the WeMOi could exert a strong influence on both the stilling and reversal phenomena, as it displays statistically significant negative trends for 1961–2010 and positive ones (non-significant) for 2010–2019, in agreement with the observed SWS trends. The use of the Jenkinson and Collison weather type classification reveals the marked influence of certain weather types in modulating SWS changes (i.e., west, northwest, and anticyclonic types). We proposed the increase in atmospheric thermal stability and the northward shift of the jet stream as principal causes of the stilling phenomenon in this region. Our results improve our understanding of wind changes, and highlight the importance of regional assessments to discern their socioeconomic and environmental impacts.

Multivariate Adaptive Step Fruit Fly Optimization Algorithm Optimized Generalized Regression Neural Network for Short-Term Power Load Forecasting

Short-term load forecasting plays a significant role in the management of power plants. In this paper, we propose a multivariate adaptive step fruit fly optimization algorithm (MAFOA) to optimize the smoothing parameter of the generalized regression neural network (GRNN) in the short-term power load forecasting. In addition, due to the substantial impact of some external factors including temperature, weather types, and date types on the short-term power load, we take these factors into account and propose an efficient interval partition technique to handle the unstructured data. To verify the performance of MAFOA-GRNN, the power load data are used for empirical analysis in Wuhan City, China. The empirical results demonstrate that the forecasting accuracy of the MAFOA applied to the GRNN outperforms the benchmark methods.

Improving the accuracy of predicting the performance of solar collectors through clustering analysis with artificial neural network models

Accurate prediction of collector performance is important for optimal planning of solar thermal systems. Here, a novel prediction method combining clustering of data with artificial neural network (ANN) model is presented. A novel all-glass straight-through tube solar collector is employed as reference solar technology. In the present approach, experimental collector performance data was first collected during different weather conditions (sunny, cloudy, rainy days) subject to a clustering analysis to screen out outlier samples. The data was then used to train and verify the neural network models. For the ANN, the Back Propagation (BP) and Convolutional Neural Network (CNN) models were used. For predicting the performance (thermal efficiency) of the solar collector, solar radiation intensity, ambient temperature, wind speed, fluid flow rate, and fluid inlet temperature were used as the input parameters in the model. The prediction accuracy of the neural network models after full-data-screening were superior to that of the pre-screening and partial-screening models. The CNN model provided somewhat better efficiency predictions than the BP model. The R2, RMSE and MAE of the CNN model prediction in sunny conditions with full-screening was 0.9693, 0.0039 and 0.0030, respectively. The average MAPE of the BP and CNN models for all three weather types dropped by 30.7% and 13.8%, respectively, when applying data pre-screening and partial-screening only. The accuracy of the ANN collector prediction model can thus be improved through data clustering, which provides an effective method for performance prediction of solar collectors.

Prediction of Short-Term Photovoltaic Power Via Self-Attention-Based Deep Learning Approach

Abstract Photovoltaic (PV) is characterized by random and intermittent. As increasing popularity of PV, it makes PV power prediction increasingly significant for efficiency and stability of the power grid. At present, prediction models of PV power based on deep learning show superior performance, but they ignore the interdependent mechanism of prediction error along the input characteristics of the neural network. This paper proposed a self-attention mechanism (SAM)-based hybrid one-dimensional convolutional neural network (1DCNN) and long short-term memory (LSTM) combined method (named 1DCNN-LSTM-SAM). In the proposed model, SAM redistributes the neural weights in 1DCNN-LSTM, and then 1DCNN-LSTM further extracts the space-time information of effective PV power. The polysilicon PV arrays data in Australia are employed to test and verify the proposed model and other five competition models. The results show that the application of SAM to 1DCNN-LSTM improves the ability to capture the global dependence between inputs and outputs in the learning process and the long-distance dependence of its sequence. In addition, mean absolute percentage error of the 1DCNN-LSTM-SAM under sunny day, partially cloudy day, and cloudy day weather types has increased by 24.2%, 14.4%, and 18.3%, respectively, compared with the best model among the five models. Furthermore, the weight distribution mechanism of self-attention to the back end of LSTM was analyzed quantitatively and the superiority of SAM was verified.

Hail disaster recognition method based on artificial intelligence with Doppler radar data

Hail weather is a strong weather phenomenon produced by a system of severe convective weather. Furthermore, hail weather has the characteristics of short duration, strong locality, and great destructive capacity and is a type of disastrous weather. Therefore, the recognition and tracking of hail weather have always been significant research topics in the field of meteorology. Doppler weather radar can obtain high precision and high frequency data by observing strong weather information with high temporal and spatial resolution. However, weather radar cannot directly recognize and inform forecasters of strong weather conditions; thus it takes considerable time and energy to identify and monitor the weather artificially. Based on the continuous development of artificial intelligence (AI) technology, combining the advantages of different observation data and using AI to process it are important ways to improve the strong weather monitoring and early warning systems. Our study presents a hail weather recognition method based on AI. In the proposed method, faster region-based convolutional neural network deep learning is applied to recognize hail weather areas, and the accuracy and precision on the test set are more than 86% and 97%, respectively. The reliability of the proposed method is verified by theoretical analysis, the actual hail situation, and comparison with the traditional hail recognition method. The experimental results provide a significant reference for the recognition and early warning of hail weather.

Concurrent Heat Waves and Extreme Ozone (O3) Episodes: Combined Atmospheric Patterns and Impact on Human Health.

More recurrent heat waves and extreme ozone (O3) episodes are likely to occur during the next decades and a key question is about the concurrence of those hazards, the atmospheric patterns behind their appearance, and their joint effect on human health. In this work, we use surface maximum temperature and O3 observations during extended summers in two cities from Morocco: Casablanca and Marrakech, between 2010 and 2019. We assess the connection between these data and climate indices (North Atlantic Oscillation (NAO), Mediterranean Oscillation (MO), and Saharan Oscillation (SaO)). We then identify concurrent heat waves and O3 episodes, the weather type behind this concurrence, and the combined health risks. Our findings show that the concurrence of heat waves and O3 episodes depends both on the specific city and the large-scale atmospheric circulation. The likely identified synoptic pattern is when the country is under the combined influence of an anticyclonic area in the north and the Saharan trough extending the depression centered in the south. This pattern generates a warm flow and may foster photochemical pollution. Our study is the first step toward the establishment of an alert system. It will help to provide recommendations for coping with concurrent heat waves and air pollution episodes.

Links between precipitation, circulation weather types and orography in central Italy

AbstractRecent analyses of satellite and surface observations reported a negative annual rainfall trend in central Italy. The complex orography of the Apennines and the strong influence of climate change in the Mediterranean basin complicates the explanation of such trends and their spatial variability. This work aims at describing the link between circulation weather types, orography and precipitation patterns observed in central Italy, as a first step towards the understanding of such climate trends. Using ERA5 reanalysis data from 1951 to 2019, four weather types are identified as most responsible for the spatial variability of rainfall in central Italy. They are associated with cyclonic circulations characterized by high water vapour transport coming from west, south‐west, south‐east and north‐east. The analysis of wind speed and precipitation climatology for the period 1951–2019, as derived from both surface observations and reanalysis, confirms a strong influence of moist south‐westerly fluxes over all the domain, while the effect of north‐easterly fluxes remains confined to the Apennines and the Adriatic coast. ERA5 overestimates annual rainfall in most parts of the region, except on the north‐central Apennines where the underestimation reaches average values of 300 mm. The analysis of circulation weather types and their associated precipitation variability demonstrates that this deficit can be ascribed to a wrong representation of the orographic precipitation component as seen by ERA5 in correspondence of cyclonic north‐easterly and westerly flows impinging towards the north‐central Apennines. Indeed the precipitation associated with north‐easterly and westerly fluxes shows the strongest modulation by orography. This interaction and its possible modification by climate change effects should be considered in future studies investigating recent climate trends in central Italy.

Impacts of meteorological parameters on the occurrence of air pollution episodes in the Sichuan basin

Episodes of fine-particulate matter (PM2.5) pollution are a widespread and common occurrence in China, and have potentially serious implications for human health. Meteorological conditions play an important role in air quality and influence the formation of regional air pollution episodes. This study applied a new classification method and daily PM2.5 concentration data to (a) evaluate different levels of air pollution in the Sichuan–Chongqing region between 2015 and 2017, and (b) investigate their relationships with meteorological parameters including atmospheric boundary layer height, wind speed, temperature inversion, weather type, and atmospheric transport. We identified a total of 40 air pollution episodes, the most severe of which occurred in winter when the atmospheric layer height was low. These heavy pollution events also coincided with low surface (10 m) wind speeds and temperature inversions, weather conditions that generally result from low pressure over the northwestern Sichuan Basin (90–102° E, 28–36° N) and southerly atmospheric transport.

Daily synoptic conditions associated with occurrences of compound events in estuaries along North Atlantic coastlines

AbstractCoastal compound flooding events occur when extreme events of rainfall, river discharge and sea level coincide and collectively increase water surface elevation, exacerbating flooding. The meteorological conditions that generate these events are usually low‐pressure systems that generate high winds and intense rainfall. In this study, we identify the types of synoptic atmospheric conditions that are typically associated with coastal compound events using a weather‐type approach, for the North Atlantic coastlines (encompassing northwest Europe and the east coast of the United States). Compound events are identified along the estuaries of the study region from 1980 to 2014 based on an impact function defined by water surface elevation that resulted from the combination of river discharge and sea level. We find that compound events are more frequent along European as opposed to U.S. coastlines. In both cases, they are associated with a few dominant weather patterns. European hotspots of compound events are concentrated in the west coast of United Kingdom, the northwest coast of the Iberian Peninsula and around the Strait of Gibraltar. These areas share the same weather patterns which represent the main pathways of storms that cross the North Atlantic Ocean. In the case of U.S. locations, the areas with highest number of compound events are located mainly in the Gulf of Mexico and along Mexico and along the mid‐eastern U.S. coastlines. In these areas, compound events are produced by transitional weather patterns, which describe storms that travel northward parallel to the coastline. Splitting the occurrence of compound events in the corresponding weather types discriminates the interannual variability based on the relationship with dominant climate indices in the North Atlantic Ocean.

A short-term prediction model to forecast power of photovoltaic based on MFA-Elman

Due to the low accuracy and reliability as well as slow prediction speed of photovoltaic (PV) power short-term prediction, a short-term PV power prediction method based on MFA-Elman neural network is proposed. K-means mean algorithm is employed firstly in this method to cluster the weather type, and then EEDM is used to decompose the PV output power on the various data obtained by clustering. Finally, each decomposition subsequence is inputted into the MFA-optimized Elman neural network to predicting the PV output power, which solves the randomness of initial weight and threshold and slow training speed of Elman neural network. The simulation results show that the MFA-Elman model has smaller prediction error, higher prediction accuracy and reliability, faster prediction speed, which can reach the requirements of short-term prediction of PV power.

Spatiotemporal Variation of Air Quality (PM and NO2) in Southern Paris during COVID-19 Lockdown Periods

In urban areas, road transportation is the main source of pollutants, and weather impacts spatiotemporal variation of air quality. In this paper, we demonstrate the spatiotemporal variabilities of particulate matter (PM10 and PM2.5) and NO2 in the south of Paris, with an emphasis on the comparison of air pollutant levels between COVID-19 lockdown and non-lockdown days according to meteorological conditions. The influence of COVID-19 lockdowns could be region-specific. Thus, it is worthwhile to conduct investigations across different regions and via different methodologies. This manuscript contains data that could be relied upon to evaluate available options for mitigation of urban air pollution. Using Airparif data and mobile survey data collected by Aeroqual 500 sensors, this study confirms that road traffic seems to be the determining factor of air quality in the southern part of Paris. The daily average concentrations of NO2, PM2.5 and PM10 calculated in 2020 show a strong spatiotemporal variability explained by the type of weather on the one hand and by the proximity of emission sources on the other hand. Mobile surveys show that during lockdowns in 2020, when the weather was stable, in 13th arrondissement of Paris, NO2 values exceeded 250 µg/m3 with PM10 values over 70 µg/m3, mainly in three locations: the area between Rue Tolbiac and Rue Nationale, along Rue de Chevaleret, and on Boulevard Périphérique.

Oil spills: impacts and perspectives of treatment technologies with focus on the use of green surfactants.

Oil spills into the oceans cause irreparable damage to marine life and harms the coastal population of the affected areas. The main measures to be taken in response to an oil spill are to reduce the impact on marine life, prevent oil from reaching the shore through its recovery, and accelerate the degradation of unrecovered oil. Any environmental damage can be reduced if the spilled oil is removed from the water quickly and efficiently. Therefore, it is essential to know the treatment strategies for spilled oils. Several technologies are currently available, including booms, skimmers, in situ burning, use of adsorbents, dispersants/surfactants, and bioremediation. The selection of the type of treatment will depend not only on the effectiveness of the technique, but mainly on the type of oil, amount spilled, location, weather, and sea conditions. In this review, the characteristics of oil spills, their origin, destination, and impacts caused, including major accidents around the world, are initially addressed. Then, the main physical, chemical, and biological treatment technologies are presented, describing their advances, advantages, and drawbacks, with a focus on the use of green surfactants. These agents will be described in detail, showing the evolution of research, recent studies, patents, and commercialized products. Finally, the challenges that remain due to spills, the necessary actions, and the prospects for the development of existing treatment technologies are discussed, which must be linked to the use of combined techniques.

The evolving role of weather types on rainfall chemistry under large reductions in pollutant emissions

Long-term change and shorter-term variability in the atmospheric deposition of pollutants and marine salts can have major effects on the biogeochemistry and ecology of soils and surface water ecosystems. In the 1980s, at the time of peak acid deposition in the UK, deposition loads were highly dependent on prevailing weather types, and it was postulated that future pollution recovery trajectories would be partly dependent on any climate change-driven shifts in weather systems. Following three decades of substantial acidic emission reductions, we used monitoring data collected between 1992 and 2015 from four UK Environmental Change Network (ECN) sites in contrasting parts of Great Britain to examine the trends in precipitation chemistry in relation to prevailing weather conditions. Weather systems were classified on the basis of Lamb weather type (LWT) groupings, while emissions inventories and clustering of air mass trajectories were used to interpret the observed patterns. Concentrations of ions showed clear differences between cyclonic-westerly-dominated periods and others, reflecting higher marine and lower anthropogenic contributions in Atlantic air masses. Westerlies were associated with higher rainfall, higher sea salt concentrations, and lower pollutant concentrations at all sites, while air mass paths exerted additional controls. Westerlies therefore have continued to favour higher sea salt fluxes, whereas emission reductions are increasingly leading to positive correlations between westerlies and pollutant fluxes. Our results also suggest a shift from the influence of anthropogenic emissions to natural emissions (e.g., sea salt) and climate forcing as they are transported under relatively cleaner conditions to the UK. Westerlies have been relatively frequent over the ECN monitoring period, but longer-term cyclicity in these weather types suggests that current contributions to precipitation may not be sustained over coming years.

Quantification and Characterisation of Pre-Production Pellet Pollution in the Avon-Heathcote Estuary/Ihutai, Aotearoa-New Zealand

Plastic pollution is threatening aquatic ecosystems and wildlife. Understanding the characteristics and extent of plastic pollution is the first step towards improving management and therefore the environmental impacts. Pre-production pellets are used in the manufacture of a range of consumer items. The Avon–Heathcote Estuary/Ihutai in Aotearoa–New Zealand, an important wildlife habitat, was assessed for the presence and characteristics of pre-production pellets. Following a visual survey of the estuary’s perimeter to establish overall levels, seven accumulation hotspots were identified, and surveyed in more detail. The enumeration and characterisation of pellet colour, size, morphology, degree of weathering and polymer type was undertaken. A total of 3819 pellets were identified, with pellets present at all sites. The pellets were predominantly clear (86%), 3 mm in size (54%), cylindrical in shape (62%), showed moderate weathering (41%) and were made of low-density polyethylene (LDPE) (53%). Pellet abundance and characteristics varied between sites. Accumulation and abundance may be influenced by river inflows along which plastic manufacturers are located, weather conditions, locality to stormwater outlets and pellet characteristics. Pellet pollution is a notable problem in the Avon–Heathcote Estuary/Ihutai and it highlights the need to better understand the sources and improve best management practices.

The influence of daily weather types on the development and intensity of the urban heat island in two Mediterranean coastal metropolises

In this study we investigated the association between daily weather types (WTs) and the Urban Heat Island (UHI) in two Mediterranean coastal metropolises. For this purpose, we employed an existing weather type classification scheme and examined which WTs influence or drive the intensity of the UHI. We used the gridded weather typing classification (GWTC), in which meteorological conditions at a single location are categorized in daily WTs. We compared these WTs with the maximum temperature differences between urban centers and rural areas in the two major metropolises of Greece (Athens and Thessaloniki). These metropolises have dissimilar geography and spatial planning as well as their urban climate characteristics have differences. We used two groups of temperature time series on a daily basis. One with high and the other with low temperature differences (upper 5% and lower 5% of the maximum temperature differences (ΔΤmax)) which reported the WTs that contribute to increase of UHI Intensity. We found that urban overheating was amplified during daytime under Humid, Humid Warm and Warm conditions in both Athens and Thessaloniki. As for nighttime, urban overheating is associated with Warm, Dry and Dry Warm conditions in Thessaloniki while in Athens increased under Humid, Humid Warm and Warm conditions.

Research on the Growth Mechanism of PM2.5 in Central and Eastern China during Autumn and Winter from 2013–2020

Haze is a majorly disastrous type of weather in China, especially central and eastern of China. The development of haze is mainly caused by highly concentrated fine particles (PM2.5) on a regional scale. Here, we present the results from an autumn and winter study conducted from 2013 to 2020 in seven highly polluted areas (27 representative stations) in central and eastern China to analyze the growth mechanism of PM2.5. At the same time, taking Beijing Station as an example, the characteristics of aerosol composition and particle size in the growth phase are analyzed. Taking into account the regional and inter-annual differences of fine particles (PM2.5) distribution, the local average PM2.5 growth value of the year is used as the boundary value for dividing slow, rapid, and explosive growth (only focuses on the hourly growth rate greater than 0). The average value of PM2.5 in the autumn and winter of each regional representative station shows a decreasing trend as a whole, especially after 2017, whereby the decreasing trend was significant. The distribution value of +ΔPM2.5 (PM2.5 hourly growth rate) in the north of the Huai River is lower than that in the south of the Huai River, and both of the +ΔPM2.5 after 2017 showed a significant decreasing trend. The average PM2.5 threshold before the explosive growth is 70.8 µg m−3, and the threshold that is extremely prone to explosive growth is 156 µg m−3 to 277 µg m−3 in north of the Huai River. For the area south of the Huai River, the threshold for PM2.5 explosive growth is relatively low, as a more stringent threshold also puts forward stricter requirements on atmospheric environmental governance. For example, in Beijing, the peak diameters gradually shift to larger sizes when the growth rate increases. The number concentration increasing mainly distributed in Aitken mode (AIM) and Accumulation mode (ACM) during explosive growth. Among the various components of submicron particulate matter (PM1), organic aerosol (OA), especially primary OA (POA), have become one of the most critical components for the PM2.5 explosive growth in Beijing. During the growth period, the contribution of secondary particulate matter (SPM) to the accumulated pollutants is significantly higher than that of primary particulate matter (PPM). However, the proportion of SPM gradually decreases when the growth rate increases. The contribution of the PPM can reach 48% in explosive growth. Compared to slow and rapid growth, explosive growth mainly occurs in the stable atmosphere of higher humidity, lower pressure, lower temperature, small winds, and low mixed layers.

Historical trend of probable maximum precipitation in Utah and associated weather types

AbstractThis study assesses the impact global climate change has had on the theoretical upper limit of precipitation for Utah's watersheds by examining historical trends in probable maximum precipitation (PMP), a metric widely used for dam construction and management. Trends in the factors of PMP driven by both frontal and monsoonal storm scenarios were studied using two networks of surface weather stations and modern gridded datasets spanning 1981–2018. It was found that the historical trend in both 3‐hr and 24‐hr PMP estimations exhibited a statistically significant increasing trend, despite that the magnitude of the trend was sensitive to which dataset was examined and the data's spatial resolution. Over the 24‐hr time scale, an interesting deviation in PMP trends was found where frontal storm events demonstrated increasing PMP trends compared to decreasing PMP trends for monsoonal storm events. However, both storm types demonstrated increasing trends over shorter 3‐hr durations, suggesting a universal increase in convective storm intensity. PMP estimates also demonstrated a dependency on the type and resolution of meteorological data used, where higher‐resolution data generally produced greater PMP values and weaker trends. A discussion on the potential causes of these dependencies is provided. This study implies the need to take the historical PMP trend into account for future PMP estimates, especially when coarse‐resolution data are used for conventional PMP studies.

Analysis of Meteorological Conditions on Riverbed Dust Aerosol in Taiwan

Extremely high concentrations of dust particles are occasionally generated from the riverbeds of Taiwan, affecting the visibility and traffic safety of the local and nearby areas. The condition is most severe during the winter monsoon when surface wind is strong. This study analyzes the concentration of particulate matter of 10 µm or less (PM10), wind direction, wind speed, temperature, and humidity of riverbed stations adjacent to the Daan, Dajia, Dadu, Zhuoshui, and Beinan Rivers in Taiwan for a period of two years. The weather conditions that cause the high concentration of PM10 are classified into typhoon and non-typhoon types, and the latter type is further classified into three stages: ahead of front, ahead of anticyclone, and behind anticyclone. The associated meteorological influences of these weather types on high-concentration events in the riverbed are explored. The monitoring data show that the hourly PM10 concentration of the four riverbed stations exceeded 125 µg m−3 for 35–465 h per year, and the maximum PM10 in the Daan (and Dajia), and Zhuoshui Rivers was more than 800 µg m−3. Weather analysis showed that the extreme PM10 concentration on the riverbed was caused by weather types: typhoon and ahead of anticyclone, in which the peak hourly concentration reached average values of more than 600 and 400 µg m−3, respectively. The high PM10 caused by the typhoon type mainly occurred in October, with an average wind speed of 6 m s−1, high temperature of 25 °C, and mostly northeasterly winds. The ahead of anticyclone type mainly occurred in December, with an average wind speed of 5 m s−1, and northeasterly and northwesterly winds. Both weather types of riverbed events were observed during the daytime, especially at noon time, when strong wind speed, high temperature, and low relative humidity is favorable for riverbed dust generation. On the other hand, the main months of the high PM10 concentrations of the ahead of front and behind anticyclone stages are February and April. The peak PM10 concentrations of these two types of riverbed events are both about 300 µg m−3, but sporadic riverbed dust in these weather stages is mixed with Asian dust or pollution transported to the rivers through weak northwesterly and northeasterly winds. The high concentrations of these two types of riverbed events can occur at any time; but for the Dadu River, the high concentrations are often observed in the morning, when land breezes from the southeast bring local pollutants to the river.

Fewer Troughs, Not More Ridges, Have Led to a Drying Trend in the Western United States

AbstractHigh‐amplitude ridges can enforce heat‐trapping systems that persist through an entire season, contributing to drought events. However, the impacts of day‐to‐day weather system changes at the meso‐ and synoptic‐scale are also important, albeit less well studied. We identify five weather types (WTs) from 500‐hPa geopotential height anomalies. These categories include WT4, which represents ridges and associated dry and warm anomalies, and WT3, which characterizes troughs and associated wetter‐ and colder‐than‐normal conditions and is the largest contributor to precipitation in the western U.S. While no discernable trend is evident for WT4 conditions, WT3 conditions have undergone a distinctive decrease in frequency at both annual and seasonal scales, thus a general drying trend across the west during this time period may be more significantly associated with fewer troughs rather than more ridges. Attribution analysis reveals that this decrease in wetter and colder weather is a likely result of anthropogenic forcing.