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The Precipitating Change Project was a 5-year development, implementation, and research study of an innovative 4-week middle school curricular unit in computational weather forecasting that integrates students' learning and use of meteorology and computational thinking (CT) concepts and practices. The project produced a list of CT skills and definitions that students use to predict the weather, CT assessment instruments, and a CT classroom observation protocol. Data was collected from 306 eighth grade (ages 13-14) students in rural indigenous communities in the Arctic and urban and suburban Northeast communities in the USA. The project met its goal of producing an intentional instructional sequence that integrates disciplinary science and CT practices to increase students' science knowledge and their ability to use CT skills and processes. The results indicate that teachers were able to use the curriculum to embed CT practices into the classroom. Students, in turn, had the opportunity to practice using these skills in class discussion as evidenced by the classroom observation data, and students' science knowledge of CT content and practices significantly increased as evidenced by their performance on the weather content and CT skills pre- and post-assessments. While statistically significant gains in science knowledge and CT skills and practices were evident in all settings (urban, suburban, and rural indigenous communities), there were noticeable differences in gains in students' CT skills and practices between the three settings and additional research is needed in a diversity of settings to understand this difference.

This study was grounded in the spatial computational thinking model developed by the "3D Weather" project funded by the NSF STEM+C program. The model reflects a discipline-based perspective towards computational thinking and captures the spatial nature of computational thinking in meteorology and the reliance of computational thinking on spatial thinking for geospatial analysis. The research was conducted among nineteen teachers attending the summer workshop offered by the project in its third project year to prepare them for teaching spatial computational thinking with IDV ("Integrated Data Viewer," downloadable at https://www.unidata.ucar.edu/software/idv/) visualization of weather data. Quantitative survey data were collected measuring these teachers' meteorology content knowledge, spatial computational thinking, self-efficacy for teaching spatial computational thinking, and epistemic cognition of teaching meteorology. The data were analyzed to examine the effects of the workshop in terms of these variables and the correlations among them were also explored.

The Kigali Amendment introduced a new family of chemical compounds, which do not contribute to stratospheric ozone depletion but present a high global warming potential, under the watch of the Montreal Protocol in 2016. Earlier this year, a press note from the World Meteorological Organization entitled "Ozone layer recovery is on track, helping avoid global warming by 0.5°C" caught our attention because of the wrong conclusions that can be potentially drawn by laypersons due to an apparent linkage of ozone depletion and global warming problems. Public communication of the Montreal Protocol since the Kigali Amendment should be more careful than ever to avoid lessening the social perception of the threat of climate change, particularly considering that society already has a distorted representation of these problems, assuming causal relations between ozone depletion and climate change, that could lead to unfounded optimism towards the climate crisis.

In the context of introductory courses in physical geography and meteorology, it is common around February 2 for curiosity to arise among students regarding the veracity of "Groundhog Day" predictions. Historically, most instructors' immediate response to such inquiries has been a definitive "NO," emphasizing the impossibility of a groundhog's shadow predicting weather weeks ahead. Yet, upon further consideration, this piece of American folklore presents a valuable educational opportunity. This lecture dissects the tale, its cultural roots, and its relevance to contemporary meteorology and climatology, exploring the substantial cultural and physical geography insights that the Groundhog Day story offers. The lecture and lesson aim to present atmospheric concepts and cultural information to a general education audience in an understandable and relatable way.

Longhorn beetles (Cerambycidae) are a very diverse group whose species vary greatly in morphology and behaviour, particularly in diel activity. The type genus Cerambyx includes two large oak‐ling sympatric species (Cerambyx cerdo and Cerambyx welensii) that exhibit a crepuscular/nocturnal (dim‐light) lifestyle, but their actual diel activity remains largely unclear.We used phototraps in the wild to assess if diel activity depended on either species, sex or annual meteorology. Phototraps were highly effective, and the imaged activities were representative of beetles' life, including interactions with some vertebrate species.Both longhorns exhibited roughly similar crepuscular/nocturnal behaviour, with daytime activity being usually residual or erratic. Diel activity increased sharply just before dusk, peaked between dusk and early dark night (23:00–2:00 h), and then dropped progressively until disappearing at dawn. Diel activity was sexually dimorphic in C. welensii, as in some years females tended to be active earlier than males.Distinctly, in 2022, the warmest and driest year recorded in the study area, diel activity curves were flatter and wider, with C. cerdo activity increasing in the late afternoon (19:00–21:30 h) and C. welensii in the second half of the night (3:00–6:00 h). Furthermore, both species boosted daytime activity (diurnality) by more than three times, and diurnality also increased over the season that year.We finally hypothesise that, under the current climate change scenario, extreme droughts impact diel activity in these species, particularly boosting diurnality, in an attempt to locate water/food sources to counteract body dehydration. [ABSTRACT FROM AUTHOR]

Despite the growing number of GNSS sites, GNSS meteorology still suffers from insufficient observational data. This deficiency is particularly evident in GNSS tomography processing in the lowermost part of the troposphere, where the highest variability of weather conditions is observed. To address this challenge, we propose an integrated tomography solution that uses both ground-based GNSS observations and radio occultation (RO) excess phase in the same tomography model. Our integrated approach benefits from improved geometry and increased number of observations. In order to verify presented methodology, we use 41 GNSS stations and 78 RO profiles gathered during four different high precipitation events over the region of southern Poland. The results are validated against radiosonde observations and WRF reanalysis. Our study indicates an enhancement in the representation of wet refractivity on the order of 20 to 30% RMSE decrease when RO are integrated into the tomography solution with the mean error of final integrated product equal 3.5 ppm. [ABSTRACT FROM AUTHOR]

The air quality (AQ) of a given location depends mostly on two factors: emissions and meteorological conditions. For most places on Earth, the meteorology of an area changes seasonally. For central Europe, winters are associated with poor dispersion conditions, which, in combination with high emissions from local heating systems, lead to significantly higher concentrations than during summer. In this study, the seasonality of AQ is analysed using hourly measurements from 44 monitoring stations in Slovakia for the years 2007–2023 for NO2, PM10 and PM2.5. Two factors are used to evaluate the seasonality—the difference and ratio of the winter and summer mean concentrations. It was found that the seasonal difference has been gradually decreasing for all pollutants since 2017. In the case of PM2.5, the seasonal ratio drops from a value of around 2.5 in 2018 to approximately 1.7 in 2023. While in the past, the seasonal ratio was the highest for PM2.5, in the last three years it is the highest for NO2 with values larger than 2. Our results imply that summer sources of PM emissions start to play a more important role for the AQ than in the past. The observed seasonality was compared with two full-year chemical-transport model simulations. [ABSTRACT FROM AUTHOR]

The Standardized Precipitation Evapotranspiration Index (SPEI) is a widely recognized and effective tool for monitoring meteorological droughts. However, existing SPEI datasets suffer from spatial discontinuity or coarse spatial resolution problems, which limits their applications at the local level for drought monitoring research. Therefore, we calculated the SPEI index at meteorological stations, combined with the Global Precipitation Measurement (GPM) Precipitation (Pre), Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST), ERA5‐Land Shortwave Radiation (SR), Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM) datasets and Random Forest Regression (RFR) model, developed a high spatial resolution (1 km) SPEI (HSPEI) datasets with multiple time scales in mainland China from 2001 to 2022. Compared to other SPEI datasets, the HSPEI datasets have higher spatial resolution and can effectively identify the detailed characteristics of drought in mainland China from 2001 to 2022. Overall, the HSPEI datasets can be effectively applied to the research of different droughts in China from 2001 to 2022. [ABSTRACT FROM AUTHOR]

The transition from the 18th to the 19th century saw significant contributions to the advancement of weather and climate sciences. Proposed interpretations of nature showed the influence of the philosophical ideas of the time. Authors such as Kant, Laplace, and Humboldt developed their theories on nature, which evolved the concepts of environmentalism of the time. In this context, Goethe published different writings on the importance of nature and its constituent elements, including the atmosphere, which merited the writing of a treatise on meteorology with novel ideas on atmospheric dynamics. With an exquisite education, Goethe developed his work in a cultural and scientific context -Weimar-propitious to promoting the arts and sciences. He participated in the activities of the Jena Circle and played an active role in the political life of the time. Goethe's contributions to the theory of nature and the understanding of atmospheric issues established a necessary intellectual link between Kantian idealism and the scientific empiricism practiced by Humboldt in the first decades of the 19th century. This work analyses the cultural and social context in which Goethe's ideas on nature developed and, in particular, on atmospheric aspects whose study and interpretation marked the life of the German polymath. [ABSTRACT FROM AUTHOR]

An introduction is presented in which editor discusses various articles within the issue on topics including disasters that occurred between the years 1802 and 1812 in South America; publication of effects of the "storm" in newpapers and knowledge of meteorological science.

This study investigates the relationship between sea surface temperature (SST) anomalies in the subtropical Atlantic Ocean, as represented by the Southern Atlantic subtropical dipole (SASD), and SST anomalies in the tropical Pacific Ocean, identified by the El Niño‐Southern Oscillation (ENSO). Contrary to the previously held notion of a weak relationship between SASD and ENSO as suggested by earlier literature, our analysis reveals a substantial inverse correlation between the two. This correlation exhibits significant multi‐decadal variability, which has notably intensified over the most recent two decades compared with the preceding two decades. This intensification in the SASD–ENSO inverse correlation may be attributed to the shift in ENSO regime from predominance of eastern Pacific El Niño to central Pacific El Niño events around the turn of the century. This transition triggers wavetrains that propagate along different paths, consequently influencing the South Atlantic subtropical high and inducing alterations in anomalous SST patterns in the subtropical Atlantic Ocean. These findings advance our comprehension of the interactions between South Atlantic and Pacific SST variations, which strongly influence rainfall patterns, particularly in South America and southern Africa. Understanding such teleconnection holds promise for improving sub‐seasonal to seasonal precipitation predictions in these regions. [ABSTRACT FROM AUTHOR]

Accurate air temperatures underpin environmental research. Most professional meteorological air temperature measurements still expose thermometers within traditional, naturally ventilated screens. Their representation of true air temperature depends on screen airflow, and therefore local winds. Accuracies of daily maximum (Tmax) and minimum (Tmin) air temperatures are assessed by comparison between a naturally ventilated large conventional screen and a co‐located aspirated reference screen. In over 1200 days' data, the naturally ventilated Tmin and Tmax both showed small (median < 0.06°C) cold bias, but, in 1% of cases, warm Tmax bias and cold Tmin bias >|1°C|. The Tmin cold bias is associated with calm clear nights, and the Tmax warm bias events with calm winter days at low sun angles, allowing solar heating of the screen. The prevalence of poor natural ventilation, potentially affecting Tmin and Tmax, is estimated across European sites. Poor ventilation occurred at Tmin for 12% of values, and at Tmax for 4%. Climatological averaging will reduce these effects, but, without corroborating wind data, statistical changes in Tmin or Tmax, including identifying "Tropical Nights" (Tmin > 20°C) or occurrences of winter extremes, may have limited value. Wider adoption of aspirated thermometer screens, with an initial overlap period, will largely eliminate these effects. [ABSTRACT FROM AUTHOR]

[Objective] Understanding and grasping the temporal and spatial changes in meteorology and reasonably predicting the future trend of climate change can provide an important theoretical basis for the formulation of regional response measures. [Methods] The Sichuan section of the Yangtze River Basin was selected as the research area. Data analysis was conducted based on measured station data from 1980 to 2014. Three future hypothetical climate scenario models (SSP1-2.6, SSP2-4.5, SSP5-8.5) were estimated using the GCM with complete scenarios in the sixth phase of the International Coupled Model Comparison Plan (CMIP6) after bias correction, and the changes in precipitation climate variables within the region were analyzed. [Results (1) There is a certain degree of underestimation of precipitation simulation in climate models, with monthly scale seasonal scale annual scale in terms of time scale. (2) After the calibration and evaluation of the climate model, the data are more consistent with the actual situation, and the quantile mapping method is very effective for the calibration of the Climate model data. (3) Compared to the benchmark period (1980-2014), the future precipitation in the study area is higher than the historical period under three scenarios, with SSP5-8.5 SSP2-4.5 SSP1-2.6. (4) The areas with significant increases in precipitation are mainly located in the northwest of the study area, showing a spatial pattern of low level in the west and high level in the east. [Conclusion] The climate model's prediction of future precipitation changes in the study area shows an overall increasing trend, but there is underestimation to a certain extent. [ABSTRACT FROM AUTHOR]

Cerebrovascular diseases are the leading cause of death and disability. The epidemiological background and predisposing factors have been the basis of many studies. We aimed to assess the effect of seasonal variability and meteorological factors on stroke incidence in Hungary. National and county-level secondary data were assessed for 2018–2019. We identified stroke with ICD codes I60, I61, I62 (hemorrhagic), I63, I65, and I66 (ischemic). The data were obtained from the University of Pécs Clinical Centre (number of patients per day according to sex and disease subtype, n = 1765). Daily average and maximum wind speed [m/s], precipitation [mm], temperature [°C], and frontal effect [warm/cold/mixed/no effect]) were provided by the Hungarian Meteorological Service. We found that 89.92% of the patients were hospitalized for ischemic and 10.08% for hemorrhagic stroke. We observed a significantly higher number of cases in the other months compared to winter (spring: +35.9%; p = 0.007, summer: +59.0%; p = 0.016, autumn: +36.5%; p = 0.01). In autumn, an increase in temperature increased the incidence of stroke (r = 0.210; p = 0.004). Temperature change affected ischemic stroke incidence (r = 0.112; p = 0.003). In contrast, the number of hemorrhagic stroke cases showed a mild but significant negative association with daily temperature change (r = −0.073; p = 0.049). Overall, a 1 °C temperature change compared to the previous day increased the daily number of admissions by 2.9% (p = 0.017). Air pressure change also affected hemorrhagic stroke incidence (r = 0.083; p = 0.025). Changes in temperature and frontal effects can increase the incidence of stroke. Modern forecasting technology can help the healthcare system prepare for possible increased workloads during critical periods. [ABSTRACT FROM AUTHOR]

The meteorology of cutoff lows over South Africa is characterized by statistical analysis of daily field data in the period 1970–2023. An index is formulated by subtracting 500 hPa geopotential height in the mid-latitudes from the subtropics. Cutoff lows (COL) are identified by positive values, mostly in autumn and spring. Statistics indicate that climate forcing is seasonal: La Nina/El Nino favors COL in March–May/September–November. Hemispheric regressions reveal anomalous highs across the southern mid-latitudes when COL are frequent over South Africa. A 14-case composite was formed from the most intense daily COL events in autumn and spring. The composite shows a NW- tilted Rossby wave and jet stream loop around the COL. Maritime easterlies induce a warm east—cool west SST pattern, but composite moist inflows are shallow, so stormy weather hugs the coastal plains. Overturning circulations meet in an upper-level "saddle" over South Africa. 500 hPa sinking motions to the Southwest are of similar strength to rising motions to the Northeast. A COL case study exhibited hourly rain rates >10 mm at Port Alfred 18–20 October 2012 fed by tropical inflow. New insights emerged from this study via composite height sections over South Africa. [ABSTRACT FROM AUTHOR]

Accurate meteorological information is crucial for the safety of civil aviation flights. Complete wind field information is particularly helpful for planning flight routes. To address the challenge of accurately reconstructing wind fields, this paper introduces a deep learning neural network method based on the Vision Mamba Decoder. The goal of the method is to reconstruct the original complete wind field from incomplete wind data distributed along air routes. This paper proposes improvements to the Vision Mamba model to fit our mission, showing that the developed model can accurately reconstruct the complete wind field. The experimental results demonstrate a mean absolute error (MAE) of wind speed of approximately 1.83 m/s, a mean relative error (MRE) of around 7.87%, an R-square value of about 0.92, and an MAE of wind direction of 5.78 degrees. [ABSTRACT FROM AUTHOR]

Similar to usual lower records, Bottom- k -lists (Kozan and Tanil, İstatistik J Turk Stat Assoc 13:73–79, 2013) have a wide range of practical applications in meteorology, hydrology, sports, etc. Also, exceedance statistics can be viewed as a close relative to tolerance limits—an important field of statistical science. In this study, an idea of combining these two important subjects together is studied and an exceedance statistic is defined based on bottom- k -lists in an independent and identically distributed (iid) continuous random sequence. Probability mass function (pmf) of a selected exceedance statistic is obtained. Also, an illustrative application of the exceedance statistic is given. [ABSTRACT FROM AUTHOR]

A study for atmospheric transport is essential for the consequence assessment of severe nuclear accidents since radionuclides could be released from the nuclear facility into the atmosphere and cause radioactive pollution in the environment. Atmospheric transport behaviors are strongly related with meteorological conditions, which can obviously influence the transport and diffusion characteristics of radioactive materials in the atmosphere; thus, it is meaningful to investigate the coupling effects between meteorological processes and transport behaviors of radioactive materials. To evaluate the influence of meteorological conditions on atmospheric transport, meteorological parameters for different seasons were first acquired by the weather research forecast model. Furthermore, atmospheric transport behaviors of radioactive materials were simulated by the meso-scale numerical model under different meteorological conditions, and numerical analyses were conducted toward transport and deposition behaviors of radioactive materials. In addition, the influence of FDDA (four-dimensional data assimilation) on meteorological parameters and atmospheric transport behaviors was researched. The present study is important for strengthening consequence assessment for severe nuclear accident and made it possible to apply the data assimilation technology in further research works. [ABSTRACT FROM AUTHOR]

The performance of numerical weather prediction models has improved dramatically recently. However, model biases remain a fundamental limitation prohibiting the direct implementation of model results. There are several ways to describe wind speed data. The Weibull and lognormal distributions are used to obtain the best-fit model for the wind speed data. This study aims to develop a statistical post-processing method based on the distribution-based scaling (DBS) approach, which scales NWP data to fit the distribution derived using recorded wind speed at that site location. The performance of the suggested method was evaluated using four different error measures. The optimal model is anticipated to have the lowest Mean Bias Error (MBE), Mean Absolute Error (MAE), Root Mean square Error (RMSE), and variance (s2) values. It was determined that employing a DBS strategy significantly improved the NWP by at least 75%. [ABSTRACT FROM AUTHOR]

Aristotle’s Meteorology 4.12 is puzzling, in part because the chapter appears to extend teleological explanation to include certain inorganic materials without natural biological functions, such as metals and stone. This paper examines two attempts to explain why such materials can have functions, and shows that they are problematic. As an alternative, I argue that raw inorganic materials—as well as separated parts of organisms—can have extrinsic functions. Extrinsic functions can explain why natural inorganic materials can be sorted into natural kinds, even if their functions are ultimately related to their uses in the productive arts. [ABSTRACT FROM AUTHOR]

Dust and high cloud interactions are critical for climate change, primarily due to the dominant roles of high cloud in the greenhouse effect and the continental precipitation. Nonetheless, disentangling the specific impacts of dust from the overlying meteorology influence on high clouds presents great challenges. In this study, we construct a meteorological pattern that successfully reveal the intricate connection between high cloud distribution and atmospheric conditions. Through this strong bounded relationship, we find that dust exhibits notable controls over the facilitation or inhibition of ice particle growth contingent upon the prevailing meteorological fields. More dust can increase precipitation rate and shorten high cloud lifetime particularly under favorable meteorology. These findings underscore the crucial role of dust concentration in mitigating global warming for future climate change. Plain Language Summary: Dust particles in the atmosphere play a crucial role in the formation and dissipation of high clouds, which can significantly impact global climate and weather patterns. By analyzing satellite observations and climate model data, we discovered that dust can either enhance or suppress the growth of ice crystals, depending on the atmospheric conditions. In moist environments with favorable weather, increased dust leads to larger ice crystals and more precipitation, accelerating the life cycle of high clouds. Conversely, in dry conditions with unfavorable weather, excessive dust competes for limited moisture, resulting in smaller ice crystals, although it can still increase rainfall during the dissipation stage of high clouds. As climate change progresses and dust storms become more frequent, the increased presence of dust in the atmosphere may lead to more extreme weather events and potentially mitigate some of the warming effects caused by high clouds. Understanding these complex interactions between dust, high clouds, and weather conditions is essential for improving climate models and predicting future changes in Earth's water cycle and energy balance. Key Points: Extracted first principal component, highly correlated with high cloud fraction, isolating meteorological effects on cloudsDust facilitates ice crystal formation and precipitation, catalyzing cloud lifecycle and ultimately modifying radiative effectsIncreased dust accelerates high cloud lifecycle, causing more extreme weather and mitigating climate warming [ABSTRACT FROM AUTHOR]

This study examines the influence of shallow cumulus clouds on the excessive summertime heat in the Houston‐Galveston metropolitan area, a coastal urban area in the warm Southeast United States. Specifically, it aims to improve our understanding of how both the clouds and the relatively cool, moist afternoon sea breeze impact the Urban Heat Island (UHI) and Heat Index (HI). During the warm season, the afternoon sea breeze phenomenon in this coastal city acts as a natural air conditioner for city residents, facilitating the dispersion of moisture, heat, and pollutants. To investigate the relationship among urbanization, clouds, and land‐sea interactions, we conducted cloud‐ and urban‐resolving simulations at a 900 m grid resolution and perform simulation scenarios aiming to isolate urbanization, clouds and land‐sea circulations. Results show that urbanization correlates with the presence of shallow cumulus clouds, higher cloud bases, and increased cloud duration over the Galveston‐Houston region compared to rural areas. These urban clouds benefit from the enhanced moist static energy that is favored by intensifying vertical mixing and moisture flux convergence. Urbanization raises the mean HI while mitigating its afternoon HI high. We found that the urban circulation dome overwhelms the sensitivity of the sea breeze to the urbanization. Instead, the influence of urbanization on cloud enhancement emerges as a crucial pathway responsible for reducing the high afternoon HI values. Moreover, uncertainties in SSTs are closely linked to the sensitivities of land‐sea circulations, which in turn modulate UHI and HI. Plain Language Summary: Urbanization influences the meteorology by creating a warmer environment that enhances excessive heat during the summer. Additionally, the warmer environment and the urban buildings increase friction, leading to more mixing and in turn favoring the development of low‐level clouds. We developed computer simulations aiming to understand these processes and their interaction with the sea‐breeze in this coastal city, and we found that these clouds help ameliorate the excessive heat during the afternoon. Key Points: Urbanization correlates with the presence of shallow cumulus cloudsUrban clouds are driven by the enhanced sensible heat and dynamic drag imparted by the urban landscapeUrbanization cloud enhancement emerges as a crucial pathway responsible for reducing the afternoon Heat Index values [ABSTRACT FROM AUTHOR]

As our planet warms, Arctic sea ice coverage continues to decline, resulting in complex feedbacks with the climate system. The core objective of NASA's Salinity and Stratification at the Sea Ice Edge (SASSIE) mission is to understand how ocean salinity and near-surface stratification affect upper-ocean heat content and thus sea ice freeze and melt. SASSIE specifically focuses on the formation of Arctic Sea ice in autumn. The SASSIE field campaign in 2022 collected detailed observations of upper-ocean properties and meteorology near the sea ice edge in the Beaufort Sea using ship-based and piloted and drifting assets. The observations collected during SASSIE include vertical profiles of stratification up to the sea surface, air–sea fluxes, and ancillary measurements that are being used to better understand the role of salinity in coupled Arctic air–sea–ice processes. This publication provides a detailed overview of the activities during the 2022 SASSIE campaign and presents the publicly available datasets generated by this mission (available at https://podaac.jpl.nasa.gov/SASSIE , last access: 29 May 2024; DOIs for individual datasets in the "Data availability" section), introducing an accompanying repository that highlights the numerical routines used to generate the figures shown in this work. [ABSTRACT FROM AUTHOR]

Effective Management And use of water resources are important in many respects, including sustaining life, agriculture, economic activities, ecosystem health, public health, climate regulation, energy production, cultural and recreational value, conflict, and cooperation. However, increasing population and rapid industrialization have put water resources under serious threat. Worldwide, while water resources are increasingly under threat, pollution and overexploitation areas have large effects on rivers, lakes, and groundwater. This situation is causing problems both in providing drinking water for people and in vital activities such as irrigation of agricultural areas. [ABSTRACT FROM AUTHOR]

The occurrence of rainfall is significantly affected by climate change around the world. While in some places this is likely to result in increases in rainfall, both winter and summer rainfall in most parts of New South Wales (NSW), Australia are projected to decrease considerably due to climate change. This has the potential to impact on a range of hydraulic and hydrologic design considerations for water engineers, such as the design and construction of stormwater management systems. These systems are currently planned based on past extreme rain event data, and changes in extreme rainfall amounts due to climate change could lead to systems being seriously undersized (if extreme precipitation events become more common and/or higher in magnitude) or oversized (if extreme rainfall events become less frequent or decrease in magnitude). Both outcomes would have potentially serious consequences. Consequently, safe, efficient, and cost-effective urban drainage system design requires the consideration of impacts arising from climate change on the approximation of design rainfall. This study examines the impacts of climate change on the probability of occurrence of daily extreme rainfall in New South Wales (NSW), Australia. The analysis was performed for 29 selected meteorological stations located across NSW. Future design rainfall in this research was determined from the projected rainfall for different time periods (2020 to 2039, 2040 to 2059, 2060 to 2079, and 2080 to 2099). The results of this study show that design rainfall for the standard return periods was, in most cases, lower than that derived employing the design rainfall obtained from the Australian Bureau of Meteorology (BoM). While most of the analysed meteorological stations showed significantly different outcomes using the climate change scenario data, this varied considerably between stations and different time periods. This suggests that more work needs to be performed at the local scale to incorporate climate change predicted rainfall data into future stormwater system designs to ensure the best outcomes. [ABSTRACT FROM AUTHOR]

The article focuses on the impact of meteorological phenomena on bird abundance during the autumn-winter season of 2023-2024 in Switzerland. It highlights the effects of storms and snowfall on various bird species, leading to unusual observations and influxes in specific regions. It presents data gathered from numerous observers, detailing the relationships between weather events and bird migrations, and emphasizes the ongoing discussions among ornithologists regarding these connections.

As a scientific approach to solving the uncertainty problems in single deterministic forecasts, ensemble forecasting has been applied in other fields besides meteorology and shows encouraging advantages. To comprehensively understand the applications of ensemble forecasting in other fields, this study offers a brief review. Several related papers are selected and outlined. The ways used to generate ensemble forecasts and the applications of ensemble forecast products in different studies are compared, the existing problems and challenges are discussed, and some suggestions are provided, with the hope that they provide useful references for researchers in related fields. [ABSTRACT FROM AUTHOR]

High concentrations of ozone in the Uinta Basin, Utah, can occur after sufficient snowfall and a strong atmospheric anticyclone creates a persistent cold pool that traps emissions from oil and gas operations, where sustained photolysis of the precursors builds ozone to unhealthy concentrations. The basin's winter-ozone system is well understood by domain experts and supported by archives of atmospheric observations. Rules of the system can be formulated in natural language ("sufficient snowfall and high pressure leads to high ozone"), lending itself to analysis with a fuzzy-logic inference system. This method encodes human expertise as machine intelligence in a more prescribed manner than more complex, black-box inference methods such as neural networks, increasing user trustworthiness of our model prototype before further optimization. Herein, we develop an ozone forecasting system, Clyfar, informed by an archive of meteorological and air-chemistry measurements. This prototype successfully demonstrates proof-of-concept despite rudimentary tuning. We describe our framework for predicting future ozone concentrations if input values are drawn from numerical weather prediction forecasts rather than observations as Clyfar initial conditions. We evaluate inferred values for one winter, finding our prototype demonstrates mixed performance but promise after optimization to deliver useful forecast guidance for decision-makers when forecast data are used as input. This early version model is the basis of ongoing optimization through machine learning. [ABSTRACT FROM AUTHOR]

Although Augmented Reality (AR) has been extensively studied in supporting Immersive Analytics (IA), there are still many challenges in visualising and interacting with big and complex datasets. To deal with these datasets, most AR applications utilise NoSQL databases for storing and querying data, especially for managing large volumes of unstructured or semi-structured data. However, NoSQL databases have limitations in their reasoning and inference capabilities, which can result in insufficient support for certain types of queries. To fill this gap, we aim to explore and evaluate whether an intelligent approach based on ontology and linked data can facilitate visual analytics tasks with big datasets on AR interface. We designed and implemented a prototype of this method for meteorological data analytics. An experiment was conducted to evaluate the use of a semantic database with linked data compared to a conventional approach in an AR-based immersive analytics system. The results significantly highlight the performance of semantic approach in helping the users analysing meteorological datasets and their subjective appreciation in working with the AR interface, which is enhanced with ontology and linked data. [ABSTRACT FROM AUTHOR]

Abstract The Standardized Precipitation Evapotranspiration Index (SPEI) is a widely recognized and effective tool for monitoring meteorological droughts. However, existing SPEI datasets suffer from spatial discontinuity or coarse spatial resolution problems, which limits their applications at the local level for drought monitoring research. Therefore, we calculated the SPEI index at meteorological stations, combined with the Global Precipitation Measurement (GPM) Precipitation (Pre), Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST), ERA5‐Land Shortwave Radiation (SR), Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM) datasets and Random Forest Regression (RFR) model, developed a high spatial resolution (1 km) SPEI (HSPEI) datasets with multiple time scales in mainland China from 2001 to 2022. Compared to other SPEI datasets, the HSPEI datasets have higher spatial resolution and can effectively identify the detailed characteristics of drought in mainland China from 2001 to 2022. Overall, the HSPEI datasets can be effectively applied to the research of different droughts in China from 2001 to 2022.

The analysis of observed fine particulate matter (PM2.5) in Jakarta, Indonesia, was conducted from January 2021 to October 2023 using hourly monitoring data from AirNow, managed by the U.S. Environmental Protection Agency. Data were collected from two air quality monitoring stations in Central and South Jakarta. Meteorological data, including wind speed, temperature, humidity, and rainfall, were obtained from NASA's global forecasting database. The objective was to understand the correlation between PM2.5 formation and meteorological conditions to identify favorable meteorology during high air pollution episodes. During the dry season (May to August), there was a notable increase in PM2.5, linked to higher temperatures, lower humidity, slower wind speeds, and minimal rainfall. The results showed that meteorological conditions significantly affected PM2.5 concentrations, influenced by seasonal changes. In contrast, the rainy season had lower PM2.5 concentrations due to higher rainfall, humidity, and wind speed, and lower temperatures. Further modeling studies are required to assess the combined impacts of emission build-up and meteorology on recurring PM2.5 episodes in Jakarta.

Tourism dynamics are profoundly influenced by weather conditions, particularly in regions celebrated for their climatic appeal. The Rías Baixas, a renowned coastal destination in northwest Spain, presents an exemplary case for examining this interplay. This study aims to enhance comprehension of how tourists alter their daily activities in response to weather changes, providing valuable insights for improved planning and management in the tourism sector. Faced with the methodological challenge of accurately measuring daily tourist flows, especially in areas such as beaches without entry registers, this study innovatively uses daily vehicle traffic intensity data in access areas to quantify tourist behavior patterns. Positive correlations are observed between the arrival of vehicles and sunshine. For tourism businesses, short-term weather forecast information, as well as a better understanding of travelers’ behavior, is crucial for decision making and operational planning.

Roughness of the surface underlying the atmospheric boundary layer causes departures of the near-surface scalar and momentum transport in comparison with aerodynamically smooth surfaces. Here, we investigate the effect of $56\times 56$ homogeneously distributed roughness elements on bulk properties of a turbulent Ekman flow. Direct numerical simulation in combination with an immersed boundary method is performed for fully resolved, three-dimensional roughness elements. The packing density is approximately $10\,\%$ and the roughness elements have a mean height in wall units of $10 \lesssim H^+ \lesssim 40$. According to their roughness Reynolds numbers, the cases are transitionally rough, although the roughest case is on the verge of being fully rough. We derive the friction of velocity and of the passive scalar through vertical integration of the respective balances. Thereby, we quantify the enhancement of turbulent activity with increasing roughness height and find a scaling for the friction Reynolds number that is verified up to $Re_\tau \approx 2700$. The higher level of turbulent activity results in a deeper logarithmic layer for the rough cases and an increase of the near-surface wind veer in spite of higher $Re_\tau$. We estimate the von Kármán constant for the horizontal velocity $\kappa _{m}=0.42$ (offset $A=5.44$) and for the passive scalar $\kappa _{h}=0.35$ (offset $\mathbb {A}=4.2$). We find an accurate collapse of the data under the rough-wall scaling in the logarithmic layer, which also yields a scaling for the roughness parameters $z$ -nought for momentum ($z_{0{m}}$) and the passive scalar ($z_{0{h}}$). [ABSTRACT FROM AUTHOR]

Objective: We here displayed the global research trends of meteorology and hemorrhagic fever with renal syndrome (HFRS) as a visual knowledge map by using bibliometrics and revealed the research directions, hotspots, trends, and frontiers in this field. Methods: Using Web of Science core collection as the data source and with CiteSpace and VOSviewer software, we collected and analyzed the annual number of papers, cooperative relationships (countries, institutions, authors, etc.), citations (literature citation, literature co-citation, literature publication, etc.), keywords (emergence, clustering, etc.) of meteorology, and HFRSrelated research data for the past 30 years, and drew a visual map. Results: In total, this study included 313 papers investigating the relationship between meteorology and HFRS. The first paper was published in 1992. Globally, United States had the largest number of publications in this field, and the Chinese Center for Disease Control and Prevention was the most influential institution conducting related research (20 articles published, and the mediation centrality was 0.24). Several small author cooperation clusters were formed; however, the number of papers published by the same scholar and the co-citation frequency were low. Cazelles Bernard (7 articles) published the highest number of articles in this field, and Gubler DJ was the author with the most co-citations (55 times). The most frequently cited journal was Emerging Infectious Diseases. In this field, the top three high-frequency keywords were "hemorrhagic fever," "transmission," and "temperature." According to keyword cluster analysis, the top three themes were dengue, dechlorane plus, and bank voles. The timeline spectrum exhibited that dengue clustering had a good temporal continuity. The trend analysis of emergent words revealed that the research on "temperature," "meteorological factors" and "Puumala hantavirus" has gradually appeared in recent years. Conclusion: This study represents the first comprehensive exploration of global trends, hotspots, frontiers, and developments in the relationship between meteorology and HFRS, utilizing CiteSpace and VOSviewer software. The findings of this study are crucial for elucidating the influence of climate change on disease transmission patterns and offering novel insights for forthcoming epidemiological research and public health interventions. [ABSTRACT FROM AUTHOR]

Based on the monitoring data of ambient air quality and meteorological observation data, the characteristics and meteorological influencing factors of air pollution in Luojiang District of Deyang City from 2018 to 2022 were analyzed. The results show that from 2018 to 2022, the main air pollutants affecting the air quality of Luojiang District of Deyang City were PM2.5 and PM10, and the primary pollutant on heavy pollution days was basically PM2.5. PM2.5 and PM10 pollution showed obvious seasonal differences, and PM2S concentration exceeded the limit mainly in spring and winter, among which it was the most serious in early spring, especially in January and February, followed by December. PMW exceeding the standard had a high seasonal correlation with PM2S exceeding the standard, mainly in spring and winter, among which it was the most serious in winter, especially in December, followed by January. PM2.5 and PM1.0 pollution showed an overall weakening trend. PM2.5 and PM1.0 concentration were closely related to meteorological factors such as temperature, relative humidity, wind speed, precipitation and air pressure, and were mainly affected by rainfall. [ABSTRACT FROM AUTHOR]

Since ancient times, calligraphy and meteorology have had an inseparable relationship. Wang Xizhi's Prologue to the Collection of Poems Composed at the Orchid Pavilion records the beautiful scenery of mild wind and bright sun, as well as the relaxed and joyful mood of people in such weather. Su Shi's Cold Food Calligraphy Copybook records the scenery of solar terms and the author's psychological changes during these solar terms through calligraphy. The Quick, Snow and Clear Time Calligraphy Copybook also reflects the grandeur of snowy days and the customs of literati recording weather and sharing it with friends. In Sun Quoting's Shupu, it is clearly stated that the third element of the "five harmony and five obedience" refers to the clear sky, humid air, and pleasant climate, and excellent climatic conditions are conducive to writing. [ABSTRACT FROM AUTHOR]

Visibility is one of the key meteorological parameters with special importance in aviation meteorology and the transportation industry. Nevertheless, it is not a straightforward task to automatize visibility observations, since the assistance of trained human observers is still inevitable. The current paper attempts to make the first step in the process of automated visibility observations: it examines, by the approaches of artificial intelligence (AI), whether light points in the target area can or cannot be automatically detected for the purposes of night-time visibility observations. From a technical point of view, our approach mimics human visibility observation of the whole circular horizon by the usage of camera imagery. We evaluated the detectability of light points in the camera images (1) based on an AI approach (convolutional neural network, CNN) and (2) based on a traditional approach using simple binary thresholding (BT). The models based on trained CNN achieved remarkably better results in terms of higher values of statistical metrics, and less susceptibility to errors than the BT-based method. Compared to BT, the CNN classification method indicated greater stability since the accuracy of these models grew with increasing pixel size around the key points. This fundamental difference between the approaches was also confirmed through the Mann–Whitney U test. Thus, the presented AI-based determination of key points' detectability in the night with decent accuracy has great potential in the objectivization of everyday routines of professional meteorology. [ABSTRACT FROM AUTHOR]

The Precipitating Change Project was a 5-year development, implementation, and research study of an innovative 4-week middle school curricular unit in computational weather forecasting that integrates students' learning and use of meteorology and computational thinking (CT) concepts and practices. The project produced a list of CT skills and definitions that students use to predict the weather, CT assessment instruments, and a CT classroom observation protocol. Data was collected from 306 eighth grade (ages 13–14) students in rural indigenous communities in the Artic and urban and suburban Northeast communities in the USA. The project met its goal of producing an intentional instructional sequence that integrates disciplinary science and CT practices to increase students' science knowledge and their ability to use CT skills and processes. The results indicate that teachers were able to use the curriculum to embed CT practices into the classroom. Students, in turn, had the opportunity to practice using these skills in class discussion as evidenced by the classroom observation data, and students' science knowledge of CT content and practices significantly increased as evidenced by their performance on the weather content and CT skills pre- and post-assessments. While statistically significant gains in science knowledge and CT skills and practices were evident in all settings (urban, suburban, and rural indigenous communities), there were noticeable differences in gains in students' CT skills and practices between the three settings and additional research is needed in a diversity of settings to understand this difference. [ABSTRACT FROM AUTHOR]

This study investigates the spatial patterns and variabilities of Seasonal Rainfall Onset Day (OD), Cessation Day (CD), Seasonal Length (SL), and Number of Rainy Days (RD) in Rwanda for the long rain season (LR) of March–April-May (MAM) and the short rain season (SR) of September–October-November–December (SOND). Data used, provided by the Rwanda Meteorology Agency, consisted of a time series of gridded rainfall and temperature from 1983 to 2021. The northern, western, and southwestern regions experience earlier OD than the remaining parts of the country, [mid-February, early March] for LR and [early September, mid-October] for SR. The entire eastern region experiences later OD ([mid-March, end March]) during LR. During SR, the central east and the southeastern regions experience later OD ([mid-October, end November]). During LR and SR, the mean SL and mean RD are highest in the northwestern and southwestern regions and lowest in the central-eastern and southeastern regions. In those regions, the mean SL and mean RD are higher during SR ([81, 116], [49, 74] days) than during LR ([77, 99], [46, 68] days). In the remaining parts of the country, they are lower during SR ([46, 81], [24, 49] days) than during LR ([55, 77], [24, 46] days). The temporal variability (coefficient of variation) is relatively high in different places. During LR, for OD ([21.5, 34] %) over the northwest, central plateau, and eastern regions, for SL ([22.5, 35] %) over the northern and eastern regions, and for RD ([24.5, 32] over the eastern region. During SR, for SL ([23, 31] %) over the southcentral and the central plateau regions, and for RD ([25.5, 38] %) over the northern, western, southern, and central plateau regions. The seasonal length and the number of rainy days are strongly dependent to rainfall intensity, but more dependent in short rains seasons. An investigation done El Nino and the Indian Ocean Dipole indicates that they may have an influence on the studied rainfall characteristics in Rwanda. Results from this study are important, as the country's economy remains dependent on rain-fed agriculture. They will help farmers, policy and decision-making for appropriate adaptation and mitigation strategies and policies. [ABSTRACT FROM AUTHOR]

[Objective] There are many environmental factors that affect vegetation ET0, and it is difficult to obtain some of them. Therefore, it is particularly important to find a simple and feasible method to simulate the response process of vegetation ET0 to environmental factors, and to quantitatively analyze the ecological water demand of typical vegetation in alpine region (taking Guide County as an example). [Method]Based on the meteorological data of Guide County from January to June, 2022, a BP network model for simulating vegetation ET0 under different algorithm optimization was established, 151 groups of data were selected to train the model, and the remaining 30 groups of data were tested and verified. [Result] The main factors affecting poplar ET0 are average temperature, 2 m wind speed, saturated vapor pressure difference, relative humidity, and the correlation from large to small is average temperature, saturated vapor pressure difference, 2 m wind speed and relative humidity. BP neural network model optimized by different algorithms is used to simulate poplar ET0, and the simulation effect is improved compared with the traditional BP network. [Conclusion] among which EWOA-BP optimization model has the best simulation result, which can be used as the first choice model to predict poplar ET0 in alpine region. [ABSTRACT FROM AUTHOR]

Land degradation, desertification and tree mortality related to global climate change have been in the spotlight of remote sensing research in recent decades since extreme climatic events could affect the composition, structure, and biogeography of forests. However, the complexity of tree mortality processes requires a holistic approach. Herein, we present the first global assessment and a historical perspective of forest tree mortality by reviewing both remote sensing and meteorological ground-based studies. We compiled 254 papers on tree mortality that make use of remotely sensed products, meteorological ground-based monitoring, and climatic drivers, focusing on their spatial and temporal patterns and the methods applied while highlighting research gaps. Our core results indicate that international publications on tree mortality are on the increase, with the main hotspots being North America (39%) and Europe (26%). Wetness indicators appear as the barometer in explaining tree mortality at a local scale, while vegetation indicators derived from multispectral optical sensors are promising for large-scale assessments. We observed that almost all of the studies we reviewed were based on less than 25 years of data and were at the local scale. Longer timeframes and regional scale investigations that will include multiple tree species analysis could have a significant impact on future research. [ABSTRACT FROM AUTHOR]

Objective To analyze the influence of meteorological factors on Aedes larvae density in Haikou City, Hainan Province, and to study its lagged effect, providing a basis for monitoring and controlling dengue vectors. Methods The Aedes larvae monitoring data and meteorological factor data were collected from 2017 to 2022. The X11 model was employed to decompose and examine the basic characteristics of seasonality and trend in the Aedes aegypti larval density time series. The correlation between Aedes aegypti larval density and meteorological factors was assessed using the Spearman correlation coefficient test. A distributed lag non-linear model was employed to examine the lag non-linear impact of meteorological factors on Aedes larval density. The significance test of relative risk at the longest lag time and the principle of minimizing Akaike's information criterion were utilized to ascertain the optimal parameters of the model. Results The time-series analysis of Aedes larval density revealed distinct seasonality and a decreasing trend. The distribution lag non-linear modeling results indicated that, compared to the median precipitation of 120 mm, with the increase in monthly average precipitation, the relative risk of Aedes larvae density first increased and then decreased, showing an inverted "U" shaped non-linear relationship. The longest lag time of the average precipitation was 1 month, with a lag effect on Aedes larvae density when the precipitation ranged from 119.89 mm to 562.14 mm (P<0.05), peaking at 397.98 mm (RR=1.57, 95%CI: 1.11-2.20). Compared to a median temperature of 25.35 °C, the relative risk to Aedes larvae density increased with the increase of monthly average temperature in a "J" shape. The longest lag time of average temperature was 1 month, with a lag effect on Aedes larvae density between 25.35 °C, and 30.10 °C, (P<0.05), peaking at 30.10 °C, (RR=2.24, 95%CI: 1.24-4.05). Conclusions High precipitation and high temperature both have a risk effect on Aedes larvae density, with a 1-month lag effect. Timely knowledge of the effects of meteorological factors on Aedes larvae density can provide a basis for vector prevention and control and early warning of dengue fever. [ABSTRACT FROM AUTHOR]

Concern about air-quality in urban areas has led to the implementation of Low Emission Zones as one of many other initiatives to control it. Recently in Spain, the enactment of a law made this mandatory for cities with a population larger than 50k inhabitants. The delimitation of these areas is not without controversy because of possible negative economic and social impacts. Therefore, clear assessments of how these initiatives decrease pollutant concentrations are to be provided. Madrid Central is a major initiative of Madrid city council for reducing motor traffic and the associated air pollution in the city centre. This Low Emission Zone starts at the end of 2018, but the first fully-operational period corresponds to the second quarter of 2019. In this work, a methodology based on the Gaussian Process to analyse the evolution of Nitrogen Dioxide inside Madrid Central is undertaken. A Gaussian Process is a stochastic process suitable for interpretable model selection and predictions. Due to its probabilistic nature it provides error estimation at predictions. After the activation of Madrid Central, a relevant reduction of Nitrogen Dioxide has been observed. However, the role of the meteorology during this period must be ascertained to correctly evaluate the role of the activation of the Low Emission Zone against a prone weather. In this work, a model based on the Gaussian Process is trained with meteorological information to predict the concentration of Nitrogen Dioxide at Madrid Central, |$[NO_{2}]$|⁠. This probabilistic description allows extracting statistical information on the reduction affected by the meteorological scenario and separately by the Madrid Central activation. [ABSTRACT FROM AUTHOR]

This paper deals with the physics of Anaximander and argues that, in his theory, the drying up of the sea cannot be accounted for in terms of the kind of conflict envisaged in his main fragment between the four basic substances: the hot, the cold, the wet and the dry (DK 12B1). In so doing, the paper takes issue with a classic interpretation of Anaximander's cosmology advocated by Jaap Mansfeld (2011). This issue also brings us to a wider philosophical problem: are meteorological phenomena in general amenable to this conflict according to Anaximander? In the conclusion, a few conjectural remarks are made regarding what may have been the real explanandum of this theory. [ABSTRACT FROM AUTHOR]

The Turkana Jet plays a pivotal role in the meteorology of East Africa across timescales, and owes its existence to both large‐scale dynamics and the representation of intricate local‐scale processes. However, much of our understanding of the jet relies on reanalysis, and these along with climate models that produce important projections do not represent these local‐scale processes. We systematically investigate the impact of changing model horizontal and vertical resolution in simulating the Turkana Jet, and associated local and large‐scale processes. We perform simulations to coincide with the Radiosonde Investigation For the Turkana Jet (RIFTJet) campaign, enabling direct model‐sonde comparisons in unprecedented detail. We find that increasing horizontal model resolution significantly increases the strength of the jet throughout the channel by up to 30%, while vertical resolution changes have little impact. Horizontal resolutions finer than 2.2 km produce a nocturnal jet ∼2 m/s stronger than observed but perform better during the day. The elevated inversion, which is strongly tied to the strength of the jet, is much better represented in resolutions as high as 1.1 km, whereas the global model at resolution O(∼10 km) is unable to produce any nocturnal elevated inversion. Predictions of jet strength are improved at higher resolution, indicating an important role of local process given that models inherit the same large‐scale state. Despite further improvements at resolutions finer than 4.4 km, we recommend that 4.4 km is the minimum horizontal resolution required to capture realistic interactions between these processes. Underestimation of the Turkana Jet could cause considerable errors in moisture advection into Africa. Plain Language Summary: The Turkana Jet plays an important role in the meteorology of East Africa across timescales, and owes its existence to both large‐scale patterns in the atmosphere as well as local factors, such as the representation of orography. However, much of our understanding of the jet relies on models with very coarse grid spacing, and climate models that produce important projections in this area are unable to represent these local processes. We systematically investigate the impact of changing model horizontal and vertical resolution in simulating the Turkana Jet and related processes. We perform simulations to coincide with the Radiosonde Investigation for the Turkana Jet (RIFTJet) campaign; enabling direct comparisons to be made with sonde releases in unprecedented detail. We find that experiments with a finer model grid significantly increase the strength of the jet throughout the channel, while changes to the number of vertical levels have little impact. The warmer air that resides around the height of the jet at night, which is strongly tied to the strength of the jet, is much better represented in resolutions as high as 1.1 km, whereas the global model at resolution O(∼10 km) is unable to produce this elevated warm layer. Key Points: Representation of the Turkana Jet and elevated inversions improves in model simulations up to 2 km resolutionThe inversion and jet strength are positively correlated, and this correlation increases with finer grid resolutionIncreased model resolution leads to improved skill in predicting the strength of the Turkana Jet, despite similar large‐scale forcing [ABSTRACT FROM AUTHOR]

Citizen science has emerged as a potent approach for environmental monitoring, leveraging the collective efforts of volunteers to gather data at unprecedented scales. Within the framework of the I-CHANGE project, MeteoTracker, a citizen science initiative, was employed to collect meteorological measurements. Through MeteoTracker, volunteers contributed to a comprehensive dataset, enabling insights into local weather patterns and trends. This paper presents the analysis and the results of the validation of such observations against the official Italian civil protection in situ weather network, demonstrating the effectiveness of citizen science in generating valuable environmental data. The work discusses the methodology employed, including data collection and statistical analysis techniques, i.e., time-series analysis, spatial and temporal interpolation, and correlation analysis. The overall analysis highlights the high quality and reliability of citizen-generated data as well as the strengths of the MeteoTracker platform. Furthermore, our findings underscore the potential of citizen science to augment traditional monitoring efforts, inform decision-making processes in environmental research and management, and improve the social awareness about environmental and climate issues. [ABSTRACT FROM AUTHOR]

We utilize ocean 10‐m wind speed (U10m) from the microwave Multi‐sensor Advanced Climatology data set to examine the coupling between convective cloud and precipitation processes, synoptic state, and U10m and to evaluate the representation of U10m in global climate models (GCMs). We find that midlatitude U10m is underestimated by GCMs relative to observations. We examine two potential mechanisms to explain this model behavior: cold pool formation in cold air outbreaks (CAOs) associated with downdrafts that enhance U10m and sea surface temperature (SST) gradients affecting U10m through thermally forced surface winds at regional scales. When the effects of the CAO index (M) and SST gradients on U10m are accounted for, a relationship between GCM horizontal resolution and U10m appears. The strongest correlation between resolution and U10m is over the western boundary currents characterized by frequent CAOs atop strong SST gradients which drives the strongest surface fluxes on Earth. Plain Language Summary: Surface wind drives the exchange of momentum and energy between the atmosphere and ocean, forcing movement of ocean surface water that impacts the circulation responsible for transporting heat and carbon. It is thus important to study how well global climate models (GCMs) simulate near surface winds. Here, we show an underestimation of ocean surface wind in the latest‐generation of GCMs and examine how an underestimation of ocean surface winds covaries with biases in surface temperature and fine‐scale precipitation‐driven circulations. We show the effect of model horizontal grid resolution on the wind speed bias and analyze surface wind speed bias occurrence in western boundary currents. Key Points: Coupled model intercomparison project (CMIP6) models underestimate midlatitude ocean surface wind speed compared to spaceborne microwave radiometersSimulated ocean surface wind speed bias scales with model resolution at a magnitude strongly influenced by meteorologyOcean surface wind speed bias is most correlated with resolution in western boundary currents [ABSTRACT FROM AUTHOR]

Atmospheric rivers (ARs) are known to produce both beneficial and extreme rainfall, leading to natural hazards in Chile. Motivated to understand moisture transport during AR events, this study performs a moisture budget analysis along 50 zonally elongated ARs reaching the western coast of South America. We identify the convergence of moist air masses of tropical/subtropical origin along the AR as the primary source of vertically integrated water vapor (IWV). Over the open ocean, moisture convergence is nearly balanced by precipitation. The advection of moisture along the AR, although smaller compared to mass convergence, significantly increases toward the landfalling region. The near conservation of IWV over the open ocean, observed by tracking a Lagrangian atmospheric column along the ARs, is the explanation behind the seemingly tropical origin of ARs in time‐lapse visualizations of IWV. Plain Language Summary: Imagine atmospheric rivers (ARs) as massive, flowing rivers in the sky, but instead of water, they carry vapor from the ocean. When these "sky rivers" travel and hit the Andes Mountains in South America, they can cause a lot of rain and snow to fall. This precipitation is often good because it helps fill reservoirs and water crops. However, sometimes there's too much rain, leading to floods and landslides, which can be dangerous. Over the ocean, the amount of water vapor these atmospheric rivers pick up is almost exactly balanced by the rain that falls from them. As these atmospheric rivers get closer to South America, the movement of moisture along the river, though generally less significant than the gathering of moist air, becomes more pronounced. This means that as the atmospheric river approaches the land, it starts carrying more moisture toward its destination. We were able to see this process in action by following a moving "slice" of the atmosphere (a Lagrangian atmospheric column) as it travels along the path of the atmospheric river. This helped us understand how atmospheric rivers maintain their water content as they move. It also shows why atmospheric rivers seem to originate from tropical areas when we look at them in time‐lapse images of water vapor. Key Points: We calculate the moisture transport budget of 50 events of zonal atmospheric rivers over the Pacific that reach South AmericaHorizontal convergence of tropical and subtropical air masses act to maintain atmospheric rivers over the ocean while advection dominates near the coastFollowing a Lagrangian column, precipitable water is roughly conserved along atmospheric rivers, except near landfalling [ABSTRACT FROM AUTHOR]

This study analyzed the applicability of five long-term precipitation datasets in the Hang-Jia-Hu Plain of eastern China based on meteorological observation data. The accuracy of each dataset at different time scales (yearly, monthly) was analyzed. Besides, their spatiotemporal distributions and differences in precipitation event frequency were also compared. The results indicate that the high-resolution (1 day, 1 km) and long-term (1961–2019) gridded dataset for temperature and precipitation across China (HRLT) exhibited the best overall performance at the annual scale, while the long-term, gauge-based gridded precipitation dataset for the Chinese mainland (CHM_PRE) performed the best at the monthly scale. The dataset of monthly precipitation with a resolution of 1 km in China from 1960 to 2020 (HHU) and the China Meteorological Forcing Dataset (CMFD) tend to overestimate the local precipitation amounts, while the other three products are characterized by an underestimation. The mean result of the five datasets indicates a slight, statistically insignificant rise in precipitation, by 4.19 mm annually, with an overall multi-year average of 1303.28 mm. The analysis of the five datasets successfully captures the spatial precipitation patterns across the Hang-Jia-Hu Plain, characterized by higher precipitation levels in the southwest and lower in the northeast. Although the interannual variability displays general consistency, there are significant discrepancies in the interannual growth rates and the spatial distribution of significance across different regions. This study can provide a reference for the accuracy of precipitation data in the fields of hydrology, meteorology, agriculture, and ecology, facilitating the analysis of uncertainties in related research. [ABSTRACT FROM AUTHOR]

This study investigates the impact of dust on radiation over the Arabian Peninsula (AP) during the reported high, low, and normal dust seasons (March–August) of 2012, 2014, and 2015, respectively. Simulations were performed using the Weather Research and Forecasting model coupled to a Chemistry module (WRF‐Chem). The simulated seasonal horizontal and vertical dust concentrations, and their interannual distinctions, match well with those from two ground‐based AERONET observations, and measurements from MODIS and CALIOP satellites. The maximum dust concentrations over the dust‐source regions in the southern AP reach vertically upto 700 hPa during the high dust season, but only upto 900–950 hPa during the low/normal dust seasons. Stronger incoming low‐level winds along the southern Red Sea and those from Iraq bring in higher‐than‐normal dust during the high dust summers. We conducted a sensitivity experiment by switching‐off the dust module to assess the radiative perturbations due to dust. The results suggest that active dust‐module improved the fidelity of simulated radiation fluxes distributions at the surface and top of the atmosphere vis‐à‐vis Clouds and the Earth's Radiant Energy System (CERES) measurements. Dust results in a 26 Wm−2 short‐wave (SW) radiative forcing in the tropospheric‐column over the AP. The SW radiative forcing increases by another 6–8 Wm−2 during the high dust season due to the increased number of extreme dust days, which also amplifies atmospheric heating. During extreme dust days, the heating rate exhibits a dipolar structure, with cooling over the Iraq region and warming of 40%–60% over the southern‐AP. Plain Language Summary: Dust is the dominant tropospheric aerosol in the Arabian Peninsula (AP). High dust loading and prolonged episodes are crucial in dust‐radiation feedback mechanisms. WRF‐Chem successfully simulated the spatial distribution of dust aerosols and their underlying dust‐transport mechanisms over the study region. Model results reveal that dust aerosols influence surface and atmospheric radiation, resulting in cooling at the surface and warming in the overlying atmosphere. Extreme dust days exhibit significant heating in the south and cooling in the north. This study enhances our understanding of dust‐aerosol interactions with regional meteorology, aiding in better weather and climate prediction in dust‐laden regions like the AP. Key Points: This study explores the influence of dust aerosols on radiative fluxes over the Arabian Peninsula (AP)WRF‐Chem model effectively captures the physical mechanisms of dust transportExtreme dustiness significantly modulates the radiative fluxes, thereby exerting a notable influence on the atmospheric heating rates [ABSTRACT FROM AUTHOR]