Meteorological Conditions Research Articles

Spatiotemporal dynamics of Phytophthora infestans airborne inoculum in Belgium

AbstractFor nearly a century, scientists have strived to model the development of Phytophthora infestans (Pi) to predict late blight infections in potatoes. This has led to the use of decision support systems (DSSs) that rely on forecasting models based on environmental parameters. All these models assume that the primary inoculum is ubiquitous. This study focuses on the spatiotemporal distribution of airborne inoculum of Pi to assess the value of incorporating quantitative data of this variable in improving the prediction of primary infection of potato late blight in fields. The daily spatiotemporal distribution of Pi airborne inoculum was studied from 2019 to 2022 at Ath, Gembloux, Libramont and Louvain-la-Neuve in Belgium by combining Burkard spore traps with a quantitative PCR assays. The quantities of Pi inoculum trapped daily ranged from 0 to 4903 (expressed as sporangia equivalent) depending on the site and the year. The appearance of late blight symptoms in untreated plots located close to spore traps was assessed shortly after the detection of airborne inoculum in all monitored sites. A comparison between airborne inoculum detection and the recommendations provided by three DSSs revealed that fungicide treatments are often recommended at the beginning of the season when airborne inoculum is absent. Two field trials performed in 2021 and 2022 showed that the number of fungicide treatments could be reduced by considering the presence of the inoculum, particularly by delaying the first application. Further knowledge of the relationships between disease pressure, airborne inoculum and meteorological conditions will provide valuable information for improving DSSs and reducing fungicide use.

Stable-isotope variability in daily precipitation: insights from a low-cost collector in SE England

ABSTRACT Precipitation stable-isotope data are often used in hydroclimatic, hydrological and hydrogeological investigations, with measurements typically undertaken on integrated monthly samples. However, daily sampling reveals overlooked aspects of controls on precipitation isotope values, including synoptic meteorological conditions. We present a one-year record of stable isotopes in daily precipitation during 2021, from a site in SE England close to Greater London. We find marked daily variability over the course of the year (–15.62 to +0.92 ‰ for δ18O, –108.7 to +2.9 ‰ for δ2H and –6.5 to +23.1 ‰ for deuterium excess). Correlations with individual meteorological variables including precipitation amount, temperature and weather type are moderate to weak suggesting complex controls on the daily rainfall isotope values. The daily data are compared with three other daily datasets from England and, by conversion to monthly values, directly with data from three long-term collection stations across Britain and Ireland. The scale of variability in the daily data from our site is consistent with that seen in other English records despite them all coming from different time periods. The monthly data show broad consistency, although there are differences that also highlight geographical variability in precipitation values across the British Isles.

Numerical study for atmospheric transport of radioactive materials for hypothetical severe nuclear accident under different meteorological conditions

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.

A Case Study of the Possible Meteorological Causes of Unexpected Fire Behavior in the Pantanal Wetland, Brazil

This study provides insights into large fires in the Pantanal by analyzing the atmospheric conditions that influenced the rapid fire evolution between 13 and 14 November 2023, when fire fronts spread rapidly, leading to critical situations for firefighters. The observation-based analysis helped us to identify some characteristics of the fire’s evolution and the meteorological conditions in the region. Furthermore, two simulations were run with the Meso-NH model, which was configured with horizontal resolutions of 2.5 km and 5 km. The fire behavior, characterized by satellite observations, revealed periods with a sudden increase in active fire numbers. High temperatures and low relative humidity in the region characterized the fire weather conditions. The simulations confirmed the critical fire condition, showing the benefits of increasing the resolution of numerical models for the Pantanal region. The convection-resolving simulation at 2.5 km showed the repeated development of gust fronts in the late afternoon and early evening. This study highlights this dynamic that, coupled with intense surface wind gusts, was crucial for the intensification of the fire spread and unexpected behavior. This study is a first step toward better understanding fire dynamics in the Pantanal through atmospheric modeling, and it can support strategies for firefighting in the region.

Model for random atmospheric inhomogeneities in engine noise auralization

AbstractModelling the impact of atmospheric turbulence is not often encountered in aircraft noise auralizations despite its relevance to human-ear perception. As sound waves propagating through turbulent media undergo modulations, observable in phase and amplitude fluctuations, the plausibility of aircraft noise auralizations is increased by taking this effect into account. This paper presents an extension of a method first developed by Rietdijk et al., for modelling these fluctuations for spherical sound waves travelling through randomly inhomogeneous media. The extensions of the method presented in this paper are: (1) Height-dependent von Kármán spectra are implemented to model slanted or vertical sound propagation. (2) An overlap-add-method is introduced to model a moving sound source when height-dependent changes of turbulence characteristics are considered. (3) Easier to measure input parameters for the (meteorological) conditions are implemented, like e.g. the day time or relative humidity. The method is integrated into a global framework dedicated to engine noise simulation, propagation and auralization, developed and validated by the German Aerospace Center (DLR). A good match with measured functions of phase and amplitude fluctuations are observed, as well as realistic reproduction of spectrograms, time signals and the psychoacoustic fluctuation strength of the auralizations with respect to real flyovers.

Analysis of the Impact of Meteorological Factors on Predicting Air Quality in South Tangerang City using Random Forest Method

Air pollution has become one of the most significant environmental problems in many cities throughout the world, which can endanger public health and the environment. Understanding the impact of meteorological conditions on air quality is very important to understanding air pollution patterns. This study investigates the influence of meteorological variables on air quality predictions in South Tangerang City, Indonesia, using the Random Forest method. Modeling is carried out by building two scenarios, namely predictions using meteorological variables and predictions without meteorological variables. Prediction performance analysis is measured using MAE, MSE, RMSE, R-square, and accuracy. The accuracy results of the research show that predictions without meteorological variables provide good prediction results with a value of 86.42%, but predictions with meteorological variables have better performance with a value reaching 98.99%. The largest error values from each model were 2.58 MAE, 71.82 MSE, and 8.4747 RMSE obtained in prediction modeling without meteorological variables, while the smallest error values were obtained in prediction modeling using meteorological variables, namely 0.00, 0.01, and 0.0219, respectively, for MAE, MSE, and RMSE. This research contributes to a better understanding of the relationship between meteorology and air pollution and air quality in urban areas and helps develop targeted mitigation strategies to improve air quality and public health, especially in South Tangerang City and the surrounding area.

The Combined Effects of the Thermal Environment and Air Quality at Recreation Places on the Physiology and Psychology of People in Urban Parks

Urban forests, crucial to urban ecosystems, are increasingly threatened by the challenges of urbanization, such as deteriorating thermal environments and declining air quality. Despite their recognized benefits to city dwellers’ quality of life, a systematic understanding of the impact of these environmental factors on public psychophysiological well-being in recreational sites is a notable gap in the literature. The objective of this research was to bridge this gap by examining the effects of the thermal environment and air quality in urban forests on the public’s perception, offering scientific evidence to inform environmental optimization and health management strategies for urban parks, essential for sustainable urban development and public health. Three urban parks in Fuzhou, Fujian Province, namely Fuzhou National Forest Park, Xihu Park, and Jinniushan Sports Park, were selected as research sites. Environmental monitoring and questionnaire surveys were conducted at 24 recreation places from October to December 2020, collecting temperature, humidity, and wind speed; the atmospheric composition includes PM2.5, PM10, negative oxygen ion, and psychophysiological data from the public. Multivariate statistical methods were employed to assess the environmental characteristics of different recreation places types and their impact on public health. The findings reveal that environmental factors explained 1.9% to 11.8% of the variation in physiological and psychological responses, mainly influenced by temperature, wind speed, and negative oxygen ions. Forests and waterfront recreation places significantly outperform canopy and open recreation places in promoting mental invigoration, stress relief, emotional tranquility, and attention restoration. Environmental monitoring results indicate that favorable meteorological conditions and good air quality are crucial for enhancing the service functions of recreation places. Notably, the positive correlation between a negative air ion concentration and psychological well-being provides a novel perspective on understanding the health benefits of urban forests. The thermal environment and air quality of urban recreation places exert a significant influence on the psychophysiological status of the public. Increasing green coverage, improving water body environments, and rationally planning recreation places layout are of great theoretical and practical significance for enhancing the environmental quality and service functions of urban forests.

Characterizing environmental contamination by plant protection products along the land-to-sea continuum:a focus on France and French overseas territories.

Environmental compartments are contaminated by a broad spectrum of plant protection products (PPPs) that are currently widely used in agriculture or, for some of them, whose use was banned many years ago. The aim of this study is to draw up an overview of the levels of contamination of soils, continental aquatic environments, seawaters and atmosphere by organic PPPs in France and the French overseas territories, based on data from the scientific publications and the grey literature. It is difficult to establish an exhaustive picture of the overall contamination of the environment because the various compartments monitored, the monitoring frequencies, the duration of the studies and the lists of substances are not the same. Of the 33 PPPs most often recorded at high concentration levels in at least one compartment, 5 are insecticides, 9 are fungicides, 15 are herbicides and 4 are transformation products. The PPP contamination of the environment shows generally a seasonal variation according to crop cycles. On a pluriannual scale, the contamination trends are linked to the level of use driven by the pest pressure, and especially to the ban of PPP. Overall, the quality of the data acquired has been improved thanks to new, more integrative sampling strategies and broad-spectrum analysis methods that make it possible to incorporate the search for emerging contaminants such as PPP transformation products. Taking into account additional information (such as the quantities applied, agricultural practices, meteorological conditions, the properties of PPPs and environmental conditions) combined with modelling tools will make it possible to better assess and understand the fate and transport of PPPs in the environment, inter-compartment transfers and to identify their potential impacts. Simultaneous monitoring of all environmental compartments as well as biota in selected and limited relevant areas would also help in this assessment.

Indica rice “Takanari” exhibited superiority to Japanese rice “Koshihikari” on N-uptake and biomass production under no-weeding practice

ABSTRACT Rice yield and whole biomass production are strongly related to the rice cultivar’s nitrogen (N) uptake ability and various meteorological conditions and management practices. Under different conditions, such as elevated CO2 with limited N-supply, the Indica cultivar” Takanari (Tak)” showed a higher N-uptake ability than the Japonica cultivar ‘Koshihikari (Kos).’ However, it is still unclear how Tak and Kos respond to N-uptake and biomass production under limited N conditions with or without weeding. For this reason, we conducted a pot experiment at Tsuruoka, Yamagata, Japan, in the 2022 rice growing season. Tak and Kos were transplanted individually and interplanted (Tak+Kos) in pots filled with 8.0 kg of air-dried alluvial soil from an organic rice farm with the main treatments of weeding and no weeding. Rice growth parameters (shoot height, tiller numbers, and leaf color) were investigated weekly, while rice biomass and N-uptake were measured after harvest. As a result, plant biomass and N-uptake were significantly different among all treatments. The rice biomass and N-uptake in weeding were significantly higher than those in no-weeding for both cultivars. Compared with Kos, Tak had significantly higher rice biomass and N-uptake for root, ear, and whole rice plants in all treatments, weeding practices, and planting modes. The ratios of aboveground biomass and N-uptake between Tak and Kos in interplanting were higher than their respective ratios in individual planting. Interplanting increased the aboveground biomass ratios between Tak and Kos by 16.24% and 27.43% in weeding and no-weeding conditions, respectively. The ratios of N-uptake in interplanting were higher than individual planting at 21.74% and 34.09% in weeding and no-weeding, respectively. Tak’s ability to compete with Kos on N-uptake and biomass production was increased when two rice cultivars were transplanted in the same pot under interplanting cultivation and no-weeding conditions.

Robots for the Energy Transition: A Review

The energy transition relies on an increasingly massive and pervasive use of renewable energy sources, mainly photovoltaic and wind, characterized by an intrinsic degree of production uncertainty, mostly due to meteorological conditions variability that, even if accurately estimated, can hardly be kept under control. Because of this limit, continuously monitoring the operative status of each renewable energy-based power plant becomes relevant in order to timely face any other uncertainty source such as those related to the plant operation and maintenance (O&M), whose effect may become relevant in terms of the levelized cost of energy. In this frame, the use of robots, which incorporate fully automatic platforms capable of monitoring each plant and also allow effective and efficient process operation, can be considered a feasible solution. This paper carries out a review on the use of robots for the O&M of photovoltaic, wind, hydroelectric, and concentrated solar power, including robot applications for controlling power lines, whose role can in fact be considered a key complementary issue within the energy transition. It is shown that various robotic solutions have so far been proposed both by the academy and by industries and that implementing their use should be considered mandatory for the energy transition scenario.

Short‐term power prediction of distributed PV based on multi‐scale feature fusion with TPE‐CBiGRU‐SCA

AbstractTo address the challenge of insufficient comprehensive extraction and fusion of meteorological conditions, temporal features, and power periodic features in short‐term power prediction for distributed photovoltaic (PV) farms, a TPE‐CBiGRU‐SCA model based on multiscale feature fusion is proposed. First, multiscale feature fusion of meteorological features, temporal features, and hidden periodic features is performed in PV power to construct the model input features. Second, the relationships between PV power and its influencing factors are modelled from spatial and temporal scales using CNN and Bi‐GRU, respectively. The spatiotemporal features are then weighted and fused using the SCA attention mechanism. Finally, TPE‐based hyperparameter optimization is used to refine network parameters, achieving PV power prediction for a single field station. Validation with data from a PV field station shows that this method significantly enhances feature extraction comprehensiveness through multiscale fusion at both data and model layers. This improvement leads to a reduction in MAE and RMSE by 26.03% and 38.15%, respectively, and an increase in R2 to 96.22%, representing a 3.26% improvement over other models.

GIS-based method for assessing the viability of solar-powered irrigation

To attain food security, sub-Saharan Africa needs to increase its use of irrigation to improve agricultural productivity and resilience to climate change-induced droughts and erratic rain patterns. Stand-alone solar-powered irrigation systems are a promising technology to power irrigation where grid electrification is unavailable. However, these systems’ economic viability and sustainability are affected by spatially varying factors such as climate, water availability, and solar potential, making it challenging to plan their roll-out. This study addresses the planning challenge by developing a novel open-source reproducible geospatial methodology to determine irrigation water demand, peak power demand, life cycle cost, and locations where solar-powered irrigation systems would be cheaper than alternative off-grid irrigation technologies. The method considers spatial–temporal data for meteorological conditions, soil quality, water distance and depth, solar resource potential, technology efficiency and local equipment and fuel costs. Maize cultivation in Kenya is taken as a case study to demonstrate the method. We find the median peak irrigation water demand in Kenya to be 65,000 l/ha/day and the median peak power demand to be 2.6 kW/ha. The levelized cost of solar-powered irrigation systems ranges from US$ 0.09/kWh to US$ 0.25/kWh, making solar irrigation cheaper than diesel-powered irrigation in all Kenyan regions. The results from the method offer valuable insights to governments, farmers and investors on where water use regulations are required for sustainable water use, where subsidies and innovative financial models are needed for the affordability of solar irrigation and where alternative uses of energy from the solar systems beyond irrigation are possible.

Enhancing Flight Delay Predictions Using Network Centrality Measures

Accurately predicting flight delays remains a significant challenge in the aviation industry due to the complexity and interconnectivity of its operations. The traditional prediction methods often rely on meteorological conditions, such as temperature, humidity, and dew point, as well as flight-specific data like departure and arrival times. However, these predictors frequently fail to capture the nuanced dynamics that lead to delays. This paper introduces network centrality measures as novel predictors to enhance the binary classification of flight arrival delays. Additionally, it emphasizes the use of tree-based ensemble models, specifically random forest, gradient boosting, and CatBoost, which are recognized for their superior ability to model complex relationships compared to single classifiers. Empirical testing shows that incorporating centrality measures improves the models’ average performance, with random forest being the most effective, achieving an accuracy rate of 86.2%, surpassing the baseline by 1.7%.

Comparative Study on the Vertical Column Concentration Inversion Algorithm of Tropospheric Trace Gas Based on the MAX-DOAS Measurement Spectrum

The tropospheric vertical column concentration (VCDtrop) of NO2, SO2, and HCHO was retrieved, respectively, by employing the geometric method (Geomtry), simplified model method (Model), and look-up table method (Table) with the observation spectra of the multi-axis differential absorption spectroscopy instrument (MAX-DOAS). The correlation and relative differences of the inversion results obtained by these three algorithms, as well as the changes in quantiles, were explored. The comparative analysis reveals that the more concentrated the vertical distribution height of gas components is in the near-surface layer, the better the conformity of the VCDtrop retrieved by different algorithms. However, the increase in relative differences is also related to the diurnal variation of gas components. The influence of aerosols on the inversion of the VCDtrop is greater than the change in the vertical distribution height of the gas component itself. The near-surface concentration and distribution height of gas components are the internal factors that give rise to relative differences in the inversion of the VCDtrop by different algorithms, while aerosols are one of the extremely important external reasons. The VCDtrop inverted by Geomtry without considering the influence of aerosols is generally larger except for NO2. Model sets up aerosols in accordance with the height and meteorological conditions of the atmospheric environment. Table can invert the aerosol profile in real time. Compared with Model, it shows a significant improvement in the refined setting of aerosols. Moreover, while obtaining the vertical distribution of aerosols, it can invert the diurnal variation of the VCDtrop. The VCDtrop inverted by Table is the smallest, and the relative difference with Model is on average about 10% smaller. The relative difference of the VCDtrop for the same height (aerosol optical thickness) quantile is 7–15% (about 25% lower on average). When comparing the inversion results of Table with the Ozone Monitoring Instrument (OMI) satellite product, the MAX-DOAS inversion results of NO2, SO2, and HCHO are all larger than the OMI product. This is related to the different observation methods of the MAX-DOAS and OMI and the configuration between the aerosol layer and the distribution height of gas components.

Remote Sensing Monitoring and Multidimensional Impact Factor Analysis of Urban Heat Island Effect in Zhengzhou City

In the 21st century, the rapid urbanization process has led to increasingly severe urban heat island effects and other urban thermal environment issues, posing significant challenges to urban planning and environmental management. This study focuses on Zhengzhou, China, utilizing Landsat remote sensing imagery data from five key years between 2000 and 2020. By applying atmospheric correction methods, we accurately retrieved the land surface temperature (LST). The study employed a gravity center migration model to track the spatial changes of heat island patches and used the geographical detector method to quantitatively analyze the combined impact of surface characteristics, meteorological conditions, and socio-economic factors on the urban heat island effect. Results show that the LST in Zhengzhou exhibits a fluctuating growth trend, closely related to the expansion of built-up areas and urban planning. High-temperature zones are mainly concentrated in built-up areas, while low-temperature zones are primarily found in areas covered by water bodies and vegetation. Notably, the Normalized Difference Built-up Index (NDBI) and the Normalized Difference Vegetation Index (NDVI) are the two most significant factors influencing the spatial distribution of land surface temperature, with explanatory power reaching 42.7% and 41.3%, respectively. As urban development enters a stable stage, government environmental management measures have played a positive role in mitigating the urban heat island effect. This study not only provides a scientific basis for understanding the spatiotemporal changes in land surface temperature in Zhengzhou but also offers new technical support for urban planning and management, helping to alleviate the urban heat island effect and improve the living environment quality for urban residents.

Spatiotemporal Variation in Absorption Aerosol Optical Depth over China

Absorbing aerosols can absorb solar radiation, affect the atmospheric radiation balance, and further have a profound influence on the global and regional climates. The absorption aerosol optical depth (AAOD) as well as the absorption Angstrom exponent (AAE) across China over 2005–2018 were systematically studied through the Ozone Monitoring Instrument (OMI) dataset. The monthly AAOD samples from the OMI generally showed a good correlation (~0.55) compared to the monthly data from AERONET at four typical sites (North: Xianghe, East: Taihu, South: Hongkong Polytechnic Univ; Northwest: Sacol) across China. The ensemble annual average of the OMI AAOD at 388 and 500 nm is 0.046 and 0.022, with minor changes during 2005–2015, and a relatively fast increase after that. The winter and spring seasons depict the maximum mean AAODs, followed by autumn, whereas summer shows minimum levels. On the contrary, the high AAE values appear in summer and low values in winter. The order of the annual average AAOD500 from 2005 to 2018 is the Tarim Basin (TB, 0.041) > the Yellow River Basin (YRB, 0.023) > Beijing and Tianjin (BT, 0.026) > the Sichuan Basin (SB, 0.023) > Nanjing and Shanghai (NS, 0.021) > the Pearl River Delta (PRD, 0.017), whereas the AAE388–500 exhibits the opposite trend except for the TB (3.058). From 2005 to 2018, the AAOD rises by nearly 1.5–2.0 fold in the six typical regions, implying a severe situation of dust and/or BC aerosol pollution in the last several years. The monthly mean AAOD388 over the TB, the SB, the YRB, BT, the PRD, and NS is estimated to be smallest at 0.072, 0.024, 0.026, and 0.027 in July, 0.024 in June, and 0.025 in September, respectively, whilst largest in January for NS, the YRB and BT, April for the TB, February for the SB, and March for the PRD with 0.055, 0.077 and 0.067, 0.123, and 0.073 and 0.075, respectively. The monthly averaged AAOD500 in each region is consistently about half of the AAOD388. The highest AAE appears in June while the lowest values are in December and January, and the daily AAE values in episode days slightly decrease as compared to non-episode days. Our study indicates that northwestern China plays an important role in the overall AAOD as a result of dust aerosols stemming from desert areas. Moreover, the meteorological conditions in winter and early spring are associated with more energy consumption conducive to the accumulation of high black carbon (BC) aerosol pollution, causing high alert levels of AAOD from November to the following March.

Impacts of Anthropogenic Emissions and Open Biomass Burning in South Asia and Southeast Asia on Air Quality and Meteorology Over Southern China

AbstractThe emissions from South Asia and Southeast Asia significantly impact air quality and meteorological conditions in China. However, the individual or joint contributions of anthropogenic emissions and/or biomass burning from outside China to surface particulate matter with aerodynamic diameters less than 2.5 μm (PM2.5) and aerosol optical depth (AOD) over southern China have not been fully investigated. Here, five experiments were designed to investigate the impacts of these emissions in January (winter), March (pre‐monsoon), and October (post‐monsoon) for 2017. Aerosols from South Asia and Southeast Asia contributed less to southern China during winter and post‐monsoon seasons, whereas the wind patterns and emission intensity during the pre‐monsoon season were conducive to the transport of aerosols. During pre‐monsoon season, the total emissions contributed approximately 5.0 μg m−3 to surface PM2.5 in Xizang Province. Biomass burning in Southeast Asia increased PM2.5 in Yunnan Province by 37.9 μg m−3, while anthropogenic emissions increased it by 8.9 μg m−3. Transboundary aerosols can be transported to Xizang Province and Yunnan Province, primarily influencing PM2.5 below 2 km height. It mainly affected PM2.5 levels above the planetary boundary layer over southeast China. Aerosols from outside China can account for 79.5% and 54.8% AOD in Yunnan Province and southeast China, respectively. These aerosols reduced surface incident solar radiation by approximately 6%, leading to decreases in air temperature, wind speed, and boundary layer height. The findings are only applicable to the pre‐monsoon season.

Biological and dust aerosols as sources of ice-nucleating particles in the eastern Mediterranean: source apportionment, atmospheric processing and parameterization

Abstract. Aerosol–cloud interactions in mixed-phase clouds (MPCs) are one of the most uncertain drivers of the hydrological cycle and climate change. A synergy of in situ, remote-sensing and modelling experiments were used to determine the source of ice-nucleating particles (INPs) for MPCs at Mount Helmos in the eastern Mediterranean. The influences of boundary layer turbulence, vertical aerosol distributions and meteorological conditions were also examined. When the observation site is in the free troposphere (FT), approximately 1 in ×106 aerosol particles serve as INPs around −25 °C. The INP abundance spans 3 orders of magnitude and increases in the following order: marine aerosols; continental aerosols; and, finally, dust plumes. Biological particles are important INPs observed in continental and marine aerosols, whereas they play a secondary, although important, role during Saharan dust events. Air masses in the planetary boundary layer (PBL) show both enriched INP concentrations and a higher proportion of INPs to total aerosol particles, compared with cases in the FT. The presence of precipitation/clouds enriches INPs in the FT but decreases INPs in the PBL. Additionally, new INP parameterizations are developed that incorporate the ratio of fluorescent-to-nonfluorescent or coarse-to-fine particles and predict >90 % of the observed INPs within an uncertainty range of a factor of 10; these new parameterizations exhibit better performance than current widely used parameterizations and allow ice formation in models to respond to variations in dust and biological particles. The improved parameterizations can help MPC formation simulations in regions with various INP sources or different regions with prevailing INP sources.

Investigating the atmospheric conditions associated with impactful shallow landslides in California (USA)

Abstract Shallow landslides are often triggered during rainfall events, which can increase subsurface soil water pressure and destabilize hillslopes. The likelihood of regional shallow landslide initiation is often assessed through a comparison of rainfall intensity and duration to pre-established thresholds. While informative for landslide warning, this exclusive focus on rainfall exceeding thresholds does not consider the meteorological conditions producing the rainfall. Here, we ask the question, are there common meteorological characteristics that lead to landslide triggering precipitation? We develop a catalog of 18 post-1995 widespread, impactful shallow landslide events occurring within 13 storms across California, USA, where initiation time could be constrained to a <=6-hour window. We examine storm characteristics during the landslide initiation window using atmospheric reanalysis products, radar observations, and quantitative precipitation estimates. We find that, while there are some common atmospheric characteristics across landslide events, they can occur under a range of atmospheric conditions. For example, all northern California landslide events assessed are associated with moderate to strong atmospheric rivers (ARs) while southern California landslides feature non-AR to strong AR conditions. The storm events evaluated herein share many characteristics of hydrologically important storms in California that did not necessarily result in landslides, thus atmospheric characteristics alone may not be sufficient to determine whether landslides will occur. However, documenting the characteristics of landslide-triggering storms defines the conditions under which landslides tend to occur, provides analog events that can be useful in forecast applications, helps to define future research directions relating atmospheric conditions and landslides, and supports interdisciplinary research efforts.

Methodology for Performing Bathymetric and Photogrammetric Measurements Using UAV and USV Vehicles in the Coastal Zone

The coastal zone is constantly exposed to marine erosion, rising water levels, waves, tides, sea currents, and debris transport. As a result, there are dynamic changes in the coastal zone topography, which may have negative effects on the aquatic environment and humans. Therefore, in order to monitor the changes in landform taking place in the coastal zone, periodic bathymetric and photogrammetric measurements should be carried out in an appropriate manner. The aim of this review is to develop a methodology for performing bathymetric and photogrammetric measurements using an Unmanned Aerial Vehicle (UAV) and an Unmanned Surface Vehicle (USV) in a coastal zone. This publication shows how topographic and bathymetric monitoring should be carried out in this type of zone in order to obtain high-quality data that will be used to develop a Digital Terrain Model (DTM). The methodology for performing photogrammetric surveys with the use of a drone in the coastal zone should consist of four stages: the selection of a UAV, the development of a photogrammetric flight plan, the determination of the georeferencing method for aerial photos, and the specification as to whether there are meteorological conditions in the studied area that enable the implementation of an aerial mission through the use of a UAV. Alternatively, the methodology for performing bathymetric measurements using a USV in the coastal zone should consist of three stages: the selection of a USV, the development of a hydrographic survey plan, and the determination of the measurement conditions in the studied area and whether they enable measurements to be carried out with the use of a USV. As can be seen, the methodology for performing bathymetric and photogrammetric measurements using UAV and USV vehicles in the coastal zone is a complex process and depends on many interacting factors. The correct conduct of the research will affect the accuracy of the obtained measurement results, the basis of which a DTM of the coastal zone is developed. Due to dynamic changes in the coastal zone topography, it is recommended that bathymetric measurements and photogrammetric measurements with the use of UAV and USV vehicles should be carried out simultaneously on the same day, before or after the vegetation period, to enable the accurate measurement of the shallow waterbody depth.