Meteorological Conditions Research Articles

Monitoring Dissolved Organic Carbon Concentration and Flux in the Qiantang Riverine System Using Sentinel-2 Satellite Images

Real-time monitoring of riverine-dissolved organic carbon (DOC) and its controlling factors is critical for formulating strategies regarding the river basin and marginal seas pollution prevention and control. In this study, we established a linear regression formulation that relates the permanganate index (CODMn) to the DOC concentration based on in situ measurements collected on five field surveys in 2023–2024. This regression formulation was used on a large number of data collected from automatic monitoring stations in the Qiantang River area to construct a daily quasi-in situ database of DOC concentration. By combining the quasi-in situ DOC data and Sentinel-2 measurements, an enhanced algorithm for empirical DOC estimation was developed (R2 = 0.66) using the extreme gradient boosting (XGBoost) method and its spatial and temporal variations in the Qiantang River were analyzed from 2016 to 2023. Spatially, the main stream of the Qiantang River exhibited an overall decreasing and increasing trend influenced by population density, economic development, and pollutant discharge in the basin area, and the temporal distribution of DOC was controlled by meteorological conditions. The DOC contents had the highest in summer, primarily due to high rainfall and leaching. The inter-annual variation in DOC concentration was influenced by the total annual runoff volumes, with a minimum level of 2.24 mg L−1 in 2023 and a maximum level of 2.45 mg L−1 in 2019. The monthly DOC fluxes ranged from 6.3 to 13.8 × 104 t, with the highest values coinciding with the maximum river discharge volumes in June and July. The DOC levels in the Qiantang River remained relatively high in recent years (2016–2023). This study enables the concerned stakeholders and researchers to better understand carbon transportation and its dynamics in the Qiantang River and its coastal areas.

Climate change mitigation and adaptation for rice-based farming systems in the Red River Delta, Vietnam

Abstract Background Rice is a major contributor to anthropogenic greenhouse gas (GHG) emissions, primarily methane, and at the same time will be negatively impacted by regional climate changes. Identifying rice management interventions to reduce methane emissions while improving productivity is, therefore, critical for climate change mitigation, adaptation, and food security. However, it can be challenging to conduct multivariate assessments of rice interventions in the field owing to the intensiveness of data collection and/or the challenges in testing long-term changes in meteorological and climate conditions. Process-based modeling, evaluated against site-based data, provides an entry point for evaluating the impacts of climate change on rice systems and assessing the impacts, co-benefits, and trade-offs of interventions under historical and future climate conditions. Methods We leverage existing site-based management data to model combined rice yields, methane emissions, and water productivity using a suite of process-based coupled crop-soil model experiments for 83 growing sites across the Red River Delta, Vietnam. We test three rice management interventions with our coupled crop-soil model, characterized by Alternate Wetting and Drying (AWD) water management and other principles representing the System of Rice Intensification (SRI). Our simulations are forced with historical as well as future climate conditions, represented by five Earth System Models for a high-emission climate scenario centered on the year 2050. We evaluate the efficacy of these interventions for combined climate change mitigation and adaptation under historical and future climate change. Results Two SRI interventions significantly increased yields (one by over 50%) under historical climate conditions while also reducing (or not increasing) methane emissions. These interventions also increase yields under future climate conditions relative to baseline management practices, although climate change decreases absolute yields across all management practices. Generally, where yield improved, so did crop water-use efficiency. However, impacts on methane emissions were mixed across the sites under future climate conditions. Two of the interventions resulted in increased methane emissions, depending on the baseline management point of comparison. Nevertheless, one intervention reduced (or did not significantly increase) methane under both historical and future climate conditions and relative to all baseline management systems, although there was considerable variation across five selected climate models. Conclusions SRI management principles combined with high-yielding varieties, implemented for site-specific conditions, can serve climate change adaptation and mitigation goals, although the magnitude of future climate changes, particularly warming, may reduce the efficacy of these interventions with respect to methane reductions. Future work should better bracket important sensitivities of coupled crop-soil models and disentangle which management and climate factors drive the responses shown. Furthermore, future analyses that integrate these findings into socio-economic assessment can better inform if and how SRI/AWD can potentially benefit farmer livelihoods now and in the future, which will be critical to the adoption and scaling of these management principles.

Indian Ocean temperature anomalies modulate the interannual variability of springtime smoke aerosols over the Indochina Peninsula

Abstract Smoke aerosols released through frequent springtime fire activity over the Indochina Peninsula (ICP) seriously affect regional air quality, climate, and human health. However, the mechanisms driving the interannual variability of these smoke aerosols are not well understood. By analyzing multi-source historical (1980–2020) smoke aerosols and meteorological reanalysis data, we explore the response of springtime smoke aerosol changes over the ICP to the interannual variability of the Indian Ocean (IO) sea-surface temperature (SST). Our findings show a positive correlation between the variability of springtime smoke aerosol loading and the preceding winter Southeast IO (SEIO) SST anomalies. Warmer SEIO SST tends to weaken the trans-equatorial flow (TEF) and the local Hadley circulation. This weakening of the TEF impede cyclones development in the Bay of Bengal (BOB), thereby reducing southwest water vapor transport. Simultaneously, enhanced westerly winds over the northern BOB are blocked by the northwestern mountains of ICP. These winds converge and rise on the windward slopes, while descending on the leeward side with diminished humidity. Collectively, these dynamics lead to drier and hotter local meteorological conditions that favored fire-induced smoke aerosol emissions. Our findings highlight the role of the SEIO in regulating smoke aerosol variability and provide a scientific basis for developing strategies to manage smoke aerosol emissions over the ICP.

Atmospheric Turbulence Parameters Deduced from VerticalProfile of Cn2

The proposed framework can capture realistic spatiotemporal variations in atmospheric turbulence parameters based on local meteorological conditions, such as Fried's parameter r0, isoplanatic angles θ0, seeing ε, scintillation rate , and the wavefront coherence time τ0. Those parameters have been deduced from the refractive index structure parameter Two Iraqi locations were compared, specifically the middle and southern borders, and atmospheric turbulence parameters relevant to astronomical observation were measured, lines were drawn at latitudes 34.26° and 28.31°, respectively. The methodology included collecting meteorological data from both sites and performing numerical simulations based on atmospheric turbulence parameters. According to the criteria of air turbulence, the results showed that the middle of Iraq was better than the southern location for astronomical observation.

Climatic Specific Energy Rating Analysis of Outdoor PV Field Data

ABSTRACTThe IEC 61853 standard series define the Climatic Specific Energy Rating (CSER), which can be used within the photovoltaic (PV) community as a reliable and accurate tool to differentiate between PV modules performance for different reference climates. The CSER is assumed to represent the expected PV device operation under real‐world conditions. The rating model combines a set of measurements on modules with a numerical approach. So far, research has mainly focused on the implementation of algorithms to determine the input parameters and to calculate the CSER values based on indoor measurement performance data. There is lack of information and technical specification how to obtain these parameters from outdoor PV field data in natural sunlight. In the present study, we first estimated the CSER parameter (according to IEC 61853‐3) for a PV module tested indoor using input values measured or defined according to IEC 61853‐1&2 with associated measurement uncertainties. Then, we analysed long‐term outdoor field data of the same device for a period of 36 consecutive months in order to understand how the length and seasonality of the data acquisition period impacts the reliability of irradiance and temperature power matrix values, particularly in relation to CSER calculations. Uncertainties estimations for the outdoor procedure have been also defined for rating accuracy. The PV outdoor performance has been validated against its indoor counterpart. The results concluded that a period of 9 or 12 months provides a good compromise between accuracy, time and cost‐effectiveness. The time period may be reduced to 6 months if the meteorological conditions are sufficiently diverse in this time frame. The study might raise worthwhile discussion regarding the revision of the IEC 61853‐1 for determining the (G‐T) matrix of PV modules from outdoor measurements under natural sunlight. It could enhance important insights to the PV community since the CSER parameter may become a compulsory requirement for each PV module produced, imported or sold in Europe if the potential European Union (EU) Ecodesign Directive and the Energy Labelling Regulation will be implemented.

Development of seasonal ozone maximum reactivity scales for Beijing, China.

We designed scenarios based on urban emissions to develop a Maximum Incremental Reactivity (MIR) scale for 41 conventional volatile organic compounds (VOCs) in Beijing and quantitatively analyzed the various factors affecting MIR in urban areas. We explored the effects of updating the representative urban atmosphere used for ozone reactivity calculations by incorporating updated meteorological data, emission rates, initial condition concentrations, and VOC profile compositions. A box model equipped with the Master Chemical Mechanism (MCM) was used to localize the MIR scale for Beijing based on urban emission model inputs, and this was compared with scenarios in the United States. The MIR differences for most VOC species were minimal; however, Beijing exhibited higher MIR values for aromatic hydrocarbons in the summer, attributed to the higher emission intensity of aromatics in the city. Due to differences in meteorological conditions and emission scenarios, the MIR showed significant seasonal characteristics across different months. It is necessary to calculate independent MIR scales for different urban agglomerations under specific ozone pollution seasons and emission conditions. This study provides recommendations for the application of the MIR scale and can serve as a reference for VOC control strategies in China and other countries facing ozone pollution.

Heatwave-amplified atmospheric oxidation in a multi-province border area in Xuzhou, China

Ozone formation is closely tied to emissions of precursors, meteorological conditions, and atmospheric chemistry. In June 2024, Xuzhou City, located at the intersection of Jiangsu, Shandong, Henan, and Anhui provinces in East China, experienced a series of ozone pollution events. The continuous pollution episodes were characterized by consistently high levels of ozone, with daytime peaks reaching 130 ppb. By combining observations of atmospheric oxidation and the use of the Observation-Based Model model, it was determined that the pollution was the result of a “heatwave-ozone” co-occurring extreme event triggered by elevated temperatures, low humidity, and intense radiation. The heatwave led to increased emissions of VOCs from both natural and human-related sources, with more pronounced contribution from Bio-alkenes and OVOCs. This, in turn, resulted in higher levels of oxidizing agents and ozone formation potential, exacerbating the co-occurrence of heatwaves and ozone extremes. Sensitivity tests on enhanced controls showed that reducing NOx had a significant adverse effect on ozone levels, whereas reducing VOCs had positive benefits, particularly for controlling alkenes. Despite ongoing reductions in anthropogenic VOCs, the elevated temperatures led to an increase in natural VOCs emissions. On average, a 1°C temperature decrease could reduce the reactivity ratio of VOCs to NOx (VOCR/NOxR) by 0.12, thereby enhancing the advantages of emission reductions. Therefore, implementing measures to alleviate extreme heatwaves, such as limiting high-energy consumption and inducing artificial rainfall, can simultaneously reduce the intensity and reactivity of VOC emissions, aiding in the effective implementation of ozone pollution control policies.

Climatic and ecological factors responsible for distribution of Phlebotomine Sand flies and occurrence of Cutaneous Leishmaniasis, in Himachal Pradesh India

Background & objectives: Subalpine valley along the Sutlej River has been identified as an endemic focus for Cutaneous Leishmaniasis (CL) in Himachal Pradesh (H.P.). The present study was undertaken with aim to find out the climatic and ecological determinants responsible for distribution of sand flies and occurrence of Cutaneous Leishmaniasis in Himachal Pradesh. Methods: Collections of sand flies were made in the Shimla, Kullu, and Nichar districts of H.P. during 2017 and 2019. The climatic data were procured from Indian Meteorological centre, Shimla (H.P). The satellite images (Indian remote sensing satellite (IRS)-Linear imaging self-scanner (LISS)-IV sensor multispectral data) having resolution of 5.8m were procured from National Remote Sensing Centre Hyderabad, India. The relationship between the incidence of CL and Land use was analysed. Results: A total of 332 sand flies were collected. The transmission of CL was favoured by temperatures exceeding 20°C over a period of six months, specifically from April to September. The valley with an elevation ranging between 1000 to 1250 meters reported 90% of the CL cases. The correlation between CL incidence and land use patterns revealed a notable rise in barren land (8%) and scrub land (5%) from 2013 to 2017. Interpretation & conclusion: The findings reveals that specific meteorological conditions and land cover play significant role in determining the presence of sand flies in Himachal Pradesh. To effectively control CL, it is recommended to implement control programs primarily between June and September, focusing particularly on regions with elevations ranging from 1000 to 1250 meters.

A Nation-by-Nation Assessment of the Contribution of Southeast Asian Open Biomass Burning to PM2.5 in Thailand Using the Community Multiscale Air Quality-Integrated Source Apportionment Method Model

This study utilized the Community Multiscale Air Quality (CMAQ) model to assess the impact of open biomass burning (OBB) in Thailand and neighboring countries—Myanmar, Laos, Cambodia, and Vietnam—on the PM2.5 concentrations in the Bangkok Metropolitan Region (BMR) and Upper Northern Region of Thailand. The Upper Northern Region was further divided into the west, central, and east sub-regions (WUN, CUN, and EUN) based on geographical borders. The CMAQ model was used to simulate the spatiotemporal variations in PM2.5 over a wide domain in Asia in 2019. The Integrated Source Apportionment Method (ISAM) was utilized to quantify the contributions from OBB from each country. The results showed that OBB had a minor impact on PM2.5 in the BMR, but transboundary transport from Myanmar contributed to an increase in PM2.5 levels during the peak burning period from March to April. In contrast, OBB substantially impacted PM2.5 in the Upper Northern Region, with Myanmar being the major contributor in WUN and CUN and domestic burning being the major contributor to EUN during the peak months. Despite Laos having the highest OBB emissions, meteorological conditions caused the spread of PM2.5 eastward rather than into Thailand. These findings highlight the critical impact of regional transboundary transport and emphasize the necessity for collaborative strategies for mitigating PM2.5 pollution across Southeast Asia.

Low-Cost Sensors for the Measurement of Soil Water Content for Rainfall-Induced Shallow Landslide Early Warning Systems

Monitoring soil water content (SWC) can improve the effectiveness of early warning systems (EWSs) designed to mitigate rainfall-induced shallow landslide risk. In extensive areas, like along linear infrastructures, the adoption of cost-effective sensors is critical for the EWS implementation. The present study aims to evaluate the reliability of different low-cost SWC sensors (frequency domain reflectometry and capacitance-based) in capturing soil moisture conditions critical for EWS, without performing soil-specific calibration. The reliability of the low-cost sensors is assessed through a comparative analysis of their measurements against those from high-cost and well-established sensors (time domain reflectometry) over a two-year period in a shallow landslide-prone area of Oltrepò Pavese, Italy. Although no landslides are observed during the monitoring period, meteorological conditions are reconstructed and statistical analysis of sensor’s responses to different rainfall events is conducted. Results indicate that, despite differences in absolute readings, low-cost sensors effectively capture relative SWC variations and demonstrate sensitivity to rainfall events across both cold and warm periods. The presented low-cost sensors can serve as reliable indicators of soil infiltration and saturation levels, highlighting their potential for real-time monitoring within extensive networks for EWS.

Dynamics of CO2 fluxes and environmental responses in a Poplar plantation

Forest plantations cover a large percentage of global forest landscapes contributing significantly to carbon sequestration. By using continuous eddy covariance technique, we observed net ecosystem CO2 exchange (NEE), gross primary production (GPP), ecosystem respiration (ER), and meteorological variables from August 2018 to December 2019 in a Poplar plantation. The Poplar plantation ecosystem was a carbon sink overall, with high carbon uptake in growing season and limited uptake/emission in non-growing season. The annual cumulative NEE, GEP, and ER were −763.61, 1542.19, and 778.58 g C m−2 yr−1, respectively. Photosynthetically active radiation (PAR) significantly influenced NEE both at half-hourly and daily scale (P < 0.01 for both), while relative humidity (RH) and vapor pressure deficit (VPD) only significantly affected NEE at half-hourly scale (P < 0.01). The prevailing wind direction throughout 2019 was southeast and it varied between seasons. Southeast wind was the prevailing wind direction in summer and winter, while southwest and northeast wind were the dominant wind direction in spring and autumn, respectively. Our results highlight that polar plantations play an important role in storing carbon, and that understanding meteorological conditions is crucial in investigating ecosystem-atmosphere interactions and their impacts on carbon cycling.

Impact of Meteorological Conditions on Overhead Transmission Line Outages in Lithuania

This study investigates the impact of meteorological conditions on unplanned outages of overhead transmission lines (OHTL) in Lithuania’s 0.4–35 kV power grid from January 2013 to March 2023. Data from the Lithuanian electricity distribution network operator and the Lithuanian Hydrometeorological Service were integrated to attribute outage events with weather conditions. A Bayesian change point analysis identified thresholds for these meteorological factors, indicating points at which the probability of outages increases sharply. The analysis reveals that wind gust speeds, particularly those exceeding 21 m/s, are significant predictors of increased outage rates. Precipitation also plays a critical role, with a 15-fold increase in the relative number of outages observed when 3 h accumulated rainfall exceeds 32 mm, and a more than 50-fold increase for 12 h snowfall exceeding 22 mm. This study underscores the substantial contribution of lightning discharges to the number of outages. In forested areas, the influence of meteorological conditions is more significant. Furthermore, the research emphasizes that combined meteorological factors, such as strong winds accompanied by rain or snow, significantly increase the risk of outages, particularly in these forested regions. These findings emphasize the need for enhanced infrastructure resilience and targeted preventive measures to mitigate the impact of extreme weather events on Lithuania’s power grid.

An Innovative TOPSIS–Mahalanobis Distance Approach to Comprehensive Spatial Prioritization Based on Multi-Dimensional Drought Indicators

This research explores a new methodological framework that blends the TOPSIS (technique for order of preference by similarity to ideal solution) and Mahalanobis Distance methods, allowing for the prioritization of nine major watersheds in China based on the integration of multi-dimensional drought indicators. This integrated approach offers a robust prioritization model by accounting for spatial dependencies between indices, a feature not commonly addressed in traditional multi-criteria decision-making applications in drought studies. This study utilized three drought indices—the Standardized Precipitation Evapotranspiration Index (SPEI), Vegetation Health Index (VHI), and Palmer Drought Severity Index (PDSI). Over years of significant drought prevalence, three types of droughts occurred simultaneously across various watersheds in multiple years, particularly in 2001, 2002, 2006, and 2009, with respective counts of 16, 17, 19, and 18 concurrent episodes. The weights derived from Shannon’s entropy emphasize the importance of the Potential Drought Severity Index (PDSI) in evaluating drought conditions, with PDSI-D (drought duration) assigned the highest weight of 0.267, closely followed by VHI-D (Vegetation Health Index under drought conditions) at 0.232 and SPEI-F (drought frequency) at 0.183. The results demonstrated considerable spatial variability in drought conditions across the watersheds, with Watersheds 1 and 4 exhibiting the highest drought vulnerability in terms of meteorological and agricultural droughts, while Watersheds 6 and 3 showed significant resilience to hydrological drought after 2012. In particular, the severe meteorological drought conditions at Watershed 1 highlight the urgent need for rainwater harvesting and strict water use policies, and in contrast, the conditions at Watershed 4 show the need for the modernization of irrigation to mitigate agricultural drought impacts. This integrated framework allows for targeted drought management solutions that directly relate to the specific contexts of the watersheds, while being more conducive to planning and prioritizing resource allocations for regions facing the highest drought vulnerability.

A Forest Fire Prediction Model Based on Meteorological Factors and the Multi-Model Ensemble Method

More than half of South Korea’s land area is covered by forests, which significantly increases the potential for extensive damage in the event of a forest fire. The majority of forest fires in South Korea are caused by humans. Over the past decade, more than half of these types of fires occurred during the spring season. Although human activities are the primary cause of forest fires, the fact that they are concentrated in the spring underscores the strong association between forest fires and meteorological factors. When meteorological conditions favor the occurrence of forest fires, certain triggering factors can lead to their ignition more easily. The purpose of this study is to analyze the meteorological factors influencing forest fires and to develop a machine learning-based prediction model for forest fire occurrence, focusing on meteorological data. The study focuses on four regions within Gangwon province in South Korea, which have experienced substantial damage from forest fires. To construct the model, historical meteorological data were collected, surrogate variables were calculated, and a variable selection process was applied to identify relevant meteorological factors. Five machine learning models were then used to predict forest fire occurrence and ensemble techniques were employed to enhance the model’s performance. The performance of the developed forest fire prediction model was evaluated using evaluation metrics. The results indicate that the ensemble model outperformed the individual models, with a higher F1-score and a notable reduction in false positives compared to the individual models. This suggests that the model developed in this study, when combined with meteorological forecast data, can potentially predict forest fire occurrence and provide insights into the expected severity of fires. This information could support decision-making for forest fire management, aiding in the development of more effective fire response plans.

Environmental factors influencing the development and spread of resistance in erythromycin-resistant streptococcus pneumoniae.

Bacterial drug resistance is becoming increasingly serious, this study aims to investigate the relationship between the resistance rate of erythromycin-resistant Streptococcus pneumoniae (SP) and reasons for the epidemic under complex geographical and climatic factors in China. Data spanning from 2014 to 2021, including drug resistance rates, isolate rates, meteorological variables, and demographic statistics, were collected from the China Antimicrobial Resistance Monitoring System, the China Statistical Yearbook and China Meteorological Website. Our analysis involved nonparametric tests and the construction of multifaceted regression models for rigorous multivariate analysis. Single-factor analysis revealed significant differences in the resistance rate and isolate rate of erythromycin-resistant SP across different regions characterized by Hu Huanyong lines or different climate types. Multivariate regression analysis indicated positive correlations between the drug resistance rate and temperature, Subtropical climate, Gross Domestic Product (GDP), Hu Huanyong line, and the highest temperature in the past period (Tm); the isolate rate showed a positive correlation with regional GDP and a negative correlation with monsoon climate. The model developed in this study provides valuable insights into the resistance rate and potential relationships of erythromycin-resistant SP under complex meteorological conditions in China.

Convective and Orographic Origins of the Mesoscale Kinetic Energy Spectrum

AbstractThe mesoscale spectrum describes the distribution of kinetic energy in the Earth's atmosphere between length scales of 10 and 400 km. Since the first observations, the origins of this spectrum have been controversial. At synoptic scales, the spectrum follows a −3 spectral slope, consistent with two‐dimensional turbulence theory, but a shallower −5/3 slope was observed at the shorter mesoscales. The cause of the shallower slope remains obscure, illustrating our lack of understanding. Through a novel coarse‐graining methodology, we are able to present a spatio‐temporal climatology of the spectral slope. We find convection and orography have a shallowing effect and can quantify this using “conditioned spectra.” These are typical spectra for a meteorological condition, obtained by aggregating spectra where the condition holds. This allows the investigation of new relationships, such as that between energy flux and spectral slope. Potential future applications of our methodology include predictability research and model validation.

Earthquake Prediction and Alert System Using IoT Infrastructure and Cloud-Based Environmental Data Analysis

Earthquakes are one of the most life-threatening natural phenomena, and their prediction is of constant concern among scientists. The study proposes that abrupt weather parameter value fluctuations may influence the occurrence of shallow seismic events by focusing on developing an innovative concept that combines historical meteorological and seismic data collection to predict potential earthquakes. A machine learning (ML) model utilizing the ML.NET framework was designed and implemented. An analysis was undertaken to identify which modeling approach, value prediction, or data classification performs better in forecasting seismic events. The model was trained on a dataset of 8766 records corresponding to the period from 1 January 2001 to 5 October 2024. The achieved accuracy of the model was 95.65% for earthquake prediction based on weather conditions in the Vrancea region, Romania. The authors proposed a unique alerting algorithm and conducted a case study that evaluates multiple predictive models, varying parameters, and methods to identify the most effective model for seismic event prediction in specific meteorological conditions. The findings demonstrate the potential of combining Internet of Things (IoT)-based environmental monitoring with AI to improve earthquake prediction accuracy and preparedness. An IoT-based application was developed using C# with ASP.NET framework to enhance earthquake prediction and public warning capabilities, leveraging Azure cloud infrastructure. The authors also created a hardware prototype for real-time earthquake alerting, integrating the M5Stack platform with ESP32 and MPU-6050 sensors for validation. The testing phase and results describe the proposed methodology and various scenarios.

The Global Forest Fire Emissions Prediction System version 1.0

Abstract. The Global Forest Fire Emissions Prediction System (GFFEPS) is a model that estimates biomass burning in near-real time for global air quality forecasting. The model uses a bottom-up approach, based on remotely sensed hotspot locations, and global databases linking burned area per hotspot to ecosystem-type classification at a 1 km resolution. Unlike other global fire emissions models, GFFEPS provides dynamic estimates of fuel consumption, fire behaviour and fire growth based on the Canadian Forest Fire Danger Rating System, plant phenology as calculated from daily global weather and burned-area estimates using near-real-time Visible Infrared Imaging Radiometer Suite (VIIRS) satellite-detected hotspots and historical burned-area statistics. Combining forecasts of daily fire weather and hourly meteorological conditions with a global land classification, GFFEPS produces fuel consumption and emission predictions in 3 h time steps (in contrast to non-dynamic models that use fixed consumption rates and require a collection of burned area to make post-burn estimates of emissions). GFFEPS has been designed for use in operational forecasting applications as well as historical simulations for which data are available. A study was conducted showing GFFEPS predictions through a 6-year period (2015–2020). Regional annual total smoke emissions, burned area and total fuel consumption per unit area as predicted by GFFEPS were generated to assess model performance over multiple years and regions. The model's fuel consumption per unit area results clearly distinguished regions dominated by grassland (Africa) from those dominated by forests (boreal regions) and showed high variability in regions affected by El Niño and deforestation. GFFEPS carbon emissions and burned area were then compared to other global wildfire emissions models, including the Global Fire Assimilation System (GFAS), the Global Fire Emissions Database (GFED4.1s) and the Fire INventory from NCAR (FINN 1.5 and 2.5). GFFEPS estimated values lower than GFAS and GFED (80 % and 74 %) and had values similar to FINN 1.5 (97 %). This was largely due to the impact of fuel moisture on consumption rates as captured by the dynamic weather modelling. Model evaluation efforts to date are described – an ongoing effort is underway to further validate the model, with further developments and improvements expected in the future.

Multiyear high-temporal-resolution measurements of submicron aerosols at 13 French urban sites: data processing and chemical composition

Abstract. This paper presents a first comprehensive analysis of long-term measurements of atmospheric aerosol components from aerosol chemical speciation monitor (ACSM) and multiwavelength Aethalometer (AE33) instruments collected between 2015 and 2021 at 13 (sub)urban sites as part of the French CARA (Chemical Characterization of Particles) program. The datasets contain the mass concentrations of major chemical species within submicron aerosols (PM1), namely organic aerosols (OAs), nitrate (NO3-), ammonium (NH4+), sulfate (SO42-), non-sea-salt chloride (Cl−), and equivalent black carbon (eBC). Rigorous quality control, technical validation, and environmental evaluation processes were applied, adhering to both guidance from the French Reference Laboratory for Air Quality Monitoring (LCSQA) and the Aerosol, Clouds, and Trace Gases Research Infrastructure (ACTRIS) standard operating procedures. Key findings include geographical differences in the aerosol chemical composition, seasonal variations, and diel patterns, which are influenced by meteorological conditions, anthropogenic activities, and proximity to emission sources. Overall, OA dominates PM1 at each site (43 %–60 % of total mass), showing distinct seasonality with higher concentrations (i) in winter, due to enhanced residential heating emissions, and (ii) in summer, due to increased photochemistry favoring secondary aerosol formation. NO3 is the second most important contributor to PM1 (15 %–30 %), peaking in late winter and early spring, especially in northern France, and playing a significant role during pollution episodes. SO4 (8 %–14 %) and eBC (5 %–11 %) complement the major fine-aerosol species, with their relative contributions strongly influenced by the origin of air masses and the stability of meteorological conditions, respectively. A comparison with the 3D chemical transport model (CTM) CHIMERE shows high correlations between simulations and measurements, albeit with an OA concentration underestimation of 46 %–76 %. Regional discrepancies in NO3 concentration levels emphasize the importance of these datasets with respect to validating air quality models and tailoring air pollution mitigation strategies. The datasets can be found at https://doi.org/10.5281/zenodo.13318298 (Chebaicheb et al., 2024).

Short-term forecast of solar irradiance components using an alternative mathematical approach for the identification of cloud features

Solar energy technologies require precise solar forecasting to reduce power generation losses and protect equipment from irradiance fluctuations. This study introduces an alternative methodology for short-term forecasting of direct normal irradiance (DNI) and global horizontal irradiance (GHI) utilizing ground-based sky images captured by a single device. A low-cost all-sky imager (ASI) was developed, which implements an angular transformation and an optical flow technique to extract cloud features such as shape and velocity. A mathematical model calculates cloud transmittance based on pixel intensity, eliminating complex training steps. Results from a 30-day experimental campaign, incorporating diverse meteorological conditions, were compared against a secondary standard solarimetric station, a smart persistence model, and state-of-the-art approaches. The DNI forecast achieved an RMSE (relative error) of 46.79 W/m2 (11.99%) for 1-min intervals and 90.21 W/m2 (17.54%) for 10-min intervals, while GHI ranged from 31.73 W/m2 (4.68%) to 75.02 W/m2 (13.63%). Pearson correlation coefficients exceeded 0.9 overall, reaching 0.98 and 0.99 for the 1-min DNI and GHI forecasts, and 0.91 and 0.96 for the 10-min DNI and GHI forecasts, respectively, underscoring the system’s accuracy and robustness in complex meteorological scenarios.