Meteorological Institute Research Articles

1.P. Round table: How could a perfect monitoring and response system look like?

Abstract Robert FitzRoy, captain of the Beagle, was ridiculed when he started to assemble a network of telegraphs to be able to follow storms at sea: in the 19th century, predicting the weather, even in the short term, was considered impossible. 150 years later, meteorological institutes around the world may have helped prevent millions of deaths. Dynamic and complex systems are particularly difficult to predict: we still don’t know exactly when the glaciers will collapse, when the next variant of a virus will appear, how intense the next earthquake will be. In fact, and as the COVID-19 pandemic has shown, most services are unprepared to adjust to such fast evolving-situations: in general, we are limited to trying to react and adapt, but systematically fail at prediction and planning, with important consequences. Moreover, disease outbreaks, scientific research, public health response(s) and legislation happen at completely different time scales, and are not prepared to communicate with (or even understand) each other. Therefore, a fundamental question is how we can create structures that, similar to what FitzRoy imagined, are prepared to anticipate and respond to health threats, when they appear. During this round table, we will bring together experts that use different perspectives (One Health, Digital Epidemiology, Health Policy) and discuss what is needed to build the “perfect” system, in an ideal world. We will argue in favor of 1) creating efficient systems for collecting and processing good quality data, fundamental for any decision making; 2) training multidisciplinary teams to work on the full pipeline from performing complex analyses, using mathematical and computational tools, to communicating with the decision-makers and the population; 3) prepare institutions to network, nationally and internationally; and 4) simulate different scenarios and design medium-term strategies. We will recognize and highlight many of the challenges of such an integrated approach, from data availability and sharing, to communicating under uncertainty, but also the risks of not even trying to do so. We expect to make very brief presentations, to leave abundant time for audience feedback and discussion. Key messages • It is urgent to create structures prepared to anticipate and respond to health threats. These should be local but integrated at the international level. • Their creation faces many challenges, from data collection and sharing to decision making and communication, that should be recognized and tackled using multidisciplinary, integrative approaches. Speakers/Panelists Joana Gonçalves-Sá Social Physics and Complexity Lab, Lisbon, Portugal Ana Bento Cornell University, Ithaca, USA Daniela Paolotti ISI Foundation, Turin, Italy André Peralta-Santos ENSP, Lisbon, Portugal

Sensitivity of Dust Deposition for Parabolic Trough Collector Mirrors to Different Meteorological Drivers

This abstract presents a soiling forecasting tool (SFT) for assessing the deposition of dust on parabolic trough collector (PTC) mirrors and its cumulative impact on their reflectivity. The SFT was developed by the University of Patras in the frame of the Smart Solar System (S3) project (Horizon 2020 Solar-Era.net). An initial version of the model was presented at the SolarPACES 2022 conference, where the adaptation of the model to dust transport phenomena was demonstrated. The subsequent step in the evolution of the algorithm concerned the calibration of the SFT under different atmospheric conditions. In addition, the model was modified to incorporate meteorological and aerosol forecast data as inputs. The atmospheric predictions of dust and aerosol optical depth are from the global atmospheric forecasts of the Copernicus Atmosphere Monitoring Service. Regarding the meteorological data, three independent sources are used. Those are the Norwegian Meteorological Institute's meteorological forecasts (YR), the METAR meteorological observations from the closest airport, and lastly, the data from the automatic weather station at the location of the PTC system at the KEAN Soft Drinks Ltd factory in Limassol, Cyprus. The aim of this paper is to assess the impact of the three distinct meteorological input sources on the modelled reflectivity and its comparison to measurements taken during the experimental campaign.

Baltic sea ice thickness estimation based on X-band SAR data and background information

Abstract In this study an operational sea ice thickness (SIT) estimation algorithm, based on HH-polarized X-band synthetic aperture radar (SAR) data, background information from the most recent, typically from the previous day, available daily Baltic Sea ice chart and the operational Finnish Meteorological Institute (FMI) thermodynamic ice model, was developed and evaluated. The algorithm was designed to complement the C-band SAR SIT algorithm developed earlier at FMI and applied daily as part of the operational Copernicus Marine Service (CMS). The X-band SIT algorithm was developed by utilizing the sea ice thickness measurements made onboard the Finnish and Swedish ice breakers during two winters seasons: 2021–2022 and 2022–2023. The former season measurements were used for defining the algorithm parameters and the later season for evaluation of the algorithm performance. According to the evaluation metrics the X-band algorithm performance is slightly better than that of the operational CMS C-band SAR SIT algorithm, indicating its suitability for operational use in CMS.

Performance of the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) Model in the Northeast Region of Brazil

Study is presented on the potential for using the tools contained in the Google Earth Engine (GEE) geospatial data processing and analysis platform in climatological studies in the Northeast Region of Brazil (NEB), in particular the validation of estimates of total annual rainfall obtained by the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) model in seven homogeneous regions of the NEB. This data is made up of various sources of meteorological information, including information from satellites, numerical models and observations at weather stations in operational and research centers across the globe. The performance of the CHIRPS model was assessed using climatological data obtained from the National Meteorological Institute's (INMET) meteorological database, metrics and statistical tests. The results showed that the CHIRPS model has potential for use in six of the seven regions studied, with low errors and strong statistical correlation, and limitations in one of these regions. The conclusion is that this technological tool, which has a low operating cost and is free of charge, can be used to assist in climatological studies, planning and the development of strategies for mitigation, adaptation and coexistence with the dry periods observed in the sub-regions of the NEB, especially in regions that are in a situation of socio-economic vulnerability, as is the case in the semi-arid region of the NEB.

Physiological responses to heat exposure in a general population cohort in Denmark: the Lolland-Falster Health Study.

Rising global temperatures due to climate change pose a health risk. Mortality and morbidity increase during heat events affects various organ systems. While warmer countries face higher risks, even colder regions show elevated mortality during hot periods. This study examines physiological responses to heat exposure using data from the general Danish population cohort Lolland-Falster Health Study (LOFUS) during the summers of 2016-2019. In this cross-sectional study, we analysed health data from 3804 individuals aged ≥15 years. Data were analysed across organ systems: cardiovascular system, lung function, renal system, inflammation, coagulation, and liver function. Meteorological data from the Danish Meteorological Institute provided information on temperature and humidity. Heat exposure was defined as one day ≥28°C heat index the day prior to examination. Adjusted multiple linear regression was applied to analyse differences between the two groups. There were 46 of 368 days with temperatures ≥28°C heat index. In total, 396 participants were heat-exposed (exposure group), while 3408 constituted the unexposed group. Heat exposure was associated with lower systolic blood pressure (-3.82 mm Hg [-5.72; -1.93]), higher heart rate (1.71 beats/min [0.45; 2.98]), lower oxygen saturation (-0.28% [-0.45; -0.10]), higher sodium (0.56 mmol/l [0.33; 0.79]), and higher urine albumin (0.14 mg/l [0.02; 0.27]). No significant differences were observed in inflammation, coagulation, or liver function. This study reveals early physiological responses to heat with one day of heat exposure ≥28°C, particularly in the cardiovascular, pulmonary, and renal systems. These findings underline the need for tailored strategies to mitigate health risks associated with rising temperatures.

Synoptic map heritage of the National Research Institute of Meteorology of Academia Sinica in the 1930s

Synoptic map heritage of the National Research Institute of Meteorology of <i>Academia Sinica</i> in the 1930s

Spatiotemporal climate variability and food security implications in the Central Ethiopia Region

Studies focusing on the spatiotemporal distribution of climatic parameters and meteorological drought are of paramount significance for countries like Ethiopia, where climate change and variability cause major losses to rain-dependent agriculture. In this study, the National Meteorology Institute of Ethiopia provided an Enhanced National Climate Services (ENACTS) dataset at a spatial resolution of approximately 4 km by 4 km over 38 years (1981–2018) was used to study climate trends, spatiotemporal variability, and meteorological drought in the Central Ethiopia Region. Coefficient of variation (CV), Standardized Rainfall Anomaly (SRA), Standardized Precipitation Index (SPI), Mann-Kendall trend test, and Sen's slope were used for the analysis. The findings suggest that Belg rainfall (also known as "small-rain") varied greatly in space and time over the study area, with area-averaged CV of 29 % and pixel-level CVs ranging from 63 to 93 %. The average precipitation during Belg experienced a 15 % decrease from 2000 to 2019 compared to the preceding two decades, from 1981 to 1999. The maximum temperature has increased significantly during the Annual, Belg, and Bega seasons. The SPI and SRA showed that there have been multiple drought episodes with rising negative rainfall anomalies, with a drought occurring every 2.9 years during the Kiremt (also called "big rain", spanning from June to September) and Belg seasons. The growing negative rainfall anomaly, high CV, and highly significant increase in mean maximum temperature during the Belg season is concerning for food security and poverty eradication. The notable rise in rainfall during the June (sowing period) and November (harvesting period) also hurts crop production during the main cropping season. Thus, developing appropriate adaptation strategies and policies oriented toward climate-resilient agriculture is crucial to meet the global sustainable development goals (SDGs) and Africa Union's Agenda 2063.

Analysis of WRF-solar in the estimation of global horizontal irradiation in Amapá, northern Brazil

Global horizontal irradiance (GHI) was simulated using version 4.3.1 of the WRF-Solar model over the state of Amapá in the northern region of Brazil. The performance of the Fast All-sky Model for Solar Applications (FARMS) algorithm, called WsolarAPf, was evaluated. The algorithm proposed by the Baseline Surface Radiation Network was used to treat hourly data from the automatic meteorological station of the National Institute of Meteorology in the city of Macapá-AP. Two cumulus parameterizations were needed to represent the rainy and less rainy periods. The modeling used input data from ERA5 reanalysis. Using cluster analysis, it was possible to estimate the typical meteorological year representative for a large area. The use of the FARMS algorithm reduced the relative RMSE of the GHI during winter (JJA) and austral spring (SON), from 26 % to 16 % in austral winter and from 17 % to 12 % in austral spring. The average daily maximum of GHI occurs at 13:00 local time during all seasons, and WRF-Solar showed it at 13:00 and 14:00 in the rainy season and 12:00 and 13:00 in the less rainy season. The suggestion to explore the development of an optimal combination of the two model predictions can be implemented using FARMS.

Spatiotemporal variability and trends of intra-seasonal rainfall and temperature in the drought-prone districts of Northwestern Ethiopia

Agriculture in Ethiopia is highly dictated by spatial patterns and temporal distributions of climate variables. The analysis of these climate variables is crucial for understanding the impacts on agricultural productivity. This study aimed to analyze spatiotemporal variability and trends of intra-seasonal rainfall and temperature using site-specific daily data from the Ethiopian Meteorology Institute (1992–2021). Standardized methods explore variability, while Mann–Kendall tests identify trends, using the Modified version for data with autocorrelation. Inverse Distance Weighted interpolation was employed for spatial analysis of rainfall, length of growing season, and temperature. The findings identified that Kiremt dominated the mono-modal rainfall pattern, contributing 72%-86% of total annual rainfall. The study found that the season typically begins early on June 13 in Adiszemen, and July 6 in Arbgebiya and ends between October 6 and October 26. The duration of the season varied across locations, averaging 95 days at Ebenat and 148 days at Adiszemen. The seasonal rainfall anomaly index shows identical patterns between ENSO episodes and seasonal rainfall. These findings inform decision-making and adaptation strategies for ENSO-driven rainfall variability. Temperatures showed predictable seasonal patterns, but have significantly increased over time, with maximum and minimum temperatures rising 0.014 °C to 0.421 °C and 0.027 °C to 0.485 °C per year respectively. This warming trend is negatively impacting water, crops, and livestock, requiring adaptation measures to build regional resilience. This study underscores the critical impact of climate variability on agriculture in the study area. The findings reveal shifts in rainfall patterns and temperature trends, providing essential insights for adapting agricultural practices.

Comparison between evapotranspiration estimate methods in the state of Rio Grande do Sul

The characterization of evapotranspiration requires time and financial investment, but from meteorological data, it is possible to estimate the values of this phenomenon by means of indirect methods. The Penman-Monteith FAO (PM) method is considered the most accurate by the Food and Agriculture Organization of the United Nations (FAO), which recommends its use because it takes into account solar radiation, temperature, relative humidity, and wind speed, data that are not always available in some locations in Brazil, making it necessary to use more simplified methods. Therefore, the objective of this work is to compare the evapotranspiration estimated by the Penman-Monteith FAO method with the evapotranspiration estimated by the methods of Penman-Monteith Simplified (PMS), Priestley-Taylor (PT), and Hargraves-Samani (HS) for the 10 weather stations of the National Institute of Meteorology (INMET) distributed in the Pampa biome in the state of Rio Grande do Sul. The results obtained indicated some divergences between the compared methods. However, the PT method showed more accurate results, with the best performance among the proposed methods. This indicates that this method can be used in future studies in the region, especially in cases of a lack of meteorological data.

Extended aerosol and surface characterization from S5P/TROPOMI with GRASP algorithm. Part II: Global validation and Intercomparison

This paper is the second part of companion papers describing the development of GRASP approach for aerosol and surface retrieval from Sentinel-5P/TROPOMI. Here we focus on the S5P/TROPOMI GRASP aerosol and surface products global validation and systematic intercomparison with other products from independent instruments and algorithms. Specifically, we have validated the S5P/TROPOMI GRASP, Suomi-NPP/VIIRS DB and MODIS/TERRA DT + DB aerosol products with the ground-based AERONET referenced measurements using the same methodology and intercompare the validation results. In addition, the global pixel-to-pixel intercomparisons of the aerosol products (AOD, fine/coarse mode AOD and SSA) are performed over different surfaces, i.e., ocean and land surface with different NDVIs. Besides, we compared the S5P/TROPOMI GRASP, MODIS MCD43 surface BRDF/albedo as well as OMI, GOME-2 and SCIAMACHY Lambertian-Equivalent Reflectivity (LER) albedo climatology developed by Royal Netherlands Meteorological Institute (KNMI) with the surface reference dataset generated based on the synergetic retrieval of AERONET and S5P/TROPOMI measurements. Finally, the intercomparisons of the surface BRDF and albedo datasets were performed globally at the UV, VIS, NIR and SWIR parts of the spectrum. Overall, generally good agreement was observed between independent aerosol and surface datasets with a high percentage of pixels satisfying the Optimal and Target requirements. We would emphasize two advantages for TROPOMI/GRASP aerosol and surface products: (i) it provides spectral AOD together with detailed aerosol properties, such as fine/coarse mode AOD, spectral AAOD and SSA at UV, VIS, NIR and SWIR wavelengths, which are important for constraining aerosol environmental and climate effects; (ii) the TROPOMI/GRASP aerosol and surface products are globally retrieved simultaneously in a fully consistent manner.

Evaluation of Seasonal and Climatic Variations Associated With Femoral Fracture Incidence Among the Elderly Population in Brazil.

Femur fractures represent a significant public health concern for individuals over 60 years of age. In Brazil, the incidence of these fractures has shown a rising trend in line with population aging. Besides intrinsic risk factors like osteoporosis, seasonal and climatic variables have been suggested to significantly influence the occurrence of these fractures. Epidemiological data were sourced from the Hospital Information System of the Unified Health System (SIH-SUS) using the TABNET tool provided by theDepartment of Informatics of the Unified Health System (DATASUS). Information on hospital admissions for femur fractures in individuals over 60 years, categorized by diagnosis, region, and month, was collected from 2008 to 2023. Climatic data were obtained from the Brazilian National Institute of Meteorology (INMET) for the period from 1991 to 2020. Monthly average temperatures were compared with epidemiological data and analyzed to uncover correlations using ANOVA and Tukey's honestly significant difference (HSD) test. During Brazil's winter months (Juneto August: 22.8 to 23.5º Celsius), average temperatures are at their lowest. This period also coincides with the peak incidence of femur fractures among the elderly. ANOVA revealed significant differences in fracture rates across various temperature ranges. Subsequent analysis using Tukey's HSD test identified significant differences between temperature ranges of 22-23°C and 25-26°C, as well as 23-24°C and 25-26°C. These results indicate that lower temperatures are associated with a higher incidence of femur fractures among the elderly. The analysis reveals a seasonal pattern in fracture incidence among older adults, with a notable increase during the colder months. To mitigate this risk, it is recommended to implement strategies such as heightened surveillance during colder months, targeted fall prevention measures, and effective osteoporosis management. These interventions aim to reduce the incidence of fractures in this vulnerable population.

Severe Weather Warnings for Sofia, Bulgaria: May – September 2010–2019

Severe weather monitoring and forecasting is the prime task of the National Meteorological Services according to World Meteorological Organization Guidelines (WMO). Since 2009 Bulgarian National Institute of Meteorology and Hydrology (NIMH) issues 48 h ahead severe weather warnings as a contribution to the European warning system (METEOALARM). A total of 243 days with warnings for heat, thunderstorms and intense rain were issued for the period May-September 2010–2019. The monthly maximum for heat is 42 days in July, for thunderstorms it is 26 days in June, and for intense rain it is 27 days in June. Objective circulation classification is conducted which shows that the majority of heat warnings are associated with Anticyclonic and West flow circulation types. About 40% of thunderstorm warnings are of Cyclone type followed by 28.6% of Cyclonic Directional types. For rain warnings, most of the days are with Cyclone type (46.2%) and Cyclonic Directional types (27.5%). Analysis of Global Navigation Satellite Systems (GNSS) derived Integrated Water Vapour (IWV), from the Bulgarian station in Sofia, shows consistently higher values for the days with METEOALARM warnings for heat, thunderstorm and rain.

Utilizing Multi-Source Datasets for the Reconstruction and Prediction of Water Temperature in Lake Miedwie (Poland)

Water temperature is a fundamental parameter of aquatic ecosystems. It directly influences most processes occurring within them. Hence, knowledge of this parameter’s behavior, based on long-term (reliable) observations, is crucial. Gaps in these observations can be filled using contemporary methodological solutions. Difficulties in reconstructing water temperature arise from the selection of an appropriate methodology, and overcoming them involves the proper selection of input data and choosing the optimal modeling approach. This study employed the air2water model and Landsat satellite imagery to reconstruct the water temperature of Lake Miedwie (the fifth largest in Poland), for which field observations conducted by the Institute of Meteorology and Water Management—National Research Institute ended in the late 1980s. The approach based on satellite images in this case yielded less accurate results than model analyses. However, it is important to emphasize the advantage of satellite images over point measurements in the spatial interpretation of lake thermal conditions. In the studied case, due to the lake’s shape, the surface water layer showed no significant thermal contrasts. Based on the model data, long-term changes in water temperature were determined, which historically (1972–2023) amounted to 0.20 °C per decade. According to the adopted climate change scenarios by the end of the 21st century (SSP245 and SSP585), the average annual water temperature will be higher by 1.8 °C and 3.2 °C, respectively. It should be emphasized that the current and simulated changes are unfavorable, especially considering the impact of temperature on water quality. From an economic perspective, Lake Miedwie serves as a reservoir of drinking water, and changes in the thermal regime should be considered in the management of this ecosystem.

Climatic features in the Serra do Sincorá geopark, in Chapada Diamantina, in the state of Bahia, Brazil

Climate studies and their elements are important, because in addition to research and weather forecasting, other sectors of the community need information about weather and climate, such as agriculture, aviation, civil defense, water resources management, navigation and the energy sector. In this sense, the present study aimed to analyze the variability of some elements of the climate of the Bahian municipalities of Andaraí, Lençóis, Mucugê and Palmeiras, the area of proposition for the creation of the Serra do Sincorá Geopark, in Chapada Diamantina, based on air temperature, rainfall and evapotranspiration in the perspective of understanding the behavior of the local climate and its possible effects in the region. For this analysis, specific time intervals were selected for each municipality, available at the National Institute of Meteorology (INMET) and Climatempo. The data were tabulated for statistical analysis of the behavior of the climate elements over the months and years, using as parameters the sum, minimum, maximum, average and the correlation between the meteorological variables, with the generation of climograms and comparative graphs. The relevance of the study lies in understanding the climatic variations of the municipalities in the proposed area of the Serra do Sincorá Geopark in relation to its climatic dimension, since the results may offer subsidies regarding environmental planning, in addition to a contribution to the local population, since there are few studies in the field of climatology in the region. With the analysis of meteorological data, it is proven that the municipalities of Andaraí, Lençóis, Mucugê and Palmeiras, due to climate factors, such as altitude, latitude and longitude, on a regional scale, influence the behavior of climate elements.

A Climate Education Experience in the Community of Morro Santa Terezinha in the City of Fortaleza in the State of Ceará (Brazil)

The study presents the evaluation of a teaching-learning process in climate education through active methodologies, as well as the perception of students in the final years of elementary school about the effects of climate variability and the occurrence of natural disasters in the region where they live in Morro Santa Terezinha in Fortaleza/CE. Thus, a questionnaire was applied to diagnose the level of knowledge of these students, the results of which supported discussions of the content in pedagogical meetings, lectures and analysis of climate information obtained from the National Institute of Meteorology and the Ceará Foundation of Meteorology and Water Resources, as well as information on the occurrence of natural disasters obtained from the Municipal Coordination of Protection and Civil Defense. This methodology enabled the students to associate scientific knowledge with the climatic reality observed in the region under study and fostered meaningful learning and protagonism, as recommended in the National Common Curriculum Base, in addition to the preparation and publication of an educational leaflet that was distributed in the school community. It was therefore concluded that the work met the objectives of motivating and arousing the interest of students and the school community in climate education and environmental preservation.

Code 1583 - preliminary steps for studies to assess the safety of an earth dam in terms of use and lowering the reservoir level for public supply during the water crisis

The spring 2021 climate forecast report from the National Institute of Meteorology (INMET) and the National Institute for Space Research (INPE) indicated an increased possibility of below-average rainfall in almost the entire region of the state of Paraná, which is consistent with the historical trend of decreasing rainfall in the Paraná River basin since the 1990s, with an intensification in the 2000s. This led to a water emergency situation in Paraná, which was decreed by the state government in 2020 and 2021. The Curitiba region depends on the Curitiba and Metropolitan Region Integrated Supply System (SAIC). Due to the intense drought in 2020 and 2021, SAIC's reservoirs suffered a significant reduction in stored water levels and volumes, reaching 28% of their total capacity in August 2020. This was the biggest water crisis in the last 90 years in the history of the state of Paraná, with several reservoirs reaching levels close to historic lows, but maintaining constant discharges in order to maintain power generation or public supply. In these cases, with the reservoirs at lower levels and with a smaller volume, the variation in the water level on the upstream slope of the earth dam varies differently from the levels when the dam and reservoir are in normal operation or outside of a water crisis. In view of the atypical behavior of reservoir levels, and based on good dam engineering practice, when the operating regime changes or the reservoir water level drops, the safety aspects of these structures should be reassessed through geotechnical analyses for conditions of rapid lowering, in order to learn how these structures behave under these conditions. This article sheds light on the important stages of this thorough technical investigation of the extensive documentation of a project like this, data on instrumentation and performance, how they were built and behaved during the construction and filling of the reservoir, knowledge of geotechnical parameters of the materials of the dam massif and foundation, recorded field observations made at the time of preliminary investigation during the design, project and construction phase; obtaining hydro-meteorological data from the region, in order to obtain the "as-built" geometry of the structure, with the aim of modeling the typical section reliably to the structure in the field in order to infer this numerical model and vice versa, and obtain results on its performance during this atypical period.

Integration of ten years of daily weather, traffic, and air pollution data from Norway’s six largest cities

This study integrates ten years of daily weather, traffic, and air pollution data across the six largest Norwegian cities, utilizing data from the Norwegian Public Roads Administration, the Norwegian Institute of Air Research, and the Norwegian Meteorological Institute. The compilation of this dataset involved detailed selection and verification of monitoring stations to ensure consistency and accuracy. Initial data collection focused on the top ten most populous cities in Norway, with the subsequent examination of traffic and air pollution monitoring sites. Weather variables were then matched to the selected sites, resulting in a comprehensive dataset from 2009 to 2018. The resulting dataset encompasses extensive information, including harmful pollutants such as Nitric oxide (NO), Nitrogen dioxide (NO2), Nitrogen oxides (NOx), Particulate Matter less than 2.5 micrometers in diameter (PM2.5), and Particulate Matter less than 10 micrometers in diameter (PM10). The dataset’s potential for further analysis and its utility in informing policy decisions underscore its significance. This integrated dataset is a valuable resource for researchers and policymakers alike, facilitating comprehensive studies on the intersection of weather, traffic, and air pollution in urban environments.

A multiclass short-term lightning predictor for the Amazon region using ground-based meteorological data and artificial neural network

Lightning prediction is a complex task due to the intricate nature of this phenomenon. Various sectors of society, including aviation and civil defense, can benefit from accurate lightning occurrence information. Methodologies that can predict lightning occurrence are crucial to minimize the issues related to this phenomenon. In this work, we propose a novel approach using the machine learning model called Artificial Neural Network (ANN) to predict lightning occurrence one hour ahead. We use ground-based meteorological data from the National Institute of Meteorology (INMET) and the number of lightning per hour to feed the ANN model. We used a lightning dataset of six years of observation (2015–2020) from GLD360 to train, validate, and test machine learning algorithm models. A model was developed for each weather station. We use data from 24 stations distributed throughout the Brazilian Legal Amazon region. The multiclass predictor was based on three categories: no lightning, low-intensity, and high-intensity of lightning. We also developed a solution comprising two cascade ANN models, each performing binary prediction. The first predicts the occurrence or no occurrence of lightning, and the second predicts the intensity: low or high. The cascade approach showed superior performance compared to the regular approach.

The Role of Physical Literacy in the Association Between Weather and Physical Activity: A Longitudinal Multilevel Analysis With 951 Children.

Numerous studies showed an effect of weather on physical activity (PA) levels in children. However, no study has yet examined the relevance of personal factors in this relationship. Therefore, this study analyzes (1) whether there are systematic interindividual differences in the extent to which weather affects the PA behavior and (2) whether physical literacy (PL) moderates the weather-PA association in children. A total of 951 children in 12 Danish schools (age 9.76 [1.59]y; 54.3% girls) completed objective PA assessments via accelerometry (moderate to vigorous PA, light PA, and sedentary behavior). Local weather data (precipitation, wind speed, temperature, and sunshine duration) were provided by the Danish Meteorological Institute. Participants' PL was measured employing the Danish version of the Canadian Assessment of Physical Literacy-2. The 4116 accelerometer days underwent longitudinal multilevel analyses while considering their nesting into pupils and school classes (n = 51). Fluctuations in all PA indicators were significantly explained by variations in weather conditions, especially precipitation (P ≤ .035). Significant interindividual differences were found for 9 of 12 analytical dimensions, suggesting that weather changes influence PA behavior differently across individuals (especially moderate to vigorous PA, χ2[4] ≥ 11.5, P ≤ .021). However, PL moderated the relationship between weather and PA in only 2 of the 48 analytical constellations. Despite the varying impact of weather on PA across individuals, the present study favors a main effect model in which weather and PL exert independent effects on children's PA. The insufficient support for PL as a moderating factor calls for future studies to test alternative mechanisms in the weather-PA association.