Hasil untuk "Meteorology. Climatology"

Fabrina Bolzan Martins, Larissa Vieira da Silva, Fernando Yuri da Silva Reis et al.

Resumo A temperatura do ar e o fotoperíodo são duas variáveis meteorológicas que desempenham um papel fundamental no desenvolvimento vegetativo e reprodutivo de espécies florestais por influenciar o aparecimento de estruturas vegetativas e reprodutivas. O objetivo deste trabalho foi avaliar a resposta de seis métodos de cálculo dos graus-dia e a influência do fotoperíodo na variável filocrono em duas espécies florestais nativas na fase de muda. Foi instalado um experimento a campo, sob delineamento inteiramente casualizado, em esquema fatorial 2 × 12, sendo duas espécies florestais nativas (pau ferro - Caesalpinia ferrea Mart. ex. Tul. Var. leiostachya Benth e angico vermelho - Anadenanthera macrocarpa (Benth) Brenan) e doze épocas de semeadura, com cinco repetições por tratamento. A variável filocrono é influenciada pelos métodos de cálculo dos graus-dia, sendo o melhor método, aquele que considera as três temperaturas cardinais. Foi verificada também a influência do fotoperíodo nos valores de filocrono, em que os menores valores para as duas espécies concentraram-se em épocas de temperaturas mais elevadas e maior fotoperíodo, instaladas em 20/10, 20/11 e 20/12.

Piyush M. Mehta, Richard J. Licata

Abstract Physics‐based models of the ionosphere‐thermosphere system have been touted as the next big thing in the context of drag modeling and space operations for decades. However, the computational complexity of such models have primarily kept them being used operationally. We recently demonstrated a proof‐of‐concept for developing what we call a reduced order probabilistic emulator (ROPE) for the thermosphere using the thermosphere ionosphere electrodynamics ‐ general circulation model (TIE‐GCM). The methodology uses a page out of dynamical systems theory to first reduce the order of the state using dimensionality reduction and then modeling the temporal dynamics in the reduced state space. The methodology uses an ensemble of temporal dynamic models to provide uncertainty estimates in the prediction. This work focuses on the dimensionality reduction step of the ROPE development process and addresses three limitations of the proof‐of‐concept: (a) extending the altitude upper boundary from 450 km to nearly 1000 km, (b) employing deep learning for nonlinear dimensionality reduction over principal component analysis (PCA) for improved performance during storm periods, and (c) maintaining the spatial resolution of the physical TIE‐GCM model, without down‐sampling, to preserve the spatial scales and variations. Results show overall performance boost over PCA for the high‐resolution and extrapolated data set as well as reduced reconstruction errors during storm‐time conditions. This work represents a major step toward operationalization.

Christopher J. Mertens, Guillaume P. Gronoff, Daniel B. Phoenix

Abstract The Nowcast of Aerospace Ionizing RAdiation System (NAIRAS) model predicts dosimetric and radiative flux quantities for assessing human radiation exposure levels and radiation effects on flight electronic systems from the surface of the Earth to deep space. NAIRAS version 3 includes many updates and improvements to the model. In this paper, NAIRAS version 3 dosimetric quantities are compared to observations of the atmospheric radiation environment taken during the Radiation Dosimetry Experiment (RaD‐X) flight campaign. The RaD‐X campaign consisted of a high‐altitude balloon flight, with altitudes up to 40 km, and four coordinated aircraft flights covering an altitude range from 6 to 20 km. The RaD‐X measurement data from the different flight platforms were acquired in September 2015 and at nearly the same vertical cutoff rigidity (3–4 GV). High quality, flight‐averaged measurement data were provided at seven altitudes, which correspond to regions of fundamentally different radiation transport and collisional interaction physics. The NAIRAS model agrees with the RaD‐X measurements to within 30% over the entire altitude domain of the flight campaign (0–40 km), which is within the measurement uncertainty.

Ayustina Samudin, Rizal Adi Saputra, Fabelina Agsaria et al.

Southeast Sulawesi is one of the provinces in Indonesia characterized by diverse topography and climate, making it challenging to accurately identify and predict rainfall patterns. The aim of this study is to enhance our understanding of weather behavior in Southeast Sulawesi and provide a foundation for developing more advanced and region-specific weather prediction methods. The data used in this research consists of historical weather records obtained from the official BMKG (Meteorology, Climatology, and Geophysics Agency) website, containing features that significantly contribute to rainfall prediction. The method employed in this study is the Naive Bayes classification model, which involves several stages including data collection, pre-processing, and preparation for the modeling phase, ultimately generating rainfall prediction outputs. The results of the study yielded a rainfall prediction accuracy of 74.66%. For the rainfall class (0.0), the model achieved a precision of 82%, recall of 66%, and F1-score of 73%. Meanwhile, for the rainfall class (1.0), the model achieved a precision of 69%, recall of 84%, and F1-score of 76%. Despite some prediction errors, these findings indicate that the Naive Bayes method can serve as a solid foundation for the development of more sophisticated and tailored weather prediction models for the Southeast Sulawesi region.

Reshma Sunkur, John Mauremootoo

Invasive alien species (IAS) constitute a large and growing environmental and socio-economic problem. Tropical islands, one of the richest habitats in the world, are especially vulnerable to invasions because of their island-specific flora and fauna. The aim of this study is thus to assess the viability of monitoring IAS distributions on small tropical islands using Geographic Information Systems (GIS) and remote sensing techniques, focusing on the invasive plant species Vachellia nilotica on the island of Rodrigues as a case study. Freely available satellite images are used to conduct the analysis with resulting classified maps having accuracy levels in the high 70s. The results reveal a significant increase in Vachellia coverage from 2013 to 2023 especially along the coasts while the simulation for 2033 indicates an inward migration from coasts to the central plateau which could have severe repercussions on the native vegetation and human activities. Given the high invasive potential of Vachellia, the present findings can support conservation actions and decision making and even support community participation in managing this IAS. In the broader context, the study demonstrates the potential of GIS and remote sensing as cost-effective tools for monitoring certain invasive plant species.

Detlef Jahn

Abstract In this paper, I offer a way to measure the stringency of climate change legislation for macro-comparative studies using publicly available data. The paper is innovative in that it examines the complex interrelationship between national, subnational, and supranational legislation using data from the FaoLex database on environmental laws and policies. It is also novel in that it conducts a contextualized comparison that takes into account different national conditions for climate change policies. The resulting index is the Gross Potential Impact (GPI), which is a measure of the stimulus of legislation before taking into account implementation issues. To demonstrate the validity of the index, findings from case study research on the European Union’s 2020 and 2030 climate and energy frameworks are used. The rest of the paper presents the GPI for 21 OECD countries for the period between 1990 and 2020.

Christoph Schünemann, Tim Felix Kriesten, Uta Moderow et al.

To what extent can outdoor heat adaptation measures in urban districts help to reduce high indoor temperatures in buildings and thus enhance indoor thermal conditions? To answer this question microscale meteorological simulation (MMS) and building performance simulation (BPS) are combined in a model chain approach. Two existing residential German districts with different urban designs are modelled in the MMS tool ENVI-met. For both districts, a representative residential building (one from the Wilhelminian period and one large panel construction type) is modelled using the BPS tool IDA-ICE. Different scenarios of heat adaptation measures are applied to analyse how changes in urban and building design (e.g. white (cool) roofs (high albedo), white traffic areas (high albedo), intensive green roofs, urban trees, facade insulation or facade greening) affect outdoor and indoor temperatures. The MMS results highlight that the district from the Wilhelminian period is less heat resilient and that the efficacy of heat adaptation measures on heat reduction in open space depends on the urban design and the daytime. Regarding the efficacy of heat adaptation measures on indoor thermal conditions, our findings indicate that the larger share of the indoor cooling effect is not caused by the outdoor air temperature reduction by the outdoor heat adaptation measures but by the change of the building physics in the BPS model (e.g. changing the surface reflectance of the white roofs). White roofs and intensive green roofs show the largest cooling effect by reducing the operative room temperature by more than 1 Kelvin. Our findings also demonstrate that facade insulation can act as both, climate adaptation and mitigation measures.

Xiao-Qi Zheng, Xiang-Bo Xu, Chang Li et al.

The Pan-Third Pole region comprises multiple nations affected by climate vulnerability and energy inequality, wherein promoting energy transitions in rural households would provide a path to combat climate change. Identifying the factors that drive rural household energy consumption and the transition is important. This study performed a micro-survey of 1060 rural households in five countries in the Pan-Third Pole region and empirically analyzed the relationships between off-farm income, energy consumption, and energy transitions. The off-farm income of rural households was found to have a significantly positive effect (p < 0.01) on energy expenditure, indicating that energy expenditure increased with increasing off-farm income. Off-farm income has a significantly positive effect (p < 0.01) on the proportion of commercial energy but a significantly negative effect on the proportion of noncommercial energy. These results indicate that increasing off-farm income can adjust the energy consumption structure of rural households. Furthermore, a robustness check by substituting independent variables, instrumental variable method (IV), and propensity score matching method (PSM) provided strong evidence to prove the robustness of the results. The heterogeneity analysis showed that the effects of off-farm income on energy expenditure and transitions differed among countries, off-farm income had no significant impact on energy expenditure in Cambodia and Myanmar, but it worked for China, Nepal, and Thailand. Finally, policy implications are proposed to promote energy transition in the Pan-Third pole region: providing more full-time or part-time off-farm employment opportunities, employment assistance or skill training by local governments; increasing the pace of infrastructure construction to solve energy inaccessibility; multiple measures to promote the education and environmental knowledge.

Hiep Nguyen Duc, Merched Azzi, Yang Zhang et al.

The emission of black carbon (BC) particles, which cause atmospheric warming by affecting radiation budget in the atmosphere, is the result of an incomplete combustion process of organic materials. The recent wildfire event during the summer 2019–2020 in south-eastern Australia was unprecedented in scale. The wildfires lasted for nearly 3 months over large areas of the two most populated states of New South Wales and Victoria. This study on the emission and dispersion of BC emitted from the biomass burnings of the wildfires using the Weather Research Forecast–Chemistry (WRF–Chem) model aims to determine the extent of BC spatial dispersion and ground concentration distribution and the effect of BC on air quality and radiative transfer at the top of the atmosphere, the atmosphere and on the ground. The predicted aerosol concentration and AOD are compared with the observed data using the New South Wales Department of Planning and Environment (DPE) aethalometer and air quality network and remote sensing data. The BC concentration as predicted from the WRF–Chem model, is in general, less than the observed data as measured using the aethalometer monitoring network, but the spatial pattern corresponds well, and the correlation is relatively high. The total BC emission into the atmosphere during the event and the effect on radiation budget were also estimated. This study shows that the summer 2019–2020 wildfires affect not only the air quality and health impact on the east coast of Australia but also short-term weather in the region via aerosol interactions with radiation and clouds.

Weihua Song, Xiaowei Han, Jifei Qi

In order to improve the prediction accuracy of gas emission in the mining face, a method combining least absolute value convergence and selection operator (LASSO), whale optimization algorithm (WOA), and extreme gradient boosting (XGBoost) was proposed, along with the LASSO-WOA-XGBoost gas emission prediction model. Aiming at the monitoring data of gas emission in Qianjiaying mine, LASSO is used to perform feature selection on 13 factors that affect gas emission, and 9 factors that have a high impact on gas emission are screened out. The three main parameters of n_estimators, learning_rate, and max_depth in XGBoost are optimized through WOA, which solves the problem of difficult parameter adjustment due to the large number of parameters in the XGBoost algorithm and improves the prediction effect of the XGBoost algorithm. "When comparing PCA-BP, PCA-SVM, LASSO-XGBoost, and PCA-WOA-XGBoost prediction models, the results indicate that utilizing LASSO for feature selection is more effective in enhancing model prediction accuracy than employing principal component analysis (PCA) for dimensionality reduction." The average absolute error of the LASSO-WOA-XGBoost model is 0.1775, and the root mean square error is 0.2697, which is the same as other models. Compared with the four prediction models, the LASSO-WOA-XGBoost prediction model reduced the mean absolute error by 7.43%, 8.81%, 4.16%, and 9.92%, respectively, and the root mean square error was reduced by 0.24%, 1.13%, 5.81%, and 8.78%. It provides a new method for predicting the gas emission from the mining face in actual mine production.

Rudolf Brázdil, Kateřina Chromá, Pavel Zahradníček et al.

Fatalities associated with severe weather, collected from newspapers and other documentary sources, were used to create a corresponding database for the 1961–2020 period for the Czech Republic. Fatalities attributed to floods, windstorms, convective storms, snow and glaze ice, frost, fog, and other severe weather, on the one hand, and vehicle accident fatalities connected with rain, snow, glaze ice, fog, and inclement weather, on the other, were analysed separately for two standard periods, 1961–1990 and 1991–2020. The number of weather-related fatalities between these two periods increased in the flood, windstorm, and especially frost categories, and decreased for the convective storm and fog categories. For snow and glaze ice they were the same. Despite significant differences in both 30-year periods, the highest proportions of fatalities corresponded to the winter months, and in individual fatality characteristics to males, adults, direct deaths, deaths by freezing or hypothermia, and to hazardous behaviour. A statistically significant (<i>p</i> < 0.05) Spearman rank correlation between fatalities and climate variables was only found in the 1991–2020 period for snow/glaze ice-related fatalities, with the number of days with snow cover depth and frost-related fatalities having days with daily minimum temperatures below −5 °C or −10 °C. Despite the highest proportions of the rain and wet road categories being in the number of vehicle accident fatalities, a statistically significant correlation was only found for the category of snow-related fatalities in the number of days with snowfall. The results and conclusions of this study have to be evaluated in the broader context of climatological, political, economic, and societal changes within the country, and have the potential to be used in risk management.

Cristian Mattar, Rodrigo Fuster, Tomás Perez

Snow cover area is dramatically decreasing across the Los Andes Mountains and the most relevant water reservoir under drought conditions. In this sense, monitoring of snow cover is key to analyzing the hydrologic balance in snowmelt-driven basins. MODIS Snow Cover daily products (MOD10A1 and MYD10A1) allow snow cover to be monitored at regular time intervals and in large areas, although the images often are affected by cloud cover. The main objective of this technical note is to evaluate the application of an algorithm to remove cloud cover in MODIS snow cover imagery in the Chilean Andes mountains. To this end, the northern region of Chile (Pulido river basin) during the period between December 2015 and December 2016 was selected. Results were validated against meteorological data from a ground station. The cloud removal algorithm allowed the overall cloud cover to be reduced from 26.56% to 7.69% in the study area and a snow cover mapping overall accuracy of 86.66% to be obtained. Finally, this work allows new cloud-free snow cover imagery to be produced for long term analysis and hydrologic models, reducing the lack of data and improving the daily regional snow mapping.

Min He, Junhui Chen, Yuming He et al.

As one of the most populated regions in China, Sichuan province had been suffering from deteriorated air quality due to the dramatic growth of economy and vehicles in recent years. To deal with the increasingly serious air quality problem, Sichuan government agencies had made great efforts to formulate various control measures and policies during the past decade. In order to better understand the emission control progress in recent years and to guide further control policy formulation, the emission trends and source contribution characteristics of SO₂, NO_X, PM₁₀ and PM_2.5 from 2013 to 2017 were characterized by using emission factor approach in this study. The results indicated that SO₂ emission decreased rapidly during 2013–2017 with total emission decreased by 52%. NO_X emission decreased during 2013–2015 but started to increase slightly afterward. PM₁₀ and PM_2.5 emissions went down consistently during the study period, decreased by 26% and 25%, respectively. In summary, the contribution of power plants kept decreasing, while contribution of industrial combustion remained steady in the past 5 years. The contribution of industrial processes increased for SO₂ emission, and decreased slightly for NO_X, PM₁₀ and PM_2.5 emissions. The on-road mobile sources were the largest emission contributor for NO_X, accounting for about 32–40%, and its contribution increased during 2013–2015 and then decreased. It was worth mentioning that nonroad mobile sources and natural gas fired boilers were becoming important NO_X contributors in Sichuan. Fugitive dust were the key emission sources for PM₁₀ and PM_2.5, and the contribution kept increasing in the study period. Comparison results with other inventories, satellite data and ground observations indicated that emission trends developed in this research were relatively credible.

Joshua J. Ruck, David R. Themens

Abstract Over‐the‐horizon radar (OTHR) systems operating in the high‐frequency (HF) band (3–30 MHz) are unique in their ability to detect targets at extreme ranges, offering cost‐effective large‐area surveillance. Due to their reliance on the reflective nature of the ionosphere in this band, OTHR systems are extremely sensitive to ionospheric conditions and can expect significant variations in operational performance. At high latitudes, the presence of auroral enhancements in the E‐Region electron density can substantially modify the coverage area and frequency management of OTHR systems. In this study, HF raytracing is utilized to investigate these impacts for a hypothetical radar under different auroral conditions simulated using the Empirical Canadian High Arctic Ionospheric Model. Aurora were seen to increase maximum usable frequency from 8.5 to 26 MHz whilst also reducing median available range from 2,541 to 1,226 km and changing coverage area by −50.4% to 58.6%, for the greatest differences. Target interception showed large variations in path coverage of between 33%–115% and 0%–107% for two flight paths tested with precipitation toggled. Two distinct auroral propagation modes were observed, noted as the F‐E ducted and Auroral E‐modes. Long‐range coverage provided by the auroral F‐E ducted mode was of limited capacity with low solar activity due to reduced NmF2. F‐mode propagation transitioned to the dominating Auroral E‐mode between Auroral Electrojet index values of 50‐ and 200‐nT. The significant variations in both frequency and coverage observed within this study highlight some aspects of the importance of considering aurora in OTHR modeling and design.

A. Fedchuk

У статті узагальнено напрями урізноманітнення туристичної та суміжної неурядової діяльності в Антарк­тиці за останнє десятиріччя. За зведеними звітами Міжнародної асоціації антарктичних туроператорів складено класифікаційний перелік усіх відомих на даний час напрямів антарктичного туризму, включаючи його базові напрями, з використанням доступних видів транспорту (морський, повітряний, комбінований), на основі яких організовуються наземні (прибережні та внутрішньоконтинентальні) туристичні експедиції, а також визначено специфічні вузькоцільові види діяльності спортивного або пригодницького характеру як складові традиційних антарктичних турів. З огляду на включення питань розвитку наземного пригод­ниць­кого туризму до пріоритетних завдань багаторічного стратегічного плану роботи Консультативних нарад з Договору про Антарктику визначено першочергові проблеми регулювання цього сегмента антарктичного туризму – відповідність міжнародно-правовому режимові, співробітництво з національними антарк­тичними програмами, безпека під час подорожей, а також належна охорона навколишнього середовища.

R. Williams, R. Duvall, V. Kilaru et al.

The United States Environmental Protection Agency held an international two-day workshop in June 2018 to deliberate possible performance targets for non-regulatory fine particulate matter (PM2.5) and ozone (O3) air sensors. The need for a workshop arose from the lack of any market-wide manufacturer requirement for documented sensor performance evaluations, the lack of any independent third party or government-based sensor performance certification program, and uncertainty among all users as to the general usability of air sensor data. A multi-sector subject matter expert panel was assembled to facilitate an open discussion on these issues with multiple stakeholders. This summary provides an overview of the workshop purpose, key findings from the deliberations, and considerations for future actions specific to sensors. Important findings concerning PM2.5 and O3 sensors included the lack of consistent performance indicators and statistical metrics as well as highly variable data quality requirements depending on the intended use. While the workshop did not attempt to yield consensus on any topic, a key message was that a number of possible future actions would be beneficial to all stakeholders regarding sensor technologies. These included documentation of best practices, sharing quality assurance results along with sensor data, and the development of a common performance target lexicon, performance targets, and test protocols. Keywords: Low-cost air quality sensors, Performance targets, PM2.5, Ozone

Josemir Araujo Neves, Silvio de Barros Melo, Everardo Valadares de Sá Barreto Sampaio

Abstract This work presents the development of a composite Index of Susceptibility to Drought (ISD) for semiarid Brazilian Northeast that considers climatology, physical properties, soil usage, social and economic aspects, the risk of harvest losses and the shortage of human and animal drinking water. The index started with the Index FUNCEME of drought Severity (IFS), developed by FUNCEME. Then, it evolved to use some tools proposed by the Joint Research Centre/Organization for Economic Co-operation and Development (JRC/OECD), as well as techniques of multiple imputation for missing data and data winsorization. The work was tested and validated with real data from Rio Grande do Norte State in three climatologic scenarios (dry, regular and rainy). A multivariate analysis test and a Monte Carlo simulation were also produced for a sensibility and strength analysis of the developed model. These analyses validated the composition model and the obtained results with real data. The ISD can be used as a tool to support decision makers in various government levels to help guide the actions for the drought-affected areas.

Gil Cruz Lemus, Rosabel Zerquera Díaz, Ayleen Morales Montejo et al.

El presente trabajo desarrolla un proceso de descubrir conocimiento en bases de datos en el grupo de Medio Ambiente del Instituto Superior Politécnico José Antonio Echeverría (CUJAE), en colaboración con el Centro de Gestión de la Información y Desarrollo de la Energía (CUBAENERGÍA), con el objetivo de obtener un modelo de datos para estimar el comportamiento de parámetros meteorológicos secundarios a partir de datos de superficie. Se detallan algunos aspectos relacionados con la minería de datos y su aplicación en el entorno meteorológico, así como se seleccionay se describe la metodología CRISP-DM y la herramienta de análisis de datos WEKA. Asimismo, se utilizan las tareas de selección de atributos y de regresión, la técnica de redes neuronales de tipo perceptrón multicapas y los algoritmos CfsSubsetEval, BestFirst y MultilayerPerceptron. Se obtienen modelos de estimación para los parámetros meteorológicos secundarios: altura de la capa de mezcla convectiva, altura de la capa de mezcla mecánica, velocidad de fricción, flujo de calor superficial y velocidad convectiva de escala, necesarios para el estudio de los modelos de dispersión de contaminantes en la zona de la CUJAE. Los resultados obtenidos constituyen un precedente para futuras investigaciones, así como para la continuidad de esta.

D. Handorf, K. Dethloff, R. Jaiser et al.

The response of the Arctic atmosphere to low and high sea ice concentration phases based on European Center for Medium-Range Weather Forecast (ECMWF) Re-Analysis Interim (ERA-Interim) atmospheric data and Hadley Centre&#x0027;s sea ice dataset (HadISST1) from 1989 until 2010 has been studied. Time slices of winter atmospheric circulation with high (1990&#x2013;2000) and low (2001&#x2013;2010) sea ice concentration in the preceding August/September have been analysed with respect to tropospheric interactions between planetary and baroclinic waves. It is shown that a changed sea ice concentration over the Arctic Ocean impacts differently the development of synoptic and planetary atmospheric circulation systems. During the low ice phase, stronger heat release to the atmosphere over the Arctic Ocean reduces the atmospheric vertical static stability. This leads to an earlier onset of baroclinic instability that further modulates the non-linear interactions between baroclinic wave energy fluxes on time scales of 2.5&#x2013;6&#x2009;d and planetary scales of 10&#x2013;90&#x2009;d. Our analysis suggests that Arctic sea ice concentration changes exert a remote impact on the large-scale atmospheric circulation during winter, exhibiting a barotropic structure with similar patterns of pressure anomalies at the surface and in the mid-troposphere. These are connected to pronounced planetary wave train changes notably over the North Pacific.

Maria de Nazaré Alves da Silva, Francisco Carlos Lira Pessoa, Rafaela Nazareth Pinheiro de Oliveira Silveira et al.

Resumo O presente estudo determinou regiões homogêneas de precipitação média anual na bacia hidrográfica do Rio Tapajós por intermédio do método de agrupamento hierárquico de Ward, utilizando variáveis físico-climáticas como: a localização (latitude e longitude), a altitude e a precipitação média anual (PMA). Definiram-se 3 regiões homogêneas de precipitação, as quais foram validadas pelo índice de Silhouette. Aplicaram-se testes não-paramétricos (Mann Kendall, Spermann e o Estimador Sem) para analisar as tendências de precipitação em cada uma das regiões formadas. Os testes indicaram que existe tendência positiva nos dados de PMA na região composta pelas nascentes da bacia; por outro lado, na região central e próximo do exutório não ocorreram tendências positiva ou negativa nos dados monitorados, quer dizer, as séries possuem estacionariedade. A determinação dessas regiões homogêneas de precipitação é uma importante contribuição para o entendimento do comportamento climático na bacia hidrográfica, podendo auxiliar na gestão dos recursos hídricos.