Field Of Meteorology Research Articles

Between fascination and science: Goethe and the atmosphere

In the final decades of the eighteenth century and the first decades of the nineteenth century, there was a phase of cultural effervescence in Europe that impacted the progress of philosophical and scientific thought. In addition to making their mark in their fields of knowledge, key figures in Western culture, such as Kant, Humboldt and Goethe, contributed greatly to the advancement of natural sciences, particularly atmospheric science. Goethe stands out for his work on barometric measurements, his knowledge of clouds and his ideas on the movement of the air. His justification of atmospheric dynamics based on internal causes of the Earth was quickly dismissed in light of the air physics discoveries taking place at the time. He was passionate about the shapes of clouds and the form of the sky, which he described and drew. His literary work is replete with references to the atmosphere. This study analyses the meteorological writings of Goethe, examining his contributions to the atmospheric sciences of his day and how his passion for the skies and clouds permeated his literary works. Goethe's relationship with the fields of meteorology and climate science is not well known, but his studies constituted a valuable contribution to the cultural and scientific context of the Europe that was taking shape at that time.

Predict Weather with Machine Learning

Everything, from farming to air travel, in our contemporary world is dependent on the weather forecast. However, traditional methods often ignore the intrinsic uncertainty in weather patterns leading to inaccurate predictions. This paper presents a groundbreaking method of predicting weather using Bayesian deep learning models; specifically, Bayesian neural networks are employed to capture the probabilistic nature of weather events and produce more accurate and reliable forecasts. These models represent a significant milestone in foretelling strategies as they are evaluated against popular metrics and trained with archived atmospheric data. Additionally, gradient boosting classifiers and decision tree classifiers have been performed as queries for performing comparative analyses about different machine learning algorithms used for predicting occurrence of rain tomorrow or any other type of climate-related information. For enhanced interpretability, this paper has developed interactive visualization tools that allow users to interactively explore predicted weather patterns and analyze levels of uncertainty with an aim of advancing the field of meteorology through providing actionable insights for responsible decision making within weather sensitive domains.

ВАЛИДАЦИЯ ВЫСОТЫ НИЖНЕЙ ГРАНИЦЫ ОБЛАЧНОСТИ В РЕАНАЛИЗЕ ERA-5 ПО ПОКАЗАНИЯМ СУДОВОГО ЛИДАРА

The Cloud Base Height (CBH) is a key parameter that influences climate conditions and aviation safety. Additionally, in some cases, CBH can serve as an estimate of the thickness of the planetary boundary layer. Accurate assessment of CBH is important for meteorological forecasts and the evaluation of the atmospheric climate characteristics and their trends. This article presents the results of the validation of the Cloud Base Height (CBH) obtained from the ERA5 reanalysis using high-precision data collected from the shipborne lidar of the research vessel “Akademik Fedorov” during the 69th Russian Antarctic Expedition (RAE-69). For validation, the model estimates of CBH from the ERA5 reanalysis, provided at a spatial resolution of 0.25°×0.25° and a temporal resolution of 1 hour, are compared with the lidar data, which were obtained in monitoring measurements with a 1-minute interval. The analysis results showed that the ERA5 data exhibit systematic deviations from the instrumental measurements, which is confirmed by the sample evaluation of the mean bias and standard deviation of the errors in the model estimates of CBH. A similar conclusion can be drawn by examining the quantile-quantile diagram of CBH values in the ERA5 model estimates and the lidar measurement data. The error map analysis of ERA5 reveals an uneven spatial distribution and a significant dependence on the latitude of observations.The results of this research can be used for further research in the fields of meteorology and climatology, as well as for improving the methods of modeling the Cloud Base Height.

Weathering energy geographies: Engineering meteorology in the North Atlantic

This article traces how the development of meteorology in the North Atlantic during the twentieth century relied on the expansion of electrical infrastructures, which in turn were dependent on meteorological knowledge production. Specifically, it follows the engagements between a subset of actors—electrical engineers and meteorologists—who collaborated between the 1930s and 1960s and founded the field of Engineering Meteorology in order to make meteorological knowledge applicable to energy industry concerns. Through examining the modes of knowing and doing that emerged through these collaborations, this article traces the ways in which understandings of weather came to be embedded in the design, management, and modification of energy infrastructures. It further illustrates how the entanglements between engineering and meteorology shaped pollution trajectories and environmental violence, resulting in lasting implications for how energy geographies are weathered differently across time and space. In bringing together decolonial and feminist engagements with weather and energy, this article proposes a new approach to thinking about the weather-energy-society nexus by exploring how energy geographies and weather worlds are co-produced, a framework I call weathering energy geographies.

Evaluating the impact of communities-based fisheries education program on local communities attitudes towards sustainable fishing practices

Effective fisheries management requires environmentally literate persons who can make wise judgments. Programs for environmental education that emphasize fisheries may contribute to the development of environmental literacy. The scholarly field of overseeing and it is designated "fisheries schooling." with an end goal to introduce an extensive image of fisheries, this multidisciplinary science consolidates components from limnology, oceanography, freshwater science, sea life science, meteorology, preservation, environment, populace elements, financial matters, measurements, choice examination, the board, and numerous different fields to understand fisheries. In many instances, new fields have developed, such as fisheries law and bioeconomics. Fisheries scientists frequently employ techniques from a wide range of academic fields because the field is so comprehensive. Fish stocks (populations) have declined in many areas over the past few decades, and worries about the effects of intense fishing on freshwater and marine biodiversity have grown.

Looking to the future of energy meteorology

Bridging the fields of renewable energy and meteorology leads to better energy forecasting and technological innovation.

Research on information leakage in time series prediction based on empirical mode decomposition

Time series analysis predicts the future based on existing historical data and has a wide range of applications in finance, economics, meteorology, biology, engineering, and other fields. Although the combination of decomposition techniques and machine learning algorithms can effectively solve the problem of predicting nonstationary sequences, this kind of decomposition-integration-prediction strategy of the prediction method has serious defects. After the decomposition of the division of the training set and the test set, the information of the test set in the process of decomposition of the information leakage ultimately shows a high accuracy of the prediction of the illusionary. This paper proposes three improvement strategies for this type of “information leakage” problem: sliding window decomposition (SW-EMD), single training and multiple decomposition (STMP-EMD), and multiple training and multiple decomposition (MTMP-EMD). They are combined with a bidirectional multiscale temporal convolutional network (MSBTCN), bidirectional long- and short-term memory network (BiLSTM), and attention mechanism (DMAttention), which introduces a dependency matrix based on cosine similarity to be applied to water quality prediction. The experimental results show that the model achieves good performance in the prediction of three water quality indicators (pH, DO and KMnO4), and the accuracies of the three models proposed in this paper are improved by 1.958% and 0.853% in terms of the RMSE and MAPE, respectively, compared with those of the mainstream LSTM models. The key contributions of this study include the following: (1) three methods are proposed to improve the class EMD decomposition, which can effectively solve the problem of “information leakage” that exists in the current models via class EMD decomposition; (2) the CEEMDAN-MSBTCN-BiLSTM-DMAttention model structure is innovated by combining improved class EMD decomposition methods; and (3) the three improved decomposition methods proposed in this paper can effectively solve the problem of “information leakage” and optimize the prediction model at the same time. This study provides an effective experimental method for water quality prediction and can effectively address the problem of “overfitting” models via class EMD decompositions during model training and testing.

Underlying factors for gold price prediction based on ARIMA models

Abstract. The purpose of this study is to explore the application of ARIMA model and deep learning technology in time series diagram prediction and the comparison of their effects. First, this paper analyzes the time series data in detail and models them using the ARIMA model. The ARIMA model effectively captures the seasonal and trending characteristics of the data through autoregressive, moving average, and differential steps. At the same time, the method of introducing deep learning models based on recurrent neural networks (RNNs), especially long short-term memory networks (LSTMs), to deal with the nonlinear characteristics and long-term dependency of time series data. By comparing and analyzing the performance of the ARIMA model and the deep learning model in terms of prediction accuracy, computational efficiency and model generalization ability, finding that the deep learning model has higher prediction accuracy when processing complex time series data. In addition, this study also uses time series diagrams to visually display the prediction results, which further verifies the advantages of deep learning models in capturing dynamic changes in data. This study provides a new perspective and method for time series data analysis and prediction, and has important practical significance for finance, meteorology, energy and other fields.

Weather as a driver of the energy transition – present and emerging perspectives of energy meteorology

Energy meteorology is an applied research field of meteorology that focuses on the study and prediction of weather conditions and events that affect energy production and use. This field has become increasingly important as the energy industry has become more dependent on weather conditions, especially in the areas of renewable energy sources such as wind energy, solar energy, and hydropower. The following paper has been written by experts of the Committee on Energy Meteorology of the German Meteorological Society summarizing their more than 30 years of experience and lessons learnt. It gives an overview of activities in energy meteorology that are already essential for the transformation of energy systems to systems with high shares of renewable energies. Building on this, the experts have created a vision of future topics that describe the future research landscape of energy meteorology. The authors explain that work in energy meteorology in recent years has primarily been concerned with the physically based modeling of wind and solar power generation and the development of short-term forecasting systems. In future years, a significant expansion of work in the areas of energy system modeling, digitalization, and climate change is expected. This includes the detailed consideration of regionally specified spatiotemporal variability for system design, the integration of artificial intelligence skills, the development of weather-related consumption based on smart meters, and the mapping of the effects of climate change on the energy system in planning and operating processes.

PERBANDINGAN METODE ARIMA DAN MODEL FUNGSI TRANSFER UNTUK MERAMALKAN CURAH HUJAN DI JAWA TENGAH PERIODE TAHUN 2023-2024

Analysis data of time series is basically used for analysis data that makes the effect of observations on the previous period. The role of forecasting has explored various fields, including the field of meteorology which concerns weather forecasting. The ARIMA model is a forecasting model for analyzing single time series data and only look into the dependence of rainfall on past data without involving other variables. The transfer function has an output tier (yt) which is expected to be affected by the input tier (xt) or other inputs which called the noise tier (nt). The purpose of this research is to find out which method is most appropriate to use in forecasting rainfall in the region of Central Java. Forecasting results with the transfer function model will be compared with the rainfall ARIMA model in Central Java to find out which method is more preferable. The data used in the research are rainfall data and air temperature, in Central Java from January 2016 to December 2022. The results showed that the MAPE value with transfer function model is 38,21%, while the ARIMA model has an MAPE value of 43,23%. From the MAPE value we can conclude that transfer function method is the best forecasting model for rainfall forecasting rather than ARIMA, because the MAPE value is tinier.

Accelerated surface brightening in China: The decisive role of reduced anthropogenic aerosol emissions

Accelerated surface brightening in China: The decisive role of reduced anthropogenic aerosol emissions

A Study of the High-Performance Computing Parallelism in Solving Complexity of Meteorology Data and Calculations

This paper investigates the high-performance computing (HPC) implementation in the field of meteorology, the challenges, and the future benefits. It is associated with utilizing HPC parallelism to simultaneously execute multiple tasks or operations for meteorological research. Merely a few people are aware of HPC's role in generating weather forecasting, climate modeling, and data assimilation. Our investigation elaborates on, identifies, and analyzes the features and characteristics of parallel computing that are utilized in it. The paper also focuses on examining parallelization modeling, the algorithms involved, and optimization strategies employed in HPC-enabled meteorological simulations. By addressing significant aspects of HPC in meteorological research, it helps the scientific community identify emerging trends and future directions for leveraging HPC in meteorology. Further issues can be studied for integrating big data analytics and machine learning into HPC computing architectures.

The Visual Monitoring Camera (VMC) on Mars Express: A new science instrument made from an old webcam orbiting Mars

The Visual Monitoring Camera (VMC) on Mars Express: A new science instrument made from an old webcam orbiting Mars

Lev Gutman—A Pioneer in Theoretical Mesoscale Meteorology

Abstract On 5 March 2023, Professor Lev Gutman would have been 100 years old. This article describes Professor Gutman’s legacy in the field of dynamic mesoscale meteorology and numerical weather prediction. Gutman developed his career as a mathematician and meteorologist in the Soviet Union, where he built a school of specialists in mesoscale meteorology from the 1950s through the 1970s. He primarily worked on analytical methods to solve complex nonlinear problems, such as the structure of sea breezes, mountain–valley circulations, and thermal convection over heated terrain. Gutman pioneered the development of theories of cumulus clouds, tornadoes, and other atmospheric phenomena. In the 1960s, he carried out numerous research investigations on these topics with his doctoral students and collaborators at the High-Altitude Geophysical Institute in Nalchik in the northern Caucasus and later at the Siberian Scientific Center near Novosibirsk. Gutman compiled the results from these studies into a monograph titled “Introduction to the Nonlinear Theory of Mesoscale Meteorological Processes,” which was published in Russian in 1969 and later translated into English, Chinese, and Japanese. This monograph became a major textbook for specialists in mesoscale meteorology, remaining relevant to this day. After Professor Gutman immigrated to Israel in 1978, his collaborations expanded to include Israeli and western scientists from Europe and the United States. Gutman did not receive the recognition he deserved due to the political realities of the time. His book and his seminal analytical solutions should still be useful for early career scientists in mesoscale meteorology and atmospheric dynamics.

Improving visibility forecasting during haze-fog processes in shanghai and eastern China: The significance of aerosol and hydrometeor extinction

Improving visibility forecasting during haze-fog processes in shanghai and eastern China: The significance of aerosol and hydrometeor extinction

Evaluation of Information Security at the Radin Inten II Lampung Meteorological Station Using the KAMI Index

Information security is a way to protect information assets from various potential threats. BMKG is a Non-Departmental Government Institution (LPND) in Indonesia whose main duties involve carrying out government duties in the fields of meteorology, climatology and geophysics. In connection with delivering information services appropriately and precisely to stakeholders, the Radin Inten II Lampung Meteorological Station needs to carry out an independent assessment in terms of security to evaluate the information system in each work unit, with the aim of understanding the level of readiness and maturity of information security. This research aims to measure the level of information security maturity at the Radin Inten II Lampung Meteorological Station. The analysis method used in this research is using the KAMI Index version 5.0 based on the ISO/IEC 27001:2022 standard. The research results indicate that the implementation of the ISO 27001:2022 standard in the information system of the Radin Inten II Lampung Meteorological Station is considered good. The total score obtained reached 591 based on analysis and questionnaires using the KAMI Index. With this score, the Radin Inten II Lampung Meteorological Station information system is categorized at level III, which indicates that some improvements are still needed.

Evaluation and Comparison of Five Long-Term Precipitation Datasets in the Hang-Jia-Hu Plain of Eastern China

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

Using multi-layer perceptron to predict sea surface temperature

Abstract This study employs a Multi-layer Perceptron (MLP) model to predict Sea Surface Temperature (SST) using Sea Surface Salinity (SSS) data collected by NASA over a period of 55 years. SSS is closely related to SST, as both are influenced by similar factors such as solar radiation, evaporation, and precipitation. The accuracy of these predictions is then evaluated through an error analysis, conducted on both annual and monthly scales. The results of this study indicate that the MLP model can effectively utilize SSS data to predict SST. However, it was observed that the model’s predictive performance varies across different seasons and regions. This study demonstrates that the MLP model is an effective tool for predicting SST based on SSS data. By employing a MLP to predict SST based on SSS data, this study contributes to the field of meteorology in several ways. However, further research and optimization of the model are needed to improve its predictive accuracy. Additionally, more data needs to be collected and the model’s performance needs to be validated across a wider temporal and spatial scale.

Embedded monitoring system and teaching of artificial intelligence online drug component recognition.

Drug testing has many test elements. It aims to prevent unqualified drugs from entering the market and ensure drug safety. The existing artificial intelligence (AI) online monitoring system identifies active ingredients in the process of use. Owing to their openness, data are easy to be lost, failing to meet user needs and inducing a specific impact on the use of the monitoring system. With the continuous development of computer and measurement technologies, various biochemical data are increasing at an unprecedented speed, and numerous databases are emerging. Extracting patterns from considerable known data and experimental facts is an essential task for a wide range of biological and chemical workers. Pattern recognition is one of the essential technologies for data mining. It is widely used in industry, agriculture, national defense, biomedicine, meteorology, astronomy, and other fields. To improve the effect of the online drug ingredient recognition system, this study used AI to design an online drug ingredient recognition-embedded monitoring system and applied AI to the teaching field to improve teaching efficiency. First, this study constructed the framework of the AI online drug ingredient recognition-embedded monitoring system and introduced the process of online drug ingredient recognition. Then, it introduced the pattern recognition method, constructed the pattern recognition system, and presented the pattern recognition algorithm and the algorithm evaluation index. Afterward, it used pattern recognition to conduct a qualitative analysis of the infrared spectrum of drug components and introduced the overall process of the qualitative analysis. In addition, this study employed AI to implement changes to the embedded system instruction in colleges and universities, summarizing the current issues. The impact of drug component recognition and the educational impact of embedded systems were investigated in the experimental portion. The experimental findings demonstrated the excellent accuracy, sensitivity, specificity, and Matthew correlation coefficient of the online drug component recognition-integrated monitoring system in this work. Compared with that of other systems, its average drug component recognition accuracy was above 0.85. Students in five majors reported high levels of satisfaction with the embedded system teaching, which is better for delivering college instruction.

Precipitation prediction based on variational mode decomposition combined with the crested porcupine optimization algorithm for long short-term memory model

Accurate precipitation prediction is very important for meteorological disaster prevention, water resources management, and agricultural decision making. To improve the accuracy of precipitation prediction, a hybrid model based on variational mode decomposition (VMD), crested porcupine optimization algorithm (CPO), and long short-term memory model (LSTM) is proposed in this paper. The model first uses VMD to decompose the precipitation time series into intrinsic mode functions of different frequencies to capture the multi-scale characteristics of precipitation data. Then, the CPO algorithm is used to optimize LSTM adaptive parameters to improve the global search ability and robustness of the model. Finally, the decomposed precipitation component is input into the LSTM network to learn the spatiotemporal dependence relationship and improve the ability of long-term prediction. The experimental results show that compared with the traditional LSTM model, CPO-LSTM model, and VMD-LSTM model, the hybrid model achieves better performance in many evaluation indices and effectively improves the accuracy of precipitation prediction. The application of the model can provide an effective tool for the fields of meteorology and water resources management, as well as provide new ideas for related research.