Comparative Analysis of SOAP and REST for Optimising Web Services in Mobile Weather Forecast Applications: Evaluating Performance and Scalability

Weather forecasting plays a pivotal role in numerous sectors, from agriculture to disaster management, yet traditional methods often face limitations in accuracy and scalability. In response, machine learning (ML) techniques have emerged as potent tools for revolutionizing weather prediction. This review provides a comprehensive overview of ML applications in weather forecasting, delving into algorithms like regression, classification, and neural networks, which exploit vast datasets to capture intricate spatiotemporal patterns in weather phenomena. Ensemble learning strategies further enhance forecast accuracy by amalgamating multiple models. However, challenges persist, including data quality issues and computational demands. Overcoming these obstacles requires innovative approaches, such as integrating ML with physical models and developing explainable AI techniques. Future research directions also include exploring new data sources like remote sensing and social media data. By harnessing the synergy between ML and weather forecasting, we can advance predictive capabilities, aiding decision-making and bolstering resilience against extreme weather events.

The precision of weather forecasting and climate analysis is crucial for various sectors, including agriculture, disaster management, transportation, and public health. While traditional meteorological methods, which primarily depend on physical models and historical data, have made considerable progress, the increasing complexity of atmospheric phenomena and the massive volume of data generated from diverse sources necessitate more advanced approaches. In this context, machine learning (ML) has emerged as a transformative technology, providing innovative techniques to enhance predictive accuracy and gain deeper insights into climatic patterns. This research delves into the numerous applications of machine learning in weather prediction and climate analysis, offering a thorough review of current methodologies and notable achievements. The proposed ML model achieves an impressive accuracy of 97%, with a mean absolute error (MAE) of 0.407 and a root mean square error (RMSE) of 0.203. These findings underscore the model's effectiveness in improving both short-term weather forecasts and long-term climate predictions, thereby enhancing preparedness and mitigation strategies for climate-related risks. Moreover, the integration of ML with advanced technologies, such as cloud computing and big data analytics, further enhances its potential, establishing a robust framework for comprehensive weather and climate analysis. This paper also addresses the challenges and future directions in this rapidly evolving field, highlighting the essential role of advanced analytics in managing the complexities of weather and climate phenomena.

In our "Weather Forecast Application" project, The increasing unpredictability of weather patterns necessitates reliable, real-time weather forecasting tools. This project presents a mobile weather forecast application designed to provide users with accurate and timely weather information tailored to their specific locations. Utilizing advanced APIs for weather data retrieval, the application features an intuitive user interface that presents current conditions, hourly forecasts, and extended outlooks, all presented in a visually appealing and easily navigable format. To improve forecast accuracy, the application employs machine learning algorithms that analyze historical weather data and trends, enabling it to provide more reliable predictions. The app also features a community-driven feedback system, allowing users to report local weather conditions, which helps refine and enhance the data provided. Key functionalities of the application include customizable notifications for severe weather alerts, enabling users to stay informed about critical weather events in real time.. Users can also personalize their experience by saving favorite locations, allowing for quick access to weather updates in multiple areas

Weather Forecasting Applications in Agriculture

Wireless Sensor Networks (WSNs) are becoming ubiquitous in everyday life due to their applications in weather forecasting, surveillance, implantable sensors for health monitoring and other plethora of applications. WSN is equipped with hundreds and thousands of small sensor nodes. As the size of a sensor node decreases, critical issues such as limited energy, computation time and limited memory become even more highlighted. In such a case, network lifetime mainly depends on efficient use of available resources. Organizing nearby nodes into clusters make it convenient to efficiently manage each cluster as well as the overall network. In this paper, we extend our previous work of grid-based hybrid network deployment approach, in which merge and split technique has been proposed to construct network topology. Constructing topology through our proposed technique, in this paper we have used analytical network process (ANP) model for cluster head selection in WSN. Five distinct parameters: distance from nodes (DistNode), residual energy level (REL), distance from centroid (DistCent), number of times the node has been selected as cluster head (TCH) and merged node (MN) are considered for CH selection. The problem of CH selection based on these parameters is tackled as a multi criteria decision system, for which ANP method is used for optimum cluster head selection. Main contribution of this work is to check the applicability of ANP model for cluster head selection in WSN. In addition, sensitivity analysis is carried out to check the stability of alternatives (available candidate nodes) and their ranking for different scenarios. The simulation results show that the proposed method outperforms existing energy efficient clustering protocols in terms of optimum CH selection and minimizing CH reselection process that results in extending overall network lifetime. This paper analyzes that ANP method used for CH selection with better understanding of the dependencies of different components involved in the evaluation process.

The ongoing use of satellites is to get information about the earth's features by sensing the radiation reflected/emitted by various objects on the earth’s surface. Since the launch of the first satellite in the year 1957 for defence purposes only, satellites have now become an integral part of human life and play an important role in different sectors including communication, navigation, monitoring weather conditions, etc. These satellites are placed in the earth’s orbit continuously imaging the earth’s surface and generating data of huge sizes. The analysis of this large size data with the help of current generation classical computers is a time-consuming process resulting in a delay in observing the real-time phenomena happening on the earth's surface. The high processing capabilities of quantum computing have emerged as a promising solution for quick analysis of data received from the satellite which may help in monitoring hazards in real time. The quantum computer further will help improve the weather forecasting models and improve the capabilities of artificial neural networks to get useful information from large size data. Here, we will review the usage of quantum computation in existing satellite data processing, its applications in weather forecasting, and real-time monitoring of natural hazard situations through satellites.

Knowledge of the planetary boundary layer height (PBLH) is crucial for various applications in atmospheric and environmental sciences. Lidar measurements are frequently used to monitor the evolution of the PBLH, providing more frequent observations than traditional radiosonde‐based methods. However, lidar‐derived PBLH estimates have substantial uncertainties, contingent upon the retrieval algorithm used. In addressing this, we applied the Different Thermo‐Dynamic Stabilities (DTDS) algorithm to establish a PBLH data set at five separate Department of Energy's Atmospheric Radiation Measurement sites across the globe. Both the PBLH methodology and the products are subject to rigorous assessments in terms of their uncertainties and constraints, juxtaposing them with other products. The DTDS‐derived product consistently aligns with radiosonde PBLH estimates, with correlation coefficients exceeding 0.77 across all sites. This study delves into a detailed examination of the strengths and limitations of PBLH data sets with respect to both radiosonde‐derived and other lidar‐based estimates of the PBLH by exploring their respective errors and uncertainties. It is found that varying techniques and definitions can lead to diverse PBLH retrievals due to the inherent intricacy and variability of the boundary layer. Our DTDS‐derived PBLH data set outperforms existing products derived from ceilometer data, offering a more precise representation of the PBLH. This extensive data set paves the way for advanced studies and an improved understanding of boundary‐layer dynamics, with valuable applications in weather forecasting, climate modeling, and environmental studies.

CHAMP is a German BMBF-funded geophysical minisatellite mission of GFZ (GeoFors-chungsZentrum, Potsdam, Germany) in cooperation with DLR. The satellite was built by the German space industry with the intent to foster high-tech capabilities especially in the East-German space industry. The S/C prime contractor is DJO (Jena Optronic GmbH) in Jena, a daughter of DASA (now Astrium). The overall science objectives are in the following fields of investigation: Global long- to medium-wavelength recovery of the static and time variable earth gravity field from orbit perturbation analyses for use in geophysics (solid Earth), geodesy (reference surface), and oceanography (ocean currents and climate), supported by a feasibility test of GPS altimetry for ocean and ice surface monitoring Global Earth magnetic field recovery (solid Earth and solar-terrestrial physics) Atmosphere/ionosphere sounding by GPS radio occultation with applications in weather forecasting, navigation, space weather, and global climate change.

Cloud classification is a critical task in meteorology, with applications in weather forecasting, climate modelling, and environmental monitoring. Traditionally, cloud observations are made visually by experienced observers, which can introduce human errors and inconsistencies. This study aims to develop accurate deep learning models for automated cloud classification from ground‐based images. A dataset of over 200,000 cloud photographs was collected from professional observers at meteorological stations of the Institute of Meteorology and Water Management–National Research Institute. The images were preprocessed and annotated with cloud type labels. Convolutional neural networks and transformer models were employed for feature extraction, combined with multilayer perceptrons for classification. Different architectures were explored, including ResNet, EfficientNet, Vision Transformers, and their variants. Hyperparameter tuning and data augmentation techniques like RandAugment were applied to improve generalisation. The best‐performing model achieved 97.4% accuracy for classifying four common cloud genera: cirrus, cumulus, stratus, and clear sky. Additional experiments classified up to 11 genera, with accuracy decreasing as complexity increased, due to data limitations. Error analysis revealed confusions between visually similar classes. While promising, limitations exist from using partial cloud views versus whole‐sky imagery. We start with four classes and end with 11 classes, progressively showing subsequent errors in the cloud genera classification process, something not presented in other publications. Future work involves collecting a more balanced dataset with standardised protocols. Integration of all‐sky cameras could help address current restrictions. This research demonstrates the viability of deep learning for automated cloud observation, with opportunities to advance meteorological applications through continued methodological refinement.

The USA is the world’s most powerful country in terms of aerospace technology. Since 1960 when it launched its first Earth observation satellite, TIROS, which was intended for television infrared observation, the country has made a great progress in Earth observation technology. The National Aeronautics and Space Administration (NASA) planned to use the satellite to determine whether Earth observation technology could be used to study Earth. Because meteorological satellites have prospects for widespread applications in weather forecasting and disaster warning, the USA initially focused on the development of meteorological satellites.

The smoothing problem is here considered for Gauss-Markov random fields defined on a kind of spherical lattice. Various observation models are included in the setting of this paper, such as the case of Gaussian noisy (even correlated) observations available only on a subset of sites, as well as a variable number of process components being measured. An efficient optimal smoothing algorithm is derived, based on the sparse representation of the potential matrix of the random field and on gaussian elimination. In view of applications in weather forecasting, an example using real data is presented, showing the capability of the proposed setting in a task of reconstruction of temperature maps.

The world is relying more and more on renewable energy sources to cater the global energy demand. Consequently, the renewable energy systems are becoming more and more intricate and therefore require state-of-the-art machine learning methodologies to operate and manage. This chapter presents a detailed overview of some key applications of artificial intelligence (AI) and machine learning (ML) for renewable energy with a particular focus on the challenges, available resources, and potential future research opportunities. In detail, the chapter discusses AI and ML applications in weather forecasting, power production, energy consumption forecasting, smart grids, and prognostic maintenance of renewable energy systems. An overview of the most commonly used AI and ML algorithms in the domain along with a detailed description of some of the publicly available datasets for training and evaluation of these algorithms to carry out different tasks in the renewable energy sector is also provided.

Meteorological sounding data provided by atmospheric imaging sounders have applications in weather forecasting, atmospheric chemistry, and climate monitoring. Realistic scenes for these instruments vary in both spatial and spectral content and such variations can impact the radiometric performance of these instruments. As sounders are developed to provide climate records with demanding long-term radiometric accuracy requirements, it becomes increasingly important to understand the effect of scene variations on the performance of these instruments. We have examined the noise performance and radiometric accuracy of two geostationary sounder architectures in cloudy scenes: a Fourier transform spectrometer (FTS) and a dispersive spectrometer. Factors such as stray light, ghosting, scattering, and line-ofsight jitter in the presence of scene inhomogeneities are considered. For each sounder architecture, quantitative estimates of the radiometric errors associated with sounding in cloudy scenes are made. We find that in a dispersive system the dominant error in a cloudy scene originates from ghosting within the instrument, while in an FTS the dominant error originates from scene modulation created by line-of-sight jitter in a partially cloudy scene coupling into signal modulation over the scale of the changing optical path length of the interferometer. In this paper we describe the assumptions made and the modeling performed. We also describe how each factor influences the radiometric performance for that architecture.

The Flexible Combined Imager (FCI), developed as the next-generation imager for the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Meteosat Third Generation (MTG) satellite series, represents a significant advancement over its predecessor, SEVIRI, on the Meteosat Second Generation (MSG) satellites. FCI offers more spectral bands, higher spatial resolution, and faster imaging capabilities, supporting a wide range of applications in weather forecasting, climate monitoring, and environmental analysis. On 13 January 2024, the FCI onboard MTG-I1 (renamed Meteosat-12 in December 2024) experienced a critical anomaly involving the failure of its onboard Calibration and Obturation Mechanism (COM). As a result, the use of the COM was discontinued to preserve operational safety, leaving the instrument dependent on alternative calibration methods. This loss of onboard calibration presents immediate challenges, particularly for the infrared channels, including image artifacts (e.g., striping), reduced radiometric accuracy, and diminished stability. To address these issues, EUMETSAT implemented an external calibration approach leveraging algorithms from the Global Space-based Inter-Calibration System (GSICS). The inter-calibration algorithm transfers stable and accurate calibration from the Infrared Atmospheric Sounding Interferometer (IASI) hyperspectral instrument aboard Metop-B and Metop-C satellites to FCI’s infrared channels daily, ensuring continued data quality. Comparisons with Cross-track Infrared Sounder (CrIS) data from NOAA-20 and NOAA-21 satellites using a similar algorithm is then used to validate the radiometric performance of the calibration. This confirms that the external calibration method effectively compensates for the absence of onboard blackbody calibration for the infrared channels. For the visible and near-infrared channels, slower degradation rates and pre-anomaly calibration ensure continued accuracy, with vicarious calibration expected to become the primary source. This adaptive calibration strategy introduces a novel paradigm for in-flight calibration of geostationary instruments and offers valuable insights for satellite missions lacking onboard calibration devices. This paper details the COM anomaly, the external calibration process, and the broader implications for future geostationary satellite missions.

Freezing rain (FR) event is a highly catastrophic event, significantly impact human habitats. However, there is still a substantial lack of gridded FR data. Here, we present a comprehensive gridded FR dataset across China from January 1, 2000, to December 31, 2019, utilizing station data from the China Meteorological Administration combined with ERA5-land and pressure level data. Employing Deep Kernel Learning (DKL), we effectively classified and predicted FR occurrences, demonstrating significant advancements in capturing complex atmospheric conditions conducive to FR. The DKL model, validated against ERA5 data for the winter of 2024 and the Ramer Scheme in 2008, 2011, and 2018, showcases superior classified power over traditional methods, achieving remarkable accuracy of 0.991, Area Under the Curve (AUC) of 0.999, recall of 0.973, and precision of 0.989. The implications of this research are profound, offering a robust database for academic and practical applications in weather forecasting, climate modelling, and disaster management, thereby enhancing our understanding and mitigation strategies for FR impacts.