Weather Map Research Articles

Supervised Learning System for Pest Disease Identification and Recommendations using Wireless Sensor Network in Agriculture Domain

Designing wireless sensor networks (WSNs) that will sustain for longer time have been a challenging issue. For achieving this, efficient utilization of available energy should be anticipated from energy-constrained sensors running autonomously for long periods. In the field of agricultural research, farming related activities, assistance to farmers using advanced IoT and sensor network technologies is a need of time. Initially this paper briefed about smartphone based application systems that forewarns the farmer about pests/diseases that can occur on the crop. The designed system assists farmers to foresee the crop disease using machine learning algorithm. The disease can be predicted based on the environmental parameters as soil moisture, temperature and humidity obtained from sensor node and other parameters like rain fall, evaporation rate and sun shine hour obtained from open weather map application programming interfaces. A prototype is developed using ultra low powered micro-controller with interconnected sensor’s module. While comparing with the Arduino micro-controller based system, it was observed that the developed prototype system utilized less energy with effectively forewarned the farmers about crop diseases using Internet of Things (IoT). Thus it would assist the farmers proactively to reduce the losses due to crop diseases.

Анализ синоптического процесса с сильным ветром в Центрально-Черноземном районе 14–15 января 2022 года

The case of occurrence of a strong wind, which is rare for the winter period, in the gradation of a dangerous phenomenon (≥25 m/s) in the Central Chernozem region (Stary Oskol, Belgorod region) is considered. For comparison, the examples of similar cases of strong wind in the same area in 2008 and 2011 are given. Conditions for the occurrence of the considered dangerous phenomenon are analyzed on the basis of upper-air and satellite observations, as well as using the forecast fields of WAFC London significant weather maps. It is suggested that wind speed has reached the criterion of a dangerous phenomenon when such factors as the passage of active atmospheric fronts and the low-level jet match. Keywords: strong wind, winter period, atmospheric fronts, low-level jet

An overview of the tropical cyclone database at the Central Weather Bureau of Taiwan

Taiwan’s Central Weather Bureau (CWB) has established a new version of tropical cyclone (TC) database to provide users with comprehensive TC data services. As of June 2022, the database includes 1673 TCs originating in the western North Pacific and South China Sea regions since the CWB began TC forecasting operations in 1958. It collects not only the track dataset, satellite imagery, and synoptic weather charts, but also more detailed data and products of TCs that have impacted Taiwan, such as radar reflectivity imagery, high-density surface observations, dropwindsonde data from aircraft observations, and mesoscale analysis maps of rainfall, wind, pressure, and temperature. Such detailed data realistically preserves the TC’s journey across Taiwan and is a feature of the CWB TC database. In addition, the TC database website (https://rdc28.cwb.gov.tw/TDB/) provides tools that allow users to search for TCs of interest based on track, rainfall, and wind. Digital best track data since 1958 is available (https://rdc28.cwb.gov.tw/TDB/manager/obs_data_download/) for users to download and conduct TC research. Also included are technical reports summarizing extensive historical observational research focusing on the mesoscale variations during a TC’s passage over Taiwan. The TC database has been widely used in academic research, disaster prevention, crop revenue insurance, offshore wind power, and other fields. It will continue to develop useful products and integrate more diverse observations to meet the needs of different users.

A Smart Vision Based System for Assisting Visually Impaired People

Eyes are most important sensory organ of a human body. A person is visually impaired if he cannot see objects as clearly as usual. So its difficult for a visually impaired one to live as normal people can do. They always required some assistive devices or assistants to guide them. To address the concerns, the suggested system will perform multiple tasks as quickly as feasible and accurately without requiring any specific talents.The proposed system bring together the deep learning and cloud APIs for smooth functioning of multiple tasks like face recognition, currency recognition, object labeling, text recognition, online news paper reading, current location ,weather and and date and time accessing.The user interact with the system by providing specific voice command like “who is infront of me” and system trigger corresponding module and return voice output. Face recognition is carried based ondlib’s face recognition. Image labeling and text recognition by Google cloud vision API, currency recognition for Indian currency notes is proposed to do on deep transfer learning model Resnet 101 ,online news paper reading is with the help of Google News API, Weather access is by Open Weather MAp API , location can be accessed with the help of IP address and datetime module of python provide current date and time.

Data Collection Framework for Context-Aware Virtual Reality Application Development in Unity: Case of Avatar Embodiment.

Virtual Reality (VR) has been adopted as a leading technology for the metaverse, yet most previous VR systems provide one-size-fits-all experiences to users. Context-awareness in VR enables personalized experiences in the metaverse, such as improved embodiment and deeper integration of the real world and virtual worlds. Personalization requires context data from diverse sources. We proposed a reusable and extensible context data collection framework, ManySense VR, which unifies data collection from diverse sources for VR applications. ManySense VR was implemented in Unity based on extensible context data managers collecting data from data sources such as an eye tracker, electroencephalogram, pulse, respiration, galvanic skin response, facial tracker, and Open Weather Map. We used ManySense VR to build a context-aware embodiment VR scene where the user’s avatar is synchronized with their bodily actions. The performance evaluation of ManySense VR showed good performance in processor usage, frame rate, and memory footprint. Additionally, we conducted a qualitative formative evaluation by interviewing five developers (two males and three females; mean age: 22) after they used and extended ManySense VR. The participants expressed advantages (e.g., ease-of-use, learnability, familiarity, quickness, and extensibility), disadvantages (e.g., inconvenient/error-prone data query method and lack of diversity in callback methods), future application ideas, and improvement suggestions that indicate potential and can guide future development. In conclusion, ManySense VR is an efficient tool for researchers and developers to easily integrate context data into their Unity-based VR applications for the metaverse.

Estimating Solar and Wind Power Production Using Computer Vision Deep Learning Techniques on Weather Maps

Accurate renewable energy production forecasting has become a priority as the share of intermittent energy sources on the grid increases. Recent work has shown that convolutional deep learning models can successfully be applied to forecast weather maps. Building on this capability, a ResNet‐inspired model that estimates solar and wind power production based on weather maps is proposed. By capturing both spatial and temporal correlations using convolutional neural networks with stacked input frames, the model is designed to capture the complex dynamics governing these energy sources. A dataset that focuses on the state of California is constructed and made available as a secondary contribution of the work. It is demonstrated that the novel model outperforms traditional deep learning techniques: it predicts an accurate power production profile that is smooth and includes high‐frequency details.

Measuring Displacement Errors with Complex Wavelets

Abstract When highly resolved precipitation forecasts are verified against observations, displacement errors tend to overshadow all other aspects of forecast quality. The appropriate treatment and explicit measurement of such errors remains a challenging task. This study explores a new verification technique that uses the phase of complex wavelet coefficients to quantify spatially varying displacements. Idealized and realistic test cases from the MesoVICT project demonstrate that our approach yields helpful results in a variety of situations where popular alternatives may struggle. Potential benefits of very high spatial resolutions can be identified even when the observational dataset is coarsely resolved itself. The new score can furthermore be applied not only to precipitation but also variables such as wind speed and potential temperature, thereby overcoming a limitation of many established location scores. Significance Statement One important requirement for a useful weather forecast is its ability to predict the placement of weather events such as cold fronts, low pressure systems, or groups of thunderstorms. Errors in the predicted location are not easy to quantify: some established quality measures combine location and other error sources in one score, others are only applicable if the data contain well-defined and easily identifiable objects. Here we introduce an alternative location score that avoids such assumptions and is thus widely applicable. As an additional benefit, we can separate displacement errors into different spatial scales and localize them on a weather map.

Study of suitability location of eel (Anguilla sp) culture in Sukabumi region, West Java province

The research objective is to evaluate the suitability status of the Sukabumi region to produce eel. The output is a spatial distribution map of suitable locations ecologically to develop Anguilla sp grow out. Spatial overlay and weighting methods were used to process the data of contour map, soil map, weather map, diurnal temperature map, season temperature map, and watershed map. Almost all of the Sukabumi region is 80% suitable relatively for developing Anguilla sp culture or 3.620 Km2. Very suitable location 873 Km2, suitable 1.926 Km2, marginal 821 Km2, and not suitable 541 Km2.

Microbially induced sedimentary structures (MISS) generated by episodic storm surges in a temperate coast

This study recognizes the creation of primary sedimentary structures in a modern environment that reports valuable insights into the role of microbiota in sculpting sedimentary structures, and it may explain many previously confusing sedimentary structures in the rock record. The research considered the frequent flooding events on a coastal sedimentary flat colonized by microbial mats and the effect produced by severe storm surge events. Large-scale microbially induced sedimentary structures (MISS) were created; thus, the interaction between meteorologic, oceanographic, and geomorphologic conditions necessary to form them was analysed. The study was developed in Paso Seco, Argentina (40°38′31” S - 62°12′54” W), a sedimentary flat behind a sand spit, coated by thick microbial mats recurrently affected by storm events. Water level measurements were recorded continuously for five years (measurement interval 10 min). The seawater inundations were related to synoptic weather maps and oceanographic parameters (wave height, period, and tides) to analyse the cause of storm surges. Two situations of severe storms were studied in 2018 and 2019. The inundation of the study area occurs when a cold front in association with a low-pressure centre in the Atlantic Ocean, moving in an SW-NE direction, produces strong SW winds higher than 45 km h−1 and wave height higher than 2 m. Strong winds and storm duration (two or three days) in co-occurrence with spring tides create an extreme erosional event that affects the sedimentary microbial flat. In sum, the interaction between storm surge, high tide, wave setups and wave heights in Paso Seco produces coastal hinterland flooding that creates meter-sized MISS such as roll-ups, erosional pockets, and remnants mats structures. The impact of storm surges on the coast is common and catastrophic in modern times but is challenging to identify in the sedimentary record. Therefore, the microbial structures presented here could help interpret high-energy events occurrence, favourable to assessing past environmental conditions.

Auto Climate Monitoring System

In this work we present a detailed conception of weather monitoring system which displays weather, cloud and air purity also, we represent it by using graphs and bar graph. In our web application a user can get the weather information upto 7 days. Here we used an Application programming interface (API). An application programming interface, or API, enables companies to open up their applications’ data and functionality to external third-party developers, business partners, and internal departments within their companies. In this framework the climate parameters estimations taken are temperature, moistness, wind course, and wind speed. In this proposed work we will monitor the live weather’s parameter of entire world. With the help of this proposed system, we measure the weather condition of whichever city entered in search bar. After getting results from API(Open weather map), it is observed that our proposed model achieves better results in comparison with the standard weather parameters

ON THE POSSIBILITY OF USING MAPS OF METEOROLOGICAL EVENTS FROM RESEARCH CENTRE «PLANET» FOR STUDYING AND PREDICTING THUNDERSTORMS ON THE URAL TERRITORY

The diagnosis and prediction of convective events are a difficult and challenging process. One of the most promising methods used for such tasks are hydrodynamic modelling and remote sensing. The aim of this research is to make a comparative analysis of meteorological events maps and data from observational network to find the possibility of using the maps from research center «Planet» for studying and predicting thunderstorms on the territory of the Ural. The spatiotemporal features of thunderstorms were identified. The repeatability and success of detection of this event were also determined, depending on the barium field, synoptic situation and specific characteristics of thunderstorms. During the study it was found that about 20 % of thunderstorms observed by weather stations were not predicted on weather maps; the high probability prediction was better than the medium and low probability forecasts of thunderstorms; spatial and temporal shifts were common in the prediction of thunderstorms. For a more detailed and objective analysis, it is recommended to work with a larger sample and use data from other sources like meteorological radiolocators, stormscopes, and others.

Climatology of Lake-Effect Snow Days Along the Southern Shore of Lake Michigan: What Is the Sensitivity to Environmental Factors and Snowband Morphology?

The Laurentian Great Lakes have substantial influences on regional climatology, particularly with impactful lake-effect snow events. This study examines the snowfall, cloud-inferred snow band morphology, and environment of lake-effect snow days along the southern shore of Lake Michigan for the 1997–2017 period. Suitable days for study were identified based on the presence of lake-effect clouds assessed in a previous study and extended through 2017, combined with an independent classification of likely lake-effect snow days based on independent snowfall data and weather map assessments. The primary goals are to identify lake-effect snow days and evaluate the snowfall distribution and modes of variability, the sensitivity to thermodynamic and flow characteristics within the upstream sounding at Green Bay, WI, and the influences of snowband morphology. Over 300 lake-effect days are identified during the study period, with peak mean snowfall within the lake belt extending from southwest Michigan to northern Indiana. Although multiple lake-effect morphological types are often observed on the same day, the most common snow band morphology is wind parallel bands. Relative to days with wind parallel bands, the shoreline band morphology is more common with a reduced lower-tropospheric zonal wind component within the upstream sounding at Green Bay, WI, as well as higher sea-level pressure and 500-hPa geopotential height anomalies to the north of the Great Lakes. Snowfall is sensitive to band morphology, with higher snowfall for shoreline band structures than for wind parallel bands, especially due south of Lake Michigan. Snowfall is also sensitive to thermodynamic and flow properties, with a greater sensitivity to temperature in southwest Michigan and to flow properties in northwest Indiana.

Analysis of Solar Chimney Power Plants Based on the Bam's Climate

One of the ways to generate power from the solar energy is to use the solar chimney technology. In the present study, by examining the weather map of Iran and using GIS software for selecting the appropriate area for the power plant site, the four physical models proposed studied for the solar chimney. This study is a numerical simulation (by MATLAB) regarding four physical models of solar chimneys in the city of Bam site, in Kerman province. The difference between the four mentioned models of the solar chimney is in storage system and the use of air diffuser. The behaviors of all four models were examined according to the valid meteorological information on a specific day from the target site. According to the results of the simulation, which includes airflow speed in several internal parts of the chimney and the production capacity of each of the mentioned models, the best and most proper model of solar chimney can be selected for the studies site and a proper estimation of the final production can be obtained.

Teaching a Weather Forecasting Class in the 2020s

Abstract We report on redesigning the undergraduate course in synoptic meteorology and weather forecasting at Wageningen University (the Netherlands) to meet the current-day requirements for operational forecasters. Weather strongly affects human activities through its impact on transportation, energy demand planning, and personal safety, especially in the case of weather extremes. Numerical weather prediction (NWP) models have developed rapidly in recent decades, with reasonably high scores, even on the regional scale. The amount of available NWP model output has sharply increased. Hence, the role and value of the operational weather forecaster has evolved into the role of information selector, data quality manager, storyteller, and product developer for specific customers. To support this evolution, we need new academic training methods and tools at the bachelor’s level. Here, we present a renewed education strategy for our weather forecasting class, called Atmospheric Practical, including redefined learning outcomes, student activities, and assessments. In addition to teaching the interpretation of weather maps, we underline the need for twenty-first-century skills like dealing with open data, data handling, and data analysis. These skills are taught using Jupyter Python Notebooks as the leading analysis tool. Moreover, we introduce assignments about communication skills and forecast product development as we aim to benefit from the internationalization of the classroom. Finally, we share the teaching material presented in this paper for the benefit of the community.

Разработка веб-сервиса для владельцев малого речного транспорта Санкт-Петербурга

The geographical location and tourist features of St. Petersburg require a specialized web service for owners and tenants of river transport, aggregating all the information necessary for them. Currently, such information is available in various news sources, web portals and groups in social networks. The work presents a prototype of a service for small river transport users. Information needs and functional and requirements for it were identified during in-depth interviews with stakeholders of the project. The web service database is implemented in the PostgreSQL DBMS with the PostGIS extension. The work presents the structure of the service, which includes 10 pages, describes its main sections, such as current regulatory documents, news, interactive maps, reference information, as well as technical information about ships and restrictions on their movement due to technical characteristics. During its development, an analysis of existing solutions related to each section was carried out. During the "Maps" section development, an analysis of existing web tools for displaying interactive maps and the selection of the best solutions was carried out. Using the Google Maps designer, interactive maps were created for the web service being developed. The first of them is a map of the city's water bodies, containing such layers as: floating gas stations, lighthouses, customs, checkpoint and Ministry of Emergencies, private yacht clubs and marinas, berths and quayes, bridges, etc. The interactive weather map allows the user to get acquainted with the weather conditions, as well as their 5-day forecast. One of the priority functional requirements of the web service, called stakeholders, was the determination of water barriers and bridges that were inappropriate in the size of the vessel. The "Your Ship" section of the developed prototype web service offers a solution to meet this requirement. The developed web service is one of the favorable incentives for the development of the water tourism sphere and the use of river transport in St. Petersburg.

A Numerical Simulation of the “1907” Kaiyuan Tornado Weather Process in Liaoning, Northeast China

From 17:00 to 18:00 local standard time (LST) on 3 July 2019, a rare strong tornado occurred in Kaiyuan, Liaoning Province, northeast China. NCEP/NCAR 0.25° × 0.25° reanalysis data and WRF4.0 numerical prediction models were used to carry out the numerical simulation. Double nesting was adopted, and the horizontal grid distance was 9 km by 3 km. Based on the observation data of China meteorological observation stations, surface and upper charts, Doppler radar data, Himawari(HMW)-8 satellite images and numerical simulation results, the mesoscale structure and mechanism of the tornado were studied. The results show that: (1) At the northwest edge of the subtropical high, and the northeast cold vortex located in Northeast China, when the transverse trough moves southward, cold air is supplied continuously. Under the joint influence of the surface northeast cyclone, these are the main synoptic features of the tornado; (2) The northeast cold vortex cloud system was located at the junction of Heilongjiang and Jilin Provinces, and a squall line cloud system is formed. The tornado occurred at the tail of the squall line, and the strongest echo reached 65 dBZ. A mesocyclone, a 20 km northwest–southeast convergence belt, V-shaped gap, echo overhang structure and tornado vortex feature (TVS) were detected by the Doppler radar; (3) Before the tornado occurred, dry and cold air intruded from the northwest of the cold vortex, and a water vapor convergence zone appeared south of the squall line. The water vapor saturation zone with 80% relative humidity in northeast China was concentrated at 700 hPa, and the 20% dry column dropped down to 500 hPa between 115 and 124° E from the west. On the 850 hPa physical fields, there was a −20 × 10−5 s−1 convergence zone, and a 16 × 10−5 s−1 divergence belt appeared south and north of the squall line. A negative vorticity belt and a positive vorticity belt appeared south and north of the squall line, respectively. Kaiyuan is located at the smallest vertical shear, which is the junction place of three large vertical shear belts; (4) After 10:00 LST, the westerly wind 20 (10) m·s−1 dropped to 400 (800) hPa between 126 and 127° E. The northerly gale at 300 hPa north of 45° N moved southward. The rising center of the low level at 17:00 LST at approximately 45° N moved southward, and a sinking center appeared above it; (5) Several pairs of positive and negative vorticity columns formed between the lower troposphere and the place where the tornado occurred. There was convective instability at the lower level. CAPE increased, 0–3 km vertical wind shear increased, and LCL decreased remarkably during the afternoon.

Wind and Sea Breeze Characteristics for the Offshore Wind Farms in the Central Coastal Area of Taiwan

Renewable energy is crucial for achieving net zero emissions. Taiwan has abundant wind resources and most major wind farms are offshore over the Taiwan Strait due to a lack of space on land. A thorough study that includes time series modeling of wind speed and sea breeze identification and evaluation for Taiwan’s offshore wind farms was conducted. The time series modeling identified two periodic (annual and diurnal) components and an autoregressive model for multiple-year wind speed time series. A new method for sea breeze type identification and magnitude evaluation is proposed. The method (named as EACH) utilizes a vector and an ellipse to represent the wind condition of a day. Verification of the type identification determined by the new method in two cases of different seasons has been conducted by using surface weather charts and wind data measured by lidar. It is a concise, effective, and programmable way to filter a number of dates for type identification and speed change precursor of sea breeze. We found that the typical daily wind power production of corkscrew sea breeze in Central Taiwan is more than 33 times that of pure sea breeze and more than 9 times that of backdoor sea breeze, which highlights the impact of sea breeze types on wind power.

Analysis of a Cold Wave Weather Process of China from December 23rd to 26th, 2021

Cold wave weather process is a large-scale strong cold air activity process, will bring drastic cooling and wind, as well as rain and snow weather. This research mainly studies the evolution and cause of a cold wave weather process of China from December 23rd to 26th, 2021. The weather map from the National Climate Center and the geopotential height and sea level pressure reanalysis data from December 23rd to 26th, 2021 provided by National Centers for Environmental Prediction of United States are analyzed by the comparative analysis method and the high altitude situation forecast equation. As a result, this research found that this cold wave was considered a nationwide process. Besides, this cold wave process was mainly caused by the invasion of cold air from Siberia. In addition, under the joint action of the warm high pressure ridge in the Ural Mountains, the polar vortex split and presented a multi-polar distribution. One of them was located near West Siberia, which was conducive to the accumulation of cold air here. With the rotation of the transverse trough, cold air moved southward, and cold wave broke out.

Weather pattern associated with climate change during Canadian Arctic wildfires: A case study in July 2019

Wildfire is one of the major novel disturbances to the Arctic forest ecosystem. Relevant Weather and climate regimes are the most important elements affecting fire activity. However, these factors could be considered under changing due to Arctic warming. This paper discusses understanding relevant atmospheric weather and climate conditions over western Canada in July 2019 with a significant burned area fraction over western Canada, Alaska, and Siberia. Here, we show that daytime area-averaged Methane mole fraction for the month of July during recent two decades obtained from Modern-Era Retrospective Analysis for Research and Applications (MERRA) database has significant growth trend over Siberia-Alaska and western Canada with the highest value for the west part of Canada in July 2019. Also, the Pearson correlation coefficient displays the strong negative relationship between north hemisphere sea ice extent and daytime areas averaged Methane mole fraction during the month of July for Siberia-Alaska and western Canada at −0.72 and −0.69 correspondingly. However, during the last two decades, the average Carbone monoxide (CO) column burden for these areas has been slightly decreased. This research demonstrates that the Arctic region has faced an albedo decrease with a maximum value of around 40% departure from mean values in some parts mostly over the northern Canadian Arctic and northern Alaska boundary for this time. Monthly weather and climate map analysis have revealed that Canadian July 2019 wildfires were linked to different factors including the rather lower humidity (with a maximum of 17.5% in North Territories) and higher surface air temperatures. Besides, the composite mean weather map analysis for the middle troposphere shows strong climate–fire links, with effects of the local blocking anti-cyclonic high-pressure systems dominated over the western Canadian Arctic, Alaska, Siberia (mostly over the south and central parts). Daily weather analysis for the peak day, July 24 reveals a tilted mid-tropospheric long-wave ridge formation accompanies by a very dry surface air (relative humidity ∼30-% 40) and warm tongue extended from the northwest of the United State to western Canada and Alaska. During the wildfire case of July 24, 2019, south-westerlies to south easterlies winds caused the fire to spread mostly over western and southwestern areas in the local afternoon time.

Research on Sand-Dust Storm Forecasting Based on Deep Neural Network With Stacking Ensemble Learning

Weather forecasting is an important factor affecting production and life. With the development of technology, weather forecasting methods such as weather map forecasting, numerical weather forecasting, and quantitative forecasting methods have emerged. However, these traditional data analysis methods have shortcomings such as incomplete analysis, insufficient objectivity, inability to quantitatively predict the weather, and low prediction accuracy. The import of neural networks into the field of weather forecasting helps to alleviate the above shortcomings and improve the accuracy of weather forecasting. In this paper, the LSTM network and the CNN network are used to predict the sand-dust storm weather. In order to improve the prediction performance, we use the Stacking integration algorithm to fuse the LSTM and CNN models. To improve the experimental scientific and comprehensive, using fully connected network and support vector machine as meta-classifiers, two LSTM-CNN integrated sand-dust storm prediction models are established. At last, the above integrated model is used in the prediction of sand-dust storms in Inner Mongolia. The experimental results show that compared with a single LSTM or CNN model, the Stacking ensemble model has different degrees of improvement in model evaluation indicators such as accuracy, precision, recall, and f1-score. The Stacking ensemble model uses the fully connected network model as the meta-classifier is even better. These prove that the Stacking ensemble algorithm improves the sand-dust storm classification effect and generalization ability of a single neural network to a certain extent.