Weather Reports Research Articles

Machine Learning for Fog-and-Low-Stratus Nowcasting from Meteosat SEVIRI Satellite Images

Fog and low stratus (FLS) are meteorological phenomena that have a significant impact on all ways of transportation and public safety. Due to their similarity, they are often grouped together as a single category when viewed from a satellite perspective. The early detection of these phenomena is crucial to reduce the negative effects that they can cause. This paper presents an image-based approach for the short-term nighttime forecasting of FLS during the next 5 h over Morocco, based on geostationary satellite observations (Meteosat SEVIRI). To achieve this, a dataset of hourly night microphysics RGB product was generated from native files covering the nighttime cold season (October to April) of the 5-year period (2016–2020). Two optical flow techniques (sparse and dense) and three deep learning techniques (CNN, Unet and ConvLSTM) were used, and the performance of the developed models was assessed using mean squared error (MSE) and structural similarity index measure (SSIM) metrics. Hourly observations from Meteorological Aviation Routine Weather Reports (METAR) over Morocco were used to qualitatively compare the FLS existence in METAR, where it is also shown by the RGB product. Results analysis show that deep learning techniques outperform the traditional optical flow method with SSIM and MSE of about 0.6 and 0.3, respectively. Deep learning techniques show promising results during the first three hours. However, their performance is highly dependent on the number of filters and the computing resources, while sparse optical flow is found to be very sensitive to mask definition on the target phenomenon.

An investigation of the effects of sand and dust storms in the North East Sahara Desert on Turkish airports and PM10 values: 7 and 8 April, 2013 events.

Between April 7 and April 10, 2013, a cyclone with a value of 995hPa that developed in the central Mediterranean transported dust from the Sahara Desert towards Turkey. At 13 airports in Turkey, dust haze and widespread dust were seen during different occasions in this period and caused the observation of so-called "Blowing dust events." This cyclone blew dust towards the Cappadocia airport, and the prevailing visibility decreased to 3800m, making it the lowest value measured during the transition of this cyclone. In this study, Aviation Routine Weather Report (Metar) and Aviation Selected Special Weather Report (Speci) observations of airports in North Africa and Turkey were evaluated for the period between April 3 and April 11, 2013. With this cyclone the prevailing visibility at Benina Airport in Libya decreased to 50m on April 6, 2013. This study aims to evaluate long-distance dust transport's effects on meteorological visibility at airports in Turkey and examine the episodic changes of PM10 values measured by air quality monitoring stations. Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model outputs were used to determine the trajectories of long-distance dust particles. Powder red, green, and blue (RGB) and Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images, Cloud-Aerosol LIDAR Infrared Pathfinder Satellite Observations (CALIPSO) images, the Barcelona Supercomputing Center-Dust Regional Atmosphere Model (BSC-DREAM8b) outputs, and Global Forecast System (GFS) synoptic maps were used for analysis. In addition, PM10 values obtained from air quality monitoring stations were examined. According to the data obtained from the CALIPSO images, the dust concentration on the Eastern Mediterranean reaches up to 5km. The episodic values obtained from certain air quality measurement stations are Adana 701, Gaziantep 629, Karaman 900, Nevşehir 1343, and Yozgat 782µg/m3 on an hourly average.

An Overview of the Development of an IOT-based Weather Reporting System

An Overview of the Development of an IOT-based Weather Reporting System

Congestion pricing and information provision under uncertainty: Responsive versus habitual pricing

In the face of capacity disruptions (due, for example, to traffic incidents or poor weather), information provision and congestion pricing are alleviating policies. We compare responsive pricing, whereby tolls vary with known or predicted traffic conditions, with habitual pricing, which only considers the probability distribution of possible traffic conditions. We do so under perfect information and imperfect information where travelers receive information from, for example, a weather report or route planning app. We find analytically that the habitual toll is a weighted average of the expected marginal external costs (MECs) over all states/information ‘signals’, with weights depending on the capacity distribution and the ‘quality’ of the information. The responsive toll depends on the information received and equals the information-specific expected MEC. The two tolls will be more similar the more imperfect the information quality or the lower the uncertainty, and they are identical under no information or no uncertainty. Although responsive pricing raises welfare and lowers travel prices, the differences in effects between the two tolls tend to be tiny even under perfect information and high uncertainty. Considering that responsive pricing may be even more unpopular with the populace and costly to implement than habitual tolls, our study reveals the significance of the quality of information and the degree of uncertainty in deciding how to manage our roads.

VIRTUAL RECOGNITION USING ARTIFICIAL INTELLIGENCE

Voice recognition are programs on digital devices that listen and respond to verbal commands. A user can say, “What’s the weather?” and the voice Recognition will answer with the weather report for that dayand location. They could say, “Tell me a story,” and the Recognition will jump into a tale. Numerous gadgets are becoming more astute in their own particular manner to cooperate with human in a simple language. Programs that are capable of responding tohuman voices via an integrated voice system are known as desktop-based voice recognition. Voice assistants are a significant AI innovation that has the potential to significantly alter people’s daily lives. We reviewed studies that used voice assistants for various tasks in this context.

The ‘Deluge’ of 25 October 1822 in Genoa, Italy

The ‘Deluge’ of 25 October 1822 in Genoa, Italy

Impact of Aerosols on NPP in Basins: Case Study of WRF−Solar in the Jinghe River Basin

Aerosols impact vegetation productivity by increasing diffuse radiation and changing temperature and humidity conditions. In this study, climate simulations of the Jinghe River Basin in 2020 based on aerosol and aerosol−free scenarios were carried out using the control variable method and the aerosol optical depth parameter as the external input data of Weather Report Forecast (WRF)−solar. These two output results were used as input data for the Carnegie Ames Stanford Approach (CASA) model to calculate the impact of aerosols on vegetation productivity. The results showed that WRF−solar accurately simulated changes in meteorological factors such as temperature, rainfall, solar radiation, and relative humidity in the Jinghe River Basin, with a correlation coefficient above 0.85. Aerosols significantly change the ratio of diffuse to direct radiation, act as a cooling function to reduce temperature, and affect rainfall by interacting with clouds. The scenario simulation results showed that under the influence of aerosols, the total solar radiation was reduced by 224.98 MJ/m2, accounting for 3.44% of the total annual radiation. Correspondingly, the average net primary productivity of vegetation in the Jinghe River Basin in 2020 decreased by 26.64 gC/m2, which was not conducive to vegetation photosynthesis and carbon fixation in the basin.

Rain by Cynthia Rylant (review)

Reviewed by: Rain by Cynthia Rylant Kate Quealy-Gainer, Editor Rylant, Cynthia Rain; illus. by Lisa Congdon. Beach Lane/Simon, 2023 [48p] Trade ed. ISBN 9781442465091 $18.99 E-book ed. ISBN 9781442465107 $10.99 Reviewed from digital galleys R 3-6 yrs "There is a softness and a quiet before the rain comes," and the preparations various creatures (including humans) take as the clouds move in are depicted in this clever but soothing picture book. First, the birds swoop in with their weather reports, and the trees tell the squirrels to head home. A disappearing sun and graying sky send the children on the playground indoors; alerted by their sensitive whiskers, the cats find their favorite window perch to watch the first drops; and the dogs, who [End Page 266] have smelled the rain since yesterday, stay a little bit longer outside before finding cover. When the skies finally open, each animal enjoys the weather in their own way until everything clears again, with a rainbow beckoning everyone outside. Rylant's buoyant text has a warm and welcoming feel, like a comfy blanket on a gloomy day, and the shifts in perspectives among the animals from page to page ensure readerly engagement. Eye-catching geometric patterns pair with softer, dappled textures to echo the transition from the orderly structure of daily happenings to an almost magical world, where the falling raindrops fill the pages, dominating but not totally obscuring the vignette-like activities, as "The trees enjoy their shower. The cats have a show. The dogs get cuddled. And the birds catch up on the news." This is an idyllic, whimsical treat to pass a rainy day until the weather clears. Copyright © 2023 The Board of Trustees of the University of Illinois

Using a Continental-Scale Data Quality Monitoring Framework to Evaluate a New Nonweather Filter for Radar Observations

Abstract Removing nonweather echoes is a critical component of the quality control (QC) chain used in the context of radar data assimilation for numerical weather prediction, quantitative precipitation estimation, and nowcasting applications. Recent studies show that using a simple QC method based on the depolarization ratio (DR) performs remarkably well in many situations. Nonetheless, this method may misclassify echoes in regions affected by nonuniform beamfilling or melting particles. This study presents an updated version of this QC used to remove nonweather echoes that uses the DR-based classification together with a set of physically based rules for correcting misclassifications of hail, nonuniform beamfilling, and melting particles. The potential of the new QC is evaluated using a continental-scale monitoring framework that compares the radar observations after QC with the precipitation occurrence derived from aviation routine weather reports (METARs). For this evaluation, the study uses the radar data and the METARs available over North America during the summer of 2019 and winter of 2020. In addition, the study demonstrates the usefulness of the monitoring framework to determine the optimal QC configuration. Some practical limitations of using the METAR-derived precipitation to assess radar data quality are also discussed.

A systematic review of natural language processing applications for hydrometeorological hazards assessment.

Natural language processing (NLP) is a promising tool for collecting data that are usually hard to obtain during extreme weather, like community response and infrastructure performance. Patterns and trends in abundant data sources such as weather reports, news articles, and social media may provide insights into potential impacts and early warnings of impending disasters. This paper reviews the peer-reviewed studies (journals and conference proceedings) that used NLP to assess extreme weather events, focusing on heavy rainfall events. The methodology searches four databases (ScienceDirect, Web of Science, Scopus, and IEEE Xplore) for articles published in English before June 2022. The preferred reporting items for systematic reviews and meta-analysis reviews and meta-analysis guidelines were followed to select and refine the search. The method led to the identification of thirty-five studies. In this study, hurricanes, typhoons, and flooding were considered. NLP models were implemented in information extraction, topic modeling, clustering, and classification. The findings show that NLP remains underutilized in studying extreme weather events. The review demonstrated that NLP could potentially improve the usefulness of social media platforms, newspapers, and other data sources that could improve weather event assessment. In addition, NLP could generate new information that should complement data from ground-based sensors, reducing monitoring costs. Key outcomes of NLP use include improved accuracy, increased public safety, improved data collection, and enhanced decision-making are identified in the study. On the other hand, researchers must overcome data inadequacy, inaccessibility, nonrepresentative and immature NLP approaches, and computing skill requirements to use NLP properly.

Long Short-term Memory-Based Prediction of the Spread of Influenza-Like Illness Leveraging Surveillance, Weather, and Twitter Data: Model Development and Validation.

The potential to harness the plurality of available data in real time along with advanced data analytics for the accurate prediction of influenza-like illness (ILI) outbreaks has gained significant scientific interest. Different methodologies based on the use of machine learning techniques and traditional and alternative data sources, such as ILI surveillance reports, weather reports, search engine queries, and social media, have been explored with the ultimate goal of being used in the development of electronic surveillance systems that could complement existing monitoring resources. The scope of this study was to investigate for the first time the combined use of ILI surveillance data, weather data, and Twitter data along with deep learning techniques toward the development of prediction models able to nowcast and forecast weekly ILI cases. By assessing the predictive power of both traditional and alternative data sources on the use case of ILI, this study aimed to provide a novel approach for corroborating evidence and enhancing accuracy and reliability in the surveillance of infectious diseases. The model's input space consisted of information related to weekly ILI surveillance, web-based social (eg, Twitter) behavior, and weather conditions. For the design and development of the model, relevant data corresponding to the period of 2010 to 2019 and focusing on the Greek population and weather were collected. Long short-term memory (LSTM) neural networks were leveraged to efficiently handle the sequential and nonlinear nature of the multitude of collected data. The 3 data categories were first used separately for training 3 LSTM-based primary models. Subsequently, different transfer learning (TL) approaches were explored with the aim of creating various feature spaces combining the features extracted from the corresponding primary models' LSTM layers for the latter to feed a dense layer. The primary model that learned from weather data yielded better forecast accuracy (root mean square error [RMSE]=0.144; Pearson correlation coefficient [PCC]=0.801) than the model trained with ILI historical data (RMSE=0.159; PCC=0.794). The best performance was achieved by the TL-based model leveraging the combination of the 3 data categories (RMSE=0.128; PCC=0.822). The superiority of the TL-based model, which considers Twitter data, weather data, and ILI surveillance data, reflects the potential of alternative public sources to enhance accurate and reliable prediction of ILI spread. Despite its focus on the use case of Greece, the proposed approach can be generalized to other locations, populations, and social media platforms to support the surveillance of infectious diseases with the ultimate goal of reinforcing preparedness for future epidemics.

Toward a Super-COP? Timing, Temporality, and Rethinking World Climate Governance

Toward a Super-COP? Timing, Temporality, and Rethinking World Climate Governance

A New Paradigm for Medium-Range Severe Weather Forecasts: Probabilistic Random Forest–Based Predictions

Abstract Historical observations of severe weather and simulated severe weather environments (i.e., features) from the Global Ensemble Forecast System v12 (GEFSv12) Reforecast Dataset (GEFS/R) are used in conjunction to train and test random forest (RF) machine learning (ML) models to probabilistically forecast severe weather out to days 4–8. RFs are trained with ∼9 years of the GEFS/R and severe weather reports to establish statistical relationships. Feature engineering is briefly explored to examine alternative methods for gathering features around observed events, including simplifying features using spatial averaging and increasing the GEFS/R ensemble size with time lagging. Validated RF models are tested with ∼1.5 years of real-time forecast output from the operational GEFSv12 ensemble and are evaluated alongside expert human-generated outlooks from the Storm Prediction Center (SPC). Both RF-based forecasts and SPC outlooks are skillful with respect to climatology at days 4 and 5 with diminishing skill thereafter. The RF-based forecasts exhibit tendencies to slightly underforecast severe weather events, but they tend to be well-calibrated at lower probability thresholds. Spatially averaging predictors during RF training allows for prior-day thermodynamic and kinematic environments to generate skillful forecasts, while time lagging acts to expand the forecast areas, increasing resolution but decreasing overall skill. The results highlight the utility of ML-generated products to aid SPC forecast operations into the medium range. Significance Statement Medium-range severe weather forecasts generated from statistical models are explored here alongside operational forecasts from the Storm Prediction Center (SPC). Human forecasters at the SPC rely on traditional numerical weather prediction model output to make medium-range outlooks and statistical products that mimic operational forecasts can be used as guidance tools for forecasters. The statistical models relate simulated severe weather environments from a global weather model to historical records of severe weather and perform noticeably better than human-generated outlooks at shorter lead times (e.g., day 4 and 5) and are capable of capturing the general location of severe weather events 8 days in advance. The results highlight the value in these data-driven methods in supporting operational forecasting.

Improvement in Dust Storm Simulation by Considering Stone Coverage Effects for Stony Deserts in East Asia

AbstractApproximately 50% of the Earth's deserts are covered with stony surfaces, not dunes. The stony surfaces often block or diminish mineral dust aerosol emissions through area fraction and roughness element effects. Incorporating these stone coverage effects is crucial for climate and environmental modeling research. Based on our field observations, this study combined the stone coverage effects into a dust simulation model for East Asia using two regression formulas and some constants. The double regression scheme assumed that the stone roughness density could be derived from the coarse fragment fraction of the SoilGrids 2.0 data set. According to the data set, the stone coverage is higher in Western Mongolia and Dzungaria and lower in the Chinese Gobi Desert and the Loess Plateau. Consequently, the model reproduced fewer dust aerosols in the higher coverage areas and more in the lower coverage areas. This simulation result was consistent with the World Meteorological Organization's current weather reports and satellite aerosol observations. The improved model reproduced the diversity of soil erodibility and was well balanced in performance statistics. This study is the first successful investigation of stone coverage effects on dust storm simulation using a realistic stone coverage map to the authors' best knowledge.

Centennial analysis in tropical cyclone-induced precipitation in Korea

Tropical cyclone (TC)-induced precipitation has undergone significant multidecadal changes over the last 117 years (1904–2020) on the Korean Peninsula, as determined through the analysis of affected TCs and surface-observed precipitation at ten stations in the Republic of Korea (hereafter Korea). Information on TCs that have affected Korea over a century was collected from the National Typhoon Center of the Korea Meteorological Administration (KMA). The information was cross-validated by referring to the International Best Track Archive for Climate Stewardship archive, while the information for 1904–1950 was cross-validated by also examining extreme weather reports in newspapers. A significant regime shift in the annual total heavy rainfall (confined to rain events ≥100 mm day−1), and its frequency associated with the affected TCs was found before and after 1979. The regime-shift signal is robust regardless of the methodology. Heavy rainfall from non-TC events, however, has presented a regime-shift increase since 1997. Based on a detailed review of historical documents describing instrument type, measuring method, altitude, and site location, this rainfall increase in regime-shift is likely a non-climatic signal coming from updates in measurement tools (i.e., the weather station automation project of KMA) during 1995–2000. However, increases in heavy rainfall of TC events in 1979 are comprehensively supported by the intensity and track changes in Korea affecting TC and changes in large-scale atmospheric and oceanic environments. The present results provide an earnest validation of TC records over the Korean Peninsula and confirm a regime-shift increase in TC hazards over Korea, thus providing grounds for further global analysis.

Real Time Weather Monitoring System using IoT

The proposed project aims to use IoT technology to build a weather monitoring and reporting system which has the potential to improve the accuracy and reliability of weather data. Weather monitoring systems can collect real-time data on various weather conditions, such as temperature and humidity, using sensors and other IoT devices. This data can then be analysed and used to generate accurate and timely weather reports, which individuals and organizations can use for various purposes, such as agriculture, aviation, and disaster management. The aim of real-time weather monitoring using IoT is to collect, analyse, and disseminate accurate and timely information about the current weather conditions to individuals and organizations through web and mobile dashboards for decision-making purposes.

Field Station Weather Reports

Field Station Weather Reports

Western Kansas Field Station Weather Report: 2022 Growing Season

Western Kansas Field Station Weather Report: 2022 Growing Season

Arduino-Based Weather Monitoring System

The use of technology has revolutionized our lives in many ways, making our day-to-day activities more convenient and efficient. One such technological advancement is the Arduino-based weather monitoring system. This system has become more well-known recently as a result of its ability to accurately measure and monitor weather conditions in real-time. In this article, we will explore what an Arduino-based weather monitoring system is, how it works, and its benefits. An Arduino-based weather monitoring system is a compact and cost-effective system that uses Arduino microcontrollers to collect and analyze weather data. The system consists of various sensors that measure different weather parameters such as temperature, humidity, pressure, and rainfall. The collected data is then processed and displayed on an LCD screen or transmitted to a computer or mobile device for further analysis. In this article, we're going to build an Arduino-based Weather monitor system that can provide us with real-time weather reports for your area, including air quality, temperature, humidity, atmospheric pressure, and light intensity. The Arduino-based weather monitoring system is a remarkable innovation that has made monitoring weather conditions more accessible and affordable. Its accuracy, portability, and customizable features make it a valuable tool for various industries. With the continuous development of technology, we can expect to see further advancements in this field, making weather monitoring more efficient and reliable.

PENGARUH BESAR PENGETAHUAN METEOROLOGI MARITIM DALAM KEMENANGAN PERTEMPURAN NORMANDIA 1944

The Battle and Landing of Normandy 1944 was four years in the making. General Dwight D. Eisenhower as the Allied Commander wanted the invasion of Normandy, D-Day to be carried out immediately, but the operation plan almost failed due to bad weather. The Battle of Normandy 1944 began with the German conquest of France and the Allies wanted to free France from German occupation. The writing methodology uses a descriptive method by analyzing the facts that occurred at the Battle of Normandy 1944 supported by literature studies, literature, and scientific journals. Meanwhile, Maritime Meteorological information at the time of the Normandy 1944 landing was obtained from the analysis of weather reports and literature studies. From this incident, it can be concluded that the TNI, especially the Navy, must take maritime weather into account in supporting operations at sea and needs to collaborate with BMKG to obtain Maritime Meteorological information. BMKG provides impact-based maritime weather that issues weather impact map information to sailing ships. The Impact-Based Weather Map provided will provide a color that indicates the risk status of maritime weather based on the type of ship, which can help TNI-AL's equipment in navigation at sea. Maritime Meteorological information and data are obtained using BMKG's ocean prediction model - Ocean Forecasting System (BMKG - OFS) and BMKG's InaWIS (Indonesia Weather Information for Shipping). The InaWIS system is integrated with the Automatic Identification System (AIS) shipping navigation data that will be able to identify ship risks to maritime weather such as wind, currents, and waves. InaWIS is also equipped with real-time weather observation data that is able to model sea predictions for the next 10 (ten) days which makes it easier for TNI AL's equipment to navigate at sea to avoid areas that have the potential for extreme weather and also to support landing operations.