Weather Reports Research Articles

Textual data for electricity load forecasting

AbstractTraditional mid‐term electricity forecasting models rely on calendar and meteorological information such as temperature and wind speed to achieve high performance. However depending on such variables has drawbacks, as they may not be informative enough during extreme weather. While ubiquitous, textual sources of information are hardly included in prediction algorithms for time series, despite the relevant information they may contain. In this work, we propose to leverage openly accessible weather reports for electricity demand and meteorological time series prediction problems. Our experiments on French and British load data show that the considered textual sources allow to improve overall accuracy of the reference model, particularly during extreme weather events such as storms or abnormal temperatures. Additionally, we apply our approach to the problem of imputation of missing values in meteorological time series, and we show that our text‐based approach beats standard methods. Furthermore, the influence of words on the time series' predictions can be interpreted for the considered encoding schemes of the text, leading to a greater confidence in our results.

Quasi-Linear Convective Systems and Derechos across Europe: Climatology, Accompanying Hazards, and Societal Impacts

Abstract In this work, we use 8 years (2014–21) of Operational Programme for the Exchange of Weather Radar Information (OPERA) radar data, ESWD severe weather reports, and arrival time difference (ATD) lightning detection network (ATDnet) data to create a climatology of quasi-linear convective systems (QLCSs) across Europe. In the first step, 15-min radar scans were used to identify 1475 QLCS polygons. Severe weather reports, lightning data, and morphological properties were used to classify QLCSs according to their intensity into 1151 marginal (78.0%), 272 moderate (18.5%), and 52 derecho (3.5%) events. The manual evaluation led to the recognition of QLCS morphological and precipitation archetypes, areal extent, duration, speed, forward motion, width, length, accompanying hazards, injuries, and fatalities. Results indicate that QLCSs are the most frequent during summer in central Europe, while in southern Europe, their occurrence is extended to late autumn. A bow echo feature occurred in around 29% of QLCS cases, while a mesoscale convective vortex occurred in almost 9%. Among precipitation modes, trailing and embedded stratiform types accounted for around 50% of QLCSs. The most frequent hazard accompanying QLCSs was lightning (taking up on average 94.4% of the area impacted by QLCS), followed by severe wind gusts (7.9%), excessive precipitation (6.1%), large hail (2.9%), and tornadoes (0.5%). Derechos had the largest coverage of severe wind reports (49.8%), while back-building QLCSs were the most prone to excessive precipitation events (13.5%). QLCSs caused 104 fatalities and 886 injuries. Severe wind gusts were responsible for 87.6% of fatalities and 73.6% of injuries. Nearly half of all fatalities and injuries were associated with only the 10 most impactful QLCS events, mostly warm-season derechos.

Evaluation and Improvement of a CALIPSO-Based Algorithm for Cloud Base Height in China

Clouds are crucial in regulating the Earth’s energy budget. Global cloud top heights have been easily retrieved from satellite measurements, but there are few methods for determining cloud base height (CBH) from satellite measurements. The Cloud Base Altitude Spatial Extrapolator (CBASE) algorithm was proposed to derive the height of the lower-troposphere liquid cloud base by using the Cloud-Aerosol Lidar with Orthogonal polarization cloud aerosol LiDAR (CALIOP) profiles and weather observations at airports from aviation routine and special weather report (METARs and SPECIs, called METAR) observation data in the United States. A modification to the CBASE algorithm over China (CNMETAR-CBASE) is presented in this paper. In this paper, the ability of the CBASE algorithm to calculate CBH in China is evaluated, and METAR observations over China (CNMETAR) were then used to modify the CBASE algorithm. The results including CNMETAR observation data in China can better retrieve CBH over China compared with the results using the original CBASE algorithm, and the accuracy of the global CBH results has been improved. Overestimations of CBH with the original algorithm range from 500 to 800 m in China, which have been reduced to about 300 m with an improved algorithm. The deviations calculated by the algorithm also have a significant reduction, from 480 m (CBASE) to 420 m (CNMETAR-CBASE). In conclusion, the modified CBASE algorithm not only calculates the CBH more accurately in China but also improves the results of the global CBH retrieved from satellites.

Weather Report Australia: James Geurts – Weatherman; Roots, and a Very Long Leash: Peter Hill in Conversation with artist Franziska Furter

Weather Report Australia: James Geurts – Weatherman; Roots, and a Very Long Leash: Peter Hill in Conversation with artist Franziska Furter

Toward a South American High-Impact Weather Reports Database

Abstract Despite southern South America being recognized as a hotspot for deep convective storms, little is known about the socioenvironmental impacts of high-impact weather (HIW) events. Although there have been past efforts to collect severe weather reports in the region, they have been highly fragmented among and within countries, sharing no common protocol, and limited to a particular phenomenon, a very specific region, or a short period of time. There is a pressing need for a more comprehensive understanding of the present risks linked to HIW events, specifically deep convective storms, on a global scale as well as their variability and potential future evolution in the context of climate change. A database of high-quality and systematic HIW reports and associated socioenvironmental impacts is essential to understand the regional atmospheric conditions leading to hazardous weather, to quantify its predictability, and to build robust early warning systems. To tackle this problem and following successful initiatives in other regions of the world, researchers, national weather service members, and weather enthusiasts from Argentina, Brazil, Chile, Paraguay, and Uruguay have embarked on a multinational collaboration to generate a standardized database of reports of HIW events principally associated with convective storms and their socioenvironmental impacts in South America. The goal of this paper is to describe this unprecedented initiative over the region, to summarize first results, and to discuss the potential applications of this collaboration.

문장의 수와 장르의 사회언어학적 관계 연구

Objectives The purpose of this paper is to compare the numbers of sentences and genre characteristics. To do so, we analyze the sociolinguistic implications of the differences in the number of occurrences of culminative suffixes in different genres. Methods Fifteen genres of texts were selected for the test, according to the type of socio-symbolic activity of systemic functional linguistics. For each of them, we counted the number of sentences with the following grammatical categories: the number of sentence types, the number of mood types, the number of stance types, the number of addressee deference types, and the number of minor sentences. To verify the results statistically, a Chi-squared Test of Independence was performed. Results The number of sentences per genre was highest in private conversations, followed by fairy tales, weather reports, and fiction texts. On the contrary, the lowest number of sentences were found in judgments. In terms of mood, five types of mood were found in private conversations and four in fairy tales, but only a single type of mood was found in other genres. The number of stance types by genre was 4 in private conversations and 3 in fairy tales, and none in elders. The number of addressee deference types by genre tended to be fixed at one type or another depending on the audience of the text. The number of minor sentences per genre was overwhelmingly high in everyday conversation, with 15. Conclusions The statistical tests of the results of the above analysis show that the grammatical categories performed by the culminative suffixes in the 15 genres have significant differences in the variance of their ratings, and that, although the culminative suffixes do not combine, minor sentences also fulfill the interpersonal functions of culminative suffixes.

“We are 9 degrees and sunny”: the use of personal pronouns with weather predicates

AbstractThis squib discusses a construction heard during weather reports on CBC Radio One in Manitoba whereby personal pronouns appear as the subjects of weather predicates. We show that the use of we/you in statements like we are minus 15 degrees and Brandon, you are sunny is unlike other non-prototypical uses for personal pronouns that have been noted in the literature and argue they index place rather than person. We note that the deictic coordinates of the utterance that are spelled out by these pronouns are necessary for a felicitous interpretation of weather statements, even though they are typically implicit. This implicit deixis, in turn, sheds light on a long-standing claim that weather-it, in contrast to a true expletive, is ‘quasi-argumental’ (Chomsky 1981). That is, we suggest that the deictic coordinates of an utterance are ‘quasi-arguments’.

Weather Report Provider And Location Provider Using Google Map

The purpose of this research title is to present a weather forecast report on various factors and attributes so we can learn more about them such as temperature, wind speed, clouds, sunrise and sunset times, etc. In addition to displaying the correct location, Google Maps makes it easier for people who don't know a particular place and are new to it to understand it more easily, which makes it a great deal easier for them to make any type of decision like traveling to that place. This type of information can be extremely helpful. Using our app, the map can be accessed directly to give you directions to a location by using the map feature on Google directly. One of the biggest benefits of our application is its effectiveness, ease of use, and ability to collect so much information on one page. Keywords—Weather Forecast, Weather Report, Google Maps, Machine Learning, Data Preprocessing.

Classification of Rainfall Intensity and Cloud Type from Dash Cam Images Using Feature Removal by Masking

Weather Report is an initiative from Weathernews Inc. to obtain sky images and current weather conditions from the users of its weather app. This approach can provide supplementary weather information to radar observations and can potentially improve the accuracy of forecasts However, since the time and location of the contributed images are limited, gathering data from different sources is also necessary. This study proposes a system that automatically submits weather reports using a dash cam with communication capabilities and image recognition technology. This system aims to provide detailed weather information by classifying rainfall intensities and cloud formations from images captured via dash cams. In models for fine-grained image classification tasks, there are very subtle differences between some classes and only a few samples per class. Therefore, they tend to include irrelevant details, such as the background, during training, leading to bias. One solution is to remove useless features from images by masking them using semantic segmentation, and then train each masked dataset using EfficientNet, evaluating the resulting accuracy. In the classification of rainfall intensity, the model utilizing the features of the entire image achieved up to 92.61% accuracy, which is 2.84% higher compared to the model trained specifically on road features. This outcome suggests the significance of considering information from the whole image to determine rainfall intensity. Furthermore, analysis using the Grad-CAM visualization technique revealed that classifiers trained on masked dash cam images particularly focused on car headlights when classifying the rainfall intensity. For cloud type classification, the model focusing solely on the sky region attained an accuracy of 68.61%, which is 3.16% higher than that of the model trained on the entire image. This indicates that concentrating on the features of clouds and the sky enables more accurate classification and that eliminating irrelevant areas reduces misclassifications.

Researchers identify best spots for Californian solar water heaters

Using weather reports, solar-resource availability, and installation data, researchers identified where Californians solar-powered water heaters can best replace gas alternatives.

Automated Spoken-to-Coded Pilot Weather Reports in General Aviation

A pilot weather report (PIREP) is a firsthand report of actual meteorological phenomena encountered by an aircraft in flight or on the ground. In general aviation (GA), PIREPs are a critical method of communicating weather information with pilots, controllers, and forecasters. However, GA pilots may find submitting PIREPs a cumbersome, manually intensive, and latent process. Automation of the radio PIREP submission process could lead to an increase in the quantity and quality (i.e., accuracy, timeliness, and usefulness) of GA PIREPs. This paper discusses the development and evaluation of a three-tier system to automatically convert spoken PIREPs to coded PIREPs using speech recognition, machine learning (ML), and natural-language-processing techniques. Specifically, this paper describes 1) the development, evaluation, and limitations of a trained commercial-of-the-shelf speech recognition system to generate text transcriptions of the spoken PIREPs, 2) an ML-based Named Entity Recognition model for segregating and labeling information from PIREP transcriptions, and 3) a final deterministic encoder to generate PIREP codes. If implemented in practice, this approach could significantly reduce interferences associated with submitting PIREPs, increase the number of PIREP submissions, improve the quality of PIREPs, and ultimately make flying safer for GA pilots.

Generative Ensemble Deep Learning Severe Weather Prediction from a Deterministic Convection-Allowing Model

Abstract An ensemble postprocessing method is developed for the probabilistic prediction of severe weather (tornadoes, hail, and wind gusts) over the conterminous United States (CONUS). The method combines conditional generative adversarial networks (CGANs), a type of deep generative model, with a convolutional neural network (CNN) to postprocess convection-allowing model (CAM) forecasts. The CGANs are designed to create synthetic ensemble members from deterministic CAM forecasts, and their outputs are processed by the CNN to estimate the probability of severe weather. The method is tested using High-Resolution Rapid Refresh (HRRR) 1–24-h forecasts as inputs and Storm Prediction Center (SPC) severe weather reports as targets. The method produced skillful predictions with up to 20% Brier skill score (BSS) increases compared to other neural-network-based reference methods using a testing dataset of HRRR forecasts in 2021. For the evaluation of uncertainty quantification, the method is overconfident but produces meaningful ensemble spreads that can distinguish good and bad forecasts. The quality of CGAN outputs is also evaluated. Results show that the CGAN outputs behave similarly to a numerical ensemble; they preserved the intervariable correlations and the contribution of influential predictors as in the original HRRR forecasts. This work provides a novel approach to postprocess CAM output using neural networks that can be applied to severe weather prediction. Significance Statement We use a new machine learning (ML) technique to generate probabilistic forecasts of convective weather hazards, such as tornadoes and hailstorms, with the output from high-resolution numerical weather model forecasts. The new ML system generates an ensemble of synthetic forecast fields from a single forecast, which are then used to train ML models for convective hazard prediction. Using this ML-generated ensemble for training leads to improvements of 10%–20% in severe weather forecast skills compared to using other ML algorithms that use only output from the single forecast. This work is unique in that it explores the use of ML methods for producing synthetic forecasts of convective storm events and using these to train ML systems for high-impact convective weather prediction.

Evaluation and prediction of fibrous filters' operating performance in residential fresh air system

Fibrous filters play a crucial role in determining indoor air quality and protect people from bacteria, particulates, and viruses such as COVID-19. However, there is a lack of comprehensive research on methods for effectively evaluating and predicting the operating performance of these filters. In this study, we conducted laboratory and field tests on coarse (G4) and medium (F9 or F7) filters from four different brands to assess both their initial and operating performance. We discovered that a fluffy fiber structure in coarse filters could achieve an impressive threefold increase in dust holding capacity (DHC) compared to normal filters. Utilizing initial pressure drop, initial particle capture efficiency, and DHC, we introduced a new quality factor (QF) that integrates these parameters to evaluate the filter's long-term performance. This approach addresses the limitations of traditional quality factor, which neglects DHC and tends to underestimate the performance of filters with high DHC. Additionally, we proposed a simple method to predict the filter's operating pressure drop, using particle concentration data from weather report. This allows for convenient assessment of the fresh air system's operating status, offering potential integration into a user-friendly mobile application or computer software. These findings contribute innovative ideas for enhancing the intelligent and energy-efficient operation of fresh air systems.

First Measurement Campaign by a Multi-Sensor Robot for the Lifecycle Monitoring of Transformers

Transformers are a very important asset in the electrical transmission grid, and they can suffer from destructive events—e.g., rare transformer fires. Unfortunately, destructive events often lead to a lack of data available for investigators during post-event forensics and failure analysis. This fact has motivated our design and implementation of a robotic multi-sensor platform and cloud backend solution for the lifecycle monitoring, inspection, diagnostics, and condition assessment of transformers. The robotic platform collects data from specific viewpoints around the transformer during operation and at specific relevant lifecycle milestones of the transformer (e.g., at the factory acceptance test) in an automated, repetitive, precise, and reliable manner. The acquired data are stored in the cloud backend, which also provides computing resources and data access to relevant in- and off-premises services (e.g., respectively, SCADA systems, and weather reports). In this paper, we present the results of our first measurement campaign to showcase the value of our solution for transformer lifecycle monitoring, for anomaly detection, and as a crucial tool for post-event forensics in the case of destructive events.

Precocious Natural Mummification in Hanging

Mummification is regarded as a particular form of decomposition. External factors such as sun, wind, humidity, and temperature are known to accelerate or slow down the mummification period. The process can take weeks or months to complete. Consequently, the diversity of external factors makes postmortem interval (PMI) estimations difficult. This case study reports on the first example of precocious natural mummification in Northern Cyprus and involves a 20-year-old male found hanging from the railing in the stairwellof an unfinished building. The individual had been missing only seven days. The body underwent complete computed tomography scanning, autopsy, and toxicology analysis. A fractured hyoid with associated ecchymosis is documented, indicating death by asphyxiation. The crime scene environmental factors and seven-day weather report during which the individual was missing were assessed to understand the factors that influenced this rapid mummification. Reporting on this case contributes to PMI studies in studies in Cyprus and other locations with similar environmental factors.

Digitizing observations from the 1861–1875 Met Office Daily Weather Reports using citizen scientist volunteers

AbstractWe describe the transcription and quality control processes for rescuing around 570,000 sub‐daily and daily weather observations which were recorded in the UK Met Office Daily Weather Reports during the 1861–1875 period. These data are from the start of coordinated weather observations and were collected with the aim of making the first‐ever weather forecasts. The observations were rescued thanks to 3500 volunteers and include sub‐daily sea‐level pressure, dry and wet bulb temperatures, daily maximum and minimum temperatures, and daily rainfall amounts from 70 different locations across Western Europe, and one in Canada. We highlight how these observations will be used to fill gaps in existing pressure and temperature datasets and use two case studies to show how the pressure observations will likely better constrain the atmospheric circulation during two severe storms. We also compare a sub‐sample of the newly rescued observations with data that were previously digitized for a small number of locations for the same dates, finding good agreement in general, although some discrepancies remain.

Evaluation of the Correlation Between Climate Change and the Most Reported Cases of Diseases Due to Climate Change in the Study Area Over the Period of 11 Years (2010-2020) in Selected South-Western States (Ekiti, Osun and Ondo), Nigeria.

Climate change, acting via less direct mechanisms, would affect the transmission of many infectious diseases (especially water, food and vector-borne diseases). The aim of this study is to evaluate the correlation between climate change and emerging and re-emerging diseases in selected south-west states (Ekiti, Osun and Ondo) Nigeria. This study covered the entire population of three south western states (Ekiti, Osun and Ondo) and relied more on secondary data from the Health records department (Data Bank) from the Federal Ministries of Health with additional data from NIMET (weather data or reports). Selection of health care facilities in each of the three states, health data indicating disease distribution and records from 2010 -2020 alongside climate data from Ekiti, Osun and Ondo State. An integrated approach using correlation study was employed in assessing the effect of climate change in particular reference to emerging and re-emerging disease in the study areas. Findings revealed that the higher the temperature the higher the emergence of Measles and Chicken pox, but the lower the temperature the higher the emergence of influenza and Pneumonia. The emergence of Lassa Fever however does not depend on the climatic change either with high or low temperature in South-West, Nigeria. Hence it was concluded that there is correlation between climate change and emerging and re-emerging of diseases.

Patterns of hurricane induced displacement in The Bahamas: Building equitable resilience in small island developing states

The extent and severity of disaster displacement in small island developing states (SIDS) often go unreported or underreported in global assessments due to the total number of affected people falling below established thresholds. Additionally, post-disaster assessments prepared by various international relief agencies often present conflicting evidence, and largely do not offer substantial insights into national and subnational spatial and temporal patterns of displacement, particularly with respect to the disproportionate risk that certain localities, communities and populations face over time. This article is a case study of hurricane induced displacement in a Caribbean SIDS – The Bahamas. It triangulates data from a publicly-available global disaster database, weather and post-disaster reports from national government departments and agencies, and newspaper articles. Its qualitative-dominant synthesis represents the best available evidence of hurricane risk across the archipelago between 2004 and 2019, organized according to hazard (winds, storm surge, flooding), exposure (people, livelihoods, assets etc. adversely affected), and vulnerability (the propensity or predisposition for adverse impacts). It finds that 11 hurricanes across three periods caused displacement in one or more of the 17 major islands. In identifying the emerging spatial and temporal patterns, it proposes two alternative core-periphery models for The Bahamas. These models not only provide a more accurate account of the islands’ exposure and sensitivity to hurricanes, but also highlight the geographical factors that should be considered as the basis for future plans, actions, strategies or policies that seek to build equitable resilience to these and other climate-amplified hydrometeorological events in SIDS.

Kansas River Valley Experiment Field: Field Station Weather Reports: 2023 Growing Season

Kansas River Valley Experiment Field: Field Station Weather Reports: 2023 Growing Season

East Central Kansas Experiment Field: Field Station Weather Reports: 2023 Growing Season

East Central Kansas Experiment Field: Field Station Weather Reports: 2023 Growing Season