scholarly journals Internet of Things Based Weather Forecast Monitoring System

2018 ◽  
Vol 9 (3) ◽  
pp. 555 ◽  
Author(s):  
Atul Kulkarni ◽  
Debajyoti Mukhopadhyay
Keyword(s):  
Weather Forecasting ◽  
Weather Forecast ◽  
Severe Weather ◽  
Weather Data ◽  
Partial Explanation ◽  
Significant Function ◽  
Weather Events ◽  
Weather Stations ◽  
Communication Methods ◽  
Cost Components

<p>Weather forecasting is a significant function in meteorology and has been one of the most systematically challenging troubles around the world.This scheme deals with the structure of a weather display method using small cost components so that any electronics hobbyist can construct it. As a replacement for using sensors to collect the weather data, the development gets the information from weather stations placed around the world through a global weather data supplier. Severe weather phenomena challengedifficult weather forecast approach with the partial explanation. Weather events have numerous parameters that are not possible to detail and compute. Growing on communication methods enables weather predictsspecialist systems to combine and share possessions and thus hybrid systems have emerged. Still, though these improvements on climate predict, these expert systems can’t be entirely reliable while weather forecast is central problem.</p>

scholarly journals Three novel copula-based bias correction methods for daily ECMWF air temperature data

2017 ◽  
Author(s):  
Fakhereh Alidoost ◽  
Alfred Stein ◽  
Zhongbo Su ◽  
Ali Sharifi
Keyword(s):  
Air Temperature ◽  
Bias Correction ◽  
Weather Forecast ◽  
Correction Method ◽  
Temperature Data ◽  
Weather Data ◽  
Conditional Quantiles ◽  
New Methods ◽  
Conditional Copula ◽  
Weather Stations

Abstract. Data retrieved from global weather forecast systems are typically biased with respect to measurements at local weather stations. This paper presents three copula-based methods for bias correction of daily air temperature data derived from the European Centre for Medium-range Weather Forecasts (ECMWF). The aim is to predict conditional copula quantiles at different unvisited locations, assuming spatial stationarity of the underlying random field. The three new methods are: bivariate copula quantile mapping (types I and II), and a quantile search. These are compared with commonly applied methods, using data from an agricultural area in the Qazvin Plain in Iran containing five weather stations. Cross-validation is carried out to assess the performance. The study shows that the new methods are able to predict the conditional quantiles at unvisited locations, improve the higher order moments of marginal distributions, and take the spatial variabilities of the bias-corrected variable into account. It further illustrates how a choice of the bias correction method affects the bias-corrected variable and highlights both theoretical and practical issues of the methods. We conclude that the three new methods improve local refinement of weather data, in particular if a low number of observations is available.

scholarly journals DEVELOPMENT OF AN AUTOMATED SYSTEM FOR WEATHER DATA COLLECTION AND FORECASTING CLIMATE EVENTS BY MEANS OF RADIO FREQUENCY

2020 ◽  
Vol 28 ◽  
pp. 415-424
Author(s):  
Caio Cesar Oba Ramos ◽  
Mário Lúcio Roloff
Keyword(s):  
Decision Making ◽  
Weather Forecasting ◽  
Field Tests ◽  
Automated System ◽  
Extreme Weather Events ◽  
Weather Data ◽  
Weather Events ◽  
Forecasting Method ◽  
Climate Events ◽  
Support Decision Making

The weather forecasting method is based on the analysis of data collected to support decision-making. This paper describes the development of a weather station (prototype) to assist in monitoring plantations and forecasting extreme weather events. The strategy adopted is to use a set of stations connected via radiofrequency forming a communications network to better understand and anticipate such events. The weather forecasting method is based on analysis of data collected to support decision making. The laboratory tests were successful, but field tests had several difficulties and unforeseen events that will be described at the end of this paper.

scholarly journals A novel weather parameters prediction scheme and their effects on crops

2022 ◽  
Vol 12 (1) ◽  
pp. 639
Author(s):  
Naveen Lingaraju ◽  
Hosaagrahara Savalegowda Mohan
Keyword(s):  
Weather Forecasting ◽  
Fitness Function ◽  
Research Work ◽  
Geographical Location ◽  
Weather Forecast ◽  
Forecast Model ◽  
Weather Data ◽  
Prediction Scheme ◽  
Weather Parameters ◽  
Technological World

Weather forecast is significantly imperative in today’s smart technological world. A precise forecast model entails a plentiful data in order to attain the most accurate predictions. However, a forecast of future rainfall from historical data samples has always been challenging and key area of research. Hence, in modern weather forecasting a combo of computer models, observation, and knowledge of trends and patterns are introduced. This research work has presented a fitness function based adaptive artificial neural network scheme in order to forecast rainfall and temperature for upcoming decade (2021-2030) using historical weather data of 20 different districts of Karnataka state. Furthermore, effects of these forecasted weather parameters are realized over five major crops of Karnataka namely rice, wheat, jowar, maize, and ragi with the intention of evaluation for efficient crop management in terms of the passing relevant messages to the farmers and alternate measures such as suggesting other geographical locations to grow the same crop or growing other suitable crops at same geographical location. A graphical user interface (GUI) application has been developed for the proposed work in order to ease out the flow of work.

Real-Time Weather Analytics

2020 ◽  
Vol 17 (4) ◽  
pp. 15-31
Author(s):  
Lavanya K. ◽  
Sathyan Venkatanarayanan ◽  
Anay Anand Bhoraskar
Keyword(s):  
Data Storage ◽  
Weather Forecasting ◽  
Modern Science ◽  
Weather Forecast ◽  
Forecast Model ◽  
Data Availability ◽  
Third Party ◽  
Weather Data ◽  
Storage Devices ◽  
Weather Forecast Model

Weather forecasting is one of the biggest challenges that modern science is still contending with. The advent of high-power computing, technical advancement of data storage devices, and incumbent reduction in the storage cost have accelerated data collection to turmoil. In this background, many artificial intelligence techniques have been developed and opened interesting window of opportunity in hitherto difficult areas. India is on the cusp of a major technology overhaul with millions of people's data availability who were earlier unconnected with the internet. The country needs to fast forward the innovative use of available data. The proposed model endeavors to forecast temperature, precipitation, and other vital information for usability in the agrarian sector. This project intends to develop a robust weather forecast model that learns automatically from the daily feed of weather data that is input through a third-party API source. The weather feed is sourced from openweathermap, an online service that provides weather data, and is streamed into the forecast model through Kafka components. The LSTM neural network used by the forecast model is designed to continuously learn from predictions and perform actual analysis. The model can be architected to be implemented across very large applications having the capability to process large volumes of streamed or stored data.

Colorful Language: Investigating Public Interpretation of the Storm Prediction Center Convective Outlook

2021 ◽  
Author(s):  
Sean Ernst ◽  
Joe Ripberger ◽  
Makenzie J. Krocak ◽  
Hank Jenkins-Smith ◽  
Carol Silva
Keyword(s):  
Statistical Tests ◽  
Weather Forecast ◽  
Severe Weather ◽  
Logistic Regression Models ◽  
Public Interpretation ◽  
Severe Thunderstorms ◽  
Weather Events ◽  
The Us ◽  
Storm Prediction ◽  
Future Work

AbstractAlthough severe weather forecast products, such as the Storm Prediction Center (SPC) convective outlook, are much more accurate than climatology at day-to-week time scales, tornadoes and severe thunderstorms claim dozens of lives and cause billions of dollars in damage every year. While the accuracy of this outlook has been well documented, less work has been done to explore the comprehension of the product by non-expert users like the general public. This study seeks to fill this key knowledge gap by collecting data from a representative survey of U.S. adults in the lower 48 states about their use and interpretation of the SPC convective outlook. Participants in this study were asked to rank the words and colors used in the outlook from least to greatest risk, and their answers were compared through visualizations and statistical tests across multiple demographics. Results show that the US public ranks the outlook colors similarly to their ordering in the outlook but switch the positions of several of the outlook words as compared to the operational product. Logistic regression models also reveal that more numerate individuals more correctly rank the SPC outlook words and colors. These findings suggest that the words used in the convective outlook may confuse non-expert users, and that future work should continue to use input from public surveys to test potential improvements in the choice of outlook words. Using more easily understood words may help to increase the outlook’s decision support value and potentially reduce the harm caused by severe weather events.

scholarly journals Severe weather over the Highveld of South Africa during November 2016

Water SA ◽  
2018 ◽  
Vol 44 (1 January) ◽  
Author(s):  
Lee-ann Simpson ◽  
Liesl L Dyson
Keyword(s):  
South Africa ◽  
Prediction Models ◽  
Weather Forecast ◽  
Severe Weather ◽  
Severe Thunderstorm ◽  
Weather Events ◽  
Circulation Patterns ◽  
Conditional Instability ◽  
Synoptic Circulation

November months are notorious for severe weather over the Highveld of South Africa. November 2016 was no exception and a large number of severe events occurred. Very heavy rainfall, large hail and tornadoes were reported. The aim of this paper is to compare the synoptic circulation of November 2016 with the long-term mean November circulation and to investigate some sounding derived parameters. Furthermore, a few of the severe weather events are described in detail. The surface temperatures and dewpoint temperatures were found to be higher than normal resulting in increased conditional instability over the Highveld. Low-level moisture originated over the warm Mozambique Channel and the 500 hPa temperature trough was located favourably over the Highveld; further east than normal. The combination of these factors and weak steering winds resulted in flash flooding on the 9th while favourable wind shear conditions caused the development of a tornado on 15 November. The favourable circulation patterns and moisture gave rise to an atmosphere in which severe weather was a possibility, and the awareness of such factors is used as one of many tools when considering the severe weather forecast. The consideration of the daily variables derived from sounding data were good precursors for the prediction of severe thunderstorm development over the Highveld during November 2016. It is recommended that an operational meteorologist incorporates upper air sounding data into the forecasting process and not to rely on numerical prediction models exclusively.

scholarly journals Initial results of the project SINOPTICA (Satellite-borne and IN-situ Observations to Predict The Initiation of Convection for ATM) 

2021 ◽  
Author(s):  
Laura Esbri ◽  
Maria Carmen Llasat ◽  
Tomeu Rigo ◽  
Massimo Milelli ◽  
Vincenzo Mazzarella ◽  
...  
Keyword(s):  
Weather Prediction ◽  
Wrf Model ◽  
Remote Sensing Data ◽  
Weather Forecast ◽  
Relevant Information ◽  
Air Traffic ◽  
Severe Weather ◽  
Radar Images ◽  
Weather Events ◽  
Adverse Weather

&lt;p&gt;In the framework of the SINOPTICA project (EU H2020 SESAR, 2020 &amp;#8211; 2022), different meteorological forecasting techniques are being tested to better nowcast severe weather events affecting Air Traffic Management (ATM) operations. Short-range severe weather forecasts with very high spatial resolution will be obtained starting from radar images, through an application of nowcasting techniques combined with Numerical Weather Prediction (NWP) model with data assimilation. The final goal is to integrate compact nowcast information into an Arrival Manager to support Air Traffic Controllers (ATCO) when sequencing and guiding approaching aircraft even in adverse weather situations. The guidance-support system will enable the visualization of dynamic weather information on the radar display of the controller, and the 4D-trajectory calculation for diversion coordination around severe weather areas. This meteorological information must be compact and concise to not interfere with other relevant information on the radar display of the controller.&lt;/p&gt;&lt;p&gt;Three severe weather events impacting different Italian airports have been selected for a preliminary radar analysis. Some products are considered for obtaining the best radar approach to characterize the severity of the events for ATM interests. Combining the Vertical Integrated Liquid and the Echo Top Maximum products, hazard thresholds are defined for different domains around the airports. The Weather Research and Forecasting (WRF) model has been used to simulate the formation and development of the aforementioned convective events. In order to produce a more accurate very short-term weather forecast (nowcasting), remote sensing data (e.g. radar, GNSS) and conventional observations are assimilated by using a cycling three-dimensional variational technique. This contribution presents some preliminary results on the progress of the project.&lt;/p&gt;

scholarly journals Detection of Severe Weather Events in a High-Resolution Ensemble Prediction System Using the Extreme Forecast Index (EFI) and Shift of Tails (SOT)

2018 ◽  
Vol 33 (4) ◽  
pp. 901-908
Author(s):  
Laure Raynaud ◽  
Benoît Touzé ◽  
Philippe Arbogast
Keyword(s):  
Reference Model ◽  
Weather Forecast ◽  
Ensemble Forecast ◽  
Severe Weather ◽  
Ensemble Prediction ◽  
False Alarms ◽  
Prediction System ◽  
Ensemble Prediction System ◽  
Wind Gusts ◽  
Weather Events

Abstract The extreme forecast index (EFI) and shift of tails (SOT) are commonly used to compare an ensemble forecast to a reference model climatology, in order to measure the severity of the current weather forecast. In this study, the feasibility and the relevance of EFI and SOT computations are examined within the convection-permitting Application of Research to Operations at Mesoscale (AROME-France) ensemble prediction system (EPS). First, different climate configurations are proposed and discussed, in order to overcome the small size of the ensemble and the short climate sampling length. Subjective and objective evaluations of EFI and SOT for wind gusts and precipitation forecasts are then presented. It is shown that these indices can provide relevant early warnings and, based on a trade-off between hits and false alarms, optimal EFI thresholds can be determined for decision-making.

Environments of Northeast U.S. Severe Thunderstorm Events from 1999 to 2009

2014 ◽  
Vol 29 (1) ◽  
pp. 3-22 ◽  
Author(s):  
Melissa M. Hurlbut ◽  
Ariel E. Cohen
Keyword(s):  
Large Scale ◽  
Weather Forecasting ◽  
Convective Available Potential Energy ◽  
Severe Weather ◽  
Severe Thunderstorms ◽  
Weather Events ◽  
Upper Level ◽  
Upper Level Jet ◽  
Lapse Rates ◽  
Jet Structure

Abstract An investigation of the environments and climatology of severe thunderstorms from 1999 through 2009 across the northeastern United States is presented. A total of 742 severe weather events producing over 12 000 reports were examined. Given the challenges that severe weather forecasting can present in the Northeast, this study is an effort to distinguish between the more prolific severe-weather-producing events and those that produce only isolated severe weather. The meteorological summer months (June–August) are found to coincide with the peak severe season. During this time, 850–500- and 700–500-hPa lapse rates, mixed layer convective inhibition (MLCIN), and downdraft convective available potential energy (DCAPE) are found to be statistically significant in discriminating events with a large number of reports from those producing fewer reports, based on observed soundings. Composite synoptic pattern analyses are also presented to spatially characterize the distribution of key meteorological variables associated with severe weather events of differing magnitudes. The presence of a midlevel trough and particular characteristics of its tilt, along with an accompanying zone of enhanced flow, are found in association with the higher-report severe weather events, along with cooler midlevel temperatures overlaying warmer low-level temperatures (i.e., contributing to the steeper lapse rates). During the meteorological fall and winter months (September–February), large-scale ascent is often bolstered by the presence of a coupled upper-level jet structure.

scholarly journals Site-specific analog weather-forecast system for northwest Himalaya, India

2008 ◽  
Vol 49 ◽  
pp. 224-230 ◽  
Author(s):  
Dan Singh ◽  
Amreek Singh ◽  
Ashwagosha Ganju
Keyword(s):  
Weather Forecasting ◽  
Forecast Accuracy ◽  
Weather Forecast ◽  
Analog Model ◽  
Site Specific ◽  
Northwest Himalaya ◽  
Weather Forecasts ◽  
The Past ◽  
Weather Events ◽  
Weather Situation

AbstractIn an analog weather-forecasting procedure, recorded weather in the past analogs corresponding to the current weather situation is used to predict future weather. Consistent with the procedure, a theoretical framework is developed to predict weather at a specific site in the Pir Panjal range of the northwest Himalaya, India, using surface weather observations of the past ten winters (1991/92 to 2001/02) 3 days in advance. Weather predictions were made as snow day with quantitative snowfall category or no-snow day, for day1 through day3. As currently deployed, the procedure routinely provides a 3 day point weather forecast as guidance information to a weather and avalanche forecaster. Forecasts by analog model are evaluated by the various accuracy measures achieved for an independent dataset of three winters (2002/03 to 2004/05). The results indicate that weather forecasts by analog model are quite reliable, in that forecast accuracy corresponds closely to the relative frequencies of observed weather events. Moreover, qualitative weather (snow day or no-snow day) and quantitative categorical snowfall forecasts (quantitative snowfall category for snow day) are better than reference forecasts based on persistence and climatology for day1 predictions. Site-specific snowfall forecast guidance may play a major role in assessing avalanche danger, and accordingly formulating an avalanche forecast for a given area in advance.

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