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Journal articles on the topic 'Weather forecast'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Singh, Dan, Amreek Singh, and Ashwagosha Ganju. "Site-specific analog weather-forecast system for northwest Himalaya, India." Annals of Glaciology 49 (2008): 224–30. http://dx.doi.org/10.3189/172756408787815013.

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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 pr

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Firanj Sremac, Ana, Branislava Lalić, Milena Marčić, and Ljiljana Dekić. "Toward a Weather-Based Forecasting System for Fire Blight and Downy Mildew." Atmosphere 9, no. 12 (2018): 484. http://dx.doi.org/10.3390/atmos9120484.

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The aim of this research is to present a weather-based forecasting system for apple fire blight (Erwinia amylovora) and downy mildew of grapevine (Plasmopara viticola) under Serbian agroecological conditions and test its efficacy. The weather-based forecasting system contains Numerical Weather Prediction (NWP) model outputs and a disease occurrence model. The weather forecast used is a product of the high-resolution forecast (HRES) atmospheric model by the European Centre for Medium-Range Weather Forecasts (ECMWF). For disease modelling, we selected a biometeorological system for messages on t

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Roads, JO, K. Ueyoshi, SC Chen, J. Alpert, and F. Fujioka. "Medium-range fire weather forecasts." International Journal of Wildland Fire 1, no. 3 (1991): 159. http://dx.doi.org/10.1071/wf9910159.

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The forecast skill of theNational Meteorological Center's medium range forecast (MRF) numerical forecasts of fire weather variables is assessed for the period June 1,1988 to May 31,1990. Near-surface virtual temperature, relative humidity, wind speed and a derived fire weather index (FWI) are forecast well by the MRF model. However, forecast relative humidity has a wet bias during the winter and a slight dry bias during the summer, which has noticeable impact on forecasts of the derived fire weather index. The FWI forecasts are also strongly affected by near-surface wind forecast errors. Still

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Chen, Jie, François P. Brissette, and Zhi Li. "Postprocessing of Ensemble Weather Forecasts Using a Stochastic Weather Generator." Monthly Weather Review 142, no. 3 (2014): 1106–24. http://dx.doi.org/10.1175/mwr-d-13-00180.1.

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Abstract This study proposes a new statistical method for postprocessing ensemble weather forecasts using a stochastic weather generator. Key parameters of the weather generator were linked to the ensemble forecast means for both precipitation and temperature, allowing the generation of an infinite number of daily times series that are fully coherent with the ensemble weather forecast. This method was verified through postprocessing reforecast datasets derived from the Global Forecast System (GFS) for forecast leads ranging between 1 and 7 days over two Canadian watersheds in the Province of Q

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Demuth, Julie L., Jeffrey K. Lazo, and Rebecca E. Morss. "Exploring Variations in People’s Sources, Uses, and Perceptions of Weather Forecasts." Weather, Climate, and Society 3, no. 3 (2011): 177–92. http://dx.doi.org/10.1175/2011wcas1061.1.

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Abstract Past research has shown that individuals vary in their attitudes and behaviors regarding weather forecast information. To deepen knowledge about these variations, this article explores 1) patterns in people’s sources, uses, and perceptions of everyday weather forecasts; and 2) relationships among people’s sources, uses, and perceptions of forecasts, their personal characteristics, and their experiences with weather and weather forecasts. It does so by performing factor and regression analysis on data from a nationwide survey of the U.S. public, combined with other data. Forecast uses

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Compton, Josh. "When Weather Forecasters Are Wrong: Image Repair and Public Rhetoric After Severe Weather." Science Communication 40, no. 6 (2018): 778–88. http://dx.doi.org/10.1177/1075547018799101.

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Weather forecasts are a unique type of prediction rhetoric—science communication with inherent uncertainty and multiple potential interpretations from diverse audiences. When forecasts are wrong, audiences often turn their ire toward the weather forecaster. This rhetorical analysis considers image repair efforts of a meteorologist following a botched winter storm forecast. Implications for communication efforts of weather forecasters are offered, in addition to consideration of insight into the larger realm of the rhetoric of science.

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Hallgren, Christoffer, Stefan Ivanell, Heiner Körnich, Ville Vakkari, and Erik Sahlée. "The smoother the better? A comparison of six post-processing methods to improve short-term offshore wind power forecasts in the Baltic Sea." Wind Energy Science 6, no. 5 (2021): 1205–26. http://dx.doi.org/10.5194/wes-6-1205-2021.

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Abstract. With a rapidly increasing capacity of electricity generation from wind power, the demand for accurate power production forecasts is growing. To date, most wind power installations have been onshore and thus most studies on production forecasts have focused on onshore conditions. However, as offshore wind power is becoming increasingly popular it is also important to assess forecast quality in offshore locations. In this study, forecasts from the high-resolution numerical weather prediction model AROME was used to analyze power production forecast performance for an offshore site in t

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Novak, David R., Christopher Bailey, Keith F. Brill, et al. "Precipitation and Temperature Forecast Performance at the Weather Prediction Center." Weather and Forecasting 29, no. 3 (2014): 489–504. http://dx.doi.org/10.1175/waf-d-13-00066.1.

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Abstract The role of the human forecaster in improving upon the accuracy of numerical weather prediction is explored using multiyear verification of human-generated short-range precipitation forecasts and medium-range maximum temperature forecasts from the Weather Prediction Center (WPC). Results show that human-generated forecasts improve over raw deterministic model guidance. Over the past two decades, WPC human forecasters achieved a 20%–40% improvement over the North American Mesoscale (NAM) model and the Global Forecast System (GFS) for the 1 in. (25.4 mm) (24 h)−1 threshold for day 1 pre

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Wible, B. "Weather Forecast." Science 329, no. 5998 (2010): 1443. http://dx.doi.org/10.1126/science.329.5998.1443-a.

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Syed, Shah Waqi Ali, Kumar Rohit, Rai Anish, Kumar Ashish, Deepak N. R. Dr., and B. Omprakash. "Weather Forecast Prediction." Recent Trends in Computer Graphics and Multimedia Technology 7, no. 2 (2025): 22–30. https://doi.org/10.5281/zenodo.15331762.

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<em>Weather forecasting involves the use of science and technology to forecast weather conditions for a particular area continues to be a major challenge worldwide. This project focuses on estimating weather conditions through predictive analysis. To achieve this, an evaluation of various data mining techniques is essential prior to implementation. This study proposes a classification-based approach for weather prediction, utilizing algorithms such as Naive Bayes and Chi-Square for classification tasks.</em> <em>The system is designed as a web application with an intuitive graphical user inter

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Morss, Rebecca E., Julie L. Demuth, and Jeffrey K. Lazo. "Communicating Uncertainty in Weather Forecasts: A Survey of the U.S. Public." Weather and Forecasting 23, no. 5 (2008): 974–91. http://dx.doi.org/10.1175/2008waf2007088.1.

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Abstract Weather forecasts are inherently uncertain, and meteorologists have information about weather forecast uncertainty that is not readily available to most forecast users. Yet effectively communicating forecast uncertainty to nonmeteorologists remains challenging. Improving forecast uncertainty communication requires research-based knowledge that can inform decisions on what uncertainty information to communicate, when, and how to do so. To help build such knowledge, this article explores the public’s perspectives on everyday weather forecast uncertainty and uncertainty information using

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Vercelli, David J. "Automatic Monitoring of Aviation Terminal Forecasts." Bulletin of the American Meteorological Society 66, no. 3 (1985): 292–96. http://dx.doi.org/10.1175/1520-0477-66.3.292.

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One of the goals of the Automation of Field Operations and Services (AFOS) was to implement a computer program to automatically monitor aviation terminal forecasts at National Weather Service (NWS) forecast offices. Such a program has now been developed. The program can be initiated either automatically at predefined times or by the forecaster at any time. Once initiated, the program compares the information in the latest terminal forecast and surface airway observation at each designated terminal to a set of amendment and alert criteria. It then notifies the forecaster via an audio or visual

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Hughes, G., N. McRoberts, and F. J. Burnett. "Resolution of Probabilistic Weather Forecasts with Application in Disease Management." Phytopathology® 107, no. 2 (2017): 158–62. http://dx.doi.org/10.1094/phyto-07-16-0256-r.

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Predictive systems in disease management often incorporate weather data among the disease risk factors, and sometimes this comes in the form of forecast weather data rather than observed weather data. In such cases, it is useful to have an evaluation of the operational weather forecast, in addition to the evaluation of the disease forecasts provided by the predictive system. Typically, weather forecasts and disease forecasts are evaluated using different methodologies. However, the information theoretic quantity expected mutual information provides a basis for evaluating both kinds of forecast

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Loken, Eric D., Adam J. Clark, and Christopher D. Karstens. "Generating Probabilistic Next-Day Severe Weather Forecasts from Convection-Allowing Ensembles Using Random Forests." Weather and Forecasting 35, no. 4 (2020): 1605–31. http://dx.doi.org/10.1175/waf-d-19-0258.1.

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AbstractExtracting explicit severe weather forecast guidance from convection-allowing ensembles (CAEs) is challenging since CAEs cannot directly simulate individual severe weather hazards. Currently, CAE-based severe weather probabilities must be inferred from one or more storm-related variables, which may require extensive calibration and/or contain limited information. Machine learning (ML) offers a way to obtain severe weather forecast probabilities from CAEs by relating CAE forecast variables to observed severe weather reports. This paper develops and verifies a random forest (RF)-based ML

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Sutikno, Sutikno, Fajar Dwi Cahyoko, Fernaldy Wananda Putra, et al. "Calibration Indonesian-Numerical Weather Prediction using Geostatistical Output Perturbation." Jurnal Meteorologi dan Geofisika 24, no. 2 (2024): 105–13. http://dx.doi.org/10.31172/jmg.v24i2.1037.

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Indonesian-Numerical Weather Prediction (INA-NWP) is a numerical-based weather forecast method that has been developed by the Meteorology, Climatology and Geophysics Agency. However, the forecast is still unable to produce accurate weather forecasts. Geostatistical Output Perturbation (GOP) is a weather forecast method derived from only one deterministic output. GOP takes into consideration the spatial correlation among multiple locations simultaneously. GOP is capable to identify spatial dependency patterns that are associated with error models. This study aims to obtain calibrated forecasts

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Guan, Hong, and Yuejian Zhu. "Development of Verification Methodology for Extreme Weather Forecasts." Weather and Forecasting 32, no. 2 (2017): 479–91. http://dx.doi.org/10.1175/waf-d-16-0123.1.

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Abstract In 2006, the statistical postprocessing of the National Centers for Environmental Prediction (NCEP) Global Ensemble Forecast System (GEFS) and North American Ensemble Forecast System (NAEFS) was implemented to enhance probabilistic guidance. Anomaly forecasting (ANF) is one of the NAEFS products, generated from bias-corrected ensemble forecasts and reanalysis climatology. The extreme forecast index (EFI), based on a raw ensemble forecast and model-based climatology, is another way to build an extreme weather forecast. In this work, the ANF and EFI algorithms are applied to extreme col

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Zarnani, Ashkan, Soheila Karimi, and Petr Musilek. "Quantile Regression and Clustering Models of Prediction Intervals for Weather Forecasts: A Comparative Study." Forecasting 1, no. 1 (2019): 169–88. http://dx.doi.org/10.3390/forecast1010012.

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Information about forecast uncertainty is vital for optimal decision making in many domains that use weather forecasts. However, it is not available in the immediate output of deterministic numerical weather prediction systems. In this paper, we investigate several learning methods to train and evaluate prediction interval models of weather forecasts. The uncertainty models of weather predictions are trained from a database of historical forecasts/observations. They are developed to investigate prediction intervals of weather forecasts using various quantile regression methods as well as clust

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Sen, Dr P. N., Sunny Chug, and Arti Bandgar. "Journey of Operational Weather Forecasting in India." MAUSAM 76, no. 1 (2025): 65–78. https://doi.org/10.54302/mausam.v76i1.6406.

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India Meteorological Department (IMD) was established in 1875. Its mandate was to study climate and weather of India as a whole and to issue storm and other warnings and daily weather forecast. IMD issues Cyclone Forecasts and Warnings; Day to day Forecasts, Medium Range and Extended Range and Long Range Forecasts; Forecast for Aviation activities; Nowcasting; Quantitative Precipitation Forecast for Flood warning etc. Initially, forecasting was done by Synoptic and Statistical methods. Nowadays, forecasts are issued using Numerical methods. Surface and upper air data along with Radar and Satel

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CHATTOPADHYAY, N., K. GHOSH, K. MALATHI, S. K. ROY BHOWMIK, and K. K. SINGH. "Verification of district level weather forecast." MAUSAM 67, no. 4 (2021): 829–40. http://dx.doi.org/10.54302/mausam.v67i4.1410.

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IMD started issuing quantitative district level weather forecast upto 5 days on operational basis from 1st June, 2008. The products comprise of quantitative forecasts for seven weather parameters, viz., rainfall, maximum and minimum temperatures, wind speed and direction, relative humidity and cloudiness. The rainfall forecast is generated based on multi model-ensemble techniques (MME). For other parameters, ECMWF forecasts (presently IMDGFS) are used. These forecast products are further value added, by the respective MCs/RMCs and forwarded to 130 Agrometeorological Field Units (AMFUs) for pre

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Zalenski, Grace, Witold F. Krajewski, Felipe Quintero, Pedro Restrepo, and Steve Buan. "Analysis of National Weather Service Stage Forecast Errors." Weather and Forecasting 32, no. 4 (2017): 1441–65. http://dx.doi.org/10.1175/waf-d-16-0219.1.

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Abstract This paper explores the skill of river stage forecasts produced by the National Weather Service (NWS). Despite the importance of the verification process in establishing a reference that allows advancement in river forecast technology, there is relatively little literature on this topic. This study aims to contribute to this subject. The study analyzed the North Central River Forecast Center’s river stage forecasts for 51 gauges in eastern and central Iowa between 1999 and 2014. The authors explored forecast skill dependence characteristics such as upstream area, water travel time, an

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Ivanov, Evgeniy, Aleksey Gubin, Sergey Lesovoi, and Ramses Zaldivar Estrada. "SOLAR SPECTROPOLARIMETER FOR SPACE WEATHER FORECAST." Solar-Terrestrial Physics 5, no. 4 (2019): 21–26. http://dx.doi.org/10.12737/stp-54201903.

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We propose a project of the meter wavelength range solar spectropolarimeter designed for a ground-based network developing for space weather forecast. The Software-Defined Radio (SDR) solution is chosen to meet such instrument network requirements as specification identity, low cost, possibility of controlling and transmitting data remotely via the Internet. Along with these requirements, the proposed SDR solution allows us to measure Stokes I and V easily, which contrasts the proposed instrument with e-CALLISTO network spectropolarimeters, most of which can record only one linear polarization

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Morss, Rebecca E., and F. Martin Ralph. "Use of Information by National Weather Service Forecasters and Emergency Managers during CALJET and PACJET-2001." Weather and Forecasting 22, no. 3 (2007): 539–55. http://dx.doi.org/10.1175/waf1001.1.

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Abstract Winter storms making landfall in western North America can generate heavy precipitation and other significant weather, leading to floods, landslides, and other hazards that cause significant damage and loss of life. To help alleviate these negative impacts, the California Land-falling Jets (CALJET) and Pacific Land-falling Jets (PACJET) experiments took extra meteorological observations in the coastal region to investigate key research questions and aid operational West Coast 0–48-h weather forecasting. This article presents results from a study of how information provided by CALJET a

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Pelosi, Anna, Paolo Villani, Salvatore Falanga Bolognesi, Giovanni Battista Chirico, and Guido D’Urso. "Predicting Crop Evapotranspiration by Integrating Ground and Remote Sensors with Air Temperature Forecasts." Sensors 20, no. 6 (2020): 1740. http://dx.doi.org/10.3390/s20061740.

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Water use efficiency in agriculture can be improved by implementing advisory systems that support on-farm irrigation scheduling, with reliable forecasts of the actual crop water requirements, where crop evapotranspiration (ETc) is the main component. The development of such advisory systems is highly dependent upon the availability of timely updated crop canopy parameters and weather forecasts several days in advance, at low operational costs. This study presents a methodology for forecasting ETc, based on crop parameters retrieved from multispectral images, data from ground weather sensors, a

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O'Malley, Mary. "The Weather Forecast." Estudios Irlandeses, no. 11 (March 15, 2016): 232–33. http://dx.doi.org/10.24162/ei2015-6535.

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O'Malley, Mary. "The Weather Forecast." Estudios Irlandeses, no. 11 (March 15, 2016): 232–33. http://dx.doi.org/10.24162/ei2016-6535.

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Werner, Kevin, David Brandon, Martyn Clark, and Subhrendu Gangopadhyay. "Incorporating Medium-Range Numerical Weather Model Output into the Ensemble Streamflow Prediction System of the National Weather Service." Journal of Hydrometeorology 6, no. 2 (2005): 101–14. http://dx.doi.org/10.1175/jhm411.1.

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Abstract This study introduces medium-range meteorological ensemble inputs of temperature and precipitation into the Ensemble Streamflow Prediction component of the National Weather Service River Forecast System (NWSRFS). The Climate Diagnostics Center (CDC) produced a reforecast archive of model forecast runs from a dynamically frozen version of the Medium-Range Forecast (MRF) model. This archive was used to derive statistical relationships between MRF variables and historical basin-average precipitation and temperatures. The latter are used to feed the Ensemble Streamflow Prediction (ESP) co

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Gautam, Dilip Kumar, and Raju Dhar Pradhananga. "Verification and Bias Correction of Rainfall and Temperature Forecasts over the Babai River Basin of Nepal." Journal of Hydrology and Meteorology 12, no. 1 (2024): 1–10. https://doi.org/10.3126/jhm.v12i1.72624.

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Weather Research & Forecasting model is known to exhibit systematic biases for weather variables. These biases need to be post-processed to get an optimal result before applying the weather forecast data in hydrological modeling or similar applications. In this paper, we examined the performance of Weather Research & Forecasting Model forecasts for rainfall and temperature over the Babai River basin of Nepal considering various performance indicators using statistical approach. The model was able to capture the rainfall event forecast (Rain/No Rain) sufficiently. However, the model sho

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Stamoulis, Dimitrios S., and Panos A. Giannopoulos. "“My personal forecast”: the digital transformation of the weather forecast communication using a fuzzy logic recommendation system." Advances in Science and Research 19 (March 23, 2022): 9–12. http://dx.doi.org/10.5194/asr-19-9-2022.

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Abstract. Communicating the scientific data of the weather forecasts to the general public has always been a challenge. Using computer graphics' visual representations to convey the message to average people has certainly helped a lot to popularize the weather forecast consumption by the general public. However, these representations are not information rich since they are abstractions; moreover they are not very actionable on the receiver side to help one decide how s/he will “live” the forecast weather conditions and prepare appropriately. Therefore, there is a need to personalize the foreca

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Roeger, Claudia, David M. McClung, and Roland Stull. "Verified combination of numerical weather- and avalanche-prediction models at Kootenay Pass, British Columbia, Canada." Annals of Glaciology 38 (2004): 215–22. http://dx.doi.org/10.3189/172756404781815301.

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AbstractThe combination of numerical weather prediction (NWP) and snow avalanche forecasting has been performed using the output of two weather models run at the University of British Columbia, Canada, and a local numerical avalanche-forecasting model developed for Kootenay Pass (McClung and Tweedy, 1994). The main motivations for this work are. (1) to extend the lead time of avalanche forecasts by using NWP forecasts of meteorological variables as input to statistical avalanche-threat models (instead of the traditional method of using current/past observed meteorological variables as input);

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Giunta, G., R. Salerno, A. Ceppi, G. Ercolani, and M. Mancini. "Effects of Model Horizontal Grid Resolution on Short- and Medium-Term Daily Temperature Forecasts for Energy Consumption Application in European Cities." Advances in Meteorology 2019 (May 8, 2019): 1–12. http://dx.doi.org/10.1155/2019/1561697.

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A short-term forecast of energy consumption is affected by different factors related to the demand in residential, commercial, thermoelectric, and industrial sectors. This demand can be strongly constrained by weather variables, especially temperatures, whose forecast may be very useful to predict the balances between supply and demand, minimizing the risk of price volatility. Energy companies use the relationship between meteorological forecast output and energy request to provide an effective scheduling of national gas and power grids and reduce operational costs in critical periods. This wo

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Hegdahl, Trine J., Kolbjørn Engeland, Ingelin Steinsland, and Lena M. Tallaksen. "Streamflow forecast sensitivity to air temperature forecast calibration for 139 Norwegian catchments." Hydrology and Earth System Sciences 23, no. 2 (2019): 723–39. http://dx.doi.org/10.5194/hess-23-723-2019.

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Abstract. In this study, we used meteorological ensemble forecasts as input to hydrological models to quantify the uncertainty in forecasted streamflow, with a particular focus on the effect of temperature forecast calibration on the streamflow ensemble forecast skill. In catchments with seasonal snow cover, snowmelt is an important flood-generating process. Hence, high-quality air temperature data are important to accurately forecast streamflows. The sensitivity of streamflow ensemble forecasts to the calibration of temperature ensemble forecasts was investigated using ensemble forecasts of t

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Wang, Yunheng, Jidong Gao, Patrick S. Skinner, et al. "Test of a Weather-Adaptive Dual-Resolution Hybrid Warn-on-Forecast Analysis and Forecast System for Several Severe Weather Events." Weather and Forecasting 34, no. 6 (2019): 1807–27. http://dx.doi.org/10.1175/waf-d-19-0071.1.

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Abstract A real-time, weather adaptive, dual-resolution, hybrid Warn-on-Forecast (WoF) analysis and forecast system using the WRF-ARW forecast model has been developed and implemented. The system includes two components, an ensemble analysis and forecast component, and a deterministic hybrid three-dimensional ensemble–variational (3DEnVAR) analysis and forecast component. The goal of the system is to provide on-demand, ensemble-based, and physically consistent gridded analysis and forecast products to forecasters for making warning decisions. Both components, the WRF-DART system with 36 ensemb

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Berrocal, Veronica J., Adrian E. Raftery, and Tilmann Gneiting. "Combining Spatial Statistical and Ensemble Information in Probabilistic Weather Forecasts." Monthly Weather Review 135, no. 4 (2007): 1386–402. http://dx.doi.org/10.1175/mwr3341.1.

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Abstract Forecast ensembles typically show a spread–skill relationship, but they are also often underdispersive, and therefore uncalibrated. Bayesian model averaging (BMA) is a statistical postprocessing method for forecast ensembles that generates calibrated probabilistic forecast products for weather quantities at individual sites. This paper introduces the spatial BMA technique, which combines BMA and the geostatistical output perturbation (GOP) method, and extends BMA to generate calibrated probabilistic forecasts of whole weather fields simultaneously, rather than just weather events at i

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Li, Peng, Liang He, Xuetong Wang, et al. "The Forecasting Yield of Highland Barley and Wheat by Combining a Crop Model with Different Weather Fusion Methods in the Study of the Northeastern Tibetan Plateau." Atmosphere 16, no. 5 (2025): 551. https://doi.org/10.3390/atmos16050551.

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Obtaining precise seasonal yield estimates is challenging, with weather forecast accuracy being a key factor. This study examines the impact of different weather data forecasting methods on yield estimation. Initially, we evaluated the suitability of the WOFOST model for highland barley (HB) and wheat on the northeastern Tibetan Plateau. Yield forecasts were conducted using nine historical weather selection methods under two scenarios, differing in their use of 10-day TIGGE data. The results showed that different weather data fusion methods led to varying forecasted yields. For HB, sequential

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Drost, Robert, Jay Trobec, Christy Steffke, and Julie Libarkin. "Eye Tracking: Evaluating the Impact of Gesturing during Televised Weather Forecasts." Bulletin of the American Meteorological Society 96, no. 3 (2015): 387–92. http://dx.doi.org/10.1175/bams-d-13-00217.1.

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Abstract Televised media is one of the most frequently accessed sources of weather information. The local weathercaster is the link between weather information and the public, and as such weathercaster characteristics, from vocal cadence to physical appearance, can impact viewer understanding. This study considers the role of weathercaster gesturing on viewer attention during weather forecasts. Two variations of a typical weather forecast were viewed by a total of 36 students during an eye tracking session. The first forecast variation contained physical gestures toward forecast text by the ne

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Lee, Ki-Kwang, and In-Gyum Kim. "Social Media Data Analytics to Enhance Sustainable Communications between Public Users and Providers in Weather Forecast Service Industry." Sustainability 12, no. 20 (2020): 8528. http://dx.doi.org/10.3390/su12208528.

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The weather forecast service industry needs to understand customers’ opinions of the weather forecast to enhance sustainable communication between forecast providers and recipients particularly influenced by inherent uncertainty in the forecast itself and cultural factors. This study aims to investigate the potential for using social media data to analyze users’ opinions of the wrong weather forecast. Twitter data from Korea in 2014 are analyzed using textual analysis and association rule mining to extract meaningful emotions or behaviors from weather forecast users. The results of textual ana

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Wall, Tamara U., Timothy J. Brown, and Nicholas J. Nauslar. "Spot Weather Forecasts: Improving Utilization, Communication, and Perceptions of Accuracy in Sophisticated User Groups." Weather, Climate, and Society 9, no. 2 (2017): 215–26. http://dx.doi.org/10.1175/wcas-d-15-0055.1.

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Abstract Spot weather forecasts (SWFs) are issued by Weather Service offices throughout the United States and are primarily for use by wildfire and prescribed fire practitioners for monitoring local-scale weather conditions. This paper focuses on use of SWFs by prescribed fire practitioners. Based on qualitative, in-depth interviews with fire practitioners and National Weather Service forecasters, this paper examines factors that influence perceptions of accuracy and utilization of SWFs. Results indicate that, while several well-understood climatological, topographical, and data-driven factors

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Suess, Elizabeth J., Cinzia Cervato, William A. Gallus, and Jonathon M. Hobbs. "Weather Forecasting as a Learning Tool in a Large Service Course: Does Practice Make Perfect?" Weather and Forecasting 28, no. 3 (2013): 762–71. http://dx.doi.org/10.1175/waf-d-12-00105.1.

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Abstract Each spring roughly 200 students, mostly nonmajors, enroll in the Introduction to Meteorology course at Iowa State University and are required to make at least 25 forecasts throughout the semester. The Dynamic Weather Forecaster (DWF) forecasting platform requires students to forecast more than just simple “numeric” forecasts and includes questions on advection, cloudiness, and precipitation factors that are not included in forecast contests often used in meteorology courses. The present study examines the evolution of forecasting skill for students enrolled in the class in spring 201

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Ghirardelli, Judy E., and Bob Glahn. "The Meteorological Development Laboratory’s Aviation Weather Prediction System." Weather and Forecasting 25, no. 4 (2010): 1027–51. http://dx.doi.org/10.1175/2010waf2222312.1.

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Abstract The Meteorological Development Laboratory (MDL) has developed and implemented an aviation weather prediction system that runs each hour and produces forecast guidance for each hour into the future out to 25 h covering the major forecast period of the National Weather Service (NWS) Terminal Aerodrome Forecast. The Localized Aviation Model Output Statistics (MOS) Program (LAMP) consists of analyses of observations, simple advective models, and a statistical component that updates the longer-range MOS forecasts from the Global Forecast System (GFS) model. LAMP, being an update to GFS MOS

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Shukla, S., N. Voisin, and D. P. Lettenmaier. "Value of medium range weather forecasts in the improvement of seasonal hydrologic prediction skill." Hydrology and Earth System Sciences 16, no. 8 (2012): 2825–38. http://dx.doi.org/10.5194/hess-16-2825-2012.

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Abstract. We investigated the contribution of medium range weather forecasts with lead times of up to 14 days to seasonal hydrologic prediction skill over the conterminous United States (CONUS). Three different Ensemble Streamflow Prediction (ESP) based experiments were performed for the period 1980–2003 using the Variable Infiltration Capacity (VIC) hydrology model to generate forecasts of monthly runoff and soil moisture (SM) at lead-1 (first month of the forecast period) to lead-3. The first experiment (ESP) used a resampling from the retrospective period 1980–2003 and represented full clim

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Shukla, S., N. Voisin, and D. P. Lettenmaier. "Value of medium range weather forecasts in the improvement of seasonal hydrologic prediction skill." Hydrology and Earth System Sciences Discussions 9, no. 2 (2012): 1827–57. http://dx.doi.org/10.5194/hessd-9-1827-2012.

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Abstract. We investigated the contribution of medium range weather forecasts with lead times up to 14 days to seasonal hydrologic prediction skill over the Conterminous United States (CONUS). Three different Ensemble Streamflow Prediction (ESP)-based experiments were performed for the period 1980–2003 using the Variable Infiltration Capacity (VIC) hydrology model to generate forecasts of monthly runoff and soil moisture (SM) at lead-1 (first month of the forecast period) to lead-3. The first experiment (ESP) used a resampling from the retrospective period 1980–2003 and represented full climato

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Lu, Chungu, Huiling Yuan, Barry E. Schwartz, and Stanley G. Benjamin. "Short-Range Numerical Weather Prediction Using Time-Lagged Ensembles." Weather and Forecasting 22, no. 3 (2007): 580–95. http://dx.doi.org/10.1175/waf999.1.

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Abstract A time-lagged ensemble forecast system is developed using a set of hourly initialized Rapid Update Cycle model deterministic forecasts. Both the ensemble-mean and probabilistic forecasts from this time-lagged ensemble system present a promising improvement in the very short-range weather forecasting of 1–3 h, which may be useful for aviation weather prediction and nowcasting applications. Two approaches have been studied to combine deterministic forecasts with different initialization cycles as the ensemble members. The first method uses a set of equally weighted time-lagged forecasts

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Denholm-Price, J. C. W. "Can an ensemble give anything more than Gaussian probabilities?" Nonlinear Processes in Geophysics 10, no. 6 (2003): 469–75. http://dx.doi.org/10.5194/npg-10-469-2003.

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Abstract. Can a relatively small numerical weather prediction ensemble produce any more forecast information than can be reproduced by a Gaussian probability density function (PDF)? This question is examined using site-specific probability forecasts from the UK Met Office. These forecasts are based on the 51-member Ensemble Prediction System of the European Centre for Medium-range Weather Forecasts. Verification using Brier skill scores suggests that there can be statistically-significant skill in the ensemble forecast PDF compared with a Gaussian fit to the ensemble. The most significant incr

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Cherubini, Tiziana, Ryan Lyman, and Steven Businger. "Forecasting seeing for the Maunakea observatories with machine learning." Monthly Notices of the Royal Astronomical Society 509, no. 1 (2021): 232–45. http://dx.doi.org/10.1093/mnras/stab2916.

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ABSTRACT The staff at the Maunakea Weather Center (MKWC) has provided daily forecasts of optical turbulence for the summit of Maunakea for more than 20 yr. Observational measures of optical turbulence at Maunakea with which to validate official MKWC forecasts have been available since mid-2009. This paper presents a machine-learning approach to translate the MKWC experience into a forecast of the nightly average optical turbulent state of the atmosphere. Maunakea observational and forecast data were collected to build a predictive model of the total and free atmospheric seeing for the followin

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Dupuy, Florian, Gert-Jan Duine, Pierre Durand, Thierry Hedde, Eric Pardyjak, and Pierre Roubin. "Valley Winds at the Local Scale: Correcting Routine Weather Forecast Using Artificial Neural Networks." Atmosphere 12, no. 2 (2021): 128. http://dx.doi.org/10.3390/atmos12020128.

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In regions of complex topography, local flows are difficult to forecast on a routine basis, especially in stable conditions, due to the coarse resolution of operational models. The Cadarache valley (southeastern France) features this sort of complex topography. The Weather Research and Forecasting (WRF) model is run daily to forecast the weather in this region with a horizontal resolution of 3 km. Such a resolution cannot resolve all topography details of the small Cadarache valley, and therefore its local wind patterns. Other variables, however, that are less dependent on the subgrid topograp

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Bolelov, E. A. "Increase success rate of weather forecasts for the airfield by integration of measurements of meteorological parameters of the atmosphere." Civil Aviation High Technologies 22, no. 5 (2019): 43–53. http://dx.doi.org/10.26467/2079-0619-2019-22-5-43-53.

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Accuracy of aviation weather forecasts is one of the main indicators characterizing the quality of meteorological support of flights. A significant influence of the quality of meteorological support on flight safety and regularity is confirmed by the results of the annual tests conducted by the Federal Agency for Air Transport of Russia and "Aviamettelecom of Roshydromet". Currently, the quality of meteorological support of flights is still at a low level compared to countries that are recognized leaders in the aviation industry. To develop high-quality weather forecasts for the airfield weath

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Chakraborty, Debasish, Saurav Saha, Bira Kishore Sethy, et al. "Usability of the Weather Forecast for Tackling Climatic Variability and Its Effect on Maize Crop Yield in Northeastern Hill Region of India." Agronomy 12, no. 10 (2022): 2529. http://dx.doi.org/10.3390/agronomy12102529.

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Weather forecasts are important for the planning of agricultural operations, especially during times of heightened climatic variability. This study analyzed and verified the medium-range weather forecast issued by the India Meteorological Department (IMD) for different weather parameters over four locations in the northeastern hill (NEH) region of India considering five years of daily datasets. Results revealed good overall accuracy of the forecast over the NEH region. The accuracy of relative humidity (>80%), rainfall (>79%), and wind speed (>70%) were good, and the accuracy of tempe

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Clive, Mary Anne T., Emma E. H. Doyle, Sally H. Potter, Chris Noble, and David M. Johnston. "How Visual Design of Severe Weather Outlooks Can Affect Communication and Decision-Making." Weather, Climate, and Society 15, no. 4 (2023): 979–97. http://dx.doi.org/10.1175/wcas-d-23-0010.1.

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Abstract Multiday severe weather outlooks can inform planning beyond the hour-to-day windows of warnings and watches. Outlooks can be complex to visualize, as they represent large-scale weather phenomena overlapping across several days at varying levels of uncertainty. Here, we present the results of a survey (n = 417) that explores how visual variables affect comprehension, inferences, and intended decision-making in a hypothetical scenario with the New Zealand MetService Severe Weather Outlook. We propose that visualization of the time window, forecast area, icons, and uncertainty can influe

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Khan,, Mohd Amaan. "WEATHER APP." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem34353.

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In today's world, the challenge of instantly accessing accurate weather updates for specific locations is significant. Weather forecasting involves complex skills, requiring the observation and processing of vast amounts of data. From small, short-lived thunderstorms to large-scale weather systems spanning thousands of miles and lasting for days, the variability of weather phenomena adds to the difficulty of obtaining precise forecasts. This often leads to inconvenience and challenges for individuals. However, technology offers a solution to this problem. Through the development of an Android

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Martin, Max, S. Abhilash, Vijaykumar Pattathil, et al. "Should I Stay or Should I Go? South Indian Artisanal Fishers’ Precarious Livelihoods and Their Engagement with Categorical Ocean Forecasts." Weather, Climate, and Society 14, no. 1 (2022): 113–29. http://dx.doi.org/10.1175/wcas-d-20-0044.1.

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Abstract Ocean State Forecasts contribute to safe and sustainable fishing in India, but their usage among artisanal fishers is often limited. Our research in Thiruvananthapuram district in the southern Indian state of Kerala tested forecast quality and value and how fishers engage with forecasts. In two fishing villages, we verified forecast accuracy, skill, and reliability by comparing forecasts with observations during the 2018 monsoon season (June–September; n = 122). We assessed forecast value by analyzing fishers’ perceptions of weather and risks and the way they used forecasts based on 8

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