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Journal articles on the topic 'Weather pattern prediction'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Root, Benjamin, Paul Knight, George Young, et al. "A Fingerprinting Technique for Major Weather Events." Journal of Applied Meteorology and Climatology 46, no. 7 (2007): 1053–66. http://dx.doi.org/10.1175/jam2509.1.

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Abstract Advances in numerical weather prediction have occurred on numerous fronts, from sophisticated physics packages in the latest mesoscale models to multimodel ensembles of medium-range predictions. Thus, the skill of numerical weather forecasts continues to increase. Statistical techniques have further increased the utility of these predictions. The availability of large atmospheric datasets and faster computers has made pattern recognition of major weather events a feasible means of statistically enhancing the value of numerical forecasts. This paper examines the utility of pattern reco

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N, Thushika, and Premaratne S. "A Data Mining Approach for Parameter Optimization in Weather Prediction." International Journal on Data Science 1, no. 1 (2020): 1–13. http://dx.doi.org/10.18517/ijods.1.1.1-13.2020.

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More than two decades, there is a number of weather-related websites are available which approximately predict the weather and climate. By extracting essential data from the websites, a predictive data pattern can be produced to show the next day’s weather is with rain or not. By applying different types of web mining and analyzing techniques those extracted weather-related data can be visualized to a typical pattern for weather forecasting with the main deciding factors of weather. With the use of these approaches, reasonably precise forecasts can be made up to about four to five days in adva

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Nigro, Melissa A., John J. Cassano, and Mark W. Seefeldt. "A Weather-Pattern-Based Approach to Evaluate the Antarctic Mesoscale Prediction System (AMPS) Forecasts: Comparison to Automatic Weather Station Observations." Weather and Forecasting 26, no. 2 (2011): 184–98. http://dx.doi.org/10.1175/2010waf2222444.1.

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Abstract Typical model evaluation strategies evaluate models over large periods of time (months, seasons, years, etc.) or for single case studies such as severe storms or other events of interest. The weather-pattern-based model evaluation technique described in this paper uses self-organizing maps to create a synoptic climatology of the weather patterns present over a region of interest, the Ross Ice Shelf for this analysis. Using the synoptic climatology, the performance of the model, the Weather Research and Forecasting Model run within the Antarctic Mesoscale Prediction System, is evaluate

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Rew, Jehyeok, Sungwoo Park, Yongjang Cho, Seungwon Jung, and Eenjun Hwang. "Animal Movement Prediction Based on Predictive Recurrent Neural Network." Sensors 19, no. 20 (2019): 4411. http://dx.doi.org/10.3390/s19204411.

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Observing animal movements enables us to understand animal behavior changes, such as migration, interaction, foraging, and nesting. Based on spatiotemporal changes in weather and season, animals instinctively change their position for foraging, nesting, or breeding. It is known that moving patterns are closely related to their traits. Analyzing and predicting animals’ movement patterns according to spatiotemporal change offers an opportunity to understand their unique traits and acquire ecological insights into animals. Hence, in this paper, we propose an animal movement prediction scheme usin

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Richardson, Doug, Hayley J. Fowler, Christopher G. Kilsby, Robert Neal, and Rutger Dankers. "Improving sub-seasonal forecast skill of meteorological drought: a weather pattern approach." Natural Hazards and Earth System Sciences 20, no. 1 (2020): 107–24. http://dx.doi.org/10.5194/nhess-20-107-2020.

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Abstract. Dynamical model skill in forecasting extratropical precipitation is limited beyond the medium-range (around 15 d), but such models are often more skilful at predicting atmospheric variables. We explore the potential benefits of using weather pattern (WP) predictions as an intermediary step in forecasting UK precipitation and meteorological drought on sub-seasonal timescales. Mean sea-level pressure forecasts from the European Centre for Medium-Range Weather Forecasts ensemble prediction system (ECMWF-EPS) are post-processed into probabilistic WP predictions. Then we derive precipitat

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Guo, Jia, Teng Li, Rong Cheng, and Lingfeng Tan. "Research on weather classification pattern recognition based on support vector machine." E3S Web of Conferences 218 (2020): 04023. http://dx.doi.org/10.1051/e3sconf/202021804023.

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weather is the most important factor affecting the photovoltaic power generation.In this paper, the irradiance data of a photovoltaic power station in crodora in 2020 are collected, and the daily out of ground irradiance and the measured irradiance curve of that day are compared and observed, then the weather of that year is classified by human work, and then the daily irradiance data records are counted for the relevant indicators, with the maximum third order Based on the attributes of difference value, discrete difference and normalized variance, it is unified with the classified weather ty

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Lin, Yisha, Zongxiang Lu, Ying Qiao, Mingjie Li, and Zhifeng Liang. "Medium and long-term wind energy forecasting method considering multi-scale periodic pattern." E3S Web of Conferences 182 (2020): 01002. http://dx.doi.org/10.1051/e3sconf/202018201002.

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Medium and long-term weather sequence forecast becomes unreliable beyond two weeks since the weather is a chaotic system. Using values of same months for electricity prediction of wind power is the usual method. This approach defaults wind power output with annual cycle law. However, the periodic pattern can be very complicated in fact with multiple time scales. This paper proposes an approach with multi-scale periodic pattern considered. The application of parametric estimation on cumulative distribution function avoids the difficulty of predicting the power curve. Meteorological condition is

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Tyagi, Himani, Shweta Suran, and Vishwajeet Pattanaik. "Weather - Temperature Pattern Prediction and Anomaly Identification using Artificial Neural Network." International Journal of Computer Applications 140, no. 3 (2016): 15–21. http://dx.doi.org/10.5120/ijca2016909252.

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Gristey, Jake J., J. Christine Chiu, Robert J. Gurney, et al. "Insights into the diurnal cycle of global Earth outgoing radiation using a numerical weather prediction model." Atmospheric Chemistry and Physics 18, no. 7 (2018): 5129–45. http://dx.doi.org/10.5194/acp-18-5129-2018.

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Abstract. A globally complete, high temporal resolution and multiple-variable approach is employed to analyse the diurnal cycle of Earth's outgoing energy flows. This is made possible via the use of Met Office model output for September 2010 that is assessed alongside regional satellite observations throughout. Principal component analysis applied to the long-wave component of modelled outgoing radiation reveals dominant diurnal patterns related to land surface heating and convective cloud development, respectively explaining 68.5 and 16.0 % of the variance at the global scale. The total varia

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Shaukat, Muhammad Haroon, Ijaz Hussain, Muhammad Faisal, et al. "Monthly drought prediction based on ensemble models." PeerJ 8 (September 8, 2020): e9853. http://dx.doi.org/10.7717/peerj.9853.

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Drought is a natural hazard, which is a result of a prolonged shortage of precipitation, high temperature and change in the weather pattern. Drought harms society, the economy and the natural environment, but it is difficult to identify and characterize. Many areas of Pakistan have suffered severe droughts during the last three decades due to changes in the weather pattern. A drought analysis with the incorporation of climate information has not yet been undertaken in this study region. Here, we propose an ensemble approach for monthly drought prediction and to define and examine wet/dry event

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Wei, Chih-Chiang, and Chen-Chia Hsu. "Real-Time Rainfall Forecasts Based on Radar Reflectivity during Typhoons: Case Study in Southeastern Taiwan." Sensors 21, no. 4 (2021): 1421. http://dx.doi.org/10.3390/s21041421.

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This study developed a real-time rainfall forecasting system that can predict rainfall in a particular area a few hours before a typhoon’s arrival. The reflectivity of nine elevation angles obtained from the volume coverage pattern 21 Doppler radar scanning strategy and ground-weather data of a specific area were used for accurate rainfall prediction. During rainfall prediction and analysis, rainfall retrievals were first performed to select the optimal radar scanning elevation angle for rainfall prediction at the current time. Subsequently, forecasting models were established using a single r

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Kim, Yong-Hyuk, and Yourim Yoon. "Spatiotemporal Pattern Networks of Heavy Rain among Automatic Weather Stations and Very-Short-Term Heavy-Rain Prediction." Advances in Meteorology 2016 (2016): 1–13. http://dx.doi.org/10.1155/2016/4063632.

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Spatiotemporal pattern networks of heavy rain among automatic weather stations, which reflect the mobility of heavy rain, were constructed and analyzed based on the hourly precipitation data over the last ten years (from 2003 to 2012) in South Korea. Moreover, a new algorithm applying the constructed heavy-rain pattern networks to very-short-term heavy-rain prediction was developed, and significant prediction results could be obtained.

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Dai, Panxi, and Benkui Tan. "The Nature of the Arctic Oscillation and Diversity of the Extreme Surface Weather Anomalies It Generates." Journal of Climate 30, no. 14 (2017): 5563–84. http://dx.doi.org/10.1175/jcli-d-16-0467.1.

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Through a cluster analysis of daily NCEP–NCAR reanalysis data, this study demonstrates that the Arctic Oscillation (AO), defined as the leading empirical orthogonal function (EOF) of 250-hPa geopotential height anomalies, is not a unique pattern but a continuum that can be well approximated by five discrete, representative AO-like patterns. These AO-like patterns grow simultaneously from disturbances in the North Pacific, the North Atlantic, and the Arctic, and both the feedback from the high-frequency eddies in the North Pacific and North Atlantic and propagation of the low-frequency wave tra

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Rawat, Shraddha, R. K. Singh, and A. S. Nain. "Analyzing Spatial Pattern of Weather Induced Yield Variability in Indian Mustard for Formation of Homogeneous Zones in North Western Himalaya and Indo-Gangetic Plains of India." Current Agriculture Research Journal 6, no. 3 (2018): 278–85. http://dx.doi.org/10.12944/carj.6.3.07.

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Yield prediction plays an important role to decide the economy of farmer as well as the country. It avoids the under and over cropping of the particular crop. The production of not only mustard crop but all the agricultural crops is mainly affected by the weather variables. The changing weather condition affects the growth and development of crop causing intra seasonal yield variability. In addition, with weather variations, the spatial variability and crop management practices also plays a decisive role. As a result, yield forecasting represents an important tool for optimizing crop yield and

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15

Nayak, Munir Ahmad, and Subimal Ghosh. "Prediction of extreme rainfall event using weather pattern recognition and support vector machine classifier." Theoretical and Applied Climatology 114, no. 3-4 (2013): 583–603. http://dx.doi.org/10.1007/s00704-013-0867-3.

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Núñez, M., R. Fidalgo, M. Baena, and R. Morales. "The influence of active region information on the prediction of solar flares: an empirical model using data mining." Annales Geophysicae 23, no. 9 (2005): 3129–38. http://dx.doi.org/10.5194/angeo-23-3129-2005.

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Abstract. Predicting the occurrence of solar flares is a challenge of great importance for many space weather scientists and users. We introduce a data mining approach, called Behavior Pattern Learning (BPL), for automatically discovering correlations between solar flares and active region data, in order to predict the former. The goal of BPL is to predict the interval of time to the next solar flare and provide a confidence value for the associated prediction. The discovered correlations are described in terms of easy-to-read rules. The results indicate that active region dynamics is essentia

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Sun, Xia, Lian Xie, Fredrick Semazzi, and Bin Liu. "Effect of Lake Surface Temperature on the Spatial Distribution and Intensity of the Precipitation over the Lake Victoria Basin." Monthly Weather Review 143, no. 4 (2015): 1179–92. http://dx.doi.org/10.1175/mwr-d-14-00049.1.

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Abstract A series of sensitivity experiments are performed to investigate the response of precipitation over the Lake Victoria basin (LVB) to the changes of lake surface temperature (LST) using the Weather Research and Forecasting (WRF) Model. It is shown that the default LST initialized from NCEP FNL (Final) Operational Global Analysis is deficient for simulating the rainfall over the LVB. Comparative experiments demonstrate the unambiguous impact of LST on the intensity and pattern of the precipitation over LVB. Intensification/weakening of precipitation over the lake occur with increasing/d

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18

Zhang, Yu Long, and Jian Zhang. "Rainfall Characteristics Joint Regionalization Based on MST-Clustering Algorithm for Rain Attenuation Prediction." Applied Mechanics and Materials 543-547 (March 2014): 1694–97. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.1694.

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Rain attenuation prediction model of earth-satellite link, based on numerical weather forecast (NMF) statistics, require regionalization synthesizing the precipitation, rain-top height , and rainfall pattern (or time distribution) information, which ITU regionalization ignored. Thus, based on theMST-Clustering regionalization of average precipitation,themean delta binary(MDB)codingis presented to record rainfall pattern information, andRainfall Characteristics Joint Regionalizationis put forward to combine the precipitation and rain-top information. Finally, on the basis of Chinese meteorologi

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Black, Jiaxin, Nathaniel C. Johnson, Stephen Baxter, Steven B. Feldstein, Daniel S. Harnos, and Michelle L. L’Heureux. "The Predictors and Forecast Skill of Northern Hemisphere Teleconnection Patterns for Lead Times of 3–4 Weeks." Monthly Weather Review 145, no. 7 (2017): 2855–77. http://dx.doi.org/10.1175/mwr-d-16-0394.1.

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The Pacific–North American pattern (PNA), North Atlantic Oscillation (NAO), and Arctic Oscillation (AO) are three dominant teleconnection patterns known to strongly affect December–February surface weather in the Northern Hemisphere. A partial least squares regression (PLSR) method is adopted in this study to generate wintertime two-week statistical forecasts of these three teleconnection pattern indices for lead times of up to five weeks over the 1980–2013 period. The PLSR approach generates forecasts for the teleconnection pattern indices by maximizing the variance explained by predictor ind

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Khasanah, Farida Nur, and Fhira Nhita. "Weather Forecasting in Bandung Regency based on FP-Growth Algorithm." International Journal on Information and Communication Technology (IJoICT) 4, no. 2 (2019): 1. http://dx.doi.org/10.21108/ijoict.2018.42.203.

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<p>Weather change is one of the things that can affect people around the world in doing activities, including in Indonesia. The area of Indonesia, especially in Bandung regency has a high intensity of rainfall, compared with other regions. The people of Bandung Regency mostly have livelihoods in the fields of industry and agriculture, both of which are closely related to the effects of weather. Weather prediction is used for reference, so the future of society can prepare all possible weather before the move. One method of data mining used to predict weather is the association rule metho

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Chao, Zeyi, Fangling Pu, Yuke Yin, Bin Han, and Xiaoling Chen. "Research on Real-Time Local Rainfall Prediction Based on MEMS Sensors." Journal of Sensors 2018 (June 26, 2018): 1–9. http://dx.doi.org/10.1155/2018/6184713.

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A more accurate and timely rainfall prediction is needed for flood disaster reduction and prevention in Wuhan. The in situ microelectromechanical systems’ (MEMS) sensors can provide high time and spatial resolution of weather parameter measurement, but they suffer from stochastic measurement error. In order to apply MEMS sensors in real-time rainfall prediction in Wuhan, firstly, seasonal trend decomposition using Loess (STL) algorithm is utilized to decompose the observed time series into trend, seasonal, and remainder components. The trend of the observed series is compared with the correspo

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Chen, Zhen, and Wei Fan. "Data analytics approach for travel time reliability pattern analysis and prediction." Journal of Modern Transportation 27, no. 4 (2019): 250–65. http://dx.doi.org/10.1007/s40534-019-00195-6.

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Abstract Travel time reliability (TTR) is an important measure which has been widely used to represent the traffic conditions on freeways. The objective of this study is to develop a systematic approach to analyzing TTR on roadway segments along a corridor. A case study is conducted to illustrate the TTR patterns using vehicle probe data collected on a freeway corridor in Charlotte, North Carolina. A number of influential factors are considered when analyzing TTR, which include, but are not limited to, time of day, day of week, year, and segment location. A time series model is developed and u

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Flores, R. A. A. "GEOVISUAL ANALYTICS ON THE VERIFICATION OF THE PAGASA OPERATIONAL NUMERICAL WEATHER PREDICTION MODEL RAINFALL FORECAST." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W19 (December 23, 2019): 215–22. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w19-215-2019.

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Abstract. Assessment of NWP model performance is an integral part of operational forecasting as well as in research and development. Understanding the bias propagation of an NWP model and how it propagates across space can provide more insight in determining underlying causes and weaknesses not easily determined in traditional methods. The study aims to introduce the integration of the spatial distribution of error in interpreting model verification results by assessing how well the operational numerical weather prediction system of PAGASA captures the country’s weather pattern in each of its

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Tseng, Kai-Chih, Eric Maloney, and Elizabeth A. Barnes. "The Consistency of MJO Teleconnection Patterns on Interannual Time Scales." Journal of Climate 33, no. 9 (2020): 3471–86. http://dx.doi.org/10.1175/jcli-d-19-0510.1.

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AbstractThe Madden–Julian oscillation (MJO) excites strong variations in extratropical geopotential heights that modulate extratropical weather, making the MJO an important predictability source on subseasonal to seasonal time scales (S2S). Previous research demonstrates a strong similarity of teleconnection patterns across MJO events for certain MJO phases (i.e., pattern consistency) and increased model ensemble agreement during these phases that is beneficial for extended numerical weather forecasts. However, the MJO’s ability to modulate extratropical weather varies greatly on interannual t

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Xu, Jing Wen, Jun Fang Zhao, Wan Chang Zhang, and Xiao Xun Xu. "A Novel Soil Moisture Predicting Method Based on Artificial Neural Network and Xinanjiang Model." Advanced Materials Research 121-122 (June 2010): 1028–32. http://dx.doi.org/10.4028/www.scientific.net/amr.121-122.1028.

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Soil moisture plays an important role in agricultural drought predicting, therefore there is an increasing demand for detailed predictions of soil moisture, especially at basin scales. However, so far soil moisture predictions are usually obtained as a by-product of climate and weather prediction models coupled with a land surface parameterization scheme, and there has been little dedicated work to meet this urgent need at basin scales. In order to improve the basin hydrological models’ performance in the soil moisture forecasting, an integrated soil moisture predicting model based on Artifici

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Khan, Md Nasim, and Mohamed M. Ahmed. "Snow Detection using In-Vehicle Video Camera with Texture-Based Image Features Utilizing K-Nearest Neighbor, Support Vector Machine, and Random Forest." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 8 (2019): 221–32. http://dx.doi.org/10.1177/0361198119842105.

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Snowfall negatively affects pavement and visibility conditions, making it one of the major causes of motor vehicle crashes in winter weather. Therefore, providing drivers with real-time roadway weather information during adverse weather is crucial for safe driving. Although road weather stations can provide weather information, these stations are expensive and often do not represent real-time trajectory-level weather information. The main motivation of this study was to develop an affordable in-vehicle snow detection system which can provide trajectory-level weather information in real time. T

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Fan, Hongdou, Lin Wang, Yang Zhang, Youmin Tang, Wansuo Duan, and Lei Wang. "Predictable Patterns of Wintertime Surface Air Temperature in Northern Hemisphere and Their Predictability Sources in the SEAS5." Journal of Climate 33, no. 24 (2020): 10743–54. http://dx.doi.org/10.1175/jcli-d-20-0542.1.

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AbstractBased on 36-yr hindcasts from the fifth-generation seasonal forecast system of the European Centre for Medium-Range Weather Forecasts (SEAS5), the most predictable patterns of the wintertime 2-m air temperature (T2m) in the extratropical Northern Hemisphere are extracted via the maximum signal-to-noise (MSN) empirical orthogonal function (EOF) analysis, and their associated predictability sources are identified. The MSN EOF1 captures the warming trend that amplifies over the Arctic but misses the associated warm Arctic–cold continent pattern. The MSN EOF2 delineates a wavelike T2m patt

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Aprillia, Happy, Hong-Tzer Yang, and Chao-Ming Huang. "Short-Term Photovoltaic Power Forecasting Using a Convolutional Neural Network–Salp Swarm Algorithm." Energies 13, no. 8 (2020): 1879. http://dx.doi.org/10.3390/en13081879.

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The high utilization of renewable energy to manage climate change and provide green energy requires short-term photovoltaic (PV) power forecasting. In this paper, a novel forecasting strategy that combines a convolutional neural network (CNN) and a salp swarm algorithm (SSA) is proposed to forecast PV power output. First, the historical PV power data and associated weather information are classified into five weather types, such as rainy, heavy cloudy, cloudy, light cloudy and sunny. The CNN classification is then used to determine the prediction for the next day’s weather type. Five models of

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Ghutake, Ishita, Ritesh Verma, Rohit Chaudhari, and Vidhate Amarsinh. "An intelligent Crop Price Prediction using suitable Machine Learning Algorithm." ITM Web of Conferences 40 (2021): 03040. http://dx.doi.org/10.1051/itmconf/20214003040.

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Planning of crops for the next season has been a tedious task for the farmers as it is a difficult prediction about metrics of prices that their crop will fetch in a particular season which will be typically based on dynamic weather conditions. This leads to inaccurate prediction of crops’ prices by farmers, and they happen to wrongly select the crops or in haste they happen to sell their crops early without storing and thus earning less than what the same crop would have fetched them in the future. This problem could be addressed by an ML model which will predict the prices of crops in advan

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Kim, Kyosik, Byunghyun Kim, and Kun-Yeun Han. "Performance Evaluation of Effective Drought Prediction Using Machine Learning." Journal of the Korean Society of Hazard Mitigation 21, no. 2 (2021): 195–204. http://dx.doi.org/10.9798/kosham.2021.21.2.195.

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There has been much research recently to improve the prediction of drought, but the frequency and pattern of drought displays an irregular time series that limits its predictability, making it difficult to predict with only a single model, and high-level predictions cannot be made even when many models are applied. Therefore, many studies have been conducted to improve predictions by using explanatory variables such as precipitation, temperature, sunshine duration, and air volume as input data. The purpose of this study is to devise a method for predicting drought using the Standard Precipitat

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Lin, Hsin-Hung, Chih-Chien Tsai, Jia-Chyi Liou, et al. "Multi-Weather Evaluation of Nowcasting Methods Including a New Empirical Blending Scheme." Atmosphere 11, no. 11 (2020): 1166. http://dx.doi.org/10.3390/atmos11111166.

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This study utilized a radar echo extrapolation system, a high-resolution numerical model with radar data assimilation, and three blending schemes including a new empirical one, called the extrapolation adjusted by model prediction (ExAMP), to carry out 150 min reflectivity nowcasting experiments for various heavy rainfall events in Taiwan in 2019. ExAMP features full trust in the pattern of the extrapolated reflectivity with intensity adjustable by numerical model prediction. The spatial performance for two contrasting events shows that the ExAMP scheme outperforms the others for the more accu

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Patnaik, Naresh, and F. Baliarsingh. "Weather Forecasting in Coastal Districts of Odisha and Andhra Pradesh by Using Time Series Analysis." International Journal of Emerging Research in Management and Technology 6, no. 8 (2018): 85. http://dx.doi.org/10.23956/ijermt.v6i8.122.

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Climate change in world is always one of the most important topics in Water Resources. Now the issue is so predominant that it is gradually restricting out social life, peace and harmony. Climate change is a change in the statistical distribution of weather pattern of an area, when such changes occur for a long period of time. Weather is the state of atmosphere at a particular place and time. Climate is the long term statistical expression of short term weather. This study presents a comprehensive assessment of the future climate pattern/weather prediction by taking different climatic paramete

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Sengupta, Saheli, Aritra Ghosh, Tapas K. Mallick, et al. "Model Based Generation Prediction of SPV Power Plant Due to Weather Stressed Soiling." Energies 14, no. 17 (2021): 5305. http://dx.doi.org/10.3390/en14175305.

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Solar energy is going to be a major component of global energy generation. Loss due to dust deposition has raised a great concern to the investors in this field. Pre-estimation of this reduced generation and hence the economic loss will help the operators’ readiness for efficient and enhanced economic energy management of the system. In an earlier article, a physics–based model is proposed for assessment of dust accumulation under various climatic conditions which is validated by data of a single location. In this paper, the universality of this model is established and is used to demonstrate

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Allen, Michael J., Thomas R. Allen, Christopher Davis, and George McLeod. "Exploring Spatial Patterns of Virginia Tornadoes Using Kernel Density and Space-Time Cube Analysis (1960–2019)." ISPRS International Journal of Geo-Information 10, no. 5 (2021): 310. http://dx.doi.org/10.3390/ijgi10050310.

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This study evaluates the spatial-temporal patterns in Virginia tornadoes using the National Weather Service Storm Prediction Center’s Severe Weather GIS (SVRGIS) database. In addition to descriptive statistics, the analysis employs Kernel Density Estimation for spatial pattern analysis and space-time cubes to visualize the spatiotemporal frequency of tornadoes and potential trends. Most of the 726 tornadoes between 1960–2019 occurred in Eastern Virginia, along the Piedmont and Coastal Plain. Consistent with other literature, both the number of tornadoes and the tornado days have increased in V

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Basu, Soumik, and David Sauchyn. "An Unusual Cold February 2019 in Saskatchewan—A Case Study Using NCEP Reanalysis Datasets." Climate 7, no. 7 (2019): 87. http://dx.doi.org/10.3390/cli7070087.

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In February 2019, central Canada, and especially the province of Saskatchewan, experienced extreme cold weather. It was the coldest February in 82 years and the second coldest in 115 years. In this study, we examine National Centers for Environmental Prediction (NCEP)/National Center for Atmospheric Research (NCAR) Reanalysis 1 data to understand the atmospheric processes leading to this cold snap. A detailed investigation of surface air temperature, sea level pressure, surface fluxes, and winds revealed a linkage between the North Pacific storm track and the February cold snap. A shift in the

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AghaKouchak, Amir, Nasrin Nasrollahi, Jingjing Li, Bisher Imam, and Soroosh Sorooshian. "Geometrical Characterization of Precipitation Patterns." Journal of Hydrometeorology 12, no. 2 (2011): 274–85. http://dx.doi.org/10.1175/2010jhm1298.1.

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Abstract Satellite estimates and weather forecast models have made it possible to observe and predict precipitation over large spatial scales. Despite substantial progress in observing patterns of precipitation, characterization of spatial patterns is still a challenge. Quantitative assessment methods for spatial patterns are essential for future developments in prediction of the spatial extent and patterns of precipitation. In this study, precipitation patterns are characterized using three geometrical indices: (i) a connectivity index, (ii) a shape index, and (iii) a dispersiveness index. Us

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Duell, Rebecca S., and Matthew S. Van Den Broeke. "Climatology, Synoptic Conditions, and Misanalyses of Mississippi River Valley Drylines." Monthly Weather Review 144, no. 3 (2016): 927–43. http://dx.doi.org/10.1175/mwr-d-15-0108.1.

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Abstract The dryline is an important focal point for convection initiation. Although drylines most commonly occur on the southern Great Plains, dryline passages and subsequent severe weather outbreaks have been documented in the Mississippi River valley. This study presents a 15-yr (1999–2013) climatology of these Mississippi River valley drylines and associated severe weather. Additionally, synoptic patterns are identified that may result in drylines moving atypically far eastward into the Mississippi River valley. In total, 39 Mississippi River valley drylines (hereafter referred to as MRV d

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Jiang, Xiaowei, Jun Li, Zhenglong Li, et al. "Evaluation of Environmental Moisture from NWP Models with Measurements from Advanced Geostationary Satellite Imager—A Case Study." Remote Sensing 12, no. 4 (2020): 670. http://dx.doi.org/10.3390/rs12040670.

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The distribution of tropospheric moisture in the environment is highly associated with storm development. Therefore, it is important to evaluate the uncertainty of moisture fields from numerical weather prediction (NWP) models for better understanding and enhancing storm prediction. With water vapor absorption band radiance measurements from the advanced imagers onboard the new generation of geostationary weather satellites, it is possible to quantitatively evaluate the environmental moisture fields from NWP models. Three NWP models—Global Forecast System (GFS), Unified Model (UM), Weather Res

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Wang, Yizhen, Ningqing Zhang, and Xiong Chen. "A Short-Term Residential Load Forecasting Model Based on LSTM Recurrent Neural Network Considering Weather Features." Energies 14, no. 10 (2021): 2737. http://dx.doi.org/10.3390/en14102737.

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With economic growth, the demand for power systems is increasingly large. Short-term load forecasting (STLF) becomes an indispensable factor to enhance the application of a smart grid (SG). Other than forecasting aggregated residential loads in a large scale, it is still an urgent problem to improve the accuracy of power load forecasting for individual energy users due to high volatility and uncertainty. However, as an important variable that affects the power consumption pattern, the influence of weather factors on residential load prediction is rarely studied. In this paper, we review the re

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Tajbakhsh, S., P. Ghafarian, and F. Sahraian. "Instability indices and forecasting thunderstorms: the case of 30 April 2009." Natural Hazards and Earth System Sciences 12, no. 2 (2012): 403–13. http://dx.doi.org/10.5194/nhess-12-403-2012.

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Abstract. In this paper, one meteorological case study for two Iranian airports are presented. Attempts have been made to study the predefined threshold amounts of some instability indices such as vertical velocity and relative humidity. Two important output variables from a numerical weather prediction model have been used to survey thunderstorms. The climatological state of thunder days in Iran has been determined to aid in choosing the airports for the case studies. The synoptic pattern, atmospheric thermodynamics and output from a numerical weather prediction model have been studied to eva

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Wirahma, Samba, Ibnu Athoillah, and Sutrisno . "PERBANDINGAN PREDIKSI CURAH HUJAN GFS METEOROGRAM DENGAN CURAH HUJAN TRMM DI DAS RIAM KANAN KALIMANTAN SELATAN." Jurnal Sains & Teknologi Modifikasi Cuaca 16, no. 2 (2015): 73. http://dx.doi.org/10.29122/jstmc.v16i2.1049.

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Teknologi Modifikasi Cuaca (TMC) yang diterapkan oleh BPPT di Kalimantan Selatan dilakukan guna mengatasi kekurangan debit air yang terjadi pada DAS Riam Kanan. Untuk melaksanakan TMC yang efektif dan efisien dibutuhkan prediksi cuaca harian yang akurat dan mendetail pada catchment area (daerah tangkapan hujan) tersebut, khususnya prediksi curah hujan harian. TMC yang diterapkan oleh BPPT menggunakan prediksi yang salah satunya diambil dari Global Forecast System (GFS) Meteorogram. Prediksi tersebut bisa menjadi referensi untuk mengolah dan menganalisis parameter cuaca dengan baik, serta meren

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Wang, Yan, Hong-Li Ren, Fang Zhou, et al. "Multi-Model Ensemble Sub-Seasonal Forecasting of Precipitation over the Maritime Continent in Boreal Summer." Atmosphere 11, no. 5 (2020): 515. http://dx.doi.org/10.3390/atmos11050515.

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The Maritime Continent (MC) is a critical region with unique geographical conditions and significant monsoon activities that plays a vital role in global climate variation. In this study, the weekly prediction of precipitation over the MC during boreal summer (from May to September) was analyzed using the 12-year reforecasts data from five Sub-seasonal to Seasonal (S2S) models, including the China Meteorological Administration (CMA), the European Centre for Medium-Range Weather Forecasts (ECMWF), Environment and Climate Change Canada (ECCC), the National Centers for Environmental Prediction (N

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Tseng, Kai-Chih, Eric Maloney, and Elizabeth Barnes. "The Consistency of MJO Teleconnection Patterns: An Explanation Using Linear Rossby Wave Theory." Journal of Climate 32, no. 2 (2018): 531–48. http://dx.doi.org/10.1175/jcli-d-18-0211.1.

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Abstract The Madden–Julian oscillation (MJO) excites strong variations in extratropical atmospheric circulations that have important implications for subseasonal-to-seasonal (S2S) prediction. A previous study showed that particular MJO phases are characterized by a consistent modulation of geopotential heights in the North Pacific and adjacent regions across different MJO events, and demonstrated that this consistency is beneficial for extended numerical weather forecasts (i.e., lead times of two weeks to one month). In this study, we examine the physical mechanisms that lead some MJO phases t

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Li, Youru, Zhenfeng Zhu, Deqiang Kong, Meixiang Xu, and Yao Zhao. "Learning Heterogeneous Spatial-Temporal Representation for Bike-Sharing Demand Prediction." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 1004–11. http://dx.doi.org/10.1609/aaai.v33i01.33011004.

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Bike-sharing systems, aiming at meeting the public’s need for ”last mile” transportation, are becoming popular in recent years. With an accurate demand prediction model, shared bikes, though with a limited amount, can be effectively utilized whenever and wherever there are travel demands. Despite that some deep learning methods, especially long shortterm memory neural networks (LSTMs), can improve the performance of traditional demand prediction methods only based on temporal representation, such improvement is limited due to a lack of mining complex spatial-temporal relations. To address this

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Mei, Bin, Licheng Sun, and Guoyou Shi. "Full-Scale Maneuvering Trials Correction and Motion Modelling Based on Actual Sea and Weather Conditions." Sensors 20, no. 14 (2020): 3963. http://dx.doi.org/10.3390/s20143963.

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Aiming at the poor accuracy and difficult verification of maneuver modeling induced by the wind, waves and sea surface currents in the actual sea, a novel sea trials correction method for ship maneuvering is proposed. The wind and wave drift forces are calculated according to the measurement data. Based on the steady turning hypothesis and pattern search algorithm, the adjustment parameters of wind, wave and sea surface currents were solved, the drift distances and drift velocities of wind, waves and sea surface currents were calculated and the track and velocity data of the experiment were co

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Samy, V. Sakthivel, Koyel Pramanick, Veena Thenkanidiyoor, and Jeni Victor. "Data Analysis and Visualization in Python for Polar Meteorological Data." International Journal of Data Analytics 2, no. 1 (2021): 32–60. http://dx.doi.org/10.4018/ijda.2021010102.

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The aim of this study is to analyze meteorological data obtained from the various expeditions made to the Indian stations in Antarctica over recent years and determine how significantly the weather has shown a marked change over the years. For any time series data analysis, there are two main goals: (a) the authors need to identify the nature of the phenomenon from the sequence of observations and (b) predict the future data. On account of these goals, the pattern in the time series data and its variability are to be accurately identified. This paper can then interpret and integrate the patter

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Hou, Jie, Ping Wang, and Shuo Zhuang. "A New Method of Characterizing Flow Patterns of Vortices and Detecting the Centers of Vortices in a Numerical Wind Field." Journal of Atmospheric and Oceanic Technology 34, no. 1 (2017): 101–15. http://dx.doi.org/10.1175/jtech-d-15-0197.1.

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AbstractA vortex in a wind field is an important aspect of a weather system; vortices often result in hazardous weather, such as rainstorms, windstorms, and typhoons. As the availability of numerical meteorological data increases, traditional manual analysis no longer provides an efficient means of timely analysis of observed and predicted atmospheric vortices. Therefore, a method was proposed to automatically characterize flow patterns of vortices and to detect the centers of vortices in complex wind fields generated from numerical weather prediction (NWP) models. First, a statistical feature

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Ivanov, Serguei, Silas Michaelides, and Igor Ruban. "Mesoscale Resolution Radar Data Assimilation Experiments with the Harmonie Model." Remote Sensing 10, no. 9 (2018): 1453. http://dx.doi.org/10.3390/rs10091453.

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This study presents a pre-processing approach adopted for the radar reflectivity data assimilation and results of simulations with the Harmonie numerical weather prediction model. The proposed method creates a 3D regular grid in which a horizontal size of meshes coincides with the horizontal model resolution. This minimizes the representative error associated with the discrepancy between resolutions of informational sources. After such preprocessing, horizontal structure functions and their gradients for radar reflectivity maintain the sizes and shapes of precipitation patterns similar to thos

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Bierkens, M. F. P., and L. P. H. van Beek. "Seasonal Predictability of European Discharge: NAO and Hydrological Response Time." Journal of Hydrometeorology 10, no. 4 (2009): 953–68. http://dx.doi.org/10.1175/2009jhm1034.1.

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Abstract In this paper the skill of seasonal prediction of river discharge and how this skill varies between the branches of European rivers across Europe is assessed. A prediction system of seasonal (winter and summer) discharge is evaluated using 1) predictions of the average North Atlantic Oscillation (NAO) index for the coming winter based on May SST anomalies of the North Atlantic; 2) a global-scale hydrological model; and 3) 40-yr European Centre for Medium-Range Weather Forecasts Re-Analysis (ERA-40) data. The skill of seasonal discharge predictions is investigated with a numerical expe

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Wang, Yuanbing, Yaodeng Chen, and Jinzhong Min. "Impact of Assimilating China Precipitation Analysis Data Merging with Remote Sensing Products Using the 4DVar Method on the Prediction of Heavy Rainfall." Remote Sensing 11, no. 8 (2019): 973. http://dx.doi.org/10.3390/rs11080973.

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In this study, the China Hourly Merged Precipitation Analysis (CHMPA) data which combines the satellite-retrieved Climate Prediction Center Morphing (CMORPH) with the automatic weather station precipitation observations is firstly assimilated into the Weather Research and Forecasting (WRF) model using the Four-Dimensional Variational (4DVar) method. The analyses and subsequent forecasts of heavy rainfall during Meiyu season occurred in July 2013 over eastern China is evaluated. Besides, the sensitivity of rainfall forecast skill of assimilating the CHMPA data to the rainfall error, the rainfal

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