Relevant bibliographies by topics / Weather pattern prediction

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Weather pattern prediction'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Weather pattern prediction"

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Root, Benjamin, Paul Knight, George Young, Steven Greybush, Richard Grumm, Ron Holmes, and Jeremy Ross. "A Fingerprinting Technique for Major Weather Events." Journal of Applied Meteorology and Climatology 46, no. 7 (July 1, 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 recognition in assisting the prediction of severe and major weather in the Middle Atlantic region. An important innovation in this work is that the analog technique is applied to NWP forecast maps as a pattern-recognition tool rather than to analysis maps as a forecast tool. A technique is described that employs a new clustering algorithm to objectively identify the anomaly patterns or “fingerprints” associated with past events. The potential refinement and applicability of this method as an operational forecasting tool employed by comparing numerical weather prediction forecasts with fingerprints already identified for major weather events are also discussed.
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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 (April 17, 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 advance. For the weather prediction analysis, we need to discover deciding factors of the next day’s weather. Particularly, common weather dependent factors and the relationship of the prediction to the particular phenomenon. The solution proposed by this research can be used to analyze a large amount of weather data which are in different forms in each source. By using predictive mining task our solution allows us to make predictions for future instances according to the model what we have created. Evaluation measurements for the selected data mining technique such as accuracy percentage, TP & FP Rate, Precision, F-Measure, ROC area, SSE, and loglikelihood for classification and clustering leads to create a high-quality model of prediction. Knowledge flow interface provides the data flow to show the processing and analyzing data with precise association rules. In order to evaluate the model, SSE values and time to build the model, are considered in an effective manner.
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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 (April 1, 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 evaluated for each of the objectively identified weather patterns. The evaluation process involves classifying each model forecast as matching one of the weather patterns from the climatology. Subsequently, statistics such as model bias, root-mean-square error, and correlation are calculated for each weather pattern. This allows for the determination of model errors as a function of weather pattern and can highlight if certain errors occur under some weather regimes and not others. The results presented in this paper highlight the potential benefits of this new weather-pattern-based model evaluation technique.
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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 (October 11, 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 using a predictive recurrent neural network architecture. To do that, we first collect and investigate geo records of animals and conduct pattern refinement by using random forest interpolation. Then, we generate animal movement patterns using the kernel density estimation and build a predictive recurrent neural network model to consider the spatiotemporal changes. In the experiment, we perform various predictions using 14 K long-billed curlew locations that contain their five-year movements of the breeding, non-breeding, pre-breeding, and post-breeding seasons. The experimental results confirm that our predictive model based on recurrent neural networks can be effectively used to predict animal movement.
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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 (January 14, 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 precipitation estimates and dichotomous drought event probabilities by sampling from the conditional distributions of precipitation given the WPs. We compare this model to the direct precipitation and drought forecasts from the ECMWF-EPS and to a baseline Markov chain WP method. A perfect-prognosis model is also tested to illustrate the potential of WPs in forecasting. Using a range of skill diagnostics, we find that the Markov model is the least skilful, while the dynamical WP model and direct precipitation forecasts have similar accuracy independent of lead time and season. However, drought forecasts are more reliable for the dynamical WP model. Forecast skill scores are generally modest (rarely above 0.4), although those for the perfect-prognosis model highlight the potential predictability of precipitation and drought using WPs, with certain situations yielding skill scores of almost 0.8 and drought event hit and false alarm rates of 70 % and 30 %, respectively.
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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 type.Then, the SVM prediction model of weather category is established based on radial basis function, and the optimal model parameters are determined by cross validation, so that a large number of historical date weather categories can be classified and predicted.This is obviously different from the traditional prediction method based on linear statistical theory, and the results show that it has a good effect.
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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 considered to some extent via multi-scale periodic pattern explored basing on historical energy data. This work is an exploration for medium and long-term wind power forecasting that can well adapt to existing conditions. It has better prediction accuracy than the method without multi-scale periodicity considered.
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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 (April 15, 2016): 15–21. http://dx.doi.org/10.5120/ijca2016909252.

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Gristey, Jake J., J. Christine Chiu, Robert J. Gurney, Cyril J. Morcrette, Peter G. Hill, Jacqueline E. Russell, and Helen E. Brindley. "Insights into the diurnal cycle of global Earth outgoing radiation using a numerical weather prediction model." Atmospheric Chemistry and Physics 18, no. 7 (April 16, 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 variance explained by these first two patterns is markedly less than previous regional estimates from observations, and this analysis suggests that around half of the difference relates to the lack of global coverage in the observations. The first pattern is strongly and simultaneously coupled to the land surface temperature diurnal variations. The second pattern is strongly coupled to the cloud water content and height diurnal variations, but lags the cloud variations by several hours. We suggest that the mechanism controlling the delay is a moistening of the upper troposphere due to the evaporation of anvil cloud. The short-wave component of modelled outgoing radiation, analysed in terms of albedo, exhibits a very dominant pattern explaining 88.4 % of the variance that is related to the angle of incoming solar radiation, and a second pattern explaining 6.7 % of the variance that is related to compensating effects from convective cloud development and marine stratocumulus cloud dissipation. Similar patterns are found in regional satellite observations, but with slightly different timings due to known model biases. The first pattern is controlled by changes in surface and cloud albedo, and Rayleigh and aerosol scattering. The second pattern is strongly coupled to the diurnal variations in both cloud water content and height in convective regions but only cloud water content in marine stratocumulus regions, with substantially shorter lag times compared with the long-wave counterpart. This indicates that the short-wave radiation response to diurnal cloud development and dissipation is more rapid, which is found to be robust in the regional satellite observations. These global, diurnal radiation patterns and their coupling with other geophysical variables demonstrate the process-level understanding that can be gained using this approach and highlight a need for global, diurnal observing systems for Earth outgoing radiation in the future.
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Shaukat, Muhammad Haroon, Ijaz Hussain, Muhammad Faisal, Ahmad Al-Dousari, Muhammad Ismail, Alaa Mohamd Shoukry, Elsayed Elsherbini Elashkar, and Showkat Gani. "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 events. Initially, the drought events were identified by the short term Standardized Precipitation Index (SPI-3). Drought is predicted based on three ensemble models i.e., Equal Ensemble Drought Prediction (EEDP), Weighted Ensemble Drought Prediction (WEDP) and the Conditional Ensemble Drought Prediction (CEDP) model. Besides, two weighting procedures are used for distributing weights in the WEDP model, such as Traditional Weighting (TW) and the Weighted Bootstrap Resampling (WBR) procedure. Four copula families (i.e., Frank, Clayton, Gumbel and Joe) are used to explain the dependency relation between climate indices and precipitation in the CEDP model. Among all four copula families, the Joe copula has been found suitable for most of the times. The CEDP model provides better results in terms of accuracy and uncertainty as compared to other ensemble models for all meteorological stations. The performance of the CEDP model indicates that the climate indices are correlated with a weather pattern of four meteorological stations. Moreover, the percentage occurrence of extreme drought events that have appeared in the Multan, Bahawalpur, Barkhan and Khanpur are 1.44%, 0.57%, 2.59% and 1.71%, respectively, whereas the percentage occurrence of extremely wet events are 2.3%, 1.72%, 0.86% and 2.86%, respectively. The understanding of drought pattern by including climate information can contribute to the knowledge of future agriculture and water resource management.
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Dissertations / Theses on the topic "Weather pattern prediction"

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Hepplewhite, C. L. "Radiometric observation of the atmospheric boundary layer : the ROSSA project." Thesis, University of Oxford, 1989. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.329921.

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Harlim, John. "Errors in the initial conditions for numerical weather prediction a study of error growth patterns and error reduction with ensemble filtering /." College Park, Md. : University of Maryland, 2006. http://hdl.handle.net/1903/3430.

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Thesis (Ph. D.) -- University of Maryland, College Park, 2006.
Thesis research directed by: Applied Mathematics and Scientific Computation Program. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
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Books on the topic "Weather pattern prediction"

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California Weather Symposium (1994 Sierra College). Predicting heavy rainfall events in California: A symposium to share weather pattern knowledge : Sierra College, Rocklin, California, June 25, 1994. Rocklin, Calif: Sierra College Science Center, 1994.

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Richard, Somerville, Volfson Leonid B, and United States. National Aeronautics and Space Administration., eds. Pattern recognition of satellite cloud imagery for improved weather prediction: Final report. San Diego, Calif: Chase Consulting, Inc., 1987.

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illustrator, Gower Neil, ed. How to read water: Clues and patterns from puddles to the sea : learn to gauge depth, navigate, forecast weather and make other predictions with water. The Experiment, 2016.

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Book chapters on the topic "Weather pattern prediction"

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Alshareef, Almahdi, Azuraliza Abu Bakar, Abdul Razak Hamdan, Sharifah Mastura Syed Abdullah, and Othman Jaafar. "Sequential Pattern Discovery for Weather Prediction Problem." In Emerging Trends and Advanced Technologies for Computational Intelligence, 223–40. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-33353-3_12.

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Paz, Andrea, Marcelo Reginato, Fabián A. Michelangeli, Renato Goldenberg, Mayara K. Caddah, Julián Aguirre-Santoro, Miriam Kaehler, Lúcia G. Lohmann, and Ana Carnaval. "Predicting Patterns of Plant Diversity and Endemism in the Tropics Using Remote Sensing Data: A Study Case from the Brazilian Atlantic Forest." In Remote Sensing of Plant Biodiversity, 255–66. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-33157-3_11.

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AbstractWe combine remote sensing (RS) measurements of temperature and precipitation with phylogenetic and distribution data from three plant clades with different life forms, i.e., shrubs and treelets (tribe Miconieae, Melastomes), epiphytes (Ronnbergia-Wittmackia alliance, Bromeliaceae), and lianas (“Fridericia and Allies” clade, Bignoniaceae), to predict the distribution of biodiversity in a tropical hot spot: the Brazilian Atlantic Forest. We assess (i) how well RS-derived climate estimates predict the spatial distribution of species richness (SR), phylogenetic diversity (PD), and phylogenetic endemism (PE) and (ii) how they compare to predictions based on interpolated weather station information. We find that environmental descriptors derived from RS sources can predict the distribution of SR and PD, performing as well as or better than weather station-based data. Yet performance is lower for endemism and for clades with a high number of species of small ranges. We argue that this approach can provide an alternative to remotely monitor megadiverse groups or biomes for which species identification through RS are not yet feasible or available.
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Rutenberg, Isaac, Arthur Gwagwa, and Melissa Omino. "Use and Impact of Artificial Intelligence on Climate Change Adaptation in Africa." In African Handbook of Climate Change Adaptation, 1107–26. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-45106-6_80.

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AbstractAlthough Climate Change is a global phenomenon, the impact in Africa is anticipated to be greater than in many other parts of the world. This expectation is supported by many factors, including the relatively low shock tolerance of many African countries and the relatively high percentage of African workers engaged in the agricultural sector. High-income countries are increasingly turning their focus to climate change adaptation, and Artificial Intelligence (AI) is a critical tool in those efforts. Algorithms using AI are making better predictions on the short- and long-term effects of climate change, including predictions related to weather patterns, floods and droughts, and human migration patterns. It is not clear, however, that Africa is (or will be) maximally benefitting from those AI tools, particularly since they are largely developed by highly developed countries using data sets that are specific to those same countries. It is therefore important to characterize the efforts underway to use AI in a way that specifically benefits Africa in climate change adaptation. These efforts include projects undertaken physically in Africa as well as those that have Africa as their focus. In exploring AI projects in or about Africa, this chapter also looks at the sufficiency of such efforts and the variety of approaches taken by researchers working with AI to address climate change in Africa.
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Mishra, Partha Sarathi, and Debabrata Nandi. "Deep Learning for Feature Engineering-Based Improved Weather Prediction." In Advances in Data Mining and Database Management, 195–217. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-6659-6.ch011.

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Weather prediction has gained a point of attraction for many researchers of variant research communities. The emerging deep learning techniques have motivated many researches to explore hidden hierarchical pattern in the great mass of weather dataset for weather prediction. In this chapter, four different categories of computationally efficient deep learning models—CNN, LSTM, CNN-LSTM, and ConvLSTM—have been critically examined for improved weather prediction. Here, emphasis has been given on supervised learning techniques for model development by considering the importance of feature engineering. Feature engineering plays a vital role in reducing dimension, decreasing model complexity as well as handling the noise and corrupted data. Using daily maximum temperature, this chapter investigates the performance of different deep learning models for improved predictions. The results obtained from different experiments conducted ensures that the feature engineering based deep learning study for the purpose of predictive modeling using time series data is really an encouraging approach.
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Ma, Liang, Cheng Huang, and Zhong-Sheng Liu. "The Application of Artificial Neural Network to Predicting the Drainage from Waste Rock Storages." In Deep Learning Applications. IntechOpen, 2021. http://dx.doi.org/10.5772/intechopen.96162.

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Reliable prediction of drainage flow rate and drainage chemistry is essential to the treatment of drainage from waste rock storages at mine sites. The traditional predictive models require simplification and assumption of geo-bio-chemical processes followed by intensive characterization, and sometimes lead to poor prediction accuracy. In the big data era, various sensors are installed in field to constantly monitor mine sites, which enables machine learning to utilize the generated monitoring data and study the underlying pattern behind the data. This chapter describes an approach to use artificial neural network to predict the drainage flow rate and drainage chemistry based on weather monitoring data collected at mine sites. The advantage of this approach is that generally no additional characterization are required to make prediction because the relevant geo-bio-chemical mechanisms are embedded naturally in the monitoring data, which can be captured through machine learning process.
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van den Dool, Huug. "Analogues." In Empirical Methods in Short-Term Climate Prediction. Oxford University Press, 2006. http://dx.doi.org/10.1093/oso/9780199202782.003.0014.

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In 1999 the Earth’s atmosphere was gearing up for a special event. Towards the end of July, the 500 mb flow in the extra-tropical SH started to look more and more like a flow pattern observed some 22 years earlier in May 1977. Two trajectories in the N-dimensional phase space, N as defined in Chapter 6, were coming closer together. Figure 7.1 shows the two states at the moment of closest encounter, with the appropriate climatology subtracted. These two states are, for a domain of this size, the most similar looking patterns in recorded history. But are these good analogues? They do look alike, nearly every anomaly center has its counterpart, but they are certainly not close enough to be indistinguishable within observational error, the anomaly correlation being only 0.81. The rms difference between the two states in Figure 7.1 is 71.6 gpm, far above observational error (<10 gpm). The close encounter did not make it to the newspapers and, more telling, not even to a meteorological journal. The idea of situations in geophysical flow that are analogues to each other has always had tremendous appeal, at least in meteorology. Even lay people may comment that the weather today or this season reminds them of the weather in some year past. The implications of true analogues would be enormous. If two states many years apart were nearly identical in all variables on the whole domain (of presumed relevance) , including boundary conditions, then their subsequent behavior should be similar for some time to come. In fact one could make forecasts that way, if only it was easy to find analogues from a “large enough” data set. The analogue method was fairly widely used for weather forecasting at one time (Schuurmans 1973) but currently is rarely used for forecasts per sé (for all the reasons explained in Section 7.1). Rather analogues are used to specify one field given another, a process called “specification” or downscaling, or to learn about predictability (Lorenz 1969). In Section 7.1 we review the idea and limitations of naturally occurring analogues, and explain why/when it is (un)likely to find analogues.
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Şen, Zekai. "Techniques to Predict Agricultural Droughts." In Monitoring and Predicting Agricultural Drought. Oxford University Press, 2005. http://dx.doi.org/10.1093/oso/9780195162349.003.0010.

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In general, the techniques to predict drought include statistical regression, time series, stochastic (or probabilistic), and, lately, pattern recognition techniques. All of these techniques require that a quantitative variable be identified to define drought, with which to begin the process of prediction. In the case of agricultural drought, such a variable can be the yield (production per unit area) of the major crop in a region (Kumar, 1998; Boken, 2000). The crop yield in a year can be compared with its long-term average, and drought intensity can be classified as nil, mild, moderate, severe, or disastrous, based on the difference between the current yield and the average yield. Regression techniques estimate crop yields using yield-affecting variables. A comprehensive list of possible variables that affect yield is provided in chapter 1. Usually, the weather variables routinely available for a historical period that significantly affect the yield are included in a regression analysis. Regression techniques using weather data during a growing season produce short-term estimates (e.g., Sakamoto, 1978; Idso et al., 1979; Slabbers and Dunin, 1981; Diaz et al., 1983; Cordery and Graham, 1989; Walker, 1989; Toure et al., 1995; Kumar, 1998). Various researchers in different parts of the world (see other chapters) have developed drought indices that can also be included along with the weather variables to estimate crop yield. For example, Boken and Shaykewich (2002) modifed the Western Canada Wheat Yield Model (Walker, 1989) drought index using daily temperature and precipitation data and advanced very high resolution radiometer (AVHRR) satellite data. The modified model improved the predictive power of the wheat yield model significantly. Some satellite data-based variables that can be used to predict crop yield are described in chapters 5, 6, 9, 13, 19, and 28. The short-term estimates are available just before or around harvest time. But many times long-term estimates are required to predict drought for next year, so that long-term planning for dealing with the effects of drought can be initiated in time.
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Deo, Ravinesh C., Sujan Ghimire, Nathan J. Downs, and Nawin Raj. "Optimization of Windspeed Prediction Using an Artificial Neural Network Compared With a Genetic Programming Model." In Advances in Computational Intelligence and Robotics, 328–59. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-4766-2.ch015.

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The precise prediction of windspeed is essential in order to improve and optimize wind power prediction. However, due to the sporadic and inherent complexity of weather parameters, the prediction of windspeed data using different patterns is difficult. Machine learning (ML) is a powerful tool to deal with uncertainty and has been widely discussed and applied in renewable energy forecasting. In this chapter, the authors present and compare an artificial neural network (ANN) and genetic programming (GP) model as a tool to predict windspeed of 15 locations in Queensland, Australia. After performing feature selection using neighborhood component analysis (NCA) from 11 different metrological parameters, seven of the most important predictor variables were chosen for 85 Queensland locations, 60 of which were used for training the model, 10 locations for model validation, and 15 locations for the model testing. For all 15 target sites, the testing performance of ANN was significantly superior to the GP model.
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Deo, Ravinesh C., Sujan Ghimire, Nathan J. Downs, and Nawin Raj. "Optimization of Windspeed Prediction Using an Artificial Neural Network Compared With a Genetic Programming Model." In Research Anthology on Multi-Industry Uses of Genetic Programming and Algorithms, 116–47. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-8048-6.ch007.

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The precise prediction of windspeed is essential in order to improve and optimize wind power prediction. However, due to the sporadic and inherent complexity of weather parameters, the prediction of windspeed data using different patterns is difficult. Machine learning (ML) is a powerful tool to deal with uncertainty and has been widely discussed and applied in renewable energy forecasting. In this chapter, the authors present and compare an artificial neural network (ANN) and genetic programming (GP) model as a tool to predict windspeed of 15 locations in Queensland, Australia. After performing feature selection using neighborhood component analysis (NCA) from 11 different metrological parameters, seven of the most important predictor variables were chosen for 85 Queensland locations, 60 of which were used for training the model, 10 locations for model validation, and 15 locations for the model testing. For all 15 target sites, the testing performance of ANN was significantly superior to the GP model.
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Lovejoy, Shaun. "Macroweather predictions and climate projections." In Weather, Macroweather, and the Climate. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780190864217.003.0011.

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“Does the Flapping of a Butterfly’s Wings in Brazil Set off a Tornado in Texas?” This was the provocative title of an address given by Edward Lorenz, the origin for the (nearly) household expression “butterfly effect.” It was December 1972 and it had been nearly ten years since he had discovered it,1 yet its significance was only then being recognized. Lorenz explained: “In more technical language, is the behavior of the atmosphere unstable to small perturbations?” His answer: “Although we cannot claim to have proven that the atmosphere is unstable, the evidence that it is so is overwhelming.” Imagine two planets identical in every way except that on one there is a butterfly that flaps its wings. The butterfly effect means that their future evolutions are “sensitively dependent” on the initial conditions, so that a mere flap of a wing could perturb the atmosphere sufficiently so that, eventually, the weather patterns on the two planets would evolve quite differently. On the planet with the Brazilian butterfly, the number of tornadoes would likely be the same. But on a given day, one might occur in Texas rather than Oklahoma. This sensitive dependence on small perturbations thus limits our ability to predict the weather. For Earth, Lorenz estimated this predictability limit to be about two weeks. From Chapters 4 and 5 and the discussion that follows, we now understand it as the slightly shorter weather– macroweather transition scale. In Chapter 1, we learned that the ratio of the nonlinear to linear terms in the (deterministic) equations governing the atmosphere is typically about a thou­sand billion. The nonlinear terms are the mathematical expressions of physical mechanisms that can blow up microscopic perturbations into large effects. Therefore, we expect instability. Chapter 4, we examined instability from the point of view of the higher level statistical laws— the fact that, at weather scales, the fluctuation exponents H for all atmospheric fields are positive (in space, up to the size of the planet; in time, up to the weather– macroweather transition scale at five to ten days).
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Conference papers on the topic "Weather pattern prediction"

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Alshareef, Almahdi, Azuraliza Abu Bakar, Abdul Razak Hamdan, Sharifah Mastura Syed Abdullah, and Othman Jaafar. "Pattern discovery algorithm for weather prediction problem." In 2015 Science and Information Conference (SAI). IEEE, 2015. http://dx.doi.org/10.1109/sai.2015.7237200.

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KIM, Jaekwang. "Seasonal Heavy Rain Forecasting Method." In 2nd International Conference on Soft Computing, Artificial Intelligence and Machine Learning (SAIM 2021). AIRCC Publishing Corporation, 2021. http://dx.doi.org/10.5121/csit.2021.111002.

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In this study, we study the technique for predicting heavy / non-rain rainfall after 6 hours from the present using the values of the weather attributes. Through this study, we investigated whether each attribute value is influenced by a specific pattern of weather maps representing heavy and non-heavy rains or seasonally when making heavy / non-heavy forecasts. For the experiment, a 20-year cumulative weather map was learned with Support Vector Machine (SVM) and tested using a set of correct answers for heavy rain and heavy rain. As a result of the experiment, it was found that the heavy rain prediction of SVM showed an accuracy rate of up to 70%, and that it was seasonal variation rather than a specific pattern that influenced the prediction.
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Manalu, Darwis Robinson, Muhammad Zarlis, Herman Mawengkang, and Opim Salim Sitompul. "Forest Fire Prediction in Northern Sumatera using Support Vector Machine Based on the Fire Weather Index." In 9th International Conference on Signal, Image Processing and Pattern Recognition (SPPR 2020). AIRCC Publishing Corporation, 2020. http://dx.doi.org/10.5121/csit.2020.101915.

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Forest fires are a major environmental issue, creating economical and ecological damage while dangering human lives. The investigation and survey for forest fire had been done in Aek Godang, Northern Sumatera, Indonesia. There is 26 hotspot in 2017 close to Aek Godang, North Sumatera, Indonesia. In this study, we use a data mining approach to train and test the data of forest fire and the Fire Weather Index (FWI) from meteorological data. The aim of this study to predict the burned area and identify the forest fire in Aek Godang areas, North Sumatera. The result of this study indicated that Fire fighting and prevention activity may be one reason for the observed lack of correlation. The fact that this dataset exists indicates that there is already some effort going into fire prevention.
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Yang, Dandan, Shuanghe Shen, and Lingling Shao. "A study on blending radar and numerical weather prediction model products in very short-range forecast and nowcasting." In Sixth International Symposium on Multispectral Image Processing and Pattern Recognition, edited by Henri Maître, Hong Sun, Bangjun Lei, and Jufu Feng. SPIE, 2009. http://dx.doi.org/10.1117/12.832627.

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Selvik, Ørjan, Tor Einar Berg, and Dariusz Eirik Fathi. "Drifting Paths of Disabled Vessels." In ASME 2015 34th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/omae2015-41921.

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Many maritime emergency situations involve drifting vessels, and tools to predict drifting patterns have been developed by meteorology institutes, class societies and research companies. It is important to be able to predict a vessel’s drifting path and to estimate when it will drift into waters where grounding is a possible outcome. Such a prediction tool would provide valuable input to the planning of an emergency towing operation to prevent the vessel from grounding or to reduce the impact of the grounding. In this paper we present the outcomes of a study that investigated the drifting pattern of a vessel with an engine shut-down in the Barents Sea. As part of the ongoing A-Lex project [1], MARINTEK has prepared a VeSim [2] model to investigate the drifting path of a cargo vessel. The outcomes of the study will be used to draw up a technical specification for work to be done to develop an improved ship drift model in Norwegian Meteorological Institute’s (MET Norway’s, [3]) new Halo platform [4]. An improved model will be of great help to those planning emergency towing operations and for positioning of emergency preparedness units with respect to the traffic situation (especially tracks of vessels carrying dangerous goods) and weather forecasts.
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Wang, Zheng, Irena Koprinska, and Mashud Rana. "Solar power prediction using weather type pair patterns." In 2017 International Joint Conference on Neural Networks (IJCNN). IEEE, 2017. http://dx.doi.org/10.1109/ijcnn.2017.7966395.

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7

Kearns, Patrick A., and Moncef Krarti. "Residential Energy Analysis: Regression Analysis of Heating Degree Days With Temperature Setback for Selected ASHRAE Climate Zones." In ASME 2011 5th International Conference on Energy Sustainability. ASMEDC, 2011. http://dx.doi.org/10.1115/es2011-54738.

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Heating Degree Days (HDDs), calculated from hourly weather data, are often used to estimate energy savings for a variety of energy efficiency measures (EEMs) to be applied to conditioned spaces in buildings. More specifically, application of HDDs is useful for estimating savings from weather-dependent EEMs. For first order estimation, it is often problematic to calculate HDDs for a given base temperature, when temperature setbacks are used in the conditioned spaces. This paper provides a set of correlations to characterize HDDs for selected ASHRAE Climate Zones as functions of three key parameters including the base temperature, setback temperature level (delta-T), and setback duration. In addition to the well-documented pattern of decreasing HDDs for decreasing base temperature, it was also shown that HDDs are inversely proportional to both setback duration and temperature setback differential levels. In the analysis presented in this paper, corrections to estimate HDDs when temperature setbacks are used for typical residential space heating schedules during unoccupied periods which occurred from 8 am to 5 pm Monday through Friday. In particular, regression correlations using two- and three-parameter models have been developed to estimate HDDs for multiple US locations that account for the impact of temperature setbacks on the heating requirements of residential buildings. For the two-parameter model, the input variables for the regression correlations are setback hours and delta T; for the three-parameter model, the input variables for the correlations include setback hours, delta T, and base temperature. The prediction accuracy for the energy savings, due to a set of EEMs, obtained from the HDD method —using the developed correlations— is tested against whole-building detailed energy simulation analysis for two single family homes. Detailed energy audits including utility data analysis have been carried out for both homes to calibrate the detailed simulation model and evaluate the effectiveness of the EEMs in reducing building energy use. The results from the detailed simulation analysis are then compared to those obtained from the HDD with temperature setbacks.
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Perez-Ortiz, M., P. A. Gutierrez, P. Tino, C. Casanova-Mateo, and S. Salcedo-Sanz. "A mixture of experts model for predicting persistent weather patterns." In 2018 International Joint Conference on Neural Networks (IJCNN). IEEE, 2018. http://dx.doi.org/10.1109/ijcnn.2018.8489179.

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9

Wang, Zhijia, Liang He, Xu Cheng, and Jieqiong He. "Method for short-term photovoltaic generation power prediction base on weather patterns." In 2014 China International Conference on Electricity Distribution (CICED). IEEE, 2014. http://dx.doi.org/10.1109/ciced.2014.6991696.

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Hakii, Takeru, Koshi Shimada, Takafumi Nakanishi, Ryotaro Okada, Keigo Matsuda, Ryo Onishi, and Keiko Takahashi. "Weather Map Prediction Using RGB Metaphorical Feature Extraction for Atmospheric Pressure Patterns." In 2021 IEEE/ACIS 19th International Conference on Computer and Information Science (ICIS). IEEE, 2021. http://dx.doi.org/10.1109/icis51600.2021.9516859.

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