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Relevant bibliographies by topics / Weather Mathematical models

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Academic literature on the topic 'Weather Mathematical models'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 January 2023

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Journal articles on the topic "Weather Mathematical models"

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BISWAS, B. C. "Forecasting for agricultural application." MAUSAM 41, no. 2 (February 22, 2022): 188–93. http://dx.doi.org/10.54302/mausam.v41i2.2630.

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Methods for agro-meteorological forecasts are mainly based on crop-weather relationship and statistical/mathematical models. Models developed from historic data make it possible to obtain the expected values fairly in advance so that appropriate action may be taken to avail of beneficial aspect of weather and minimise or avoid detrimental effect. Validity of these models under different conditions is imperative as the climatic conditions of general field may be quite different from those of experimental one. This paper discusses the work done on the above aspects.

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Hewage, Pradeep, Ardhendu Behera, Marcello Trovati, Ella Pereira, Morteza Ghahremani, Francesco Palmieri, and Yonghuai Liu. "Temporal convolutional neural (TCN) network for an effective weather forecasting using time-series data from the local weather station." Soft Computing 24, no. 21 (April 23, 2020): 16453–82. http://dx.doi.org/10.1007/s00500-020-04954-0.

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Abstract Non-predictive or inaccurate weather forecasting can severely impact the community of users such as farmers. Numerical weather prediction models run in major weather forecasting centers with several supercomputers to solve simultaneous complex nonlinear mathematical equations. Such models provide the medium-range weather forecasts, i.e., every 6 h up to 18 h with grid length of 10–20 km. However, farmers often depend on more detailed short-to medium-range forecasts with higher-resolution regional forecasting models. Therefore, this research aims to address this by developing and evaluating a lightweight and novel weather forecasting system, which consists of one or more local weather stations and state-of-the-art machine learning techniques for weather forecasting using time-series data from these weather stations. To this end, the system explores the state-of-the-art temporal convolutional network (TCN) and long short-term memory (LSTM) networks. Our experimental results show that the proposed model using TCN produces better forecasting compared to the LSTM and other classic machine learning approaches. The proposed model can be used as an efficient localized weather forecasting tool for the community of users, and it could be run on a stand-alone personal computer.

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Jing, Li, Liu Ying-di, Li Hu-ming, and Chen Gong-xi. "Mathematical models for some ecophysiological characteristics in different weather condition in moss (Plagiomnium acutum)." Wuhan University Journal of Natural Sciences 5, no. 1 (March 2000): 123–26. http://dx.doi.org/10.1007/bf02828327.

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Pooja, S. B., and Siva R. V. Balan. "An Investigation Study on Clustering and Classification Techniques for Weather Forecasting." Journal of Computational and Theoretical Nanoscience 16, no. 2 (February 1, 2019): 417–21. http://dx.doi.org/10.1166/jctn.2019.7742.

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Weather forecasting is the prediction of atmosphere state for particular location by using principles of physics provided by many statistical and empirical techniques. Weather forecasts are frequently made by collecting quantitative data about current state of atmosphere through scientific understanding of atmospheric processes to illustrate how atmosphere changes in future. Current weather conditions are collected through the observation from the ground, ships, aircraft, radio sounds and satellites. The information is transmitted to the meteorological centers where the data are collected and examined for prediction. There are diverse techniques included in weather forecasting, from relatively simple observation of sky to complex computerized mathematical models. But, the existing techniques failed to predict the weather with higher accuracy and lesser time. In order to improve the prediction performance, the machine learning and ensemble techniques are introduced.

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Duan, Q., Z. Di, J. Quan, C. Wang, W. Gong, Y. Gan, A. Ye, et al. "Automatic Model Calibration: A New Way to Improve Numerical Weather Forecasting." Bulletin of the American Meteorological Society 98, no. 5 (May 1, 2017): 959–70. http://dx.doi.org/10.1175/bams-d-15-00104.1.

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Abstract Weather forecasting skill has been improved over recent years owing to advances in the representation of physical processes by numerical weather prediction (NWP) models, observational systems, data assimilation and postprocessing, new computational capability, and effective communications and training. There is an area that has received less attention so far but can bring significant improvement to weather forecasting—the calibration of NWP models, a process in which model parameters are tuned using certain mathematical methods to minimize the difference between predictions and observations. Model calibration of the NWP models is difficult because 1) there are a formidable number of model parameters and meteorological variables to tune, and 2) a typical NWP model is very expensive to run, and conventional model calibration methods require many model runs (up to tens of thousands) or cannot handle the high dimensionality of NWP models. This study demonstrates that a newly developed automatic model calibration platform can overcome these difficulties and improve weather forecasting through parameter optimization. We illustrate how this is done with a case study involving 5-day weather forecasting during the summer monsoon in the greater Beijing region using the Weather Research and Forecasting Model. The keys to automatic model calibration are to use global sensitivity analysis to screen out the most important parameters influencing model performance and to employ surrogate models to reduce the need for a large number of model runs. Through several optimization and validation studies, we have shown that automatic model calibration can improve precipitation and temperature forecasting significantly according to a number of performance measures.

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Du, Hailiang, and Leonard A. Smith. "Pseudo-Orbit Data Assimilation. Part II: Assimilation with Imperfect Models." Journal of the Atmospheric Sciences 71, no. 2 (January 31, 2014): 483–95. http://dx.doi.org/10.1175/jas-d-13-033.1.

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Abstract Data assimilation and state estimation for nonlinear models is a challenging task mathematically. Performing this task in real time, as in operational weather forecasting, is even more challenging as the models are imperfect: the mathematical system that generated the observations (if such a thing exists) is not a member of the available model class (i.e., the set of mathematical structures admitted as potential models). To the extent that traditional approaches address structural model error at all, most fail to produce consistent treatments. This results in questionable estimates both of the model state and of its uncertainty. A promising alternative approach is proposed to produce more consistent estimates of the model state and to estimate the (state dependent) model error simultaneously. This alternative consists of pseudo-orbit data assimilation with a stopping criterion. It is argued to be more efficient and more coherent than one alternative variational approach [a version of weak-constraint four-dimensional variational data assimilation (4DVAR)]. Results that demonstrate the pseudo-orbit data assimilation approach can also outperform an ensemble Kalman filter approach are presented. Both comparisons are made in the context of the 18-dimensional Lorenz96 flow and the two-dimensional Ikeda map. Many challenges remain outside the perfect model scenario, both in defining the goals of data assimilation and in achieving high-quality state estimation. The pseudo-orbit data assimilation approach provides a new tool for approaching this open problem.

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Bi, HuiJing, HongYan Zhang, YueMei Jiang, and XiLan Zhao. "A New Security Warning Model about Power Grid." MATEC Web of Conferences 160 (2018): 04005. http://dx.doi.org/10.1051/matecconf/201816004005.

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In recent years, lots of power grid blackouts at home and abroad indicates that the meteorological disasters caused by external meteorological conditions has gradually rose to major contradiction of power grid security. Basing on release information of meteorology, lightning monitoring information and power transmission equipment monitoring information, it established a weather warning model about power grid security, combined with real-time security analysis. Firstly, mathematical models of various types of weather conditions and weather risk assessment model grid were built, then actual operating conditions of a certain regional power grid and model results were compared, the comparison result prove the accuracy of the warning model, and provides a strong recommendation for decision-making.

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Ilie, Constantin Ovidiu, Dănuț Grosu, Oana Mocian, Radu Vilău, and Daniela Bartiș. "Using Statistically Based Modeling for Vehicle Dynamics." Advanced Materials Research 1036 (October 2014): 564–67. http://dx.doi.org/10.4028/www.scientific.net/amr.1036.564.

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This paper is a result of a research focused on statistical vehicles dynamics. Its main purpose is to establish mathematical description of vehicle dynamics based on statistically sufficient experimental data and using statistical instruments. The results are analytical expressions and graphical representations that can be used in situations other than those the data were obtained. Experimental research program objective was to obtain a variety of data to define the dynamics of a vehicle. It involved a large number of tests, more than 100, on different runways, pavement, mosaic tiles or asphalt. They were performed in various weather conditions, sunny and warm weather or rain or sleet and snow. The driving style varied between normal and sport ones. The experimental data were used in obtaining mathematical models that define certain dependency between dynamic parameters. There were issued multiple linear regressions with one resulting parameter. If we analyzed the models we issued we notice that the more factorial parameters are involved, the higher the accuracy of the model we get.

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Hjelkrem, Anne-Grete Roer, Andrea Ficke, Unni Abrahamsen, Ingerd Skow Hofgaard, and Guro Brodal. "Prediction of leaf Bloch disease risk in Norwegian spring wheat based on weather factors and host phenology." European Journal of Plant Pathology 160, no. 1 (February 17, 2021): 199–213. http://dx.doi.org/10.1007/s10658-021-02235-6.

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AbstractLeaf blotch diseases (LBD), such as Septoria nodorum bloch (Parastagnospora nodorum), Septoria tritici blotch (Zymoseptoria tritici) and Tan spot (Pyrenophora tritici-repentis) can cause severe yield losses (up to 50%) in Norwegian spring wheat (Triticum aestivum) and are mainly controlled by fungicide applications. A forecasting model to predict disease risk can be an important tool to optimize disease control. The association between specific weather variables and the development of LBD differs between wheat growth stages. In this study, a mathematical model to estimate phenological development of spring wheat was derived based on sowing date, air temperature and photoperiod. Weather factors associated with LBD severity were then identified for selected phenological growth stages by a correlation study of LBD severity data (17 years). Although information regarding host resistance and previous crop were added to the identified weather factors, two purely weather-based risk prediction models (CART, classification and regression tree algorithm) and one black box model (KNN, based on K nearest neighbor algorithm) were most accurate to predict moderate to high LBD severity (>5% infection). The predictive accuracy of these models (76–83%) was compared to that of two existing models used in Norway and Denmark (60 and 61% accuracy, respectively). The newly developed models performed better than the existing models, but still had the tendency to overestimate disease risk. Specificity of the new models varied between 49 and 74% compared to 40 and 37% for the existing models. These new models are promising decision tools to improve integrated LBD management of spring wheat in Norway.

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ALAM, MAHBOOB, and MOHD AMJAD. "A precipitation forecasting model using machine learning on big data in clouds environment." MAUSAM 72, no. 4 (November 1, 2021): 781–90. http://dx.doi.org/10.54302/mausam.v72i4.3546.

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Numerical weather prediction (NWP) has long been a difficult task for meteorologists. Atmospheric dynamics is extremely complicated to model, and chaos theory teaches us that the mathematical equations used to predict the weather are sensitive to initial conditions; that is, slightly perturbed initial conditions could yield very different forecasts. Over the years, meteorologists have developed a number of different mathematical models for atmospheric dynamics, each making slightly different assumptions and simplifications, and hence each yielding different forecasts. It has been noted that each model has its strengths and weaknesses forecasting in different situations, and hence to improve performance, scientists now use an ensemble forecast consisting of different models and running those models with different initial conditions. This ensemble method uses statistical post-processing; usually linear regression. Recently, machine learning techniques have started to be applied to NWP. Studies of neural networks, logistic regression, and genetic algorithms have shown improvements over standard linear regression for precipitation prediction. Gagne et al proposed using multiple machine learning techniques to improve precipitation forecasting. They used Breiman’s random forest technique, which had previously been applied to other areas of meteorology. Performance was verified using Next Generation Weather Radar (NEXRAD) data. Instead of using an ensemble forecast, it discusses the usage of techniques pertaining to machine learning to improve the precipitation forecast. This paper is to present an approach for mapping of precipitation data. The project attempts to arrive at a machine learning method which is optimal and data driven for predicting precipitation levels that aids farmers thereby aiming to provide benefits to the agricultural domain.

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Dissertations / Theses on the topic "Weather Mathematical models"

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Melton, Roy Wayne. "Parallelizing the spectral method in climate and weather modeling." Diss., Available online, Georgia Institute of Technology, 2004:, 2003. http://etd.gatech.edu/theses/available/etd-04062004-164733/unrestricted/melton%5Froy%5Fw%5F200312%5Fphd.pdf.

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Nyulu, Thandekile. "Weather neutral models for short-term electricity demand forecasting." Thesis, Nelson Mandela Metropolitan University, 2013. http://hdl.handle.net/10948/d1018751.

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Energy demand forecasting, and specifically electricity demand forecasting, is a fun-damental feature in both industry and research. Forecasting techniques assist all electricity market participants in accurate planning, selling and purchasing decisions and strategies. Generation and distribution of electricity require appropriate, precise and accurate forecasting methods. Also accurate forecasting models assist producers, researchers and economists to make proper and beneficial future decisions. There are several research papers, which investigate this fundamental aspect and attempt var-ious statistical techniques. Although weather and economic effects have significant influences on electricity demand, in this study they are purposely eliminated from investigation. This research considers calendar-related effects such as months of the year, weekdays and holidays (that is, public holidays, the day before a public holiday, the day after a public holiday, school holidays, university holidays, Easter holidays and major religious holidays) and includes university exams, general election days, day after elections, and municipal elections in the analysis. Regression analysis, cate-gorical regression and auto-regression are used to illustrate the relationships between response variable and explanatory variables. The main objective of the investigation was to build forecasting models based on this calendar data only and to observe how accurate the models can be without taking into account weather effects and economic effects, hence weather neutral models. Weather and economic factors have to be forecasted, and these forecasts are not so accurate and calendar events are known for sure (error-free). Collecting data for weather and economic factors is costly and time consuming, while obtaining calendar data is relatively easy.

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Yan, Hanjun. "Numerical methods for data assimilation in weather forecasting." HKBU Institutional Repository, 2018. https://repository.hkbu.edu.hk/etd_oa/555.

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Data assimilation plays an important role in weather forecasting. The purpose of data assimilation is try to provide a more accurate atmospheric state for future forecast. Several existed methods currently used in this field fall into two categories: statistical data assimilation and variational data assimilation. This thesis focuses mainly on variational data assimilation. The original objective function of three dimensional data assimilation (3D-VAR) consists of two terms: the difference between the pervious forecast and analysis and the difference between the observations and analysis in observation space. Considering the inaccuracy of previous forecasting results, we replace the first term by the difference between the previous forecast gradients and analysis gradients. The associated data fitting term can be interpreted using the second-order finite difference matrix as the inverse of the background error covariance matrix in the 3D-VAR setting. In our approach, it is not necessary to estimate the background error covariance matrix and to deal with its inverse in the 3D-VAR algorithm. Indeed, the existence and uniqueness of the analysis solution of the proposed objective function are already established. Instead, the solution can be calculated using the conjugate gradient method iteratively. We present the experimental results based on WRF simulations. We show that the performance of this forecast gradient based DA model is better than that of 3D-VAR. Next, we propose another optimization method of variational data assimilation. Using the tensor completion in the cost function for the analysis, we replace the second term in the 3D-VAR cost function. This model is motivated by a small number of observations compared with the large portion of the grids. Applying the alternating direction method of multipliers to solve this optimization problem, we conduct numerical experiments on real data. The results show that this tensor completion based DA model is competitive in terms of prediction accuracy with 3D-VAR and the forecast gradient based DA model. Then, 3D-VAR and the two model proposed above lack temporal information, we construct a third model in four-dimensional space. To include temporal information, this model is based on the second proposed model, in which introduce the total variation to describe the change of atmospheric state. To this end, we use the alternating direction method of multipliers. One set of experimental results generates a positive performance. In fact, the prediction accuracy of our third model is better than that of 3D-VAR, the forecast gradient based DA model, and the tensor completion based DA model. Nevertheless, although the other sets of experimental results show that this model has a better performance than 3D-VAR and the forecast gradient based DA model, its prediction accuracy is slightly lower than the tensor completion based model.

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Schiefelbein, Jon M. "Prototype development of machine-to-machine operational nephanalysis." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2006. http://library.nps.navy.mil/uhtbin/hyperion/06Mar%5FSchiefelbein.pdf.

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Schroder, Ulf P. "Development of a weather radar signal simulator to examine sampling rates and scanning schemes." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2005. http://library.nps.navy.mil/uhtbin/hyperion/05Sep%5FSchroder.pdf.

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Wahl, Douglas Timothy. "Increasing range and lethality of Extended -Range Munitions (ERMS) using Numerical Weather Prediction (NWP) and the AUV workbench to compute a Ballistic Correction (BALCOR)." Thesis, Monterey, Calif. : Naval Postgraduate School, 2006. http://bosun.nps.edu/uhtbin/hyperion.exe/06Dec%5FWahl.pdf.

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Thesis (M.S. in Meteorology and Physical Oceanography)--Naval Postgraduate School, December 2006.
Thesis Advisor(s): Wendell Nuss, Don Brutzmann. "December 2006." Includes bibliographical references (p. 107-116). Also available in print.

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Sanabia, Elizabeth R. "Objective identification of environmental patterns related to tropical cyclone track forecast errors." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2006. http://library.nps.navy.mil/uhtbin/hyperion/06Sep%5FSanabia.pdf.

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Thesis (M.S. in Meteorology and Physical Oceanography)--Naval Postgraduate School, September 2006.
Thesis Advisor(s): Patrick A. Harr, Russell L. Elsberry. "September 2006." Includes bibliographical references (p. 43). Also available in print.

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Nakakita, Kunio. "Toward real-time aero-icing simulation using reduced order models." Thesis, McGill University, 2007. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=99781.

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Even though the power of supercomputers has increased extraordinarily, there is still an insatiable need for more advanced multi-disciplinary CFD simulations in the aircraft analysis and design fields. A particular current interest is in the realistic three-dimensional fully viscous turbulent flow simulation of the highly non-linear aspects of aero-icing. This highly complex simulation is still computationally too demanding in industry, especially when several runs, such as parametric studies, are needed. In order to make such compute-intensive simulations more affordable, this work presents a reduced order modeling approach, based on the "Proper Orthogonal Decomposition", (POD), method to predict a wider swath of flow fields and ice shapes based on a limited number of "snapshots" obtained from complete high-fidelity CFD computations. The procedure of the POD approach is to first decompose the fields into modes, using a limited number of full-calculations snapshots, and then to reconstruct the field and/or ice shapes using those decomposed modes for other conditions, leading to reduced order calculations. The use of the POD technique drastically reduces the computational cost and can provide a more complete map of the performance degradation of an iced aircraft over a wide range of flight and weather conditions.

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Shorts, Vincient F. "A mathematical analysis of the Janus combat simulation weather effects models and sensitivity analysis of sky-to-ground brightness ratio on target detection." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1994. http://handle.dtic.mil/100.2/ADA289629.

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Thesis (M.S. in Applied Mathematics) Naval Postgraduate School, September 1994.
Thesis advisor(s): Bard K. Mansager, Maurice D. Weir. "September 1994." Bibliography: p. 63-64. Also available online.

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Khajehei, Sepideh. "A Multivariate Modeling Approach for Generating Ensemble Climatology Forcing for Hydrologic Applications." PDXScholar, 2015. https://pdxscholar.library.pdx.edu/open_access_etds/2403.

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Reliability and accuracy of the forcing data plays a vital role in the Hydrological Streamflow Prediction. Reliability of the forcing data leads to accurate predictions and ultimately reduction of uncertainty. Currently, Numerical Weather Prediction (NWP) models are developing ensemble forecasts for various temporal and spatial scales. However, it is proven that the raw products of the NWP models may be biased at the basin scale; unlike model grid scale, depending on the size of the catchment. Due to the large space-time variability of precipitation, bias-correcting the ensemble forecasts has proven to be a challenging task. In recent years, Ensemble Pre-Processing (EPP), a statistical approach, has proven to be helpful in reduction of bias and generation of reliable forecast. The procedure is based on the bivariate probability distribution between observation and single-value precipitation forecasts. In the current work, we have applied and evaluated a Bayesian approach, based on the Copula density functions, to develop an ensemble precipitation forecasts from the conditional distribution of the single-value precipitation. Copula functions are the multivariate joint distribution of univariate marginal distributions and are capable of modeling the joint distribution of two variables with any level of correlation and dependency. The advantage of using Copulas, amongst others, includes its capability of modeling the joint distribution independent of the type of marginal distribution. In the present study, we have evaluated the capability of copula-based functions in EPP and comparison is made against an existing and commonly used procedure for same i.e. meta-Gaussian distribution. Monthly precipitation forecast from Climate Forecast System (CFS) and gridded observation from Parameter-elevation Relationships on Independent Slopes Model (PRISM) have been utilized to create ensemble pre-processed precipitation over three sub-basins in the western USA at 0.5-degree spatial resolution. The comparison has been made using both deterministic and probabilistic frameworks of evaluation. Across all the sub-basins and evaluation techniques, copula-based technique shows more reliability and robustness as compared to the meta-Gaussian approach.

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Books on the topic "Weather Mathematical models"

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Spectral numerical weather prediction models. Philadelphia: Society for Industrial and Applied Mathematics, 2012.

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Colucci, Jay W. A comparison of model performance between the nested grid and Eta models. Monterey, Calif: Naval Postgraduate School, 1994.

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Newton, R. E. Performance comparisons for two versions of the Staniforth-Mitchell barotropic numerical weather prediction code. Monterey, Calif: Naval Postgraduate School, 1988.

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Miller, N. L. An analysis of simulated California climate using multiple dynamical and statistical techniques: Final paper. Sacramento, Calif.]: California Energy Commission, 2009.

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O'Muircheartaigh, I. G. Prediction of polytomous events: Model description, algorithm development and methodological aspects, with an application. Monterey, Calif: Naval Postgraduate School, 1987.

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Numerical weather and climate prediction. Cambridge: Cambridge University Press, 2011.

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Empirical methods in short-term climate prediction. Oxford: Oxford University Press, 2007.

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Fundamentals of numerical weather prediction. Cambridge: Cambridge University Press, 2011.

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Medina, Jon G. Estimation of cloud liquid water in winter storms on the Mogollon Rim report on Task 1. Denver, Colo: Water Augmentation Group, Research and Laboratory Services Division, Denver Office, U.S. Dept. of the Interior, Bureau of Reclamation, 1993.

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Pusan, Korea) APCN Meeting 2004 (2004. Fourth APCN Working Group Meeting, Third APCN Steering Committee Meeting. Seoul, Korea: Korea Meteorological Administration, 2004.

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Book chapters on the topic "Weather Mathematical models"

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Zouine, M., M. Akhsassi, N. Erraissi, N. Aarich, A. Bennouna, M. Raoufi, and A. Outzourhit. "Mathematical Models Calculating PV Module Temperature Using Weather Data: Experimental Study." In Lecture Notes in Electrical Engineering, 630–39. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1405-6_72.

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Quarteroni, Alfio. "Weather Forecast Models." In Modeling Reality with Mathematics, 11–27. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-96162-6_2.

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Göncü, Ahmet, Yaning Liu, Giray Ökten, and M. Yousuff Hussaini. "Uncertainty and Robustness in Weather Derivative Models." In Springer Proceedings in Mathematics & Statistics, 351–65. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-33507-0_17.

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Piotrowski, Zbigniew P., Marcin J. Kurowski, Bogdan Rosa, and Michal Z. Ziemianski. "EULAG Model for Multiscale Flows – Towards the Petascale Generation of Mesoscale Numerical Weather Prediction." In Parallel Processing and Applied Mathematics, 380–87. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14403-5_40.

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Thuburn, John. "ENDGame: The New Dynamical Core of the Met Office Weather and Climate Prediction Model." In UK Success Stories in Industrial Mathematics, 27–33. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-25454-8_4.

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Lobbe, Alexander. "Deep Learning for the Benes Filter." In Mathematics of Planet Earth, 195–210. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-18988-3_12.

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AbstractThe filtering problem is concerned with the optimal estimation of a hidden state given partial and noisy observations. Filtering is extensively studied in the theoretical and applied mathematical literature. One of the central challenges in filtering today is the numerical approximation of the optimal filter. Here, accurate and fast methods are actively sought after, especially for such high-dimensional settings as numerical weather prediction, for example. In this paper we present a brief study of a new numerical method based on the mesh-free neural network representation of the density of the solution of the filtering problem achieved by deep learning. Based on the classical SPDE splitting method, our algorithm includes a recursive normalisation procedure to recover the normalised conditional distribution of the signal process. The present work uses the Benes model as a benchmark. The Benes filter is a well-known continuous-time stochastic filtering model in one dimension that has the advantage of being explicitly solvable. Within the analytically tractable setting of the Benes filter, we discuss the role of nonlinearity in the filtering model equations for the choice of the domain of the neural network. Further, we present the first study of the neural network method with an adaptive domain for the Benes model.

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Sonechkin, D. M. "Synchroneity of the Low- Frequency Planetary Wave Dynamics and Its Use to Create a Model for the Numerical Monthly Weather Forecasting." In IUTAM Symposium on Advances in Mathematical Modelling of Atmosphere and Ocean Dynamics, 239–44. Dordrecht: Springer Netherlands, 2001. http://dx.doi.org/10.1007/978-94-010-0792-4_32.

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Jacobson, Mark Z. "Development of a Global-Through-Urban Scale Nested and Coupled Air Pollution and Weather Forecast Model and Application to the Sarmap Field Campaign." In The IMA Volumes in Mathematics and its Applications, 277–97. New York, NY: Springer New York, 2002. http://dx.doi.org/10.1007/978-1-4757-3474-4_11.

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Bacciu, Valentina, Maria Mirto, Sandro Luigi Fiore, Costantino Sirca, Josè Maria Costa Saura, Sonia Scardigno, Valentina Scardigno, et al. "An operational platform for fire danger prevention and monitoring: insights from the OFIDIA2 project." In Advances in Forest Fire Research 2022, 87–92. Imprensa da Universidade de Coimbra, 2022. http://dx.doi.org/10.14195/978-989-26-2298-9_13.

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The project OFIDIA2 (Operational FIre Danger preventIon plAtform 2), funded by the Interreg Greece-Italy 2014-2020 Programme, proposed a pragmatic approach to improve the operational capacity of the stakeholders to detect and fight forest wildfires. A data analytics system was designed and implemented within the project to manage, transform, and extract knowledge from heterogenous data sources, through forecasting models such as weather, fire danger, and fire behaviour models. The high-resolution weather forecasting network previously developed in OFIDIA1 was enhanced by using a mesoscale configuration of the WRF-ARW model over the Central Mediterranean Sea. A nested domain over the Southern Italy at ~2km horizontal resolution allows getting high-resolution weather forecasts (2x2km) and processing data into fire danger models. Fires, fuel, topography and weather data were collected from several sources and used to run and calibrate fire models (FlamMap and Wildfire Analyst) in Apulia region (Italy). Based on the analyses of recurrent weather conditions leading to large fires, fire metricsâ€™ maps for prevention and fire-fighting activities were produced. Finally, a Decision Support System (DSS) was also developed to provide support for 1) the selection of fire behaviour scenarios by means of mathematical models; and 2) the prevention of emergencies thanks to weather forecast information with fire danger indices at high resolutions.

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Dave, Divyang, Rajeev Kumar Gupta, Santosh Kumar Bharti, and Ved Prakash Singh. "Role of Meteorological Satellites and Radar in Weather Forecasting." In Artificial Intelligence of Things for Weather Forecasting and Climatic Behavioral Analysis, 16–31. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-3981-4.ch002.

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Because of global warming, pollution, and many other factors, the environment is changing at an alarming rate. Accurate forecasting can assist people in making appropriate plans for activities such as harvesting, traveling, aviation, etc. Satellites and radar have been increasingly popular in weather forecasting over the previous few decades. The information collected by the satellite and radar can be used to monitor climate movement, track hurricanes, and give barometrical estimations that can be turned into mathematical climate expectation (NWP) models for exact forecasting. Currently, more than 160 meteorological satellites are located in orbit, which generates approximately 80 million observations every day. This chapter discusses several meteorological satellites which are used to extract weather pattern. For the time being, the results of Observation System Simulation Studies (OSSE) utilising satellite information are presented in order to demonstrate the relationship between perceptions from satellite sensors and ground-based sensors.

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Conference papers on the topic "Weather Mathematical models"

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Perera, Lokukaluge P., Brage Mo, and Matthias P. Nowak. "Visualization of Relative Wind Profiles in Relation to Actual Weather Conditions of Ship Routes." In ASME 2017 36th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/omae2017-61120.

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Ship performance and navigation data are collected by vessels that are equipped with various supervisory control and data acquisition systems (SCADA). Such information is collected as large-scale data sets, therefore various analysis tools and techniques are required to extract useful information from the same. The extracted information on ship performance and navigation conditions can be used to implement energy efficiency and emission control applications (i.e. weather routing type applications) on these vessels. Hence, this study proposes to develop data visualizing methods in order to extract ship performance and navigation information from the respective data sets in relation to weather conditions. The relative wind (i.e. apparent wind) profile (i.e. wind speed and direction) collected by onboard sensors and absolute weather conditions, which are extracted from external data sources by using position and time information a selected vessel (i.e. from the recorded ship routes), are considered. Hence, the relative wind profile of the vessel is compared with actual weather conditions to visualize ship performance and navigation parameters relationships, as the main contribution. It is believed that such relationships can be used to develop appropriate mathematical models to predict ship performance and navigation conditions under various weather conditions.

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Wang, Zhongjie, Shaoqiang Zhang, Yangxun Ou, Gaojie Zhong, Ao Wang, Zongkai Zhang, and Limin Sun. "Study on temperature field effects on RC high-pier bridge." In IABSE Congress, Ghent 2021: Structural Engineering for Future Societal Needs. Zurich, Switzerland: International Association for Bridge and Structural Engineering (IABSE), 2021. http://dx.doi.org/10.2749/ghent.2021.0376.

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<p>Temperature is one of the essential factors that cause dynamic characteristics changes in concrete bridges. However, the study of temperature-induced structural frequency changes relies on a large number of measured data to establish a frequency-temperature mathematical model. The selection of temperature variables is arbitrary. This paper takes a concrete high-pier rigid-framed bridge under construction in China as the research object. Based on the continuous 120-day measured weather data at the bridge site, the finite element method (FEM) is used to simulate the bridge's temperature and frequency history without considering the crack and other damage conditions. A regression analysis model of frequency and temperature variables was established. The study found that the selection of temperature variables and mathematical models influences the frequency-temperature mathematical relationship; for high-pier bridges. Establishing a multiple linear regression model with air temperature and point temperature of the main girder and piers as variables can obtain an ideal fitting result.</p>

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Wang, Zhongjie, Shaoqiang Zhang, Yangxun Ou, Gaojie Zhong, Ao Wang, Zongkai Zhang, and Limin Sun. "Study on temperature field effects on RC high-pier bridge." In IABSE Congress, Ghent 2021: Structural Engineering for Future Societal Needs. Zurich, Switzerland: International Association for Bridge and Structural Engineering (IABSE), 2021. http://dx.doi.org/10.2749/ghent.2021.0376.

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<p>Temperature is one of the essential factors that cause dynamic characteristics changes in concrete bridges. However, the study of temperature-induced structural frequency changes relies on a large number of measured data to establish a frequency-temperature mathematical model. The selection of temperature variables is arbitrary. This paper takes a concrete high-pier rigid-framed bridge under construction in China as the research object. Based on the continuous 120-day measured weather data at the bridge site, the finite element method (FEM) is used to simulate the bridge's temperature and frequency history without considering the crack and other damage conditions. A regression analysis model of frequency and temperature variables was established. The study found that the selection of temperature variables and mathematical models influences the frequency-temperature mathematical relationship; for high-pier bridges. Establishing a multiple linear regression model with air temperature and point temperature of the main girder and piers as variables can obtain an ideal fitting result.</p>

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De Martin, Andrea, Andrea Dellacasa, Giovanni Jacazio, and Massimo Sorli. "High-Fidelity Model of Electro-Hydraulic Actuators for Primary Flight Control Systems." In BATH/ASME 2018 Symposium on Fluid Power and Motion Control. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/fpmc2018-8917.

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Hydraulic actuators are the de facto standard for primary flight control systems, since they provide low jamming probability and intrinsic damping capabilities. Electro-Hydraulic Actuators theoretically provide a number of advantages over the traditional hydraulic systems, such as the decrease in the overall power consumption, easier installation and reduced weight of the flight control system, but are so far mostly used as back-up solutions in civil applications. Flight control actuators can face an extremely wide range of operational scenarios depending on the aircraft route, weather condition, pilot behavior and components health. The use of high-fidelity models is instrumental in the design of both actuators and control laws and can enhance the definition of a Prognostics and Health Monitoring system, given its capability to simulate a huge number of possible in-flight situations. In this paper, we provide the mathematical definition of a novel high-fidelity model for primary flight control system, discuss its implementation and results in nominal and off-nominal conditions.

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Fumo, Nelson, Daniel C. Lackey, and Sara McCaslin. "Analysis of Autoregressive Energy Models of a Research House." In ASME 2015 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/imece2015-50630.

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Energy consumption from buildings is a major component of the overall energy consumption by end-use sectors in industrialized countries. In the United States of America (USA), the residential sector alone accounts for half of the combined residential and commercial energy consumption. Therefore, efforts toward energy consumption modeling based on statistical and engineering models are in continuous development. Statistical approaches need measured data but not buildings characteristics; engineering approaches need building characteristics but not data, at least when a calibrated model is the goal. Among the statistical models, the linear regression analysis has shown promising results because of its reasonable accuracy and relatively simple implementation when compared to other methods. In addition, when observed or measured data is available, statistical models are a good option to avoid the burden associated with engineering approaches. However, the dynamic behavior of buildings suggests that models accounting for dynamic effects may lead to more effective regression models, which is not possible with standard linear regression analysis. Utilizing lag variables is one method of autoregression that can model the dynamic behavior of energy consumption. The purpose of using lag variables is to account for the thermal energy stored/release from the mass of the building, which affects the response of HVAC equipment to changes in outdoor or weather parameters. In this study, energy consumption and outdoor temperature data from a research house are used to develop autoregressive models of energy consumption during the cooling season with lag variables to account for the dynamics of the house. Models with no lag variable, one lag variable, and two lag variables are compared. To investigate the effect of the time interval on the quality of the models, data intervals of 5 minutes, 15 minutes, and one hour are used to generate the models. The 5 minutes time interval is used because that is the resolution of the acquired data; the 15 minutes time interval is used because it is a common time interval in electric smart meters; and one hour time interval is used because it is the common time interval for energy simulation in buildings. The primary results shows that the use of lag variables greatly improves the accuracy of the models, but a time interval of 5 minutes is too small to avoid the dependence of the energy consumption on operating parameters. All mathematical models and their quality parameters are presented, along with supporting graphical representation as a visual aid to comparing models.

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Acton, Michael R., Geoff Hankinson, Blaine P. Ashworth, Mohsen Sanai, and James D. Colton. "A Full Scale Experimental Study of Fires Following the Rupture of Natural Gas Transmission Pipelines." In 2000 3rd International Pipeline Conference. American Society of Mechanical Engineers, 2000. http://dx.doi.org/10.1115/ipc2000-107.

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The gas industry has an excellent safety record in operating high pressure transmission pipelines. Nevertheless, it is important that pipeline operators have an understanding of the possible consequences of an accidental gas release, which may ignite, in order to help manage the risks involved. This paper describes two full scale experiments, conducted as part of a research programme into the consequences of pipeline failures, undertaken by an international collaboration of gas companies. The experiments involved the deliberate rupture of a 76km length of 914mm diameter natural gas pipeline operating at a pressure of 60 bar, with the released gas ignited immediately following the failure. Instrumentation was deployed to take detailed measurements, which included the weather conditions, the gas outflow, the size and shape of the resulting fire, and the thermal radiation levels. The results provide important data for the validation of mathematical models, used in developing risk assessment methodologies, and in establishing those standards and design codes for gas pipelines that are risk based.

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Pan, A., Siru Chen, Tsz Chung Ho, Hau Him Lee, and Chi Yan Tso. "Investigations on Improving the Performance of Solid Desiccant Cooling Systems With Passive Radiative Sky Cooling Modules." In ASME 2022 Heat Transfer Summer Conference collocated with the ASME 2022 16th International Conference on Energy Sustainability. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/ht2022-81659.

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Abstract Solid desiccant cooling (SDC) systems are alternatives to conventional vapor compression refrigeration (VCR) systems for indoor air conditioning. Also, passive radiative sky cooling (PRSC) has been shown as an effective electricity-free technology for cooling. PRSC technology has also been incorporated in some air conditioning systems, which were demonstrated to improve system performance considerably. However, the use of PRSC technology in SDC systems has not yet been reported. In this work, a new SDC system was introduced, featuring the use of the PRSC component, i.e., radiative cooling panels, for cooling the supply air to improve the system performance. Mathematical models were developed for each component in the system, after which the models were combined to establish the simulation model of the whole system. The models were validated by comparing with the results in other papers. The performance of the conventional and the newly introduced hybrid SDC systems were evaluated with the established simulation models under the typical hot and humid weather condition in Hong Kong. The performance improvement by adding the PRSC component in the hybrid SDC system was analysed by comparing with the performance of the conventional SDC system. It is shown that the use of the PRSC component can further improve the cooling performance of the SDC system by lowering the air temperature by 3 °C approximately. Therefore, the new hybrid SDC systems with the PRSC component provide a more energy efficient method for indoor air conditioning than conventional SDC systems, especially in hot and humid regions, like Hong Kong.

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Shibasaki, Satoshi, and Hideki Aoyama. "Development of Wood Grain Pattern Design System." In ASME 2009 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/detc2009-87094.

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Various approaches for generating woodgrain patterns using computer graphics have been proposed so far. However, the generation of various woodgrain patterns with conventional methods is difficult due to the need for the adjustment of numerous parameters to express a real woodgrain pattern. In this paper, a new mathematical approach for generating woodgrain patterns is proposed. Virtual trees are generated by simulating tree growing based on past actual weather information obtained from public organizations, and woodgrain patterns are then acquired by cutting the trunks of the virtual trees. In order to simulate tree growing, growth models of tree are constructed in consideration of dendrological characteristics and environmental conditions. Growth of tree is influenced by various environmental factors, such as sunlight, temperature, carbon dioxide concentration, wind, precipitation, soil nutrient, inclination of ground, survival amongst surrounding trees, etc. With this system, the growth model of trees is constructed based on precipitation, temperature, sunlight, and inclination of ground, which especially have strong effects on tree growth. With this approach, various types of virtual trees can be obtained by changing growth conditions such as period and location of growth without the need to reset complicated parameters of tree species, and then the virtual trees can be cut at arbitrary areas, thus allowing a variety of woodgrain patterns to be easily generated by one parameter setup.

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Meng, X. Z., Z. Lu, L. J. Su, X. L. Luo, L. C. Wei, L. W. Jin, and J. Chai. "Numerical and Experimental Investigation on Thermal Management of an Outdoor Battery Cabinet." In ASME 2014 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/imece2014-38229.

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Many forms of electronic equipment, of necessity, must be located in an outdoor environment. Such equipment in typical form may be battery packs or telecom-equipment. It is essential that these facilities be protected from a wide range of ambient temperatures and solar radiation. To this end, cabinet enclosures with proper thermal management have been developed to house such electronic equipment in a highly weather tight manner, especially for battery cabinet. Often the batteries are of a lead-acid construction which is known to be adversely affected by temperature extremes in terms of battery performance and life. Therefore, it is important to maintain the cabinet temperature ideally for ensuring battery stability and extending battery lifespan. In this paper, physical and mathematical models are established to investigate the flow field and temperature distribution inside an outdoor cabinet, which contains 24 batteries with two configurations of two-layer and six-layer respectively. The cabinet walls are maintained at a constant temperature by a refrigeration system and the ambient temperature is up to 50 °C according to the practical situation. The flow field and temperature distribution are analyzed with and without consideration of solar radiation. An experimental facility is then developed to measure the battery surface temperatures and to validate the numerical simulation. The differences between the CFD and experimental results are within 2%, which confirms the CFD model.

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Famelis, Ioannis, George Galanis, and Aristotelis Liakatas. "New efficient optimizing techniques for Kalman filters and numerical weather prediction models." In INTERNATIONAL CONFERENCE OF NUMERICAL ANALYSIS AND APPLIED MATHEMATICS 2015 (ICNAAM 2015). Author(s), 2016. http://dx.doi.org/10.1063/1.4952267.

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Reports on the topic "Weather Mathematical models"

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Clausen, Jay, Christopher Felt, Michael Musty, Vuong Truong, Susan Frankenstein, Anna Wagner, Rosa Affleck, Steven Peckham, and Christopher Williams. Modernizing environmental signature physics for target detection—Phase 3. Engineer Research and Development Center (U.S.), March 2022. http://dx.doi.org/10.21079/11681/43442.

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The present effort (Phase 3) builds on our previously published prior efforts (Phases 1 and 2), which examined methods of determining the probability of detection and false alarm rates using thermal infrared for buried object detection. Environmental phenomenological effects are often represented in weather forecasts in a relatively coarse, hourly resolution, which introduces concerns such as exclusion or misrepresentation of ephemera or lags in timing when using this data as an input for the Army’s Tactical Assault Kit software system. Additionally, the direct application of observed temperature data with weather model data may not be the best approach because metadata associated with the observations are not included. As a result, there is a need to explore mathematical methods such as Bayesian statistics to incorporate observations into models. To better address this concern, the initial analysis in Phase 2 data is expanded in this report to include (1) multivariate analyses for detecting objects in soil, (2) a moving box analysis of object visibility with alternative methods for converting FLIR radiance values to thermal temperature values, (3) a calibrated thermal model of soil temperature using thermal IR imagery, and (4) a simple classifier method for automating buried object detection.

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