Relevant bibliographies by topics / Rain forecasting

Academic literature on the topic 'Rain forecasting'

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Published: 14 March 2023

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Journal articles on the topic "Rain forecasting"

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Hutchins, David A. "Forecasting the rain ratio." Nature 476, no. 7358 (August 2011): 41–42. http://dx.doi.org/10.1038/476041a.

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Bridger, Nguyen, and Chiao. "Developing Spatially Accurate Rainfall Predictions for the San Francisco Bay Area through Case Studies of Atmospheric River and other Synoptic Events." Atmosphere 10, no. 9 (September 12, 2019): 541. http://dx.doi.org/10.3390/atmos10090541.

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Rainfall patterns in the San Francisco Bay Area (SFBA) are highly influenced by local topography. It has been a forecasting challenge for the main US forecast models. This study investigates the ability of the Weather Research and Forecasting (WRF) model to improve upon forecasts, with particular emphasis on the rain shadow common to the southern end of the SFBA. Three rain events were evaluated: a mid-season atmospheric river (AR) event with copious rains; a typical non-AR frontal passage rain event; and an area-wide rain event in which zero rain was recorded in the southern SFBA. The results show that, with suitable choices of parameterizations, the WRF model with a resolution around 1 km can forecast the observed rainfall patterns with good accuracy, and would be suitable for operational use, especially to water and emergency managers. Additionally, the three synoptic situations were investigated for further insight into the common ingredients for either flooding rains or strong rain shadow events.
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Adhikary, Sajal Kumar, Nitin Muttil, and Abdullah Gokhan Yilmaz. "Improving streamflow forecast using optimal rain gauge network-based input to artificial neural network models." Hydrology Research 49, no. 5 (December 5, 2017): 1559–77. http://dx.doi.org/10.2166/nh.2017.108.

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Abstract Accurate streamflow forecasting is of great importance for the effective management of water resources systems. In this study, an improved streamflow forecasting approach using the optimal rain gauge network-based input to artificial neural network (ANN) models is proposed and demonstrated through a case study (the Middle Yarra River catchment in Victoria, Australia). First, the optimal rain gauge network is established based on the current rain gauge network in the catchment. Rainfall data from the optimal and current rain gauge networks together with streamflow observations are used as the input to train the ANN. Then, the best subset of significant input variables relating to streamflow at the catchment outlet is identified by the trained ANN. Finally, one-day-ahead streamflow forecasting is carried out using ANN models formulated based on the selected input variables for each rain gauge network. The results indicate that the optimal rain gauge network-based input to ANN models gives the best streamflow forecasting results for the training, validation and testing phases in terms of various performance evaluation measures. Overall, the study concludes that the proposed approach is highly effective to achieve the enhanced streamflow forecasting and could be a viable option for streamflow forecasting in other catchments.
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Costa-Patry, Etienne, Sven Holzendorf, and Matthias Pätsch. "Car Rain Sensors as Mobile Measuring Stations in Heavy-rain Forecasting." ATZ worldwide 123, no. 5-6 (May 2021): 70–75. http://dx.doi.org/10.1007/s38311-021-0655-0.

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KAWASAKI, Shoji, Masaaki KOYAMA, Shunsuke FUKAMI, and Chisa KOBAYASHI. "Forecasting Occurrence of Localized Heavy Rain and Tornado." Journal of The Institute of Electrical Engineers of Japan 134, no. 9 (2014): 604–7. http://dx.doi.org/10.1541/ieejjournal.134.604.

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Mazzeffi, Michael A., and Jacob T. Gutsche. "Forecasting Extracorporeal Membrane Oxygenation Outcomes: Rain or Shine?" Journal of Cardiothoracic and Vascular Anesthesia 33, no. 4 (April 2019): 918–19. http://dx.doi.org/10.1053/j.jvca.2018.09.021.

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Migon, Helio S., and Ana Beatriz S. Monteiro. "Rain-fall modeling: An Application of Bayesian forecasting." Stochastic Hydrology and Hydraulics 11, no. 2 (April 1997): 115–27. http://dx.doi.org/10.1007/bf02427911.

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Mwangi, E., F. Wetterhall, E. Dutra, F. Di Giuseppe, and F. Pappenberger. "Forecasting droughts in East Africa." Hydrology and Earth System Sciences Discussions 10, no. 8 (August 8, 2013): 10209–30. http://dx.doi.org/10.5194/hessd-10-10209-2013.

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Abstract. The humanitarian crisis caused by the recent droughts (2008–2009 and 2010–2011) in the East African region have illustrated that the ability to make accurate drought predictions with adequate lead time is essential. The use of dynamical model forecasts and drought indices, such as Standardized Precipitation Index (SPI), promises to lead to a better description of drought duration, magnitude and spatial extent. This study evaluates the use of the European Centre for Medium-Range Weather Forecasts (ECMWF) products in forecasting droughts in East Africa. ECMWF seasonal precipitation shows significant skill for both rain seasons when evaluated against measurements from the available in-situ stations from East Africa. The October–December rain season has higher skill that the March–May season. ECMWF forecasts add value to the statistical forecasts produced during the Greater Horn of Africa Climate Outlook Forums (GHACOF) which is the present operational product. Complementing the raw precipitation forecasts with SPI provides additional information on the spatial extend and intensity of the drought event.
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Li, Ji, Yangbo Chen, Huanyu Wang, Jianming Qin, Jie Li, and Sen Chiao. "Extending flood forecasting lead time in a large watershed by coupling WRF QPF with a distributed hydrological model." Hydrology and Earth System Sciences 21, no. 2 (March 2, 2017): 1279–94. http://dx.doi.org/10.5194/hess-21-1279-2017.

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Abstract. Long lead time flood forecasting is very important for large watershed flood mitigation as it provides more time for flood warning and emergency responses. The latest numerical weather forecast model could provide 1–15-day quantitative precipitation forecasting products in grid format, and by coupling this product with a distributed hydrological model could produce long lead time watershed flood forecasting products. This paper studied the feasibility of coupling the Liuxihe model with the Weather Research and Forecasting quantitative precipitation forecast (WRF QPF) for large watershed flood forecasting in southern China. The QPF of WRF products has three lead times, including 24, 48 and 72 h, with the grid resolution being 20 km × 20 km. The Liuxihe model is set up with freely downloaded terrain property; the model parameters were previously optimized with rain gauge observed precipitation, and re-optimized with the WRF QPF. Results show that the WRF QPF has bias with the rain gauge precipitation, and a post-processing method is proposed to post-process the WRF QPF products, which improves the flood forecasting capability. With model parameter re-optimization, the model's performance improves also. This suggests that the model parameters be optimized with QPF, not the rain gauge precipitation. With the increasing of lead time, the accuracy of the WRF QPF decreases, as does the flood forecasting capability. Flood forecasting products produced by coupling the Liuxihe model with the WRF QPF provide a good reference for large watershed flood warning due to its long lead time and rational results.
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Shu, Zhangkang, Jianyun Zhang, Junliang Jin, Lin Wang, Guoqing Wang, Jie Wang, Zhouliang Sun, et al. "Evaluation and Application of Quantitative Precipitation Forecast Products for Mainland China Based on TIGGE Multimodel Data." Journal of Hydrometeorology 22, no. 5 (May 2021): 1199–219. http://dx.doi.org/10.1175/jhm-d-20-0004.1.

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AbstractWe evaluated 24-h control forecast products from The International Grand Global Ensemble center over the 10 first-class water resource regions of Mainland China in 2013–18 from the perspective of precipitation processes (continuous) and precipitation events (discrete). We evaluated the forecasts from the China Meteorological Administration (CMA), the Centro de Previsão de Tempo e Estudos Climáticos (CPTEC), the Canadian Meteorological Centre (CMC), the European Centre for Medium-Range Weather Forecasts (ECMWF), the Japan Meteorological Agency (JMA), the Korea Meteorological Administration (KMA), the United Kingdom Met Office (UKMO), and the National Centers for Environmental Prediction (NCEP). We analyzed the differences among the numerical weather prediction (NWP) models in predicting various types of precipitation events and showed the spatial variations in the quantitative precipitation forecast efficiency of the NWP models over Mainland China. Meanwhile, we also combined four hydrological models to conduct meteo-hydrological runoff forecasting in three typical basins and used the Bayesian model averaging (BMA) method to perform the ensemble forecast of different scenarios. Our results showed that the models generally underestimate and overestimate precipitation in northwestern China and southwestern China, respectively. This tendency became increasingly clear as the lead time rose. Each model has a high reliability for the forecast of no-rain and light rain in the next 10 days, whereas the NWP model only has high reliability on the next day for moderate and heavy rain events. In general, each model showed different capabilities of capturing various precipitation events. For example, the CMA and CMC forecasts had a better prediction performance for heavy rain but greater errors for other events. The CPTEC forecast performed well for long lead times for no-rain and light rain but had poor predictability for moderate and heavy rains. The KMA, UKMO, and NCEP forecasts performed better for no-rain and light rain. However, their forecasting ability was average for moderate and heavy rain. Although the JMA model performed better in terms of errors and accuracy, it seriously underestimated heavy rain events. The extreme rainstorm and flood forecast results of the coupled JMA model should be treated with caution. Overall, the ECMWF had the most robust performance. Discrepancies in the forecasting effects of various models on different precipitation events vary with the lead time and region. When coupled with hydrological models, NWP models not only control the accuracy of runoff prediction directly but also increase the difference among the prediction results of different hydrological models with the increase in NWP error significantly. Among all the single models, ECMWF, JMA, and NCEP have better effects than the other models. Moreover, the ensemble forecast based on BMA is more robust than the single model, which can improve the quality of runoff prediction in terms of accuracy and reliability.
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Dissertations / Theses on the topic "Rain forecasting"

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DeSordi, Steven Paul. "Utah local area model sensitivity to boundary conditions for summer rain simulations." Wright-Patterson AFB, Ohio : Dept. of the Air Force, 1996. http://stinet.dtic.mil/cgi-bin/fulcrum%5Fmain.pl?database=ft%5Fu2&searchid=0&keyfieldvalue=ADA319136&filename=%2Ffulcrum%2Fdata%2FTR%5Ffulltext%2Fdoc%2FADA319136.pdf.

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Thesis (M.S.)--University of Utah, 1996. Thesis from the University of Utah's Department of Meteorology explores the sensitivity of the pecipitation-predicting model known as the Utah Limited Area Model (LAM) to the way that the lateral and upper boundary conditions are applied. The approach is different from most past studies of LAM boundary specification because it is founded upon a medium-range simulation using real data. Many other studies of boundary conditions have used idealized cases or short-term (a few days or less) predictions.
Title from web page (viewed Oct. 30, 2003). "96-084." "August 1996." Includes bibliographical references p. [110]-112. Also available in print version.
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Li, Jing. "Clustering and forecasting for rain attenuation time series data." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-219615.

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Clustering is one of unsupervised learning algorithm to group similar objects into the same cluster and the objects in the same cluster are more similar to each other than those in the other clusters. Forecasting is making prediction based on the past data and efficient artificial intelligence models to predict data developing tendency, which can help to make appropriate decisions ahead. The datasets used in this thesis are the signal attenuation time series data from the microwave networks. Microwave networks are communication systems to transmit information between two fixed locations on the earth. They can support increasing capacity demands of mobile networks and play an important role in next generation wireless communication technology. But inherent vulnerability to random fluctuation such as rainfall will cause significant network performance degradation. In this thesis, K-means, Fuzzy c-means and 2-state Hidden Markov Model are used to develop one step and two step rain attenuation data clustering models. The forecasting models are designed based on k-nearest neighbor method and implemented with linear regression to predict the real-time rain attenuation in order to help microwave transport networks mitigate rain impact, make proper decisions ahead of time and improve the general performance.
Clustering is een van de unsupervised learning algorithmen om groep soortgelijke objecten in dezelfde cluster en de objecten in dezelfde cluster zijn meer vergelijkbaar met elkaar dan die in de andere clusters. Prognoser är att göra förutspårningar baserade på övergående data och effektiva artificiella intelligensmodeller för att förutspå datautveckling, som kan hjälpa till att fatta lämpliga beslut. Dataseten som används i denna avhandling är signaldämpningstidsseriedata från mikrovågsnätverket. Mikrovågsnät är kommunikationssystem för att överföra information mellan två fasta platser på jorden. De kan stödja ökade kapacitetsbehov i mobilnät och spela en viktig roll i nästa generationens trådlösa kommunikationsteknik. Men inneboende sårbarhet för slumpmässig fluktuering som nedbörd kommer att orsaka betydande nätverksförstöring. I den här avhandlingen används K-medel, Fuzzy c-medel och 2-state Hidden Markov Model för att utveckla ett steg och tvåstegs regen dämpning dataklyvningsmodeller. Prognosmodellerna är utformade utifrån k-närmaste granne-metoden och implementeras med linjär regression för att förutsäga realtidsdämpning för att hjälpa mikrovågstransportnät att mildra regnpåverkan, göra rätt beslut före tid och förbättra den allmänna prestandan.
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Karlsson, Magnus Sven. "NEAREST NEIGHBOR REGRESSION ESTIMATORS IN RAINFALL-RUNOFF FORECASTING." Diss., The University of Arizona, 1985. http://hdl.handle.net/10150/282088.

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The subject of this study is rainfall-runoff forecasting and flood warning. Denote by (X(t),Y(t)) a sequence of equally spaced bivariate random variables representing rainfall and runoff, respectively. A flood is said to occur at time period (n + 1) if Y(n + 1) > T where T is a fixed number. The main task of flood warning is that of deciding whether or not to issue a flood alarm for the time period n + 1 on the basis of the past observations of rainfall and runoff up to and including time n. With each decision, warning or no warning, there is a certain probability of an error (false alarm or no alarm). Using notions from classical decision theory, the optimal solution is the decision that minimizes Bayes risk. In Chapter 1 a more precise definition of flood warning will be given. A critical review (Chapter 2) of classical methods for forecasting used in hydrology reveals that these methods are not adequate for flood warning and similar types of decision problems unless certain Gaussian assumptions are satisfied. The purpose of this study is to investigate the application of a nonparametric technique referred to as the k-nearest neighbor (k-NN) methods to flood warning and least squares forecasting. The motivation of this method stems from recent results in statistics which extends nonparametric methods for inferring regression functions in a time series setting. Assuming that the rainfall-runoff process can be cast in the framework of Markov processes then, with some additional assumptions, the k-NN technique will provide estimates that converge with an optimal rate to the correct decision function. With this in mind, and assuming that our assumptions are valid, then we can claim that this method will, as the historical record grows, provide the best possible estimate in the sense that no other method can do better. A detailed description of the k-NN estmator is provided along with a scheme for calibration. In the final chapters, the forecasts of this new method are compared with the forecasts of several other methods commonly used in hydrology, on both real and simulated data.
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Gorugantula, Srikanth V. L. "A GPS-IPW Based Methodology for Forecasting Heavy Rain Events." Thesis, Virginia Tech, 2002. http://hdl.handle.net/10919/10145.

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The mountainous western Virginia is the source of the headwater streams for the New, the Roanoke, and the James rivers. The region is prone to flash flooding, typically the result of localized precipitation. Fortunately, within the region, there is an efficient system of instruments for real-time data gathering with IFLOWS (Integrated Flood Observing and Warning System) gages, WSR-88D Doppler radar, and high precision GPS (Global Positioning System) receiver. The focus of this research is to combine the measurements from these various sensors in an algorithmic framework to determine the flash flood magnitude. It has been found that the trend in the GPS signals serves as a precursor for rain events with a lead-time of 30 minutes to 2 hours. The methodology proposed herein takes advantage of this lead-time as the trigger to initiate alert related calculations. It is shown here that the sum of the rates of change of total cloud water, water vapor contents and logarithmic profiles of partial pressure of dry air and temperature in an atmospheric column is equal to the rain rate. The total water content is measurable as the profiles of integrated precipitable water (IPW) from the GPS, the vertically integrated liquid (VIL) from the radar (representing different phases of the atmospheric water) and the pressure and temperature profiles are available. An example problem is presented illustrating the involving the calculations.
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Ryall, Gill. "An automated system for generating very-short-range forecasts of precipitation." Thesis, University of Sussex, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.284079.

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Pettegrew, Brian P. "On methods of precipitation efficiency estimation /." free to MU campus, to others for purchase, 2004. http://wwwlib.umi.com/cr/mo/fullcit?p1422951.

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Michaud, Jene Diane. "RAINFALL-RUNOFF MODELING OF FLASH FLOODS IN SEMI-ARID WATERSHEDS." Department of Hydrology and Water Resources, University of Arizona (Tucson, AZ), 1992. http://hdl.handle.net/10150/614156.

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Flash floods caused by localized thunderstorms are a natural hazard of the semi -arid Southwest, and many communities have responded by installing ALERT flood forecasting systems. This study explored a rainfall- runoff modeling approach thought to be appropriate for forecasting in such watersheds. The kinematic model KINEROS was evaluated because it is a distributed model developed specifically for desert regions, and can be applied to basins without historic data. This study examined the accuracy of KINEROS under data constraints that are typical of semi -arid ALERT watersheds. The model was validated at the 150 km2, semi -arid Walnut Gulch experimental watershed. Under the conditions examined, KINEROS provided poor simulations of runoff volume and peak flow, but good simulations of time to peak. For peak flows, the standard error of estimate was nearly 100% of the observed mean. Surprisingly, when model parameters were based only on measurable watershed properties, simulated peak flows were as accurate as when parameters were calibrated on some historic data. The accuracy of KINEROS was compared to that of the SCS model. When calibrated, a distributed SCS model with a simple channel loss component was as accurate as KINEROS. Reasons for poor simulations were investigated by examining a) rainfall sampling errors, b) model sensitivity and dynamics, and c) trends in simulation accuracy. The cause of poor simulations was divided between rainfall sampling errors and other problems. It was found that when raingage densities are on the order of 1/20 km2, rainfall sampling errors preclude the consistent and reliable simulation of runoff from localized thunderstorms. Even when rainfall errors were minimized, accuracy of simulations were still poor. Good results, however, have been obtained with KINEROS on small watersheds; the problem is not KINEROS itself but its application at larger scales. The study also examined the hydrology of thunderstorm -generated floods at Walnut Gulch. The space -time dynamics of rainfall and runoff were characterized and found to be of fundamental importance. Hillslope infiltration was found to exert a dominant control on runoff, although flow hydraulics, channel losses, and initial soil moisture are also important. Watershed response was found to be nonlinear.
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Tsang, Fan Cheong. "Advances in flood forecasting using radar rainfalls and time-series analysis." Thesis, Lancaster University, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.481184.

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This thesis reports the use of a time-series analysis approach to study the catchment hydrological system of the River Ribble. Rain gauge records, radar rainfall estimates and flow data are used in the analysis. The preliminary study consists of the flow forecasting at Reedyford, Pendle Water (82 km2). Flow forecasts generated from the rain gauge records are better than the radar rainfall estimates over this small catchment. However, the catchment response to rainfall is quick and no clear advantages in extending the lead-time of the forecast can be introduced by using an artificial time delayed rainfall input. A non-linear rainfall-flow relationship has been studied using the rain gauge rainfall and flow records at the River Hodder catchment (261 km2). A calibration scheme is used to identify the non-linear function of the catchment as well as the rainfall-flow system model. Although a better time-invariant system model can be identified, the non-linear rainfall-flow process cannot be fully explained by a power law function of effective rainfall. Assuming the dynamic, nonlinear system characteristics of the catchment can be reflected by a time-varying model gain parameter, relationships between the parameter and the flow, and between the parameter and the rainfall can be evaluated. These relationships have been used to improve the flow forecast during storm events. The results indicate, however, that the approach failed to improve the flow forecast near the peak flow condition. Radar data have been incorporated to forecast the flow at Jumbles Rock (1053 km2) and Samlesbury (1140 km2), River Ribble. The radar data calibrated by the Lancaster University Adaptive Radar Calibration System appears to produce better flow forecasts than the standard radar data product calibrated by the Meteorological Office. The proposed flow forecasting scheme generates better forecasts than the current system operated by the National Rivers Authority, North West Region.
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Cataldo, Edmund F. "Evaluation of the SSM/I rain analyses for selective storms in the ERICA project." Monterey, California : Naval Postgraduate School, 1990. http://handle.dtic.mil/100.2/ADA241321.

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Thesis (M.S. in Meteorology and Physical Oceanography)--Naval Postgraduate School, September 1990.
Thesis Advisor(s): Wash, Carlyle H. Second Reader: Nuss, Wendell A. "September 1990." Description based on title screen viewed on December 17, 2009. DTIC Descriptor(s): Weather forecasting, satellite meteorology, uncertainty, polarization, ships, coastal regions, light, rates, theses, radar, regression analysis, precipitation, solutions(general), rain, winter, rainfall intensity, storms, equations, cyclones, channels, corrections, temperate regions, cyclogenesis, algorithms, temperature. DTIC Identifier(s): Rainfall intensity, erica project, ssm/i(special sensor microwave/images). Author(s) subject terms: Microwave, ERICA, SSM/I, precipitation forecasting, rain. Includes bibliographical references (p. 81-82). Also available in print.
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Karnieli, Arnon 1952. "Storm runoff forecasting model incorporating spatial data." Diss., The University of Arizona, 1988. http://hdl.handle.net/10150/191138.

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This study is concerned with design forecasting of storm hydrographs with emphasis on runoff volume and peak discharge. The objective of the study was to develop, calibrate and test a method for forecasting storm runoff from small semi-arid watersheds using an available prediction model. In order to turn the selected prediction model into a forecasting model an objective procedure in terms of an API-type model was developed for evaluating the soil moisture deficit in the upper soil layer at the beginning of each storm. Distinction was made between the physically-based parameters and the other fitting parameters. The rainfall excess calculation was computed by solving the Green and Ampt equation for unsteady rainfall conditions using the physically-based parameters. For the physically-based parameters a geographic information system was developed in order to account for the variability in time and space of the input data and the watershed characteristics and to coregister parameters on a common basis. The fitting parameters were used to calibrate the model on one subwatershed in the Walnut Gulch Experimental Watershed while the physically-based parameters remained constant. Two objective functions were selected for the optimization procedure. These functions expressed the goodness of fit between the calculated hydrograph volume and peak discharge and the observed volume and peak discharge. Linear relationships between the effective matric potential parameter and the two objective functions obtained from the sensitivity analyses made it possible to develop a bilinear interpolation algorithm to minimize, simultaneously, the difference between the calculated and observed volume and peak discharge. The prediction mode of the model was tested both on different storm events on the same subwatershed and on another subwatershed with satisfactory results. In the prediction mode the effective matric potential parameter was allowed to vary from storm to storm, however, in the forecasting mode these values were obtained from the API model. Relatively poor results were obtained in testing the forecasting mode on another subwatershed. These errors were able to be corrected by changing the channel losses fitting parameters.
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Books on the topic "Rain forecasting"

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Rainfall forecasting. Hauppauge, N.Y: Nova Science Publishers, 2011.

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Martín, Olga E., and Tricia M. Roberts. Rainfall: Behavior, forecasting, and distribution. Hauppauge, N.Y: Nova Science Publishers, 2011.

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ill, Ross Scott, ed. Brewster, the rain-makin' rooster. Austin, Tex: Eakin Press, 1999.

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Devkota, Lochan Prasad. Long range forecasting (LRF) of monsoon rainfall in Nepal. Dhaka: SAARC Meteorological Research Centre, 2006.

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Devkota, Lochan Prasad. Long range forecasting (LRF) of monsoon rainfall in Nepal. Dhaka: SAARC Meteorological Research Centre, 2006.

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Ojhā, Madhusūdana. Vr̥ṣṭividyābodhikā Kādambinī: Svopajñahindībhāṣānuvādasahitā. Jodhapuram: Paṇḍitamadhusūdanaojhāśodhaprakoṣṭhaḥ, 2003.

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Dube, Umāśaṅkara. Prācīna varshā vijñāna. Kānapura: Śrī Hanumat Jyotisha Mandira, 1989.

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Kishōdai, Ōsaka Kanku. Kyōu o motarasu senjō kōsuitai no keisei kikō tō no kaimei oyobi kōsui kyōdo idō sokudo no yosoku ni kansuru kenkyū. Tsukuba-shi: Kishō Kenkyūjo, 2010.

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Kelkar, R. R. Monsoon prediction. Hyderabad, India: BS Publications, 2009.

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Kishōdai, Ōsaka Kanku. Dankōki no kōsuikei ni okeru meso-ryōteki yohō no gijutsu kaihatsu. [Osaka]: Ōsaka Kanku Kishōdai, 1995.

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Book chapters on the topic "Rain forecasting"

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Yasuno, Takato, Akira Ishii, and Masazumi Amakata. "Rain-Code Fusion: Code-to-Code ConvLSTM Forecasting Spatiotemporal Precipitation." In Pattern Recognition. ICPR International Workshops and Challenges, 20–34. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-68787-8_2.

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Song, Haifeng, Tao Tang, Chenling Li, and Yi Ding. "Short Time Forecasting of Rail Transit Passenger Volume." In The 2nd International Symposium on Rail Transit Comprehensive Development (ISRTCD) Proceedings, 121–30. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-37589-7_12.

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Song, Haifeng, Tao Tang, Chenling Li, and Yi Ding. "Long Time Forecasting of Rail Transit Passenger Volume." In The 2nd International Symposium on Rail Transit Comprehensive Development (ISRTCD) Proceedings, 387–94. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-37589-7_37.

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Peng, Hao, Santosh U. Bobade, Michael E. Cotterell, and John A. Miller. "Forecasting Traffic Flow: Short Term, Long Term, and When It Rains." In Big Data – BigData 2018, 57–71. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-94301-5_5.

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Tang, Yuxin, Jianyuan Guo, Yalin Wang, and Jian Li. "Metro Outbound Passenger Flow Forecasting Considering Spatial-Temporal Correlation Characteristics." In Proceedings of the 5th International Conference on Electrical Engineering and Information Technologies for Rail Transportation (EITRT) 2021, 525–34. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9909-2_55.

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Zhang, Liangliang, Yuanhua Jia, Xihui Yin, and Zhong-hai Niu. "The Arrival Passenger Flow Short-Term Forecasting of Urban Rail Transit Based on the Fractal Theory." In The 2nd International Symposium on Rail Transit Comprehensive Development (ISRTCD) Proceedings, 95–101. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-37589-7_9.

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Zhang, Ke, Xinyue Xu, and Ziyue Mi. "Subway Passenger Flow Forecasting Under Station Closure with an Improved General Regression Neural Network." In Proceedings of the 5th International Conference on Electrical Engineering and Information Technologies for Rail Transportation (EITRT) 2021, 397–405. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9909-2_44.

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Tang, Jinjin, Leishan Zhou, Yong Zhao, and Jingjing Shao. "Passenger Flow Trend Analysis and Forecasting for Large High-Speed Railway Stations During Holidays: A Case Study for Beijing West Railway Station." In Proceedings of the 2015 International Conference on Electrical and Information Technologies for Rail Transportation, 399–407. Berlin, Heidelberg: Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-662-49370-0_42.

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"rain(fall) forecasting." In Dictionary Geotechnical Engineering/Wörterbuch GeoTechnik, 1074. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-41714-6_180249.

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Hudnurkar, Shilpa, Vidur Sood, Vedansh Mishra, Manobhav Mehta, Akash Upadhyay, Shilpa Gite, and Neela Rayavarapu. "Multivariate Time Series Forecasting of Rainfall Using Machine Learning." In Artificial Intelligence of Things for Weather Forecasting and Climatic Behavioral Analysis, 87–106. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-3981-4.ch007.

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Predicting rainfall is essential for assessing the impact of climatic and hydrological changes over a specific region, predicting natural disasters or day-to-day life. It is one of the most prominent, complex, and essential weather forecasting and meteorology tasks. In this chapter, long short-term memory network (LSTM), artificial neural network (ANN), and 1-dimensional convolutional neural network LSTM (1D CNN-LSTM) models are explored for predicting rainfall at multiple lead times. The daily weather parameter data of over 15 years is collected for a station in Maharashtra. Rainfall data is classified into three classes: no-rain, light rain, and moderate-to-heavy rain. The principal component analysis (PCA) helped to reduce the input feature dimension. The performance of all the networks are compared in terms of accuracy and F1 score. It is observed that LSTM predicts rainfall with consistent accuracy of 82% for 1 to 6 days lead time while the performance of 1D CNN-LSTM and ANN are comparable to LSTM.
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Conference papers on the topic "Rain forecasting"

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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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Legg, Nicholas T. "FORECASTING RAIN-INDUCED DEBRIS FLOWS ON MOUNT RAINIER, WASHINGTON." In GSA Annual Meeting in Seattle, Washington, USA - 2017. Geological Society of America, 2017. http://dx.doi.org/10.1130/abs/2017am-302794.

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Li, Jiachen, Fan Yang, Hengbo Ma, Srikanth Malla, Masayoshi Tomizuka, and Chiho Choi. "RAIN: Reinforced Hybrid Attention Inference Network for Motion Forecasting." In 2021 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE, 2021. http://dx.doi.org/10.1109/iccv48922.2021.01579.

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Komarudin and David. "Rain Forecasting for Production Capacity Planning at Open Pit Mining." In ICIBE 2019: 2019 The 5th International Conference on Industrial and Business Engineering. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3364335.3364396.

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Awan, Malik Shahzad Kaleem, and Mian Muhammad Awais. "Towards a Generic Model for Forecasting Rain Duration Using GITIC Model." In 2008 15th Annual IEEE International Conference on Engineering of Computer Based Systems (ECBS). IEEE, 2008. http://dx.doi.org/10.1109/ecbs.2008.25.

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Del Rosario, Vince Xavier C., Vhynce Joi Narca, Franz Timothy Jeanne Laconsay, and Chris Jordan Alliac. "Weather Forecasting Rain Probability in Cebu Using ANFIS and Bayesian Network." In 2021 1st International Conference in Information and Computing Research (iCORE). IEEE, 2021. http://dx.doi.org/10.1109/icore54267.2021.00026.

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Yeo, J. X., Y. H. Lee, and J. T. Ong. "Use of weather radar rain cell motion forecasting for site diversity system." In MILCOM 2016 - 2016 IEEE Military Communications Conference (MILCOM). IEEE, 2016. http://dx.doi.org/10.1109/milcom.2016.7795351.

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Khidir, Adil Mohammed, Hanan Hassan Ali Adlan, and Isam Ahmed Basheir. "Neural Networks forecasting architectures for rainfall in the rain-fed Sectors in Sudan." In 2013 International Conference on Computing, Electrical and Electronics Engineering (ICCEEE). IEEE, 2013. http://dx.doi.org/10.1109/icceee.2013.6634026.

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Yin, Zhiyuan, Fang Yang, and Tieyuan Shen. "Application of Set Pair Analysis on QPE and Rain Gauge in Flood Forecasting." In 2017 2nd International Symposium on Advances in Electrical, Electronics and Computer Engineering (ISAEECE 2017). Paris, France: Atlantis Press, 2017. http://dx.doi.org/10.2991/isaeece-17.2017.10.

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Kim, Byung Sik, Jun Bum Hong, Hung Soo Kim, and Seok Young Yoon. "Combining Radar and Rain Gauge Rainfall Estimates for Flood Forecasting Using Conditional Merging Method." In World Environmental and Water Resources Congress 2007. Reston, VA: American Society of Civil Engineers, 2007. http://dx.doi.org/10.1061/40927(243)414.

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Reports on the topic "Rain forecasting"

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Charley, William J. The Estimation of Rainfall for Flood Forecasting Using Radar and Rain Gage Data. Fort Belvoir, VA: Defense Technical Information Center, September 1988. http://dx.doi.org/10.21236/ada200802.

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