Relevant bibliographies by topics / Precipitation forecasting

Contents

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

Academic literature on the topic 'Precipitation forecasting'

Author: Grafiati
Published: 4 June 2021
Last updated: 3 March 2023

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Journal articles on the topic "Precipitation forecasting"

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Collier, Chris G., and Roman Kzyzysztofowicz. "Quantitative precipitation forecasting." Journal of Hydrology 239, no. 1-4 (December 2000): 1–2. http://dx.doi.org/10.1016/s0022-1694(00)00389-9.

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Browning, K. A. "Quantitative Precipitation Forecasting." Weather 58, no. 3 (March 1, 2003): 126–27. http://dx.doi.org/10.1256/wea.245.02.

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Asgari-Motlagh, Xaniyar, Mehdi Ketabchy, and Ali Daghighi. "Probabilistic Quantitative Precipitation Forecasting Using Machine Learning Methods and Probable Maximum Precipitation." International Academic Journal of Science and Engineering 06, no. 01 (June 4, 2019): 1–14. http://dx.doi.org/10.9756/iajse/v6i1/1910001.

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Krzysztofowicz, Roman, and Chris G. Collier. "Quantitative Precipitation Forecasting II." Journal of Hydrology 288, no. 1-2 (March 2004): 1. http://dx.doi.org/10.1016/j.jhydrol.2003.11.007.

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Tao, P., S. Tie-Yuan, Y. Zhi-Yuan, and W. Jun-Chao. "Application of quantitative precipitation forecasting and precipitation ensemble prediction for hydrological forecasting." Proceedings of the International Association of Hydrological Sciences 368 (May 6, 2015): 96–101. http://dx.doi.org/10.5194/piahs-368-96-2015.

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Abstract. The precipitation in the forecast period influences flood forecasting precision, due to the uncertainty of the input to the hydrological model. Taking the ZhangHe basin as the example, the research adopts the precipitation forecast and ensemble precipitation forecast product of the AREM model, uses the Xin Anjiang hydrological model, and tests the flood forecasts. The results show that the flood forecast result can be clearly improved when considering precipitation during the forecast period. Hydrological forecast based on Ensemble Precipitation prediction gives better hydrological forecast information, better satisfying the need for risk information for flood prevention and disaster reduction, and has broad development opportunities.
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Adams, Neil. "Precipitation Forecasting at High Latitudes." Weather and Forecasting 19, no. 2 (April 2004): 456–72. http://dx.doi.org/10.1175/1520-0434(2004)019<0456:pfahl>2.0.co;2.

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Morelli, S., and R. Santangelo. "Statistical forecasting of daily precipitation." Il Nuovo Cimento C 12, no. 2 (March 1989): 139–49. http://dx.doi.org/10.1007/bf02523787.

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Tsanis, Ioannis K., Paulin Coulibaly, and Ioannis N. Daliakopoulos. "Improving groundwater level forecasting with a feedforward neural network and linearly regressed projected precipitation." Journal of Hydroinformatics 10, no. 4 (October 1, 2008): 317–30. http://dx.doi.org/10.2166/hydro.2008.006.

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A module that uses neural networks was developed for forecasting the groundwater changes in an aquifer. A modified standard Feedforward Neural Network (FNN), trained with the Levenberg–Marquardt (LM) algorithm with five input variables (precipitation, temperature, runoff, groundwater level and specific yield) with a deterministic component, is used. The deterministic component links precipitation with the seasonal recharge of the aquifer and projects the seasonal average precipitations. A new algorithm is applied to forecast the groundwater level changes in Messara Valley, Crete, Greece, where groundwater level has been steadily decreasing due to overexploitation during the last 20 years. Results from the new algorithm show that the introduction of specific yield improved the groundwater level forecasting marginally but the linearly projected precipitation component drastically increased the window of forecasting up to 30 months, equivalent to five biannual time-steps.
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Krzysztofowicz, Roman, and Thomas A. Pomroy. "Disaggregative Invariance of Daily Precipitation." Journal of Applied Meteorology 36, no. 6 (June 1, 1997): 721–34. http://dx.doi.org/10.1175/1520-0450-36.6.721.

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Abstract Disaggregative invariance refers to stochastic independence between the total precipitation amount and its temporal disaggregation. This property is investigated herein for areal average and point precipitation amounts accumulated over a 24-h period and disaggregated into four 6-h subperiods. Statistical analyses of precipitation records from 1948 to 1993 offer convincing empirical evidence against the disaggregative invariance and in favor of the conditional disaggregative invariance, which arises when the total amount and its temporal disaggregation are conditioned on the timing of precipitation within the diurnal cycle. The property of conditional disaggregative invariance allows the modeler or the forecaster to decompose the problem of quantitative precipitation forecasting into three tasks: (i) forecasting the precipitation timing; (ii) forecasting the total amount, conditional on timing; and (iii) forecasting the temporal disaggregation, conditional on timing. Tasks (ii) and (iii) can be performed independently of one another, and this offers a formidable advantage for applications.
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Silva, Renato Ramos da, Adilson Wagner Gandú, Julia Clarinda Cohen, Paulo Kuhn, and Maria Aurora Mota. "Weather forecasting for Eastern Amazon with OLAM model." Revista Brasileira de Meteorologia 29, spe (December 2014): 11–22. http://dx.doi.org/10.1590/0102-778620130026.

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The OLAM model has as its characteristics the advantage to represent simultaneously the global and regional meteorological phenomena using the application of a grid refinement scheme. During the REMAM project the model was applied for a few case studies to evaluate its performance on numerical weather prediction for the eastern Amazon region. Case studies were performed for the twelve months of the year of 2009. The model results for those numerical experiments were compared with the observed data for the region of study. Precipitation data analysis showed that OLAM is able to represent the average mean accumulated precipitation and the seasonal features of the events occurrence, but can't predict the local total amount of precipitation. However, individual evaluation for a few cases had shown that OLAM was able to represent the dynamics and forecast a few days in advance the development of coastal meteorological systems such as the squall lines that are one of the most important precipitating systems of the Amazon.
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Dissertations / Theses on the topic "Precipitation forecasting"

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Botnen, Tore. "Precipitation forecasting using Radar Data." Thesis, Norwegian University of Science and Technology, Department of Mathematical Sciences, 2009. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-9905.

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The main task of this assignment is to filter out noise from a series of radar images and to carry out short term precipitation forecasts. It is important that the final routine is performed online, yielding new forecasts as radar images arrive with time. The data available is a time series arriving at a one hour ratio, from the Rissa radar located in Sør Trøndelag. Gaussian radial basis functions are introduced to create the precipitation field, whose movement is solely governed by its velocity field, called advection. By performing discretization forward in time, from the basis given by the differential advection equation, prior distributions can be obtained for both basis functions and advection. Assuming normal distributed radar errors, the basis functions and advection are conditioned on associating radar images, which in turn can be taken into the prior distributions, yielding new forecasts. A modification to the model, labeling the basis functions either active or inactive, enable the process of birth and death of new rain showers. The preferred filtering technique is a joint MCMC sampler, but we make some approximations, sampling from a single MCMC sampler, to successfully implement an online routine. The model yield good results on synthetic data. In the real data situation the filtered images are satisfying, and the forecast images are approximately predicting the forthcoming precipitation. The model removes statistical noise efficiently and obtain satisfying predictions. However, due to the approximation in the MCMC algorithm used, the variance is somewhat underestimated. With some further work with the MCMC update scheme, and given a higher frequency of incoming data, it is the authors belief that the model can be a very useful tool in short term precipitation forecasting. Using gauge data to estimate the radar errors, and merging online gauge data with incoming radar images using block-Kriging, will further improve the estimates.

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Chew, Serena Janine. "Comparison of quantitative precipitation forecast, a precipitation-based quantitative precipitation estimate and a radar-derived quantitative precipitation estimate." abstract and full text PDF (free order & download UNR users only), 2006. http://0-gateway.proquest.com.innopac.library.unr.edu/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:1432997.

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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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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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Zhao, Qingyun. "The incorporation and initialization of cloud water/ice in an operational forecast model /." Full-text version available from OU Domain via ProQuest Digital Dissertations, 1993.

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Tournay, Robert C. "Long-range statistical forecasting of Korean summer precipitation." Thesis, Monterey, Calif. : Naval Postgraduate School, 2008. http://bosun.nps.edu/uhtbin/hyperion-image.exe/08Mar%5FTournay.pdf.

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Thesis (M.S. in Meteorology)--Naval Postgraduate School, March 2008.
Thesis Advisor(s): Murphree, Tom ; Smarsh, David. "March 2008." Description based on title screen as viewed on May 15, 2008. Includes bibliographical references (p. 115-120). Also available in print.
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Kozyniak, Kathleen. "Integrated mesoscale-hydrometeorological modelling for flood forecasting." Thesis, University of Bristol, 2001. http://hdl.handle.net/1983/f54ba862-fc88-4ae1-9f6a-fe955dc5e581.

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In an effort to improve upon rainfall forecasts produced by simple storm advection methods (nowcasts) and to broach the gap between them and the forecasts of complex Numerical Weather Prediction (NWP) models, in terms of the spatial detail and length of lead-time each provides, the research presented explores the possibility of combining elements of each into a physically-based algorithm for rainfall forecasting. It is an algorithm that uses as its foundation the rainfall prediction model of Mark French and Witold Krajewski, developed in 1994. Their model was designed to take advantage of the high resolution rainfall observations and tracking abilities provided by weather radar and to achieve a rainfall forecast by augmenting extrapolation techniques with a representation of storm dynamics in the form of "rising parcel" theory. The new algorithm/model retains those features but incorporates NWP data to assist with forecasting, using it as a means to enable an informed choice of algorithm pathways and, more specifically, to identify the ingredients of precipitation, namely ascending air of high moisture content. A case study application of the new rainfall forecasting model to storms in Northern England shows its performance, at a lead-time of one hour, compares favourably with respect to extrapolation and persistence techniques and also NWP forecasts, and that it is able to provide more assured forecasts than persistence and nowcasts at longer lead-times. The robustness of the model is tested and confirmed by way of another case study, this time using Mediterranean storms and with predictions made in the context of urban hydrology. The case studies help to identify aspects of the model that need improvement, with representation of orographic forcing being a key one. Both the model's encouraging performance and its pinpointed weaknesses provide impetus for further research in the area of integrated mesoscale-hydrometeorological modelling for flood forecasting.
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Wild, Adrian Douglas. "Multi-sensor precipitation measurement techniques for quantitative rainfall forecasting." Thesis, University of Salford, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.360408.

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Bellone, Enrica. "Nonhomogeneous hidden Markov models for downscaling synoptic atmospheric patterns to precipitation amounts /." Thesis, Connect to this title online; UW restricted, 2000. http://hdl.handle.net/1773/8979.

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Swann, Matthew J. "Seasonal Forecasting of Extreme Wind and Precipitation Frequencies in Europe." Thesis, University of East Anglia, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.514270.

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Books on the topic "Precipitation forecasting"

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Collier, C. G. Precipitation estimation and forecasting. Geneva, Switzerland: Secretariat of the World Meteorological Organization, 2000.

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

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Hartzell, Curtis L. Quantitative precipitation forecasting for improving reservoir operations. Denver, Colo: River Systems and Meteorology Group, Water Resources Services, Technical Service Center, U.S. Dept. of the Interior, Bureau of Reclamation, 1995.

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Silas, Michaelides, ed. Precipitation: Advances in measurement, estimation, and prediction. Berlin: Springer, 2008.

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Ridge, Daniel. A candidate mesocale numerical cloud/precipitation model. Hanscom AFB, MA: Atmospheric Sciences Division, Air Force Geophysics Laboratory, 1985.

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Fuchs, Hans-Joachim. Typisierung der annuellen Niederschlagsvariationen in Nordostindien in Abhängigkeit vom indischen Monsunklima. Mainz: Geographisches Institut der Johannes Gutenberg-Universität, 2000.

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A, Matthews David. Nested model simulations of regional orographic precipitation. Denver, Colo: U.S. Dept. of the Interior, Bureau of Reclamation, Technical Service Center, 1997.

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Schwein, Noreen O. The effect of quantitative precipitation forecasts on river forecasts. Kansas City, Mo: U.S. Department of Commerce, National Oceanic and Atmospheric Administration, National Weather Service, 1996.

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Scofield, Roderick A. Instability bursts associated with extratropical cyclone systems (ECSs) and a forecast index of 3-12 hour heavy precipitation. Washington, D.C: U.S. Dept. of Commerce, National Oceanic and Atmospheric Administration, National Environmental Satellite, Data, and Information Service, 1990.

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1958-, Krahe P., Herpertz D, and International Commission for the Hydrology of the Rhine Basin., eds. Generation of hydrometeorological reference conditions for the assessment of flood hazard in large river basins: Papers presented at the international workshop held on March 6 and 7, 2001 in Koblenz. Lelystad: International Commission for the Hydrology of the Rhine Basin, 2001.

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Book chapters on the topic "Precipitation forecasting"

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Reinking, Roger F., and Joe F. Boatman. "Upslope Precipitation Events." In Mesoscale Meteorology and Forecasting, 437–71. Boston, MA: American Meteorological Society, 1986. http://dx.doi.org/10.1007/978-1-935704-20-1_19.

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Lanza, Luca, Paolo Barbera, and Franco Siccardi. "Early warnings and quantitative precipitation forecasting." In Coping with Floods, 413–35. Dordrecht: Springer Netherlands, 1994. http://dx.doi.org/10.1007/978-94-011-1098-3_24.

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Schouppe, Michel, and Anver Ghazi. "European Commission Research for Global Climate Change Studies: Towards Improved Water Observations and Forecasting Capability." In Measuring Precipitation From Space, 3–6. Dordrecht: Springer Netherlands, 2007. http://dx.doi.org/10.1007/978-1-4020-5835-6_1.

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Osuch, Marzena. "Sensitivity and Uncertainty Analysis of Precipitation-Runoff Models for the Middle Vistula Basin." In Stochastic Flood Forecasting System, 61–81. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-18854-6_5.

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Nnaji, Soronadi, Moshe Sniedovich, and Donald R. Davis. "A Systems Approach for Improving the Effectiveness of Short Term Flood Forecasting Systems." In Precipitation Analysis for Hydrologic Modeling, 28–37. Washington, D. C.: American Geophysical Union, 2013. http://dx.doi.org/10.1029/sp004p0028.

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Kung, Hsiang-Te, L. Yu Lin, and S. Malasri. "Use of Artificial Neural Networks in Precipitation Forecasting." In The GeoJournal Library, 173–79. Dordrecht: Springer Netherlands, 1996. http://dx.doi.org/10.1007/978-94-011-5676-9_9.

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Figueiredo, Karla, Carlos R. Hall Barbosa, André V. A. Da Cruz, Marley Vellasco, Marco Aurélio C. Pacheco, and Roxana J. Conteras. "Neural Networks for Inflow Forecasting Using Precipitation Information." In New Trends in Applied Artificial Intelligence, 552–61. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-73325-6_55.

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Foufoula-Georgiou, Efi, and Konstantine P. Georgakakos. "Hydrologic Advances in Space-Time Precipitation Modeling and Forecasting." In Recent Advances in the Modeling of Hydrologic Systems, 47–65. Dordrecht: Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-011-3480-4_3.

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Colton, Don E. "Precipitation Analysis for Operational Streamflow Forecasting- The Use of Meso-Scale Numerical Modeling to Enhance Estimation of Precipitation in Mountainous Areas." In Precipitation Analysis for Hydrologic Modeling, 237–47. Washington, D. C.: American Geophysical Union, 2013. http://dx.doi.org/10.1029/sp004p0237.

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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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Conference papers on the topic "Precipitation forecasting"

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Pathan, Muhammad Salman, Mayank Jain, Yee Hui Lee, Tarek Al Skaif, and Soumyabrata Dev. "Efficient Forecasting of Precipitation Using LSTM." In 2021 Photonics & Electromagnetics Research Symposium (PIERS). IEEE, 2021. http://dx.doi.org/10.1109/piers53385.2021.9694772.

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Wu, Xiaojuan, Yuan Tian, Lun Wu, Guiyun Jia, and Chenchao Xiao. "Precipitation transformation in rainfall-induced landslide forecasting." In 2010 18th International Conference on Geoinformatics. IEEE, 2010. http://dx.doi.org/10.1109/geoinformatics.2010.5567593.

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Saur, David. "Forecasting of Daily and Nightly Convective Precipitation." In 2019 International Conference on Military Technologies (ICMT). IEEE, 2019. http://dx.doi.org/10.1109/miltechs.2019.8870091.

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Zhang, Haipeng, and Xiefei Zhi. "Calibration of Extended-range Precipitation Forecasting Over China." In 2018 5th International Conference on Systems and Informatics (ICSAI). IEEE, 2018. http://dx.doi.org/10.1109/icsai.2018.8599298.

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Nong, Jifu. "Application of Nonparametric Methods in Short-Range Precipitation Forecasting." In 2009 International Joint Conference on Computational Sciences and Optimization, CSO. IEEE, 2009. http://dx.doi.org/10.1109/cso.2009.306.

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Hendawitharana, S. U., Darshana Priyasad, and R. L. H. L. Rajapakse. "Sub-basin Scale Drought Forecasting with Standard Precipitation Index by using Remotely Sensed Precipitation & LSTM." In 2018 18th International Conference on Advances in ICT for Emerging Regions (ICTer). IEEE, 2018. http://dx.doi.org/10.1109/icter.2018.8615519.

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Janál, Petr, and Tomáš Kozel. "FUZZY LOGIC BASED FLASH FLOOD FORECAST." In XXVII Conference of the Danubian Countries on Hydrological Forecasting and Hydrological Bases of Water Management. Nika-Tsentr, 2020. http://dx.doi.org/10.15407/uhmi.conference.01.10.

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The flash flood forecasting remains one of the most difficult tasks in the operative hydrology worldwide. The torrential rainfalls bring high uncertainty included in both forecasted and measured part of the input rainfall data. The hydrological models must be capable to deal with such amount of uncertainty. The artificial intelligence methods work on the principles of adaptability and could represent a proper solution. The application of different methods, approaches, hydrological models and usage of various input data is necessary. The tool for real-time evaluation of the flash flood occurrence was assembled on the bases of the fuzzy logic. The model covers whole area of the Czech Republic and the nearest surroundings. The domain is divided into 3245 small catchments of the average size of 30 km2. Real flood episodes were used for the calibration and future flood events can be used for recalibration (principle of adaptability). The model consists of two fuzzy inference systems (FIS). The catchment predisposition for the flash flood occurrence is evaluated by the first FIS. The geomorphological characteristics and long-term meteorological statistics serve as the inputs. The second FIS evaluates real-time data. The inputs are: The predisposition for flash flood occurrence (gained from the first FIS), the rainfall intensity, the rainfall duration and the antecedent precipitation index. The meteorological radar measurement and the precipitation nowcasting serve as the precipitation data source. Various precipitation nowcasting methods are considered. The risk of the flash flood occurrence is evaluated for each small catchment every 5 or 10 minutes (the time step depends on the precipitation nowcasting method). The Fuzzy Flash Flood model is implemented in the Czech Hydrometeorological Institute (CHMI) – Brno Regional Office. The results are available for all forecasters at CHMI via web application for testing. The huge uncertainty inherent in the flash flood forecasting causes that fuzzy model outputs based on different nowcasting methods could vary significantly. The storms development is very dynamic and hydrological forecast could change a lot of every 5 minutes. That is why the fuzzy model estimates are intended to be used by experts only. The Fuzzy Flash Flood model is an alternative tool for the flash flood forecasting. It can provide the first hints of danger of flash flood occurrence within the whole territory of the Czech Republic. Its main advantage is very fast calculation and possibility of variant approach using various precipitation nowcasting inputs. However, the system produces large number of false alarms, therefore the long-term testing in operation is necessary and the warning releasing rules must be set.
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Yadav, Nishant, and Auroop R. Ganguly. "A Deep Learning Approach to Short-Term Quantitative Precipitation Forecasting." In CI2020: 10th International Conference on Climate Informatics. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3429309.3429311.

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Manokij, Fuenglada, Kanoksri Sarinnapakorn, and Peerapon Vateekul. "Forecasting Thailand’s Precipitation with Cascading Model of CNN and GRU." In 2019 11th International Conference on Information Technology and Electrical Engineering (ICITEE). IEEE, 2019. http://dx.doi.org/10.1109/iciteed.2019.8929975.

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CUI, CHUN-GUANG, ZHI-YUAN YIN, and TAO ENG. "THE SPECIAL SCALING EFFECT OF QUANTITATIVE PRECIPITATION FORECAST ON FLOOD FORECASTING." In 38th IAHR World Congress. The International Association for Hydro-Environment Engineering and Research (IAHR), 2019. http://dx.doi.org/10.3850/38wc092019-0623.

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Reports on the topic "Precipitation forecasting"

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Strauss, Harald, David Rust, D. G. McGrew, and Francis Tower. Forecasting Freezing Precipitation in Central Europe. Fort Belvoir, VA: Defense Technical Information Center, November 1986. http://dx.doi.org/10.21236/ada202802.

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Sloughter, J. M., Adrian E. Raftery, and Tilmann Gneiting. Probabilistic Quantitative Precipitation Forecasting Using Bayesian Model Averaging. Fort Belvoir, VA: Defense Technical Information Center, February 2006. http://dx.doi.org/10.21236/ada454809.

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Kong, J., and M. J. Leach. Using NORAPS for forecasting heavy precipitation with topographic forcing. Office of Scientific and Technical Information (OSTI), February 1997. http://dx.doi.org/10.2172/641010.

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Berrocal, Veronica J., Adrian E. Raftery, and Tilmann Gneiting. Probabilistic Quantitative Precipitation Forecasting using a Two-Stage Spatial Model. Fort Belvoir, VA: Defense Technical Information Center, April 2008. http://dx.doi.org/10.21236/ada479737.

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Venne, Monique G., William H. Jasperson, and David E. Venne. Difficult Weather: A Review of Thunderstorm, Fog and Stratus, and Winter Precipitation Forecasting. Fort Belvoir, VA: Defense Technical Information Center, September 1997. http://dx.doi.org/10.21236/ada336642.

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South, D., J. McDonald, and W. Oakland. Regional economic forecasting models: Suitability for use in the National Acid Precipitation Assessment Program. Office of Scientific and Technical Information (OSTI), February 1990. http://dx.doi.org/10.2172/7221583.

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Desai, Jairaj, Jijo K. Mathew, Woosung Kim, Mingmin Liu, Howell Li, Jeffrey D. Brooks, and Darcy M. Bullock. Dashboards for Real-time Monitoring of Winter Operations Activities and After-action Assessment. Purdue University, 2020. http://dx.doi.org/10.5703/1288284317252.

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Abstract:
The Indiana Department of Transportation (INDOT) operates a fleet of nearly 1100 snowplows and spends up to $60M annually on snow removal and de-icing as part of their winter operation maintenance activities. Systematically allocating resources and optimizing material application rates can potentially save revenue that can be reallocated for other roadway maintenance operations. Modern snowplows are beginning to be equipped with a variety of Mobile Road Weather Information Sensors (MARWIS) which can provide a host of analytical data characterizing on-the-ground conditions during periods of wintry precipitation. Traffic speeds fused with road conditions and precipitation data from weather stations provide a uniquely detailed look at the progression of a winter event and the performance of the fleet. This research uses a combination of traffic speeds, MARWIS and North American Land Data Assimilation System (NLDAS) data to develop real-time dashboards characterizing the impact of precipitation and pavement surface temperature on mobility. Twenty heavy snow events were identified for the state of Indiana from November 2018 through April 2019. Two particular instances, that impacted 182 miles and 231 miles of interstate at their peaks occurred in January and March, respectively, and were used as a case study for this paper. The dashboards proposed in this paper may prove to be particularly useful for agencies in tracking fleet activity through a winter storm, helping in resource allocation and scheduling and forecasting resource needs.
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