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Books on the topic 'Precipitation prediction'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Dohring, Henry. Precipitation: Prediction, formation, and environmental impact. Nova Science Publishers, 2011.

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2

Michaelides, Silas, ed. Precipitation: Advances in Measurement, Estimation and Prediction. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-77655-0.

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3

Hayes, Pamela Speers. Prediction of precipitation in Western Washington State. Washington State Dept. of Transportation, Planning, Research and Public Transportation Division, 1991.

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4

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

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5

Hayes, Pamela Speers. Diagnosis and prediction of precipitation in regions of complex terrain. Washington State Dept. of Transportation, Planning, Research and Public Transportation Division, 1986.

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6

J-W, Bao, and Environmental Technology Laboratory (Oceanic and Atmospheric Research Laboratories), eds. A case study of the impact of off-shore P-3 observations on the prediction of coastal wind and precipitation. U.S. Dept. of Commerce, National Oceanic and Atmospheric Administration, Oceanic and Atmospheric Research Laboratories, Environmental Technology Laboratory, 2000.

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7

J-W, Bao, and Environmental Technology Laboratory (Oceanic and Atmospheric Research Laboratories), eds. A case study of the impact of off-shore P-3 observations on the prediction of coastal wind and precipitation. U.S. Dept. of Commerce, National Oceanic and Atmospheric Administration, Oceanic and Atmospheric Research Laboratories, Environmental Technology Laboratory, 2000.

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8

Cox, Jonathan Peter. Hydrometeorological aspects of drought management. University of Salford, 1993.

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9

J-W, Bao, and Environmental Technology Laboratory (Oceanic and Atmospheric Research Laboratories), eds. A case study of the impact of off-shore P-3 observations on the prediction of coastal wind and precipitation. U.S. Dept. of Commerce, National Oceanic and Atmospheric Administration, Oceanic and Atmospheric Research Laboratories, Environmental Technology Laboratory, 2000.

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10

Alberto, Montanari, Bárdossy András, Reeves A. D, Duck R. W, and European Geophysical Society, eds. I. Predicting and estimating extremes of precipitation. Pergamon, 2001.

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11

Haines, Terry A. Predicting and evaluating the effects of acidic precipitation on water chemistry and endemic fish populations in the northeastern United States. Office of Research and Development, U.S. Environmental Protection Agency, 1986.

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12

California Weather Symposium (1998 Sierra College). Proceedings of 1998 California Weather Symposium: Theme "Predicting California precipitation and floods" : Sierra College, Rocklin, California, June 27, 1998. Sierra College Natural History Museum, 1998.

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13

Haines, Terry A. Predicting and evaluating the effects of acidic precipitation on water chemistry and endemic fish populations in the northeastern United States. Office of Research and Development, U.S. Environmental Protection Agency, 1986.

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14

Haines, Terry A. Predicting and evaluating the effects of acidic precipitation on water chemistry and endemic fish populations in the northeastern United States. Fish and Wildlife Service, U.S. Dept. of the Interior, 1986.

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15

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

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16

Grotch, Stanley L. Regional intercomparisons of general circulation model predictions and historical climate data. U.S. Dept. of Energy, Office of Energy Research, Office of Basic Energy Sciences, Carbon Dioxide Research Division, 1988.

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17

Grotch, Stanley L. An intercomparison of general circulation model predictions of regional climate change: Presented at the International Conference on "Modelling of Global Climate Change and Variability," Hamburg, Federal Republic of Germany, September 1989. Available from National Technical Information Service, 1990.

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18

Michaelides, Silas. Precipitation: Advances in Measurement, Estimation and Prediction. Springer, 2009.

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19

Michaelides, Silas. Precipitation: Advances in Measurement, Estimation and Prediction. Springer, 2010.

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20

Cao, Zuohao, Huaqing Cai, and Xiaofan Li, eds. Improving the Understanding, Diagnostics, and Prediction of Precipitation. MDPI, 2023. http://dx.doi.org/10.3390/books978-3-0365-7606-0.

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21

National Aeronautics and Space Administration (NASA) Staff. Canonical Ensemble Correlation Prediction Model for Seasonal Precipitation Anomaly. Independently Published, 2018.

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22

Boken, Vijendra K., Arthur P. Cracknell, and Ronald L. Heathcote. Monitoring and Predicting Agricultural Drought. Oxford University Press, 2005. http://dx.doi.org/10.1093/oso/9780195162349.001.0001.

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Agricultural droughts affect whole societies, leading to higher food costs, threatened economies, and even famine. In order to mitigate such effects, researchers must first be able to monitor them, and then predict them; however no book currently focuses on accurate monitoring or prediction of these devastating kinds of droughts. To fill this void, the editors of Monitoring and Predicting Agricultural Drought have assembled a team of expert contributors from all continents to make a global study, describing biometeorological models and monitoring methods for agricultural droughts. These models

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23

Cook, Kerry H. Climate Change Scenarios and African Climate Change. Oxford University Press, 2018. http://dx.doi.org/10.1093/acrefore/9780190228620.013.545.

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Accurate projections of climate change under increasing atmospheric greenhouse gas levels are needed to evaluate the environmental cost of anthropogenic emissions, and to guide mitigation efforts. These projections are nowhere more important than Africa, with its high dependence on rain-fed agriculture and, in many regions, limited resources for adaptation. Climate models provide our best method for climate prediction but there are uncertainties in projections, especially on regional space scale. In Africa, limitations of observational networks add to this uncertainty since a crucial step in i

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24

Tibaldi, Stefano, and Franco Molteni. Atmospheric Blocking in Observation and Models. Oxford University Press, 2018. http://dx.doi.org/10.1093/acrefore/9780190228620.013.611.

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The atmospheric circulation in the mid-latitudes of both hemispheres is usually dominated by westerly winds and by planetary-scale and shorter-scale synoptic waves, moving mostly from west to east. A remarkable and frequent exception to this “usual” behavior is atmospheric blocking. Blocking occurs when the usual zonal flow is hindered by the establishment of a large-amplitude, quasi-stationary, high-pressure meridional circulation structure which “blocks” the flow of the westerlies and the progression of the atmospheric waves and disturbances embedded in them. Such blocking structures can hav

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25

Carvalho, Carlos, and Jill Rickershauser. Characterizing the uncertainty of climate change projections using hierarchical models. Edited by Anthony O'Hagan and Mike West. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198703174.013.20.

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This article focuses on the use of Bayesian hierarchical models for integration and comparison of predictions from multiple models and groups, and more specifically for characterizing the uncertainty of climate change projections. It begins with a discussion of the current state and future scenarios concerning climate change and human influences, as well as various models used in climate simulations and the goals and challenges of analysing ensembles of opportunity. It then introduces a suite of statistical models that incorporate output from an ensemble of climate models, referred to as gener

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26

Rosenzweig, Cynthia, and Daniel Hillel. Climate Variability and the Global Harvest. Oxford University Press, 2008. http://dx.doi.org/10.1093/oso/9780195137637.001.0001.

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The Earth's climate is constantly changing. Some of the changes are progressive, while others fluctuate at various time scales. The El Niño-la Niña cycle is one such fluctuation that recurs every few years and has far-reaching impacts. It generally appears at least once per decade, but this may vary with our changing climate. The exact frequency, sequence, duration and intensity of El Niño's manifestations, as well as its effects and geographic distributions, are highly variable. The El Niño-la Niña cycle is particularly challenging to study due to its many interlinked phenomena that occur in

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27

Goswami, B. N., and Soumi Chakravorty. Dynamics of the Indian Summer Monsoon Climate. Oxford University Press, 2017. http://dx.doi.org/10.1093/acrefore/9780190228620.013.613.

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Lifeline for about one-sixth of the world’s population in the subcontinent, the Indian summer monsoon (ISM) is an integral part of the annual cycle of the winds (reversal of winds with seasons), coupled with a strong annual cycle of precipitation (wet summer and dry winter). For over a century, high socioeconomic impacts of ISM rainfall (ISMR) in the region have driven scientists to attempt to predict the year-to-year variations of ISM rainfall. A remarkably stable phenomenon, making its appearance every year without fail, the ISM climate exhibits a rather small year-to-year variation (the sta

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