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Books on the topic 'Rainfall estimates'

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

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1

Grimes, D. I. F. Rainfall estimation workbook: A guide to rainfall estimates for the seasonally arid tropics. Natural Resources Institute, 1998.

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2

Meynink, W. J. C. Incorporating uncertainty and risk in rainfall-based flood estimates. University of Queensland, Dept. of Civil Engineering, 1993.

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3

author, Rajbhandari Rupak, Bajracharya Sagar R. author, and International Centre for Integrated Mountain Development, eds. Validation of NOAA CPC_RFE satellite-based rainfall estimates in the Central Himalayas. International Centre for Integrated Mountain Development, 2013.

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4

Zurndorfer, E. A. Probable maximum and TVA precipitation estimates with areal distribution for Tennessee River drainages less that 3,000 Miø in area. U.S. Dept. of Commerce, National Oceanic and Atmospheric Administration, 1986.

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5

Xie, Juying. Satellite-derived rainfall estimates and propagation characteristics associated with mesoscale convective systems (MCSs). U.S. Dept. of Commerce, National Oceanic and Atmospheric Administration, National Environmental Satellite, Data, and Information Service, 1989.

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6

Xie, Juying. Satellite-derived rainfall estimates and propagation characteristics associated with mesoscale convective systems (MCSs). U.S. Dept. of Commerce, National Oceanic and Atmospheric Administration, National Environmental Satellite, Data, and Information Service, 1989.

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7

Fortune, Michael A. Automated satellite-based estimates of precipitation: An assessment of accuracy. U.S. Dept. of Commerce, National Oceanic and Atmospheric Administration, National Environmental Satellite, Data, and Information Service, 1998.

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8

Fortune, Michael A. Automated satellite-based estimates of precipitation: An assessment of accuracy. U.S. Dept. of Commerce, National Oceanic and Atmospheric Administration, National Environmental Satellite, Data, and Information Service, 1998.

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9

Fortune, Michael A. Automated satellite-based estimates of precipitation: An assessment of accuracy. U.S. Dept. of Commerce, National Oceanic and Atmospheric Administration, National Environmental Satellite, Data, and Information Service, 1998.

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10

Fortune, Michael A. Automated satellite-based estimates of precipitation: An assessment of accuracy. U.S. Dept. of Commerce, National Oceanic and Atmospheric Administration, National Environmental Satellite, Data, and Information Service, 1998.

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11

Fortune, Michael A. Automated satellite-based estimates of precipitation: An assessment of accuracy. U.S. Dept. of Commerce, National Oceanic and Atmospheric Administration, National Environmental Satellite, Data, and Information Service, 1998.

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12

Devito, Kevin John. Errors in estimating stream discharge in small headwater catchments: Influence on interpretation of catchment yields and input-output budget estimates. Queen's Printer for Ontario, 1993.

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13

Hedgecott, S. An estimate of annual rainfall over the North Sea. Water Research Centre, 1989.

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14

Weiss, Linda S. Investigation of techniques to estimate rainfall-loss parameters for Illinois. Dept. of the Interior, U.S. Geological Survey, 1987.

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15

Weiss, Linda S. Investigation of techniques to estimate rainfall-loss parameters for Illinois. Dept. of the Interior, U.S. Geological Survey, 1987.

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16

Weiss, Linda S. Investigation of techniques to estimate rainfall-loss parameters for Illinois. Dept. of the Interior, U.S. Geological Survey, 1987.

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17

Kiggundu, Lawrence. Distribution of rainfall erosivity in Swaziland: For use in the universal soil loss equation (USLE) and the soil loss estimator for southern Africa (SLEMSA) to estimate soil loss due to sheet and rill erosion. Social Science Research Unit and Research and Publications, University of Swaziland, 1986.

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18

Barrett, Eric C. Satellite rainfall estimates for the western Sahel. Remote Sensing Unit, University of Bristol, 1987.

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19

Matthew, Sittel, and National Climatic Data Center (U.S.). Research Customer Service Group, eds. A comparison of NEXRAD rainfall estimates with recorded amounts. National Climatic Data Center, Research Customer Service Group, 1996.

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20

Rainfall probability estimates as an aid in dry land farming: A technical review report. Directorate of Economics & Statistics, Govt. of Karnataka, 1985.

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21

Optimal combining of ground-based sensors for the purpose of validating satellite-based rainfall estimates: Final report. Dept. of Civil and Environmental Engineering and Iowa Institute of Hydraulic Research, 1991.

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22

United States. National Environmental Satellite, Data, and Information Service., ed. Automated satellite-based estimates of precipitation: An assessment of accuracy. U.S. Dept. of Commerce, National Oceanic and Atmospheric Administration, National Environmental Satellite, Data, and Information Service, 1998.

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23

A final report to the National Aeronautics and Space Administration for antecendent precipitation index determined from CST estimates of rainfall: Grant # NAG-1478 ... for the period of 15 November 1990 to 15 February 1992. Space Science and Engineering Center at the University of Wisconsin-Madison, 1992.

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24

L, Crosson William, and United States. National Aeronautics and Space Administration., eds. Assessment of rainfall estimates using a standard Z-R relationship and the probability matching method applied to composite radar data in central Florida. National Aeronautics and Space Administration, 1996.

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25

United States. National Aeronautics and Space Administration., ed. A final report to the National Aeronautics and Space Administration for antecedent precipitation index determined from CST estimates of rainfall: Grant # NAG5-1478 for the period of 15 November 1990 to 15 February 1992. Space Science and Engineering Center at the University of Wisconsin-Madison, 1992.

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26

Kucharski, Fred, and Muhammad Adnan Abid. Interannual Variability of the Indian Monsoon and Its Link to ENSO. Oxford University Press, 2017. http://dx.doi.org/10.1093/acrefore/9780190228620.013.615.

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Abstract:
The interannual variability of Indian summer monsoon is probably one of the most intensively studied phenomena in the research area of climate variability. This is because even relatively small variations of about 10% to 20% from the mean rainfall may have dramatic consequences for regional agricultural production. Forecasting such variations months in advance could help agricultural planning substantially. Unfortunately, a perfect forecast of Indian monsoon variations, like any other regional climate variations, is impossible in a long-term prediction (that is, more than 2 weeks or so in advance). The reason is that part of the atmospheric variations influencing the monsoon have an inherent predictability limit of about 2 weeks. Therefore, such predictions will always be probabilistic, and only likelihoods of droughts, excessive rains, or normal conditions may be provided. However, even such probabilistic information may still be useful for agricultural planning. In research regarding interannual Indian monsoon rainfall variations, the main focus is therefore to identify the remaining predictable component and to estimate what fraction of the total variation this component accounts for. It turns out that slowly varying (with respect to atmospheric intrinsic variability) sea-surface temperatures (SSTs) provide the dominant part of the predictable component of Indian monsoon variability. Of the predictable part arising from SSTs, it is the El Niño Southern Oscillation (ENSO) that provides the main part. This is not to say that other forcings may be neglected. Other forcings that have been identified are, for example, SST patterns in the Indian Ocean, Atlantic Ocean, and parts of the Pacific Ocean different from the traditional ENSO region, and springtime snow depth in the Himalayas, as well as aerosols. These other forcings may interact constructively or destructively with the ENSO impact and thus enhance or reduce the ENSO-induced predictable signal. This may result in decade-long changes in the connection between ENSO and the Indian monsoon. The physical mechanism for the connection between ENSO and the Indian monsoon may be understood as large-scale adjustment of atmospheric heatings and circulations to the ENSO-induced SST variations. These adjustments modify the Walker circulation and connect the rising/sinking motion in the central-eastern Pacific during a warm/cold ENSO event with sinking/rising motion in the Indian region, leading to reduced/increased rainfall.
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