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5 - Satellite Remote Sensing of Precipitation

Published online by Cambridge University Press:  aN Invalid Date NaN

Faisal Hossain
Affiliation:
University of Washington
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Summary

In this chapter, we will cover the remote sensing of precipitation to understand how precipitation is tracked. Precipitation is considered one of the most important components of the water cycle that drives the availability of water and its management. For example, precipitation leads to runoff and streamflow, irrigates a field of crops and provides the water for crop growth, fills up lakes, reservoirs and ponds that are a key source for water management. The understanding of precipitation remote sensing will pave the way for learning more complex water management applications that are being increasingly carried out around the world today using satellite water data. We will first cover the history of precipitation remote sensing that began with using active sensing and ground radar. Next, we will cover satellite-based sensing where the challenges and complexities are different. The pros and cons of using various electromagnetic wavelengths will be covered. Finally, we will cover the topic of multi-sensor precipitation estimation based on the synergistic use of multiple satellite sensors spanning different wavelengths of the electromagnetic spectrum.

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Publisher: Cambridge University Press
Print publication year: 2025

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References

References

Battan, L. J. (1959). Radar Meteorology. University of Chicago Press.Google Scholar
Griffith, C. G. Woodley, W. L., Grube, P. G., et al. (1978). Rain estimation from geosynchronous satellite imagery – visible and infrared, studies. Monthly Weather Review, vol. 106, 11531171. https://doi.org/10.1175/1520-0493(1978)106<1153:REFGSI>2.0.CO;22.0.CO;2>CrossRefGoogle Scholar
Huffman, G. J., Adler, R. F., Bolvin, D. T., and Nelkin, E. J. (2010). The TRMM Multi-Satellite Precipitation Analysis (TMPA). In Gebremichael, M. and Hossain, F. (eds.) Satellite Rainfall Applications for Surface Hydrology, 322. Springer. https://doi.org/10.1007/978-90-481-2915-7_1CrossRefGoogle Scholar
Levizzani, V. and Cattani, E. (2019). Satellite remote sensing of precipitation and the terrestrial water cycle in a changing climate. Remote Sensing, vol. 11, 2301. https://doi.org/10.3390/rs11192301CrossRefGoogle Scholar
Tian, Y., Peters-Lidard, C. D., Eylander, J. B., et al. (2009). Component analysis of errors in satellite-based precipitation estimates. Journal of Geophysical Research, vol. 114, D24101. https://doi.org/10.1029/2009JD011949CrossRefGoogle Scholar
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Suggested Reading

Joyce, R. J., Janowiak, J. E., Arkin, P. A., and Xie, P. (2004). CMORPH: a method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution. Journal of Hydrometeorology, vol. 5, 487503. https://doi.org/10.1175/1525-7541(2004)005<0487:CAMTPG>2.0.CO;22.0.CO;2>CrossRefGoogle Scholar
Levizzani, V., Kidd, C., Kirschbaum, D. B., et al. (eds.) (2020). Satellite Precipitation Measurement. Springer. https://doi.org/10.1007/978-3-030-24568-9Google Scholar
Sorooshian, S., AghaKouchak, A., Arkin, P., et al. (2011). Advancing the remote sensing of precipitation. Bulletin of the American Meteorological Society, vol. 92, 12711272. http://dx.doi.org/10.1175/bams-d-11-00116.1 Retrieved from https://escholarship.org/uc/item/0hz1d89zCrossRefGoogle Scholar

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