Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Satellite Precipitation MeasurementPERSIANN-CDR for Hydrology and Hydro-climatic Applications

Satellite Precipitation Measurement: PERSIANN-CDR for Hydrology and Hydro-climatic Applications [Satellite-retrieved precipitation datasets represent a promising input data source to be utilized in hydroclimatic and hydrologic applications. Due to their characteristics of high spatiotemporal resolution, near real-time availability and quasi global coverage, satellite-retrieved precipitation datasets promise to provide a remedy for the long-standing issues associated with ground rainfall information. In this article, we shed light on the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks – Climate Data Records (PERSIANN-CDR) dataset and its use in hydroclimatic and hydrologic applications. In particular, we highlight the use of PERSIANN-CDR for rainfall trend analysis, observation of extreme rainfall events such as Hurricanes, and evaluation of climate models’ simulations of precipitation based on their historical performance. Regarding the use of PERSIANN-CDR for hydrologic applications, we show examples of utilizing the dataset in rainfall-runoff modeling as well as its use in rainfall frequency analysis and the development of intensity-duration-frequency (IDF) curves.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Satellite Precipitation MeasurementPERSIANN-CDR for Hydrology and Hydro-climatic Applications

Part of the Advances in Global Change Research Book Series (volume 69)
Editors: Levizzani, Vincenzo; Kidd, Christopher; Kirschbaum, Dalia B.; Kummerow, Christian D.; Nakamura, Kenji; Turk, F. Joseph

Loading next page...
 
/lp/springer-journals/satellite-precipitation-measurement-persiann-cdr-for-hydrology-and-OhMgB5swh8

References (53)

Publisher
Springer International Publishing
Copyright
© Springer Nature Switzerland AG 2020
ISBN
978-3-030-35797-9
Pages
993 –1012
DOI
10.1007/978-3-030-35798-6_26
Publisher site
See Chapter on Publisher Site

Abstract

[Satellite-retrieved precipitation datasets represent a promising input data source to be utilized in hydroclimatic and hydrologic applications. Due to their characteristics of high spatiotemporal resolution, near real-time availability and quasi global coverage, satellite-retrieved precipitation datasets promise to provide a remedy for the long-standing issues associated with ground rainfall information. In this article, we shed light on the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks – Climate Data Records (PERSIANN-CDR) dataset and its use in hydroclimatic and hydrologic applications. In particular, we highlight the use of PERSIANN-CDR for rainfall trend analysis, observation of extreme rainfall events such as Hurricanes, and evaluation of climate models’ simulations of precipitation based on their historical performance. Regarding the use of PERSIANN-CDR for hydrologic applications, we show examples of utilizing the dataset in rainfall-runoff modeling as well as its use in rainfall frequency analysis and the development of intensity-duration-frequency (IDF) curves.]

Published: Apr 15, 2020

Keywords: Precipitation; Rainfall; PERSIANN-CDR; PERSIANN-CCS; TMPA; CMORPH; GPCP; Hydrology; Hydroclimatology; Neural networks; Rain gauges; Brightness temperature; Mann-Kendall test; ETCCDI; CCI; JCOMM; CLIVAR; Extreme value theory; CMIP5; IDF curves

There are no references for this article.