Estimating storm areal average rainfall intensity in field experiments

Estimating storm areal average rainfall intensity in field experiments Estimates of areal mean precipitation intensity derived from rain gages are commonly used to assess the performance of rainfall radars and satellite rainfall retrieval algorithms. Areal mean precipitation time series collected during short‐duration climate field studies are also used as inputs to water and energy balance models which simulate land‐atmosphere interactions during the experiments. In two recent field experiments (1987 First International Satellite Land Surface Climatology Project (ISLSCP) Field Experiment (FIFE) and the Multisensor Airborne Campaign for Hydrology 1990 (MAC‐HYDRO '90)) designed to investigate the climatic signatures of land‐surface forcings and to test airborne sensors, rain gages were placed over the watersheds of interest. These gages provide the sole means for estimating storm precipitation over these areas, and the gage densities present during these experiments indicate that there is a large uncertainty in estimating areal mean precipitation intensity for single storm events. Using a theoretical model of time‐ and area‐averaged space‐ time rainfall and a model rainfall generator, the error structure of areal mean precipitation intensity is studied for storms statistically similar to those observed in the FIFE and MAC‐HYDRO field experiments. Comparisons of the error versus gage density trade‐off curves to those calculated using the storm observations show that the rainfall simulator can provide good estimates of the expected measurement error given only the expected intensity, coefficient of variation, and rain cell diameter or correlation length scale, and that these errors can quickly become very large (in excess of 20%) for certain storms measured with a network whose size is below a “critical” gage density. Because the mean storm rainfall error is particularly sensitive to the correlation length, it is important that future field experiments include radar and/or dense rain gage networks capable of accurately characterizing the rainstorm spatial and temporal correlation structure. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Water Resources Research Wiley

Estimating storm areal average rainfall intensity in field experiments

Loading next page...
 
/lp/wiley/estimating-storm-areal-average-rainfall-intensity-in-field-experiments-AxnfbzQ9rZ
Publisher
Wiley
Copyright
Copyright © 1994 by the American Geophysical Union.
ISSN
0043-1397
eISSN
1944-7973
DOI
10.1029/94WR00558
Publisher site
See Article on Publisher Site

Abstract

Estimates of areal mean precipitation intensity derived from rain gages are commonly used to assess the performance of rainfall radars and satellite rainfall retrieval algorithms. Areal mean precipitation time series collected during short‐duration climate field studies are also used as inputs to water and energy balance models which simulate land‐atmosphere interactions during the experiments. In two recent field experiments (1987 First International Satellite Land Surface Climatology Project (ISLSCP) Field Experiment (FIFE) and the Multisensor Airborne Campaign for Hydrology 1990 (MAC‐HYDRO '90)) designed to investigate the climatic signatures of land‐surface forcings and to test airborne sensors, rain gages were placed over the watersheds of interest. These gages provide the sole means for estimating storm precipitation over these areas, and the gage densities present during these experiments indicate that there is a large uncertainty in estimating areal mean precipitation intensity for single storm events. Using a theoretical model of time‐ and area‐averaged space‐ time rainfall and a model rainfall generator, the error structure of areal mean precipitation intensity is studied for storms statistically similar to those observed in the FIFE and MAC‐HYDRO field experiments. Comparisons of the error versus gage density trade‐off curves to those calculated using the storm observations show that the rainfall simulator can provide good estimates of the expected measurement error given only the expected intensity, coefficient of variation, and rain cell diameter or correlation length scale, and that these errors can quickly become very large (in excess of 20%) for certain storms measured with a network whose size is below a “critical” gage density. Because the mean storm rainfall error is particularly sensitive to the correlation length, it is important that future field experiments include radar and/or dense rain gage networks capable of accurately characterizing the rainstorm spatial and temporal correlation structure.

Journal

Water Resources ResearchWiley

Published: Jul 1, 1994

References

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$360/year

Save searches from
Google Scholar,
PubMed

Create folders to
organize your research

Export folders, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month

PDF Discount

20% off