Estimating Near Real-Time Hourly Evapotranspiration Using Numerical Weather Prediction Model Output and GOES Remote Sensing Data in Iowa

Wonsook S Ha; George R Diak; Witold F Krajewski

doi:10.3390/rs12142337

Back

Estimating Near Real-Time Hourly Evapotranspiration Using Numerical Weather Prediction Model Output and GOES Remote Sensing Data in Iowa

Journal article

Open access

Peer reviewed

Estimating Near Real-Time Hourly Evapotranspiration Using Numerical Weather Prediction Model Output and GOES Remote Sensing Data in Iowa

Wonsook S Ha, George R Diak and Witold F Krajewski

Remote sensing (Basel, Switzerland), Vol.12(14), p.2337

07/21/2020

DOI: 10.3390/rs12142337

Files and links (1)

url

https://doi.org/10.3390/rs12142337View

Published (Version of record) Open Access

Abstract

This study evaluates the applicability of numerical weather prediction output supplemented with remote sensing data for near real-time operational estimation of hourly evapotranspiration (ET). Rapid Refresh (RAP) and High-Resolution Rapid Refresh (HRRR) systems were selected to provide forcing data for a Penman-Monteith model to calculate the Actual Evapotranspiration (AET) over Iowa. To investigate how the satellite-based remotely sensed net radiation ( R n ) estimates might potentially improve AET estimates, Geostationary Operational Environmental Satellite derived R n (GOES- R n ) data were incorporated into each dataset for comparison with the RAP and HRRR R n -based AET evaluations. The authors formulated a total of four AET models—RAP, HRRR, RAP-GOES, HRRR-GOES, and validated the respective ET estimates against two eddy covariance tower measurements from central Iowa. The implementation of HRRR-GOES for AET estimates showed the best results among the four models. The HRRR-GOES model improved statistical results, yielding a correlation coefficient of 0.8, a root mean square error (mm hr−1) of 0.08, and a mean bias (mm hr−1) of 0.02 while the HRRR only model results were 0.64, 0.09, and 0.04, respectively. Despite limited in situ observational data to fully test a proposed AET estimation, the HRRR-GOES model clearly showed potential utility as a tool to predict AET at a regional scale with high spatio-temporal resolution.

Details

Title: Subtitle: Estimating Near Real-Time Hourly Evapotranspiration Using Numerical Weather Prediction Model Output and GOES Remote Sensing Data in Iowa
Creators: Wonsook S Ha
George R Diak
Witold F Krajewski
Resource Type: Journal article
Publication Details: Remote sensing (Basel, Switzerland), Vol.12(14), p.2337
DOI: 10.3390/rs12142337
ISSN: 2072-4292
eISSN: 2072-4292
Language: English
Date published: 07/21/2020
Academic Unit: Civil and Environmental Engineering
Record Identifier: 9984066351502771

Metrics

25 Record Views

7 Times Cited - Web of Science