TY - JOUR
T1 - An evaluation of gridded weather data sets for the purpose of estimating reference evapotranspiration in the United States
AU - Blankenau, Philip A.
AU - Kilic, Ayse
AU - Allen, Richard
N1 - Funding Information:
We wish to give special thanks to the weather station networks that gathered and distributed data that were used in this study, including the New Mexico State Climate Center that provided data for New Mexico and Dr. Dave DuBois who provided hourly weather data for several locations. We also acknowledge the long-term science and development work by the US and other international governments and universities that have formulated the weather data assimilation systems and products. This work was supported by the Nebraska and Idaho Agricultural Experiment Stations , Google, Inc. , NASA , and the Landsat Science Team . The authors wish to acknowledge and thank all three reviewers for their thorough, thoughtful, and helpful reviews and comments.
Funding Information:
We wish to give special thanks to the weather station networks that gathered and distributed data that were used in this study, including the New Mexico State Climate Center that provided data for New Mexico and Dr. Dave DuBois who provided hourly weather data for several locations. We also acknowledge the long-term science and development work by the US and other international governments and universities that have formulated the weather data assimilation systems and products. This work was supported by the Nebraska and Idaho Agricultural Experiment Stations, Google, Inc., NASA, and the Landsat Science Team. The authors wish to acknowledge and thank all three reviewers for their thorough, thoughtful, and helpful reviews and comments.
Publisher Copyright:
© 2020 Elsevier B.V.
PY - 2020/12/1
Y1 - 2020/12/1
N2 - This study assessed the quality of gridded weather data for calculating reference evapotranspiration (ETref), which, by definition, represents a near maximum ET occurring in a well-watered agricultural environment. Six gridded weather data sets – GLDAS-1, NLDAS-2, the CFSv2 operational analysis, gridMET, RTMA, and NDFD – were compared to weather data collected from 103 weather stations located in well-watered settings across the conterminous United States. ETref along with the weather variables used to compute it – near-surface air temperature, vapor pressure, wind speed, and shortwave solar radiation – were compared. The gridded weather data sets generally overestimated the standardized Penman-Monteith ETref produced from weather station data, with median biases ranging from 12 to 31 %. The overestimation was mainly due to chronic overstatement of air temperature, shortwave radiation, and wind speed and understatement of humidity. These results indicate that gridded data should be carefully evaluated before being substituted for agricultural weather station data. Bias correction procedures may make these gridded data more suitable for generating ETref. RTMA was generally the best performing gridded data set for all variables and NLDAS was the worst for all variables except vapor pressure. NDFD one-day forecasts outperformed most of the analysis products, likely due to its initialization with RTMA. gridMET temperature agreed relatively well with the station data due to its dependence on the PRISM station-interpolated data set. However, its performance was similar to its other parent product, NLDAS, for the remaining variables which reduced its ETref performance. The low-resolution products GLDAS and CFSv2 performed better than the finer resolution NLDAS product suggesting that spatial resolution is not a primary factor determining correspondence to station data. The excellent performance of gridMET temperature and RTMA indicates that the degree to which gridded data depend on station data is a primary factor determining correspondence.
AB - This study assessed the quality of gridded weather data for calculating reference evapotranspiration (ETref), which, by definition, represents a near maximum ET occurring in a well-watered agricultural environment. Six gridded weather data sets – GLDAS-1, NLDAS-2, the CFSv2 operational analysis, gridMET, RTMA, and NDFD – were compared to weather data collected from 103 weather stations located in well-watered settings across the conterminous United States. ETref along with the weather variables used to compute it – near-surface air temperature, vapor pressure, wind speed, and shortwave solar radiation – were compared. The gridded weather data sets generally overestimated the standardized Penman-Monteith ETref produced from weather station data, with median biases ranging from 12 to 31 %. The overestimation was mainly due to chronic overstatement of air temperature, shortwave radiation, and wind speed and understatement of humidity. These results indicate that gridded data should be carefully evaluated before being substituted for agricultural weather station data. Bias correction procedures may make these gridded data more suitable for generating ETref. RTMA was generally the best performing gridded data set for all variables and NLDAS was the worst for all variables except vapor pressure. NDFD one-day forecasts outperformed most of the analysis products, likely due to its initialization with RTMA. gridMET temperature agreed relatively well with the station data due to its dependence on the PRISM station-interpolated data set. However, its performance was similar to its other parent product, NLDAS, for the remaining variables which reduced its ETref performance. The low-resolution products GLDAS and CFSv2 performed better than the finer resolution NLDAS product suggesting that spatial resolution is not a primary factor determining correspondence to station data. The excellent performance of gridMET temperature and RTMA indicates that the degree to which gridded data depend on station data is a primary factor determining correspondence.
KW - GLDAS gridMET
KW - Gridded weather data
KW - Irrigation
KW - NDFD
KW - NLDAS
KW - RTMA
KW - Reference evapotranspiration
UR - http://www.scopus.com/inward/record.url?scp=85086522749&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85086522749&partnerID=8YFLogxK
U2 - 10.1016/j.agwat.2020.106376
DO - 10.1016/j.agwat.2020.106376
M3 - Article
AN - SCOPUS:85086522749
SN - 0378-3774
VL - 242
JO - Agricultural Water Management
JF - Agricultural Water Management
M1 - 106376
ER -