This page summarizes a comparison of the ETo component of the Soil Moisture Estimator (SME) to NMSU's ZiaMet weather stations. A brief description of the ETo can be found here.

New Mexico State University ZiaMet:

New Mexico State University makes weather data publicly available through the ZiaMet website. Numerically predicted values of ETo from the SME platform were compared to weather-based ETo calculations (Hargreaves and Samani) over nine ZiaMet weather stations throughout New Mexico with dates ranging from 2017-05-25 to 2018-07-31. Not all stations spanned the given range and one station was not used at all due to insufficient data. Results are given in the table below:

Site Regression R^2 Samples SME total ETo (mm) H&S total ETo (mm) % Error
Artesia ASC 0.837x + 0.315 0.90 274 1305 1181 10.5
Clovis ASB 0.824x + 0.449 0.86 432 2095 1921 9.0
Fabian Garcia SC* 0.706x + 0.978 0.88 432 2435 2143 13.6
Farmington ASC 0.861x + 0.483 0.94 432 1887 1834 2.9
JTH Forestry RC 0.880x + 0.407 0.89 285 947 950 0.3
Leyendecker II PSRC 0.824x + 0.695 0.86 432 2251 2156 4.4
Los Lunas ASC 0.933x + 0.465 0.88 307 1190 1254 5.1
NMSU Main* 0.717x + 1.024 0.88 432 2435 2190 11.1
Tucumcari ASC 0.818x + 0.468 0.83 432 2151 1961 9.7
*NOTE: The Fabian Garcia and NMSU Main stations share the same WRF pixel location.

You can download the daily data CSV files here.


1) This study brought out the fact that previous SME calculations over-estimated the ETo values due to an error in the net radiation calculations. This has been corrected and the results given above are from the new ETo calculations from 2017-05-25 to 2018-07-31.

Data for the currently available 10 sectors will be updated using the corrected ETo in two phases. The first phase will be for dates 2017-01-01 through 2018-07-31. The second phase will be for dates 2018-08-01 through 2019-05-31 and will include new physics options (see below). As of 2019-06-01 current data server uploads will use the corrected ETo and new physics options.

2) There still appears to be a small bias toward over-estimating ETo, when compared to the Hargreaves and Samani calculation. This is probably due to WRF implementation. We will be adjusting WRF physics model input parameters to determine appropriate values. For example, WRF is known to over-estimate night time wind speeds and so options will be investigated to address this. Another example is to bring in more cloud factoring which will diminish radiation values on cloudy days.

3) This is a preliminary study. An augmented study for ZiaMet will include data from 2017-01-01 to 2017-05-24 and will be available as soon as the data are ready.

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