A procedure is described for “conditioning” (i.e., adjusting) ambient weather data collected in ‘arid' ambient environments having low vapor fluxes and high sensible heat fluxes so that the data better represent near surface air properties that might have been measured in the same climate, but over an evaporating, “reference” surface that is, by definition, well-watered. These adjustments are necessary before applying ‘reference evapotranspiration' methods such as the ASCE-EWRI standardized Penman-Monteith equation, since these methods generally assume equilibrium between the reference, evaporating surface and the near surface air. Following conditioning, the “equilibrium reference ET” can be scaled using crop coefficients or other parameters to estimate actual ET for a variety of vegetation. The conditioning of the weather data completes the feedback processes between surface evaporation and near surface air properties and fulfills the equilibrium requirements assumed in the PM equation.

The procedure extrapolates T, vapor and wind speed profiles to and from a blended height of 50 to 200 m using ambient and reference vapor (going upward) and sensible heat fluxes (going back downward). Ambient ET and H are estimated using complementary theory or daily soil water balance. ‘Reference' ET and H are estimated using an iteration between near surface temperature, wind and vapor pressure and the Penman-Monteith estimate for the reference surface. A ‘full' data set containing measurements of air temperature, humidity, solar radiation and wind speed is utilized. The procedure can be applied to hourly or daily weather data. In the case of daily weather data, an energy balance and extrapolation to the blending height is done at both maximum and minimum air temperature conditions for the day. These are assumed to occur at 2 pm (approximate time of Tmax) and at 2 am (approximate time of Tmin).

The procedure is intended to be applied to measured weather data from ambient settings such as airports where local dryness tends to elevate near surface temperature measurements by as much as 5 C, and reduces vapor pressure by up to one-half, when compared to measurements made over an evaporating surface. The procedure can also be applied to gridded weather data from WRF-Noah and other land simulation models that, due to their large grid sizes, ‘see' only ambient conditions regulated by precipitation inputs, only, with no irrigation. These systems generally assimilate data from weather stations that are in dry environments having low precipitation inputs, only, with no irrigation.

The approach uses standard Monin-Obhukov similarity theory and provides estimates of conditioned (adjusted) near surface (2 m) air temperature, vapor pressure and wind speed. In applications in southern Idaho, the procedure estimated a 3-4 C decrease in 2 m air temperature, doubling of 2m vapor pressure, and a 20% reduction in 2m wind speed over an evaporating surface as compared to the original ‘desert' condition. These differences were confirmed by data measurements over an irrigated alfalfa field. The conditioned data caused a reduction in estimated reference ET of 20%. The 20% reduction in reference ET estimate (defined as the ET from an extensive surface of well-watered grass or other full-cover crop) suggests that reference (and potential) ET would be overestimated by 20% if based on the ambient weather data. This overestimation would have significant, negative consequences for ET estimates used in irrigation water management, in water balance studies, or water rights transfers, that, in the future, may be based on gridded weather data from the WRF-Noah and similar models.