Energy2green Wind And Solar Power System

Two approaches can be utilized to overcome the problem of missing windspeed data.

Use of Wind-Speed Data from a Nearby Weather Station. Importing wind-speed data from a nearby station, as for Rs discussed earlier, relies on the fact that airflow above a homogeneous region may have relatively large space variations during the course of a day but small variation over longer periods or even over the day itself. This occurs when air masses are of the same origin or when the same fronts govern airflows in the region where both importing and exporting stations are located. Thus, when a location exists within a given region where wind-speed data are available (U2), it may be possible to utilize the same U2 at a nearby station where wind-speed data are missing.

The decision to import U2 data from another station has to be taken after checking the regional climate and relief. In areas with relief, the nearest station may not necessarily be the best one for providing data but rather more distant a station with similar elevation and exposure to the dominant winds may be more appropriate. This may vary from one season to another.

Use of wind-speed data from a nearby weather station is acceptable for daily estimates if climate homogeneity is checked and if daily estimates are to be summed or averaged to several-day periods (7 or 10 days, or time intervals between irrigations) in irrigation scheduling computations. To check validity of imported wind-speed data, trends of other meteorological variables have to be checked. Strong winds often are associated with a decrease in air moisture, and low winds are common with high RH. Thus, trends in variation of daily RHmax and RHmin (or Tdry, Twet, and Tdry - 2wet) should be similar in both locations.

Use of an Empirical Monthly Estimate of Wind Speed. When it is not possible to find a weather station within the climatic region from which to import wind-speed data, then the user may adopt monthly estimates of wind-speed data. Errors in the resulting monthly ET0 estimates may not be very substantial, particularly when ET is energy driven.

Average wind estimates should be selected from the information available on the climate of the region [80] and may change with the seasonal changes in climate. Indicative values (ms-1) are

Low to moderate wind U ^ 2.0

Moderate to strong wind U ^ 3.0

U2 = 2 ms-1 is an average value for much of the globe. Lower values are frequent in humid tropical regions. Caution in selecting an empirical wind-speed estimate is recommended.

Humidity data are required to compute the actual vapor pressure ea. The daily minimum temperature is suggested as an approximation to the dewpoint temperature:

This approximation provides better estimates for low values of Tmin and when the air is moist. It often can be expected that Tmin should be decreased by 2°C to overcome problems resulting from aridity of the weather station [78], namely, outside rainy periods. Tmin should be decreased by 2°C in semiarid climates and by 3°C in arid ones.

Case When Only T^,^ Is Available

Because of nonlinearity of the e0 (T) curve, the daily saturation vapor pressure es should be computed from Tmax and Tmin [Eq. (5.19)]. However, for some data sets, only the mean daily temperature Tmean is available. Computing es from Tmean would provide underestimation of es and of VPD, and thus of ET0. It is then advisible to estimate Tmax and Tmin from Tmean.

Adopting the general relationship given by the Eq. (5.44), it is possible to compute Tmax = Tmean + ^(5.9 Rs / Ra )2 (5.46)

The empirical coefficient 5.9 corresponds in Eq. (5.44) to Kr = 0.17. This coefficient may be modified for specific regional climatic conditions.

Comments on Using ET0 PM with Estimates of Missing Weather Data

Approaches suggested earlier intend to perform ET0 calculations with only one standard equation, the FAO-PM, that is, Eq. (5.14), thus avoiding the problems of using alternative ET equations whose behavior could differ from that of the FAO-PM. Deviations from the approximations proposed to the full FAO-PM are expected to be in the range of those resulting from the use of an alternative ET equation or even less.

Use of the FAO-PM equation with only maximum and minimum temperature data, that is, estimating Rs from the nearest station [Eq. (5.43)], Td = Tmin and U2 = 2 ms-1, provided an average standard error of estimate (SEE) equal to 0.60 mm day-1 for the monthly estimates of ET0 relative to the FAO-PM equation with full data sets when applied to 918 locations in 20 countries, using the CLIMWAT database. Application of the Hargreaves-Samani equation (5.50) to the same data set resulted in a higher SEE = 0.85 mm day-1. The FAO-PM equation with Rs estimated from (Tmax — Tmin), Td = Tmin,and U2 = 2 ms-1 was compared with daily lysimeter data from Davis, California, resulting in SEE = 1.01 mm day-1, which was only 6% higher than the SEE = 0.95 mm day-1 obtained from the Hargreaves-Samani equation (5.50). If imported wind could be used, a lower SEE could be expected. This indicates that a proper application of methodologies described in the preceding sections also can be used for estimating daily ET0, mainly when these values are averaged over several-day periods, as in irrigation scheduling computations.

Approximations proposed earlier can be validated at the regional level (see "Calibration," below). Sensitivity analysis should be performed to check causes (and limits) for the method utilized to import the missing data.

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