Figure 2. Climatological 10-m wind of the Marshalltown airport for the months of (a) January and (b) July. Figure 3. Wind for (a) CWEX-10 6.5-m winds at the reference flux tower (NLAE 1), (b) CWEX-11 10-m winds at the reference flux tower (NCAR 1), and (c) CWEX-11 80-m winds from the upwind wind cube (WC 68). Dashed lines denote wind directions for turbine wakes on downwind stations. Figure 4. CWEX-10 differences (downwind – upwind) of normalized wind speed and normalized TKE, 9-m air temperature, and uncorrected sensible heat flux as functions of upwind flux tower thermal stability (z/L0): for the westerly no-wake case (a),(d),(g),and (j); for the SW B2 turbine wake case (b),(e),(h),and (k); and for the SSE case between the wakes of turbine B2 and B3 (c),(f),(i),and (l). Figure 4. (continued)
3. Same as in Table 2 but for downwind-upwind latent heat flux differences (E) for each direction composite as presented in Table 1. a) DAY Rnet > 300 W Wind direction  E  Lower 95% Upper 95% Skewness Kurtosis category (W m-2) (W m-2) (W m-2) (W m-2) EW -1.70 39.88 -15.40 12.00 0.09 -0.36 EOFF 3.25 57.26 -8.48 14.98 -0.76 1.57 EON 3.58 44.15 -1.18 8.34 0.25 0.66 EWSW 31.20 41.05 14.25 48.14 0.64 0.45 ESW 26.57 55.00 6.40 46.75 0.23 0.18 ESSW 15.87 39.78 6.96 24.78 0.11 0.09 ES-SSE -8.42 40.10 -14.04 -2.80 0.09 0.86
4. (continued) b) Rnet < 0 W Wind direction  fc fc Lower 95% Upper 95% Skewness Kurtosis category (mg m-2 s-1) (mg m-2 (mg m-2 (mg m-2 fcW -0.01 0.49 -0.14 0.12 -4.83 34.18 fcOFF 0.00 0.22 -0.04 0.05 1.55 11.75 fcON 0.09 0.43 0.05 0.13 5.28 56.56 0.20 0.44 0.06 0.33 0.22 0.83 fcSW 0.40 0.36 0.22 0.57 0.95 0.79 fcSSW 0.30 0.75 0.09 0.52 3.23 13.36 fcS-SSE 0.04 0.36 0.00 0.07 7.26 112.73 1) 1)
Table 5. Tests of statistical significance (t-test, and Wilcoxon-pair) for the means of the flux differences of sensible heat flux (H) for each of the comparison categories tested in the DAY case (a) and the NIGHT case (b). Statistical significance is identified by bold red labeling if the mean differences are not equal according to a probability threshold at or exceeding 95%. a) DAY Rnet > 300 W Directional comparison t-test p-value Wilcoxon pair null hypothesis (=0.95) (=0.95) HON = HW 0.25 0.30 HWSW = HW 0.30 0.27 HSW = HW 0.73 0.73 HSSW = HW 0.17 0.19 HS-SSE = HW 0.25 0.33 HON = HOFF 0.02 0.05 HWSW = HOFF 0.13 0.10 HSW = HOFF 0.99 0.86 HSSW = HOFF 0.02 0.04 HS-SSE = HOFF 0.02 0.08 b) NIGHT Rnet < 0 W m-2 Directional comparison t-test p-value Wilcoxon pair p-value null hypothesis (=0.95) (=0.95) HON = HW <0.001 <0.0001 HWSW = HW <0.001 <0.0001 HSW = HW <0.001 <0.0001 HSSW = HW <0.0001 <0.0001 HS-SSE = HW 0.003 <0.0001 HON = HOFF <0.001 <0.001 HWSW = HOFF <0.0001 <0.0001 HSW = HOFF <0.0001 <0.001 HSSW = HOFF <0.001 0.001 HS-SSE = HOFF 0.01 0.001
Table 6. Same as in Table 5 but for latent heat flux (E). a) DAY Rnet > 300 W Directional comparison t-test p-value Wilcoxon pair p-value null hypothesis (=0.95) (=0.95) EON = EW 0.50 0.57 EWSW = EW 0.003 0.004 ESW = EW 0.006 0.03 ESSW = EW 0.04 0.03 ES-SSE = EW 0.38 0.34 EON = EOFF 0.95 0.66 EWSW = EOFF 0.007 0.02 ESW = EOFF 0.01 0.07 ESSW = EOFF 0.07 0.14 ES-SSE = EOFF 0.04 0.02 b) NIGHT Rnet < 0 W m-2 Directional comparison t-test p-value Wilcoxon pair p-value null hypothesis (=0.95) (=0.95) EON = EW 0.46 0.80 EWSW = EW 0.98 0.01 ESW = EW 0.16 0.01 ESSW = EW 0.23 0.29 ES-SSE = EW 0.19 0.65 EON = EOFF 0.23 0.25 EWSW = EOFF 0.10 <0.001 ESW = EOFF 0.00 0.001 ESSW = EOFF 0.81 0.08 ES-SSE = EOFF 0.38 0.85
Table 7. Same as in Table 5 but for CO2 flux (fc). a) DAY Rnet > 300 W Directional comparison t-test Wilcoxon pair p-value null hypothesis (=0.95) (=0.95) fcON = fcW 0.03 0.06 fcWSW = fcW 0.004 0.003 fcSW = fcW 0.002 0.01 fcSSW = fcW 0.01 0.002 fcS-SSE = fcW 0.20 0.53 fcON = fcOFF 0.92 0.38 fcWSW = fcOFF 0.12 0.24 fcSW = fcOFF 0.09 0.35 fcSSW = fcOFF 0.44 0.60 fcS-SSE = fcOFF 0.31 0.03 b) NIGHT Rnet < 0 W m-2 Directional comparison t-test p-value Wilcoxon pair p-value null hypothesis (=0.95) (=0.95) fcON = fcW 0.09 0.34 fcWSW = fcW 0.02 0.004 fcSW = fcW <0.001 <0.0001 fcSSW = fcW <0.001 <0.0001 fcS-SSE = fcW 0.41 0.70 fcON = fcOFF 0.05 <0.001 fcWSW = fcOFF 0.008 <0.0001 fcSW = fcOFF <0.0001 <0.0001 fcSSW = fcOFF <0.0001 <0.0001 fcS-SSE = fcOFF 0.44 0.04
(Draxl et al. 2012; Storm and Basu 2010; Shin and Hong 2011) . These biases are related to the type of planetary boundary layer scheme used in the simulation and the turbulence closure parameterization techniques used for each scheme. Wind speed bias in the forecast is reduced from a combination of high-resolution models and the use of model ensembles. Recent studies by Deppe et al. (2013) and Gallus and Deppe (2011); demonstrate improved accuracy in some of the nighttime wind resource when an ensemble of hub-height wind speeds using different PBL schemes was compared with 80-m meteorological tower data within a wind farm in Northwest Iowa. Traituer et al. (2012) also indicated an improved short term forecast (1-3 hr lead time) by using an ensemble of high resolution operational models and a PBL suite of single column models and developing a calibration tool through running several days or weeks of simulations to develop a training method of determining bias corrections. Extremes in hub-height wind speed over a few hours can occur in localized meteorological conditions (frontal passage, mesoscale convective system gusts and diurnally driven transitions in the boundary layer). Ramp up and ramp down events (e.g. Gallus and Deppe 2011) have been shown to be very difficult to predict even in short lead time for the forecasts. Surface sensitivity may be one such factor in modulation of the frequency, intensity, and duration of such ramping events. Increasing skill in prediction of these ramping events may facilitate energy sale savings of $2-5 MW-hr or up to $2-4 billion -yr for nationwide implementation of these strategies under the 20% by 2030 DOE scenario (Marquis et al. 2011). Recent high-resolution modeling indicates that the spatial variability of 80-m wind speed is similar to the variations present on a surface terrain map (Personal communication, Anderson, 2012). Therefore, it is highly plausible that in addition to terrain, the heterogeneous patchwork of agricultural fields may also provide an influence on wind power and reliability that has not yet been determined. Numerical simulations with spatial variation soil moisture, roughness, soil type, and crop resistance alter boundary layer development of clouds and/or induce localized circulations similar to a lake breeze (e.g. Zhong and Duran 1997; Alapaty et al. 1997; Courault et al. 2007).