Near-Surface Wind-Speed Stilling in Alaska during 1984-2016 and Its Impact on the Sustainability of Wind Power

Based on wind-speed records of Alaska’s 19 first-order weather stations, we analyzed the near-surface wind-speed stilling for January 1, 1984 to December 31, 2016. With exception of Big Delta that indicates an increase of 0.0157 m∙s∙a, on average, all other first-order weather stations show declining trends in the near-surface wind speeds. In most cases, the average trends are less then −0.0300 m∙s∙a. The strongest average trend of −0.0500 m∙s∙a occurred at Homer, followed by −0.0492 m∙s∙a at Bettles, and −0.0453 m∙s∙a at Yakutat, while the declining trend at Barrow is marginal. The impact of the near-surface wind-speed stilling on the wind-power potential expressed by the wind-power density was predicted and compared with the wind-power classification of the National Renewable Energy Laboratory and the Alaska Energy Authority. This wind-power potential is, however, of subordinate importance because wind turbines only extract a fraction of the kinetic energy from the wind field characterized by the power efficiency. Since wind turbine technology has notably improved during the past 35 years, we hypothetically used seven currently available wind turbines of different rated power and three different shear exponents to assess the wind-power sustainability under changing wind regimes. The shear exponents 1/10, 1/7, and 1/5 served to examine the range of wind power for various conditions of thermal stratification. Based on our analysis for January 1, 1984 to December 31, 2016, Cold Bay, St. Paul Island, Kotzebue, and Bethel would be very good candidates for wind farms. To quantify the impact of a changing wind regime on wind-power sustainability, we predicted wind power for the periods January 1, 1984 to December 31, 1994 and January 1, 2006 to December 31, 2016 How to cite this paper: Kramm, G., Mölders, N., Cooney, J. and Dlugi, R. (2019) Near-Surface Wind-Speed Stilling in Alaska during 1984-2016 and Its Impact on the Sustainability of Wind Power. Journal of Power and Energy Engineering, 7, 71-124. https://doi.org/10.4236/jpee.2019.77006 Received: June 13, 2019 Accepted: July 28, 2019 Published: July 31, 2019 Copyright © 2019 by author(s) and Scientific Research Publishing Inc. This work is licensed under the Creative Commons Attribution International License (CC BY 4.0). http://creativecommons.org/licenses/by/4.0/ Open Access

McVicar et al. highlights the contribution R v has made to these declining evaporative rates.
Terrestrial stilling is, however, not omnipresent. Some positive trends were reported by McVicar et al. [1]. The situation is rather complex as indicated by the observations performed at the first-order weather station Barrow (now the City of Utqiaġvik), Alaska (for the specification, see Table 1). Based on the period 1921-2001, Lynch et al. [18] found a positive trend of 0.0047 m•s −1 •a −1 . In addition, Hartmann and Wendler [16] found an increase in wind speed of 0. The objective of our paper is two-fold: (a) to provide additional evidence to the process of near-surface wind-speed stilling in Alaska during Period I, and (b) to quantify the impact of a changing wind regime on the wind-power sustainability in the statewide region by considering the change from the period January 1, 1984 to December 31, 1994 denoted as Period II hereafter to the period January 1, 2006 to December 31, 2016 denoted as Period III hereafter. This means that the Period I was divided into three equal periods of 11 years.
To achieve these goals, we consider Alaska's 19 first-order weather stations as a testbed. First-order stations are defined as those operated by certified observers and are typically operated by the National Weather Service. These sites include a full suite of equipment to measure air pressure and temperature, cloudiness, total precipitation, snowfall, wind speed and wind direction (e.g. [16]). These stations are grouped into climate regions and their specifications are listed in Table 1.
Declining trends in the near-surface wind speeds notably affect the windpower potential [6] commonly expressed by the wind-power density (i.e., the mean kinetic energy stream density) at a certain height above the surface [19], This equation describes the transfer of mean kinetic energy (MKE) and turbulent kinetic energy (TKE) by the mean wind field and the transfer of TKE by the eddying wind field. Here, ρ and v are the air density and the velocity of the wind field at the same height, respectively. The overbar ( ) characterizes the conventional Reynolds mean and a prime ( ' ) the deviation from that. The hat (   ) denotes the density-weighted average according to Hesselberg [20] defined by  ρχ χ ρ = , (1. 2) and the double prime ( '' ) marks the departure from that. Here, χ is a field quantity like the wind vector, v , and the specific humidity 1 m . It is obvious that  0 ρ χ ρχ ′′ ′′ = = . Hesselberg's average can be related to that of Reynolds by [21] where v = v   . Apparently, the wind-power density is proportional to the cube of wind speed. The rotor of a wind turbine causes a divergence effect expressed by 0 kin ∇ ⋅ ≠ S . The wind speed is usually assumed as uniformly distributed over the rotor area of a wind turbine which is a crude assumption. Generally, at a given location, all these quantities vary with time.
As wind turbines only extract a fraction of the kinetic energy from the wind field characterized by the power efficiency (see, e.g., [19] [34] and  Table 2. Also shown are the wind-power density = ≅ (adopted from [19]).
the wind-power potential is of subordinate importance. Here, P is the extracted (or consumed) power, and is the power carried by the flow through the projection of the turbine section region onto the plane perpendicular to it, where v ∞ is the undisturbed wind speed far upstream of the wind turbine, and R A is the rotor area considered as perpendicular to the flow axis.
Therefore, it is indispensable to analyze how wind turbines respond to the declining trends in near-surface wind speeds. Since the wind turbine technology has notably improved during the past 35 years, we followed Mölders et al. [15] and used seven currently available wind turbines of different rated power to assess the wind-power sustainability under changing wind regimes. These wind tur- were also used by Mölders et al. [35] in their study on the uncertainty in windpower assessment over complex terrain.
In Section 2, we describe the data source and the methodology. Here, we also compare the wind-power potential obtained from daily mean averages of wind speed with that based on hourly mean averages of wind speed for the same time interval because the latter is generally larger than the former. This comparison serves for assessing the results of our wind-power predictions for the Periods I, II, and III that are based on daily mean averages of wind speed taken from the Global Historical Climatology Network (GHCN)-Daily [36]. The impact of the Journal of Power and Energy Engineering  Table 2 is outlined in Section 4. The results are presented in Section 5.

Wind Data and Methodology
To estimate the wind-power potential in Alaska, we considered the daily mean wind data provided by the 19 first-order weather stations in Alaska for Periods I, II, and III. The wind data are taken from the Global Historical Climatology Network (GHCN)-Daily [36]. Days for which no wind data were reported were removed from the datasets. The number of days considered is listed in Table 3.
Based on these wind data, we assessed these locations for their suitability for wind farms by answering three major questions: 1) Does the location exhibit enough wind speed to generate electrical power in a sufficient manner?
2) Is this electrical power affected by long-term trends in horizontal wind speeds?
3) Are there obstacles such as siting issues present at the location?
The first two questions relate to the basic requirements for establishing a wind farm. The third question assesses the impacts of any kind of energy producing facilities (e.g., power plants fueled with coal, oil, gas, or nuclear elements to hydroelectric dams, tide-power systems, wind farms) on the natural environment.
The impact of wind farms on endangered species, avian migrations/habitats, wetlands/protected areas, and subsistence lifestyle must be considered as well.
Additionally, for numerous Alaska areas, the ambient temperatures fall below −20˚C in winter which is below the operation range of most wind turbines, except cold climate versions like that of Senvion MM92 which has a lower limit of −30˚C. Frequently icing of rotor blades due to the occurrence of supercooled is the period under study, N is the number of days, , is the time period of a day, and  Table 3 for Period I.
As described in the Wind Energy Resource Atlas of the United States, another measure for assessing the wind-power potential at a given location is related to the wind-power class listed in Figure 2. It is based on the true wind-power density at the height of 50 m z = above ground. The average of the wind-power density is given by     i.e.,  1  1  1  3  3  3  3  , ,

( )
and in a further step the Inequality (2.8). Differences ( ) , are illustrated in Figure 3 and Figure 4 for two different cases.   The corresponding daily mean wind speeds illustrated in Figure 5 31,1983. Missing data were replaced by interpolated values to obtain an adequate dataset of daily mean wind speeds ( Figure 5). Nevertheless, 70 of these daily mean wind speeds were removed from the dataset because of too many missing hourly mean wind speeds. Apparently, these daily mean wind speeds decreased. Again, the Inequality (2.8) is always fulfilled. Since the wind-power potential is of subordinate importance, we analyze how the chosen wind turbines (see Table 2) respond to this difference in the wind-power potential (see Section 5).
The daily mean wind speed, (2.15) Thus, we obtain (2. 16) In accord with Equation (2.13), the corresponding wind speed at z amounts to , , , In case of an exponent     Table 1 for station numbers).
The respective mean wind-power density  surrounding cities and communities, respectively. In most cases, the average trends are less then −0.0300 m•s −1 •a −1 , with exception of Gulkana that only exhibits a value of −0.0043 m•s −1 •a −1 ( Table 3). The strongest trend occurred at Homer with −0.0500 m•s −1 •a −1 , on average, followed by Bettles with −0.0492 m•s −1 •a −1 and Yakutat with −0.0453 m•s −1 •a −1 (Table 3). Thus, we only discuss the predicted wind-power outcomes obtained for Barrow (1), Big Delta (4), Kotzebue (7), Nome (8), Bethel (9), King Salmon (13), Juneau (15), Annette (16), St. Paul Island (17), Kodiak (18), and Cold Bay (19) in detail. Note that Cold Bay and St. Paul Island are related to the wind-power class of 6 ("outstanding"), followed by Kotzebue that is related to a wind-power class of 4 ("good"), Barrow, Bethel and Kodiak are related to a wind-power class of 3 ("fair"), Big Delta and Nome are related to a wind-power class of 2 ("marginal"). We only considered Juneau and Annette for the purpose of comparison with recent results [15]. Figure 9 shows the records of the daily mean wind speed at these weather stations for Period I.
The linear trends shown in Figure 9 indicate declining trends in the daily mean wind speeds, with exception of Big Delta that shows a positive one ( Table  3). The decrease at Barrow is marginal. At all other first-order weather stations, such declining trends in the daily mean wind speed occurred as well (Table 3). However, this fact is of minor importance because , kin z S is already very low ( Figure 8). Except for Barrow, all results are statistically significant according to a two-side t-test at 95% confidence.
We assess the impact on the sustainability of wind power by quantifying the change in the predicted wind power from Period II (January 1, 1984 to December 31,1994) to Period III (January 1, 2006 to December 31, 2016).

The Impact of the Long-Term Wind-Speed Decrease on Energy Conversion at the Interface Earth-Atmosphere and Wind Power
The reasons for the long-term decrease of the mean horizontal wind speed as Journal of Power and Energy Engineering  Table 1 for anemometer heights, R z , at these stations).
documented by 18 of the 19 first-order weather stations in Alaska are unknown.
This long-term decrease, however, may impact not only the generation of electricity using wind power, but also the near-surface air temperature. To address the latter, we considered the energy flux balance at the interface atmosphere assuming bare soil 1 for simplification. For a given location (characterized, for instance, by the zenith angle, θ , and the azimuthal angle, ϕ ) it reads (only the components normal to the horizontal surface element play a role) Here, is local zenith angle of the Sun's center, The inclusion of a vegetation canopy has been discussed, for instance, by Deardorff [49]  i.e., they have to be computed based on mean quantities derived from observations. Under horizontally homogeneous and steady-state conditions these fluxes can be parameterized by [39] (hereafter, θ and ϕ are omitted) where the vertical components of the respective gradients characterize the mo- where horizontally homogeneous and steady-state conditions are presupposed to fulfill the requirements of the Prandtl layer (also called the atmospheric surface layer, ASL, or the constant-flux layer), the lowest layer of the atmosphere of a thickness of about ten meters. Here,  R R U = v and  s s U = v are the mean horizontal wind speeds at R z (subscript R) and at the Earth's surface (subscript s), where in the case of rigid walls (like layers of soil, snow, and/or ice) the latter is equal to zero, R Θ is the potential temperature at R z , s T is the absolute temperature at the water surface, and 1,R m and 1,s m are the corresponding values of the specific humidity. Furthermore, the potential temperature is defined by

7) Journal of Power and Energy Engineering
Here, R is the calculated gas constant for moist air, 0 R is the calculated gas constant for dry air, 1 R is the gas constant for water vapor, is the mass fraction, where j ρ is the partial density for dry air ( 0 j = ), water vapor ( 1 j = ), liquid water ( 2 j = ), and ice ( 3 j = ), respectively. These partial densities obey Furthermore, h is the specific enthalpy, and is the partial specific enthalpy. Moreover, h C and 1 m C are the local transfer coefficients for sensible heat and water vapor, respectively, given by Here, κ is the von Kármán constant,  ( ) are the local similarity functions for momentum, sensible heat, and water vapor, respectively. They are based on the similarity hypothesis of Monin and Obukhov [77]. These local similarity (or stability) functions are given by is the Obukhov stability length. Furthermore, ,0 p c is the specific heat at constant pressure for dry air,  H H U = v is the mean horizontal wind speed, g is the acceleration of gravity, and  Θ is a potential temperature representative for the entire Prandtl layer.
To determine the local drag coefficient and the local similarity function for momentum, the magnitude of the friction stress vector, and in a further step the friction velocity must be computed.  ( ) This formula shows that the shear exponent explicitly depends on thermal stratification. Equations (3.4) and (3.5) suggest that the decrease in the mean horizontal wind speed as demonstrated by the global stilling can reduce the fluxes of sensible and latent heat, and, according to Equation (3.1), can increase the surface temperature. The situation, however, is rather complex because a decrease of evapotranspiration also affects the formation and depletion of clouds and, subsequently, the scattering and absorption of solar radiation and the emission of infrared radiation by hydrometeors. Analyzing such interrelations requires the support by non-hydrostatic models of the meso-scales β γ [79]- [85].
Usually, MKE can be converted into TKE. In the inertial range, for instance, the TKE is transferred from lower to higher wave numbers until the far-dissipation range is reached, where kinetic energy is converted into heat energy by direct dissipation, : ∇ J v  , and turbulent dissipation, : ′′ ∇ J v , where J is the Stokes stress tensor [19]. Wind turbines may generate even more TKE. Heating of air due to the dissipation of TKE has been discussed and investigated (e.g., [86] [87] [88]). It has been assessed as being marginal.
The use of wind farms, however, will contribute to a further decrease of the mean horizontal wind speed [89] [90] [91] [92] [93] and may notably affect the energy conversion at the interface Earth-atmosphere either directly as described before or indirectly, for instance, by altering the cloudiness over and/or the lee-side regions of wind-farm areas [94].
The axial momentum theory [95] [96] [97], for instance, leads to the power efficiency [19] ( )( ) 2 1 1 1 2  Table 4. Based on these results, a notable change of the local or regional climate-depending on the size of the wind farm-is to be expected. Note that these estimates presuppose that the thermal stratification and, hence, the shear exponents are unaffected by the wind turbines. However, we must expect the interaction between the wind field, wind turbine generating vortices, and enhancing turbulence in its wake may cause that thermal stratification tends to neutral conditions.

The Prediction of Wind Power
The wind power is predicted based on current state-of-the-art wind turbines of different rated powers listed in Table 2  with a turbulence intensity ranging from 0.10 to 0.12. Figure 11 shows the corresponding power efficiencies.
Based on discrete power-curve data, we determined the empirical fitting parameters A, K, Q, B, S, and u of the general logistic function where ( ) P v represents the power generated by the respective wind turbine at the wind speed v at hub height. The parameters obtained are listed in Table 5.    Table 2 (adopted from [19]). Figure 11. Power efficiencies of the seven wind turbines listed in Table 2 (adopted from [19]).
where W k and W c represent the shape and scale parameters, respectively (e.g., [16] [70] [71]). The scale factor has units of speed and is closely related to the mean wind speed at hub height. The shape parameter is a non-dimensional quantity inversely related to the variance of the wind speed [101]. The integration of Equation (4.3) leads to the cumulative distribution function [100] ( ) 1 exp . The probabilities 1 P , 2 P , and 3 P that the daily mean wind speed will be in one of these colored areas are defined by Equations (4.5) to (4.7). Journal of Power and Energy Engineering shows both the fitted normalized cumulative frequency for Period I and the corresponding Weibull distribution at hub height obtained for Cold Bay using 1 7 p = . The results are listed in Table 6. Obviously, the scale parameters obtained for Periods II and III also indicate the near-surface wind-speed stilling, with exception of Barrow and Big Delta.
The probability that the daily mean wind speed does not meet the cut-in speed requirements of the different wind turbines considered in this study is given by (4.5) Furthermore, the probability that the daily mean wind speed is in the range between ci v and the wind speed of the rated power, pr v , is given by The probability that the daily mean wind speed exceeds pr v results in For the purpose of simplification, a common cut-in wind speed of Both wind speeds are averages derived from the wind turbine specifications ( Table 2). The sum 2 3 P P + broadly coincides with the operating range of a modern wind turbine.
The ratio of the average power output, WT P , provided by a wind turbine to its rated power, R P , is the capacity factor WT F R P C P = . (4.8) The capacity factor may empirically be related to the ratio ( ) 3 2 3 P R P P P = + .

Hourly Mean Wind Speeds versus Daily Mean Wind Speeds
Because of Inequality (2.8), we must expect that hourly mean wind speeds pro- at Bethel performed from January 1, 1979 to December 31, 1983. Results obtained at 80 m z = are illustrated in Figure 13 and Figure 14, where, again, 1 10 p = , 1 7 p = and 1 5 p = were used. Figure 13 shows the probability density functions of (a) the hourly mean wind speeds and (b) the daily mean wind speeds at the hub height. Obviously, these probability density functions remarkably differ so that-according to Equation (4.2)-different amounts of wind power were predicted for each of the seven wind turbines considered. Figure 14 shows the predicted wind power, , obtained from the hourly mean wind speeds and the corresponding

Daily Mean Wind Speeds
Our predicted wind power obtained for Barrow, Big Delta, Kotzebue, Nome, Bethel, King Salmon, Juneau, St. Paul Island, Kodiak, and Cold Bay for Periods I, II, and III using 1 10 p = , 1 7 p = and 1 5 p = , is listed in Table 7. The effect of the shear exponent on the probability density function and the wind-power output predicted for Periods I, II, and III is exemplarily shown in Figure 15 to Figure 17 for Cold Bay. Figure 15 and Figure 16 show the results obtained for the seven wind turbines of different rated power (see Table 2). Generally, the power output predicted at G. Kramm et al. . Consequently, the capacity factor for each wind turbine is the lowest for 1 10 p = and the highest for 1 5 p = (see Table 8).    (Table 2) as well as 1 10 p = , 1 7 p = , and 1 5 p = . Figure 17. As in Figure 16, but for the predicted capacity factor  The typical distribution of the capacity factor is illustrated in Figure 17.
Again, the General Electric 1.6 -82.5 and Senvion MM92 have the highest and second highest capacity factor, respectively [15]. Nonetheless, capacity factors of more than 50% for 1 7 p = and more than 60% for 1 5 p = as obtained in Period I for most of these wind turbines in case of Cold Bay are extraordinary. Figure 18 shows the relative decrease.   is that predicted for Period III. Besides Big Delta that suggests a relative increase in wind power of up to 12% for 1 7 p = , we found notable relative decreases in the predicted wind power of about 38% for Annette, followed by Kodiak (≈30%), King Salmon (≈26%), and Kotzebue (≈24%), where the effect of the shear exponents was marginal in these instances. Bethel with about 17% for 1 5 p = and about 20% for 1 10 p = and 1 7 p = , Juneau with about 18% hardly affected by the shear exponents, and Cold Bay with about 14% for 1 10 p = to 10% for 1 5 p = also show remarkable relative decreases in predicted wind power. In case of Nome, the relative decrease in the predicted wind power is less than 12%. However, the results notably depend on both the chosen wind turbine and the shear exponent. St. Paul Island exhibits a small relative decrease of about 8% hardly affected by the shear exponents. Barrow shows a relative increase mainly for 1 7 p = and 1 5 p = , but this in-Journal of Power and Energy Engineering crease is less than 5%. In case of Annette, predicted wind power dramatically decreased due to the near-surface wind-speed stilling from Period II to Period III, but with respect to Period I, wind-power generation at Annette is generally ineffective. The same is true for Juneau despite the relative decrease in the predicted wind power is twice as small as compared with Annette. At Cold Bay, Bethel and Kotzebue, which were very good candidates for wind farms based on Period I, the near-surface wind-speed stilling notably shrinks wind-power generation. The same is true in the case of Kodiak and King Salmon.
For the purpose of simplification, the probabilities 1 P , 2 P , and 3 P given by Equations (4.5) to (4.7) were computed for Periods I, II, and III using a common cut-in wind speed of  Table 9.

Discussion and Conclusions
Based on wind-speed record of its 19 first-order weather stations, we analyze the  currently prevents sustainability of wind power at these two communities. As mentioned before, wind-power generation at Annette and Juneau is generally ineffective.
Cold Bay located in the Aleutians East Borough is an ideal site for a wind farm. It has a very high wind-power potential expressed by the wind-power class of 6, termed as "outstanding". As illustrated in Figure 15, For all three periods, the probability 1 P is very low, but the 3 P is very high (see Table 9). The combination of these issues leads to extraordinarily high capacity factors. As illustrated in Figure 19, Again, the results obtained for Periods II and III indicate a slight effect due to the near-surface wind-speed stilling (see Figure 18).
According to the Alaska Energy Data Gateway Icing of the rotor blades, however, may occur during the cold season.
There is, however, a significant drawback. The Alaska-breeding population of Steller's Eider currently listed as threatened under the Endangered Species Act (ESA) and a State of Alaska species of special concern, regularly occurs on Izembek National Wildlife Refuge, near Cold Bay [104].
With respect to all three periods, the differences in wind-power generation  density functions determined for all three shear exponents and all three periods slightly differ from those of Cold Bay. Compared with those of Cold Bay, the modes are slightly shifted to lower wind speeds leading to a slightly higher probability 1 P and a slightly lower probability 3 P . Thus, the capacity factors determined for St. Paul Island are somewhat lower than the corresponding ones of Cold Bay. As illustrated in Figure 19, the average capacity factor ranges for The electricity was mainly produced using natural gas from nearby gas, only a very small amount was generated using oil. A power consumption of about 50 GWh would require numerous medium-scale wind turbines.
The generation of wind power at Barrow is strongly limited by the operating temperature range. The near-surface air temperature ranges from −48.9˚C observed on February 3, 1924 to 26.1˚C observed on July 13, 1993 [103]. Based on the period from 1971 to 2000, the lowest mean monthly minimum temperature is −30.0˚C (February) and the highest mean monthly temperature is 8.1˚C (July) [103]. Consequently, Barrow would require cold climate versions of wind turbines with limits of −30˚C and lower. Senvion's MM92 CCV nearly fulfils this requirement. Icing of the rotor blades, however, may occur during the cold season. Beside the low temperature range, Barrow's landscape has a great deal of lakes, ponds, and birds migrating. Wind turbines could impede the wildlife in the area by disrupting the migration and habitats of these animals. Based on these facts, we do not recommend the use of wind power at Barrow under the current conditions.
Kotzebue located at the north-western corner of the Baldwin Peninsula in the Kotzebue Sound, has a notable wind-power potential expressed by the wind-power class of 4, termed as "good". The wind-power density and average wind speed at hub height are relatively high signifying that it could generate a lot of power. The probability density functions determined for all three shear expo-Journal of Power and Energy Engineering nents and all three periods, however, notably differ from those of Cold Bay.
There is a notable shift in the modes to lower mean wind speeds ranging from 2 m•s −1 to 4 m•s −1 or so. Thus, 1 P is notably higher and 3 P is notably lower, and, hence, the capacity factors determined for Kotzebue are notably lower than the corresponding ones of Cold Bay and St. Paul Island. As illustrated in Figure 19, the average capacity factor ranges for Period I from 26 The results obtained for the Periods II and III indicate a notable effect due to the near-surface wind-speed stilling (see Figure 18). During the short Arctic summers, large numbers of white-fronted geese and tundra swans arrive along with sandhill cranes and a horde of other shorebirds.
Lastly, Bethel is the largest community on the Kuskokwim River, approximately 80 km upstream from where the river flows into Kuskokwim Bay. Bethel has a remarkable wind-power potential expressed by the wind-power class of, at least, 3 termed as "fair". The probability density functions for all three shear exponents determined for all periods notably differ from those of Cold Bay. There is a shift in the modes by more than 2 m•s −1 to lower mean wind speeds. Thus, 1 P is remarkably higher and 3 P is notably lower, and, hence, the capacity factors determined for Bethel are notably lower than the corresponding ones of  Figure 19). As shown in Journal of Power and Energy Engineering Nevertheless, icing of the rotor blades may occur during the cold season.
A potential problem with Bethel is that it is surrounded by the Yukon Delta National Wildlife Refuge which supports one of the largest aggregations of water birds in the world. Thus, wind turbines could strongly impact the wildlife in that area by killing countless birds.
Based on our study, one may conclude that wind-stilling affects wind-power generation in Alaska to a notable degree. Thus, prior to installing new wind farms, assessments of suitability for power generation should look at the entire record of available data to identify trends in 1 P , 2 P , and 3 P . Obviously, the distribution of these probabilities affects productivity. These aspects may also optimize the choice of turbine and sustainability of wind power. Also, like in the permitting process of power plants and other industrial complexes, a full environmental impact assessment must be performed to protect the subsistence lifestyle in the immediate area of the potential farm, wildlife, eco-systems and local climate. While the assessment of the impacts on endangered species, migrating birds and birds that are part of a subsistence lifestyle is straight forward, assessment of the impacts on local climate requires numerical modeling techniques.
The mixing of air due to the rotor blades and the consequent more frequent neutral conditions alter the cloud and precipitation formation in the near-field.
Such changes in the water cycle are known to affect ecosystems again with potential impacts on birds, fish and game and hence a subsistence lifestyle.