Downloaded 10/10/21 07:22 PM UTC Projects, but Has Also Built up a Public Meteorologi- Cal Database Over Previ- Ously Data-Void Regions

WIND ENERGY EXPLORATION OVER THE ATACAMA DESERT A Numerical Model–Guided Observational Program

Ricardo C. Muñoz, Mark J. Falvey, Mario Arancibia, Valentina I. Astudillo, Javier Elgueta, Marcelo Ibarra, Christian Santana, and Camila Vásquez

A Chilean program explores winds over the Atacama Desert region and is producing a public model and observational database in support of the development of wind energy projects.

enewable energy, especially wind and solar, is an and execute measurement programs, and for the increasingly important field in applied meteorol- modeling of resource variability. Renewable energy Rogy and climate science. From the initial studies poses special challenges compared to more traditional that explore the availability of these energy resources meteorological applications (Emeis 2013). In the case in a given area, to the forecasting models required of wind power, for example, most phenomena of inter- to optimize the operation of wind or solar power est occur within the atmospheric boundary layer over plants, meteorological expertise is needed to design horizontal scales ranging from the microscale to the mesoscale. Moreover, the viability of many renewable energy projects depends on the accuracy of the AFFILIATIONS: Muñoz and Astudillo—Department of Geophys- ics, University of Chile, Santiago, Chile; Falvey and Ibarra—De- meteorological variables measured and modeled, with partment of Geophysics, University of Chile, and Meteodata Ltd., even small errors having large financial implications. Santiago, Chile; Arancibia and Elgueta—Department of Geophysics, On the other hand, the emergence of the renewable University of Chile, and Airtec Ltd., Santiago, Chile; Santana and energy industry has led to a significant increase in the Vásquez—Ministerio de Energía, Santiago, Chile number of measuring sites being deployed worldwide, CORRESPONDING AUTHOR: Ricardo C. Muñoz, often in locations previously devoid of meteorologi- [email protected] cal data. The commercial market for meteorological The abstract for this article can be found in this issue, following the instrumentation has also responded, with the devel- table of contents. opment of sensors tailored to satisfy specific renew- DOI:10.1175/BAMS-D-17-0019.1 able energy requirements. While this enhanced data A supplement to this article is available online (10.1175/BAMS-D-17-0019.2) availability and new instrumentation certainly has In final form 12 April 2018 great scientific potential, it is true that because of the ©2018 American Meteorological Society commercial interests behind most renewable energy For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy. projects, the associated meteorological information is usually not fully available to the general scientific

AMERICAN METEOROLOGICAL SOCIETY OCTOBER 2018 | 2079 Unauthenticated | Downloaded 10/10/21 07:22 PM UTC projects, but has also built up a public meteorological database over previously data-void regions. The objective of this paper is to introduce this database to the meteorological community, describing the measurements and model results it contains, the tools available to access the information, and high- lighting some interesting climatic features revealed by the observations. With a latitudinal extent that spans from the tropics to the Southern Ocean (18°S to about 56°S in latitude and roughly along the 70°W meridian), Chile is subject to a large variety of climatic regimes. North of about 30°S, climate is largely con- ditioned by the southeast Pacific anticyclone, providing for a very dry and stable free troposphere. To the south, the weather is deter- mined by the midlatitude westerlies and the synoptic modulation provided by the successive migration of low and high pressure systems. Zonally, the basic surface contrast provided by the ocean–continent interface to the west is further modu- lated inland by complex Fig. 1. Location map showing 45 meteorological stations over the Atacama Desert (inset shows location of the region in South America). Colors denote terrain that rises in less than the mean wind speed field simulated with the EE for 2010 at 100 m AGL. 300 km from sea level up to Letters and dashed ellipses indicate selected main zones for wind prospection. the Andes Cordillera along The bottom layer corresponds to a 30-m Shuttle Radar Topography Mission the eastern border of Chile, (SRTM) hillshade relief map. with maximum altitudes above 5,000 m north of community (e.g., Kusiak 2016). The present work 30°S. While this wide variety of climates and topog- describes a measurement and modeling program raphy led naturally to the assumption that there could funded mainly by the Chilean government over the be many regions in Chile where solar and wind energy last several years that is aimed at generating public potentials were high, the national energy agency (Min- information about the potential of renewable energy istry of Energy) realized in the early 2000s that a lack resources across the country. As such, it has not only of reliable measurements and quantitative information provided information upon which private investors about these resources was one of the main barriers have been able to develop new renewable energy for the development of renewable energy projects. A

2080 | OCTOBER 2018 Unauthenticated | Downloaded 10/10/21 07:22 PM UTC Fig. 2. Timeline of the national wind and solar exploration program. (top) Description of the observational efforts and measurement periods for the different selected zones, with color intensity representing higher station density. Blue is for 20-m towers, green for 60/80-m towers, yellow for solar monitoring sites, and red for PER stations. (bottom) Description of the modeling initiatives and the evolution of the EE: WF indicates wind farms in operation and LH and LV refer to the Loma del Hueso and Lengua de Vaca stations, respectively, which are the stations with the longest records in the observational database.

series of projects were then initiated with the collabo- measure and quantify the solar energy resource being ration of both national universities and international documented in other reports (Rondanelli et al. 2015; assistance agencies, dedicated to the compilation of Molina et al. 2017). historical databases, measurement campaigns, and numerical modeling of wind and solar resources over EXPLORATION PROGRAM. Evolution. The the country (Santana et al. 2014). While the spatial first efforts of the national energy agency to gather extent of some of these activities encompasses all of information on wind potential over Chile involved Chile, the focus of the present contribution is on the the compilation of existing wind data from private northern part of the country, where the Atacama Des- and public sources (DGF 1993; UNTEC 2003; CERE ert is located (Fig. 1). This area was prioritized by the Table 1. Configuration of the WRF Model used for the EE. energy agency because its WRF version 3.2 energy demand is very high as a result of the presence Dynamical core Advanced Research core of the WRF (ARW) of a large copper mining Horizontal resolution (km) 27, 9, 3, 1 industry, while the aridity of Nesting method One way the region makes hydroelec- Vertical grid 42 sigma levels, 10-m spacing in the first 100 m tric energy generation (the Upper boundary (hPa) 50 principal renewable energy Model topography SRTM source in southern Chile) Time step (s) 40, 40, 13.33, 4.44 impossible. Therefore, in Time period simulated 2010 calendar year northern Chile solar and wind generation constitutes Data save interval (min) 60 a very attractive alterna- Lateral boundary condition 6-h operational analyses from the Global Forecast System tive to the fossil fuel–based Planetary boundary layer QNSE generation that has histori- Surface layer Monin–Obukhov cally supplied energy to the Solar radiation Rapid Radiative Transfer Model (RRTM) region. The focus of the Longwave radiation RRTM paper is further restricted to Cloud microphysics WRF single moment (5 class) the exploration of the wind resource, with the efforts to Convection scheme Kain–Fritsch (27- and 9-km grids only)

AMERICAN METEOROLOGICAL SOCIETY OCTOBER 2018 | 2081 Unauthenticated | Downloaded 10/10/21 07:22 PM UTC EXAMPLES OF CIRCULATION FEATURES OVER THE ATACAMA DESERT

ased on the data gathered by these zone B (b21 and T80CN in Fig. SB1) projected. While a diurnal cycle in wind Bprojects, a picture of the near-sur- are located along broad valleys running direction is noticeable in the annual face wind regime over several points from high elevations to the west down climatology of this station, the diurnal in the Atacama Desert is beginning to the lowlands in the east. Both show phase during winter is much reduced to emerge. An overriding character- a conspicuous nocturnal wind regime and strong northerly winds dominate, istic of this regime is a strong diurnal characterized by very persistent down- except for a brief weakening during the variability in wind speed and direction, valley wind directions (easterly in the evening transition (not shown). The hardly surprising given the extreme case of b21 and northerly in the case of location of these sites near the top insolation over the desert surface and T80CN) and relatively high wind speeds, of a zonally oriented mountain chain the general lack of synoptic variability. traits that are more marked during win- around 2,000 m MSL suggests that the The top panels in Fig. SB1 show the ter (not shown). Furthermore, the 80-m topography may be interacting here mean spatial pattern of mean diurnal towers in this zone show that the verti- with a northerly barrier jet existing in (left) and nocturnal (right) wind speed cal profiles of these nocturnal winds this region at this altitude (Rutllant et al. at 80 m simulated by the WRF Model. have a nose-like shape, with maximum 2013), a hypothesis that could eventu- The model shows the diurnal wind speeds occurring between 20 and 60 m ally be tested with numerical model regime to be spatially homogenous, AGL (see the “Nocturnal drainage flows diagnostics of these winds. Finally, as characterized by westerly oriented along valleys” sidebar). Muñoz et al. predicted by the EE modeling results, upslope flows that align with prevailing (2013) classified these winds as drainage the highest wind speeds are found in northwesterly winds at higher alti- winds and provided a more complete the high-elevation zone E (station e02 in tudes. In contrast, the nocturnal wind observational characterization of them. Fig. SB1). The dominant wind direction regime shows a more complex spatial Stations in zones A and C are located is northwesterly, with weak diurnal structure characterized by a diverse along coastal valleys. As such, the wind and annual modulation. Wind speeds range of flows, including stagnation regimes of stations a7 and c71 in Fig. SB1 can exceed 25 m s−1 at 10 m AGL, be- zones, strong drainage systems (which show a strong and well-defined diurnal ing larger in the afternoon during the in some locations exceed the diurnal phase with a marked westerly wind di- summer (not shown). While knowledge maximum), and synoptically driven rection and wind speeds that can reach of these features of the near-surface regimes at higher-elevation locations. up to 20 m s−1. A possible superposition wind climatology is essential for wind The bottom panels in Fig. SB1 pres- of coastal and valley breeze forcings energy projects, the database produced ent data for each of the five measuring may explain these high diurnal winds, by these projects should be of interest zones A–E. The two panels for each considering that stations closer to the to scientists in diverse disciplines and station describe the diurnal variation coast have lower speeds (not shown). at this time its use in geomorphological of the wind direction frequencies and Station d02 in zone D illustrates the and biological studies of the Atacama the association between wind speeds wind regime in the Taltal area, where a Desert is already under way (M. Reyers and wind directions. The stations in significant wind energy development is 2018, personal communication).

▶ Fig. SB1. Wind regimes at different locations over the Atacama Desert. (top) The mean wind speed maps correspond to EE simulations for the year 2010 at 80 m AGL, for diurnal (1200–1800 UTC − 4 h) and nocturnal (0000–0600 UTC − 4 h) conditions. (bottom) The near-surface (10 m AGL) wind regimes at six observing sites (WD and WS correspond to wind direction and wind speed, respectively).

2005). Numerical modeling and the acquisition of targeting off-grid rural communities that could use new measurements were only a small part of these wind energy as a local source of electricity (Canales efforts. Given that most of the data compiled were 2011). Under this program, 31 wind monitoring sta- not originally intended for wind energy purposes, tions were operated over 1–3-yr periods between 2002 but for other objectives like air quality or aeronauti- and 2007. A second line of work targeted potential cal safety, it rapidly became apparent that the avail- locations for wind farms that could supply energy able information did not provide a true picture of to the main electrical grids of the country. This the wind resource across the country. Therefore, work started in 2008 with a project that applied the subsequent efforts included the installation of new Weather Research and Forecasting (WRF) Model measurement sites and numerical modeling spe- to produce detailed wind fields for selected regions cifically oriented to wind potential evaluation. One (UNTEC 2008a,b). The modeling results, together line of work was the so-called Program for Rural with information on technical, geographical, and Electrification (PER) program, a United Nations land-ownership constraints for possible locations of Development Program–funded measurement effort wind farms, were used to select measurement sites

2082 | OCTOBER 2018 Unauthenticated | Downloaded 10/10/21 07:22 PM UTC EXAMPLES OF CIRCULATION FEATURES OVER THE ATACAMA DESERT

AMERICAN METEOROLOGICAL SOCIETY OCTOBER 2018 | 2083 Unauthenticated | Downloaded 10/10/21 07:22 PM UTC the most popular mesoscale modeling system in the scientific community worldwide, and has proven to be a standard tool for wind energy prospecting at the regional scale (e.g., Mattar and Borvarán 2016; Nawri et al. 2014; Carvalho et al. 2014; Storm et al. 2009). As described in the previous section, the WRF modeling system was first applied to the northern region of the country, as part of a two-stage project involving regional model simulations followed by a 20-site prospecting campaign where the site selection was based on the model results. Given the favorable evaluation of this early project, the Chilean government continued to fund the development and application of the modeling system in parallel with the measurement campaigns, extending the spatial domain of the model to eventually cover the entire country. The model configuration used to simulate the Atacama region underwent a rapid evolution during the early stages of the project. The initial version of Fig. 3. Scatterplot of annual mean wind speeds be- the model used an essentially “off the shelf” configu- tween the measured and modeled data. Modeled data correspond to EE simulations for 2010. Measured data ration with low spatial resolution (3 km) and default correspond to averages of the year 2010 when available vertical spacing. Because of limited computational (red dots), or to averages over the maximum number resources, these simulations were restricted to just of complete years when not available (blue dots). The four months (January, April, July, and September) dashed line represents a perfect fit and R2 is the deter- of a single year (2006). Despite the lack of any real mination coefficient. customization, these early experiments were able to successfully guide the initial deployment of the for wind prospection in the northern region of the 20-m tower sites that were installed during 2009. country, as described in the “Measurement sites” sec- As the observations started to come in and a direct tion below). The WRF modeling efforts, on the other model evaluation was made possible, there began hand, were subsequently extended and refined, giving a rapid process of refinement and optimization of rise to the so-called Wind Energy Explorer described the original WRF setup. By 2011 a definitive model in the following section. A timeline of the modeling configuration was established, considered to be of and measurement activities carried out over the years sufficient quality for long-term model runs with is presented in Fig. 2. public dissemination in mind. This setup was used initially to provide complete simulations of the 2010 Modeling description. Since 2008 the use of moderate- calendar year for the central and northern portions resolution mesoscale models, in particular the WRF of the country at 1-km resolution, and was later ex- Model, has played an increasing and pivotal role tended to cover the southern portion of Chile, along in the Chilean government’s efforts to improve the with Easter Island (Chilean territory in the Pacific). resource information available to wind energy stake- The model configuration used for the definitive holders. We use the words “moderate resolution” to simulations is summarized in Table 1. Given the large distinguish the regional (~1-km resolution) modeling extent of the region to be simulated at high horizontal efforts described here from the very high-resolution resolution, it was necessary to divide the entire spatial (10–100 m) large-eddy simulation (LES) or com- domain into 22 subdomains (5 over the Atacama putational fluid dynamics (CFD) models that have region) that were run independently. Each subdo- become popular in the industry for micrositing and main consisted of four nested computational grids other wind farm–scale applications (e.g., Uchida and that scale down from 27- to 1-km resolution. The Ohya 2008; Sanderse et al. 2011). average size of the innermost (1 km) grids is about All modeling results presented in this work are 250 km × 250 km and together they cover practically based on simulations with the version 3.2 of the all of continental Chile. The simulation period cho- WRF mesoscale model (Skamarock et al. 2008). sen was the year 2010. In general, the specific model The WRF Model was, and remains today, perhaps settings were chosen based on sensitivity testing and

2084 | OCTOBER 2018 Unauthenticated | Downloaded 10/10/21 07:22 PM UTC evaluation against the newly available field data, or other desert regions worldwide. Interestingly, les- to reduce computational burden. Although many sons learned in configuring the WRF Model for aspects of the model configuration do not deviate the drainage winds over the Atacama Desert aided considerably from WRF standard practice, there are subsequently the modeling of surface wind fields some important departures that deserve mention. over Antarctica, where katabatic winds are common First, a dense vertical spacing of 10 m was applied (Falvey and Rojo 2016). below the first 100 m above the model surface. This was found to be very important to correctly resolving Measurement sites. Guided by the modeling results de- the often-complex vertical structure of the nocturnal scribed above, a series of wind measurement campaigns flow regime over the Atacama Desert characterized in northern Chile started in 2009, originally funded by by the presence of very strong low-level jets [see the the German International Cooperation Agency (GIZ). “Nocturnal drainage flows along valleys” sidebar and The most common installation configuration consists Muñoz et al. (2013)]. Needless to say, a high vertical of a 20-m mast erected on a concrete foundation and resolution in the lowest levels is also of great practical guyed to three lateral supporting points. Wind speed importance for wind prospecting applications. After measurements are performed at 10 and 20 m AGL, considerable sensitivity testing, the use of the quasi- while a wind vane registers wind direction at 10 m normal scale elimination (QNSE; Sukoriansky et al. AGL. Depending on the site, additional variables like 2005, 2006) turbulence scheme was chosen based on temperature, relative humidity, and atmospheric pres- its generally better performance for the nighttime sure are also measured. A datalogger records 10-min regime. Given the extreme topographic gradients over averages of the variables and 3-monthly visits allow for the Atacama region, a small time step was required data collection and station checkup. on the outermost domain to ensure numerical stabil- Figure 1 shows the locations of the measurement ity, and a smoothing filter was applied to the terrain sites along with the mean velocity field at 100 m based elevation in regions of particularly steep terrain. The on the currently available 1-km-resolution simulation reduction of both the roughness length and default for 2010. This wind map differs from that available moisture content of the desert surface also proved for the initial prospecting campaign, but the same helpful to better model the near-surface wind, tem- salient features are present in both, and it is easy to perature, and humidity. This is most probably due appreciate how the selected measurement sites cluster to the particularly extreme dryness and absence of around “hot spots” of higher mean speed simulated vegetation over much of the Atacama compared to by the model.

Table 2. Correlation coefficient R of wind speed (WS) averages between measured and modeled values. Nov–Feb (NDJF) are summer months and May–Aug (MJJA) are winter months. Diurnal hours are 0800– 1600 UTC – 4 h and nocturnal hours are 1600–0800 UTC – 4 h. Note asterisk means R is not statistically significant (p value: 0.05). Annual WS NDJF WS MJJA WS Daily Diurnal Nocturnal Daily Diurnal Nocturnal Daily Diurnal Nocturnal a02 0.77 0.79 0.42 0.32 0.22 0.29 0.63 0.53 0.41 a7 0.59 0.73 0.14 0.19 0.21 0.17* 0.43 0.43 0.21 b21 0.71 0.69 0.82 0.70 0.58 0.69 0.68 0.69 0.86 b31 0.75 0.65 0.85 0.75 0.60 0.72 0.65 0.60 0.85 b41 0.80 0.77 0.75 0.48 0.42 0.50 0.81 0.81 0.72 b42 0.69 0.75 0.62 0.48 0.52 0.54 0.73 0.80 0.59 b51 0.78 0.45 0.85 0.72 0.64 0.73 0.80 0.41 0.88 b61 0.73 0.50 0.79 0.67 0.58 0.67 0.73 0.43 0.80 c71 0.77 0.81 0.17 0.36 0.24 0.27 0.54 0.63 −0.10* d01 0.84 0.83 0.77 0.69 0.73 0.59 0.84 0.82 0.81 d02 0.73 0.81 0.55 0.68 0.74 0.55 0.73 0.79 0.55 d04 0.88 0.88 0.83 0.78 0.80 0.72 0.83 0.85 0.76 d05 0.84 0.85 0.78 0.75 0.80 0.67 0.81 0.83 0.75 d09 0.82 0.85 0.71 0.76 0.81 0.66 0.83 0.85 0.73

AMERICAN METEOROLOGICAL SOCIETY OCTOBER 2018 | 2085 Unauthenticated | Downloaded 10/10/21 07:22 PM UTC NOCTURNAL DRAINAGE FLOWS ALONG VALLEYS

ne of the more interesting features amount of observational and model been studied by Muñoz et al. (2013) Oof the low-level circulation over data were available, including those and Jacques-Coper et al. (2015). The the Atacama Desert is the presence of taken at the 80-m prospecting tower WRF Model simulations have also particularly strong nocturnal drainage at Sierra Gorda and vertical wind proven quite successful at capturing flows with hourly averaged down-valley profiles from a sodar instrument that the salient features of the jets, includ- speeds reaching up to about 20 m s−1 was deployed specifically to verify ing their spatial pattern, jet height, during the cold season. The strongest the existence of the nocturnal jets. and temporal variability. Indeed, the of the jet systems are found in broad, Both observations and models show presence of the nocturnal flows was gently sloping valleys that extend from that the flows are concentrated along largely unknown before the wind en- the Andes to the lower plains of the the central axis of the valley with the ergy prospecting initiative began, and Atacama Desert in the vicinity of the nighttime flow direction coinciding the placement of measurement sites at mining town of Calama, and are of with the terrain slope. The verti- the primary jet locations was a direct great interest both as potential sites cal wind profile within the jet region result of the guidance provided by the for wind farms and also for the trans- shows a nose-shaped structure with WRF Model simulations. An animation port and dispersion of air pollutants a mean nocturnal maximum close to of surface winds model results for a produced by mining operations. 10 m s−1, although wind speeds may 4-day case of drainage flows in the Figure SB2 shows representa- reach up to 20 m s−1 on occasion. The same region is provided in the online tive examples taken from the period observed characteristics of these wind supplemental material (https://doi 16–23 April 2010, during which a large regimes and their predictability have .org/10.1175/BAMS-D-17-0019.2).

Fig. SB2. Observed and simulated data in the Sierra Gorda valley (SGORD site in Fig. 1). All three plots are based on observed and modeled data from 15 to 24 Apr 2010, a period during which several meteorological stations were deployed in the valley, including the SGORD 80-m mast and a sodar vertical wind profiler that was installed near the SGORD site specifically to verify the presence of the low-level nocturnal wind maximum. (top left) The 10-min observed (red circles) and hourly modeled winds at 40-m height at the SGORD station. The gray bands highlight the nighttime periods (0000–0800 LT). (right) The mean nocturnal vertical wind profiles derived from the 80-m meteorological tower (red dots), the sodar (green line, measurements at 10-m inter- vals), and from a high-vertical-resolution WRF Model simulation that was run for this observing period (blue line). There was significant missing data above the jet region in the sodar dataset and the shaded area denotes the fraction of missing sodar measurements as a function of altitude. (bottom left) A 3D visualization of the mean nocturnal wind velocity (40 m) overlaid onto the 3D shaded topography of the Sierra Gorda valley. The wind velocity is represented by arrows pointing in the mean flow direction with length and colors associated with the wind speed. An animation of the model results for a 4-day case of drainage flows in the same region is provided in the online supplemental material.

2086 | OCTOBER 2018 Unauthenticated | Downloaded 10/10/21 07:22 PM UTC Fig. 4. Screenshot of the website hosting the observational database named Campaña de medición del recurso Eólico y Solar (Measurement campaign of solar and wind energy resources). The site includes (top left) a list of the stations, (top right) a location map, (bottom left) location information for the stations, and (bottom right) an instrument register table. Additionally, all metadata are available for each station, and the original raw data and consolidated files are available via download links (arrowed boldface blue words).

Over time, five main zones of interest have been as will be shown later, several measuring sites have investigated: zone A close to the mouth of the Loa completed more than five years of operation. In paral- River, zone B along the Loa River valley near the mining lel, seven solar radiation monitoring sites were also towns of Calama and Sierra Gorda, zone C to the east maintained over the same period, each having wind of the city of Antofagasta, zone D on an elevated plain measurements typically at 5 m AGL. Currently, there to the northwest of the coastal town of Taltal, and the are eight zone D stations and four solar monitoring sites high-altitude zone E in the Chilean Altiplano, at nearly active in the Atacama Desert region. Together with the 4,500 m MSL (Fig. 1). Motivated by the promising winds modeling results, all these observation sites are provid- found in some of the zones, six high instrumented tow- ing a more complete picture of the wind climatology ers (60/80 m) were erected during 2010–12 in zones B over the region (see sidebar “Examples of circulation and D. Although all sites have minimum measuring features over the Atacama Desert”). periods of one year, many have been discontinued or relocated based upon budget availability, analysis of the Validation of model results. The previous sections have wind data, and changes in energy policies. Nonetheless, alluded to the good performance of the WRF Model

AMERICAN METEOROLOGICAL SOCIETY OCTOBER 2018 | 2087 Unauthenticated | Downloaded 10/10/21 07:22 PM UTC Table 3. Summary of the meteorological variables measured and their measurement heights (values in meters) available for 45 observation sites in the Atacama Desert. Station Wind Wind Relative Air Atmospheric Radiation ID speed direction humidity temp pressure a11 20, 10 10 5 5 — — a02 20, 10 10 5 5 — — a31 20, 10 10 5 5 — — a06 20, 10 10 5 5 5 — a7 20, 10 10 5 5 — — b21 20, 10 10 5 5 — — b21a 20, 10 10 5 5 — — b21b 20, 10 10 5 5 — — b31 20, 10 10 5 5 5 — b31a 20, 10 10 5 5 — — b04 20, 10 10 5 5 — — b41 20, 10 10 5 5 5 5 b42 20, 10 10 5 5 — — b51 20, 10 10 5 5 5 5 b61 20, 10 10 5 5 — — b08 20, 10 10 5 5 — — c11 20, 10 10 5 5 5 5

20-m towers c71 20, 10 10 5 5 5 − c8 20, 10 10 5 5 5 5 d01 20, 10 10 5 5 — — d02 20, 10 10 5 5 5 — d04 20, 10 10 5 5 — — d05 20, 10 10 5 5 5 5 d05a 20, 10 10 5 5 — — d05b 20, 10 10 5 5 — — d06 20, 10 10 5 5 — — d08 20, 10 10 5 5 — — d09 20, 10 10 5 5 — — d10 20, 10 10 5 5 5 5 e01 20, 10 10 5 5 — — e02 20, 10 10 5 5 — 5 T80CN 80, 60, 40, 20, 10 80, 40, 10 2 80, 40, 2 2 2 T80CO 80, 60, 40, 20, 10 80, 40, 10 2 80, 40, 2 2 2 T80TT 80, 60, 40, 20 60 2 40, 10, 2 2 2 SGORE 80, 60, 40, 20, 10 80, 40, 10 2 80, 40, 2 2 2 towers 60/80-m 60/80-m SGORD 80, 60, 40, 20 60 2 40, 10, 2 2 2 ARMAZ 50, 40, 20, 10 58, 18 — 3 2 — Crucero2 12.6 12 2 2 — 2 ARMA 4 — 2 2 — 2 CRUC 4 — 2 2 — 2 SLAR 4 — 2 2 — 2

sites PALM 4, 2 — 2 2 — 2 CAMA 4, 5 — 2 2 — 2

Solar monitoring Solar monitoring PANG 4 — 2 2 — 2 SPED 4, 2.5 — 2 2 — 2

for site selection in northern Chile. In this section the definitive model configuration described in the we provide a brief quantitative description of model “Modeling description” section. The model skill in performance, focusing on both spatial and temporal predicting the spatial pattern of the wind field is as- variability. The model results are those obtained from sessed by comparing the annual wind speed means

2088 | OCTOBER 2018 Unauthenticated | Downloaded 10/10/21 07:22 PM UTC obtained by the model and the observations, as shown in Fig. 3. With a small bias, a determination coefficient of 0.85, and a root-mean- square error of 0.71 m s−1, the model is clearly able to detect regions with higher and lower mean wind speeds. The evaluation of the time variability of the WRF wind speed simulation is assessed by the correlation coefficients presented in Table 2, which were computed for concurrent models and observations in terms of daily, diurnal, and nocturnal averages, and for the full 2010 year, as well as distinguishing between the warm and cold seasons. Stations closer to the coast in zones A and C, which have strong diurnal and annual cycles in wind speed, show larger correlation coefficients for the diurnal phase at the annual scale. In contrast, zone B stations subject to a strong drainage wind regime (see the “Noc- turnal drainage flows along valleys” sidebar ) tend to show the largest correlations in the nocturnal phase during the cold season, when these winds are stronger. Finally, stations in zone D show relatively high correlations that change little between the diurnal and nocturnal phases.

Fig. 5. Time periods with wind data availability for selected sites in the ob- PRODUCTS DESCRIP- servational database. When 20-m data were nonexistent, other heights were TION. Observations considered, such as 12, 10, and 4 m AGL. database. The observational database compiled in the projects described earlier Desert, as shown in Fig. 1 (31 towers of 20-m height, is publically available at http://walker.dgf.uchile.cl six high towers, and eight solar radiation stations). /Mediciones/ (Fig. 4), and includes data for a total As of July 2017, the program maintains eight active of 83 stations over the entire country. Figure 5 and 20-m towers in zone D, two 20-m towers at the coast Table 3 show the measuring periods and variables around 30° and 31°S (operating since 2006), and four registered at the 45 stations located over the Atacama solar sites. For every station, the website provides

AMERICAN METEOROLOGICAL SOCIETY OCTOBER 2018 | 2089 Unauthenticated | Downloaded 10/10/21 07:22 PM UTC Fig. 6. Screenshot of the EE. This example shows the main capabilities of the site. An interactive map allows the display of mean wind fields for user-defined time periods and altitudes. In the example shown, the mean 45-m wind field at 0400 UTC – 4 h during Aug 2010 is shown and the zones where strong katabatic low-level jets form may be clearly distinguished. By clicking on the map, users may request quick-look plots (the VISOR) showing climatological profiles (in the example, monthly mean vertical profiles at a nocturnal jet location have been selected) or download a wind resource assessment report along with data files containing the complete hourly data.

access to the original raw data as well as to consoli- The EE is an Internet-based tool that provides dated files in Excel and ASCII formats. Metadata rapid and free access to the results of the WRF simula- are also available in the form of installation reports, tions described in the “Modeling description” section visit logs, instrument descriptions, and calibration above (Fig. 6). Unlike many wind atlas products, the certificates when available. Current developments EE aimed to provide not just static wind maps, but involving the database include 1) a detailed qual- full and dynamic access to the underlying time series ity control revision of the data, eliminating and of wind data. Such data are necessary to provide a documenting periods with suspicious data and 2) genuine understanding of the behavior of the wind the generation of figures for each site, providing a resource at sites of interest. rapid depiction of the climatological regime revealed The EE interface is centered around an interactive by these measurements (diurnal and annual cycles, map over which wind data may be displayed. Tools are wind and direction distributions, etc.). provided that allow users to select locations on the map and subsequently bring up rapid visualizations Modeling products. Although the initial intention of of key wind parameters, extract detailed site reports, the WRF simulations was to guide site selection for and download data files with complete hourly data. wind prospecting in the northern region of Chile, Additional capabilities include calculating wind an important by-product of these efforts was the turbine generation from a public domain database of development of the so-called Explorador Eólico (EE; over 600 turbine types and an experimental statistical Wind Energy Explorer), which has since proved to be reconstruction method for estimating long-term vari- a valuable tool for wind energy prospection, resource ability based on correlating the WRF results for 2010 assessment, policy making, and outreach. with large-scale variables from the National Centers

2090 | OCTOBER 2018 Unauthenticated | Downloaded 10/10/21 07:22 PM UTC for Environmental Prediction–National Center for technical assistance from international agencies. Atmospheric Research (NCEP–NCAR) reanalysis, While coordinating the interaction of all these and then using the relationships to reconstruct winds different organizations has not always been easy, over an extended time period (1980–2016). Since its the experience has been replicated with different inception in 2012, the EE has supported a growing nuances to explore Chile`s solar, hydroelectric, and user base (since January 2016, an average of 46 ses- tidal renewable energies (Santana et al. 2014). The sions each day) and has generated more than 70,000 publicly available products of all these efforts, in- site resource assessment reports. Work is currently cluding measurements and model results (accessible under way to implement a new, more modern interface online at www.energia.gob.cl/energias-renovables), for the EE and to add new WRF simulations that have have been in part responsible for the increasing share recently been performed for the 2015 calendar year. of nonconventional renewable energy sources in Chile’s energy matrix. CONCLUSIONS. The program described herein has demonstrated synergistic interactions along ACKNOWLEDGMENTS. This article was sup- several axes. Considering a modeling/measurement ported by the Chilean Ministry of Energy under axis, for example, the fact that the location of most Exempt Decrees 348 of 2015 and 645 of 2017. The of the prospecting sites was initially decided based measurement campaigns in northern Chile and the mainly on numerical model results, and later the Wind Energy Explorer were partially funded by the observed data helped in validating and improving German International Cooperation (GIZ), the Chil- the model configuration, closes a virtuous circle that ean National Energy Commission (pre-2010), and the is not frequently seen at this scale in South America. Chilean Ministry of Energy (post-2010). The authors The projects have also demonstrated clear synergies appreciate the comments and suggestions provided between applied and scientific research. While the by two anonymous reviewers. projects were driven by a very concrete and applied objective related to fostering wind energy development, it has produced a public database that is al- REFERENCES ready being used in academic pursuits (Rondanelli Canales, C. C., 2011: Barriers elimination for the use et al. 2015; Watts et al. 2016). The scientific use of of renewable energies as electricity sources in rural these observational and modeling results has led to areas (in Spanish). Ministry of Energy Final Rep., the documentation of strong drainage winds along Project GEF-Ministerio de Energía-PNUD CHI/00/ valleys traversing the Atacama Desert (Muñoz et al. G32 (11799), 151 pp. [Available from the authors.] 2013; Jacques-Coper et al. 2015), while the interest- Carvalho, D., A. Rocha, M. Gomez, and C. Santos, ing wind regime in the Taltal region may deserve a 2014: WRF wind simulation and wind energy more detailed study in the near future as well. Re- production estimates forced by different reanaly- cent wind energy developments in northern Chile, ses: Comparison with observed data for Portugal. on the other hand, provide a clear demonstration Appl. Energy, 117, 116–126, https://doi.org/10.1016/j that the applied objective of the projects is being .apenergy.2013.12.001. accomplished: based on the model results and the CERE, 2005: Improvement of knowledge and adminis- observations in zone D, the Chilean government has tration of wind information in Chile: Second stage reserved close to 300,000 Ha of public land for wind (in Spanish). Centro de Estudio de los Recursos En- farm developments, the first of which, with 99-MW ergéticos Tech. Rep., University of Magallanes, Punta nominal capacity, has been supplying energy to the Arenas, Chile, 74 pp., www.cne.cl/archivos_bajar national electric grid since 2014 and has become one /INFORME_FINAL_CERE_para_CNE_2005 of the wind farms with the best performance indices _after_DMC2.pdf. in the country. Other wind energy projects have also DGF, 1993: EOLO Project: Evaluation of the national been developed in the Calama and Sierra Gorda wind potential (in Spanish). Dept. of Geophysics areas (zone B), and several more are in the planning Tech. Rep., University of Chile, Santiago, Chile, 159 stages elsewhere. Finally, the projects also provide a pp., www.jimic.cl/appv/Congreso/Proyecto_EOLO compelling example of a fruitful interinstitutional /INFORME/. collaboration because they were led by governmen- Emeis, S., 2013: Wind Energy Meteorology. Springer, tal agencies, received support for the planning and 196 pp. execution of the observations and modeling by Falvey, M., and P. Rojo, 2016: Application of a re- university units, and benefited from financial and gional model to astronomical site testing in western

AMERICAN METEOROLOGICAL SOCIETY OCTOBER 2018 | 2091 Unauthenticated | Downloaded 10/10/21 07:22 PM UTC Antarctica. Theor. Appl. Climatol., 125, 841–862, 158 pp., www.minenergia.cl/archivos_bajar/Estudios https://doi.org/10.1007/s00704-016-1794-x. /Potencial_ER_en_Chile_AC.pdf. Jacques-Coper, M., M. Falvey, and R. C. Muñoz, 2015: Skamarock, W. C., and Coauthors, 2008: A description Inter-daily variability of a strong thermally-driven of the Advanced Research WRF version 3. NCAR wind system over the Atacama Desert of South Tech. Note NCAR/TN-475+STR, 113 pp., https:// America: Synoptic forcing and short-term predict- dx.doi.org/10.5065/D68S4MVH. ability using the GFS global model. Theor. Appl. Storm, B., J. Dudhia, S. Basu, A. Swift, and I. Giammanco, Climatol., 121, 211–223, https://doi.org/10.1007 2009: Evaluation of the Weather Research and Fore- /s00704-014-1231-y. casting Model on forecasting low-level jets: Impli- Kusiak, A., 2016: Renewables: Share data on wind energy. cations for wind energy. Wind Energy, 12, 81–90, Nature, 529, 19–21, https://doi.org/10.1038/529019a. https://doi.org/10.1002/we.288. Mattar, C., and D. Borvarán, 2016: Offshore wind Sukoriansky, S., B. Galperin, and I. Staroselsky, 2005: power simulation by using WRF in the central coast A quasinormal scale elimination model of turbu- of Chile. Renewable Energy, 94, 22–31, https://doi lent flows with stable stratification. Phys. Fluids, 17, .org/10.1016/j.renene.2016.03.005. 085107, https://doi.org/10.1063/1.2009010. Molina, A., M. Falvey, and R. Rondanelli, 2017: A solar —, —, and V. Perov, 2006: A quasi-normal scale radiation database for Chile. Sci. Rep., 7, 14283, elimination model of turbulence and its application to https://doi.org/10.1038/s41598-017-13761-x. stably stratified flows. Nonlinear Processes Geophys., Muñoz, R. C., M. J. Falvey, M. Araya, and M. Jacques- 13, 9–22, https://doi.org/10.5194/npg-13-9-2006. Coper, 2013: Strong down-valley low-level jets over Uchida, T., and Y. Ohya, 2008: Micro-siting technique the Atacama Desert: Observational characterization. for wind turbine generators by using large-eddy sim- J. Appl. Meteor. Climatol., 52, 2735–2752, https://doi ulation. J. Wind Eng. Ind. Aerodyn., 96, 2121–2138, .org/10.1175/JAMC-D-13-063.1. https://doi.org/10.1016/j.jweia.2008.02.047. Nawri, N., G. Nína, H. Bjornsson, A. Hahmann, UNTEC, 2003: Improvement of the knowledge on K. Jonasson, C. Hasager, and N. Clausen, 2014: The wind resources in northern and central Chile (in wind energy potential of Iceland. Renewable En- Spanish). Foundation for Technological Transfer ergy, 69, 290–299, https://doi.org/10.1016/j.renene (UNTEC) Tech. Rep., University of Chile, Santiago, .2014.03.040. Chile, 131 pp., www.cne.cl/archivos_bajar/Informe Rondanelli, R., A. Molina, and M. Falvey, 2015: The FinalEolo2003.pdf. Atacama surface solar maximum. Bull. Amer. Me- —, 2008a: Selection of sites of interest for wind teor. Soc., 96, 405–418, https://doi.org/10.1175/BAMS prospection in the northern electric grid region: -D-13-00175.1. Stage 1 (in Spanish). Foundation for Technological Rutllant, J. A., R. C. Muñoz, and R. D. Garreaud, Transfer Tech. Rep., University of Chile, Santiago, 2013: Meteorological observations on the northern Chile, 98 pp. [Available from the authors.] Chilean coast during VOCALS-Rex. Atmos. Chem. —, 2008b: Selection of sites of interest for wind Phys., 13, 3409–3422, https://doi.org/10.5194/acp-13 prospection in the northern electric grid region: -3409-2013. Stage 2 (in Spanish). Foundation for Technological Sanderse, B., S. P. Van der Pijl, and B. Koren, 2011: Re- Transfer Tech. Rep., University of Chile, Santiago, view of computational fluid dynamics for wind tur- Chile, 155 pp. [Available from the authors.] bine wake aerodynamics. Wind Energy, 14, 799–819, Watts, D., N. Osses, and R. Pérez, 2016: Assessment https://doi.org/10.1002/we.458. of wind energy potential in Chile: A project-based Santana, C., M. Falvey, M. Ibarra, and M. García, regional wind supply function approach. Renew- 2014: Renewable energies in Chile: Wind, solar, and able Energy, 96, 738–755, https://doi.org/10.1016/j hydroelectric potentials from Arica to Chiloé (in .renene.2016.05.038. Spanish). Ministry of Energy Rep., Santiago, Chile,

2092 | OCTOBER 2018 Unauthenticated | Downloaded 10/10/21 07:22 PM UTC