Analysis of WRF Model Wind Estimate Sensitivity to Physics Parameterization Choice and Terrain Representation in Andalusia (Southern Spain)

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F. J. SANTOS-ALAMILLOS,D.POZO-VA´ ZQUEZ,J.A.RUIZ-ARIAS, V. LARA-FANEGO, AND J. TOVAR-PESCADOR Physics Department, University of Jaen, Jaen, Andalusia, Spain

(Manuscript received 23 July 2012, in ﬁnal form 24 February 2013)

ABSTRACT

This paper reports on an evaluation of the relative roles of choice of parameterization scheme and terrain representation in the Weather Research and Forecasting (WRF) mesoscale model, in the context of a regional wind resource assessment. As a first step, 32 configurations using two different schemes for microphysics, cumulus, planetary boundary layer (PBL), or shortwave and longwave radiation were evaluated. In a second step, wind estimates that were obtained from various experiments with different spatial resolution (1, 3, and 9 km) were assessed. Estimates were tested against data from four stations, located in southern Spain, that provided hourly wind speed and direction data at 40 m above ground level. Results from the first analysis showed that wind speed standard deviation (STD) and bias values were mainly sensitive to the PBL parameterization selection, with STD differences up to 10% and bias differences between 215% and 10%. The second analysis showed a weak influence of spatial resolution on the STD values. On the other hand, the bias was found to be highly sensitive to model spatial resolution. The sign of the bias depended on terrain morphology and the spatial resolution, but absolute values tended to be much higher with coarser spatial resolution. Physical configuration was found to have little impact on wind direction distribution estimates. In addition, these estimates proved to be more sensitive to the ability of WRF to represent the terrain morphology around the station than to the model spatial resolution itself.

1. Introduction a comprehensive description of the atmospheric system, using initial and boundary conditions from atmospheric Reliable wind resource assessment is a key issue in the reanalysis. On the basis of dynamical downscaling tech- evaluation of wind energy potential and in studies re- niques, current NWP models resolve local and regional lated to integration of wind energy electricity produc- circulation patterns, taking into account local topo- tion (GE Energy 2010). Such wind resource assessment graphic surface features to a certain extent. Thus, these can be divided into two steps (Sempreviva et al. 2008)— models can provide accurate meteorological informa- a regional wind assessment, which evaluates resources of tion, such as wind speed and direction, over large regions a region as a first approach, and a site-specific assess- at high temporal (a few minutes) and spatial (a few ment, which is a detailed evaluation of specific areas that kilometers) resolution. exhibit high potential in the regional assessment. At this Despite the high resolution of current NWP models time, regional wind assessment is mostly based on nu- (;km), there are still two main problems in the esti- merical weather prediction (NWP) models (Frank et al. mation of surface meteorological variables, especially 2001; Landberg et al. 2003; Lavagnini et al. 2006; Zagar near-surface wind speed. These problems are 1) repre- et al. 2006; Kanamitsu and Kanamaru 2007; Byrkjedal sentation of topography and terrain morphology in the and Berge 2008; Jimenez et al. 2007; Hahmann et al. modeled domains and 2) representation of the numer- 2010; Shimada and Ohsawa 2011). NWP models facilitate ous physical processes at subgrid scale. Topography and terrain morphology have a marked influence on surface meteorological variables, especially Corresponding author address: D. Pozo-Va´zquez, Physics De- partment, University of Jaen, Campus Las Lagunillas s/n Edif A3, on surface wind speed and direction. Surface winds re- E23071, Jaen, Spain. sult from interaction between mesoscale circulation and E-mail: [email protected] other more local factors, many of which are related to

DOI: 10.1175/JAMC-D-12-0204.1

Ó 2013 American Meteorological Society Unauthenticated | Downloaded 10/05/21 11:39 AM UTC JULY 2013 S A N T O S - A L A M I L L O S E T A L . 1593 topographic characteristics (elevation, aspect, and slope) evaluation of different parameterization versions of the and terrain morphology (hills, valleys, and others). First, various schemes, for the given study area and applica- wind speed changes with elevation in the planetary tion, is advisable. boundary layer (PBL) as a result of reduced turbulent The WRF (Skamarock et al. 2008) model is one of drag. Second, terrain features can accelerate wind flow. the most widely used NWP models, being a reference As it passes a summit, an approaching air mass is often for regional wind resource assessment. Over recent squeezed into a thinner layer and therefore speeds up. decades, many different parameterizations have been Aside from elevation, terrain morphology (terrain aspect proposed for this model, especially for the PBL. PBL and slope) can also modify wind flow. Over a ridge, there parameterizations are responsible for vertical subgrid- is maximum acceleration when wind blows perpendicular scale fluxes, allowing eddy transports through the entire to the ridge line. Terrain morphology is also important; atmospheric column. Wind distribution is affected by wind flow accelerates through passes along valleys factors such as PBL height, wind shear, and entrainment aligned with the flow (channeling). In a similar way, of free atmospheric air into the PBL, which determines topography may produce local areas of reduced wind momentum, heat, and moisture exchanges at the top of speed, such as sheltered valleys and areas in the lee of the layer (Arya 2001). These processes may vary con- a mountain ridge. Last, it is also well known that in siderably depending on, for instance, the study region or complex-topography areas strong horizontal tempera- spatial and temporal scales. As a consequence, many ture gradients, caused by the sloping terrain, generate authors have reported strong sensitivity of WRF (and mountain and valley wind regimes. If the study region is other NWP) surface wind estimates to PBL parame- composed of flat and homogeneous land, Weather Re- terization, in many areas of the world: the United States search and Forecasting model (WRF) terrain repre- (Zhang and Zheng 2004; Watson et al. 2009; Hu et al. sentation may be accurate enough to provide reliable 2010; Gibbs et al. 2011), South Korea (Kim et al. 2005; near-surface wind estimates. This representation, how- Kwun et al. 2009), Spain (Perez-Landa et al. 2007; Borge ever, may provide unreliable surface wind estimates in et al. 2008), and Sweden (Miao et al. 2008). complex-topography areas, even using high spatial res- For other NWP parameterizations, the influence of olutions. This issue is usually addressed in specific site selected schemes on surface wind speed estimates, al- assessment, using microscale models such as the Wind though less important, could be significant. For instance, Atlas Analysis and Application Program (WAsP; MPH schemes include explicitly resolved water vapor, Mortensen et al. 1998). This type of model provides site- cloud, and precipitation processes. According to Rajeevan specific wind estimates that are based on terrain in- et al. (2010), cloud microphysical processes are important formation and wind measurements from nearby stations because of their direct influence on cold-pool strength and are at very high spatial resolution (;100 m). Wind (from rainfall evaporation) and latent heating (from estimates from WAsP are constrained by the availability condensation). Therefore, MPH processes can affect of local measurement data and by the complexity of the wind fields close to the surface. SWR parameterizations study area. In the last few years, several methods have include visible and near-visible wavelengths, whereas been proposed for using NWP wind estimates as input to LWR parameterizations include infrared or longwave microscale models like WAsP (Frank et al. 2001; Berge radiation absorbed and emitted by gases and surfaces. In et al. 2007). complex topography, differences in surface radiation Physical subgrid processes are taken into account in generate important thermal contrasts that in turn create NWP models by the use of parameterization schemes. In local wind regimes. CMS schemes represent subgrid- particular, most models include schemes to account for scale effects of convective and/or shallow clouds. These cloud microphysics (MPH), cumulus formation (CMS), schemes are mainly intended to account for vertical PBL processes, surface-layer physics, land surface layer, fluxes from unresolved updrafts and downdrafts, and or shortwave and longwave radiation (SWR and LWR, compensating motions outside clouds. CMS parame- respectively). Stensrud (2007) presents some of these terization schemes are largely evaluated for precip- parameterizations. In the last decades, many different itation estimates (Kotroni and Lagouvardos 2001), parameterization schemes have been proposed. Their and literature focusing on wind estimates is scarce validity is highly dependent on the study region, season (Cohen 2002). Clouds have a significant effect on PBL of the year, and spatial and temporal scales of interest, processes, however, affecting hydrologic processes however. In fact, given a spatial and temporal configu- through redistributions of sensible and latent heats ration, the skill of a model mainly depends on these and momentum (Arakawa 2004). Therefore, different physical parameterizations (Lynn et al. 2004; Fernańdez CMS parameterization schemes may produce varying et al. 2007). As a consequence, a prior performance surface wind estimates.

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FIG. 1. Location (inset at bottom left) and principal topographic and geographic characteristics of the study region. The locations of the four validation stations are highlighted with crosses. Grayscale colors indicate elevation above sea level as based on a 90-m-resolution DEM. The scale at the right indicates elevation in meters above sea level.

Some authors (Jimenez and Dudhia 2012) have re- conditions that make the evaluation especially chal- ported a positive bias in WRF wind speed estimates over lenging. Estimates are tested against data from four 2 plains and valleys [about 1 m s 1at 10 m above ground stations that provide hourly wind speed and direction level (AGL)] and a negative bias over hills and mountains data at 40 m AGL. Station locations are representative 2 (about 23ms 1 at 10 m AGL). The main reason for the of different topographic and climatic conditions, thus positive bias seems to be unresolved topographic fea- allowing a comprehensive evaluation of the WRF wind tures, such as hills or mountains in complex terrain. These estimates. can produce additional drag, which is not parameterized To sum up, the main objectives of this work are 1) to in the WRF, in addition to the drag generated by vege- obtain a general model setup to be used for a long in- tation (Beljaars et al. 2004; Jimenez and Dudhia 2012). tegration to be used toward evaluating wind resources in The magnitude of this bias is related to the differences the study region, 2) to evaluate the extent to which this between the actual and WRF-modeled station elevations. single setup can provide accurate wind estimates in This work evaluates the relative roles of terrain rep- a region that is characterized by strongly variable cli- resentation and choice of parameterization schemes in mate conditions, and 3) to assess the extent to which an the WRF as they relate to regional wind resource as- increase in WRF spatial resolution gives better esti- sessment. To evaluate the sensitivity of WRF wind es- mates in a region that is characterized by the existence of timates to the choice of parameterization schemes, we areas with complex topography. first evaluated 32 physical configurations resulting from The work is organized as follows: section 2 describes combination of two different PBL, MPH, CMS, SWR, the study region, terrain characteristics, and data. Section 3 and LWR parameterization schemes. For each scheme, details the methods, and section 4 gives the results. we evaluated one widely used parameterization and The main conclusions are presented in section 5. a new one available in the recent WRF release (version 3.2; V3.2). Therefore, a by-product of this work is a 2. Study region and data comparative evaluation of the new schemes against the old ones. In a second step, to evaluate sensitivity of the The study region (Fig. 1) of Andalusia is in the south- wind estimates to the terrain representation and mor- ern Iberian Peninsula (IP). It extends over 87 000 km2. phology in the WRF, we also evaluated wind speed and The region (358300–388300N, 78300–18300W) is in a tran- direction estimates obtained from different experiments sition zone between temperate and subtropical climates. at 1-, 3-, and 9-km spatial resolution. From a topographic point of view, the region may be The work applies to the region of Andalusia (southern split into two different parts. The western part, covering Spain). Although relatively small, this region is charac- ;40 000 km2, is a nearly homogeneous flat area that is terized by varied topographic, geographic, and weather around 100 m MSL. The eastern part has very complex

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TABLE 1. The main topographic characteristics at the station locations. Displayed are elevations at station locations from a 90-m-spatial- resolution DEM and from the WRF model using three different spatial resolutions (9, 3, and 1 km). The standard deviation of elevation (denoted as s) and the relief (denoted as mde) parameters are also shown for each station. Both parameters were derived from the DEM and from the 1-km-spatial-resolution WRF domain and were computed over an area of 3 km 3 3 km centered at each station location. All values are presented in meters.

Station Elev DEM Elev WRF, 9 km Elev WRF, 3 km Elev WRF, 1 km s DEM mde DEM s WRF 1 km mde WRF 1 km Centenar 236 229 253 253 17 152 13 36 Gomera 640 405 464 515 67 310 49 178 Almirez 1195 1052 1178 1182 58 270 55 215 Los Nietos 1056 1262 1013 1055 38 158 33 108 topography with several mountain areas, reaching were furnished by the wind division of Gamesa Corpo- 3482 m MSL in Sierra Nevada National Park (the highest ration and include hourly-averaged wind speed and di- elevation on the IP). rection data measured at 40 m AGL for 16 days in 2005. The atmospheric circulation over most of the IP is The 16 days at each station were selected as represen- dominated by a semipermanent, subtropical high pres- tative of different periods in that year—data from 2 to 5 sure center over the Azores Islands, particularly so in February, from 9 to 12 April, from 28 to 31 August, and the south part of the study region. The position and in- from 7 to 10 October were used to represent winter, tensity of this center changes throughout the year. spring, summer, and autumn, respectively. We conducted During winter, the high is at lower latitudes, allowing the a quality-control procedure that was based on Jimenez region to be affected by zonal circulations from the west. et al. (2010) and found no suspicious data. We used During summer, the high migrates northward, blocking wind data collected at 40 m AGL for the evaluation off westerly circulations over most of the IP and par- rather than the much more common wind data at 10 m ticularly over the study region (Pozo-Va´zquez et al. AGL that are measured at most meteorological sta- 2001; Trigo et al. 2002; Castro-Dıez et al. 2002). Al- tions. This procedure gives the results greater repre- though the entire region is under the influence of the sentativeness for regional wind resource assessment, Azores subtropical high, it has a wide range of climate mainly because wind speeds measured at the lower conditions. This range is related to topographic fea- elevation are frequently influenced by local features tures. The eastern part is less influenced by the Azores near the meteorological stations (buildings, vegetation, high and is more affected by the Mediterranean Sea and others), which are definitely not captured in an (Esteban-Parra et al. 1998; Rodrıguez-Puebla et al. 1998; NWP model. Ramos-Calzado et al. 2008). The western part is the The areas containing the four masts have very dif- lower Guadalquivir River basin. It is open to the At- ferent terrain complexity and morphology. Since these lantic Ocean and to direct influence by atmospheric flow factors have a marked influence on surface wind speed controlled by the Azores high. The Guadalquivir valley and direction, a topographic characterization was at- is exposed to mild and moist air from the Atlantic year- tempted. This characterization is important to the dis- round except during summer. Winds are channeled cussion of the evaluation result in section 4. Many through this valley, generating strong mean surface wind different parameters for characterizing terrain com- speeds in the upper valley (Alsamamra 2009). The plexity have been defined in the literature (Huaxing eastern part is partially isolated from this Atlantic in- 2008). In this work, we consider two types of parameters: fluence by the Sierra Nevada and Cazorla Mountain the standard deviation of elevation (denoted as s) and ranges (Fig. 1). Annual precipitation over the region the difference between the highest and lowest elevation 2 consequently ranges from 300 mm yr 1 in coastal, semi- in a specific region (denoted as mde). We derived both 2 desert southeast areas to more than 2500 mm yr 1 in parameters from a 90-m-spatial-resolution digital ele- mountains near the Strait of Gibraltar (Ramos-Calzado vation model (DEM; Farr et al. 2007). In particular, on et al. 2008). In addition, the study region is bounded on the basis of the elevations reported by this DEM, we the south by the Atlantic (western part) and the Medi- computed s and mde on an area of 3 km 3 3 km cen- terranean (eastern part), representing about 900 km tered at each mast location. Both s and mde represent of coastline. One of the most important wind features subgrid-scale topographic complexity at the station lo- is strong, semipermanent winds near the Strait of cations. Table 1 shows these parameters for the four Gibraltar coast. stations, along with station elevations derived from the Validation of WRF estimates was carried out by using DEM and WRF. The terrain topography and morphology data from four meteorological masts (Fig. 1). These data surrounding the stations are represented in Figs. 2 and 3.

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FIG. 2. Topographic maps for the (a) Centenar, (b) Gomera, (c) Almirez, and (d) Los Nietos stations. Each map is 10 km 3 10 km, centered at the station location, and was obtained by using a 10-m-resolution DEM. The scale at the right indicates elevation in meters above sea level.

Figure 2 shows a detailed elevation map around the complexity. In fact, this area has the smallest values of station locations that was obtained from a 10-m-spatial- s and mde (17 and 152 m, respectively) and therefore resolution DEM. Figure 3 also shows elevations maps the least topographic complexity among the analyzed around the stations, derived from a 90-m DEM and the stations (Table 1). Elevation in the WRF integrations WRF domains using the three spatial resolutions (1, 3, ranges from 229 (WRF 9 km) to 253 (WRF 1 km) m MSL, and 9 km). For better understanding of the terrain whereas the elevation in the DEM is 236 m MSL. This morphology, observed wind direction distributions for WRF under/overestimation of station elevation seems to the whole evaluation period (16 days) at the four sta- be associated with the spatial averaging, since there are tions are also shown in Fig. 3. hills near the station (Figs. 2a and 3a). In this case, the Centenar station (Fig. 1) is in the western part of the coarser spatial resolution better represented the terrain study region, relatively close to the Atlantic Ocean. elevation. Nevertheless, as observed in Fig. 3d, terrain Thus, the climatological behavior at this site is strongly morphology is more realistic at 1-km spatial resolution inﬂuenced by mild and humid Atlantic winds through- than at coarser resolutions (Figs. 3b and 3c). Wind di- out the year. The station is atop a hill (Fig. 2a), and rection variability at Centenar is substantially higher than the surrounding area has relatively low topographic at the other stations (Fig. 3e).

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FIG. 3. Terrain morphology and observed wind direction distribution for the four validation stations for the whole study period (16 days). Shown are the (a) elevation map of a 50 km 3 50 km area centered on Centenar station, as derived from 90-m DEM, and the elevation maps used in the (b) 9-, (c) 3-, and (d) 1-km-spatial-resolution WRF setups for the Centenar station, also showing areas of 50 km 3 50 km centered on the station. The scales at the right in (a)–(d) indicate elevation in meters above sea level. (e) Wind direction distribution observed at Centenar station at 40 m AGL. The values in the circle indicate percentage of observations; the color scale at right 2 indicates ranges of wind speed (m s 1). Also shown are plots as in (a)–(e), but for the (f)–(j) Gomera, (k)–(o) Almirez, and (p)–(t) Los Nietos stations.

Gomera station is in the middle of the study region, on second greatest topographic complexity after Gomera the southern boundary of the Guadalquivir valley (Fig. 1). (Table 1). As in the previous case, differences in station The station is also atop a hill (Fig. 2b), at an elevation elevation from the different WRF configurations are of 640 m MSL. The area around the station has the considerable—from 1052 (WRF 9 km) to 1182 (WRF greatest topographic complexity among the analyzed 1 km) m MSL. Morphology of the surrounding topog- stations, as derived from values of s (67 m) and mde raphy clearly dominates the wind direction distribution (310 m) (Table 1). As a consequence and as expected, (Fig. 2o), enhancing the frequency corresponding to the differences in the station elevation from the various southeast and northwest directions. Note that the WRF configurations are considerable—from 405 (WRF strongest winds are mainly from the northwest. Unlike 9 km) to 515 (WRF 1 km) m MSL. In this case, elevation in the previous cases, station elevation (Table 1) and and, of note, terrain morphology are much better rep- surrounding terrain morphology seem reasonably represented by the 1-km WRF integration (Fig. 3i). The resented in both the 3- and 1-km WRF integrations hilltop location and morphology of the surrounding (Figs. 3k, 3m, and 3n). This will be confirmed when an- terrain clearly determine the wind direction distribution alyzing the results in section 4. (Fig. 3j), through enhancing the southeast direction. Los Nietos station is also in the east, at high elevation Almirez station is in the far east of the region (Fig. 1), (1056 m MSL) (Fig. 1), with climate conditions that are on the middle of a plateau of considerable elevation very similar to those of Almirez. The main feature of this (.1000 m MSL), facilitating continental climate condi- station is its location in the middle of a valley, with rel- tions. Station elevation is 1195 m MSL, in the foothills of atively low topographic complexity (Fig. 2d). As a con- a mountain range and near a valley (Fig. 2c). It has the sequence, this station has the second-lowest s and mde

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FIG. 4. Spatial configuration of domains used for numerical simulations: (a) the three nested domains at 27-, 9-, and 3-km spatial resolution for Andalusia (southern Spain) that were used in the first analysis and (b) the four domains centered on Centenar station at 27-, 9-, 3-, and 1-km spatial resolutions that were used in the second analysis. Similar spatial configurations, but centered on the corresponding station locations, were used for the remaining stations. values after Centenar (Table 1). Thus, as expected, direction estimates. In this endeavor, the model config- terrain morphology (Figs. 3p, 3r, and 3s) and station uration included three nested domains, with 27-, 9-, and elevation (Table 1) are reasonably reproduced in both 3-km spatial resolutions (Fig. 4a). Data were ultimately the 3- and 1-km WRF configurations. Wind direction at analyzed for the third nested domain (3 km), centered this station is clearly controlled by the valley effect, and on the study region. A two-way interaction was used in its distribution is the narrowest of the four that were the three domains, with 36 vertical levels, 8 of which analyzed (Fig. 3t). were within the first 1000 m above ground. The vertical To sum up, the four station locations have different levels were configured following Hahmann et al. (2010), climate conditions, topographic complexity, and terrain Draxl (2012), and Horvath et al. (2012). The first five morphology. Los Nietos and Almirez stations are at el- lowest vertical levels—at approximately 4, 18, 43, 64, evations above 1000 m MSL in the mountainous eastern and 76 m above the modeled ground level—were used in part of the study region and therefore have cold con- a vertical interpolation to estimate the wind speed and tinental climates. In contrast, the Centenar station is direction at the measurement elevation (40 m AGL). at relatively low elevation in the eastern Guadalquivir Topography, land use, and land/water-mask datasets River basin, which is an area that is characterized by were interpolated from the U.S. Geological Survey a mild oceanic climate. For topography, the Gomera (USGS) land cover dataset (Loveland et al. 2000), with area has the highest topographic complexity, followed appropriate spatial resolution for each domain (50,10, by Almirez. On the other hand, Los Nietos and, espe- and 3000, respectively). cially, Centenar are in areas of relatively low topographic The second study was on the effect of topography rep- complexity. The effect of terrain morphology near each resentation on the wind speed and wind direction estimates. station is important for wind direction distribution— In this effort, the model configuration included four nested particularly for Gomera, Almirez, and Los Nietos. domains, with 27-, 9-, 3-, and 1-km spatial resolutions. In this case, a one-way interaction was used in all domains. Moreover, as in the previous study, we also used 36 levels 3. Methods for the vertical configuration of the domains. An example The WRF model, version 3.2, was evaluated in this of this spatial configuration is shown in Fig. 4b (Centenar work. Model runs were conducted for the days men- case). Topography, land use, and land/water-mask data- tioned in section 2, and two different spatial configura- sets were interpolated from the USGS land cover, with tions were used, depending on the study. appropriate spatial resolution for each domain (50 and 20 The first study was on the influence of choice of pa- for the domains with 27- and 9-km spatial resolution, rameterization scheme on the wind speed and wind respectively, and 3000 for the 3- and 1-km domains).

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TABLE 2. Schemes used to conﬁgure the 32 experiments for PBL, MPH, CMS, or SWR and LWR parameterizations. The center column shows widely used schemes that were implemented in older versions of the WRF model and the right column shows schemes that are newly implemented in WRF, version 3.2. Here, MYNN is Mellor–Yamada–Nakanishi–Niino.

Parameterization Routine schemes New schemes in WRF V3.2 PBL YSU (P1; Hong et al. 2006) MYNN (P2; Nakanishi and Niino 2004) MPH Morrison-2M (M1; Morrison and Gettelman 2008) Thompson (M2) CMS Kain–Fritsch (C1; Kain and Fritsch 1993) Grell-3D (C2) SWR Goddard (S1; Chou and Suarez 1994) RRTMG_SW (S2) LWR RRTM (L1; Mlawer et al. 1997) RRTMG_LW (L2)

In the first study, 32 physical configurations (Table 2) variations of the PBL parameterization were consid- were evaluated. This evaluation was conducted with a ered, keeping the rest of the parameterizations fixed. 3-km spatial resolution. These configurations were com- This approach considerably reduces the complexity of posed as a combination of two different schemes for the the analysis. In particular, in the second study, we PBL, MPH, CMS, SWR, and LWR parameterizations— evaluated the WRF wind estimates at three different one widely used and referenced scheme (first column in spatial resolutions (1, 3, and 9 km) and used two alter- Table 2) and a new one, available in a recent WRF native PBL parameterizations. In addition, for the last model release (version 3.2; second column in Table 2). domain (1-km spatial resolution) the cumulus parame- Selection of the old schemes for the various physical terization was disabled. configurations was based on previous evaluations of Initial and boundary conditions for the simulation WRF performance in the study region (Fernańdez et al. were generated using the Interim European Centre for 2007; Borge et al. 2008; Ruiz-Arias et al. 2008). Table 3 Medium-Range Weather Forecasts Re-Analysis (ERA- shows the parameterization in each experiment. We Interim; Uppala et al. 2008) with 0.78 spatial resolution note that a computer-code error (‘‘bug’’) in the Yonsei and 6-h time interval. Newtonian relaxation or nudging University (YSU) PBL scheme that was used in this is one method of four-dimensional data assimilation study that affects the simulation of the stable boundary implemented in mesoscale models. This technique aims layer and its consistency with thermal roughness length to relax the model state toward gridded analyses that are has been recently reported (see the WRF V3.4.1 updates based on observations (analysis nudging) or directly at http://www.wrf-model.org). Nevertheless, we think it toward the individual observations (observation nudg- would not affect seriously the results presented in sec- ing). This method is attempted by adding artificial tion 4. As explained in section 4, results of the first study forcing terms to the model predictive equations on the show that the wind estimates were sensitive mainly to basis of the difference between both states weighted the choice of PBL parameterization. As a consequence, by nudge coefficients (Seaman 2000). Several works in for the sake of conciseness in the second study, only the literature have shown that the use of nudging in

TABLE 3. Nomenclature of the 32 physical conﬁgurations evaluated. Experiments E15 and E31, highlighted in boldface type, were used in analysis of terrain representation inﬂuence.

Nomenclature Physical conﬁguration Nomenclature Physical conﬁguration E1 P1-M1-C1-R1-L1 E17 P2-M1-C1-R1-L1 E2 P1-M1-C1-R1-L2 E18 P2-M1-C1-R1-L2 E3 P1-M1-C1-R2-L1 E19 P2-M1-C1-R2-L1 E4 P1-M1-C1-R2-L2 E20 P2-M1-C1-R2-L2 E5 P1-M1-C2-R1-L1 E21 P2-M1-C2-R1-L1 E6 P1-M1-C2-R1-L2 E22 P2-M1-C2-R1-L2 E7 P1-M1-C2-R2-L1 E23 P2-M1-C2-R2-L1 E8 P1-M1-C2-R2-L2 E24 P2-M1-C2-R2-L2 E9 P1-M2-C1-R1-L1 E25 P2-M2-C1-R1-L1 E10 P1-M2-C1-R1-L2 E26 P2-M2-C1-R1-L2 E11 P1-M2-C1-R2-L1 E27 P2-M2-C1-R2-L1 E12 P1-M2-C1-R2-L2 E28 P2-M2-C1-R2-L2 E13 P1-M2-C2-R1-L1 E29 P2-M2-C2-R1-L1 E14 P1-M2-C2-R1-L2 E30 P2-M2-C2-R1-L2 E15 P1-M2-C2-R2-L1 E31 P2-M2-C2-R2-L1 E16 P1-M2-C2-R2-L2 E32 P2-M2-C2-R2-L2

Unauthenticated | Downloaded 10/05/21 11:39 AM UTC 1600 JOURNAL OF APPLIED METEOROLOGY AND CLIMATOLOGY VOLUME 52 a meteorological model leads to improved meteorolog- Direction data have traditionally been analyzed using ical simulations (Stauffer and Seaman 1994; Seaman graphical methods in the literature. In particular, the et al. 1995; Seaman 2000; Otte 2008). The nudging to- comparison of wind direction distributions (wind-rose ward gridded analysis was used in this study, and nudge plots) has been used for gaining an understanding of di- coefficients for temperature and water mixing ratio were rectional characteristics. In this work, however, we wan- 2 2 selected following Otte (2008), that is, 3 3 10 4 s 1 for ted to provide a quantitative measure of the performance 2 2 temperature, 1.0 3 10 5 s 1 for water mixing ratio, and of the wind direction estimates, as in the wind speed case. 2 2 3 3 10 4 s 1 for the horizontal wind components. Since A possibility is the deterministic forecast of multiple the use of analysis nudging in high-resolution domains categories [described in WMO (2000)]. This method and near the surface may inhibit the simulation of im- mainly consists of a categorical comparison that is based portant mesoscale features, the analysis nudging was on a contingency table, which is built by ranking the wind applied only for the first domain (27 km) and only for the direction intervals. From this analysis, several parameters last 15 vertical levels. The simulations were conducted can be derived, such as the reliability, accuracy, and the with a spinup of 24 h and outputs were saved every skill score (WMO 2000). In particular, the accuracy is 10 min, and then horizontal wind components were calculated as the sum of the diagonal of the contingency hourly averaged. matrix, divided by the sum of the whole matrix. This Following the method of Draxl (2012), the model grid parameter has limitations, however, because it could be points that are representative for the station locations were that simulated and observed directions were in different selected by comparing the modeled and actual elevation categories although the circular residual was low. and distance to the coast. To this end, the model elevation To avoid this problem, in this work we propose a new and distance to the coast of all of the grid points inside parameter for analyzing the accuracy of the wind di- a window of 3 3 3 (9) cells centered at each station loca- rection estimates: the directional accuracy (DACC) pa- tion were compared with the corresponding values derived rameter. This parameter is based on the circular distance, from the DEM. For each station, the grid point that min- defined as the smaller of the two arc lengths between two imized the accumulated squared errors was selected. points along a circumference (Jammalamadaka and Validation of the different model configurations was Sengupta 2001). For angles a and b, the circular dis- done on the basis of various statistics for both wind tance is defined as speed and direction. First, we used the standard deviation (STD) of residuals between observed and mod- Du(a, b) 5 min[a 2 b, 36082(a 2 b)]. eled wind speed, which estimates the amount of scatter of the wind speed errors: Then, we define the DACC to account for the percentage of times in which the circular distance between the 2 31/2 å (m 2 o )2 observed and modeled wind direction is lower than 6 i i 7 8 STD 5 4 i 5 or a threshold, chosen as 30 here: N 8 # Du # 8 å1 (if 0 i 30 ) 1/2 i å 2 2 5 0 (else) 3 N (mi oi) DACC 100. 5 i 3 N relative STD å 100, oi i Results presented in section 4 correspond to the whole dataset (16 days). Nevertheless, these results are shown to where N is the number of records and mi and oi are be generally consistent across the four periods (one per modeled and observed wind speed values, respectively. season) of simulations. It is important to mention that this Second, we used the mean error or bias, which quantifies work focused on the influence of physics parameteriza- overall overestimation or underestimation of modeled tion and terrain configuration in the WRF model wind- wind speed estimates: estimate errors. Nevertheless, other sources of errors—such as wind measurements errors, errors associated with the å 2 (mi oi) initialization/boundary conditions, land surface parame- bias 5 i or N ter representation errors, or errors associated with an unresolved horizontal thermal gradient—were not con- å (m 2 o ) i i sidered in the evaluation procedure. In addition, differing 5 i 3 relative bias å 100. nesting parameterization schemes could be another oi i source of discrepancies in model predictions.

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FIG. 5. Relative STD (%) of residuals between observed and modeled wind speed for 32 different experiments at the (a) Centenar, (b) Gomera, (c) Almirez, and (d) Los Nietos stations. The conﬁguration of each experiment can be determined from Tables 2 and 3.

4. Results and discussion lower STD than do experiments E17–E32, which use the P2 PBL scheme. This is confirmed in Fig. 8. In particular, a. Analysis of sensitivity to parameterization choice Fig. 8a shows that use of P1 PBL instead of P2 for Figures 5–7 show, respectively, relative STD, relative Centenar reduces STD by 6% on average (37% vs 43%). bias, and DACC values obtained for the 32 experiments The other stations show smaller STD differences, of less at each station. Figure 8 shows the sensitivity of STD, than 3%, associated with the PBL scheme choice (Figs. bias, and DACC values to the parameterization choice 8b–d). Sensitivity of STD to the other parameterizations for each station. To this end, the figure shows mean is smaller for all stations. Therefore, aside from the PBL, values of these parameters resulting from the 16 ex- there is no clear and systematic effect of parameteriza- periments in which each parameterization was involved. tion selection on STD at any of the four stations. Nev- The STD values (Fig. 5) show considerable differ- ertheless, there are certain combinations of schemes ences among stations and are higher for the Almirez and that perform better than others. These combinations Centenar stations. Almirez shows the highest values, unfortunately vary among stations. For instance, for between 48% and 55% for all experiments. In contrast, Centenar, STD varies from about 35% (E12: best ex- Los Nietos has the lowest values, between 33% and 37% periment) to 45% (E27: worst experiment). The best for most experiments. Dependence of STD on physical experiment for Gomera is E14 (34%) and the worst is configuration varies considerably among stations. Cen- E28 (40%). The best experiments for Almirez and Los tenar is the most sensitive by a large amount, followed Nietos are E1 (48%) and E15 (33%), respectively. by Gomera. Recall that these are the lowest-elevation Similar to STD, bias values (Fig. 6) show a notable stations, in the western part of the study area. A clear dependence on the station and physical configura- dependence of results on the PBL scheme is observed tion, ranging from 215% to 10%. There is a clear ten- for these two stations. In particular, experiments E1– dency to underestimate wind speed at Almirez and Los E16, which use the P1 PBL scheme, produce considerably Nietos (Figs. 6c,d). The P1 parameterization performs

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FIG. 6. As in Fig. 5, but for relative bias (%). considerably better than P2 at these stations. At Cen- PBL scheme) show a negative bias, whereas experi- tenar and Gomera stations (Figs. 6a,b), however, bias ments E17–E32 (using the P2 scheme) produce slightly associated with the different experiments strongly var- positive or near zero bias. Gomera had considerably ies. At Centenar, experiments E1–E16 (using the P1 lower bias associated with the P1 scheme. This high

FIG. 7. As in Fig. 4, but for the DACC parameter (%).

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FIG. 8. Results of the sensitivity analysis of STD (black bars), bias (gray bars), and wind direction DACC parameter (white bars) to choice of parameterization. Mean values of these parameters, resulting from 16 experiments in which each parameterization is involved, are displayed for the (a) Centenar, (b) Gomera, (c) Almirez, and (d) Los Nietos stations. sensitivity of bias to the PBL parameterization is con- WRF than to the choice of parameterization. This firmed by Fig. 8. In particular, use of P1 for Centenar finding will be further analyzed in the next section. leads to a 210% bias on average, whereas P2 leads to To conclude, the PBL parameterization has a signifi- a value near zero. In contrast, P1 performs better than cant influence on both the STD and bias, and the P1 P2 for the rest of the stations, although differences are parameterization scheme performs better overall. This smaller than for Centenar. As with STD, the other influence is highly dependent on the station and the schemes had little influence on bias. Again, no single statistics, however. It is notable that for the Centenar experiment performed best for all stations, but ex- station P1/P2 provides lower/higher STD values and periment E14 performed reasonably at Almirez and higher/lower bias values. Los Nietos (high elevations). b. Analysis of sensitivity to terrain representation For DACC (Fig. 7), Gomera (between 60% and 70%) and, especially, Los Nietos (above 75%) have the Results of the previous analysis show that the WRF highest values. As highlighted earlier, these stations wind estimates in the study region are mainly sensitive have wind directions that are strongly conditioned by to the selection of PBL parameterization. As a conse- nearby terrain morphology (Figs. 2 and 3). Therefore, it quence, in the analysis described in this section, we only seems that terrain morphology at these stations is rea- consider variations in this parameterization, keeping sonably reproduced by WRF at 3-km spatial resolution; the rest of the parameterizations fixed. This approach this will be confirmed in the next section. Centenar and greatly reduces the complexity of this examination of Almirez had lower DACC values of close to 60%. The terrain influence. As revealed in the previous section, DACC parameter shows little sensitivity to the choice of none of the scheme combinations provided a clearly parameterization. This is confirmed by Fig. 8. Only small better representation of both wind speed and direction differences (below 2%) associated with the PBL are at the four stations. Nevertheless, among all experi- observed. Therefore, wind direction estimation appears ments, E15 seemed to provide the best overall estimates. to be more sensitive to the terrain representation in This experiment produced the lowest, or near the lowest,

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21 FIG. 9. Results of the sensitivity analysis of STD to spatial resolution FIG. 10. As in Fig. 9, but for bias (m s ). of WRF setup: STD values of WRF wind speed estimates using the 21 E15 (P1) and E31 (P2) configurations for three different spatial res- improvement (about 0.2 m s ) of the STD was obtained olutions (9, 3, and 1 km) at the (a) Centenar, (b) Gomera, (c) Almirez, by increasing spatial resolution. At Los Nietos and and (d) Los Nietos stations. The left-hand scale represents the STD Almirez, STD values were slightly lower at 9 km than at 2 in meters per second, and the right-hand scale represents the STD in the 3- and 1-km spatial resolutions (about 0.2 m s 1). percent with respect to the mean value of the observations. The reasons for this last result are difficult to assess and may be associated with error sources that are not related values of STD and bias for all stations (Figs. 5 and 6). The to the terrain representation, such as those listed at the DACC score (Fig. 7) showed little variation among ex- end of section 3. periments. Therefore, we have selected E15 as a basis for A general tendency to underestimate wind speed, the analysis presented here. We performed six experi- regardless of PBL parameterization and resolution, can ments, involving the two physical configurations (E15 and be discerned from Fig. 10. Except for Centenar station, E31) and the three spatial resolutions (1, 3, and 9 km), to the use of the P1 experiment provides lower bias values. evaluate the effect of terrain representation on the WRF Figure 10 also reveals a significant and complex de- wind estimates. This implies maintaining the parameteri- pendence of the bias on the spatial resolution. At Los zation M2, C2, R2, and L1 in both experiments, changing Nietos, the bias was positive at 9 km and was negative only the PBL scheme. We refer to the experiment that uses at 3 and 1 km (Fig. 10d). As shown in Table 1, WRF the E15 physical configuration as P1 and to the experiment overestimated station elevation at 9 km (1262 m MSL) that uses the E31 configuration as P2 (Table 3). while underestimating it at 3 km (1013 m MSL) and 1 km Figures 9–11 show the STD, bias, and DACC values, (1055 m MSL)—the DEM shows 1056 m MSL. The signs respectively, for the two physical configurations and of the biases at Los Nietos may be caused by this over- three different spatial resolutions. The STDs (Fig. 9) estimation/underestimation of station elevation. Since show considerable dependence on the PBL, station, and this station is in a flat area with low topographic com- spatial resolution. Overall, at the four stations and for plexity, according to Jimenez and Dudhia (2012), a 2 the three spatial resolutions, the P1 scheme generated positive bias (about 1 m s 1 at 10 m AGL) is expected lower STDs, with greater differences at Centenar (Fig. 9a). because of an unresolved drag effect of the terrain. Our Dependence on spatial resolution was much more com- results are compatible with the existence of a small 2 plex, however. The Centenar station showed no de- positive bias of substantially lower than 1 m s 1. This is pendence. At Gomera with the P1 scheme (Fig. 9b), slight mainly because the measuring elevation considered in

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station. This underestimation is clearly observed at 9 km, for which the station elevation from WRF is close to actual. At 1 and 3 km, the negative bias is marginally less. This reduction is probably caused by a compensating effect in the wind speed estimates that is associated with the overestimation of station elevation (about 17 m) at these spatial resolutions. Several conclusions can be drawn from Fig. 11 re- garding DACC. To aid interpretation of the results, Fig. 12 presents wind direction distributions from WRF, using P1 at the three spatial resolutions. The observed distribution is also shown. The DACC estimates show a small but signiﬁcant dependence on the PBL parameterization. The P1 scheme provides DACC values at Centenar at 1-km spatial resolution that are about 3% higher (Fig. 11a). On the other hand, P2 provides better DACC values at Gomera; the differences reach 8% at 1-km spatial resolution (Fig. 11b). For the other stations, differences are smaller. There are notable differences among stations in the accuracy of wind direction estimates. The highest DACC values were observed for Los Nietos, and the lowest were observed for Centenar. The spatial resolution had a relevant inﬂuence on DACC, especially for Los Nietos station (Fig. 11d). As highlighted in section 2, this station presents low wind direction variability, with this direction highly conditioned FIG. 11. As in Fig. 9, but for wind direction DACC parameter (%). by nearby terrain morphology. The location of Los Nietos station, in the middle of a valley (Fig. 2d and Figs. this evaluation is 40 m AGL, at which height the drag 3p,t) clearly modulates the wind direction distribution. effect should be much lower than it is at 10 m AGL. The valley morphology at this station seems reasonably Results for Gomera (Fig. 10b) clearly show the effect represented at 3 and 1 km but not at 9-km resolution of elevation misrepresentation of mountains and hills on (Figs. 3p–s). As a result, wind direction distribution was the wind estimates. This is a hilltop station, and WRF reasonably reproduced at 3 and 1 km (Figs. 12m,o,p), estimates of station elevation (Table 1) range from and the DACC parameter is considerable larger at these 405 m MSL (9-km resolution) to 515 m MSL (1-km two spatial resolutions (Fig. 11d). Gomera (Fig. 2b) and resolution)—the DEM gives 640 m MSL. As a conse- Almirez (Fig. 2c) present a low dependence of the quence, the station has a negative bias in both the P1 and DACC on the spatial resolution and also show low wind P2 experiments that decreases in magnitude with in- direction variability (Fig. 12). Unlike the case of Los creasing spatial resolution. At Almirez (Fig. 10c) in the Nietos, however, the three spatial resolutions provide foothill area, the elevation misrepresentation effect is similar results in terms of wind direction distribution also evident. At 9-km resolution, station elevation de- (Figs. 12f–h, j–l). As highlighted in section 2, these two rived from the WRF was 1052 m MSL, which is consid- stations present the highest topographic complexity erably lower than the reference DEM elevation of among the four analyzed. It seems that the terrain fea- 1195 m MSL. This results in the negative bias that is seen tures that constrain the wind direction variability at 2 in Fig. 10c (about 20.4 m s 1). Using the 3- and 1-km these two stations are so complex that they are not spatial resolutions, however, the WRF station elevations properly reproduced in the WRF, even using the 1-km are close to the DEM value, and only a slight positive spatial resolution. Therefore, low DACC values, and 2 bias (about 0.2 m s 1) is observed. Centenar station, lo- a weak dependence of this parameter on the spatial cated above a smooth hill, presents low topographic resolution, are found for both stations. Centenar had the complexity (Fig. 2a and Fig. 3a). Elevations from WRF worst DACC values (Fig. 11a), with little dependence vary from 229 m at 9 km to 253 m MSL at 1 and 3 km; the on WRF spatial resolution. At this station with low to- DEM indicates 236 m MSL. Results in Fig. 10a indicate pographic complexity, terrain morphology had little a tendency for WRF to underestimate wind speed at this effect on the wind direction distribution (Figs. 3a,e).

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FIG. 12. Modeled [from the best-performing WRF conﬁguration (E15) and using different spatial resolutions] and observed wind direction distributions, at the 40-m AGL measurement elevation of four stations. Shown are the (a) observed and (b)–(d) modeled (using 9-, 3-, and 1-km spatial resolutions, respectively) wind distributions for the Centenar station. Also shown are plots as in (a)–(d), but for the (e)–(h) Gomera, (i)–(l) Almirez, and (m)–(p) Los Nietos stations. The values in the circle indicate the percentage of observations; the 2 color scale at right indicates ranges of wind speed (m s 1).

Therefore, differences in terrain representation at the channeling effect of surrounding topography (Figs. 2d three WRF resolutions (Figs. 3b–d) had little effect on and 3p,t). The wind distribution was also reasonably re- the wind distribution estimates (Figs. 12b–d). produced at Gomera station (Figs. 12e,h), but with a bias To sum up, from Figs. 9–12, it is concluded that the of ;258 toward the southeast. At Almirez (Fig. 12l), the WRF setup that is based on experiment E15 (P1) and principal wind directions were fairly well reproduced, 1-km spatial resolution provides the best wind speed and with no bias; not so the distribution. The model indicated direction estimates overall at the four stations. Never- more cases than were observed in the south through theless, there are signiﬁcant differences among stations in east directions and fewer cases than were observed in the reliability of these estimates. The STD ranged from the north through west directions. The poorest esti- 2 about 2 (Centenar) to 2.8 (Gomera) m s 1. The bias was mation of the wind direction distribution is for Centenar 2 from 20.4 (Centenar) to 0.3 (Almirez) m s 1,andDACC (Figs. 12a,d). The model was able to estimate some of the ranged from about 57% at Centenar to 85% at Los Nietos. predominant wind directions, but some of the modeled Wind direction distribution was fairly reproduced at directions are not found in the observed data. Los Nietos (Figs. 12m–p). The predominant southeast wind direction was clearly reproduced by the model, 5. Summary and conclusions although there is overestimation of values for the north through west directions. As highlighted earlier, this In this work, we evaluated the relative roles of pa- predominant wind direction can be explained by the rameterization choice and terrain representation in the

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Weather Research and Forecasting mesoscale model, as version (RRTMG) (SWR) parameterization schemes, they pertain to regional wind resource assessment. In performed the best. Since results of the first analysis a first part, to evaluate sensitivity of WRF wind esti- showed that WRF wind estimates in the study region mates to parameterization choice, 32 configurations were mainly sensitive to the PBL parameterization se- (experiments) of the microphysics, cumulus, planetary lection, only variations of this parameterization were boundary layer, or shortwave and longwave radiation considered in the second analysis. Six experiments were parameterization schemes were assessed. These experi- conducted, with three spatial resolutions (1, 3, and 9 km) ments, with 3-km spatial resolution and 1-h output fre- and two PBL parameterizations. The remaining pa- quency, were conducted for the four seasons in 2005. In rameterizations were chosen on the basis of the best- a second part, to evaluate the sensitivity of the wind es- performing experiment from the first analysis. Although timates to terrain representation in WRF, experiments the spatial configuration and, in particular, the nesting were conducted with three different spatial resolutions— approaches in the first and second part of this study were 9, 3, and 1 km. The study region was Andalusia (southern different, we consider that results from the first part are Spain). Although relatively small, this region is charac- still applicable for the analysis of the second part, mainly terized by varied topographic, geographic, and weather because we believe that the weak influence that the conditions that made the evaluation especially chal- choice of the MPH, CMS, SWR, and LWR parameter- lenging. Estimates were obtained with data collected at izations schemes showed on the wind estimates in the four stations, the locations of which are representative first analysis should not change significantly just by of different climatic conditions and varied topographic changing the nesting approach. complexity and morphology in the study region. The In the second analysis, STDs showed a dependence on four stations provided hourly wind speed and direction the PBL parameterization, with the P1 (YSU) scheme 2 data at 40 m AGL. The WRF estimates were assessed performing better (differences up to 0.3 m s 1). On the using different statistics that represented both wind other hand, the influence of the spatial resolution on speed and wind direction. The standard deviation of the STD values was found to be considerably weak residuals between observed and modeled wind speed and highly dependent on the evaluation station. Biases and the bias were used to evaluate wind speed pre- in the WRF wind speed estimates were very sensitive dictions. For evaluating wind direction, we used an ac- to model spatial resolution. This was mainly because higher curacy parameter (DACC) that considers the percentage resolution improved the representation of terrain eleva- of times for which the circular distance between observed tion. The sign of the bias depended on terrain morphology and modeled wind direction is less than 308. and the spatial resolution, but absolute values tended to be Results of the first analysis show STD values of the much higher with coarser spatial resolution (9 km). wind speed estimates to be sensitive mostly to the PBL Physical configuration was found to have little im- parameterization choice. For some of the stations, dif- pact on wind direction distribution estimates. These ferences up to 10% were observed, depending on the estimates proved to be more sensitive to the repre- PBL parameterization. Sensitivity of the wind speed sentation of terrain morphology around the station STD values to other parameterizations was not signif- than to the model spatial resolution. In this regard, for icant. Nevertheless, some combinations of schemes stations that are located in areas of high topographic (experiments) performed better than others. These complexity, the use of 3- or even 9-km grid resolution in combinations unfortunately differed among stations. the model may provide similar (low) accuracy in the The bias values of wind speed showed considerable de- wind direction estimates to that from 1-km grid reso- pendence on the station and choice of PBL parameter- lution. In other cases, when the terrain is not so com- ization. Values ranged from 215% to 10%, depending plex, the increment in the spatial resolution provide on the station and experiment. The DACC values a substantial improvement in the accuracy of the wind showed a large sensitivity only to the choice of the PBL direction estimates. parameterization scheme, although only small differ- Experiment E15 at 1-km grid resolution provided the ences (below 2%) were observed. On the other hand, overall best estimates of wind speed and direction at there were substantial differences among stations in the four stations. Nevertheless, at the stations where the DACC values, indicating that WRF wind direction es- terrain morphology and elevations are fairly reproduced timates are highly sensitive to terrain characteristics. at 3-km grid resolution (Almirez and Los Nietos), re- Among all 32 experiments, experiment number 15, sults for this resolution are similar to the results obtained which used the YSU (PBL), Morrison two-moment for the 1-km grid resolution. In addition, there were (Morrison-2M) (MPH), Grell-3D (CMS), Rapid Radia- significant differences among stations in the reliability tive Transfer Model (RRTM) (LWR), and RRTM GCM of these estimates. The STD ranged from about 2 to

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2 2 more than 2.8 m s 1. The bias was from 20.4 to 0.3 m s 1, Risø-PhD-84(EN), 104 pp. [Available online at http://orbit. and the DACC ranged from approximately 57% to dtu.dk/files/10246050/Ris_PhD_84.pdf.] ı 85%. Wind direction distribution was reliably repro- Esteban-Parra, M. J., F. Rodrigo, and Y. Castro-D ez, 1998: Spatial and temporal patterns of precipitation in Spain for the period duced with this WRF setup for the stations that present 1880–1992. Int. J. Climatol., 18, 1557–1574. marked terrain morphology features (viz., the Los Farr, T. G., and Coauthors, 2007: The shuttle radar topography mis- Nietos, Almirez, and Gomera stations). Nevertheless, sion. Rev. Geophys., 45, RG2004, doi:10.1029/2005RG000183. at some stations the model showed bias in the pre- Fernańdez, J., J. P. Monta´vez, J. Saénz, J. F. Gonza´lez-Rouco, dominant directions, and at other stations it output wind and E. Zorita, 2007: Sensitivity of the MM5 mesoscale model to physical parameterizations for regional climate studies: directions that were not observed. Annual cycle. J. Geophys. Res., 112, D04101, doi:10.1029/ 2005JD006649. Acknowledgments. The Consejerıa de Innovacion, Frank, H. P., O. Rathmann, N. G. Mortensen, and L. Landberg, Ciencia y Empresa (CICE) of Junta de Andalucıa (Spain) 2001: The numerical wind atlas—The KAMM/WAsP method. (Project P07-RNM-02872) and FEDER funds financed Risø National Laboratory Tech. Rep. Risø-R-1252(EN), 60 pp. [Available online at http://www.risoe.dk/rispubl/VEA/veapdf/ this study. This work has been also supported by the ris-r-1252.pdf.] TEP-220 research group. The authors thank Gamesa GE Energy, 2010: Western wind and solar integration study. Na- Co. for providing the wind measurements. We also tional Renewable Energy Laboratory Tech. Rep. NREL/SR- thank the reviewers for their very helpful comments. 550-47434, 536 pp. [Available online at http://www.nrel.gov/ docs/fy10osti/47434.pdf.] Gibbs, J. A., E. Fedorovich, and A. Eijk, 2011: Evaluating Weather REFERENCES Research and Forecasting (WRF) model predictions of tur- Alsamamra, H., 2009: Using geostatistical techniques to obtain bulent flow parameters in a dry convective boundary layer. high spatial resolution climate maps for Andalusia (southern J. Appl. Meteor. Climatol., 50, 2429–2444. Spain). Ph.D. thesis, University of Jaen, 262 pp. [Available Hahmann, A., D. Rostkier-Edelstein, F. Vandenberghe, Y. Liu, online at http://refbase.iecolab.es/files/husainalsamamra/2009/ S. Swerdlin, T. Warner, and R. Babarsky, 2010: A reanalysis 1147_HusainAlsamamra2009.pdf.] system for the generation of mesoscale climatographies. Arakawa, A., 2004: The cumulus parameterization problem: Past, J. Appl. Meteor. Climatol., 49, 954–972. present, and future. J. Climate, 17, 2493–2525. Hong, S., Y. Noh, and J. Dudhia, 2006: A new vertical diffusion Arya, S., 2001: Introduction to Micrometeorology. Academic Press, package with an explicit treatment of entrainment processes. 420 pp. Mon. Wea. Rev., 134, 2318–2341. Beljaars, A. C. M., A. R. Brown, and N. Wood, 2004: A new pa- Horvath, D., D. Koracin, R. Vellore, and J. Jiang, 2012: Sub- rametrization of turbulent orographic form drag. Quart. kilometer dynamical downscaling of near-surface winds in J. Roy. Meteor. Soc., 130, 1327–1347. complex terrain using WRF and MM5 mesoscale models. Berge, E., R. E. Bredesen, and K. Mollestad, 2007: Combining J. Geophys. Res., 117, D11111, doi:10.1029/2012JD017432. WAsP with WRF mesoscale model: Evaluation of wind re- Hu, X. M., J. W. Nielsen-Gammon, and F. Zhang, 2010: Evaluation source assessment for three Norwegian wind farm areas. Proc. of three planetary boundary layer schemes in the WRF Model. EWEC2007, Milan, Italy, European Wind Conference and J. Appl. Meteor. Climatol., 49, 1831–1844. Exhibition. Huaxing, L. U., 2008: Modelling terrain complexity. Advances in Borge, R., V. Alexandrov, J. Del Vas, J. Lumbreras, and Digital Terrain Analysis, Springer, 159–176. E. Rodrıguez, 2008: A comprehensive sensitivity analysis of Jammalamadaka, S., and A. Sengupta, 2001: Topics in Circular the WRF model for air quality applications over the Iberian Statistics. World Scientific, 322 pp. Peninsula. Atmos. Environ., 42, 8560–8574. Jimenez, B., F. Durante, B. Lange, T. Kreutzer, and J. Tambke, Byrkjedal, Ø., and E. Berge, 2008: The use of WRF for wind re- 2007: Offshore wind resource assessment with WAsP and source mapping in Norway. Preprints, Ninth Annual WRF MM5: Comparative study for the German Bight. Wind En- User’s Workshop, Boulder, CO, NCAR, P9.18., 4 pp. [Avail- ergy, 10, 121–134. able online at http://www.mmm.ucar.edu/wrf/users/workshops/ Jimenez, P. A., and J. Dudhia, 2012: Improving the representation WS2008/abstracts/P9-18.pdf.] of resolved and unresolved topographic effects on surface wind Castro-Dıez, Y., D. Pozo-Va´zquez, F. Rodrigo, and M. J. Esteban- in the WRF model. J. Appl. Meteor. Climatol., 51, 300–316. Parra, 2002: NAO and winter temperature variability in southern ——, J. F. Gonza´lez-Rouco, J. Navarro, J. P. Monta´vez, and Europe. Geophys. Res. Lett., 29, doi:10.1029/2001GL014042. E. Garcıa-Bustamante, 2010: Quality assurance of surface Chou, M.-D., and M. J. Suarez, 1994: Technical report series on wind observations from automated weather stations. J. Atmos. global modeling and data assimilation, Volume 3: An efficient Oceanic Technol., 27, 1101–1122. thermal infrared radiation parameterization for use in general Kain, J., and J. Fritsch, 1993: Convective parameterization for circulation models. NASA Tech. Memo. 104606, Vol. 3, 85 pp. mesoscale models: The Kain–Fritsch scheme. The Represen- [Available online at http://archive.org/details/nasa_techdoc_ tation of Cumulus Convection in Numerical Models, Meteor. 19950009331.] Monogr., No. 46, Amer. Meteor. Soc., 165–170. Cohen, C., 2002: A comparison of cumulus parameterizations in Kanamitsu, M., and H. Kanamaru, 2007: Fifty-seven-year California idealized sea-breeze simulations. Mon. Wea. Rev., 130, 2554– reanalysis downscaling at 10 km (CaRD10). Part I: System detail 2571. and validation with observations. J. Climate, 20, 5553–5571. Draxl, C., 2012: On the predictability of hub height winds. Tech- Kim, Y., J. H. Jeong, J. H. Bae, S. K. Song, and J. W. Seo, 2005: Study nical University of Denmark (DTU) Wind Energy PhD-Rep. on planetary boundary layer schemes suitable for simulation

Unauthenticated | Downloaded 10/05/21 11:39 AM UTC JULY 2013 S A N T O S - A L A M I L L O S E T A L . 1609

of sea surface wind in the southeastern coastal area. J. Environ. Ramos-Calzado, P., J. Gomez-Camacho, P. Perez-Bernal, and Sci., 14, 1015–1026. M. Pita-Lopez, 2008: A novel approach to precipitation series Kotroni, V., and K. Lagouvardos, 2001: Precipitation forecast skill completion in climatological datasets: Application to Anda- of different convective parameterization and microphysical lusia. Int. J. Climatol., 28, 1525–1534. schemes: Application for the cold season over Greece. Geo- Rodriguez-Puebla, C., A. H. Encinas, S. Nieto, and J. Garmendia, phys. Res. Lett., 28, 1977–1980. 1998: Spatial and temporal patterns of annual precipitation Kwun, J., Y. Kim, J. Seo, J. Jeong, and S. You, 2009: Sensitivity of variability over the Iberian Peninsula. Int. J. Climatol., 18, MM5 and WRF mesoscale model predictions of surface winds 299–316. in a typhoon to planetary boundary layer parameterizations. Ruiz-Arias, J., D. Pozo-Va´zquez, N. Sańchez-Sańchez, J. Monta´vez, Nat. Hazards, 51, 63–77. A. Hayas-Barru, and J. Tovar-Pescador, 2008: Evaluation of Landberg, L., L. Myllerup, O. Rathmann, E. Petersen, B. Jørgensen, two MM5-PBL parametrizations for solar radiation and tem- J. Badger, and N. Mortensen, 2003: Wind resource estimation perature estimation in the south-eastern area of the Iberian an overview. Wind Energy, 6, 261–271. Peninsula. Nuovo Cimento C, 31, 825–842. Lavagnini, A., A. Sempreviva, C. Transerici, C. Accadia, M. Casaioli, Seaman, N. L., 2000: Meteorological modeling for air-quality as- S. Mariani, and A. Speranza, 2006: Offshore wind climatology sessments. Atmos. Environ., 34, 2231–2259. over the Mediterranean basin. Wind Energy, 9, 251–266. ——, D. R. Stauffer, and A. M. Lario, 1995: A multiscale four- Loveland, T. R., J. F. Reed, D. O. Brown, J. Ohlen, Z. Zhu, dimensional data assimilation system applied in the San L. Yang, and J. W. Merchant, 2000: Development of a global Joaquin Valley during SARMAP. Part I: Modeling design land-cover characteristics database and IGBP DISCover from and basic performance characteristics. J. Appl. Meteor., 34, 1-km AVHRR data. Int. J. Remote Sens., 21, 1303–1330. 1739–1761. Lynn, B. H., and Coauthors, 2004: Sensitivity of present and future Sempreviva, A., R. Barthelmie, and S. Pryor, 2008: Review of surface temperatures to precipitation characteristics. Climate methodologies for offshore wind resource assessment in Res., 28, 53–65. European seas. Surv. Geophys., 29, 471–497. Miao, J.-F., and Coauthors, 2008: Evaluation of MM5 mesoscale Shimada, S., and T. Ohsawa, 2011: Accuracy and characteristics model at local scale for air quality applications over the of offshore wind speeds simulated by WRF. SOLA, 7, 21– Swedish west coast: Influence of PBL and LSM parameteri- 24. zations. Meteor. Atmos. Phys., 99, 77–103. Skamarock, W., and Coauthors, 2008: A description of the Ad- Mlawer, E., S. Taubman, P. Brown, M. Iacono, and S. Clough, 1997: vanced Research WRF version 3. NCAR Tech. Note NCAR/ Radiative transfer for inhomogeneous atmospheres: RRTM, TN-47511STR, 125 pp. a validated correlated-k model for the longwave. J. Geophys. Stauffer, D. R., and N. L. Seaman, 1994: Multiscale four- Res., 102, 16 663–16 682. dimensional data assimilation. J. Appl. Meteor., 33, 416–434. Morrison, H., and A. Gettelman, 2008: A new two-moment bulk Stensrud, D., 2007: Parameterization Schemes: Keys to Under- stratiform cloud microphysics scheme in the Community At- standing Numerical Weather Prediction Models. Cambridge mosphere Model, version 3 (CAM3). Part I: Description and University Press, 459 pp. numerical tests. J. Climate, 21, 3642–3659. Trigo, R., T. Osborn, and J. Corte-Real, 2002: The North Atlantic Mortensen, N. G., L. Landberg, I. Troen, and E. L. Petersen, 1998: oscillation influence on Europe: Climate impacts and associ- Wind Atlas Analysis and Application Program (WAsP), ated physical mechanisms. Climate Res., 20, 9–17. Vol. 1: Getting started. Riso National Laboratory Tech. Rep. Uppala, S., D. Dee, S. Kobayashi, P. Berrisford, and A. Simmons, Risø-I-666(EN), 30 pp. 2008: Towards a climate data assimilation system: Status up- Nakanishi, M., and H. Niino, 2004: An improved Mellor–Yamada date of ERA-Interim. ECMWF Newsletter, No. 155, ECMWF, level-3 model with condensation physics: Its design and veri- Reading, United Kingdom, 12–18. fication. Bound.-Layer Meteor., 112, 1–31. Watson, L., W. Bauman, and B. Hoeth, 2009: Weather Research Otte, T. L., 2008: The impact of nudging in the meteorological and Forecasting Model Wind Sensitivity Study at Edwards Air model for retrospective air quality simulations. Part II: Eval- Force Base, CA. Preprints, 13th Conf. on Integrated Observing uating collocated meteorological and air quality observations. and Assimilation Systems for Atmosphere, Oceans, and Land J. Appl. Meteor. Climatol., 47, 1868–1887. Surface (IOAS-AOLS), Phoenix, AZ, Amer. Meteor. Soc., Perez-Landa, G., P. Ciais, M. Sanz, B. Gioli, F. Miglietta, J. L. 6.1. [Available online at https://ams.confex.com/ams/89annual/ Palau, G. Gangoiti, and M. M. Millań, 2007: Mesoscale cir- techprogram/paper_144873.htm.] culations over complex terrain in the Valencia coastal region, WMO, 2000: Guidelines on performance assessment of public Spain—Part 1: Simulation of diurnal circulation regimes. At- weather services. World Meteorological Organization Tech. mos. Chem. Phys., 7, 1835–1849. Rep. WMO/TD 1023, 67 pp. [Available online at http://www. Pozo-Va´zquez, D., M. Esteban-Parra, F. Rodrigo, and Y. Castro- wmo.int/pages/prog/amp/pwsp/pdf/TD-1023.pdf.] Dıez, 2001: A study of NAO variability and its possible non- Zagar, N., M. Zagar, M. Cedilnik, C. Gregori, and J. Rakovec, linear influences on European surface temperature. Climate 2006: Validation of mesoscale low-level winds obtained by Dyn., 17, 701–715. dynamical downscaling of ERA40 over complex terrain. Tel- Rajeevan, M., A. Kesarkar, S. Thampi, T. Rao, B. Radhakrishna, lus, 58A, 445–455. and M. Rajasekhar, 2010: Sensitivity of WRF cloud microphysics Zhang, D., and W. Zheng, 2004: Diurnal cycles of surface winds to simulations of a severe thunderstorm event over Southeast and temperatures as simulated by five boundary layer pa- India. Ann. Geophys., 28, 603–619. rameterizations. J. Appl. Meteor., 43, 157–169.

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