Digitalization of scanning lidar measurement campaign planning

By using multiple wind measurements when designing wind farms, it is possible to decrease the uncertainty of wind farm energy assessments since the extrapolation distance between measurements and wind turbine locations is reduced. A WindScanner system consisting of two synchronized scanning lidars potentially represents a cost-effective solution for multipoint measurements, especially in complex terrain. However, the system limitations and limitations imposed by the wind farm site are detrimental to the installation of scanning lidars and the number and location of the measurement points. To simplify the process of finding suitable measurement positions and associated installation locations for the WindScanner system, we have devised a campaign planning workflow. The workflow consists of four phases. In the first phase, based on a preliminary wind farm layout, we generate optimum measurement positions using a greedy algorithm and a measurement “representative radius”. In the second phase, we create several Geographical Information System (GIS) layers such as exclusion zones, line-of-sight (LOS) blockage and lidar range constraint maps. These GIS layers are then used in the third phase to find optimum positions of the WindScanner systems with respect to the measurement positions considering the WindScanner measurement uncertainty and logistical constraints. In the fourth phase, we optimize and generate a trajectory through the measurement positions by applying the traveling salesman problem (TSP) on these positions. The described workflow has been digitalized into a Python package named campaign-planning-tool, which gives users an effective way to design measurement campaigns with WindScanner systems. In this study, the Python package has been tested on three different sites characterized by different terrain complexity and wind farm dimensions and layouts. With minimal effort, the Python package can optimize measurement positions and suggest possible lidar installation locations for carrying out resource assessment campaigns.

To account for the seasonal and inter-annual variations of the wind the observed wind climate is long-term corrected using long-term reference data from a nearby meteorological station, reanalysis data or meso-scale models (Carta et al., 2013). The long-term corrected wind climate is then extrapolated vertically and horizontally, typically using a flow model such as WAsP (Mortensen et al., 2014) to estimate the wind resource at hub height for every wind turbine location.
The single mast approach is affordable but can cause large uncertainties. Specifically, in complex terrain (mountainous and 5 forested areas), the spatial extrapolation becomes challenging as the topography can significantly influence the flow. The ideal scenario would be to measure the local wind climate at every planned wind turbine position. However, erecting as many masts as wind turbines would be extremely costly and in some areas impossible.
Many large wind farm projects in complex terrain are developed using multiple masts. Combining one fixed mast and one or several roaming profiling lidars moved to different positions during the campaign is another option. The advantage of roaming of the methodology for multi-lidar experiments on wind resource assessment campaigns (Vasiljević et al., 2017), which was previously used in planning of more than 20 measurement campaigns (Vasiljevic, 2018) and especially those conveyed in the New European Wind Atlas (NEWA) project (Mann et al., 2017), such as Perdigao-2015 (Vasiljević et al., 2017) and Perdigao-2017(Fernando et al., 2019. On the other hand, the CPT has previously been conceptualized during the WindScanner.eu project (see 'WindScanner locator' in Vasiljevic and Hasager, 2015). 5 The paper is organized as follows. Section 2 describes the workflow and corresponding elements of CPT. In Section 3 we present results of applying CPT for planning campaigns at three sites. We discuss the results and future work in Section 4, while we provide our concluding remarks in Section 5.
2 Methodology 2.1 Overview 10 We assume that the location and the layout of the wind farm are known. These initial information are inputs to the campaign planning workflow which consists of four sequential phases graphically represented in Figure 1. First of all, the measurement positions are optimized based on the wind farm layout. Afterwards, the measurement positions are used in combination with lidar and site constraints to generate the map that highlights best lidar installation locations. In the next phase, the positions of the scanning lidars are determined by minimizing a dual-Doppler measurement uncertainty of horizontal wind speed while 15 identifying existing road and power infrastructure. Finally, considering the measurement positions and positions of scanning lidars the trajectory of the laser beams through all the reachable measurement points is optimized and afterwards generated. In the sections that follow each phase will be described in details followed by a short summary how the entire workflow has been digitalized and thus converted into the CPT.

Phase 1 -Measurement positions optimization 20
The wind farm layout is a required input for the campaign planning workflow. For small wind farms (either a limited number of turbines and-or a limited spatial extent) we can coincide the measurement positions with the wind turbine positions. For larger wind farms, the number of measurement points needs to be reduced. However, the reduced set of measurement points should be adequately distributed over the wind farm site to avoid long wind resource extrapolation distances. The simplest approach is to group the wind turbine locations, which are close to each other in clusters, and to assign a single measurement location 25 per cluster. MEASNET (2016) suggests that measurements from a single location represent the wind climate over a certain area described by 'representativeness radius' (R r ). R r has different values for different terrain types. For example, in complex terrain, the radius should be smaller than 2 km as suggested by MEASNET (2016). By solving a disc covering problem (e.g., Biniaz et al., 2017), in which we aim to find a minimum number of discs with a radius equal to R r that cover all locations of wind turbines, we cluster the wind turbines and optimize the measurement locations. As stated in Ghasemalizadeh  Aerial image Intersecting angle

Combine layers
Trajectory optimization Trajectory generation (2012) there are several ways in solving disc covering problem. One of them is a greedy approach which we adapted to suite our purpose.
In our case, the greedy algorithm implementation yields the set D of m unique disks with the radius R r covering the set T of n wind turbine positions (T = { T 1 ,T 2 ,...,T n } ). We are solving the disc covering problem in two dimensions (2D) by omitting height coordinate (i.e., z) of turbine positions. The greedy algorithm implementation can be described in the algorithmic sense 5 with following steps: 1. Initialize the set D (D = ∅) 2. For any unique pair of wind turbine positions (there is p = n! 2(n−2)! unique pairs) calculate a midpoint, which is considered as a potential disc center and add it to the set M = { M 1 ,M 2 ,...,M p } 3. Find the elements of the set T that are covered by each element of the set M and form an additional set S which will 10 contain these information. To do this, calculate the distance between each element of the two sets: where x m , y m , x t and y t are coordinates of disc centers and turbine positions, and compare d i,j to R r ( d i,j <= R r condition must be satisfied for a disc M i to cover a point T j ). Through the comparisons the set S is formed. The elements of S are actually sets themselves containing wind turbine positions covered by each disc from the set M . If 15 for example, a disc M k covers turbine positions T 1 , T 2 and T n (i.e., d k,1 , d k,2 and d k,n are smaller or equal to R r ) the corresponding element of the set S, that is S k will contain these elements (i.e., S k = { T 1 , T 2 and T n }). Alternatively, if a disc M k does not cover any turbine position the corresponding element of the set S will be an empty set (i.e., S k = ∅).
4. Select a disc from the set M which covers the maximum number of points of the set T (this process is aided using the set S). Let this disc be M i . 20 5. The disc M i is added to D and removed from M : 6. Points covered by M i provided in S i are removed from the set T and from any subset of the set S: 7. Steps 1 to 6 are repeated until either the set T or S are empty : 8. If T is not an empty set after Step 7, then the remaining elements are added to the set D : At the end of this process elements of the set D, that is measurement points, contain only x and y coordinates. Using the 5 digital elevation model (DEM) from the Shuttle Radar Topography Mission (SRTM, Farr et al. (2007)) we can attach the height information to the elements of the set D. It is important to additionally add the hub height of future wind turbines to these height information. As the last step of the first phase we generate a mesh of equally spaced points over the site with the measurement point in the mesh center (see Figure 2). Lets denote the mesh as G and treat it as a set of elements . The mesh resolution should be equal to the land cover and terrain data resolution which will be 10 used in the second phase (typically |x 2 -x 1 | = |y 2 -y 1 | =100 m). This avoids any interpolation of the land cover or topography datasets to our mesh.

Phase 2 -Highlighting best lidar installation locations
In this phase we will create a Geographical Information System (GIS) layer which includes site and lidar constraints while highlighting the best lidar installation locations. We will denote this GIS layer as the combined layer and treat it as a set Cl 15 containing elements Cl i,j . To create this layer, first we acquire land cover data, orthography data and aerial image corresponding to the extent of the previously generated mesh ( Figure 2). For land cover data we can use CORINE Land Cover dataset. In case of the orography, the previously mentioned SRTM DEM datasets serves this purpose, while for the aerial image we can use the Google map server. All three data sources are publicly available. The acquired stack of data will be a base material for the combined layer creation.

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At present we consider 5 types of constraints which are detrimental for a lidar installation: zones where a lidar cannot be installed (e.g., lakes, forests, etc.); topographical features that can block the beam; keeping the lidar elevation angle below a certain threshold to avoid measurement contamination with the vertical component of the wind; the maximum lidar range; practical matters such as access roads. To create the combined layer which contains all the above-listed constraints, first we will generate GIS layers for each individual constraint and afterwards merge them. These individual GIS layers are: (1) Exclusion 25 zones layer, (2) LOS blockage layer, (3) Elevation angle layer, (4) Lidar range layer and (5) Logistical layer.
To create the exclusion zone layer we will use the land cover data and according to the land cover type (e.g., water surface, forest, etc.) classify areas of the site as suitable or not for a lidar installation. The land cover data can be treated as a set Lc equally spaced elements Lc i,j containing integer values which represent so-called CLC codes that indicate the land cover type (e.g., water bodies have CLC code from 40 to 44). To generate the exclusion zone layer we make a copy of the mesh (an empty 30 mesh), walk through the mesh (going from one mesh point to another), fetch the corresponding information on land cover type if CLC code is equal to 12 that is 'Arable land'). An example of a fictive exclusion zone layer is shown in Figure 3.
To generate the LOS blockage layer we need to create dataset contain summed height of the terrain and canopy. To do this we will add 20 m at each location in the DEM dataset where the CORINE Land Cover dataset contains code which correspond 5 to the forest (CLC code equal to 23, 24 and 25). Afterwards, we make a copy of the mesh, walk through the mesh, fetch the corresponding elevation from the DEM dataset, perform a viewshed analysis (Izraelevitz, 2003) from the selected mesh point to each measurement point that returns which measurement points are visible, and assign the visible measurement points to the mesh point (e.g., if measurement points D 1 ,D 2 and D n are visible from  and good (Gi,j = 1) locations for a lidar installation respectively, i corresponds to x coordinate or Easting and j corresponds to y coordinate or Northing.
considering the summed height dataset. The result of this process is the LOS blockage layer which mesh points contain a set of measurement points to which there is an unobstructed LOS.
As mentioned, our focus is to design a dual-Doppler measurement campaign in order to retrieve the horizontal wind speed. Accordingly, a low elevation beam angle is required to avoid contamination of the LOS speed measurement with the vertical component of the wind vector. We create the elevation angle layer to serve this purpose. This layer is created through following 5 steps: we make a copy of the mesh, walk through each mesh point, fetch the height information from the DEM dataset, calculate the elevation angle from the mesh point to each measurement point, compare the calculated angle to a threshold value (e.g., a maximum of 5 • suggested by Vasiljević et al. (2017)), and assign measurement points to the mesh point for which the elevation angle is below the threshold value.
In the lidar range layer mesh points contain measurement points which are within reach of the lidar taking into account the 10 expected range of the lidar for the given site. The layer is created in a similar fashion like the previous one.
To create the combined layer, we will treat the four previously derived layers as sets Ez (exclusion zone layer), Lb (LOS blockage layer), Ea (elevation angle layer) and Lr (lidar range layer). Since each set is made using the same mesh, each set contains the same number of elements. The combined layer, treated as a set Cl containing elements Cl i,j , is derived as following: Therefore, the mesh points of the combined layer will contain which and how many measurement points are reachable considering the first four above described constraints.
Finally, the aerial image of the site (the logistical layer) is kept separate, it will serve the important purpose of identifying 5 existing road and power infrastructure.

Phase 3 -Placement of the lidars
The combined layer together with underlying aerial image highlights the 'best' locations for the placement of individual lidars considering all the above-described constraints. However, designing the campaign for a dual-Doppler system, where beams from two lidars need to synchronously cross at every measurement positions, adds one more constraint: the limitation on 10 the beams intersection angle. The measurement uncertainty of a dual-Doppler system increases when the intersecting angle between the laser beam gets small (see Vasiljevic and Courtney (2017)). Therefore the position of the second lidar is very much determined by the position of the first lidar. Considering that we have chosen the first lidar location using the combined layer and the logistical layer, now we need to calculate an additional layer to which we will refer as the intersecting angle layer. This layer is created as following: we make a copy of the mesh, walk through each mesh point considering each mesh 15 point as a second lidar position, calculate intersecting angles between the two laser beams at each measurement point, and add those measurement points to the mesh point for which the intersecting angle is bigger than a specific value (e.g., at least 30 • suggested by (Davies-Jones, 1979;Vasiljevic and Courtney, 2017)). Lets treat this GIS layer as a set Ia with elements Ia i,j . To highlight the best locations for the second lidar installation the intersecting angle layer should be intersected with the combined layer, i.e. : where Sl is a set corresponding to the newly created GIS layer for the second lidar placement. The process of selecting a position for the first lidar, followed with the generation of the layer for locating the second lidar and selection of the second lidar position should be performed several times to generate several potentials experiment designs, since only during a field visit it will be possible to determine the most likely design for the measurement campaign. Once the second lidar position is 25 determined, we derive a set of reachable measurement points Dr by both lidars which is actually a subset of the set D (Dr ∈ D).

Phase 4 -Trajectory optimization and generation
The fourth phase consists of the optimization of the path through the measurement points and the generation of the corresponding trajectory.
In the previous phases, we derived the measurement locations and dual-Doppler campaign layout(s). A next task is to optimize the path through those positions such that the motion of the scanner heads required to steer the beams takes the 5 least amount of time (i.e., increasing sampling rate). One way to achieve this is to adapt the solution for the traveling salesman problem (TSP). In the regular TSP, the goal is to find the shortest path through a set of n cities that a traveling salesman needs to visit. There are multiple approaches to solve the TSP (Reinelt, 1994). One of the simplest implementation of the TSP solution is Nearest Neighbor Heuristics (NNH). As stated in (Reinelt, 1994): This heuristic for constructing a traveling salesman tour is near at hand. The salesman starts at some city and then visits the city nearest to the starting city. From there he visits the 10 nearest city that was not visited so far, etc., until all cities are visited, and the salesman returns to the start.
In our case, we have a single set of measurement points D c which needs to be simultaneously visited by the two laser beams. Since typically two scanning lidars will not be symmetrically positioned with respect to the measurement points we will have two different sets of steering angles D s1 and D s2 corresponding to the first and second lidar respectively which enable 'visiting' the measurement points with the laser beams. Therefore, we cannot directly apply the above described heuristics. The

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TSP NNH solution needs to be adapted.
Lets consider that the set D c is defined as: while D s1 and D s2 are defined as: additionally we will make a set I which will contains indexes of the sets' elements: The adapted TSP NNH solution for dual-Doppler trajectory can be described in the algorithmic sense with following steps: 25 1. Initialize empty sets T l1 and T l2 (T l1 = T l2 = ∅), which will contain ordered elements of the optimized trajectory.
2. Select an arbitrary index j from the set I.
3. Set an element l to j (l = j).
5. Add the elements D s1,l and D s2,l to the set T l1 and T l2 respectively, and remove index j and elements D s1,l and D s2,l from the set I, D s1 and D s2 respectively: 6. Calculate sets ∆α 1 and ∆α 2 which contains elements ∆α 1,il and ∆α 2,il (i takes values from the set I), defined as: 10 that describe relative angular moves for the two lidars from the measurement point described by the last element of the sets T l1 and T l2 (i.e., D s1,l and D s2,l respectively) to all remaining measurement points described by elements of D s1 and D s2 .
The main modifications of a standard TSP NNH solution is the addition of Step 7 which secures that the trajectory will be optimal for both lidars instead of only one. The difference between the standard and adapted TSP NNH solution can seen from an example shown in Figure 4.

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To get the lidars to actually follow the optimized trajectory, we need to describe the motion of the scanners as a function of time. In other words, we need to 'attach' the time component of the trajectory to the spatial description we yielded in the previous steps. When calculating the timing for the trajectory, we assume that the lidars will stop at each measurement point and sample wind speed before they continue to the next measurement points. Therefore, we expect that lidars will perform so-called step-stare trajectories. There are several reasons for selecting step-stare trajectories instead of continuously scanning the flow through the trajectory described by the measurement points. The most important reason is that the current commercial scanning lidars allow only step-stare implementation of complex trajectories. Also, the timing for the step-stare scans can be calculated using a simple solution for Kinematics Elevator Problem (KEP) (e.g., Al-Sharif, 2014) considering an infinite jerk: where T move is a minimum time required to perform an angular motion ∆β considering a maximum allowed acceleration of the lidar scanner head A max .
Since we have two lidars that move to from one to another measurement point, we will generally have two different moving times to perform angular motions. To keep the lidar measurements in sync we take the maximum of the two derived values. 3 Results

Overview
In this section, the campaign planning workflow is demonstrated through the application of CPT on three different wind farm 20 sites, which are named by their country of origin: Scotland (Vasiljevic and Bechmann, 2019a), Italy (Vasiljevic and Bechmann, 2019b) and Turkey (Vasiljevic and Bechmann, 2019c). The only information needed for each site is the wind turbine positions and their hub height. This input could be generated arbitrarily, but to make the examples realistic actual operating wind farms have been chosen. For all three sites, we aim to design the campaign for the long-range WindScanner system configured in a dual-Doppler mode (i.e., the system will have two scanning lidars). The system is described in details in Vasiljevic et al. 25 (2016). To demonstrate the workflow, the most essential bits of information is the maximum range of the lidars, which is 6 km, and maximum acceleration of the scanner heads, which is 100 • /s 2 . Results which will be described in the following sections are accessible as a data collection (Vasiljevic et al., 2019) or as an individual datasets (Vasiljevic and Bechmann, 2019a, b, c).

Site 1 -Scotland
The Scottish site consists of 22 wind turbines with 47-m hub-heights and has a quite compact layout ( Figure 5). The distance between adjacent turbines is about 300 m (5 rotor diameters). The wind farm is placed on a 300-m tall hill surrounded by rolling hills of farmland with windbreaks and patches of forest. The hill is quite steep with maximum slopes of 20% from the main south-western wind direction. The site is located 17 km from the coast and can, therefore, be considered an inland site. Due to the compact design of the wind farm, we decided to skip the measurement position optimization and try to generate a measurement campaign in which we intend to measure at every wind turbine position. Considering that the site is relatively close to the coast, surrounded by agricultural land, and the altitude is about 300 m above sea level (asl.), thus relatively low, the site should experience a good concentration of aerosols. Nevertheless, we cannot expect that the WindScanners will have 6 km range all the time and assume that on average the WindScanners would have a range of at least 3 km at the selected site 10 (i.e., a half of the maximum claimed range). This estimation is based on our experience in doing measurement campaigns at various locations and in different atmospheric conditions.
Using this range together with the map extent, the CPT tool outputs the combined layer (see top image in Figure 6). The dark red color areas show positions from where an individual scanning lidar can reach out to all measurement positions. Those areas are relatively large because the wind farm layout is compact. For the purpose of this example, we chose to place the first 15 the intersecting angle between the respective lidars' beams. There is only one area of the map where the placement of the second lidar assures that all measurement points are within reach and measurable with fair accuracy (bottom image in Figure   6). By selecting the position of the second lidar (coordinates of 1600 m, 400 m and 318 m in Easting, Northing and altitude asl. respectively relative to the map center coordinates), we complete the generation of one measurement campaign layout. In 5 practice, we would generate several layouts (for different positions of WindScanner 1 and WindScanner 2), and assess their feasibility by inspecting aerial images, e.g. looking for access roads and nearby power lines or houses. However, for the sake of demonstrating the workflow, we have generated only a single layout.   table in Vasiljevic and Bechmann (2019a)). Overall, one complete scan of all measurement points will take about 36 s, which results in about 16 samples of each measurement point per 10-min period. Typically we aim at having at least ten samples per 10-min period which is satisfied with this configuration.

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The Italian wind farm consists of 36 wind turbines with a 78-m hub-height. The turbines are distributed over a large area (see From there, the workflow was applied in the same way as it was for the Scottish site. In comparison to the Scottish site, the Italian wind farm is even closer to the sea, and it is surrounded by an urban area that in our experience increases the 5 aerosol concentration resulting in an improved lidar range. Therefore, for the Italian site, we can expect to have an average measurement range of 4 km for the WindScanners. The combined layer generated by the CPT is shown as the top image in The layer for the second lidar placement (the bottom image in Figure 10) shows that the second lidar can only reach 7 measurement positions at most and this can only be achieved from a few locations. Of these locations, we selected one which assures that we cover the largest extent of the wind farm, thus getting good spatial information on the farm wind resources.

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The coordinates of a selected location for the second lidar are 1600 m, 110 m and 278 m in Northing, Easting and height asl.
relative to the map center coordinates (the bottom image in Figure 10).
Considering the positions of WindScanners, reachable measurement points, and kinematic limits, we derived an optimum trajectory through the measurement points and calculated the timing for the synchronized scanner head motions ( Figure 11). wind turbines measurement points discs  (2019b)). This provides us with about 28 measurement samples at each measurement point within a 10-min period.

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The Turkish wind farm consists of 22 wind turbines with a 80-m hub-height. The wind farm extends 8 km from North to South (see Figure 12) with the three most northerly turbines separated by about 2 km from the rest. The inter-turbine distance is 400-500 m (4-5 rotor diameters) for most turbines. The turbines are located along a 1600 m tall North-South ridge and the main wind direction from North-East (i.e., perpendicular to the ridge line). In the main wind direction the mean terrain slopes are about 12% and with extremes reaching 50% the site should be regarded as very complex. The land cover is sparse vegetation 10 with a patch of forest along Western facing slopes.
For this site, we assumed the average lidar measurement range to be 3 km, and we used the representativeness radius of 500 m. Our assumption on the average range in case of the Turkish site is probably closer to what a lidar would probably achieve in field operation (thus less conservative) due to operation in high altitude where we usually experience low aerosol concentration and often low clouds and fog. On the other hand, the selected representative radius is 100 m lower than in the case of the Italian Northing [m] wind turbines measurement points discs The designed WindScanner layout can provide measurements in 6 out of 10 measurement points which cover the middle part of the wind farm ( Figure 15). The upper and lower quarter of the wind farm area are not reachable with the current layout.
In principle, we would probably need two WindScanner systems to cover the entire wind farm (i.e., four scanning lidars).
Considering the WindScanners and measurement locations together with the kinematic limits as the input for the last phase of

Discussing results
The primary purpose of the described workflow and corresponding CPT tool up to date is to design a measurement campaign for wind resource assessment (WRA) using a long-range WindScanner system (Vasiljevic et al., 2016) configured in a dual-Doppler mode. This scope follows the RECAST project ambition which is focused on developing a new way of measuring 5 the wind over a site for resource assessment, based on multiple measurement points using WindScanners. This has driven the choice of examples for Section 3 of this paper. However, the workflow and CPT described in this paper are not limited to only planning WRA campaigns. It can be used to design any campaign using one or several scanning lidars. It can easily be applied to any type of scanning lidars since it only requires lidar specifications, which are maximum lidar range and scanner head kinematic limits (i.e., maximum acceleration).

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Planning the measurement campaign thoroughly especially with such complex instruments as scanning lidars ensures higher data availability during the campaign and eventually saves time and money. Lidars are very mobile and allow agile measurement campaigns compared to a met mast, but too often the ease of deployment is mistaken with a limited (underestimated) need of planning. This study and the CPT, in general, show the main constraints to lidar measurements in complex terrain and give a practical solution by providing the most suitable positions where the lidar can be placed.

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The point of this tool is also to carefully consider the relevance or value of using scanning lidars for a measurement campaign.
In the example of the Scottish site, it is relevant to question how much improvement measuring at all turbine positions makes for such a small wind farm. Is it worth using a WindScanner system instead or in addition to one met mast if we compare costs versus uncertainty in horizontal and vertical extrapolation? One way to trade off for costs is to use scanning lidars for a short period, less than the 12 months required by best practices. The challenge then is the long term correction of the measurement and the related uncertainty.
This study has shown that, for a large site like the Italian or Turkish examples, one set of two WindScanners cannot measure over the whole wind farm area. This is very important to realize at the campaign planning stage when there is still time to either 5 give priority to one part of the site or consider using a second set of two WindScanners to cover the rest of the site.
Another major constraint that must be considered before the lidars deployment is the access roads to or near the lidar locations and possible access to a power source (e.g. existing houses, wind turbines). This is the purpose of the Satellite image used as background for the various GIS layer produced by the CPT.
In order to get around those very strong constraints, as already mentioned, it is, in any case, recommended to generate several 10 campaign designs and to make a site visit with thorough inspection of the possible lidar positions and verification that the data used in the CPT were accurate and up to date (e.g. obstacles, tree height).

Improving workflow
The presented workflow and developed tool (CPT) can already solve many important challenges regarding the scanning lidar deployment. Nevertheless, we envisage the development of several additional modules which will improve the workflow and 15 the developed tool.
In the current application of CPT, we were predicting the lidar range based on our experience. We plan to extend the 'Lidar range' module to be able to predict the lidar range by developing a lidar simulator. The lidar simulator will take inputs from external databases of global atmospheric visibility or aerosol optical depth for a given site and predict the expected lidar range.
In mean time, our suggestion when planning the lidar campaign is to generate campaign layouts considering a conservative 20 approach in which the expected range of the lidar should be in the range between 75 % to 50 % of the claimed range by the lidar manufacturers.
Directly connected to the range prediction is the development of a module which will predict the lidar data availability at any desired range during the planned measurement period. This module will take inputs such as the predicted range from the Lidar range module as well as the cloud height, fog or mist occurrence from the WRF model to predict the data availability. 25 Furthermore, the module for optimizing measurement positions will be extended by considering other criteria for finding measurement positions apart from the representativeness radius. These are for example terrain elevation, speed-up factors, roughness changes, local obstacles, etc. In principle, we will strive to incorporate anything that can cause local changes in the flow. In other words, the optimization of measurement positions will consider drivers of flow model uncertainty when finding measurement positions.

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Finally, we intend to develop an eye safety module that will produce yet another restriction zone (GIS) layer for the placement of lidars. The module will impose geometrical limitations when designing campaign layout to avoid that the laser beam is steered over the site at a height where we could expect that the human eyes can be directly exposed to it.
In this paper, we have provided an exhaustive description of the workflow we recommend for planning measurement campaigns using scanning lidars or WindScanner systems. The purpose is to find the most suitable positions for the lidars given the measurement positions, the characteristics of the site (topography), the characteristics of the lidars (measurement range, kinematic limits) and the position of the two lidars relative to each other. The workflow is available through a Python library, 5 named the Campaign Planning Tool, which will be made public during the RECAST project. The CPT has been demonstrated for planning campaigns for resource assessment on three different sites. For a small wind farm layout, the WindScanners could be placed so that measurements could be made at all turbine positions. For the other sites, that were larger, the number of measurement points was needed to be optimized and a set of two lidars could only cover some part of the site. In any case, it is recommended to generate several possible campaign layouts and to make a site visit to take the final decision.

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The CPT is easy and fast and helps to design realistic lidar measurement campaigns. Measurement campaigns are costly and risky, especially when using advanced measurement technology. The CPT helps to avoid many pitfalls that can be predicted before the start of the campaign, limiting the risks to the campaign itself.