# The RUNE Experiment--A Database of Remote-Sensing Observations of Near-Shore Winds
## 1 Introduction  
Near-shore wind measurements (i.e., within \(\approx\)12 km from the coast) are both scarce and sparse, and are needed for a number of reasons. First, a number of coastal wind farms are being planned and erected and their design and financial viability highly depends on the accuracy of wind resource estimates. Second, models that estimate atmospheric and oceanic parameters (used in a number of disciplines including wind-power meteorology) become uncertain in coastal areas due to the complexities of the land-sea interactions, the validity of their parametrizations and numerical approximations, and local topographic features that are not adequately captured [1; 2]. Evaluation of such models with high-quality observations is therefore needed. For wind resource estimation near the coast, microscale models such as the one included in the Wind Atlas Analysis and Application Program (WAsP) are normally used [3], although mesoscale models are becoming increasingly popular [4; 5; 6]. One of the latter examples is the Weather Research and Forecasting (WRF) model, whose outputs can been 'downscaled' with a microscale model to provide detailed wind resource estimations [7]. This downscaling is a two-way process: first, the mesoscale model output is 'generalized' to remove the effects of mesoscale topographic features and second the generalized wind climate is downscaled adding the effects of microscale topographic features [7]. Near the coast, downscaling has not been thoroughly tested and the RUNE database represents a unique opportunity for model and model-chain evaluation. The uncertainty in wind resource estimations can be reduced significantly by using measurements at the site of interest. Traditionally, these are performed with cup anemometers on meteorological masts, but these are often expensive to install (particularly offshore) and have a long lead time. Profiling lidars, i.e., those vertically profiling the wind using velocity azimuth display (VAD) scanning, have been commercially available for several years [8] and are generally accepted as wind resource tools [9]. Recently, scanning lidars that are able to perform a number of scanning trajectories due to a steerable scanning head have also appeared on the market or have been developed by research institutes and can be of great advantage when performing wind resource estimation in complex areas [10] or areas with limited access. One of these scanning lidars is the long-range WindScanner; it can be used to map the line-of-sight (LOS) wind velocities over a predetermined scanning pattern [11; 12; 13] and, by assuming certain flow properties in the scanned volume, the wind speed components can be estimated. Three spatially-separated WindScanners can be synchronized and the flow field estimated without making any assumptions, while with two WindScanners the horizontal wind vector can be estimated by assuming that the vertical component is zero. Finally, one WindScanner can be used to estimate the horizontal wind speed by scanning a certain azimuthal sector and assuming the wind direction in this sector is constant. Detailed information about the pointing accuracy and the measuring volume of the WindScanners is given in [11]. For RUNE, it is important to determine whether it is sufficient to use one or two WindScanners for coastal wind resource assessment. Recently, lidars mounted on buoys and floating lidars have become available. In RUNE, we use measurements from a lidar buoy to compare to those from onshore-located WindScanners that scanned the flow over water. Here, we present the lidar systems, the locations and the topographic conditions of the RUNE coastal site (Section 2). Section 2.1 shows the timeline of the campaign and the details of the scanning patterns. Settings of the lidars are given in Section 3. In Section 4, we present the wind climate, how the filtering of the measurements affected the data availability and an inter-comparison of the wind speeds estimated from the lidars. Finally, we provide conclusions and an outlook in Section 5.