A system for determining and displaying a wind field and a method of use

Abstract

Described is a system for determining and displaying a wind field. The system comprises one or more or more doppler LiDAR wind sensors directed towards an area comprising a wind-affected region, a display, and a controller. The controller is configured to receive data from each of the one or more doppler LiDAR wind sensors; control each of the one or more doppler LiDAR wind sensors to repeatedly sweep over the area and measure a wind speed at a series of speed grid points within the area; repeatedly determine a wind velocity, at a series of velocity grid points within the area; and overlay a representation of the determined wind velocity at each of the velocity grid points on a spatial representation, that comprises at least a portion of the region, on the display. Also described is a method for determining and displaying a wind field.

Description

A SYSTEM FOR DETERMINING AND DISPLAYING A WIND FIELD AND A METHOD OF USETECHNICAL FIELD

[0001] The present invention relates to a system for determining and displaying a wind field and a method of using the system.BACKGROUND

[0002] The ability to determine the wind conditions within and surrounding a wind- affected region (such as a sailing course) is key to understanding how a wind-affected object (such as a vehicle) may behave in such a region. Wind sensors exist that are capable of collecting raw wind speed data across an area comprising a wind-affected region. One example of such a wind sensor is a doppler LiDAR wind sensor.

[0003] The prior art discloses systems that take raw wind speed data relating to an area at a point in time and perform basic processing steps to convert that data into simple and limited representations of the wind conditions at that point in time. These systems provide very limited insights and representations of the wind conditions. A more sophisticated system for determining a wind field could enable better understanding of the wind conditions and how these conditions may affect any wind-affected objects in the wind- affected region. A more sophisticated system for determining a wind field could also enable more informative visualisations of the wind conditions and how these conditions may affect any wind-affected objects in the wind-affected region.

[0004] It is desired to address or ameliorate one or more disadvantages or limitations associated with the prior art, provide a system for determining and displaying a wind field, a method for determining and displaying a wind field for an area comprising a wind-affected region, or to at least provide the public with a useful alternative.SUMMARY

[0005] According to a first aspect, the present disclosure describes a system for determining and displaying a wind field, comprising: two or more wind sensors positioned to measure wind speed within an area comprising a wind-affected region; a display; and a controller configured to:344273.1■ receive data from each of the two or more wind sensors;■ control each of the wind sensors to repeatedly measure a wind speed at a speed grid point corresponding to its position;■ repeatedly determine a wind velocity, at a series of velocity grid points within the area, as a function of at least two measured wind speeds, wherein, for one or more of the velocity grid points, at least one of the measured wind speeds is a previously measured wind speed from a speed grid point that is spatially proximate to a different velocity grid point within the area; and■ overlay a representation of the determined wind velocity at each of the velocity grid points on a spatial representation, that comprises at least a portion of the region, on the display.

[0006] According to another aspect, the present disclosure describes a system for determining and displaying a wind field, comprising: one or more doppler LiDAR wind sensors directed towards an area comprising a wind- affected region; a display; and a controller configured to:■ receive data from each of the one or more doppler LiDAR wind sensors;■ control each of the one or more doppler LiDAR wind sensors to repeatedly sweep over the area and measure a wind speed at a series of speed grid points within the area;■ repeatedly determine a wind velocity, at a series of velocity grid points within the area, as a function of at least two measured wind speeds in different directions, wherein, for one or more of the velocity grid points, at least one of the measured wind speeds is a previously measured wind speed from a speed grid point that is spatially proximate to a different velocity grid point within the area; and■ overlay a representation of the determined wind velocity at each of the velocity grid points on a spatial representation, that comprises at least a portion of the region, on the display.

[0007] According to another aspect, the present disclosure describes a method for determining and displaying a wind field for an area comprising a wind-affected region, comprising: receiving, at a controller, data from one or more doppler LiDAR wind sensors that are configured to sweep over the area and repeatedly measure a wind speed at a series of speed grid points within the area;344273.1repeatedly determining, with the controller, a wind velocity, at a series of velocity grid points within the area, as a function of at least two measured wind speeds in different directions, wherein, for one of more of the velocity grid points, at least one of the measured wind speeds is a previously measured wind speed from a speed grid point that is spatially proximate to a different velocity grid point within the area; and overlaying, with the controller, a representation of the determined wind velocity at each of the velocity grid points on a spatial representation, that comprises at least a portion of the region, on a display.

[0008] In one configuration, one or more of the wind sensors is a doppler LiDAR wind sensor.

[0009] In one configuration, one or more of the wind sensors is an anemometer supported by a floating object.

[0010] In one configuration, one or more of the wind sensors is a drone holding station within the wind-affected region, sensing the wind velocity from its own thrust required to maintain its position.

[0011] In one configuration, one or more of the wind sensors is an anemometer supported above the area by a drone.

[0012] In one configuration, the system comprises two or more doppler LiDAR wind sensors directed towards the area comprising the wind-affected region.

[0013] In one configuration, the controller is configured to repeatedly determine a wind velocity, at a series of velocity grid points within the area, as a function of at least two measured wind speeds in different directions and measured by different doppler LiDAR wind sensors.

[0014] In one configuration, the series of speed grid points are unique to each sweep of each of the doppler LiDAR wind sensors.

[0015] In one configuration, each wind velocity to be determined is a wind velocity at the present time, so that the representation of the determined wind velocity at each of the velocity grid points illustrates present wind conditions.

[0016] In one configuration, each wind velocity to be determined is a wind velocity at a future time, so that the representation of the determined wind velocity at each of the velocity grid points illustrates future wind conditions.344273.1

[0017] In one configuration, for one or more of the velocity grid points, at least one of the measured wind speeds is:■ a most recently measured wind speed or a previously measured wind speed;■ a wind speed from a speed grid point that is spatially proximate to the velocity grid point or a wind speed from a speed grid point that is spatially proximate to a different velocity grid point; and■ a wind speed measured by a first doppler LiDAR wind sensor, a wind speed measured by a second doppler LiDAR wind sensor, or a wind speed measured by a different type of wind sensor.

[0018] In one configuration, the controller is configured to select the speed grid point from which to take each previously measured wind speed based on the magnitude and direction of the average wind velocity within the area.

[0019] In one configuration, the controller is configured to repeatedly determine a wind velocity, at one or more velocity grid points, as a function of a previously determined wind velocity at that velocity grid point, as well as the two measured wind speeds in different directions.

[0020] In one configuration, the controller is configured to repeatedly determine the wind velocity, at one or more of the velocity grid points within the area, as a function of at least three measured wind speeds:■ a most recently measured wind speed measured by a first doppler LiDAR wind sensor at a speed grid point that is spatially proximate to the velocity grid point;■ a previously measured wind speed measured by the first doppler LiDAR wind sensor at a speed grid point that is spatially proximate to a different velocity grid point; and■ a most recently measured wind speed measured by a second doppler LiDAR wind sensor at a speed grid point that is spatially proximate to the velocity grid point.

[0021] In one configuration, the controller is configured to repeatedly determine the wind velocity, at the one or more velocity grid points within the area, as a function of at least four measured wind speeds, wherein the fourth measured wind speed is a previously measured wind speed measured by the second doppler LiDAR wind sensor at a speed grid point that is spatially proximate to a different velocity grid point.344273.1

[0022] In one configuration, the controller is configured to apply a weighting value to each of the measured wind speeds it uses to determine each wind velocity and to determine each wind velocity as a function of the applicable weighting values.

[0023] In one configuration, each weighting value is a function of one or more of:■ distance from the speed grid point at which the wind speed was measured to the velocity grid point at which the velocity is being determined;■ time since the wind speed was measured;■ average wind velocity within the area since the wind speed was measured;■ signal-to-noise ratio of the wind speed measurement;■ type of sensor used to measure the wind speed; and■ angle between the direction at which the wind speed was measured and a direction of a most-recently determined velocity at the velocity grid point at which the velocity is being determined.

[0024] In one configuration, one or more velocity grid points in the series of velocity grid points is located inside the wind-affected region, and wherein, the controller is configured to determine the wind velocity, at the one or more velocity grid points located inside the wind-affected region, as a function of a previously measured wind speed from a speed grid point that is located outside the wind- affected region.

[0025] In one configuration, the spatial representation is a camera feed, so that the display shows the representation of the determined wind velocity at each of the velocity grid points overlayed on the camera feed of the portion of the region.

[0026] In one configuration, the spatial representation is a virtual representation, so that the display shows the representation of the determined wind velocity at each of the velocity grid points overlayed on the virtual representation of the portion of the region.

[0027] In one configuration, the representation of the determined wind velocity at each of the velocity grid points comprises one or more of:■ a number indicating an absolute or relative magnitude of the determined wind velocity;■ an arrow indicating one or more of an absolute or relative direction or magnitude of the determined wind velocity;■ shading indicating an absolute or relative magnitude of the determined wind velocity;344273.1■ colour indicating an absolute or relative magnitude of the determined wind velocity;■ one or more isobars indicating one or more of an absolute or relative direction or magnitude of the determined wind velocity.

[0028] In one configuration, the controller is configured to determine a future position of a wind-affected object in the region at a time tn+1as a function of:■ a position of the wind-affected object at a time tn;■ one or more determined wind velocities at one or more velocity grid points surrounding the wind-affected object at tn; and■ a model that relates the one or more wind velocities surrounding the wind-affected object to the velocity of the wind-affected object for a given heading; and wherein the controller is configured to overlay a representation of the future position of the wind-affected object at time tn+1on the spatial representation on the display.

[0029] In one configuration, the method comprises receiving, at the controller, data from two or more doppler LiDAR wind sensors that are configured to sweep over the area and repeatedly measure a wind speed at a series of speed grid points within the area;

[0030] In one configuration, the method comprises repeatedly determining, with the controller, a wind velocity, at a series of velocity grid points within the area, as a function of at least two measured wind speeds in different directions and measured by different doppler LiDAR wind sensors.

[0031] In one configuration, the controller selects the speed grid point from which to take each previously measured wind speed based on the magnitude and direction of the average wind velocity within the area.

[0032] In one configuration, the controller applies a weighting value to each of the measured wind speeds it uses to determine each wind velocity and determines each wind velocity as a function of the applicable weighting values.

[0033] The term “determining” (and variations thereof) as used in the specification and claims refers to active calculation performed by a controller within the system.

[0034] The term “displaying” (and variations thereof) as used in the specification and claims refers to a process actively performed by a controller within the system and may encompass generating / rendering an image or series of images for display. It may also344273.1encompass displaying the image or series of images on one or more displays within the system.

[0035] The term “comprising” as used in the specification and claims means “consisting at least in part of”. When interpreting each statement in this specification that includes the term “comprising”, features other than that or those prefaced by the term may also be present. Related terms “comprise” and “comprises” are to be interpreted in the same manner.

[0036] The reference in this specification to any prior publication (or information derived from it), or to any matter which is known, is not, and should not be taken as, an acknowledgement or admission or any form of suggestion that that prior publication (or information derived from it) or known matter forms part of the common general knowledge in the field of endeavour to which this specification relates.

[0037] As used herein “(s)” following a noun means the plural and / or singular forms of the noun.

[0038] As used herein the term “and / or” means “and” or “or” or both.

[0039] It is intended that reference to a range of numbers disclosed herein (for example, 1 to 10) also incorporates reference to all rational numbers within that range (for example, 1 , 1 .1 , 2, 3, 3.9, 4, 5, 6, 6.5, 7, 8, 9 and 10) and also any range of rational numbers within that range (for example, 2 to 8, 1 .5 to 5.5 and 3.1 to 4.7) and, therefore, all sub-ranges of all ranges expressly disclosed herein are hereby expressly disclosed. These are only examples of what is specifically intended and all possible combinations of numerical values between the lowest value and the highest value enumerated are to be considered to be expressly stated in this application in a similar manner.

[0040] This invention may also be said broadly to consist in the parts, elements and features referred to or indicated in the specification of the application, individually or collectively, and any or all combinations of any two or more said parts, elements or features, and where specific integers are mentioned herein which have known equivalents in the art to which this invention relates, such known equivalents are deemed to be incorporated herein as if individually set forth.BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 shows a schematic representation of a system for determining and displaying a wind field as described.344273.1

[0042] Figure 2 shows a velocity vector as described.

[0043] Figure 3 shows a schematic representation of an area for which a wind field is to be determined by the system as described.

[0044] Figure 4 shows a schematic representation of an area for which a wind field is to be determined by the system as described.

[0045] Figure 5 shows a schematic representation of an area for which a wind field is to be determined by the system as described.

[0046] Figure 6 shows a schematic representation of a series of steps for determining a future position of a wind-affected object.DETAILED DESCRIPTION

[0047] Described is a system for determining and displaying a wind field, comprising: one or more doppler LiDAR wind sensors directed towards an area comprising a wind- affected region; a display; and a controller. The controller is configured to receive data from each of the one or more doppler LiDAR wind sensors. The controller is also configured to control each of the one or more doppler LiDAR wind sensors to repeatedly sweep over the area and measure a wind speed at a series of speed grid points within the area. The controller is also configured to repeatedly determine a wind velocity, at a series of velocity grid points within the area, as a function of at least two measured wind speeds in different directions. For one or more of the velocity grid points, at least one of the measured wind speeds may be a previously measured wind speed from a speed grid point that is spatially proximate to a different velocity grid point within the area. The controller is also configured to overlay a representation of the determined wind velocity at each of the velocity grid points on a spatial representation, that comprises at least a portion of the region, on the display.

[0048] Described is a method for determining and displaying a wind field for an area comprising a wind-affected region. The method comprises receiving, at a controller, data from one or more doppler LiDAR wind sensors that are configured to sweep over the area and repeatedly measure a wind speed at a series of speed grid points within the area. The method also comprises repeatedly determining, with the controller, a wind velocity, at a series of velocity grid points within the area, as a function of at least two measured wind speeds in different directions. For one or more of the velocity grid points, at least one of the measured wind speeds may be a previously measured wind speed from a speed grid point that is spatially proximate to a different velocity grid point within the area. The method also344273.1comprises overlaying, with the controller, a representation of the determined wind velocity at each of the velocity grid points on a spatial representation, that comprises at least a portion of the region, on a display.

[0049] As shown in Fig. 1 , the system 100 comprises one or more wind sensors 70. One example of a wind sensor is a local wind sensor. A local wind sensor is only capable of measuring wind speed at its own location. An anemometer is an example of a local wind sensor. Another example of a wind sensor is a remote wind sensor. A remote wind sensor is a wind sensor that is capable of measuring wind speed at one or more points remote to its position. A doppler LiDAR wind sensor is an example of a remote wind sensor. The system 100 may comprise any combination of one or more local and one or more remote wind sensors.

[0050] The one or more wind sensors 70 are configured to measure wind speed within an area for which the wind field is to be determined. Any local sensors used will be positioned within the area. Any remote sensors used may be positioned within the area or outside the area - as long as they are configured (e.g. directed) to measure wind speed within the area. At least one of the wind sensors may be positioned on a land structure such as a building. At least one of the wind sensors may be positioned on a floating structure such as a boat or buoy. At least one of the wind sensors may be integral to (e.g. positioned on) a flying object such as a drone.

[0051] At least one of the wind sensors 70 may be a drone holding station configured to be positioned within the wind-affected region. The term drone holding station describes a drone that is configured to maintain a position in space. A drone holding station may determine wind velocity at its position as a function of the thrust required to maintain its position.

[0052] The one or more wind sensors 70 may be configured to repeatedly measure wind speed within the area.

[0053] As an example, the system 100 may comprise two or more doppler LiDAR wind sensors directed towards the area.

[0054] As another example, the system 100 may comprise one or more local wind sensors and one or more remote wind sensors. One or more local wind sensors may be used to generate wind speed measurements to supplement wind speed measurements from one or more remote wind sensors. For example, one or more local wind sensors may be344273.1used to provide additional data at points not well covered by one or more remote wind sensors.

[0055] The area may comprise a wind-affected region. The area may extend beyond the wind-affected region. Alternatively, the wind-affected region may take up or encompass the entire area. One or more wind-affected objects may be located in the wind-affected region.

[0056] One example of a wind-affected region is a wind-affected course. A wind- affected course is a course over which a wind-affected object, such as a vehicle, may travel (and optionally race). One example of a wind-effected course is a sailing course, such as a sailing race course. Another example of a wind-affected course is a motor racing circuit. Another example of a wind-affected course is a skiing course. Another example of a wind- affected region is a stadium used for ball sports. One example of a wind-affected object is a sailing boat. Another example of a wind-affected object is a land yacht. Another example of a wind-affected object is a windsurfing board. Another example of a wind-affected object is a kiteboarding setup. Another example of a wind-affected object is a wing foiling setup.Another example of a wind-affected object is a car used in motor racing. Another example of a wind-affected object is a skier or ski-jumper. Another example of a wind-affected object is a ball or projectile used in sporting events. Another example of a wind-affected object is an aircraft. Another example of a wind-affected region is an airport or landing area used for aircraft operations. Another example of a wind-affected object is an aircraft engaged in landing or take-off manoeuvres.

[0057] The system 100 further comprises one or more displays 90. Each of the one or more displays 90 may comprise, for example, a television, a computer screen, a smartphone screen, a tablet screen, or a projector screen.

[0058] The system 100 further comprises a controller 80. The controller 80 may comprise a processor, such as a CPU. The controller 80 may comprise a computer. The controller 80 may comprise two or more coupled controllers.

[0059] The controller 80 is coupled to the one or more wind sensors 70 in such a way that the controller 80 can receive data from (and optionally control) each of the one or more wind sensors 70. The controller 80 may be physically coupled to one or more of the wind sensors 70, by one or more cables, for example. The controller 80 may be remotely coupled to one or more of the wind sensors 70, by bluetooth, cellular network, television network, or WiFi, for example.344273.1

[0060] The controller 80 is coupled to the one or more displays 90 in such a way that the controller 80 can send data to (and optionally control) each of the one or more displays 90. The controller 80 may be physically coupled to one or more of the displays 90, by one or more cables, for example. The controller 80 may be remotely coupled to one or more of the displays 90, by bluetooth, cellular network, television network, or WiFi, for example.

[0061] The controller 80 is configured to receive data from the one or more sensors 70, process the data to form a wind field, and display a representation of the wind field on the display 90.

[0062] The controller 80 may be configured to control each of the one or more wind sensors 70 to measure wind speed within the area. If one or more of the wind sensors is a doppler LiDAR wind sensor, the controller 80 will control it to measure wind speed using the doppler effect.

[0063] If the system comprises one or more remote wind sensors (such as one or more doppler LiDAR wind sensors), the controller 80 may be configured to control the direction of measurement of one or more of the remote wind sensors. The controller 80 may be configured to control one or more of the remote wind sensors to sweep over an area to be measured. Each sweep may form an arc. Each sweep may by a full 360° rotation. The controller 80 may be configured to control one or more of the remote wind sensors to repeatedly sweep over the area. That is, the controller 80 may be configured to control one or more of the remote wind sensors to sweep back and forth over the area. The controller 80 may be configured to control one or more of the remote wind sensors to make multiple measurements during each sweep. The measurements may be taken at varying distances from the corresponding remote wind sensor. The measurements may be taken at varying angles of sweep.

[0064] The controller 80 may be configured to associate each wind sensor speed measurement with a point on a speed grid. In other words, the controller 80 may be configured to record the location that each wind sensor speed measurement applies to. Similarly, the controller 80 may be configured to associate each wind sensor speed measurement with a point in time. In other words, the controller 80 may be configured to record the time at which each wind sensor speed measurement was taken. Each wind speed measurement may be available for use by the controller 80 as soon as it is made.344273.1

[0065] As described above, each of the wind sensors is capable of measuring wind speed. However, a wind field comprises a grid of wind velocities over an area. Therefore, the controller 80 is configured to determine wind velocity from wind speed.

[0066] The controller 80 is configured to determine a wind velocity at a series of velocity grid points within the area. The controller 80 may be configured to repeatedly determine the wind velocity at the series of velocity grid points. The series of velocity grid points may be unique to each determination. Alternatively, the controller 80 may be configured to determine the velocity at one or more of the same velocity grid points multiple times. For example, the controller 80 may be configured to determine the velocity at the same series of velocity grid points for each determination.

[0067] The controller 80 may be configured to determine each wind velocity as a function of at least two measured wind speeds in different directions.

[0068] As an example, the controller may be configured to repeatedly determine a wind velocity, at a series of velocity grid points within the area, as a function of at least two measured wind speeds in different directions and measured by different doppler LiDAR wind sensors.

[0069] As shown in the example of Fig. 2, the controller 80 may determine each wind velocity as a wind velocity vector 24 by combining a first wind speed measured in a first measurement direction 9 with a second wind speed measured in a second measurement direction 10. Furthermore, the controller 80 may determine each wind velocity as a velocity vector by combining more than two wind speed measurements in at least two different directions. Regardless of whether two or more measured wind speeds are used, the controller 80 may determine each wind velocity by resolving the wind speeds to be used into x and y components based the direction in which each speed was measured relative to the coordinate system for the velocity grid. An example coordinate system for a velocity grid is shown in Fig. 2. The controller 80 may then calculate a weighted average of the x components and a weighted average of the y components to form the velocity vector.

[0070] As mentioned above, the system 100 comprises a display 90. The controller 80 may be configured to generate a representation of the determined velocity at one or more of the velocity grid points and display it on the display 90. For example, the controller 80 may be configured to overlay a representation of the determined wind velocity at one or more of the velocity grid points on a spatial representation of at least a portion of the area, that comprises at least a portion of the region (or course), on the display 90. For example, the344273.1controller 80 may be configured to overlay a representation of the determined wind velocity at each of the velocity grid points on a spatial representation, that comprises at least a portion of the region (or course), on the display 90.

[0071] The spatial representation of at least a portion of the area may be a camera feed (e.g. a live camera feed). In such a case, the display 90 may show a representation of the determined wind velocity at one or more (e.g. each) of the velocity grid points overlayed on the camera feed of the portion of the region (or course).

[0072] The spatial representation of at least a portion of the area may be a virtual representation (e.g. a computer-generated representation or a computer-augmented representation). In such a case, the display 90 may show a representation of the determined wind velocity at one or more (e.g. each) of the velocity grid points overlayed on the virtual representation of the portion of the region (or course).

[0073] The representation of the determined wind velocity at one or more (e.g. each) of the velocity grid points may comprise a number indicating the magnitude of the determined wind velocity. Additionally or alternatively, the representation of the determined wind velocity at one or more (e.g. each) of the velocity grid points may comprise an arrow indicating one or more of the direction and magnitude of the determined wind velocity. Additionally or alternatively, the representation of the determined wind velocity at one or more (e.g. each) of the velocity grid points may comprise shading indicating the magnitude of the determined wind velocity. Additionally or alternatively, the representation of the determined wind velocity at one or more (e.g. each) of the velocity grid points may comprise colour indicating the magnitude of the determined wind velocity. Additionally or alternatively, the representation of the determined wind velocity at one or more (e.g. each) of the velocity grid points may comprise one or more isobars indicating one or more of the direction and magnitude of the determined wind velocity.

[0074] Each representation of magnitude may be an absolute magnitude. Alternatively, each representation of magnitude may be a relative magnitude. For example, relative to a reference magnitude or an average magnitude. Each representation of direction may be an absolute direction. Alternatively, each representation of direction may be a relative direction. For example, relative to a reference direction or an average direction.

[0075] Displaying one or more representations of the wind velocity at one or more of the velocity grid points on a spatial representation, as described above, may enable greater insights into the effect that the wind field may have on one or more wind-affected objects344273.1within the area. This may in turn enable more engaging commentary and a more engaging viewing experience. For example, if multiple objects (e.g. yachts or cars) are racing on a wind-affected course within the area, displaying one or more representations of the current wind velocity at one or more of the velocity grid points on a spatial representation, as described, may provide insights concerning the parts of the course that currently contain the most favourable wind conditions. As another example, if multiple objects (e.g. yachts or cars) are racing on a wind-affected course within the area, displaying one or more representations of a determined future wind velocity at one or more of the velocity grid points on a spatial representation, as described, may provide insights concerning the parts of the course that may contain the most favourable wind conditions in the future.

[0076] In a first example embodiment, the system 100 comprises one doppler LiDAR wind sensor (the first sensor). Figure 3 shows a schematic representation of an area 20 for which a wind field is to be determined by the system of this first example embodiment. The area 20 comprises a wind-affected course 21 (i.e. a wind-affected region). The wind-affected course is bounded by a course boundary 22.

[0077] In the first example embodiment, the controller 80 is configured to control the first sensor 1 to sweep over a first sensor area 3. The first sensor area 3 is bounded by a first sensor area boundary 5. In this example embodiment, the area 20 is the same as the first sensor area 3.

[0078] In the first example embodiment, the first sensor 1 may measure a wind speed at a series of first sensor speed grid points 7 within the first sensor area 3 during each sweep. The first sensor speed grid points 7 may be unique to each sweep of the first sensor 1 over the first sensor area 3. Alternatively, the first sensor 1 may measure a speed at one or more of the same first sensor speed grid points 7 during multiple sweeps. The same can be said for any sweeping sensor in any embodiment.

[0079] In the first example embodiment, the controller 80 is configured to repeatedly determine a wind velocity at each of the velocity grid points 23 within the area 20 as a function of at least two measured wind speeds in different directions.

[0080] In the first example embodiment, and whenever the wind sensor is a doppler LiDAR wind sensor, the direction of each wind speed measurement is dictated by the direction that the wind sensor is facing while taking the measurement. This is because a doppler LiDAR wind sensor can only measure wind speed in the direction it is facing. As an344273.1example, the two possible wind speed measurement directions 9 for one of the first sensor speed grid points 7 is shown in Fig. 3.

[0081] In a second example embodiment, the system 100 comprises at least two doppler LiDAR wind sensors (the first sensor and the second sensor). Figure 4, shows a schematic representation of an area 20 for which a wind field is to be determined by the system of this second example embodiment. The area 20 comprises a wind-affected course 21 (i.e. a wind-affected region). The wind-affected course is bounded by a course boundary 22.

[0082] In the second example embodiment, the controller 80 is configured to control the first sensor 1 to sweep over a first sensor area 3 and the second sensor 2 to sweep over a second sensor area 4. The first sensor area 3 is bounded by a first sensor area boundary 5. The second sensor area 4 is bounded by a second sensor area boundary 6. In this example embodiment, the area 20 may be defined by, for example, the overlap of the first sensor area 3 and the second sensor area 4. Alternatively, the area 20 may exceed the overlap of the first sensor area 3 and the second sensor area 4 and be defined by, for example, the combined areas of the first sensor 1 and the second sensor 2.

[0083] In any embodiments with more than two sensor areas, the area 20 may be defined by the overlap of, or the combined areas of, for example, two, three, four, or any number of sensors in the system 100, or all of the sensors in the system 100.

[0084] In the second example embodiment, the first sensor 1 may measure a wind speed at a series of first sensor speed grid points 7 within the first sensor area 3 during each sweep. The first sensor speed grid points 7 may be unique to each sweep of the first sensor 1 over the first sensor area 3. Alternatively, the first sensor 1 may measure a speed at one or more of the same first sensor speed grid points 7 during multiple sweeps. Similarly, the second sensor 2 may measure a wind speed at a series of second sensor speed grid points 8 within the second sensor area 4 during each sweep. The series of second sensor speed grid points 8 may be unique to each sweep of the second sensor 2 over the second sensor area 4. Alternatively, the second sensor 2 may measure a speed at one or more of the same second sensor speed grid points 8 during multiple sweeps.

[0085] The first sensor speed grid points 7 may be unique to the first sensor 1 . The second sensor speed grid points 8 may be unique to the second sensor 2. Alternatively, one or more of the second sensor speed grid points 8 may be the same as one or more of the first sensor speed grid points 7.344273.1

[0086] In any embodiment with two or more wind sensors, each with their own set of one or more speed grid points, each set of speed grid points may be unique to their wind sensor. Alternatively, one or more of the speed grid points for one wind sensor may be the same as one or more of the speed grid points for one or more other wind sensors.

[0087] In the second example embodiment, the controller 80 is configured to repeatedly determine a wind velocity at each of the velocity grid points 23 within the area 20 as a function of at least two measured wind speeds in different directions. Each of these measured wind speeds may be from either the first sensor 1 or the second sensor 2.

[0088] Returning to a general description of the system, as mentioned above, the controller 80 is configured to repeatedly determine a wind velocity at a series of velocity grid points 23 within the area 20. The controller 80 may be configured to determine each wind velocity as a function of at least two measured wind speeds in different directions. Each of the measured wind speeds may come from any one of the one or more wind sensors 70.

[0089] The controller 80 may use measured wind speeds from any number of different speed grid points for the determination of each wind velocity. For one or more of the velocity grid points, at least one of the measured wind speeds may be from a speed grid point that is spatially proximate to that velocity grid point. Additionally or alternatively, for one or more of the velocity grid points, at least one of the measured wind speeds may be from a speed grid point that is spatially proximate to a different velocity grid point within the area. For example, if a velocity is to be determined at a velocity grid point within the wind-affected region (or course), at least one of the measured wind speeds may be from a speed grid point that is located outside the region (or course).

[0090] Throughout, spatially proximate may mean the closest in space. For example, the speed grid point that is closest to the velocity grid point in space. Spatially proximate may mean substantially the closest in space. For example, among the two or three closest speed or velocity grid points in space. Spatially proximate may refer to the one or more speed grid points whose most-recent wind speed measurements accurately represent the current local wind conditions at the velocity grid point in question (i.e. the velocity grid point at which the velocity is to be determined).

[0091] As further described below, allowing at least one of the measured wind speeds to be from a speed grid point that is spatially proximate to a different velocity grid point within the area means that the controller 80 can better determine the wind velocity at each velocity grid point at the current time or at a future time. This is because the controller 80 can make344273.1the determination a function of the fact that wind conditions, which have been measured or determined up to and including the present moment at a different part of the area, may have now moved to the velocity grid point in question (or may otherwise now be influencing the velocity in question), or may arrive at the velocity grid point in question in the future (or may otherwise influence the velocity in question in the future).

[0092] Regardless of whether the measured wind speed used in a velocity determination is from a speed grid point that is spatially proximate to the velocity grid point in question, the controller 80 may use measured wind speeds from any current or previous time for the determination of each wind velocity.

[0093] In one example, the controller 80 may be configured to repeatedly determine a wind velocity, at a series of grid points within the area, as a function of at least two measured wind speeds in different directions. For one or more of the velocity grid points, at least one of the measured wind speeds is a previously measured wind speed from a speed grid point that is spatially proximate to a different velocity grid point within the area.

[0094] For one or more of the velocity grid points, at least one of the measured wind speeds that the controller 80 uses to determine a velocity may be a most recently measured wind speed or a previously measured wind speed.

[0095] For one or more of the velocity grid points, at least one of the measured wind speeds that the controller 80 uses to determine a velocity may be a wind speed from a speed grid point that is spatially proximate to the velocity grid point or a wind speed from a speed grid point that is spatially proximate to a different velocity grid point.

[0096] For one or more of the velocity grid points, at least one of the measured wind speeds that the controller 80 uses to determine a velocity may be a wind speed measured by a first doppler LiDAR wind sensor, a wind speed measured by a second doppler LiDAR wind sensor, or a wind speed measured by a different type of wind sensor.

[0097] The controller 80 may use any number of wind speed measurements, from any number of speed grid points and times, to determine each velocity value. As soon as a wind speed measurement has been made, the controller 80 may use it. Using a greater number of wind speed measurements may increase the accuracy of the velocity determination, however, it may also increase the computational load. This is especially true if the controller 80 is repeatedly determining velocity values for a large number of velocity grid points, which may be helpful in generating a high-resolution wind field.344273.1

[0098] For a given velocity determination, when selecting the wind speed measurements to use, the controller 80 may consider how the wind speed at one point in time and space may affect the velocity at a different point in time and / or space. To do this, the controller 80 may make assumptions about how the wind field might evolve (i.e. move or shift) in light of the measurements made.

[0099] For example, the controller 80 may use a series of wind speed measurements and or determined velocities across the area to determine one or more average wind velocities across the area. For example, the controller 80 may divide the area up into two or more sub-areas and use a series of wind speed measurements and or determined velocities across each sub-area to determine an average wind velocity across each sub-area. The controller 80 may then use, for example, a frozen turbulence model and assume that the wind conditions at one part of the area may ‘move’ to a different part of the area over time in response to the one or more average wind velocities across the area.

[0100] Additionally or alternatively, the controller 80 may assume that the wind field evolves according to the Navier Stokes equations. A simulation of wind evolution according to these equations, using currently estimated wind velocity as initial conditions, would form the prediction step of a filter, providing a predicted state of the wind field for the next time step, to be corrected by subsequent measurements.

[0101] A statistical framework such as a Kalman filter may allow for the prediction of the wind field by Navier-Stokes equations or otherwise, and the correction of the wind field by measurements received.

[0102] Additionally or alternatively, the controller 80 may make assumptions about the evolution of the wind field by using wider meteorological data or forecasts to add boundary conditions. For example, the controller 80 may use data from one or more local weather models to add boundary conditions.

[0103] To improve the accuracy of each velocity determination, the controller 80 may apply a weighting value to one or more of the wind speed measurements it uses. For example, the controller 80 may apply a weighting value to each of the wind speed measurements it uses to determine a given wind velocity value. The controller 80 may determine a velocity as a function of the magnitude and direction of the selected wind speed measurements adjusted by their corresponding weightings.

[0104] Each weighting value may be a function of the distance from the speed grid point at which the wind speed was measured to the velocity grid point in question. At a given344273.1time, wind speed measurements from spatially proximate speed grid points may be given higher weightings as they may more accurately represent the wind conditions at the velocity grid point. For example, when calculating the current velocity at a velocity grid point, if two wind speed measurements were taken at two different speed grid points one second ago, and one of the speed grid points is 1 metre away from the velocity grid point in question while the other speed grid point is 100 metres away from the velocity grid point in question, the controller 80 may apply a higher weighting to the wind speed measurement from the speed grid point that is 1 metre away. This weighting decision reflects the fact that, all other factors being equal, the closer measurement will be a better representation of the current wind conditions at the velocity grid point in question.

[0105] Each weighting value may be a function of the time since the wind speed was measured. If, for example, a selected speed grid point is spatially proximate to the velocity grid point, a recent (e.g. most recent) wind speed measurement from that speed grid point may better represent the wind conditions at the velocity grid point, and so it may be given a higher weighting. If, for example, a selected speed grid point is spatially proximate to another velocity grid point, a previous wind speed measurement from that speed grid point may better represent the wind conditions at the velocity grid point at which the determination is being made, and so it may be given a higher weighting. This is because it may take time for the wind conditions at a distant speed grid point to significantly affect the wind conditions at the velocity grid point at which the determination is being made.

[0106] Each weighting value may be a function of one or more average wind velocities within the area since the wind speed was measured. As described above, the controller 80 may determine an average wind velocity across the area (and / or across one or more subareas). Then, if the controller 80 selects one or more wind speed measurements from speed grid points that are spatially proximate to other velocity grid points, it may give more weight to wind speed measurements that were previously taken at speed grid points that are more directly upwind of the velocity grid point in question. This is because the controller 80 may assume that wind conditions may move across the area substantially in the direction of the average wind velocity.

[0107] Each weighting value may be a function of the signal-to-noise ratio of the wind speed measurement. Wind speed measurements with a higher signal-to-noise ratio may be given higher weightings as they may better represent the wind conditions within the area.

[0108] Each weighting value may be a function of the type of sensor used to measure the wind speed. This is because some types of wind sensors are known to be more accurate344273.1at measuring wind speed than others. The controller 80 may give higher weightings to wind speed measurements taken from more accurate wind sensors.

[0109] Each weighting value may be a function of the angle between the direction at which the wind speed was measured and a direction of a most-recently determined velocity at the velocity grid point in question. For example, if a wind speed measurement that the controller 80 selects for determining a wind velocity at a velocity grid point is 80° from the direction of the most-recently determined velocity at that grid point, the controller 80 may apply only a low weight to that speed measurement. Alternatively, if, for example, a wind speed measurement that the controller 80 selects for determining a wind velocity at a velocity grid point is 4° from the direction of the most-recently determined velocity at that grid point, the controller 80 may apply a high weight to that speed measurement. This is because, the closer to parallel a wind speed measurement is with a velocity to be determined, the better it may represent that velocity.

[0110] As described above, the controller 80 may be configured to determine a wind velocity at a velocity grid point as a function of two or more wind speed measurements. Additionally or alternatively, the controller 80 may be configured to determine a wind velocity at a velocity grid point as a function of one or more previously determined wind velocities.

[0111] The controller 80 may apply a weighting value to one or more of these previously determined wind velocities based on the distance from it to the velocity grid point at which the velocity is to be determined. Previously determined wind velocities from closer velocity grid points may be given higher weighting as they may more accurately represent the wind conditions at the velocity grid point at which the velocity is to be determined.

[0112] The controller 80 may apply a weighting value to one or more of these previously determined wind velocities based on the time since the wind velocity was determined. If, for example, a selected velocity grid point is spatially proximate to the velocity grid point at which the velocity is to be determined, a recent (e.g. most recent) velocity value from that selected velocity grid point may better represent the wind conditions at the velocity grid point at which the velocity is to be determined, and so it may be given a higher weighting. If, for example, a selected velocity grid point is not spatially proximate to the velocity grid point at which the velocity is to be determined, a previous velocity value from that velocity grid point may better represent the wind conditions at the velocity grid point at which the determination is to be made, and so it may be given a higher weighting. This is because it may take time for the wind conditions at a distant velocity grid point to significantly affect the wind conditions at the velocity grid point at which the determination is to be made.344273.1

[0113] The controller 80 may apply a weighting value to one or more of these previously determined wind velocities based on the average wind velocity within the area since the wind velocity was determined. As described above, the controller 80 may determine one or more average wind velocities across the area. Then, if the controller 80 selects one or more velocity grid points that are not spatially proximate to the velocity grid point at which the determination is to be made, it may give more weight to wind velocity values from velocity grid points that are more directly upwind of the velocity grid point at which the determination is to be made. This is because the controller 80 may assume that wind conditions may move across the area substantially in the direction of the one or more average wind velocities.

[0114] Using one or more previously calculated velocities to determine a new velocity value may reduce the computational load for the controller 80 as it may mean that the controller 80 does not need to use as many individual speed measurements in the determination.

[0115] An example will now be described to further illustrate how the controller 80 may select measured speeds, previously determined velocity values, and weightings when determining a velocity value. This is an example only. In this example, as shown in Fig. 5, the controller 80 is determining a current velocity at a velocity grid point 42. In this example, the controller 80 has determined the average wind velocity 15 across the area 20.

[0116] In this example, for the determination of the velocity at the velocity grid point 42, the controller 80 has also selected two speed grid points from which to consider most recent speed measurements 40,41 , four speed grid points from which to consider previous speed measurements 50,51 ,60,61 , and two velocity grid points from which to consider previously determined velocity values 52,62.

[0117] In this example, the speed grid points 40,50,60 relate to a first doppler LiDAR wind sensor and the speed grid points 41 ,51 ,61 relate to a second doppler LiDAR wind sensor.

[0118] In this example, the controller 80 has selected the two speed grid points from which to consider most recent speed measurements 40,41 , because these speed grid points are spatially proximate to the velocity grid point 42. Therefore, the most recent speed measurements at these speed grid points should be indicative of the current wind conditions near the velocity grid point 42.344273.1

[0119] In this example, the controller 80 has selected the four speed grid points from which to consider previous speed measurements 50,51 ,60,61 , because these speed grid points are substantially upwind of the velocity grid point 42 relative to the average wind velocity. Therefore, previous speed measurements at these speed grid points may be indicative of the current wind conditions near the velocity grid point 42.

[0120] In this example, the controller 80 has selected the two velocity grid points from which to consider previously determined velocity values 52,62, because these speed grid points are substantially upwind of the velocity grid point relative to the average wind velocity. Therefore, previous velocity values for these velocity grid points may be indicative of the current wind conditions near the velocity grid point 42.

[0121] In this example, the controller 80 may apply high weightings to most recent speed measurements from the speed grid points 40,41 . The controller may also apply high weightings to previous speed measurements at the speed grid points 60,61 and one or more previously determined velocity values at velocity grid point 62 because these points are directly upwind of the velocity grid point 42. The controller may however apply lower weightings to previous speed measurements at the speed grid points 50,51 and one or more previously determined velocity values at velocity grid point 52 because these points are not as directly upwind relative to the average wind velocity. Therefore, they may not be as indicative of the current wind conditions near the velocity grid point 42.

[0122] It may be helpful to consider why, in this example, the controller would not simply use the most-recent wind speed measurement from the speed grid points 40,41 and disregard the other wind speed measurements and velocities. One reason may be that the most-recent wind speed measurements at these speed grid points 40,41 may have been taken long enough ago that using them alone may not produce the most accurate determination of the velocity at velocity grid point 42 - as the wind field may shift over time in response to the average wind velocity 15. Another reason may be that the measurement directions of the most-recent wind speed measurements at these speed grid points 40,41 may be so similar that using them alone may not produce the most accurate determination of the velocity at velocity grid point 42. In this example, and in any use case of the system 100, using multiple different wind speed measurements and velocities from different times and different parts of the area may result in the most accurate velocity determinations.

[0123] In one example embodiment, the system 100 may use every existing measured wind speed, weighted accordingly, to determine each new velocity value. In other words, the344273.1system 100 could use a filtering model to determine each new wind velocity value by incorporating weighted information from all historical measurements.

[0124] In one example embodiment, the system 100 may use every existing measured wind speed and every existing determined velocity value, weighted accordingly, to determine each new velocity value. In other words, the system 100 could use a filtering model to determine each new wind velocity value by incorporating weighted information from all historical measurements and velocities.

[0125] In one example embodiment, the system 100 may use one or more wind speed measurements and / or one or more determined velocity values from grid points downwind of the velocity grid point in question. For example, when using a Navier Stokes approach, data from downwind speed and or velocity grid points may influence the determination of the velocity at the velocity grid point in question due to pressure gradients downwind of the velocity grid point in question.

[0126] Returning to a general description of the system, as described above, the controller 80 may use measured wind speeds from any number of different speed grid points for the determination of each wind velocity. For one or more of the velocity grid points, at least one of the measured wind speeds may be from a speed grid point that is spatially proximate to a different velocity grid point within the area. This aspect means that the controller 80 can better determine the current wind velocity at each velocity grid point by making the determination a function of the fact that wind conditions, which were previously measured or determined at a different part of the area, may have now arrived at the velocity grid point in question or may otherwise now be influencing the velocity in question. In this way, each wind velocity to be determined may be a wind velocity at the present time, so that the representation of the determined wind velocity at each of the velocity grid points illustrates present wind conditions.

[0127] Furthermore, because the controller 80 may use measured wind speeds from any number of different speed grid points for the determination of each wind velocity, it may also determine (i.e. estimate) what the wind velocity at a given velocity grid point will be at a given time in the future. In the same way that the controller 80 may determine a current wind velocity by making the determination a function of the fact that wind conditions, which were previously measured or determined at a different part of the area, may have now arrived at the velocity grid point in question or may otherwise now be influencing the velocity in question, the controller 80 may also determine a future wind velocity by making the determination a function of the fact that wind conditions, which have been measured or344273.1determined up to and including the present moment at a different part of the area, may arrive at the velocity grid point in question in the future, or may otherwise influence the velocity in question in the future. In this way, each wind velocity to be determined may be a wind velocity at a future time, so that the representation of the determined wind velocity at each of the velocity grid points illustrates future wind conditions. The future may refer to, for example, a next time step, or some later time.

[0128] The way in which the controller 80 may use one or more determined velocities to estimate the future position of an object will now be described.

[0129] As mentioned above, the controller 80 may be configured to generate a representation of the determined velocity at one or more of the velocity grid points and display it on the display 90. Additionally or alternatively, the controller 80 may be configured to determine (i.e. estimate) a likely future position of a wind-affected object in the wind- affected region as a function of the determined velocity at one or more of the velocity grid points.

[0130] As shown in Fig. 6, the controller 80 may begin at stage 200 by determining the current (tn) position of the wind-affected object and determining one or more wind velocities at one or more velocity grid points surrounding the wind-affected object in the way described above.

[0131] The controller 80 may obtain data indicative of the current position via, for example, a reading from a locating device (such as a GPS device) attached to the wind- affected object. The controller 80 may determine the current position using, for example, image recognition on a live video feed of the area.

[0132] At stage 300, the controller 80 may input the determined current object position, orientation and velocity, and one or more wind velocities into a model that relates the one or more wind velocities surrounding the wind-affected object to the acceleration of the wind- affected object.

[0133] The model may include a representation of how aerodynamic forces on the object can be estimated from the current geometry, position, orientation, velocity, and angular velocity of the object with respect to the one or more surrounding wind velocities. The estimated aerodynamic forces may then be combined with other forces on the object, for example hydrodynamic forces, to determine the total resultant forces on the object. The acceleration and angular acceleration of the object may then be determine using Newton's344273.1second law of motion. The velocity, angular velocity, position and orientation of the object may then be updated via an integration step.

[0134] At stage 400, the controller 80 determines the position of the wind-affected object at the next time step (tn+1) from the model.

[0135] At stage 500, the controller 80 may progress the current time step forward one step. The controller may then repeat the process shown in Fig. 6. For each repeat, the current position of the wind-affected object at 200 may be the position determined in the previous time step and the determined wind velocities at one or more velocity grid points surrounding the wind-affected object may be updated for the current time step as described above. In this way, the controller 80 may numerically (i.e. with a series of iterative determinations) determine a position of the object multiple time steps into the future.

[0136] The controller 80 may be configured to overlay a representation of the determined likely position of the wind-affected object at one or more future time steps on the spatial representation on the display.

[0137] While the examples of Figs. 3, 4, and 5 correspond to a two-dimensional area 20, the system 100 of the present disclosure could be adapted for determining and displaying a three-dimensional wind field. Determining a three-dimensional wind field would require the wind speed measurements to collectively have components in three dimensions. This could be achieved by, for example incorporating local wind sensors at different heights within a volume in which the wind field is to be determined. Additionally or alternatively, this could be achieved by incorporating one or more remote wind sensors capable of measuring wind speed at multiple heights.

[0138] As described above, the system 100 may comprise one or more doppler LiDAR wind sensors. One example of a doppler LiDAR wind sensor is a Stream Line AllSky XR+. This product has an typical sampling range of 6.5 km. Another example of a doppler LiDAR wind sensor is a Stream Line AllSky XR. Another example of a doppler LiDAR wind sensor is a Stream Line AllSky LR.

[0139] Although embodiments have been described with reference to a number of illustrative embodiments thereof, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims. Many modifications will be apparent to those skilled in the art without departing from the scope of the present invention as herein described with reference to the accompanying drawings.344273.1

[0140] Unless specifically stated otherwise, as apparent from the following discussions, it is appreciated that throughout the specification discussions utilizing terms such as "processing," "computing," "calculating," "determining", analysing" or the like, refer to the action and / or processes of a computer or computing system, or similar electronic computing device, that manipulate and / or transform data represented as physical, such as electronic, quantities into other data similarly represented as physical quantities.

[0141] In a similar manner, the term "processor" may refer to any device or portion of a device that processes electronic data, e.g., from registers and / or memory to transform that electronic data into other electronic data that, e.g., may be stored in registers and / or memory. A "computer" or a "computing machine" or a "computing platform" may include one or more processors. The terms "processor" and "processing unit" may be used interchangeably throughout the specification.

[0142] It should be appreciated that the present disclosure can be implemented in numerous ways, including as a process, an apparatus, a system, a device, a method, or a computer-readable medium such as a computer-readable storage medium or embedded system containing computer-readable instructions or computer program code, or as a computer program product, comprising a computer-usable medium having a computer- readable program code embodied therein. The methodologies described herein are, in one embodiment, performable by one or more processors that accept computer-readable (also called machine-readable) code containing a set of instructions that when executed by one or more of the processors carry out at least one of the methods described herein. Any processor capable of executing a set of instructions (sequential or otherwise) that specify actions to be taken are included. Thus, one example is a typical processing system that includes one or more processors. Each processor may include one or more of a CPU, a graphics processing unit, and a programmable DSP unit. The processing system further may include a memory subsystem including main RAM and / or a static RAM, and / or ROM. A bus subsystem may be included for communicating between the components. The processing system further may be a distributed processing system with processors coupled by a network. If the processing system requires a display, such a display may be included, e.g., a liquid crystal display (LCD) or a cathode ray tube (CRT) display. If manual data entry is required, the processing system also includes an input device such as one or more of an alphanumeric input unit such as a keyboard, a pointing control device such as a mouse, and so forth. The term memory unit as used herein, if clear from the context and unless explicitly stated otherwise, also encompasses a storage system such as a disk drive unit. The processing system in some configurations may include a sound output device, and a344273.1network interface device. The memory subsystem thus includes a computer-readable carrier medium that carries computer-readable code (e.g., software) including a set of instructions to cause performing, when executed by one or more processors, one or more of the methods described herein. Note that when the method includes several elements, e.g., several steps, no ordering of such elements is implied, unless specifically stated. The software may reside in the hard disk, or may also reside, completely or at least partially, within the RAM and / or within the processor during execution thereof by the computer system. Thus, the memory and the processor also constitute computer-readable carrier medium carrying computer-readable code.

[0143] Furthermore, a computer-readable carrier medium may form, or be included in a computer program product.

[0144] In alternative embodiments, the one or more processors operate as a standalone device or may be connected, e.g., networked to other processor(s), in a networked deployment, the one or more processors may operate in the capacity of a server or a user machine in server-user network environment, or as a peer machine in a peer-to- peer or distributed network environment. The one or more processors may form a personal computer (PC), a tablet PC, a set-top box (STB), a Personal Digital Assistant (PDA), a cellular telephone, a web appliance, a network router, switch or bridge, or any machine capable of executing a set of instructions (sequential or otherwise) that specify actions to be taken by that machine.

[0145] Note that while diagrams only show a single processor and a single memory that carries the computer-readable code, those in the art will understand that many of the components described above are included, but not explicitly shown or described in order not to obscure the inventive aspect. For example, while only a single machine is illustrated, the term "machine" shall also be taken to include any collection of machines that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methodologies discussed herein.

[0146] Thus, one embodiment of each of the methods described herein is in the form of a computer-readable carrier medium carrying a set of instructions, e.g. a computer program that is for execution on one or more processors, e.g. one or more processors that are part of web server arrangement. Thus, as will be appreciated by those skilled in the art, embodiments of the present invention may be embodied as a method, an apparatus such as a special purpose apparatus, an apparatus such as a data processing system, or a computer-readable carrier medium, e.g. a computer program product. The computer-344273.1readable carrier medium carries computer readable code including a set of instructions that when executed on one or more processors cause the processor or processors to implement a method. Accordingly, aspects of the present invention may take the form of a method, an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of carrier medium (e.g., a computer program product on a computer-readable storage medium) carrying computer-readable program code embodied in the medium.

[0147] The software may further be transmitted or received over a network via a network interface device. While the carrier medium is shown in an exemplary embodiment to be a single medium, the term "carrier medium" should be taken to include a single medium or multiple media (e.g. a centralized or distributed database, and / or associated caches and servers) that store the one or more sets of instructions. The term "carrier medium" shall also be taken to include any medium that is capable of storing, encoding or carrying a set of instructions for execution by one or more of the processors and that cause the one or more processors to perform any one or more of the methodologies of the present invention. A carrier medium may take many forms, including but not limited to, non-volatile media, volatile media, and transmission media. Non-volatile media includes, for example, optical, magnetic disks, and magneto-optical disks. Volatile media includes dynamic memory, such as main memory. Transmission media includes coaxial cables, copper wire and fibre optics, including the wires that comprise a bus subsystem. Transmission media may also take the form of acoustic or light waves, such as those generated during radio wave and infrared data communications. For example, the term "carrier medium" shall accordingly be taken to include, but not be limited to, solid-state memories, a computer product embodied in optical and magnetic media; a medium bearing a propagated signal detectable by at least one processor of one or more processors and representing a set of instructions that, when executed, implement a method; and a transmission medium in a network bearing a propagated signal detectable by at least one processor of the one or more processors and representing the set of instructions.

[0148] It will be understood that the steps of methods discussed are performed in one embodiment by an appropriate processor (or processors) of a processing (i.e., computer) system executing instructions (computer-readable code) stored in storage. It will also be understood that the invention is not limited to any particular implementation or programming technique and that the invention may be implemented using any appropriate techniques for implementing the functionality described herein. The invention is not limited to any particular programming language or operating system.344273.1

[0149] It should be appreciated that in the above description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of the various inventive aspects. This method of disclosure, however, is not to be interpreted as reflecting an intention that the claimed invention requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the Detailed Description are hereby expressly incorporated into this Detailed Description, with each claim standing on its own as a separate embodiment of this invention.

[0150] Furthermore, while some embodiments described herein include some but not other features included in other embodiments, combinations of features of different embodiments are meant to be within the scope of the invention, and form different embodiments, as would be understood by those skilled in the art. For example, in the following claims, any of the claimed embodiments can be used in any combination.

[0151] Furthermore, some of the embodiments are described herein as a method or combination of elements of a method that can be implemented by a processor of a computer system or by other means of carrying out the function. Thus, a processor with the necessary instructions for carrying out such a method or element of a method forms a means for carrying out the method or element of a method. Furthermore, an element described herein of an apparatus embodiment is an example of a means for carrying out the function performed by the element for the purpose of carrying out the invention.

[0152] In the description provided herein, numerous specific details are set forth. However, it is understood that embodiments of the invention may be practiced without these specific details. In other instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.

[0153] Similarly, it is to be noticed that the term coupled, when used in the claims, should not be interpreted as being limited to direct connections only. The terms "coupled" and "connected," along with their derivatives, may be used. It should be understood that these terms are not intended as synonyms for each other. Thus, the scope of the expression a device A coupled to a device B should not be limited to devices or systems wherein an output of device A is directly connected to an input of device B. It means that there exists a path between an output of A and an input of B which may be a path including other devices or means. "Coupled" may mean that two or more elements are either in direct physical or344273.1electrical contact, or that two or more elements are not in direct contact with each other but yet still co-operate or interact with each other.

[0154] Thus, while there has been described what are believed to be the preferred embodiments of the invention, those skilled in the art will recognize that other and further modifications may be made thereto without departing from the spirit of the invention, and it is intended to claim all such changes and modifications as falling within the scope of the invention. For example, any formulas given above are merely representative of procedures that may be used. Functionality may be added or deleted from the block diagrams and operations may be interchanged among functional blocks. Steps may be added or deleted to methods described within the scope of the present invention.344273.1

Claims

CLAIMS:1 . A system for determining and displaying a wind field, comprising: one or more doppler LiDAR wind sensors directed towards an area comprising a wind-affected region; a display; and a controller configured to:■ receive data from each of the one or more doppler LiDAR wind sensors;■ control each of the one or more doppler LiDAR wind sensors to repeatedly sweep over the area and measure a wind speed at a series of speed grid points within the area;■ repeatedly determine a wind velocity, at a series of velocity grid points within the area, as a function of at least two measured wind speeds in different directions, wherein, for one or more of the velocity grid points, at least one of the measured wind speeds is a previously measured wind speed from a speed grid point that is spatially proximate to a different velocity grid point within the area; and■ overlay a representation of the determined wind velocity at each of the velocity grid points on a spatial representation, that comprises at least a portion of the region, on the display.

2. The system of claim 1 , comprising two or more doppler LiDAR wind sensors directed towards the area comprising the wind-affected region,3. The system of claim 1 or claim 2, wherein the controller is configured to repeatedly determine a wind velocity, at a series of velocity grid points within the area, as a function of at least two measured wind speeds in different directions and measured by different doppler LiDAR wind sensors.

4. The system of any one of claims 1 to 3, wherein the series of speed grid points are unique to each sweep of each of the doppler LiDAR wind sensors.

5. The system of claim 1 or claim 4, wherein each wind velocity to be determined is a wind velocity at the present time, so that the representation of the determined wind velocity at each of the velocity grid points illustrates present wind conditions.

6. The system of claim 1 or claim 4, wherein each wind velocity to be determined is a wind velocity at a future time, so that the representation of the determined wind velocity at each of the velocity grid points illustrates future wind conditions.344273.

17. The system of any one of claims 1 to 6, wherein, for one or more of the velocity grid points, at least one of the measured wind speeds is:■ a most recently measured wind speed or a previously measured wind speed;■ a wind speed from a speed grid point that is spatially proximate to the velocity grid point or a wind speed from a speed grid point that is spatially proximate to a different velocity grid point; and■ a wind speed measured by a first doppler LiDAR wind sensor, a wind speed measured by a second doppler LiDAR wind sensor, or a wind speed measured by a different type of wind sensor.

8. The system of any one of claims 1 to 7, wherein the controller is configured to select the speed grid point from which to take each previously measured wind speed based on the magnitude and direction of the average wind velocity within the area.

9. The system of any one of claims 1 to 8, wherein the controller is configured to repeatedly determine a wind velocity, at one or more velocity grid points, as a function of a previously determined wind velocity at that velocity grid point, as well as the two measured wind speeds in different directions.

10. The system of any one of claims 1 to 9, wherein the controller is configured to apply a weighting value to each of the measured wind speeds it uses to determine each wind velocity and to determine each wind velocity as a function of the applicable weighting values.11 . The system of claim 10, wherein each weighting value is a function of one or more of:■ distance from the speed grid point at which the wind speed was measured to the velocity grid point at which the velocity is to be determined;■ time since the wind speed was measured;■ average wind velocity within the area since the wind speed was measured;■ signal-to-noise ratio of the wind speed measurement;■ type of sensor used to measure the wind speed; and■ angle between the direction at which the wind speed was measured and a direction of a most-recently determined velocity at the velocity grid point at which the velocity is to be determined.

12. The system of any one of claims 1 to 11 , wherein one or more velocity grid points in the series of velocity grid points is located inside the wind-affected region, and344273.1wherein, the controller is configured to determine the wind velocity, at the one or more velocity grid points located inside the wind-affected region, as a function of a previously measured wind speed from a speed grid point that is located outside the region.

13. The system of claim any one of claims 1 to 12, wherein the spatial representation is a camera feed, so that the display shows the representation of the determined wind velocity at each of the velocity grid points overlayed on the camera feed of the portion of the region.

14. The system of any one of claims 1 to 12, wherein the spatial representation is a virtual representation, so that the display shows the representation of the determined wind velocity at each of the velocity grid points overlayed on the virtual representation of the portion of the region.

15. The system of any one of claims 1 to 14, wherein the representation of the determined wind velocity at each of the velocity grid points comprises one or more of:■ a number indicating an absolute or relative magnitude of the determined wind velocity;■ an arrow indicating one or more of an absolute or relative direction or magnitude of the determined wind velocity;■ shading indicating an absolute or relative magnitude of the determined wind velocity;■ colour indicating an absolute or relative magnitude of the determined wind velocity;■ one or more isobars indicating one or more of an absolute or relative direction or magnitude of the determined wind velocity.

16. The system of any one of claims 1 to 15, wherein the controller is configured to determine a future position of a wind-affected object in the region at a time tn+1as a function of:■ a position of the wind-affected object at a time tn;■ one or more determined wind velocities at one or more velocity grid points surrounding the wind-affected object at tn; and■ a model that relates the one or more wind velocities surrounding the wind-affected object to the velocity of the wind-affected object for a given heading; and wherein the controller is configured to overlay a representation of the future position of the wind-affected object at time tn+1on the spatial representation on the display.344273.

117. A method for determining and displaying a wind field for an area comprising a wind- affected region, comprising: receiving, at a controller, data from one or more doppler LiDAR wind sensors that are configured to sweep over the area and repeatedly measure a wind speed at a series of speed grid points within the area; repeatedly determining, with the controller, a wind velocity, at a series of velocity grid points within the area, as a function of at least two measured wind speeds in different directions, wherein, for one of more of the velocity grid points, at least one of the measured wind speeds is a previously measured wind speed from a speed grid point that is spatially proximate to a different velocity grid point within the area; and overlaying, with the controller, a representation of the determined wind velocity at each of the velocity grid points on a spatial representation, that comprises at least a portion of the region, on a display.

18. The method of claim 17, comprising: receiving, at the controller, data from two or more doppler LiDAR wind sensors that are configured to sweep over the area and repeatedly measure a wind speed at a series of speed grid points within the area; repeatedly determining, with the controller, a wind velocity, at a series of velocity grid points within the area, as a function of at least two measured wind speeds in different directions and measured by different doppler LiDAR wind sensors.

19. The method of claim 17 or claim 18, wherein the controller selects the speed grid point from which to take each previously measured wind speed based on the magnitude and direction of the average wind velocity within the area.

20. The method of any one of claims 17 to 19, wherein the controller applies a weighting value to each of the measured wind speeds it uses to determine each wind velocity and determines each wind velocity as a function of the applicable weighting values; and wherein each weighting value is a function of one or more of:■ distance from the speed grid point at which the wind speed was measured to the velocity grid point at which the velocity is to be determined;■ time since the wind speed was measured;■ average wind velocity within the area since the wind speed was measured;■ signal-to-noise ratio of the wind speed measurement; and.■ type of sensor used to measure the wind speed; and344273.1■ angle between the direction at which the wind speed was measured and a direction of a most-recently determined velocity at the velocity grid point at which the velocity is to be determined.344273.1

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