CA2853915A1

CA2853915A1 - Weather warning system and method

Info

Publication number: CA2853915A1
Application number: CA2853915A
Authority: CA
Inventors: Ronald Joseph Chapman; Darren Michael Cherneski; Michael Patrick Maxwell Gibbons; Andrew Webster; Jeffrey Ross Stanley Lundgren; Peter Anthony Irwin; Stoyan Trayanov Stoyanov
Original assignee: RWDI Air Inc
Current assignee: RWDI Air Inc
Priority date: 2014-06-09
Filing date: 2014-06-09
Publication date: 2015-12-09
Anticipated expiration: 2034-06-09
Also published as: CA2853915C

Abstract

A computer-implemented method and system for providing a forecast and weather- warning alert at a site is provided. The method and system can account for site-specific factors such as local topography and building effects to generate and deliver a forecast comprising one or more of wind gust, convective gust, precipitation, snow accretion, and snow shedding conditions.

Description

WEATHER WARNING SYSTEM AND METHOD
FIELD
[0001] The present subject matter relates to a system and method for providing a weather warning at a building site, existing structure or bridge.
BACKGROUND

[0002] Localized, timely, accurate and detailed wind forecast information at construction sites is critical to maximize safety, efficiency and profitability of a construction project. Wind forecast information can assist firms at construction sites in planning operations, material handling and mitigation activities as they pertain to daily operations. Additionally, wind forecast information can assist firms responsible for the maintenance of a structure. Timely, accurate and detailed weather forecast information can reduce costs and improve safety by providing predictions of favourable weather windows which allow for the scheduling of maintenance activities to occur during favourable conditions.

[0003] Local accelerations of wind can occur due to interactions of wind with a building or between multiple buildings. Wind gust acceleration factors related to local building effects, surface roughness and topography can be determined either rigorously through the use of wind tunnel testing/computational fluid dynamics (CFD) modeling or can be approximated using wind engineering best practices (see, e.g., Engineering Sciences Data Unit, "Strong winds in the atmospheric boundary layer, Part 1:
hourly-mean wind speeds", Issued September 1982 with Amendments A to E March 2002, herein "ESDU 82026"). Turbulence is a measure of the fluctuations in wind speed about a mean wind speed and can originate from the generation and decay of eddies as atmospheric wind encounters surface roughness and surface obstructions. The statistical behaviour of turbulence in response to surface roughness has been well-studied in the civil engineering field through numerous empirical studies.

[0004] Strong winds can also be associated with convective turbulence. In the summer, thunderstorms can develop rapidly and generate gust events due to convective phenomena including but not limited to updrafts, downdrafts, and spreading , cold pools. These phenomena are extremely difficult to predict as they can be smaller than what numerical weather models can resolve. In many cases the scale of a thunderstorm is too small to resolve with operational weather forecast models.

[0005] Wind gusts resulting from mechanical turbulence and convective conditions are of great concern to building operations and maintenance, among others, in the building construction industry, but existing building construction weather forecast products are limited in that they do not provide information in the resolution (time or space) required by the building construction industry. In particular, such products do not comprehensively account for various important wind forecast parameters, including upwind terrain effects, which may impact the formation and activity of wind gusts, and thus do not provide a truly site- and height-specific wind gust forecast.

[0006] Precipitation amount and type is also of concern to the use, stability, and safety of infrastructure and structures. High rates of precipitation are a primary factor in overland flooding and in colder climates where precipitation in the form of snow and ice may accumulate on structures. Snow and ice accumulation can deleteriously change the exterior dimensions and weight bearing characteristics of a structure. For example, in the case of a cable stay bridge such changes are of great concern since the cable stays may become aerodynamically unstable, or the snow/ice accumulations may fall to the ground as cohesive units and may impact vehicle and pedestrian safety.

[0007] Building construction firms among others can be at a disadvantage if they do not have access to localized, timely, accurate and detailed forecasting information pertaining to wind gusts (resulting from mechanical turbulence and/or convective conditions), precipitation, snow accretion and/or snow shedding at or near a building site.
SUMMARY

[0008] In accordance with the present disclosure, provided is a system and method for generating a wind gust, precipitation, and/or snow accretion shedding forecast and warning at a site to obviate or mitigate at least one of the above-presented disadvantages.

[0009] Disclosed is a system and method for generating and delivering site-and height specific wind gust forecasts at a site that account for site-specific factors such as building effects, surface roughness and topology in a site's upwind fetch.

[0010] Disclosed is a system and method for generating and delivering convective gust forecasts to provide an indication as to when conditions are favourable at a site for the formation of convective storms, which can comprise strong winds resulting from convective phenomena such as downdrafts, cold pool expansion, microbursts, downbursts, thunder storms, and tornadoes.

[0011] Disclosed is a system and method for combining weather forecast technology with the science of wind engineering and complex aerodynamics to provide timely and accurate site and height specific wind gust forecasts that can be used during the construction of tall buildings, bridges, and other structures as well as for the ongoing maintenance of existing structures to assist operational matters and/or for site safety.

[0012] Disclosed is a system and method for forecasting precipitation at a site and for delivering the forecast to one or more designated forecast recipients.

[0013] Disclosed is a system and method for forecasting snow accretion/shedding at a site and for delivering the forecast to one or more designated forecast recipients.

[0014] There is provided a computer-implemented method for providing a weather forecast at a site for one or more buildings or structures. The method comprises receiving and storing geographic coordinates defining the location and dimensions of a forecast domain located at the site; receiving and storing weather-related data comprising information indicative of weather conditions within the forecast domain, the weather conditions being one or more members selected from the group consisting of:
wind speed patterns, wind gust patterns, wind convection patterns, precipitation patterns, snow accretion patterns and snow shedding patterns; extracting from the weather-related data the information indicative of the weather conditions within the forecast domain; and providing the information indicative of the weather conditions within the forecast domain as a weather forecast.

[0015] The method may comprise receiving and storing geographic coordinates defining one or more bounding boxes located within the forecast domain. The steps of receiving and storing the weather-related data, extracting the information indicative of the weather conditions within the forecast domain, and providing the information indicative of the weather conditions within the forecast domain may be performed for each bounding box defined at the site.

[0016] The method may comprise applying one or more numerical weather prediction models to the weather-related data. At least one of the numerical weather prediction models may be a high-resolution model.

[0017] The method may comprise receiving and storing a matrix wherein each cell of the matrix is populated with a wind gust factor representative of one or more parameters which influence a wind speed at a specific height and for a specific compass direction at the site. One or more wind gust factors may be applied to the extracted information to generate one or more site-specific wind gust speeds. Site-specific wind gust speeds may be generated for multiple heights at the site.

[0018] The weather forecast may comprises an alert message if one or more site-specific wind gust speeds exceeds a stored threshold value.

[0019] The information indicative of the weather conditions may be information indicative of the weather conditions at one or more time-steps, wherein each time-step represents a distinct accretion of time into the future. The step of providing the information indicative of the weather conditions within the forecast domain may comprise providing the information indicative of the weather conditions at multiple time steps.

[0020] The step of providing the information indicative of the weather conditions within the forecast domain may comprise providing an alert message if the information indicative of the weather conditions comprises an indicator of the presence of one or more members selected from the group consisting of: wind convection, precipitation, . CA 02853915 2014-06-09 snow accretion, and snow shedding. The alert message may comprise an indicator that is audible or visible at the site.

[0021]
The weather forecast may be provided to a computing device located remote from the site.

[0022]
There is provided a computer system comprising at least one processor and a memory storing instructions and data, which when executed by the processor, cause the computer system to perform at least one of the methods described above.
BRIEF DESCRIPTION OF THE DRAWINGS

[0023]
Exemplary embodiments of the subject matter will now be described in conjunction with the following drawings, by way of example only, in which:

[0024]
Figure 1 shows a systems view of the components for providing a wind gust, precipitation, and/or snow accretion/shedding forecast and warning;

[0025]
Figure 2 is a schematic view showing the extraction of weather-related statistics from a spatial grid cell located at a given height in the time-step output intersecting with a site and the generation of a wind forecast, precipitation forecast, and/or a snow accretion/shedding forecast using the extracted statistics;

[0026]
Figure 3 is a schematic view of an embodiment of operations used to determine the configuration state of a site;

[0027]
Figure 4 is a schematic view of an embodiment of a home-builder site-configuration state;

[0028]
Figure 5 is a schematic view of an embodiment of a low-rise site-configuration state;

[0029]
Figure 6 is a schematic view of an embodiment of a site-specific site-configuration state;

[0030] Figure 7 shows a wind gust matrix constructed for a site;

[0031] Figure 8 is a schematic view of an embodiment of operations used to download weather-related data and/or one or more ensemble members from one or more remote servers;

[0032] Figure 9 is a schematic view of an embodiment of operations used to process weather-related data and/or one or more ensemble members;

[0033] Figure 10 is a schematic view of an embodiment of operations used to extract ensemble member data fields intersecting with one or more bounding boxes at a site;

[0034] Figure 11 is a schematic view of an embodiment of operations used to generate a wind gust forecast at a site for one or more ensemble members;

[0035] Figure 12 is a schematic view of an embodiment of operations used to assess ensemble members for indicators of wind convection at a site;

[0036] Figure 13 is a schematic view of an embodiment of operations used to generate a precipitation and/or snow accretion/shedding forecast at a site for one or more ensemble members;

[0037] Figure 14 is a schematic view of an embodiment of operations used to assess ensemble members for indicators of snow accretion/shedding at a site;

[0038] Figure 15 is a schematic view of an embodiment of operations used to generate a wind gust, precipitation, and/or snow accretion/shedding forecast and warning at a site;

[0039] Figure 16 is a flowchart illustrating an exemplary method for providing a wind gust, precipitation, and/or snow accretion/shedding forecast and warning at a site;

[0040] Figure 17 is a schematic view of components of an exemplary computing device that can be used to implement the subject matter described herein.

, DETAILED DESCRIPTION

[0041] As further described below, a computer-implemented system and method for providing a wind, precipitation and/or snow accretion/shedding forecast at a site is provided. A site can comprise any two-dimensional or three-dimensional space for which a wind, precipitation and/or snow accretion/shedding forecast is desired. Site dimensions can vary from a few meters in the x and y directions to one hundred or more kilometers in the x and y directions to encompass an entire town or a quadrant of a city.
Further, the site can comprise any shape including rectangular and circular domains of land and long, thin stretches of land (e.g. for situating a road). The height of the site can vary according to the heights of existing or anticipated buildings (if any) at the site.

[0042] The site can comprise one or more existing buildings or structures including one or more partially built buildings or structures. A building or structure can include but is not limited to a residential or commercial building or tower, a tower or structure (e.g., a windmill) for generating or facilitating the distribution of power, a bridge, a temporary structure at for example a campground or fairground, or any other type of building, structure, tower, temporary structure or the like.

[0043] The system can use one or more configuring engines to configure the dimensions and location of one or more bounding boxes at the site. Where a wind forecast is desired, the one or more configuring engines can be configured to populate a wind gust matrix, wherein each cell of the matrix defines a wind gust factor for a given height and wind direction. The bounding box parameters and optionally wind gust matrix can be transmitted from the configuring engine to a forecasting engine, which can be configured to forecast wind, precipitation, and/or snow accretion/shedding conditions at and around the site using data field statistics extracted from weather-related data downloaded from one or more remote servers. Recipients of the wind, precipitation, and/or snow accretion/shedding forecast can include, for example, a remote monitoring service, a server operating a website, and/or pre-determined individuals, businesses or government agencies. The system can also generate and remotely transmit alert messages to third parties pertaining to wind, precipitation, and/or snow accretion/shedding if the forecasted conditions at a site meet pre-determined, user-specified criteria.

[0044] Referring to Figures 1-2, shown is a system 100 for providing a forecast 150 at a site 172. The system 100 can comprise a. configuring engine 102 (which can be a component of a forecasting engine 124) to be used in configuring the site 172.

Configuring the site 172 can involve determining and/or storing a site-configuration state 104, defining and optionally sub-zoning a forecast domain 400 according to the site-configuration state 104, defining one or more conceptual bounding boxes 410 at the site 172, and optionally populating a wind gust matrix 112 at the site 172. The configuring engine 102 can transmit this information to the forecasting engine 124 which is used to generate a forecast 150 (i.e. if the site configuring engine 102 is not integrated into the forecasting engine 124).

[0045] The system 100 comprises one or more remote servers 116 with access to weather-related data 118. Weather-related data 118 can include any information that is necessary to derive an accurate, current forecast pertaining to wind, precipitation, and/or snow accretion/shedding conditions at the site 172. Weather-related data 118 can be collected from any source that generates and/or processes and/or stores data relating to current and future meteorological conditions including surface observation meteorological stations and third-party government agencies such as Environment Canada or the US National Weather Service.

[0046] The forecasting engine 124 uses weather-related data 118 obtained from (e.g. by downloading) the one or more remote servers 116 to generate a forecast 150.
Weather-related data 118 may be stored and/or processed in the forecasting engine 124 as one or more ensemble members 128. An ensemble member 128 can be defined and/or differentiated by a number of criteria including the source of the weather-related data 118, the content of the weather-related data 118 (e.g. whether the information pertains to wind, precipitation, and/or snow accretion/shedding conditions), the particular forecast models or processing scheme to which the weather-related data 118 is subjected (e.g. the same weather-related data 118 can be processed using different combinations of physics options with low-resolution and high-resolution numerical weather prediction (NWP) models), and the geographical coordinates (e.g. for a large site 172) to which the weather-related data 118 applies.

[0047] The forecasting engine 124 processes weather-related data 118 and/or ensemble members 128 to generate a forecast 150 comprising one or more of a wind forecast 166, a precipitation forecast 168, and a snow accretion/shedding forecast 170.
For example, weather-related data 118 can be subjected to one or more low-and/or high-resolution forecast models. Further processing can involve extracting meteorological data fields 202 (e.g. native wind gust field 204, reference wind speed 220, wind direction 222, convection fields 232, precipitation data field 224, snow accretion/shedding data field 226) at one or more spatial grid cells 208 at one or more heights 210, where the one or more spatial grid cells 208 intersects with the one or more bounding boxes 410. Further, forecasting operations can account for site-specific information 174 such as upwind topography and building effects (e.g. for a wind forecast 166) by applying one or more wind gust factors 706 (see Figure 7) from the wind gust matrix 112 to values of extracted meteorological data fields 202 at one or more heights 210.

[0048] Weather-related data 118 and/or ensemble members 128 can include forecast data for one or more future time-steps 206. As shown in Figure 2, time-step output 218 can be configured as for example a cube or rectangular cuboid that encompasses all or a portion of the forecast domain 400 and the one or more bounding boxes 410.

[0049] The system 100 further comprises one or more forecast recipients 180 which can include a remote monitoring service 156, one or more remote target servers operating for example a website, and/or pre-determined third parties 160 such as individuals, businesses, and/or government agencies. The forecasting engine 124 can be configured to transmit a forecast 150 to the one or more forecast recipients 180 as for example a time series comprising data derived from multiple time step outputs 206 relating to future wind, precipitation and/or snow accretion/shedding conditions. The forecasting engine 124 can also be configured to transmit an alert message 152 to forecast recipients 180 if forecasted wind, precipitation and/or snow accretion/shedding conditions include values that have been flagged by a user as cautionary and/or if the weather-related data 118 is determined to contain one or more weather alerts.

[0050] The configuring engine 102 and forecasting engine 124 typically comprise applications and software that facilitate configuration of the site 172 and the generation of a forecast 150, respectively. The applications and software are capable of executing the various operations described herein (e.g. configuration operations 300 or download operations 800) and can receive and process information inputted through a user interface 1706 of a computing device 1700 or received from a computing device over a communications network 164 (see Figure 17). It is also contemplated that configuring engine 102 and forecasting engine 124 functionality can be integrated into a single configuring/forecasting engine comprising software and applications implemented by one or more computing devices 1700. An example of software that may be executed as part of one or both of the configuring engine 102 and forecasting engine 124 is a relational database management system (RDBMS).

[0051] The forecasting engine 124 can communicate with the configuring engines 102, remote servers 116 and/or computing devices of forecast recipients 180 over any communications network 164 such as the Internet, Wi-Fi, and cellular networks.
Further, the forecasting engine 124 and/or the configuring engine 102 can be distributed systems in which storage of information (e.g. site-configuration state 104, bounding box coordinates 410, forecasts 150, and the various operations) and execution of operations (e.g. download operations 800, processing operations 900) can occur across platforms provided by a variety of computing devices.

[0052] Figure 17 illustrates physical components of a computing device 1700, which is representative of computing devices implementing the forecasting engine 124, configuring engine 102, as well as computing devices implementing the one or more remote servers 116 and computing devices of forecast recipients 180. In various embodiments, computing device 1700 can be for example a laptop, personal computer, server computer, smartphone, or personal digital assistant.

[0053] Computing device 1700 includes a network connection interface 1702, such as a wired/wireless network interface card or modem, in communication with a processor 1704. The network connection interface 1702 is connectable to the communications network 164 using an associated antenna and/or transmitter/receiver module (not shown).

[0054] Computing device 1700 has a user interface 1706 coupled to the processor 1704 to interact with a user. The user interface 1706 can be configured to receive input from one or more input devices such as but not limited to a QWERTY keyboard, a keypad, a track wheel, a stylus, a mouse, a touch screen, and a microphone.
Output can be to for example an LCD screen display and/or a speaker.

[0055] The processor 1704 is in communication with random access memory for storing of various parameters such as a site-configuration state 104, bounding box coordinates 410, forecasts 150, or various operations (e.g. configuration operations 300 or download operations 800). Further, it is recognized that the device 1700 can include persistent storage 1710 coupled to the processor 1704 for providing instructions to the processor 1704 and/or to load/update client applications (e.g. a RDBMS). The persistent storage 1710 can include hardware and/or software such as, by way of example only, magnetic disks, magnetic tape, optically readable medium such as CD/DVD ROMS, and memory cards.

[0056] The processor 1704 may be configured according to the intended functionality of the device (e.g. to implement software or applications required for the functionality of the configuring engine 102, forecasting engine 124, remote servers 116, or computing devices of forecast recipients 180). The processor 1704 facilitates performance of the device 1700 through operation of the network connection interface 1702, the user interface 1706 and other application programs of the device 1700 by executing related instructions. These related instructions can be provided by an operating system, and/or software applications (e.g. RDBMS) located in the memory.

[0057] Referring again to Figure 1, configuring the site 172 can comprise identifying site-specific information 174 likely to be material to an accurate and current forecast 150, such as the heights 210 of buildings that exist on the site 172 or will exist on the site 172, the dimensions of the site 172, local topographies, building effects, or obstructions likely to impact wind gust forecasts. Configuring the site 172 can additionally comprise executing operations (e.g. configuration operations 300) that use site-specific information 174 to generate output (e.g. the site-configuration state 104 or wind gust matrix 112) that facilitates the development of a forecast 150.

[0058] Site-specific information 174 is inputted into the configuring engine 102 using a user interface such as user interface 1706. For example, the configuring engine 102 may include an application implemented by a smartphone or other handheld device that enables a user at the site 172 to input data.

[0059] Referring to Figure 3, the configuring engine 102 can execute configuration operations 300 to designate a site-configuration state 104 according to risk of wind impact and/or the current or future heights 210 of one or more buildings that exist or will exist at the site 172. Differentiating sites 172 based on building heights 210 and/or risk of wind impact can be important to generate accurate wind forecasts 166 at a site 172, as the output of forecasting operations 176 for calculating a wind forecast 150 can vary depending on the height 210 of the site 172 (see below). In one embodiment configuration operations 300 assign the site 172 to one of three configuration states 104, namely a home-builder configuration state 106 (e.g. a site 172 with a height generally less than 10 meters containing buildings or structures with 1-2 floors), a low-rise configuration state 108 (e.g. a site 172 with a height generally 10-20 meters containing buildings or structures with 3-4 floors), or a site-specific configuration state 110 (e.g. a site 172 with a height generally greater than 20 meters containing buildings or structures with greater than 4 floors). However, it is recognized that more configuration states 104 can exist, for example by further resolving the site-specific configuration state 110 or defining different categories of risk.

[0060] Figure 3 illustrates an example embodiment of configuration operations 300.
Operations can comprise determining 302 whether or not the site 172 is high risk. In general, a high-risk site 172 comprises buildings or structures that are or will be five stories or taller, current or to-be-built aerodynamic structures that diverge from a typical building structure, and either current or to-be-built structures prone to impact by the wind (e.g. a crane with a heavy lift or tarped scaffolding). If the site 172 is determined as high-risk, then the site-configuration state 104 can be stored as a site-specific configuration 110. If the site is determined as not high-risk, then the operations can determine 306 whether or not the site 172 comprises/will comprise buildings with heights 210 of primarily 1-2 floors. If the site 172 is determined to comprise buildings with heights 210 of primarily 1-2 floors, then the site-configuration state 104 can be stored/recorded as a home-builder configuration 106. If the site 172 is determined to not comprise buildings with heights 210 of primarily 1-2 floors, then the operations can determine 310 whether or not the site 172 comprises/will comprise buildings with heights 210 of primarily 3-4 floors. If the site 172 is determined to comprise buildings with heights 210 of primarily 3-4 floors, then the site-configuration state 104 can be stored as a low-rise configuration 108. If the site 172 is determined to not comprise buildings with heights 210 of primarily 3-4 floors, then the site-configuration state 104 can be stored as a site-specific configuration 110.

[0061] If the configuring engine 102 and forecasting engine are implemented on different computing devices 1700, the configuring engine 102 can be configured to transmit the site-configuration state 104 to the forecasting engine 124 over a communications network 164. Both the configuring engine 102 and the forecasting engine 124 can be configured to store the configuration state 104 (e.g. in random access memory 1708 and/or persistent storage such as an RDBMS).

[0062] Configuring the site can also comprise defining the precise location and dimensions of a forecast domain 400. The forecast domain 400 is the two-dimensional or three-dimensional space for which a forecast is desired. The precise location and dimensions of the forecast domain 400 can be defined using for example geographical positional system (GPS) coordinates, which can be inputted into the site configuring . CA 02853915 2014-06-09 engine 102. The coordinates of the forecast domain 400 can be used in further configuring the site 172 to define the coordinates and dimensions of sub-zones 402 of the forecasting domain 400 and/or the coordinates and dimensions of one or more conceptual bounding boxes 410 superimposed on the forecast domain 400. Sub-zones 402 and/or multiple bounding boxes 410 can be employed to account for spatial variability of weather forecasts.

[0063] Example configurations of forecast domains 400, sub-zones 402, and bounding boxes 410 for three site-configuration states 104 are illustrated in Figure 4 (home-builder configuration state 106), Figure 5 (low-rise configuration state 108), and Figure 6 (site-specific configuration state 110). It is recognized that each of the configurations represented in Figures 4-6 are embodiments only and that other possibilities can exist. In general, the numbers and sizes of sub-zones 402 and bounding boxes 410 will vary depending on the area and height of the forecast domain 400.

[0064] Referring to Figures 4-6, for each configuration state 104, a three-dimensional forecast domain 400 can be defined that corresponds to the dimensions of the site 172.
In an example embodiment of a home-builder configuration state 106 (Figure 4), the forecast domain 400 can be configured as four equally sized three-dimensional sub-zones 402 comprising a north-west zone, north-east zone, south-east zone and south-west zone. Each sub-zone 402 of the forecast domain 400 can be encompassed by a three-dimensional conceptual bounding box 410 with slightly greater dimensions (e.g. <
2%) than the sub-zone 402 encompassed by the bounding box 410. A site can be defined as a number of buildings in a construction area, subdivision, or as large as a quadrant of a city.

[0065] In an example embodiment of a site with a low-rise configuration state 108 (Figure 5), the forecast domain can be configured as eight sub-zones 402 comprising four equally sized three-dimensional inner sub-zones 402a (e.g. north-west inner zone, north-east inner zone, south-east inner zone, and south-west inner zone), and four equally sized three-dimensional outer sub-zones 402b (e.g. north-west outer zone, north-east outer zone, south-east outer zone, and south-west outer zone ¨ note that only two outer sub-zones 402b are visible in Figure 5). The outer sub-zones 402b will generally encompass any buildings or structures on the immediate periphery of a site, while the inner sub-zones 402a will encompass the core of the site. Depending on the area of the site, the area of the outer sub-zones 402b can be less than, equal to, or greater than the area of the inner sub-zones 402a. A sub-zone pair comprising an inner sub-zone 402a and outer sub-zone 402b facing the same direction can be encompassed by a conceptual three-dimensional bounding box 410 with slightly greater dimensions (e.g. < 2%) than the pair of sub-zones that the bounding box 410 encompasses.

[0066] In an example embodiment of a site with a site-specific configuration state 110 (Figure 6), the forecast domain 400 can be encompassed by a single three-dimensional conceptual bounding box 410 with slightly greater dimensions (e.g.
<2%) than the forecast domain 400.

[0067] The location and dimensions of the forecast domain 400, optionally sub-zones 402 and the one or more bounding boxes 410 can be inputted into the configuring engine 102 (e.g. using the user interface 1706 of a computing device 1700) where it can be stored. If the configuring engine 102 and forecasting engine 124 are implemented by different computing devices 1700, the information can then be transmitted to the forecasting engine 124 which can be configured to store it (e.g. in random access memory 1708 and/or persistent storage such as an RDBMS).

[0068] Configuring the site 172 can also comprise developing and populating one or more wind gust matrices 112. Each wind gust matrix 112 embodies site-specific information 174 such as local topography or building effects that can affect the formation and persistence of wind gusts. Whether one or more wind gust matrices 112 are developed for a site 172 can depend on the current or future height 210 of buildings or structures that exist at a site 172 or that will exist at a site 172, as well as the risk of wind impact at a site 172. In an example embodiment, a separate wind gust matrix 112 is developed and populated for each building or structure greater than five floors (e.g.

for a site-specific configuration state 110). In a different embodiment, (e.g.
for a low-rise configuration state 108), a separate wind gust matrix 112 can be developed and populated for each sub-zone 402 of the forecast domain 400. In a further embodiment a separate wind gust matrix 112 is developed/populated at a site 172 comprising primarily buildings/structures less than 10 meters in height (e.g. for a home-builder configuration state 106).

[0069] Referring to Figure 7, a wind gust matrix 112 can comprise a matrix wherein each column 702 represents a compass direction and each row 704 represents a height 210 of the forecast domain 400 where a wind forecast 166 is required. In an example embodiment (e.g. for a low-rise configuration 108), each wind gust matrix 112 comprises sixteen columns 702 representing different wind directions, and two rows 704 representing 10 meter and 20 meter heights 210.

[0070] Each cell within the wind gust matrix 112 contains a wind gust factor 706 that accounts for various site-specific information 174 including local topography, building effects and surface roughness. In an example embodiment, the wind gust factor specifies a parameter that is part of the relationship between the reference wind speed 220 and a wind gust speed at a specific height 704 for a specific wind direction 702. In other embodiments, the wind gust factor 706 can define mean wind speeds over longer durations such as 10 minutes or 15 minutes. The wind gust matrix 112 can be populated using data obtained from the application of methods familiar to those skilled in the art of weather forecasting. Methods include site modelling using a boundary layer wind tunnel, a computational fluid dynamics (CFD) model simulation, and/or using wind engineering best practices (see, e.g., ESDU 82026). One or more wind gust factors 706 are used to make adjustments to reference wind speeds 220 to a specific height 210 to account for site-specific information 174 such as building effects, surface roughness, and topography. As would be familiar to a person skilled in the art, each wind gust factor 706 can be applied to a reference wind speed 220 extracted from weather-related data 118 and/or one or more ensemble members 128, wherein the reference wind speed corresponds to a reference height that is above tall buildings in most urban centers and is not influenced by surface roughness. For tall structures in complex boundary layers, the use of a single reference wind speed 220 may not be sufficient. Thus it is contemplated that other mechanisms such as using multiple reference wind speeds at different heights, or using turbulence kinetic energy, may be implemented.

[0071] For buildings/structures/zones that have been modelled in a boundary layer wind tunnel, wind gust factors 706 can be derived from data extracted from sensors for detecting and storing wind speeds and/or pressures that occur at specified heights 210 modelled in the boundary layer wind tunnel model. The model can be rotated for each of the 16 compass directions to determine a wind gust factor 706 for each column 702 of a row 704 of the wind gust matrix 112. If insufficient sensor data is available to populate every cell of the wind gust matrix 112 for a building/structure/zone, data from sensors on surrounding buildings/structures/zones can be used to augment the wind gust matrix 112, or data from a subset of sensors can be used.

[0072] The wind gust matrix 112 can also be populated using a CFD model wherein the wind flow patterns on the subject building/structure/zone and nearby buildings/structures/zones can be modelled computationally for the 16 compass directions represented by the columns 702 at each desired height 210 represented by the rows 704 of the wind gust matrix 112.

[0073] The wind gust matrix 112 can also be populated using wind engineering best practices, which would be familiar to a person skilled in the art and typically comprise using upwind land use profiles for each of the 16 compass directions represented by the columns 702 of the wind gust matrix 112 and assigning a length and land use classification as described in ESDU 82026. The land use classification (e.g., rural, urban residential, urban low-rise, urban high rise) can then be used to derive a surface roughness coefficient for each upwind segment. The upwind fetch for each compass direction can then be used to calculate wind gust profiles (e.g. using three-second wind gust speeds) at each desired height 210 represented by the rows 704 of the wind gust matrix 112. A wind engineer can then use knowledge based on understanding of the local effects of urban corridors for the region to relax blocking effects (e.g. allow for some east-west accelerations due to the alignment of city streets).

[0074] The information required to generate the one or more wind gust matrices 112 can be inputted into the configuring engine 102 (e.g. using the user interface 1706 of a computing device 1700), which can then use software to develop and/or populate the one or more wind gust matrices 112. If the configuring engine 102 and forecasting engine 124 are implemented by different devices, the one or more matrices 112 can be transmitted by the configuring engine 102 to the forecasting engine 124 which can be configured to store the information (e.g. in random access memory 1708 and/or persistent storage such as an RDBMS).

[0075] Once a site 172 is configured as above, the forecasting engine 124 can download and process weather-related data 118 to compute a forecast 150 for the site 172. The forecasting engine 124 can be configured to transmit download requests to one or more remote servers 116 over a communications network 164 according to for example one or more data access schedules 120. The weather-related data 118 can comprise data generated or stored by government weather agencies such as Environment Canada, the US National Weather Service and surface observation meteorological stations.

[0076] The one or more data access schedules 120 can constitute a custom schedule generated by the user or alternatively can be downloaded from the one or more remote servers 116 as weather-related data 118. For example, a data access schedule 120 can be incorporated into an NWP forecast model downloaded from the one or more remote servers 116 and then stored in the forecasting engine 124.
In an example embodiment that uses multiple data access schedules 120, weather-related data 118 can be downloaded several times daily from for example federal weather agencies and downloaded hourly from observational meteorological stations in conjunction with data access schedules 120 acquired from one or more NWP
models.

[0077] The one or more remote servers 116 can respond to download requests made by the forecasting engine 124 by transmitting current stored weather-related data 118 to the forecasting engine 124 over the communications network 164. The weather-related data 118 can be publicly available unencrypted data that is transmitted by the one or more remote servers 116 to the forecasting engine 124 upon request, or alternatively the one or more remote servers 116 can be configured to transmit the weather-related data 118 only upon receipt of a recognized password (e.g., obtained through a subscription for accessing the data) transmitted from the forecasting engine 124 to the one or more remote servers 116. The weather-related data 118 itself can be unencrypted or encrypted data unlocked using an encryption key by the forecasting engine 124. In a further embodiment, a remote server 116 may comprise a "push service" wherein the remote server 116 transmits weather-related data 118 to the forecasting engine 124 in the absence of a preceding request from the forecasting engine 124. Such a service may or may not be based on a publish/subscribe model.

[0078] Weather-related data 118 can be downloaded and/or stored by the forecasting engine 124 as one or more ensemble members 128. One or more ensemble members 128 can be obtained by for example parsing the weather-related data and/or applying one or more forecast models (e.g. low-resolution and/or high-resolution models) to the weather-related data 118. In an example embodiment the forecasting engine 124 can be configured (e.g. using a data access schedule 120 incorporated into an NWP forecast model) to make requests of the one or more remote servers 116 to transmit weather-related data 118 as an ensemble member 128. In an alternative embodiment, the forecasting engine 124 can download weather-related data 118 from the one or more remote servers 116 as unparsed raw data. The unparsed data can comprise information both relevant to and unrelated to current and future wind, precipitation, and/or snow accretion/shedding conditions at the site 172.
Software or applications executed as part of the forecasting engine 124 can be configured to parse the weather-related data 118 and/or run forecast models to obtain one or more ensemble members 128. The one or more ensemble members 128 can then be stored by the forecasting engine 124 (e.g. in random access memory 1708 and/or persistent storage such as an RDBMS) or immediately processed by the forecasting engine 124.

[0079] In another embodiment, weather-related data 118 can be downloaded and stored as unparsed raw data and further processed by the forecasting engine without assembling the weather-related data into ensemble members 128.

[0080] Figure 8 illustrates an example embodiment of download operations executed by the forecasting engine 124 to download weather-related data 118 from the one or more remote servers 116. The download operations 800 can comprise a data access schedule 120 to start 802 download operations 800 periodically (e.g.
every hour). The download operations 800 can direct the forecasting engine 124 to download 804 weather-related data 118 from one or more remote servers 116 as for example one or more ensemble members 128. Each ensemble member 128 can contain one or more meteorological data fields 202 comprising information relating to wind, precipitation, and/or snow accretion/shedding conditions at and/or around the site 172. The downloaded ensemble member 128 can be immediately processed 806 by the forecasting engine 124 and/or can be temporarily stored (e.g. in random access memory 1708 and/or persistent storage such as an RDBMS). Processing 806 can comprise for example executing a low-resolution NWP model downloaded in conjunction with the ensemble member 128 and/or executing one or more high-resolution NWP models (see below). Once a given ensemble member 128 has been downloaded from a remote server 116, the download operations 800 can determine whether or not further ensemble members 128 are scheduled to be downloaded according to the one or more data access schedules 120. If so, the forecasting engine 124 can download 804 another ensemble member 128 from the same or a different remote server 116. Downloading and storing and/or processing of the weather related data 118 or ensemble members 128 can continue in accordance with the one or more data access schedules 120. If no weather-related data 118 or ensemble member remains to be downloaded, then the download operations 800 can sleep 810 until the data access schedule 120 directs the forecasting engine 124 to begin downloading again.

[0081] In a further embodiment, the forecasting engine 124 can be configured to poll the one or more remote servers 116 over the communications network 164 according to one or more data access schedules 120 to determine if weather-related data 118 has been updated since the forecasting engine 124 last downloaded weather-related data 118, and if so, to download the updated weather-related data 118 according to the download operations 800. Alternatively, the one or more remote servers 116 can be configured to alert the forecasting engine 124 when updated weather-related data 118 becomes available.

[0082] Processing operations 900 comprising low- and high-resolution forecast models can be used to process the downloaded weather-related data 118 and/or ensemble members 128. In an example embodiment, one or more ensemble members 128 can be obtained by executing different forecast models (e.g. low-resolution and high-resolution models) on the same weather-related data 118. Processing can comprise executing a low-resolution NWP model (in one embodiment the model is downloaded in conjunction with the weather-related data 118), followed by execution of a high-resolution custom-built forecast model on the original downloaded weather-related data 118 or on the output of the low-resolution model. Any combination of downloaded NWP models and/or in-house models can be used to process the weather-related data 118 and/or one or more ensemble members 128. Weather-related data and/or ensemble members 128 can contain data representing one or more time-steps 206 (e.g. a forecast for 1 hour ahead, 3 hours ahead, etc.), and the output of processing operations 900 can comprise time-step output 218 for one or more time steps 206.
Time-step output 218 can comprise a three-dimensional cube/rectangular cuboid of spatial grid cells 208, or data points, representing the spatial domain of the weather-related data 118 and/or ensemble member 128 (see Figure 2). The forecasting engine 124 can store time-step output 218 (e.g. in random access memory 1708 and/or persistent storage such as an RDBMS) or use it directly for further processing (see below).

[0083] Figure 9 illustrates an example embodiment of processing operations 900 that can be used to process/generate one or more ensemble members 128. Software of the forecasting engine 124 can be configured to start 902 the operations at any time desired by the user, for example immediately following the downloading 804 and/or processing 806 of the weather-related data 118 or ensemble member 128 using download operations 800. In one embodiment, the successful download of weather-related data 118 is immediately followed by processing 806 of the data with a low-resolution forecast model (e.g. downloaded in conjunction with the weather-related data 118), with the completion of the processing 806 triggering the execution 904 of a high-resolution forecast model designated to further process 906 the low-resolution ensemble member 128. Execution of the high-resolution model can generate a high-resolution ensemble member 128 that is stored and/or processed separately from the low-resolution ensemble member 128. The processing operations 900 can determine 908 whether additional forecasting models have been designated for execution with either the low-resolution ensemble member 128 or high-resolution ensemble member 128, and if so, run 904 the further models until all models designated to process the one or more ensemble members 128 have been executed. Each ensemble member 128 may be stored separately (e.g. as time-step output 218 in random access memory 1708 and/or persistent storage such as an RDBMS). Once the processing operations 900 have run all models to be executed for an ensemble member 128, the operations can be configured to sleep 910 until another ensemble member 128 has been downloaded.

[0084] The forecasting engine 124 can be configured to further process weather-related data 118 and/or ensemble members 128 by extracting statistics from relevant meteorological data fields 202 intersecting with the one or more bounding boxes 410.
For example, the forecasting engine 124 can run extraction operations 1000 on time-step output 206 to extract meteorological data fields 202 relating to wind, precipitation, and/or snow accretion/shedding, where the meteorological data fields 206 intersect with the extent of the bounding boxes 410 defined during the site-configuration process.

[0085] For a wind forecast 166, extracted meteorological data fields 202 can relate to wind gusts and/or convective gusts. Extracted meteorological data fields 202 for wind gusts can comprise for example the native wind gust field 204, reference wind speed 220, and/or wind direction 222. Extracted convection fields 232 can comprise for example Convective Available Potential Energy (CAPE), Convective Inhibition (CIN), Storm Relative Helicity (SRH), Composite Reflectivity (CREF), Lifted Index (LI) and/or the presence/absence of lightning. When extracting convection fields 232 the forecasting engine 124 can be configured to run storm index calculations (e.g., Total Totals, Severe Weather Threat (SWEAT) index). In one embodiment, where multiple values are available for a particular convection field 232, the most conservative value (i.e., most likely to lead to a convective gust) is used.

[0086] For a precipitation forecast 168, the extracted precipitation data fields 224 can comprise the amount of total rain (RAINT), the amount of convective rain (RAINC) or precipitation type (e.g. rain, snow, etc.).

[0087] For a snow accretion/shedding forecast 170, the extracted snow accretion/shedding data fields 226 can include one or more values for non-convective snow (SNOWNC), the sticking ratio (SR) (i.e. the fraction of frozen precipitation, calculated by dividing the amount of frozen precipitation by the total precipitation), the ground level temperature, or the temperature at cable stay height (e.g. for a bridge).
Such models can use or be based on snow accretion models known to a person skilled in weather forecasting (e.g. ISO 12494: Atmospheric Icing of Structures).

[0088] The forecasting engine 124 can be configured to extract data fields 202 that spatially overlap, or intersect with, the one or more bounding boxes 410 by first comparing the spatial configuration of the grid cells 208 in the time-step output 218 to the location and dimensions of the bounding boxes 410, and then extracting meteorological data fields 202 from grid cells 208 located on or within the borders of the bounding box 410.

[0089] Figure 10 illustrates an example embodiment of extraction operations that can be used to extract data fields 202 from grid cells 208 that intersect with the one or more bounding boxes 410 defined during the configuration of the site 172.
The forecasting engine 124 can be configured to start 1002 the extraction operations 1000 at any time desired by the user, for example immediately following or concomitantly with execution of the download operations 800 and/or high-resolution processing operations 900. The extraction operations 1000 can direct the forecasting engine 124 to obtain 1004 the one or more bounding boxes 410 for the site from memory (e.g. in random access memory 1708 and/or persistent storage such as an RDBMS). For each time-step 206 represented by time-step output 218 and for each bounding box 410 and for each grid cell 208 that lies within the one or more bounding boxes 410 at the site, the forecasting engine 124 can execute 1006, 1008, 1010 a loop to extract 1012, 1014, 1016 meteorological data fields 202 representing wind gusts and/or convective gusts and/or precipitation and/or snow accretion/shedding. The statistics of each meteorological data field 202 can be stored 1018, 1020 for example in random access memory 1708 and/or persistent storage such as an RDBMS. Once extraction operations 1000 are complete for each grid cell 208 within the one or more bounding boxes 410 at each time step 206, the extracted meteorological data fields 202 can be stored individually or together as a time series comprising data from individual time-steps 206.

[0090] Extracted meteorological data fields 202 can be further processed to assemble a forecast 150 at the site 172. The forecast 150 can comprise one or more of a wind forecast 166 (which can comprise a wind gust forecast 214 and/or convective gust forecast 230), precipitation forecast 168, and/or snow accretion/shedding forecast 170, and can be transmitted by the forecasting engine 124 over a communications network 164 to one or more forecast recipients 180. Example embodiments of forecasting operations 176 that can be used to generate a forecast 150 at the site 172 are provided below.

[0091] Forecasting operations 176 can comprise wind forecasting operations used to derive a wind gust forecast 214 and/or convective gust forecast 230.
Wind forecasting operations 1100 can account for site-specific information 174 such as local topography and/or building effects by applying one or more wind gust factors 706 from the wind gust matrix 112 to statistics of extracted meteorological data fields 202. Wind forecasting operations 1100 can also comprise operations that derive or obtain statistics from meteorological data fields 202 without applying one or more wind gust factors 706.
Where a wind gust factor 706 is not applied, wind forecasting operations 1100 can comprise calculating and/or obtaining the native wind gust 204 statistic, which can be extracted from fields within a downloaded NWP model, or derived by executing an NWP
model (e.g. using processing operations 900) and then extracted from within the bounding box 410 using extraction operations 1000.

[0092] Figures 11-12 illustrate example wind forecasting operations 1100.
The forecasting engine 124 can be configured to start 1102 wind forecasting operations 1100 at any time including immediately following the extraction/identification of meteorological data fields 202 (e.g. via the execution of extraction operations 1000), or or on a set schedule (e.g. every hour). For an ensemble approach, a loop can be executed 1104 for each ensemble member 128, the loop comprising accessing 1106 the statistics for the meteorological data fields 202 for one or more time-steps 206 (which can be for example 1-hour apart); obtaining 1108 the one or more wind gust matrices 112 for the site; identifying 1110 the reference wind speed 220 and wind direction 222 statistics from the extracted data fields 202 for one or more time-steps 206; determining 1112 the two wind directions directly adjacent to the wind direction 222 for one or more time-steps 206; using 1114 the wind gust matrix 112 to identify wind gust factors 706 corresponding to the wind direction 222 and the wind directions directly adjacent to the wind direction 222 for each height 210 in the matrix and at one or more time-steps 206; applying each wind gust factor 706 to the reference wind speed 220 to obtain one or more site-specific wind gusts for each height 210 at one or more time-steps 206, and selecting 1116 the maximum site-specific wind gust 228 for each height 210 at one or more time-steps 206; and executing 1118 convection operations 1200 for one or more time-steps 206.

[0093] As explained above, the precise method and/or operations to apply a wind gust factor 706 to a reference wind speed 220 would be familiar to a person skilled in the art of wind forecasting. Using the two wind directions directly adjacent to the wind direction 222 obtained from the extracted meteorological data fields 202 can account for variability in wind direction 222 predicted by the weather models. The system 100 thus enables each grid cell 208 to be scaled from a reference wind speed 220 to a height-specific wind gust speed (which can be calculated for example over three seconds, 10 minutes, 15 minutes, etc.). In the event that more than one wind gust matrix 112 applies to a forecast domain 400 (e.g. for a site-specific configuration 110, where separate wind gust matrices 112 can exist for each building/structure greater than five floors), the wind forecasting operations 1100 can be run separately for each applicable wind gust matrix 112. The wind forecasting operations 1100 can then further be configured to select the maximum, for example, site-specific wind gust 228 from among the wind gusts calculated from the application of the multiple wind gust matrices 112.

[0094] Forecasting operations 176 can further comprise convection operations 1200 that can be used to derive a convective gust forecast 230. In an example embodiment, the convection operations 1200 comprise setting a convective gust flag at one or more time-steps 206 if convective phenomena are determined to exist in the meteorological data fields 202. Convective phenomena can be any factors identified/extracted from the weather-related data 118 and/or ensemble members 128 which can contribute to convective gust potential 216, and would be familiar to a person skilled in the art of weather forecasting. Indicators of convective phenomena can be derived from convection fields 232 such as Convective Available Potential Energy (CAPE), Convective Inhibition (CIN), Storm Relative Helicity (SRH), Composite Reflectivity (CREF), Lifted Index (LI), and the presence/absence of lightning.

[0095] Figure 12 illustrates an example embodiment of convection operations which can be executed 1202 alone or as part of wind forecasting operations 1100. Note that the values of pre-set thresholds would be familiar to a person skilled in the art of weather forecasting. The convection operations 1200 can determine 1204 whether the SRH and CAPE are greater than pre-set threshold values. If yes, then the time-step 206 is marked 1206 as having convective gust potential 216. If the SRH and CAPE
are determined to be not greater than the pre-set threshold values, then the operations can determine 1208 whether there are grid cells 208 with lightning indicators. If grid cells 208 with lightning indicators are determined to not exist, then the operations can determine 1212 whether CAPE is greater than a pre-set threshold and whether LI
is less than a pre-set threshold. If CAPE is determined to be greater than a pre-set threshold and if LI is determined to be less than a pre-set threshold, or if grid cells 208 with lightning indicators are determined to exist, then the convection operations 1200 can determine 1210 whether the CIN is greater than a pre-set threshold and whether CREF is greater than a pre-set threshold. If both CIN and CREF are determined to exceed their respective pre-set thresholds, then the time-step 206 is marked 1206 as having convective gust potential 216. If either CIN or CREF is determined to not exceed its pre-set threshold, then time-step 206 is determined 1214 to not have convective gust potential 216. Likewise, if CAPE is determined to not exceed its pre-set threshold value and/or if LI is determined to be not less than its pre-set threshold, then the time-step 206 (e.g. hour) is not marked with convective gust potential 216.

[0096] Wind forecasting operations 1100 and/or convection operations 1200 can further comprise operations to prepare a wind forecast 166 and/or alert message 152 for transmission to one or more forecast recipients 180. Such operations can include selecting the maximum site-specific wind gust 228 from among multiple ensemble members 128 at one or more heights 210 at one or more time-steps 206, comparing the selected maximum site-specific wind gust 228 against the value of the native wind gust field 204 and selecting the maximum value, and/or marking one or more time steps 206 as having convective gust potential 216 if convective gust potential 216 was determined to be present. Values representing the maximum site-specific wind gust 228 or native wind gust 204, and/or time-steps 206 marked with convective gust potential 216, can then be stored by the forecasting engine 124 (e.g. in random access memory and/or persistent storage such as an RDBMS) for transmission over a communications network 164 to one or more forecast recipients 180. An example embodiment of such operations is illustrated in Figure 15 (described below).

[0097] The forecasting engine 124 can store statistics calculated using the wind forecasting operations 1100 and/or convection operations 1200 (e.g. in random access memory 1708 and/or persistent storage such as an RDBMS). Stored statistics can include maximum site-specific wind gusts 228 as well as the one or more time-steps 206 marked with convective gust potential 216. Statistics derived from the execution of the wind forecasting operations 1100 and/or convection operations 1200 can be stored alone or along with one or more statistics from the meteorological data fields 202 (e.g.
the native wind field 204). Further, the statistics can be stored as a wind forecast 166 or as a forecast 150 that can comprise further statistics/conditions representing a precipitation forecast 168 and/or snow accretion/shedding forecast 170.

[0098] Forecasting operations 176 can further comprise precipitation/snow accretion/shedding forecasting operations 1300 and/or snow accretion/shedding forecasting operations 1400 to derive a precipitation forecast 168 and/or snow accretion/shedding forecast 170 at the site 172. The operations 1300, 1400 can generate forecasts by assessing weather-related data 118 and/or one or more ensemble members 128 for indicators of precipitation and/or snow accretion/shedding at one or more time steps 206. For example, operations 1300, 1400 can analyze meteorological data fields 202 extracted from the weather-related data 118 and/or one or more ensemble members 128 for indicators of precipitation and/or snow accretion/shedding. Indicators of precipitation and/or snow accretion/shedding are familiar to a person skilled in the art. For example, indicators of snow accretion/shedding can include one or more values for total rain (RA1NT), non-convective snow (SNOWNC), sticking ratio (SR) (i.e. the fraction of frozen precipitation, calculated by dividing the amount of frozen precipitation by the total precipitation), and/or ground level temperature and/or temperature at the height of the structure.

[0099] Figure 13 illustrates an example embodiment of precipitation/snow accretion/shedding forecasting operations 1300. Software of the forecasting engine 124 can be configured to start 1302 the operations at any time desired by the user, such as immediately following the extraction of statistics from meteorological data fields 202 intersecting with the one or more bounding boxes 410 or on a set schedule (e.g. every hour). For an ensemble approach, the operations can execute 1304 a loop for each ensemble member 128 comprising identifying 1306 (e.g. from random access memory 1708 and/or persistent storage such as an RDBMS) the statistical data for each meteorological data field 202 for the one or more time-steps 206 (which can be for example 1-hour apart); executing 1308 snow accretion/shedding forecasting operations 1400; and storing 1310 snow accretion/shedding values and precipitation values (e.g. in random access memory 1708 and/or persistent storage such as an RDBMS).

[00100] The forecasting operations 176 can further comprise snow accretion/shedding forecasting operations 1400 run alone or as part of the precipitation/snow accretion/
shedding forecasting operations 1300. Figure 14 illustrates an example embodiment of snow accretion/shedding forecasting operations 1400. Note that values of pre-set .
thresholds/ranges would be familiar to a person skilled in the art of weather forecasting.
Software of the forecasting engine 124 can be configured to start 1402 the snow accretion/shedding forecasting operations 1400 for example at step 1308 of the precipitation/snow accretion/shedding forecasting operations 1300. The operations can determine 1404 whether RAINT is greater than a pre-set threshold value. If RAINT is determined to be not greater than a pre-set threshold value, then a prediction 1406 can be made of no snow accretion. If RAINT is determined to be greater than a pre-set threshold value, then the operations can determine 1408 whether SNOWNC is greater than a pre-set threshold value and whether SR is within a pre-set acceptable range. If SNOWNC is determined to be not greater than a pre-set threshold or if SR is determined to not be within an acceptable range, then the operations can determine 1410 whether SR is greater than a pre-set threshold value and whether the ground level temperature is greater than a pre-set threshold value. If SR is determined to be not greater than a threshold value or if the ground level temperature is determined to be not greater than threshold, then a prediction 1406 can be made of no snow accretion. If SNOWNC is determined to be greater than a pre-set threshold value and SR is determined to be within a pre-set acceptable range, or if SR is determined to be greater than a pre-set threshold value and the ground level temperature is determined to be greater than a pre-set threshold value, then a prediction 1412 can be made of wet snow. If wet snow is predicted then the operations can compute 1414 snow accretion using an International Organization for Standardization (ISO) accretion method (e.g.
ISO 12494: Atmospheric Icing of Structures). Once the snow accretion is calculated, a separate snow shedding algorithm is used to determine whether a snow shedding event will take place. For example, the operations can determine 1416 whether the ambient temperature is rising or whether rain is predicted. If the ambient temperature is determined to be rising or if rain is predicted, then a prediction 1418 can be made that snow shedding is possible. If the ambient temperature is determined to be not rising or if rain is not predicted, then a prediction 1420 can be made that snow shedding is not possible.

[00101] Precipitation/snow accretion/shedding forecasting operations 1300 and/or snow accretion/shedding forecasting operations 1400 can further comprise operations to prepare a precipitation forecast 168 and/or snow accretion/shedding forecast 170 and/or alert message 152 for transmission to one or more forecast recipients 180.
Figure 15 (described below) illustrates an example embodiment of such operations.

[00102] The forecasting engine 124 can be configured to store (e.g. in random access memory 1708 and/or persistent storage such as an RDBMS) statistics calculated using precipitation/snow accretion/shedding forecasting operations 1300 and/or snow accretion/shedding forecasting operations 1400. The information can be stored as a precipitation forecast 168, snow accretion/shedding forecast 170, and/or as a forecast 150 comprising statistics or information representative of precipitation, snow accretion/shedding and/or wind conditions.

[00103] Forecasting operations 176 can further comprise bundling operations that prepare one or more of the wind forecast 166, precipitation forecast 168 and/or snow accretion/shedding forecast 170 as a forecast 150 for transmission to one or more forecast recipients 180. Bundling operations 1500 can comprise operations that prepare a wind forecast 166 by selecting the maximum value from among the native wind gust field 204 and the maximum site-specific wind gusts 228 of multiple ensemble members 128 at one or more heights 210 and/or one or more time-steps 206, or by selecting time-steps 206 marked with convective gust potential 216. Bundling operations can further comprise operations to prepare a wind forecast 166, precipitation forecast 168 and/or a snow accretion/shedding forecast 170 by storing values pertaining to a wind, precipitation, and/or snow accretion shedding forecast (e.g. in random access memory 1708 and/or persistent storage such as an RDBMS). Such values can comprise for example one or more statistics from the meteorological data fields 202, a maximum site-specific wind gust 228 value, native wind gust 204 value, one or more time-steps 206 marked with convective gust potential 216, and/or one or more time-steps 206 marked with a prediction of precipitation and/or snow accretion/shedding. The wind forecast 166, precipitation forecast 168, and/or snow accretion/shedding forecast 170 can be stored separately and/or together as a forecast 150. Further, the one or more forecasts 150, 166, 168, 170 can be stored as a time-series such that each time-step 206 in the time series is marked with or accompanied by data from one or more of the wind forecast 166, precipitation forecast 168, and snow accretion/shedding forecast 170 (e.g. time-step 1 can be marked with convective gust potential 216, time step 2 can be marked with a prediction for snow accretion, and time step 3 can be marked with maximum wind gusts predicted at one or more heights 210 of the forecast domain 400).

[00104] Bundling operations 1500 can further comprise operations to generate one or more alert messages 152. Generating an alert message 152 can comprise comparing maximum wind gusts at each height 210 at one or more time-steps 206 to pre-set threshold values and generating an alert message 152 if the maximum wind gust at a height 210 exceeds the pre-set threshold value for that height 210. An alert message 152 can also be generated if a time-step 206 is marked with convective gust potential 216 and/or precipitation and/or snow accretion/shedding is predicted for the time-step 206. Bundling operations 1500 can also be configured to generate an alert message 152 in response to the detection of one or more alerts embedded within downloaded weather-related data 118 and/or ensemble member 128 data (e.g. federal weather agency alerts/warnings). Alert messages 152 can contain information about the source of the alert (e.g. a specific time-step 206 marked with convective gust potential 216) and/or can instruct the recipient to access a recently transmitted forecast 150 for the details of the source of the alert message 152.

[00105] Figure 15 illustrates an example embodiment of bundling operations used to generate one or more forecasts 150, 166, 168, 170. The forecasting engine 124 can be configured to start 1502 bundling operations 1500 immediately following execution of other forecasting operations 176 (e.g. wind forecasting operations 110001 snow accretion/shedding forecasting operations 1400) or on a pre-set schedule (e.g.
every hour) or as required by a user. A forecast 150, 166, 168, 170 can be created 1504 by running 1506, 1508 a loop for each project and for each time-step 206 for which a forecast 150 is required within each project. The loop can comprise obtaining 1510, 1512 stored wind and/or precipitation and/or snow accretion/shedding forecast data (which can comprise meteorological data fields 202 and/or output from forecasting operations 176); selecting 1514 the maximum site-specific wind gust 228 for each height 210 within the project from all ensemble members 128; comparing 1516 the maximum site-specific wind gust 228 for each height 210 to the native wind gust field 204 and selecting the maximum value; marking 1518 the time-step 206 as having convective gust potential 216 if convective gust potential 216 has been found to exist using for example executed convection operations 1200; and storing 1520 the maximum site-specific wind gust 228 or native wind gust 204 value and/or the presence/absence of convective gust conditions and/or the presence/absence of a prediction of precipitation and/or the presence/absence of a prediction of snow accretion/shedding (e.g. in random access memory 1708 and/or persistent storage such as an RDBMS). The output from execution of bundling operations 1500 on multiple time-steps 206 and/or at multiple heights 210 of the forecast domain 400 can be stored individually or together as a time series.

[00106] The forecasting engine 124 can be configured to transmit the one or more forecasts 150, 166, 168, 170 and/or alert message 152 to one or more forecast recipients 180 over a communications network 164. The one or more forecast recipients 180 can comprise the general public, a remote monitoring service 156, one or more servers 158 operating a website and/or pre-determined third parties 160 such as the computing devices 1700 of individuals, businesses or government agencies. The forecasting engine 124 can be configured to transmit (using e.g. the network connection interface 1702) one or more forecasts 150, 166, 168, 170 and/or alert messages 152 to the computing devices 1700 of one or more forecast recipients 180 over a = communications network 164. Alert messages 152 can take the form of for example an email, text, page, etc. transmitted to a computing device 1700 (e.g. handheld device or laptop computer), as data that is received and interpreted by a wind gust mobile app installed on a computing device 1700, or as information (e.g. text message) sent to a cell phone.

[00107] In an alternative embodiment, a computing device 1700 implementing the forecasting engine 124 can exist at a site 172 and be linked to a warning system configured to emit an audible or visible signal as an alert message 152.

[00108] Transmission of one or more alert messages 152 can be controlled by transmission operations 1550, which can be performed in conjunction with forecasting operations 176 such as wind forecasting operations 1100, precipitation/snow accretion/shedding forecasting operations 1300, snow accretion/shedding forecasting operations 1400 and/or bundling operations 1500.

[00109] Figure 15 illustrates an example embodiment of transmission operations 1550. For each project, the operations 1550 can run 1522 a loop comprising looking up 1524 (e.g. from random access memory 1708 and/or persistent storage such as an RDBMS) a stored 1520 time-series of maximum site-specific wind gusts 228 and/or time-steps 206 marked with convective gust potential 216 and/or a prediction of precipitation and/or snow accretion/shedding; updating 1526 a website comprising transmitting the time-series of wind, precipitation and/or snow/accretion conditions (via a network connection interface 1702) as a forecast 150 to a remote monitoring service 156 and/or server 158 operating a publicly available or encrypted website (using a computing device 1700); sending 1528 an email comprising the forecast 150 to third parties 160 (e.g. pre-determined individuals, businesses, or government agencies); and checking 1530 for alerts by determining if one or more wind, precipitation and/or snow accretion/shedding conditions in the wind forecast 150 represent alert conditions and, if so, transmitting one or more alert messages 152 to designated forecast recipients 180 such as a remote monitoring service 156 or a designated third party 160 such as a building site supervisor or government agency. Once a forecast 150 has been generated for each project in the series, the operations 1550 can sleep 1532 until next required (e.g., in an hour).

[00110] Also disclosed is a computer-implemented method for providing a weather forecast at a site 172. One embodiment of the method is illustrated in Figure 16. The method 1600 can comprise a first step 1602 of receiving and storing geographical coordinates defining the location and dimensions of a forecast domain 400 at the site 172. The forecast domain 400 can comprise one or more bounding boxes 410.
Although not shown, this step may further comprise receiving and storing a wind gust matrix 112 wherein each cell of the wind gust matrix 112 is populated with a wind gust factor 706 representative of one or more local parameters which influence the wind speed at a specific height 210 and for a specific compass direction at the site 172.

[00111] The method 1600 can comprise a second step 1604 of receiving and storing weather-related data 118 comprising information indicative and/or predictive of current and/or future weather conditions within the forecast domain 400. The weather conditions can pertain to one or more of wind gust patterns, wind convection patterns, precipitation patterns, snow accretion patterns and snow shedding patterns.
The information indicative and/or predictive of weather conditions within the forecast domain 400 can be information from one or more time-steps 206.

[00112] The method 1600 can comprise a third step 1606 of extracting from the weather-related data the information indicative and/or predictive of the weather conditions within the forecast domain 400. This step can further comprise processing the weather-related data with one or more numerical weather prediction models, at least one of which can be a high-resolution model. In addition, one or more wind gust factors 706 can be applied to the extracted information (e.g. the reference wind speed 220) to generate one or more site-specific wind gust speeds and/or a maximum site-specific wind gust 228.

[00113] The method 1600 can comprise a fourth step 1608 of providing the information indicative of the weather conditions within the forecast domain 400 as a weather forecast 150, 166, 168, 170 (e.g. to one or more forecast recipients 180). The weather forecast 150 can comprise an alert message 152 if for example the maximum site-specific wind gust 228 exceeds a stored threshold value and/or if one or more wind, precipitation and/or snow accretion/shedding conditions in the wind forecast represent alert conditions.

Claims

We Claim:

1. A computer-implemented method for providing a weather forecast at a site for one or more buildings or structures, the method comprising:
receiving and storing geographic coordinates defining the location and dimensions of a forecast domain located at the site;
receiving and storing weather-related data comprising information indicative of weather conditions within the forecast domain, the weather conditions being one or more members selected from the group consisting of: wind speed patterns, wind gust patterns, wind convection patterns, precipitation patterns, snow accretion patterns and snow shedding patterns;
extracting from the weather-related data the information indicative of the weather conditions within the forecast domain; and providing the information indicative of the weather conditions within the forecast domain as a weather forecast.

2. The method of claim 1 further comprising receiving and storing geographic coordinates defining one or more bounding boxes located within the forecast domain.

3. The method of claim 2 wherein the steps of receiving and storing the weather-related data, extracting the information indicative of the weather conditions within the forecast domain, and providing the information indicative of the weather conditions within the forecast domain are performed for each bounding box defined at the site.

4. The method of any one of claims 1 to 3 further comprising applying one or more numerical weather prediction models to the weather-related data.

5. The method of claim 4 wherein at least one of the one or more numerical weather prediction models is a high-resolution model.

6. The method of any one of claims 1 to 5 further comprising receiving and storing a matrix wherein each cell of the matrix is populated with a wind gust factor representative of one or more parameters which influence a wind speed at a specific height and for a specific compass direction at the site.

7. The method of claim 6 further comprising applying one or more of the wind gust factors to the extracted information to generate one or more site-specific wind gust speeds.

8. The method of claim 7 wherein the one or more site-specific wind gust speeds is generated for multiple heights at the site.

9. The method of claim 7 or claim 8 wherein the weather forecast comprises an alert message if the one or more site-specific wind gust speeds exceeds a stored threshold value.

10. The method of any one of claims 1 to 9 wherein the information indicative of the weather conditions is information indicative of the weather conditions at one or more time-steps, wherein each time-step represents a distinct accretion of time into the future.

11. The method of claim 10 wherein the step of providing the information indicative of the weather conditions within the forecast domain comprises providing the information indicative of the weather conditions at multiple time steps.

12. The method of any one of claims 1 to 11 wherein the step of providing the information indicative of the weather conditions within the forecast domain comprises providing an alert message if the information indicative of the weather conditions comprises an indicator of the presence of one or more members selected from the group consisting of: wind convection, precipitation, snow accretion, and snow shedding.

13. The method of claim 12 wherein the alert message comprises an indicator that is audible or visible at the site.

14. The method of any one of claims 1 to 13 wherein the weather forecast is provided to a computing device located remote from the site.

15. A computer system for providing a weather forecast at a site for one or more buildings or structures, the system comprising at least one processor coupled to memory storing instructions and data, which when executed by the processor, cause the computer system to:
receive and store geographic coordinates defining the location and dimensions of a forecast domain located at the site;
receive and store weather-related data comprising information indicative of weather conditions within the forecast domain, the weather conditions being one or more members selected from the group consisting of: wind speed patterns, wind gust patterns, wind convection patterns, precipitation patterns, snow accretion patterns and snow shedding patterns;
extract from the weather-related data the information indicative of the weather conditions within the forecast domain; and provide the information indicative of the weather conditions within the forecast domain as a weather forecast.

16. The system of claim 15 further configured to receive and store geographic coordinates defining one or more bounding boxes located within the forecast domain.

17. The system of claim 16 wherein the weather forecast comprises information indicative of weather conditions at each bounding box defined at the site.

18. The system of any one of claims 15 to 17 further configured to apply one or more numerical weather prediction models to the weather-related data.

19. The system of claim 18 wherein at least one of the one or more numerical weather prediction models is a high-resolution model.

20. The system of any one of claims 15 to 19 further configured to receive and store a matrix wherein each cell of the matrix is populated with a wind gust factor representative of one or more parameters which influence a wind speed at a specific height and for a specific compass direction at the site.

21. The system of claim 20 wherein the wind gust factor is applied to the extracted information to generate one or more site-specific wind gust speeds.

22. The system of claim 20 or claim 21 wherein the one or more site-specific wind gust speeds is generated for multiple heights at the site.

23. The system of claim 21 or claim 22 wherein the weather forecast comprises an alert message if the one or more site-specific wind gust speeds exceeds a stored threshold value.

24. The system of any one of claims 15 to 23 wherein the information indicative of the weather conditions is information indicative of the weather conditions at one or more time-steps, wherein each time-step represents a distinct accretion of time into the future.

25. The system of claim 24 wherein the weather forecast comprises the information indicative of the weather conditions at multiple time steps.

26. The system of any one of claims 15 to 25 wherein the weather forecast comprises an alert message if the information indicative of the weather conditions comprises an indicator of the presence of one or more members selected from the group consisting of: wind convection, precipitation, snow accretion, and snow shedding.

27. The system of claim 26 wherein the alert message comprises an indicator that is audible or visible at the site.

28.
The system of any one of claims 15 to 27 wherein the weather forecast is provided to a computing device located remote from the site.