Method and apparatus for providing a weather volatility index

An approach is provided for generating a volatility index for weather data. The approach involves retrieving weather data collected from one or more weather sensors over a temporal domain, a spatial domain, or a combination thereof. The one or more weather sensors provide the weather data for at least one geographic point. The approach also involves processing the weather data to determine volatility data for at least one weather attribute, wherein the volatility data represents how much the at least one weather attribute changes over the temporal domain, the spatial domain, or a combination thereof. The approach further involves generating a volatility index to represent the volatility data.

BACKGROUND

Because of the popularity of real-time weather services, weather service providers have devoted significant efforts to ensuring that they provide accurate, precise, and timely weather data. One primary obstacle to this effort is the natural variability of weather data. For example, this variability generally is not uniform across all locations and can vary across different terrains, locations, etc., thereby creating significant technical challenges for the service providers to overcome to deliver consistent weather services across these different locations.

SOME EXAMPLE EMBODIMENTS

Therefore, there is a need for providing a weather volatility index for characterizing how volatile or variable a weather attribute (e.g., temperature, pressure, precipitation, etc.) is at a location or area. This volatility index can then be used to determine how a service provider uses or provides weather data to end users.

According to one embodiment, a computer-implemented method comprises retrieving weather data collected from one or more weather sensors over a temporal domain, a spatial domain, or a combination thereof. The one or more weather sensors provide the weather data for at least one geographic point. The method also comprises processing the weather data to determine volatility data for at least one weather attribute. The volatility data represents how much the at least one weather attribute changes over the temporal domain, the spatial domain, or a combination thereof. The method further comprises generating a volatility index to represent the volatility data.

According to another embodiment, an apparatus comprises at least one processor, and at least one memory including computer program code for one or more computer programs, the at least one memory and the computer program code configured to, with the at least one processor, cause, at least in part, the apparatus to retrieve weather data collected from one or more weather sensors over a temporal domain, a spatial domain, or a combination thereof. The one or more weather sensors provide the weather data for at least one geographic point. The method is further caused to process the weather data to determine volatility data for at least one weather attribute. The volatility data represents how much the at least one weather attribute changes over the temporal domain, the spatial domain, or a combination thereof. The apparatus is also caused to generate a volatility index to represent the volatility data.

According to another embodiment, a computer-readable storage medium carries one or more sequences of one or more instructions which, when executed by one or more processors, cause, at least in part, an apparatus to retrieve weather data collected from one or more weather sensors over a temporal domain, a spatial domain, or a combination thereof. The one or more weather sensors provide the weather data for at least one geographic point. The method is further caused to process the weather data to determine volatility data for at least one weather attribute. The volatility data represents how much the at least one weather attribute changes over the temporal domain, the spatial domain, or a combination thereof. The apparatus is also caused to generate a volatility index to represent the volatility data.

According to another embodiment, an apparatus comprises means for retrieving weather data collected from one or more weather sensors over a temporal domain, a spatial domain, or a combination thereof. The one or more weather sensors provide the weather data for at least one geographic point. The apparatus also comprises means for processing the weather data to determine volatility data for at least one weather attribute. The volatility data represents how much the at least one weather attribute changes over the temporal domain, the spatial domain, or a combination thereof. The apparatus also comprises means for generating a volatility index to represent the volatility data.

For various example embodiments, the following is applicable: An apparatus comprising means for performing the method of the claims.

DESCRIPTION OF SOME EMBODIMENTS

FIG. 1is a diagram of a system capable of providing a weather volatility index, according to one embodiment. Weather varies according to many factors such as terrain (e.g. mountainous regions) and the presence of water bodies such as lakes, oceans, and seas. As a result, different locations with different terrain or features (e.g., water features), can have different levels of weather variability. In some cases, by not taking into account weather variability across different locations, weather service providers may either be expending too many resources (e.g., by over sampling a weather attribute in areas with less weather variability or volatility) or too few resources (e.g., by under sampling a weather attribute in areas with more weather volatility). This can also lead to the weather service provider providing less consistent, accurate, or precise weather data to its users.

To address this problem, a system100ofFIG. 1introduces a capability to generate a volatility index based the detected variability or volatility of weather data collected from weather stations101a-101m(also collectively referred to as weather stations101) providing weather data for a geographic point or area. In one embodiment, the weather stations101are equipped with sensors for measuring one or more weather attributes (e.g., temperature, pressure, precipitation, etc.). As a result, the volatility index can be determined with respect to one or more of the individual weather attributes.

In one embodiment, the weather volatility index can be determined over a temporal domain, spatial domain, or a combination thereof. For example, a temporal weather volatility index represents how variable a weather attribute is at a given location or area over a period of time. Similarly, a spatial weather volatility index represents how variable a weather attribute at different distances in a specified area or an area around a target geographic point.

In one embodiment, this weather volatility index provides a useful mechanism that the system100can use to adapt one or more weather services. For example, for weather services that use interpolation of weather data to predict or estimate weather at one location from data collected from other location, the volatility index can be used to adjust the spatial and temporal interpolation ranges (e.g., ranges that specify over how far in time or distance interpolation of weather data can occur). For example, in areas where the volatility index is low, the system100can relax the interpolation ranges, but in areas where the volatility index is high, the system100can apply more conservative or limited interpolation ranges.

An example use-cases of the volatility index include, but are not limited to: (1) using the volatility index a prioritization of the areas for which the system100should compute and/or publish weather reports (e.g., compute or publish for areas with the highest volatility index first); and (2) allocating or recommending the allocation of weather stations101, weather sensors, etc. (e.g., allocate more weather stations101to areas with higher volatility). More specifically, weather reports or weather data generally comes from two sources, which are stationary weather stations101(e.g., weather station101asuch as those at airports) and mobile weather stations (e.g., connected cars or devices such as weathers stations101band101m). For example, each weather report can be interpolated to other locations because for each location point at which weather data is requested, there may not be a corresponding weather station101. Thus, the system100can interpolate weather data using limits on the distance and limits on the time to increase coverage. For example, for the case of temperature observations, the system100can interpolate an observation to approximately 50 km from the reporting weather station101.

Traditionally, these limits on interpolation have been fixed to a single value. However, by computing the volatility index according to the various embodiments described herein, the system100can apply dynamic interpolation limits that depend on volatility index for the location. For example, in mountainous regions where the volatility index is likely to be high, the system100can apply lower time and/or space thresholds for interpolating data (e.g., lower than the typical fixed 50 km space constraint). Therefore, if the system100receives a weather observation of temperature in the mountains (e.g., from a vehicle-based or mobile weather station101), the system100can apply a lower interpolation distance limit (e.g., lower than 50 km), and thus will not be able to interpolate that report to a location 50 km away or to any other location more than the dynamic distance threshold. On the other hand, in the plains that are far from water bodies where the volatility index should be low, the system100can apply a more relaxed interpolation thresholds on space and time cut-offs (e.g., greater than 50 km). Accordingly, interpolation the weather report to a location 50 km away may be reasonable.

In one embodiment, the system100can generate weather volatility indices for geographic points and/or geographic areas. For example, as shown inFIG. 2which illustrates an example of weather data locations used for providing a weather volatility index according to one embodiment, the system100can determine the locations201a-201d(also collectively referred to as locations201) of respective weather stations101, and then generate volatility indices individually for one or more of the locations201.

In one embodiment, the system100can use one or more of the weather stations101corresponding the locations201to determine weather data for an interpolated location203. The system100can then generate the volatility index for the interpolated location203. In yet another embodiment, any of the locations201and/or the interpolated location203can represent a geographic area205. The volatility index would then be correspond to the variability or volatility of a weather attribute within the geographic area205. The geographic area205, for instance, can correspond to a map tile of a tile-based map representation of a geographic database103. In addition or alternatively, the geographic area205can correspond to a node, a road link, an intersection, a point of interest (POI), and/or any other feature stored or represented in the geographic database103.

In one embodiment and as previously noted, the system100provides for at least two measures of volatility: (1) a temporal volatility index, and (2) a spatial volatility index. With respect to a temporal volatility index and given a geographic point or area, this index reflects how volatile weather is across time. That is, as time elapses how much the weather changes for the given attribute at the target location or area. For a spatial volatility index, given a geographic point or area, this index reflects how volatile weather is across distance. That is, as the distance in an area (e.g., a map tile) increases, how much the weather changes for the given attribute at the target location or area.

In one embodiment, the volatility index (e.g., temporal and/or spatial) is computed for each weather attribute using historical weather data. Thus, for a given geographic point or area (e.g., map tile), there can be temperature volatility index, pressure volatility index, precipitation volatility index, etc. over a time and/or spatial domain.

Assuming that we are computing the time and spaced based volatility index for Tile A inFIG. 1. Let's assume that we want to compute the time based volatility index specifically for the temperature weather attribute. The procedure is as follows:

FIG. 3Ais a diagram illustrating a process for generating a temporal volatility index for a weather attribute, according to one embodiment. As shown, for each target point or area (e.g., a map tile), the system100gathers historical weather data301(e.g., historical temperature data in this example). In one embodiment, the historical weather data301includes data that corresponds or is interpolated to the target geographic area. In an embodiment in which a geographic area such as a map tile is targeted, the weather data can correspond to a centroid of the area or any other specified location(s) within the target area. Because this example is illustrated with respect to temperature, the system100uses the weather data301to compute a temperature volatility index. It is noted that the procedure for a general weather attribute or any other weather attribute is analogous to the process described in this embodiment.

In one embodiment, the historical weather data301can be collected from any specified period of time. For example, if seasonal variations are to be captured, then historical weather data301should at least cover one year. However, if seasonal variations are to be determined, the historical weather data301can be segmented according to seasons so that separate volatility indices can be computed for each season. It is noted that the volatility index can be captured with respect to any contextual parameter as along as the weather data301is segmented according to that contextual parameter. For example, if day versus night volatility is to be differentiated, then the weather data301can be segmented into day versus night to enable calculating separate volatility indices. Other examples of contextual parameters include, but are not limited to, different weather events, different types of weather stations101, different types of weather sensors used to measure the same attribute, etc.

After retrieving the weather data301, the system100organizes the historical data301for the given point or area by time. For example, the weather data can be organized into different time epochs303a-303n(also collectively referred to as time epochs303). The time epochs303can span any period of time (e.g., 15 mins, 1 hour, etc.) depending on the level of granularity desired for determining the variability or volatility of the weather data301.

In one embodiment, the system100can then discard any outliers by throwing out unreasonable weather attribute values (e.g., temperature values). The reasonable range can be determined based on ranges that expected to normally occur in nature. For example, temperature values outside a reasonable range (e.g., −30° C. and 100° C.) can be immediately suppressed as erroneous data.

From the remaining data after outlier suppression, the system100computes a measure of weather volatility (e.g., a temporal volatility index305) for the weather attribute (e.g., for temperature). In this contemplated that any means for determining variability in a data can be used to determine volatility data from the weather data set301(e.g., standard deviation, coefficient of variation, etc.). In one embodiment, the system100considers the average difference across the different time epochs303as a measure of volatility. By way of example, the average difference is computed as follows: given time ordered historical temperature data for tile A as t1, t2, t3, t4, t5 . . . to (e.g., corresponding to time epochs303),

In one embodiment, the equation above can generalized to any other weather attribute to compute the temporal volatility index305.

After the computation of temporal volatility index305, the system100can optionally perform a normalization process. This normalization process, for instance, ensures that the volatility index values are adjusted to a common scale so that comparisons of different volatility indices can be performed more easily. In on embodiment, the normalized can include dividing the temporal volatility index305by a mean value, maximum value, minimum value, or the like for the corresponding weather attribute computed from the weather data301. It is contemplated that any means for normalizing the resulting volatility index305can be used according to the various embodiments described herein.

In one embodiment, the embodiments of the process described above that was used for temperature can also be used to compute the volatility index for the different weather attributes such as visibility, humidity, and pressure. For visibility volatility index, assume that for a given geographic point or area (e.g., a map tile), the system100can generate time ordered visibility reports as V1, V2, V3, V4, V5 . . . Vn (e.g., corresponding to the time epochs303), then

In addition or as an alternate to computing the temporal volatility index305, the system100can compute a distance based or spatial volatility index321for each weather attribute as shown inFIG. 3B. In one embodiment, the spatial volatility index321measures the volatility across the spatial or distance domain for each weather attribute. As with the temporal volatility index305, the spatial volatility index321can be any measure of variability or volatility such as an average difference.

In one embodiment, the procedure for generating the spatial volatility index321begins as described with respect to the temporal volatility index305. That is, the system100retrieves a historical weather data323that is equivalent to the weather data301ofFIG. 3A. In this case, however, the historical weather data323is collected from or interpolated to locations at various distances from the target geographic point or one or more reference points (e.g., a centroid) of a target geographic area (e.g., a map tile). In one embodiment, the weather data323consists of weather reports recorded at approximately the same time or over the same time period.

In one embodiment, the system100then organizes the weather data323according to distance, for instance, by segmenting the data into different area segments occurring at different distances within the area of interest or near the target geographic point. For example, the system100can generate a time series dataset for various radius distance search (e.g. 1 km, 2 km, 3 km, 4 km, 5 km, 6 km, etc.) within the area of interest as shown by radii325. In yet another embodiment, the time series dataset can created for areas of various cells a grid327that segments the area of interest. The radii325and the grid327are provided by way of illustration and not as limitations, and it is contemplated that any equivalent means for segmenting the weather data323by distance or areas can be used in the embodiments described herein.

Then the system100computes the variability of the weather attribute (e.g., temperature) between each different distance segment (e.g., radii325or cells of the grid327). By way of example, the volatility or variability can be computed as an average difference, standard deviation, coefficient of variation, and/or any other measure of variability between the radii325, cells of the grid327, etc.

In one embodiment, the system100can use the distance-based or spatial volatility index321to determine the interpolation cut-off distance. For example, when the system100receives an observation of weather (e.g., temperature) in an area where the distance based volatility index is high then it means that the observation cannot be used to interpolate temperature at farther locations and is only useful for a few meters. However, if the distance based volatility index is low, then it means that there is not much fluctuation of weather expected and thus the system100can interpolate the observation to far locations (e.g. several kilometers).

In one embodiment, as shown inFIG. 4, the system100can create a volatility index record401to store the generated volatility index (e.g., the temporal volatility index305and/or spatial volatility index321) in, for instance, the geographic database103. The volatility index record401can then be associated with other records in the geographic database103. For example, the volatility index record401can be applied to geographic points403(e.g., nodes or other location points), map tiles405, road links or segments407, intersections409, points of interests (POIs)411, and/or any other map feature represented in the geographic database103. The volatility index record401can then be queried or retrieved from the geographic database103using a location-based query specifying one or more of the map features as a query term. Additional description of the geographic database is provided below with respect toFIG. 5.

In one embodiment, by using historical weather data (e.g., 40 years of data from 1970 until 2010), the system100can pre-compute or generate volatility indices (e.g., temporal and/or spatial indices) for a range of weather attributes (e.g., temperature, visibility, precipitation, etc.) for selected or all available locations in the world. The historical data, for instance, can be organized in one hour epochs. The resulting volatility indices can then be stored in the geographic database103.

Returning toFIG. 1, as shown, the system100comprises one or more weather stations101with connectivity over a communication network105to a weather platform107. In one embodiment, the weather platform107performs the functions for providing a weather volatility index according to the various embodiments described herein. As previously discussed, the weather stations101can be fixed or mobile weather stations. For example, fixed weather stations101can be installed (e.g., permanently or semi-permanently) at locations selected to optimize weather data collection (e.g., a location where representative outside ambient measurements can be taken that minimizes factors that can affect weather data readings such as obstructions, direct exposure to sunlight, clear line of sight, etc.). In contrast, mobile weather stations101do not have fixed locations and can move along with a traveler and/or vehicle to which they are associated. As a result, the weather sampling locations for mobile weather stations101are highly variable.

In one embodiment, the weather stations101(e.g., either fixed or mobile) can be equipped with a range of weather sensors for sensing any number of weather attributes or parameters. For example, these sensors include, but are not limited to: (1) thermometer for measure air or surface temperatures, (2) barometer for measuring atmospheric pressure, (3) hygrometer for measuring humidity, (3) anemometer for measuring wind speed, (4) pyranometer for measuring solar radiation, (5) rain gauge for measuring liquid precipitation, (6) precipitation identification sensor for identifying type of falling precipitation, (7) disdrometer for measuring precipitation drop size distribution, (8) transmissometer for measuring visibility, (8) ceilometer for measuring cloud ceiling, and/or the like. It is contemplated that the weather stations101can be equipped with any type of weather or environmental sensor known in the art. In one embodiment, the weather stations101collect weather data (e.g., weather attribute values) that can be used to characterize current weather conditions and/or predict future weather conditions (e.g., weather forecasts).

In one embodiment, the weather stations101generate weather reports to report a range of weather attributes as a combined weather report (e.g., multiple weather attributes measured at the same time and location) in the following format and/or any other equivalent data format or structure:
BGBW|197301010000|197212312100|197212312100|N|2|*|1|*|−27|0|999|*|69|−31|−27|996.6|F|30|C|22.0|*|*|*|*|*|*|*|*|*|*|*|*|*|

In one embodiment, the weather stations101are equipped with logic, hardware, firmware, software, memory, etc. to collect, store, and/or transmit weather data measurements from their respective weather sensors continuously, periodically, according to a schedule, on demand, etc. In one embodiment, the logic, hardware, firmware, memory, etc. can be configured to perform the all or a portion of the various functions associated generating a weather volatility index according to the various embodiments described herein. The weather stations101can also include means for transmitting the collected and stored weather data over, for instance, the communication network105to weather platform107and/or any other components of the system100for generating volatility indices and/or initiating weather-related services or functions based on the volatility indices.

In one embodiment, mobile weather stations101can be associated with travelers and/or vehicles (e.g., connected and/or autonomous cars). These travelers and/or vehicles equipped with such mobile weather stations101can act as probes traveling over a road network within a geographical area represented in the geographic database103. Accordingly, the weather volatility indices generated from weather data sensed from locations along the road network can be associated with different areas (e.g., map tiles, geographical boundaries, etc.) and/or other features (e.g., road links, nodes, intersections, POIs) represented in the geographic database103. Although the vehicles are often described herein as automobiles, it is contemplated that the vehicles can be any type of vehicle, manned or unmanned (e.g., planes, aerial drone vehicles, motor cycles, boats, bicycles, etc.), and the mobile weather stations101can be associated with any of the types of vehicles or a person or thing (e.g., a pedestrian) traveling within a road or transportation network. In one embodiment, each weather station101is assigned a unique identifier (station ID) for use in reporting or transmitting weather data and/or related probe data (e.g., location data).

In one embodiment, the mobile weather stations101can be part of vehicles and/or other devices (e.g., mobile phones, portable navigation devices, etc.) that are part of a probe-based system for measuring weather and traffic conditions in a road network. In one embodiment, each weather station, vehicle, and/or device is configured to report weather data in addition to probe points. By way of example, probe points are individual data records collected at a point in time that records telemetry data for that point in time. As noted, the weather data and/or probe points can be reported from the weather stations, vehicles, and/or devices in real-time, in batches, continuously, or at any other frequency requested by the system100over, for instance, the communication network105for processing by the weather platform107.

In one embodiment, the weather platform107can use probe data or probe point information to map match locations of weather reports received from mobile weather stations101to generate weather volatility indices for the matched locations. By way of example, a probe point can include attributes such as: probe ID, longitude, latitude, speed, and/or time. The list of attributes is provided by way of illustration and not limitation. Accordingly, it is contemplated that any combination of these attributes or other attributes may be recorded as a probe point (e.g., such as those previously discussed above). For example, attributes such as altitude (e.g., for flight capable vehicles or for tracking non-flight vehicles in the altitude domain), tilt, steering angle, wiper activation, etc. can be included and reported for a probe point. In one embodiment, if the probe point data includes altitude information, the transportation network, links, etc. can also be paths through an airspace (e.g., to track aerial drones, planes, other aerial vehicles, etc.), or paths that follow the contours or heights of a road network (e.g., heights of different ramps, bridges, or other overlapping road features).

In one embodiment, the weather platform107can be a standalone server or a component of another device with connectivity to the communication network105. For example, the component can be part of an edge computing network where remote computing devices (not shown) are installed along or within proximity of a given geographical area to provide weather provider/weather station monitoring for weather data collected locally or within a local area served by the remote or edge computing device.

In one embodiment, a weather station101can be any device equipped with one or more of the weather sensors discussed above. By way of example, such a device can be any type of embedded system, mobile terminal, fixed terminal, or portable terminal including a built-in navigation system, a personal navigation device, mobile handset, station, unit, device, multimedia computer, multimedia tablet, Internet node, communicator, desktop computer, laptop computer, notebook computer, netbook computer, tablet computer, personal communication system (PCS) device, personal digital assistants (PDAs), audio/video player, digital camera/camcorder, positioning device, fitness device, television receiver, radio broadcast receiver, electronic book device, game device, or any combination thereof, including the accessories and peripherals of these devices, or any combination thereof. It is also contemplated that the device can support any type of interface to the user (such as “wearable” circuitry, etc.).

In one embodiment, the weather platform107may be a platform with multiple interconnected components. The weather platform107may include multiple servers, intelligent networking devices, computing devices, components and corresponding software for generating weather volatility indices and performing weather-related services or functions. In addition, it is noted that the weather platform107may be a separate entity of the system100, a part of one or more services111a-111j(collectively referred to as services111) of a services platform113, or included within the weather stations101.

The services platform113may include any type of service111. By way of example, the services111may include weather services, mapping services, navigation services, travel planning services, notification services, social networking services, content (e.g., audio, video, images, etc.) provisioning services, application services, storage services, contextual information determination services, location based services, news services, etc. In one embodiment, the services platform113may interact with the weather platform107, the weather stations101, and/or one or more content providers115a-115k(also collectively referred to as content providers115) to provide the services111.

In one embodiment, the content providers115may provide content or data to the weather platform107, and/or the services111. The content provided may be any type of content, such as historical weather data for various weather attributes, mapping content, textual content, audio content, video content, image content, etc. In one embodiment, the content providers115may provide content that may aid in generating weather volatility indices and/or initiating weather services and/or functions based on the volatility indices according to the various embodiments described herein. In one embodiment, the content providers115may also store content associated with the weather stations101, the weather platform107, and/or the services111. In another embodiment, the content providers115may manage access to a central repository of data, and offer a consistent, standard interface to data, such as a repository of historical or current weather data, probe data, probe features/attributes, link features/attributes, etc.

FIG. 5is a diagram of the geographic database103of system100, according to exemplary embodiments. In the exemplary embodiments, the volatility indices generated by the weather platform107and/or the weather data generated by the weather stations101can be stored, associated with, and/or linked to the geographic database103or data thereof. In one embodiment, the geographic database103includes geographic data501used for (or configured to be compiled to be used for) mapping and/or navigation-related services, such as for personalized route determination, according to exemplary embodiments. For example, the geographic database103includes node data records503, road segment or link data records505, POI data records507, weather data records509, and other data records511, for example. More, fewer or different data records can be provided. In one embodiment, the other data records511include cartographic (“carto”) data records, routing data, and maneuver data. One or more portions, components, areas, layers, features, text, and/or symbols of the POI or event data can be stored in, linked to, and/or associated with one or more of these data records. For example, one or more portions of the POI, event data, or recorded route information can be matched with respective map or geographic records via position or GPS data associations (such as using the point-based map matching embodiments describes herein), for example.

In one embodiment, the following terminology applies to the representation of geographic features in the geographic database103.

“Node”—A point that terminates a link.

In one embodiment, the geographic database103is presented according to a hierarchical or multi-level tile projection. More specifically, in one embodiment, the geographic database103may be may be defined according to a normalized Mercator projection. Other projections may be used. In one embodiment, a map tile grid of a Mercator or similar projection can a multilevel grid. Each cell or tile in a level of the map tile grid is divisible into the same number of tiles of that same level of grid. In other words, the initial level of the map tile grid (e.g., a level at the lowest zoom level) is divisible into four cells or rectangles. Each of those cells are in turn divisible into four cells, and so on until the highest zoom level of the projection is reached.

In one embodiment, the map tile grid may be numbered in a systematic fashion to define a tile identifier (tile ID). For example, the top left tile may be numbered 00, the top right tile may be numbered 01, the bottom left tile may be numbered 10, and the bottom right tile may be numbered 11. In one embodiment, each cell is divided into four rectangles and numbered by concatenating the parent tile ID and the new tile position. A variety of numbering schemes also is possible. Any number of levels with increasingly smaller geographic areas may represent the map tile grid. Any level (n) of the map tile grid has 2(n+1) cells. Accordingly, any tile of the level (n) has a geographic area of A/2(n+1) where A is the total geographic area of the world or the total area of the map tile grids. Because of the numbering system, the exact position of any tile in any level of the map tile grid or projection may be uniquely determined from the tile ID.

In one embodiment, the system100may identify a tile by a quadkey determined based on the tile ID of a tile of the map tile grid. The quadkey, for example, is a one dimensional array including numerical values. In one embodiment, the quadkey may be calculated or determined by interleaving the bits of the row and column coordinates of a tile in the grid at a specific level. The interleaved bits may be converted to a predetermined base number (e.g., base10, base4, hexadecimal). In one example, leading zeroes are inserted or retained regardless of the level of the map tile grid in order to maintain a constant length for the one dimensional array of the quadkey. In another example, the length of the one dimensional array of the quadkey may indicate the corresponding level within the map tile grid. In one embodiment, the quadkey is an example of the hash or encoding scheme of the respective geographical coordinates of a geographical data point that can be used to identify a tile in which the geographical data point is located.

In exemplary embodiments, the road segment data records505are links or segments representing roads, streets, or paths, as can be used in the calculated route or recorded route information for determination of one or more personalized routes, according to exemplary embodiments. The node data records503are end points or vertices corresponding to the respective links or segments of the road segment data records505. The road link data records505and the node data records503represent a road network, such as used by vehicles, cars, and/or other entities. Alternatively, the geographic database103can contain path segment and node data records or other data that represent pedestrian paths or areas in addition to or instead of the vehicle road record data, for example. In one embodiment, the road or path segments can include an altitude component to extend to paths or road into three-dimensional space (e.g., to cover changes in altitude and contours of different map features, and/or to cover paths traversing a three-dimensional airspace).

The road/link segments and nodes can be associated with attributes, such as geographic coordinates, street names, address ranges, speed limits, turn restrictions at intersections, and other navigation related attributes, as well as POIs, such as gasoline stations, hotels, restaurants, museums, stadiums, offices, automobile dealerships, auto repair shops, buildings, stores, parks, etc. The geographic database103can include data about the POIs and their respective locations in the POI data records507. The geographic database103can also include data about places, such as cities, towns, or other communities, and other geographic features, such as bodies of water, mountain ranges, etc. Such place or feature data can be part of the POI data records507or can be associated with POIs or POI data records507(such as a data point used for displaying or representing a position of a city).

In one embodiment, the geographic database103includes weather data records509which store the weather volatility index records401, weather data reports, and/or related probe point data. For example, the weather data records409can store weather volatility index records401that can be associated with any of the map features stored in the geographic database103(e.g., a specific road or link, node, intersection, area, POI, etc. on which the weather data was collected and used to generate a corresponding weather volatility index record401.

The geographic database103can be maintained by the content provider115in association with the services platform113(e.g., a map developer). The map developer can collect geographic data to generate and enhance the geographic database103. There can be different ways used by the map developer to collect data. These ways can include obtaining data from other sources, such as municipalities or respective geographic authorities. In addition, the map developer can employ field personnel to travel by vehicle along roads throughout the geographic region to observe features and/or record information about them, for example. Also, remote sensing, such as aerial or satellite photography, can be used.

FIG. 6is a diagram of the components of a weather platform107, according to one embodiment. By way of example, the weather platform107includes one or more components for providing a weather volatility index according to the various embodiments described herein. It is contemplated that the functions of these components may be combined or performed by other components of equivalent functionality. In this embodiment, the weather platform107includes a data module601, an index module603, a geographic database interface module605, and an application interface module607. The above presented modules and components of the weather platform107can be implemented in hardware, firmware, software, or a combination thereof. Though depicted as a separate entity inFIG. 1, it is contemplated that the weather platform107may be implemented as a module of any of the components of the system100(e.g., a component of the services platform113, the weather stations101, etc.). In another embodiment, one or more of the modules601-607may be implemented as a cloud based service, local service, native application, or combination thereof. The functions of these modules are discussed with respect toFIGS. 7 and 8below.

FIG. 7is a flowchart of a process for providing a weather volatility index, according to one embodiment. In various embodiments, the weather platform107and/or any of the modules601-607of the weather platform107as shown inFIG. 6may perform one or more portions of the process700and may be implemented in, for instance, a chip set including a processor and a memory as shown inFIG. 12. As such, the weather platform107and/or the modules601-607can provide means for accomplishing various parts of the process700, as well as means for accomplishing embodiments of other processes described herein in conjunction with other components of the system100. Although the process700is illustrated and described as a sequence of steps, its contemplated that various embodiments of the process700may be performed in any order or combination and need not include all of the illustrated steps.

In step701, the data module601retrieves weather data collected from one or more weather sensors over a temporal domain, a spatial domain, or a combination thereof, wherein the one or more weather sensors provide the weather data for at least one geographic point. By way of example, the weather sensors may affixed to one or more weather stations located at the geographic point or whose data can be used to interpolate weather data for the geographic point. In one embodiment, the at least one geographic point can be used to represent a geographic area such as a map tile or any other geographic boundary. Accordingly, the at least one geographic point can be a centroid or reference point(s) within the area. For example, in the case of a map tile of a tile-based representation of a geographic database (e.g., the geographic database103), the at least one geographic point can be a centroid of the tile, and the geographic area represented by the at least one geographic point is an area represented by the tile.

In step703, the index module603processes the weather data to determine volatility data for at least one weather attribute. As previously described, the volatility data represents how much the at least one weather attribute changes over the temporal domain, the spatial domain, or a combination thereof. In one embodiment, the volatility data includes the weather data collected in step701that has been organized by time (e.g., in the case of computing a temporal volatility index) and/or by distance (e.g., in the case of computing a spatial temporal volatility index).

In step705, the index module603generates a volatility index to represent the volatility data. In one embodiment, in the case of a temporal volatility index, the index module603computes the volatility index over the temporal domain as an average difference, a coefficient of variation, a standard deviation, and/or any other measure of variability across the different time epochs into which the weather data are organized.

In another embodiment, in the case of a spatial volatility index, the index module603computes the volatility index over the spatial domain by segmenting a geographic area represented by the at least one geographic point by distance from the at least one and computing an average difference, a coefficient of variation, a standard deviation, and/or any other measure of variability among the weather data corresponding to each segment of the segmented geographic area. Examples are this segmentation process include specifying different search radii or grid cells to organize data by distance as discussed above.

In one embodiment (e.g., when the volatility index is generated for a plurality of geographic areas or points), the index module603normalizes the volatility index for comparison among the plurality of geographic areas. For example, the volatility index can normalized to a value of the at least one weather attribute, and wherein the value includes a maximum value, a mean value, a minimum value, or a combination thereof. In other words, the index module603can divide a resulting volatility index for a weather attribute by the maximum value, mean value, minimum, or other reference value to scale the volatility index for comparison among different volatility indices.

In step707, the geographic database interface module605stores the volatility index as a record of a geographic database in association with a representation of the at least one geographic point or a geographic area represented by the at least one geographic point. For example, by creating a volatility index record in the geographic database103, the geographic database interface module605enables the weather platform107to link or associated the generated volatility index to any map feature (e.g., node, link, point, road segment, area, POI, etc.), so that a location-based query of the geographic database103for that map feature can return the corresponding volatility index. The retrieved volatility index can then be used to support various weather related services and/or functions as described below with respect toFIG. 8.

FIG. 8is a flowchart of a process for applying a weather volatility index to weather related services, according to one embodiment. In various embodiments, the weather platform107and/or any of the modules601-607of the weather platform107as shown inFIG. 6may perform one or more portions of the process800and may be implemented in, for instance, a chip set including a processor and a memory as shown inFIG. 12. As such, the weather platform107and/or the modules601-607can provide means for accomplishing various parts of the process800, as well as means for accomplishing embodiments of other processes described herein in conjunction with other components of the system100. Although the process800is illustrated and described as a sequence of steps, its contemplated that various embodiments of the process800may be performed in any order or combination and need not include all of the illustrated steps.

As described above, the process800is performed after the volatility index is generated using, for instance, the process700ofFIG. 7. In one embodiment, the steps of the process800are optional steps, and can be performed in combination or individually.

In step801, the application interface module607begins the process800by retrieving the weather volatility index. In one embodiment, the weather volatility index can be retrieved for a target geographic point or area of interest. The geographic point or area can be selected based on weather service needs or requests.

For example, a weather service may be requested to provide interpolated weather data for a given point or area. The application interface module607can then retrieve the volatility index corresponding to this point or area. Then, for temporal volatility indices, in step803, the application interface module607specifies a time limit for interpolating subsequent weather data collected from the at least one geographic point or from a geographic area represented by the at least one geographic point based on the volatility index. As previously described, in one embodiment, the application interface module607specifies a shorter time limit for interpolating weather data when the volatility index is high (e.g., above a threshold value), and a longer time limit for interpolating weather data when the volatility index is low (e.g., below a threshold value). For example, for a geographic point or area with a high volatility index, a time limit of 30 mins may be specified, so that the weather platform107will only use weather reports less than 30 mins old to interpolate weather data to other locations in or near the point or area. Conversely, a time limit of 90 mins may be specified for a point or area with a low volatility index, so that the weather platform107can use reports less than 90 mins old to interpolate weather data.

For spatial volatility indices, the application interface module607specifies a distance limit for interpolating subsequent weather data collected from the at least one geographic point or from a geographic area represented by the at least one geographic point based on the volatility index. In other words, a weather report received for a particular location would only be used to interpolate weather for another location that is not more than then distance limit away from the location of the weather report. As with the temporal volatility index, the application interface module607can retrieve the volatility index for a target geographic point or area. If the point or area has a high volatility index (e.g., above a threshold value), the application interface module607can specify a shorter distance limit for interpolation weather data. The application interface module607can then set the distance limit to a greater value if the point or areas has a low volatility index.

In step805, the application interface module607the application interface module607prioritizes a publication of the weather data for the plurality of geographic areas based on the volatility index. For example, if the weather platform107or other weather service is responsible for reporting or publishing weather data for multiple locations. The application interface module607can prioritize which of the areas to report weather data for first or how often to publish the data based on the respective volatility indices for each area. In one embodiment, areas with higher volatility indices can be published first or more frequently than for areas with lower volatility indices. This is, for instance, because weather is more likely to change over time and/or distance in areas of higher weather volatility than in areas with lower volatility.

In step807, the application interface module607allocates or recommends an allocation of the one or more weather sensors, one or more weather stations, or a combination thereof among the plurality of geographic areas based on the volatility index. In one embodiment, the system100may use weather volatility indices to optimize its allocation of weather equipment (e.g., weather stations101, weather sensors, etc.) among multiple geographic areas based on the generated weather volatility indices for those areas. For example, areas with higher volatility indices can be allocated with a higher density of weather stations101or sensors than areas with lower volatility indices.

FIGS. 9A and 9Bare diagrams illustrating example user interfaces for presenting a weather volatility index in a mapping user interface, according to various embodiments. More specifically,FIG. 9Aillustrates an example a user interface (UI)901presents time based temperature volatility indices for locations that are within the United States. As shown in UI901, the temporal volatility indices for a temperature attribute are computed and then presented as overlays on at map representation of the United States. The volatility indices shown inFIG. 9Aare consistent with is expected based on terrain. For example, mountainous areas (e.g. the Denver Colo.) generally have the highest temperature volatility index followed by the coastal areas. In particular,FIG. 9Ashows that the temperature volatility index for the Colorado region is 1 or more. In this example, since the volatility is very high in these regions, it means is that our time cut-off for interpolating temperature observations in Colorado should be the lowest. In other words, when the weather platform107receives a temperature reading in Colorado and after a very short amount time (e.g., 30 min to hour) has elapsed, the weather platform107will no longer use the report (e.g., for weather interpolation or other use) because the area is highly volatile according to the temperature volatility index.

As another example,FIG. 9Ashows that Iowa has a temperature volatility index of 0.68, while in Colorado the volatility index is 1.41. This means, for instance, that the temperature in Iowa is less volatile and varies less over time. Thus, if the weather platform107receives an observation of temperature from a weather station or a connected vehicle that is in Iowa then the observation is still useful even after several hours have elapsed (e.g., because of a longer applied time limit). However, if weather platform107receives an observation of temperature from a weather station or a connected vehicle that is in Colorado then the observation is NOT useful after several hours have elapsed (e.g., because of a shorter applied time limit).

FIG. 9Billustrates an example UI921that presents time based visibility volatility indices computed for locations that are within the United States. The temporal visibility volatility indices are computed according to the various embodiments described herein. The resulting indices shown inFIG. 9Bare also consistent with what is expected based on terrain. For example, coastal areas have the highest visibility volatility index. Accordingly, the visibility volatility indices for the Florida region, East coast, West coast, and Great Lakes region are the highest. This is, for instance, because visibility is affected by fog which is heavily influenced by nearby bodies of water

Since the visibility volatility is very high in these coastal regions, it means that time cut-off for interpolating visibility observations in these regions should be the lowest. In other words, when the weather platform107receives a visibility reading in the areas where the visibility indices are high (e.g., near coastal areas), after a very short time (e.g., 30 min to an hour) has elapsed since the observation, the weather platform107will not use that visibility report anymore because the area is highly volatile according to the visibility volatility index.

FIG. 10is a diagram illustrating an example user interface for presenting a volatility index in a tile-based map representation, according to one embodiment. In the example ofFIG. 10, geographic areas are represented using a tile-based representation. Accordingly, the weather platform107calculates weather volatility indices for each area represented by the tiles depicted in the map UI1001. In this example, the index is computed with respect a single weather attribute (e.g., temperature) and depicted in the UI1001using shading to represent different volatility ranges. As shown, lighter shades represent ranges of the volatility indices that are the lower, while darker shades represent higher volatility. In this way, instead of viewing the volatility indices numbers, the end user can quickly scan the map to identify areas with high or low volatilities based on the respective shading of the corresponding map tile.

The processes described herein for providing a weather volatility index may be advantageously implemented via software, hardware (e.g., general processor, Digital Signal Processing (DSP) chip, an Application Specific Integrated Circuit (ASIC), Field Programmable Gate Arrays (FPGAs), etc.), firmware or a combination thereof. Such exemplary hardware for performing the described functions is detailed below.

A bus1110includes one or more parallel conductors of information so that information is transferred quickly among devices coupled to the bus1110. One or more processors1102for processing information are coupled with the bus1110.

Computer system1100also includes a memory1104coupled to bus1110. The memory1104, such as a random access memory (RAM) or other dynamic storage device, stores information including processor instructions for providing a weather volatility index. Dynamic memory allows information stored therein to be changed by the computer system1100. RAM allows a unit of information stored at a location called a memory address to be stored and retrieved independently of information at neighboring addresses. The memory1104is also used by the processor1102to store temporary values during execution of processor instructions. The computer system1100also includes a read only memory (ROM)1106or other static storage device coupled to the bus1110for storing static information, including instructions, that is not changed by the computer system1100. Some memory is composed of volatile storage that loses the information stored thereon when power is lost. Also coupled to bus1110is a non-volatile (persistent) storage device1108, such as a magnetic disk, optical disk or flash card, for storing information, including instructions, that persists even when the computer system1100is turned off or otherwise loses power.

Information, including instructions for providing a weather volatility index, is provided to the bus1110for use by the processor from an external input device1112, such as a keyboard containing alphanumeric keys operated by a human user, or a sensor. A sensor detects conditions in its vicinity and transforms those detections into physical expression compatible with the measurable phenomenon used to represent information in computer system1100. Other external devices coupled to bus1110, used primarily for interacting with humans, include a display device1114, such as a cathode ray tube (CRT) or a liquid crystal display (LCD), or plasma screen or printer for presenting text or images, and a pointing device1116, such as a mouse or a trackball or cursor direction keys, or motion sensor, for controlling a position of a small cursor image presented on the display1114and issuing commands associated with graphical elements presented on the display1114. In some embodiments, for example, in embodiments in which the computer system1100performs all functions automatically without human input, one or more of external input device1112, display device1114and pointing device1116is omitted.

In the illustrated embodiment, special purpose hardware, such as an application specific integrated circuit (ASIC)1120, is coupled to bus1110. The special purpose hardware is configured to perform operations not performed by processor1102quickly enough for special purposes. Examples of application specific ICs include graphics accelerator cards for generating images for display1114, cryptographic boards for encrypting and decrypting messages sent over a network, speech recognition, and interfaces to special external devices, such as robotic arms and medical scanning equipment that repeatedly perform some complex sequence of operations that are more efficiently implemented in hardware.

Computer system1100also includes one or more instances of a communications interface1170coupled to bus1110. Communication interface1170provides a one-way or two-way communication coupling to a variety of external devices that operate with their own processors, such as printers, scanners and external disks. In general the coupling is with a network link1178that is connected to a local network1180to which a variety of external devices with their own processors are connected. For example, communication interface1170may be a parallel port or a serial port or a universal serial bus (USB) port on a personal computer. In some embodiments, communications interface1170is an integrated services digital network (ISDN) card or a digital subscriber line (DSL) card or a telephone modem that provides an information communication connection to a corresponding type of telephone line. In some embodiments, a communication interface1170is a cable modem that converts signals on bus1110into signals for a communication connection over a coaxial cable or into optical signals for a communication connection over a fiber optic cable. As another example, communications interface1170may be a local area network (LAN) card to provide a data communication connection to a compatible LAN, such as Ethernet. Wireless links may also be implemented. For wireless links, the communications interface1170sends or receives or both sends and receives electrical, acoustic or electromagnetic signals, including infrared and optical signals, that carry information streams, such as digital data. For example, in wireless handheld devices, such as mobile telephones like cell phones, the communications interface1170includes a radio band electromagnetic transmitter and receiver called a radio transceiver. In certain embodiments, the communications interface1170enables connection to the communication network105for providing a weather volatility index.

In one embodiment, the chip set1200includes a communication mechanism such as a bus1201for passing information among the components of the chip set1200. A processor1203has connectivity to the bus1201to execute instructions and process information stored in, for example, a memory1205. The processor1203may include one or more processing cores with each core configured to perform independently. A multi-core processor enables multiprocessing within a single physical package. Examples of a multi-core processor include two, four, eight, or greater numbers of processing cores. Alternatively or in addition, the processor1203may include one or more microprocessors configured in tandem via the bus1201to enable independent execution of instructions, pipelining, and multithreading. The processor1203may also be accompanied with one or more specialized components to perform certain processing functions and tasks such as one or more digital signal processors (DSP)1207, or one or more application-specific integrated circuits (ASIC)1209. A DSP1207typically is configured to process real-world signals (e.g., sound) in real time independently of the processor1203. Similarly, an ASIC1209can be configured to performed specialized functions not easily performed by a general purposed processor. Other specialized components to aid in performing the inventive functions described herein include one or more field programmable gate arrays (FPGA) (not shown), one or more controllers (not shown), or one or more other special-purpose computer chips.

The processor1203and accompanying components have connectivity to the memory1205via the bus1201. The memory1205includes both dynamic memory (e.g., RAM, magnetic disk, writable optical disk, etc.) and static memory (e.g., ROM, CD-ROM, etc.) for storing executable instructions that when executed perform the inventive steps described herein to provide a weather volatility index. The memory1205also stores the data associated with or generated by the execution of the inventive steps.

FIG. 13is a diagram of exemplary components of a mobile station (e.g., handset) capable of operating in the system ofFIG. 1, according to one embodiment. Generally, a radio receiver is often defined in terms of front-end and back-end characteristics. The front-end of the receiver encompasses all of the Radio Frequency (RF) circuitry whereas the back-end encompasses all of the base-band processing circuitry. Pertinent internal components of the telephone include a Main Control Unit (MCU)1303, a Digital Signal Processor (DSP)1305, and a receiver/transmitter unit including a microphone gain control unit and a speaker gain control unit. A main display unit1307provides a display to the user in support of various applications and mobile station functions that offer automatic contact matching. An audio function circuitry1309includes a microphone1311and microphone amplifier that amplifies the speech signal output from the microphone1311. The amplified speech signal output from the microphone1311is fed to a coder/decoder (CODEC)1313.

A radio section1315amplifies power and converts frequency in order to communicate with a base station, which is included in a mobile communication system, via antenna1317. The power amplifier (PA)1319and the transmitter/modulation circuitry are operationally responsive to the MCU1303, with an output from the PA1319coupled to the duplexer1321or circulator or antenna switch, as known in the art. The PA1319also couples to a battery interface and power control unit1320.

The encoded signals are then routed to an equalizer1325for compensation of any frequency-dependent impairments that occur during transmission though the air such as phase and amplitude distortion. After equalizing the bit stream, the modulator1327combines the signal with a RF signal generated in the RF interface1329. The modulator1327generates a sine wave by way of frequency or phase modulation. In order to prepare the signal for transmission, an up-converter1331combines the sine wave output from the modulator1327with another sine wave generated by a synthesizer1333to achieve the desired frequency of transmission. The signal is then sent through a PA1319to increase the signal to an appropriate power level. In practical systems, the PA1319acts as a variable gain amplifier whose gain is controlled by the DSP1305from information received from a network base station. The signal is then filtered within the duplexer1321and optionally sent to an antenna coupler1335to match impedances to provide maximum power transfer. Finally, the signal is transmitted via antenna1317to a local base station. An automatic gain control (AGC) can be supplied to control the gain of the final stages of the receiver. The signals may be forwarded from there to a remote telephone which may be another cellular telephone, other mobile phone or a land-line connected to a Public Switched Telephone Network (PSTN), or other telephony networks.

Voice signals transmitted to the mobile station1301are received via antenna1317and immediately amplified by a low noise amplifier (LNA)1337. A down-converter1339lowers the carrier frequency while the demodulator1341strips away the RF leaving only a digital bit stream. The signal then goes through the equalizer1325and is processed by the DSP1305. A Digital to Analog Converter (DAC)1343converts the signal and the resulting output is transmitted to the user through the speaker1345, all under control of a Main Control Unit (MCU)1303—which can be implemented as a Central Processing Unit (CPU) (not shown).

The MCU1303receives various signals including input signals from the keyboard1347. The keyboard1347and/or the MCU1303in combination with other user input components (e.g., the microphone1311) comprise a user interface circuitry for managing user input. The MCU1303runs a user interface software to facilitate user control of at least some functions of the mobile station1301to provide a weather volatility index. The MCU1303also delivers a display command and a switch command to the display1307and to the speech output switching controller, respectively. Further, the MCU1303exchanges information with the DSP1305and can access an optionally incorporated SIM card1349and a memory1351. In addition, the MCU1303executes various control functions required of the station. The DSP1305may, depending upon the implementation, perform any of a variety of conventional digital processing functions on the voice signals. Additionally, DSP1305determines the background noise level of the local environment from the signals detected by microphone1311and sets the gain of microphone1311to a level selected to compensate for the natural tendency of the user of the mobile station1301.

The CODEC1313includes the ADC1323and DAC1343. The memory1351stores various data including call incoming tone data and is capable of storing other data including music data received via, e.g., the global Internet. The software module could reside in RAM memory, flash memory, registers, or any other form of writable computer-readable storage medium known in the art including non-transitory computer-readable storage medium. For example, the memory device1351may be, but not limited to, a single memory, CD, DVD, ROM, RAM, EEPROM, optical storage, or any other non-volatile or non-transitory storage medium capable of storing digital data.

An optionally incorporated SIM card1349carries, for instance, important information, such as the cellular phone number, the carrier supplying service, subscription details, and security information. The SIM card1349serves primarily to identify the mobile station1301on a radio network. The card1349also contains a memory for storing a personal telephone number registry, text messages, and user specific mobile station settings.