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    • 专利摘要:
    METHOD AND SYSTEM FOR REFINING WEATHER FORECASTS USING SPECIAL OBSERVATIONS.Provided are non-transitory computer-readable methods, devices and storage media to generate a more accurate weather forecast using the actual observation of a weather event at a particular location and weather. For example, observation data can be received from a user's device and contain information indicative of a weather event that the user has observed. Alternatively or additionally, the observation data can be automatically generated by one or more sensors placed in certain locations and transmitted to a central server automatically when detecting an observation. Observation data can be used to predict a weather forecast for a location that corresponds to, or is otherwise related to, the particular location in which the observed event occurred.
    公开号:BR112015025342A2
    申请号:R112015025342-3
    申请日:2014-04-04
    公开日:2020-11-10
    发明作者:Andre Leblanc
    申请人:Sky Motion Research, Ulc；
    IPC主号:
    专利说明:

    [001] [001] This application claims the co-ownership and co-invention of US patent application No. 13 / 856,923 filed on April 4, 2013, US patent application No. 13 / 922,800, filed on June 20, 2013, US Patent Application No. 13 / 947,331, filed on July 22, 2013, US Provisional Application No. 61 / 839,675, filed on June 26, 2013, US Provisional Application No. 61 / 835,626, filed on 16 June 2013 and US Provisional Order No. 61 / 836,713, filed on June 19, 2013, all disclosures that are incorporated herein by reference. Background Field
    [002] [002] The matter revealed generally refers to methods for producing weather forecasts. More specifically, the article refers to requests for software to produce weather forecasts. (b) Related prior art
    [003] [003] Systems currently available to produce weather forecasts are push systems only; that is, the system collects information from sensors, a database, etc., produces a meteorological forecast based on this information and pushes the meteorological forecast to users.
    [004] [004] Users generally note that the forecast is not accurate for their location and the forecast producer has no way of determining whether their forecasts are really accurate.
    [005] [005] There is a need in the market to pull information from observers, who may still be users, located in the territory.
    [006] [006] A computer-implemented method may be provided comprising: receiving, from a remote device, observation related to meteorological data associated with the first location and the first hour, observation related to meteorological data comprising data obtained observing an event related to the weather associated with the first location and the first hour, generating a meteorological forecast associated with a second location and a second hour based on the observation related to the meteorological data and issuing the meteorological forecast to the device remote.
    [007] [007] In some modalities, the map may comprise the formation of an image of the second location, the image comprising pixels associated with the values of the weather forecast.
    [008] [008] In some modes, the map may comprise at least two images from the second location, each image being associated with a different time.
    [009] [009] In some modalities, the climate-related event may comprise at least one of hail, wind, lighting, temperature, precipitation and intensity of sunlight.
    [0010] [0010] In some modalities, the map may include modifying a meteorological forecast value associated with a pixel based on the observation related to meteorological data.
    [0011] [0011] In some modalities, the weather-related event can be observed by a user operating the remote device and the user causes the remote device to transmit the observation related to meteorological data.
    [0012] [0012] In some modalities, the remote device can automatically
    [0013] [0013] In some modalities, the map may include: receiving, from a second remote device, the second observation related to meteorological data associated with a third location and a third hour, the second observation related to meteorological data comprising data obtained from observing an event related to the weather associated with the third location and the third hour, in which the weather forecast associated with the second location and the second hour is generated based on the observation related to the associated meteorological data with the first location and the first hour and the second observation related to the meteorological data associated with the third location and the third hour.
    [0014] [0014] In some modalities, the map may include: storing the received observation related to meteorological data, producing a statistic about the stored observation related to meteorological data, the statistic comprising information indicative of a number of remote devices that received the weather forecast and send at least part of the statistic to the remote device.
    [0015] [0015] In some modalities, the map can understand: compare the observation related to meteorological data with pre-stored meteorological data and determine, based on the comparison, the weight of the observation related to meteorological data that affects the generation of the forecast weather.
    [0016] [0016] In some modalities, the map may comprise: produce a confidence classification associated with the remote device and determine, based on the confidence classification, the observation weight related to the meteorological data received from the remote device that affect the generation of weather forecast. In addition or alternatively, a trust rating can be assigned to a user who may be operating, or otherwise associated with one or more remote devices.
    [0017] [0017] In other modalities, there may be a method to refine a meteorological forecast which comprises: obtaining observations related to the climate in a particular position and at a given time and using the observations related to the climate when forming a grid image of a particular area on Earth, the image in grids comprising pixels in which each pixel has a value that corresponds to a meteorological value which, in turn, corresponds to a meteorological forecast, in which forming an image in grids comprises forming multiple grid images, each grid image corresponds to a moment in time.
    [0018] [0018] Still, there may still be a method to predict the presence of an event related to the climate in a particular position at a given time. In some modalities, the map may include: obtaining an observation related to the climate of an event related to the climate in the particular position and at a given time, changing or conforming a meteorological value to a grid image based on the observation related to the climate and produce a meteorological forecast using the altered or confirmed meteorological value.
    [0019] [0019] In other modalities, there may be a method for using observer contributions in a meteorological forecast. The method can comprise: obtain observations related to the observers 'climate, each observation being made in a particular position and at a given time, using the observations related to the observers' climate in the production of a future weather forecast for a user, calculating the statistics on observations
    [0020] [0020] In some modalities, there may be a device comprising one or more processors, a memory storing the instructions of the computer that can be executed by one or more processors so that the device makes it perform any or more of the maps described above, when the instructions are executed. In addition, there may be a medium readable by a non-transitory computer that stores such instructions. The device can be a mobile device such as, not exclusively, a portable device, a cell phone, a vehicle, etc. Brief description of the drawings
    [0021] [0021] Other features and advantages of the present disclosure will become evident from the detailed description, taken in combination with the attached drawings, in which:
    [0022] [0022] Figure 1A shows an example of a block diagram of a system to produce meteorological forecasts using point observations (a nowcaster) according to a modality;
    [0023] [0023] Figure 1B shows an example of a block diagram of a system to produce meteorological forecasts using point observations (a nowcaster) according to another modality;
    [0024] [0024] Figure 1C shows an example of a block diagram of a system for producing meteorological forecasts using point observations (a nowcaster) according to another modality.
    [0025] [0025] Figure 2 shows an example of a flowchart of a method for "crowd-sourcing" meteorological observations to produce meteorological forecasts of observations according to a fashion-
    [0026] [0026] Figure 3 shows an example of a flowchart of a method to predict hail based on specific observations and meteorological radar data according to a modality;
    [0027] [0027] Figure 4 shows an example of a flowchart of a method to use the contributions of observers in a meteorological forecast according to a modality;
    [0028] [0028] Figures 5A to 5D are snapshots showing an example of an application used to present a meteorological forecast and to obtain specific observations from observers according to a modality;
    [0029] [0029] Figure 6 shows an example of a network environment in which the modalities can be practiced; and
    [0030] [0030] Figure 7 illustrates an exemplary diagram of an adequate computational operating environment in which the modalities of the claimed matter can be practiced.
    [0031] [0031] It will be noted that, through the attached drawings, similar features are identified by similar reference numerals. Detailed Description
    [0032] [0032] The modalities will now be described more fully below with reference to the attached drawings, which form a part of these, and which show, in the form of illustration, specific modalities by which the modalities can be practiced. The modalities are further described so that the disclosure leads the scope of the invention to those skilled in the art. The modalities can, however, be incorporated in many different ways and should not be construed as limited to the modalities defined here.
    [0033] [0033] Among other things, the present modalities can be incorporated as methods or devices. Certainly, the modalities can take the form of a complementary hardware modality.
    [0034] [0034] In this specification, the following terms are intended to be defined as indicated below:
    [0035] [0035] Forecast of the moment (nowcast): The term forecast of the moment (nowcast) is a contraction of "now" and "forecast"; refers to the sets of techniques designed to make short-term predictions, typically in the range of 0 to 12 hours.
    [0036] [0036] A nowcaster is a weather forecasting device that prepares short-term forecasts (for example, 1 min., 5 minutes, minutes, 30 minutes, etc.) for a very small region on Earth (5 meters, 10 meters, 50 meters, 100 meters, 500 meters, 1,000 meters, etc.).
    [0037] [0037] A meteorological value is an amount or attribute related to meteorology of any type such as temperature, pressure, visibility, type of precipitation and intensity, accumulation, cloud cover, wind, etc.
    [0038] [0038] A predicted meteorological value is a meteorological value that is predicted by the nowcaster.
    [0039] [0039] An event related to the climate is, for example, at least one of hail, a gust of wind, lighting, a change in temperature, etc.
    [0040] [0040] Type of precipitation (PType): indicates the type of precipitation. Examples of types of precipitation include, but are not limited to,
    [0041] [0041] Precipitation rate (PRate): indicates the intensity of precipitation. Examples of rainfall rate values include, but are not limited to, without (ie none), light, moderate, heavy, extreme, etc. In one embodiment, the precipitation rate can also be expressed as a range of values such as: none to light, light to moderate, moderate to heavy, or any combination of these.
    [0042] [0042] Probability of precipitation: indicates the probability that precipitation can occur. Examples of the precipitation probability values include, but are not limited to, none, unlikely, slight chance, chance, probable, very likely, determined, etc.
    [0043] [0043] In one modality, the probability of precipitation can also be expressed as a range of values such as: none to light, light to moderate, moderate, or heavy. The probability of precipitation can also be expressed in terms of percentages; for example, 0%, 25%, 50%, 75%, 100%, etc .; or percentage ranges; for example, 0% to 25%, 25% to 50%, 50% to 75%, 75% to 100%, etc. In one embodiment, the probability of precipitation can be considered as a probability distribution.
    [0044] [0044] Categories of type of precipitation and rate of precipitation (PTypeRate): a category of PTypeRate is a combination of the type of precipitation and rate of precipitation that may be associated with the probability of occurring for a given period of time to indicate the possibility receiving a certain type of precipitation at a certain rate.
    [0045] [0045] A weather forecast is a set of one or more forecast weather values that are displayed to users.
    [0046] [0046] An observation related to the climate can be an image, a video, a free-form text (tweet, message, email, etc.), a climatic value of any kind such as temperature, pressure,
    [0047] [0047] A point observation is an observation, as defined herein, made in a particular position (sometimes referred to as a "location") at a given time.
    [0048] [0048] The particular position is the position on Earth in which the observation is made. An accuracy of 5 meters to 10 meters is appropriate for the modalities described here, but the variation in position can be greater than 25 meters, 50 meters, 100 meters, 1000 meters or more (that is, less precision). The means to obtain the particular position include any type of geolocation means or positioning system available at the time of filing this patent application. The means of geolocation or positioning system can be automated or not. The geolocation medium or automated positioning system includes global positioning systems, RF location systems, radiolocation technologies, Internet Protocol (IP) address, MAC address, WiFi, Radio Frequency Identification (RFID), etc. Positioning systems can also be manual as providing an address, street corner, building or landmark, etc.
    [0049] [0049] A given time is defined as the hour, minute and second in which the point observation is made in the time zone corresponding to the particular position. The hour, minute and second for the given time can also be recorded according to Coordinated Universal Time (UTC) or Greenwich Mean Time (GMT) so that the given time is independent of the particular position. The precision of the given time can be more or less than a second. For example, in some modalities, an accuracy of 5s, 10s, 30s, 60s or more may be sufficient for the modalities described here.
    [0050] [0050] A user is a person to whom or a machine on which a weather forecast is sent. An observer is an entity that provides automated and / or manned observations. An observer can be a person or an automated machine. An observer can also be a user as defined here.
    [0051] [0051] A grid image is an image that comprises latitude and longitude coordinates. Thus, it is a collection of geolocalized two-dimensional dots / pixels.
    [0052] [0052] Each pixel in a grid image corresponds to a position and can either represent a single climatic value, a probability distribution of values or a confidence level.
    [0053] [0053] Briefly stated, the present modalities describe a method and system implemented by computer to generate more accurate meteorological forecasts considering not only the various types of meteorological values, but also the actual observations in particular locations in particular periods. Such observations can be automated and placed in a database automatically, or they can be guided by users as individual users informing their observations. Nowcaster
    [0054] [0054] Figures 1A-1C are examples of block diagrams of a nowcaster according to the present description.
    [0055] [0055] As shown in figures 1A-1C, the nowcaster 200 receives meteorological observations from different sources 201 as sources of meteorological observations including, but not limited to: point observations 201-2 (for example, feedback provided by users and automated stations) , weather radars 201-3, satellites 201-4 and other types of weather observations 201-1, and weather forecast sources such as output from the numerical weather forecast (NWP) model 201-5 and weather and advisory forecasts 201-6.
    [0056] [0056] The nowcaster 200 comprises memory 220 and processor 210. Memory 220 comprises instructions for the map and also stores data from weather sources 201, intermediate results and weather forecasts. Processor 210 allows nowcaster 200 to perform calculations.
    [0057] [0057] The nowcaster 200 can receive information 230 from a user 150 through a communication network 254. According to one modality, this information 230 can be a chosen time increase.
    [0058] [0058] The nowcaster 200 issues a weather forecast, or a succession of weather forecasts.
    [0059] [0059] Figure 1B is a modality of the nowcaster 200. In this modality, the nowcaster 200 comprises a meteorologist of Ptype 202 distribution and a meteorologist of Prate 204 distribution. Meteorologist Ptype 202 receives meteorological observations from different sources 201 and emits a probability distribution of the precipitation type over a time interval, for the given latitude and longitude (and / or location). For example: a. Snow: 10% b. Rain: 30% c. Glacial Rain: 60% d. Hail: 0% e. Ice Grains: 0%
    [0060] [0060] Similarly, the meteorologist PRate 204 receives meteorological observations for a given latitude and longitude from different sources 201 and issues a forecast of probability distribution of a precipitation rate (PRate) in a representation that expresses uncertainty. For example, PRate can be issued as a probability distribution of precipitation rates or a range of rates over a time span, for a given latitude and longitude. In a non-limiting example, it could be: f. Without precip. : 30% g. Light: 40% h. Moderate: 20% Heavy: 10%
    [0061] [0061] The PRate and PType values issued by the meteorologist PRate 204 and the meteorologist Ptype 202 are sent to a forecast combiner 206 to combine these values into a single PTypeRate value that represents the results of the precipitation. For example, if the PType value is "Snow", and the "PRate" value is heavy, the combined PTypeRate value can be "heavy snow".
    [0062] [0062] For a given latitude and longitude, the system issues predicted PTypeRate distributions for predetermined time intervals, both fixed (ex: 1 minute) and variable (ex: 1 minute, then 5 minutes, then 10 minutes , etc). The system can either pre-calculate or store predicted PTypeRate distributions in a sequence of time intervals, or calculate immediately. A PTypeRate Distribution represents, for each time interval, the certainty or uncertainty that a PTypeRate will occur.
    [0063] [0063] With reference to figure 1B, the forecast combiner 206 receives the final PRate distribution from meteorologist Ptype 202 and the final PRate distribution from meteorologist PRate 204 to combine them into a group of PTypeRate distribution values each that represents the probability of receiving a certain type of precipitation at a certain rate. An example is provided below.
    [0064] [0064] Assuming that the PType distribution is as follows: Snow: 50%, Rain 0%, Ice Rain: 30%, Hail 0%, Grains of Ice 20%, and the PRate distribution is as follows: None: 0 %, light: 10%, moderate: 20%, Heavy: 30%, Very heavy 40%, PTypeRate distributions can be as follows:
    [0065] [0065] Certainly, the forecast combiner 206 multiplies the probability of each type of precipitation by the probability of each rate of precipitation to obtain a probability to receive a certain type of precipitation at a certain rate, for example, 20% chance of heavy snow, or 12% chance of very heavy glacial rain. In a modality, it is possible to associate the probability ranges with the textual information to display the textual information to the user instead of probabilities in numbers. For example, probabilities that are between 5% and 15% can be associated with the text: "low chance", while probabilities that are between 40% and 70% can be associated with the text "ata chance", or "very likely" etc. for which, instead of displaying: 60% chance of heavy snow, it is possible to display: "high chance of heavy snow".
    [0066] [0066] In another modality, it is possible to combine two or more different PTypeRates along one or more dimensions (dimensions including: the rate, type or probability). For example, results of such a combination may include: Rain probably light to moderate, Rain likely light to moderate or heavy snow;
    [0067] [0067] Certainly, the nowcaster 200 receives the location for which "moment forecasts" are now needed and the time and / or time interval for which "moment forecasts" (nowcasts) are needed and issues the PtypRate distribution for the given location and for the specific time.
    [0068] [0068] Figure 1C illustrates another modality of the nowcaster 200. In this modality, the nowcaster 200 comprises a PType 202-C selector / receiver and a Prate 204 distribution meteorologist.
    [0069] [0069] Similar to the modality shown in figure 1B, the distribution meteorologist Prate 204 receives meteorological observations for a given latitude and longitude from different sources 201 and issues a forecast of probability distribution of a precipitation rate (PRate ) in a representation that expresses uncertainty. For example, PRate can be issued as a probability distribution of precipitation rates or a range of rates over a time interval, for a given latitude and longitude. For example: f. Without precip .: 30% g. Light: 40% h. Moderate: 20% i. Heavy: 10%
    [0070] [0070] However, the PType 202-C selector / receiver does not emit a probability distribution associated with different types of precipitation. Instead, the PType selector / receiver 202-C receives weather observations for a given latitude and longitude from different sources 201 to select a type of precipitation from a list of different types of precipitation. For example, based on inputs received from sources 201, the PType 202-C selector / receiver selects a single type of precipitation that is most likely to occur at the given latitude and longitude (and / or location) from the following list of precipitation types : a. Snow b. Rain c. Glacial Rain d. Hail and. Grains of Ice f. Mixing (for example, a + c, a + d, b + c, a + e, c + e, d + e, etc.)
    [0071] [0071] From the list of precipitation types like the one above, only one type of precipitation is selected for a given location. For example, a mixture of snow and glacial rain can be selected as the most likely type of precipitation for a given location at a given time. The type of precipitation is not associated with a probability value. In fact, since only one type of precipitation is selected for any given location and time corresponding to the location, the type of precipitation selected will have an effective probability value of 100%.
    [0072] [0072] The list of precipitation types that are available for selection of a type may include a mixture type that represents a mixture of two different types of precipitation (for example, snow and glacial rain, hail and ice grains, etc.). A Mix Type is considered to be a distinct type of precipitation available for selection and, as shown above in (f) of the list, there may be many different types of mix representing the Mix of different pairs of various types of precipitation.
    [0073] [0073] In another mode, the type of precipitation is not selected by the PType 202-C selector / receiver, but is instead received from a source outside the nowcaster 200. In other words, the nowcaster 200 can ask a remote source (for example, a third-party weather service) to identify the type of precipitation that is most likely to occur for a given location at a given time and receive a response from the source identifying the most likely type of precipitation. In this case, the selection of the type of precipitation is not carried out by the nowcaster 200. The nowcaster 200 is simply inserted with the type of precipitation already selected and, thus, can save computational energy of the nowcaster 200 that, otherwise, it was necessary to carry out the selection.
    [0074] [0074] The type of precipitation selected and the PRate values respectively emitted by the PType selector / receiver 202-C and the distribution meteorologist Prate 204 are combined. For example, if the type of precipitation selected is snow and the PRATE values are as described above, the combined information would indicate: a. Without Snow: 30% b. Light Snow: 40% c. Moderate Snow: 20% d. Heavy Snow: 10%.
    [0075] [0075] As only one type of precipitation is involved, only a minimal amount of computational energy is needed to accomplish what combines to issue the final meteorological forecast data. Since the PType 202-C selector / receiver will emit one (1) type of precipitation for a given location and time, if the distribution meteorologist Prate 204 issues a probability distribution number m, the final weather forecast data they will comprise only a m (m * l) number of meteorological forecast distribution.
    [0076] [0076] When issuing the final weather forecast data, it is possible to associate the probability bands with textual information for
    [0077] [0077] Certainly, the nowcaster 200 receives the location for which "moment forecasts" (nowcasts) are needed and the time and / or time interval for which "moment forecasts" (nowcasts) are needed and issues the selected PType and PRate distribution for the given location and for the specific time.
    [0078] [0078] The nowcaster according to this other modality of the now-caster can be advantageous over the modality shown in figure 1B in certain circumstances in which efficiency is desired. The modality of figure 1C can be implemented using much less processing energy than the modality of figure 1B. However, the modality of figure 1B may be more suitable than this other modality described in figure 1C in providing a more detailed and accurate snapshot of weather forecast data for any given location and weather. Crowd-sourcing weather observations
    [0079] [0079] Briefly stated, the present modalities describe a system and / or method that collects and manages observations related to the climate (or referred to only as "observations") from users and / or automated machines to generate observation mosaics (for example, mosaics) for the purpose of weather forecasting. The system comprises many automated and / or managed observation sources (for example, observers) that send
    [0080] [0080] To create a mosaic, the mosaic creator chooses the observations to include based on observation time and position. Each observation is positioned in the Mosaic precisely in the geo-location provided.
    [0081] [0081] A region around the observation is defined (observation region). Each point in an observation region can contribute to the mosaic based on various factors such as distance to observation, observation time, observer confidence and reliability factor and more factors.
    [0082] [0082] Now returning to figure 2, a flowchart of a method 300 is shown for crowd-sourcing meteorological observations to produce meteorological forecasts of the observations according to a modality.
    [0083] [0083] Method 300 comprises: - obtaining observations related to the climate in a particular position and in a given time (for example, step 302); - save climate-related observations to a database (for example, step 304); and - use climate-related observations when forming a grid image of a particular area on Earth, the grid image comprising pixels where each pixel has a value that corresponds to a meteorological value that, in turn, corresponds to a forecast weather (for example, step 306).
    [0084] [0084] According to another method of method 300, the step of obtaining observations related to the climate is automated or made by the observers.
    [0085] [0085] According to another modality, a mechanism is provided to deal with overlapping observation regions. For example, an observation is reported to a database. The observation can be marked with a specific location and time so that when a second observation in the same location is later informed, it can be associated with the first informed observation. If the second observation is made in a time that is only within a short period of time in which the first observation is made, the second observation may be unrelated as a redundant report.
    [0086] [0086] According to another modality, a mechanism is provided to assess the credibility and reliability of the theological observations. For example, if a report includes information that is inconsistent with pre-stored weather data (for example, a user reports snow at a given location at a given time, but other objective weather data such as temperature, pressure, etc., indicate snow is impossible at this location and time), then the user receives a reduced confidence rating. Pre-stored meteorological data can include only data obtained from objective meteorological sources and not individually informed data from users. The confidence rating can be provided on a scale and any reports from a source with a confidence rating below a threshold may be unrelated in its entirety.
    [0087] [0087] According to another modality, the mosaic can be used in a meteorological system of the moment forecast (now cast). Hail prediction based on spot observations
    [0088] [0088] Briefly stated, the present modalities describe a system and / or method that provide hail using a hail observation, a displacement field (for example, flow field) and possibly radar data. radar weather. The position and time of a hail event (for example, observation) is transmitted to a computer. The computer tries to determine whether the signature on the weather radar is consistent with hail observation. If so, a region (for example, hail area) is created using the radar signature, or not and hail is predicted in the future by analyzing the precipitation movement captured from the radar and displacing the hail area along the movement. If no radar correlation exists, a region (for example, hail area) is created without radar. In any case, the hail region can be modified as a function of time or distance from the original hail observation. A confidence score can also be generated as a function of time or distance from the original hail observation or any other parameter.
    [0089] [0089] Now returning to figure 3, a flowchart of a method 320 is shown to predict hail based on specific observations and meteorological radar data according to a modality.
    [0090] [0090] It should be noted here that hail is an example of an event related to the climate. It should be understood that the described modalities of the present invention according to figure 3 and other figures still apply to other types of events related to the climate defined here or known to those skilled in the art.
    [0091] [0091] Method 320 comprises:
    [0092] [0092] According to another modality, method 320 still comprises creating or confirming an area of hail in a region of the image in grids using the meteorological value.
    [0093] [0093] According to another modality, method 320 also comprises obtaining a field of displacement of meteorological values in or around the image region in grids, analyzing the movement of the hail area and refining the meteorological forecast based on the movement of the hail area.
    [0094] [0094] According to another modality, method 320 still comprises determining whether a signature of the meteorological radar in the image in grids at the particular position in the given time corresponds to a hail observation and based on this determination, the meteorological value can be changed and / or a confidence score of the meteorological value can be calculated. USING OBSERVERS 'CONTRIBUTIONS TO PREVENT METEOROLOGICAL
    [0095] [0095] Briefly stated, the present modalities describe a system and method for informing a user who has made a contribution to the calculation of a forecast how another person can benefit from this contribution.
    [0096] [0096] A user makes an observation (observer) indicating the position and time of a meteorological event that is transmitted to a computer making a forecast. The forecasting computer uses information from observers to calculate forecasts for a period in the future.
    [0097] [0097] Each request by a user to obtain a forecast is analyzed to generate a list of observers who contributed to the users' forecast. Each observer is then made aware of the number of people (plus any other user characteristics) that have benefited from this observation.
    [0098] [0098] A classification can be constructed from this information. Observers can be compensated with prizes, or gains in status or badges based on the number of users impacted.
    [0099] [0099] Now returning to figure 4, a flow chart of a 330 method is shown to use the contributions of observers in a meteorological forecast according to a modality.
    [00100] [00100] Method 330 comprises: - obtaining observations related to the observers' climate, each observation being made in a particular position and at a given time (for example, step 332); - use observations related to the climate of observers to produce a future weather forecast for a user (for example, step 334); - calculate the statistics on observations related to the climate of observers (for example, step 336); and - send at least one of the observers a message comprising at least part of the statistics on the observations related to the climate of at least one of the observers (for example, step 338).
    [00101] [00101] According to another modality, method 330 still comprises saving observations related to the climate in a database.
    [00102] [00102] According to another modality of method 330, the step of calculating the statistics involves generating, from the observations related to the climate, a selection of the observers who contributed to the future weather forecast for the user and the users who benefited from the weather forecast.
    [00103] [00103] According to another modality of method 330, the step to calculate the statistics comprises generating, from the observations related to the climate and for one or more of the observers, a list of users for whom the observations of one or more of the observers were used when producing the future weather forecast.
    [00104] [00104] According to another modality of method 330, the step to calculate the statistics comprises generating, from the observations related to the climate and for each of the observers, a reliability of observations made from the respective observers.
    [00105] [00105] According to another modality of method 330, the step to calculate the statistics comprises generating, from the observations related to the climate and at least one among the number of observations and the reliability of observations, a list organization of observers.
    [00106] [00106] According to another modality, method 330 still comprises providing an advantage to at least one among the observers based on the organized list.
    [00107] [00107] Now going back to figures 5A to 5D, screen snapshots of an application used to present the meteorological forecast and to obtain specific observations from observers according to a modality are shown.
    [00108] [00108] More specifically, figure 5A is a screen snapshot of an application of the moment forecast (nowcast). The screen snapshot shows an "eye" icon at the top of the right corner. The "eye" icon is an indication to the user that an observation can be sent to the system. Selecting the "eye" icon brings up the observation pages
    [00109] [00109] Figures 58 and 5C are screen snapshots of the pages of the moment forecast application (nowcast) that provides choices for the observer to make a point observation.
    [00110] [00110] Figure 5D is a screen snapshot showing that "Trapped" is selected. The "Submit" button is now activated as a selection of a point observation has been made. Once the "Submit" button is selected, a confirmation page can be provided to confirm the observer's selection. Once confirmed, the punctual observation is sent to the nowcaster.
    [00111] [00111] Figure 6 is an example of a network environment in which the modalities can be practiced. System 200 (for example, "nowcaster") can be implemented on a server / computer 250 that is accessible by a plurality of client computers 252 over a telecommunication network 254. Client computers can include, but are not limited to : laptops, desktops, portable computer devices, tablets and the like. Using a 252 client computer, each user can specify the time interval for which you want to receive "now forecasts" and the location for which "now forecasts" are needed. necessary. For example, the user can enter the postal code / zip code, address, location on a map, or the latitude and longitude of the location for which "time predictions" (nowcasts) are needed, along with the interval time over which "nowcasting" is needed. The time interval can extend between one minute and several hours.
    [00112] [00112] Upon receiving the location information and the weather information, the server 250 can receive the weather values available for the specified location and has issued different discussed PTypeRates that represent the "forecasts of the moment" (nowcasts)
    [00113] [00113] PTypeRates produced by server 250 can then be sent to client computer 252 to display to the user. In one mode, it is possible to display PTypeRates in a series, one after another, or to display those having a higher percentage. Hardware and Operating Environment
    [00114] [00114] Figure 7 illustrates an exemplary diagram of an adequate computational operating environment in which the modalities of the invention can be practiced. The following description is associated with figure 5 and is intended to provide a brief general description of suitable computer hardware and a suitable computing environment together with which the modalities can be implemented. Not all components are necessary to practice the modalities and variations in the arrangement and type of the components can be made without departing from the spirit or scope of the modalities.
    [00115] [00115] Although not necessary, the modalities are described in the general context of instructions executable by computer, such as program modules, being executed by a computer, such as a personal computer, a portable or palm-sized computer, Smartphone , or an embedded system such as a computer in a consumer device or specialized industrial controller. Program modules typically include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific summary data types.
    [00116] [00116] In addition, technicians in the field will note that the modalities can be practiced with other computer system configurations, including portable devices, multiprocessor systems, programmable consumer electronics or based on the microprocessor, PCS of network, minicomputers, mainframe computers, cell phones, smart phones, pagers, radio frequency (RF) devices, infrared (IR) devices, Personal Digital Assistants (PDAs), notebooks, wearable computing, tablets, a device from family of IPOD or IP AD devices manufactured by Apple Computer, integrated devices that combine one or more of the above devices, or any other computer device capable of making the described maps and systems of the present invention. The modalities can also be practiced in distributed computing environments where the tasks are carried out by remote processing devices that are connected through a communications network. In a distributed computing environment, program modules can be located on both local and remote memory storage devices.
    [00117] [00117] The exemplary hardware operating environment of figure 5 includes a general purpose computer device in the form of a computer 720, including a processing unit 721, a system memory 722 and a system bus 723 that operably couples various system components including system memory to processing unit 721. There may be only one or there may be more than one processing unit 721, so that the processor of computer 720 comprises a single central processing unit ( CPU), or a plurality of processing units, generally referred to as a parallel processing environment. Computer 720 can be a conventional computer, a distributed computer, or any other type of computer; the modalities are not limited.
    [00118] [00118] The 723 system bus can be any of several types of bus structures including a memory bus or memory controller, a peripheral bus and a local bus using any variety of bus architectures. The system memory can also be referred to as simply memory and includes reading only memory (ROM) 724 and random access memory (RAM) 725. A basic input / output system (BIOS) 726, containing basic routines that help to transfer information between elements inside the computer 720, such as during startup, are stored in ROM 724. In an embodiment of the invention, the computer 720 also includes a hard disk 727 to read and write to a disk hard drive, not shown, a magnetic disk drive 728 to read or write to a removable magnetic disk 729 and an optical disk drive 730 to read or write to a removable optical disk 731 such as a CD ROM or other optical medium. In the alternative modes of the invention, the functionality provided by hard disk 727, magnetic disk 729 and optical disk drive 730 is emulated using volatile or non-volatile RAM in order to conserve energy and reduce the size of the system. In these alternative modes, the RAM can be fixed in the computer system, or it can be a removable RAM device, such as a Compact Flash memory card.
    [00119] [00119] In one embodiment of the invention, hard disk 727, magnetic disk drive 728 and optical disk drive 730 are connected to the system bus 723 via a hard disk interface 732, a magnetic disk drive interface 733 and a optical disc drive interface 734, respectively. The drives and their associated computer-readable media provide non-volatile storage of computer-readable instructions, data structures, program modules and other data to the computer 720. It should be noted by those skilled in the art that any type of media readable by computer that can store data that is accessible by a computer, such as magnetic tapes, flash memory cards, digital video discs, Bernoulli cartridges, random access memories (RAMs), read-only memories (ROMs) and the like, can be used in the exemplary operating environment.
    [00120] [00120] A number of program modules can be stored on the hard disk, magnetic disk 729, optical disk 731, ROM 724, or RAM 725, including an operating system 735, one or more application programs 736, other modules program 737 and data from program 738. A user can enter commands and information to the personal computer 720 via input devices such as a keyboard 740 and display devices 742. Other input devices (not shown) may include a microphone, joystick , game pad, satellite dish, scanner, touch sensitive pad, or similar. These and other input devices are usually connected to the processing unit 721 via a serial port interface 746 which is coupled to the system bus, but can be connected via other interfaces, such as a parallel port, game port, or a universal serial bus (USB). In addition, input to the system can be provided by a microphone to receive audio input.
    [00121] [00121] A 747 monitor or other type of display device is still connected to the system bus 723 through an interface, such as a 748 video adapter. In an invention mode, the monitor comprises a Crystal Screen Liquid (LCD). In addition to the monitor, computers typically include other peripheral output devices (not shown), such as speakers and printers. The monitor can include a touch-sensitive surface that allows the user to connect to the computer by pressing or touching the surface.
    [00122] [00122] Computer 720 can operate in a network environment using logical connections to one or more remote computers, such as a remote computer 749. These logical connections are obtained by a communication device coupled to or a part of computer 720; the mode is not limited to a particular type of communications device. The remote computer 749 can be another computer, a server, a router, a network PC, a client, a peer device, or another common network node and typically includes many or all of the elements described above with respect to computer 720, although only one memory storage device 750 has been illustrated in figure 6. The logical connections described in figure 6 include a local area network (LAN) 751 and a wide area network (WAN)
    [00123] [00123] When used in a LAN network environment, the computer 720 is connected to the local network 751 through a network interface or adapter 753, which is a type of communications device. When used in a WAN network environment, computer 720 typically includes a 754 modem, a type of communications device, or any other type of communications device to establish communications over the wide area network 752, such as the Internet. Modem 754, which can be internal or external, is connected to the system bus 723 via the serial port interface 746. In a network environment, program modules described in relation to personal computer 720, or parts of it, can be stored on the remote memory storage device. It is observed that the network connections shown are exemplary and other means and communications devices establish a communications link between the computers can be used.
    [00124] [00124] The hardware and operating environment together with which the modalities of the invention can be practiced has been described. The computer together with which the modalities of the invention can be practiced can be a conventional computer, a portable or palm-sized computer, a computer in an embedded system, a distributed computer, or any other type of computer. computer; the invention is not limited. Such a computer typically includes one or more processing units as its processor and a computer-readable medium as a memory. The computer can also include a communications device such as a network adapter or a modem, so that it is capable of communicating with other computers.
    [00125] [00125] While the preferred modalities have been described above and illustrated in the accompanying drawings, it will be evident to technicians on the subject that modifications can be made without leaving this disclosure. These modifications are considered as possible variants included in the scope of the disclosure.
    权利要求:
    Claims (25)
    [1]
    1. Computer-implemented method, characterized by understanding: receiving, from a remote device, the observation related to the meteorological data associated with a first location and a first hour, the observation related to the meteorological data including data obtained from the observation of an event related to the weather associated with the first location and the first hour, generate a weather forecast associated with a second location and a second hour based on the observation related to the weather data, and issue the weather forecast to the remote device.
    [2]
    Method according to claim 1, characterized in that it comprises the formation of an image of the second location, the image comprising pixels associated with the values of the meteorological forecast.
    [3]
    Method according to claim 2, characterized in that it comprises the formation of at least two images of the second location, each of the at least two images being associated with a different time.
    [4]
    4. Method according to any one of claims 1-3, characterized by the fact that the weather-related event comprises at least one of hail, wind, lightning, temperature, precipitation and intensity of sunlight.
    [5]
    5. Method, according to claim 2 or 3, characterized by comprising modifying a meteorological forecast value associated with a pixel based on the observation related to meteorological data.
    [6]
    6. Method according to any of the claims
    1-5, characterized by the fact that the weather-related event is observed by a user who operates the remote device and the user causes the remote device to transmit the observation related to meteorological data.
    [7]
    7. Method according to any one of claims 1-6, characterized by the fact that the remote device automatically transmits the observation related to meteorological data when detecting an observation of an event related to the weather.
    [8]
    Method according to any one of claims 1-7, characterized in that it comprises: receiving, from a second remote device, the second observation related to the meteorological data associated with a third location and a third hour, the second observation related to - linked to the meteorological data comprising data obtained from the observation of an event related to the climate associated with the third location and the third hour, in which the meteorological forecast associated with the second location and the second hour is generated based on the observation related to related to the meteorological data associated with the first location and the first hour and the second observation related to the meteorological data associated with the third location and the third hour.
    [9]
    9. Method according to any one of claims 1-8, characterized by comprising: storing the received observation related to the meteorological data, producing a statistic associated with the stored observation related to the meteorological data, the statistics comprising indicative information of a number of remote devices that received the weather forecast, and send at least part of the statistic to the remote device.
    [10]
    10. Method according to any one of claims 1-9, characterized by comprising: comparing observation related to meteorological data with pre-stored meteorological data, and determining, based on the comparison, a weight associated with observation related to meteorological data.
    [11]
    11. Method according to any one of claims 1-10, characterized by comprising: producing a confidence rating associated with the remote device, and determining, based on the confidence rating, a weight associated with the observation related to meteorological data received from the remote device.
    [12]
    12. Device to refine a weather forecast using a point observation, characterized by comprising: one or more processors, a memory that stores instructions for one or more processors, and a communication module to connect to a remote device through a network of communication, in which when one or more processors execute the instructions stored in memory, the device causes: it receives, from the remote device, the observation related to the meteorological data associated with a first location and a first hour, the observation related to the meteorological data - cos comprising data obtained from observing an event related to the climate in the first location in the first hour, generate a weather forecast in a second location
    second hour based on the observation related to the meteorological data, and send the meteorological forecast to the remote device.
    [13]
    13. Device, according to claim 12, characterized by the fact that the device makes it form an image of a second location, the image comprising pixels associated with the values of the weather forecast.
    [14]
    14. Device according to claim 13, characterized by the fact that the device causes it to form at least two images of the second location and each of the at least two images is associated with a different time.
    [15]
    15. Device according to any one of claims 12-14, characterized by the fact that the weather-related event is observed by a user operating the remote device and the user causes the remote device to transmit the observation related to related to meteorological data.
    [16]
    16. Device according to any one of claims 12-15, characterized by the fact that the remote device automatically transmits the observation related to meteorological data to the device when detecting an observation of an event related to the weather.
    [17]
    17. Device according to any one of claims 12-16, characterized by the fact that the device causes: it receives, from a second remote device, the second observation related to the meteorological data associated with a third location and a third hour, the observation related to meteorological data comprising data obtained from the observation of a weather related event at the third location in the third hour,
    where the weather forecast for the second location in the second hour is generated based on both the observation related to the meteorological data associated with the first location and the first hour and the second observation related to the weather data associated with the third location and the third hour .
    [18]
    18. Device according to any of claims 12-17, characterized by the fact that the device causes: to store the received observation related to meteorological data, to produce a statistic about the stored observation related to meteorological data, the statistic comprising information indicative of a number of remote devices that received the weather forecast, and send at least part of the statistic to the remote device.
    [19]
    19. Device according to any of claims 12-18, characterized by the fact that the device causes: to compare the observation related to meteorological data with pre-stored meteorological data, assign a confidence rating associated with the remote device, and determine, based on the comparison and the confidence rating, a weight associated with the observation related to the weather data received from the remote device.
    [20]
    20. System comprising a server and a remote device that is connected to the server over a communication network, characterized by the fact that:
    the server comprises one or more processors and a non-transitory computer-readable medium that stores a program that causes one or more processors to perform a weather forecast refinement process, the weather forecast refinement process comprising : receive, from a remote device, the observation related to the meteorological data associated with a first location and a first hour, the observation related to the meteorological data comprising data obtained from the observation of an event related to the climate in the first location in the first hour, generate a weather forecast in a second location in a second hour based on the observation related to the weather data, and send the weather forecast to the remote device, and the remote device comprises a computer, a screen and a memory. non-transitory computer that stores a program that causes the computer to run a comp process relendendo: obtain the weather forecast outputted from the tab over the communication network, and makes, on the screen, a screen of at least part of the weather forecast.
    [21]
    21. Method to refine a meteorological forecast, the map is characterized by understanding: obtaining observations related to the climate in a particular position and at a given time, and using the observations related to the climate when forming an image in degrees of a particular area on Earth, the image in degrees comprising pixels in which each pixel has a value that corresponds to a meteorological value that, in turn, corresponds to a meteorological forecast, in which the formation of an image in grids comprises - to form multiple grid images, each grid image corresponds to a moment in time.
    [22]
    22. Method for predicting the presence of events related to the climate in a particular position at a given time, the map characterized by understanding: obtaining a climate-related observation of a weather-related event at the particular position and at a given time, changing or confirm a weather value in a grid image based on the weather-related observation, and produce a weather forecast using the altered or confirmed weather value.
    [23]
    23. Method for using observer contributions in a meteorological forecast, the map is characterized by: obtaining observations related to the observers' climate, each observation being made in a particular position and at a given time, using observations related to the climate of the observers. observers in production in a future weather forecast for a user, calculate the statistics on observers' weather-related observations, weather forecasts and users who have benefited from the observations and send at least one observer a message comprising at least part of the statistics on observations related to the climate of at least one of the observers.
    [24]
    24. Device for displaying a refined weather forecast using a point observation, characterized by comprising:
    one or more processors, a memory that stores instructions for one or more processors, a communication module for connecting to a remote server over a communication network, and a screen, on which when one or more processors execute the instructions stored in memory, the device makes it: receive, from the remote tab, a weather forecast, the weather forecast being associated with a first location and a first hour and generated by the remote server based, at least, on the observation related to the weather data, in that the observation related to the meteorological data comprises data obtained from the observation of an event related to the climate in a second location in a second hour, and makes, on the screen, a screen of at least a part of the meteorological forecast received the remote tab.
    [25]
    25. Non-transient computer-readable medium characterized by stores instructions, as defined in any of claims 1- 11 and 21-23.
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    法律状态:
    2020-11-24| B06F| Objections, documents and/or translations needed after an examination request according [chapter 6.6 patent gazette]|
    2021-04-06| B08F| Application dismissed because of non-payment of annual fees [chapter 8.6 patent gazette]|Free format text: REFERENTE A 7A ANUIDADE. |
    2021-08-10| B08K| Patent lapsed as no evidence of payment of the annual fee has been furnished to inpi [chapter 8.11 patent gazette]|Free format text: REFERENTE AO DESPACHO 8.6 PUBLICADO NA RPI 2622 DE 06/04/2021. |
    2021-12-07| B350| Update of information on the portal [chapter 15.35 patent gazette]|
    优先权:
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