TW201920988A - Method for generating and displaying a nowcast in selectable time increments - Google Patents

Abstract

The present document describes a method for generating and displaying a succession of short-term weather forecasts, also called nowcasts, in selectable time increments. A system for preparing nowcasts, called nowcaster, is used for preparing short-term forecasted weather values with a default time increment between each one of them. The method receives a chosen time increment from a user and the prepared forecasted weather values. The method comprises an aggregator that re-packages the forecasted weather values in the chosen time increments. A succession of short-term weather forecasts, which is a collection of forecasted weather values at the chosen time increment, is then outputted.

Description

Method for generating and displaying nowcasts in selectable time increments

The disclosed subject matter is generally related to methods for generating weather forecasts. More specifically, the subject matter relates to software applications for generating weather forecasts.

The conventional weather forecasting system provides weather forecasts from the current time of 12 hours to several days. If a short-term forecast or a forecast with a fine time scale is required, the best information available is usually forecasted on an hourly basis.
The conventional weather forecasting system produces an average forecast of the areas in which it is equal. Therefore, a forecast for a precise location within this region may be inaccurate, and even the current weather displayed for a region may differ from the actual weather at one of the precise locations within the region.
In addition, conventional weather forecasts are shown on a time scale that is too coarse to allow a user to know when the exact location and time of a weather event occurred. Even for hourly weather forecasting, it is not possible for the user to know whether the predicted meteorological event lasts for one hour or one minute, and for the latter, it is impossible for the user to know the exact time at which the predicted event occurred within that one hour.
The market needs to generate and display short-term weather forecasts on different time scales.

This embodiment describes this method.
According to an embodiment, a computer implemented method for outputting a series of weather forecasts for a given period of time and for a given time starting at a given time is provided, the method comprising: receiving a weather value a predicted weather value for the forecaster, the forecast weather value starting at the given time and continuing for a subsequent time separated by a predetermined time increment; receiving a time increment from a user, the selection time The incremental definition begins at the given time and continues for a series of specific times of the subsequent time separated by the selected time increment, the selected time increment being less than 1 hour; for each selection of a time increment, pressing the preset time Incrementally using the forecasted meteorological values to generate a series of new weather forecasts within a time interval between the particular times; and outputting the series of meteorology within the time intervals between the specific times forecast.
According to an embodiment, receiving the predicted weather values comprises receiving a predicted weather value comprising at least one of: a rainfall rate, a rainfall type, a rainfall probability, a temperature, a pressure, a relative humidity, a wind speed, a wind direction One value relative to lightning, one value relative to hail, and one value relative to a micro storm.
According to an embodiment, generating the series of weather forecasts includes using at least one of: in addition to using the predicted weather values: a rainfall rate, a rainfall type, a rainfall probability, a temperature, a pressure, a relative humidity, a wind speed , a wind direction, a value relative to lightning, a value relative to the hail and a value relative to a micro storm.
According to an embodiment, generating the series of weather forecasts within the time intervals between the specific times comprises: selecting between the predicted weather values prepared in the preset time increments for each specific time At least one of the forecasted meteorological values.
According to another embodiment, generating the series of weather forecasts within the time intervals between the specific times comprises: averaging preparing for a time within a time range of one of the specific times and pressing the preset The meteorological value selected between the forecasted meteorological values prepared by the time increment.
According to an embodiment, outputting the series of weather forecasts includes presenting the series of weather forecasts to the user.
According to an embodiment, outputting the series of weather forecasts includes outputting the series of weather forecasts within a given period of less than 6 hours.
According to an embodiment, receiving one of the time increments comprises selecting to receive one of the time increments from a previous usage save.
According to an embodiment, generating the series of weather forecasts in the selected time increment comprises generating the series of weather forecasts by one of a time increment of one minute, five minutes, fifteen minutes, or thirty minutes.
According to an embodiment, receiving one of the time increments comprises selecting to select one of the variable ones of the time increments within the given period.
According to an embodiment, generating the series of weather forecasts beginning at the given time comprises generating the series of weather forecasts beginning at a current time.
According to an embodiment, outputting a series of weather forecasts for a given zone includes: outputting a series of weather forecasts defined as one of a minimum range of resolutions between 5 meters and 1,000 meters.
According to an embodiment, outputting a series of weather forecasts for a very small area comprises: outputting a series of weather forecasts for one of the current positions of the user.
According to an embodiment, outputting a series of weather forecasts of one of the current positions of the user comprises: outputting a series of meteorological conditions through one of the current positions enabled by a communication network or by a GPS navigation device forecast.
According to an embodiment, receiving one of the time increments from a user comprises receiving any real number specified by the user.
According to an embodiment, receiving one of the time increments from a user comprises receiving the selected time increment greater than or equal to the preset time increment.
In another aspect, a system for outputting a series of weather forecasts for a given period of time and for a given time beginning at a given time is provided, the system comprising: for receiving by a Entering one of the forecast meteorological values prepared by the meteorological value predictor, the predicted weather value starting at the given time and continuing for a subsequent time separated by a predetermined time increment; for receiving a time increment from a user One of the selections is input, the selection time increment defines a series of specific times starting at the given time and continuing for the subsequent time separated by the selection time increment, the selection time increment being less than 1 hour; a weather forecast generation Means for generating, for each selection of a time increment, a series of new weather forecasts within a time interval between the specific times using the predicted weather values; and an output for outputting The series of weather forecasts within such time intervals between the specific times.
Definitions In this specification, the following terms mean the following definitions:
Nowcast: The term nowcast is an abbreviation of “now” and “forecast”; it refers to a collection of technologies envisioned for short-term forecasting (usually in the range of 0 to 12 hours).
One of the short-term areas (5, 10, 50, 100, 500, 1,000, etc.) of the Earth's very small area (the resolution of 5 meters, 10 meters, 50 meters, 100 meters, 500 meters, 1,000 meters, etc.) For example, 1 minute, 5 minutes, 15 minutes, 30 minutes, etc.) one of the weather forecasting devices. The proximity predictor includes a weather value predictor for preparing a forecast meteorological value and a weather forecast generator for generating a weather forecast by selecting a forecast meteorological value between the prepared forecast meteorological values.
A meteorological value is a meteorologically relevant quantity or attribute of any class, such as a rainfall rate, a rainfall type, a rainfall probability, a temperature, a pressure, a relative humidity, a wind speed, a wind direction, a value relative to lightning, Relative to the value of hail, relative to a value of a micro-storm, an amount of ice, a cloud, and so on.
A forecast meteorological value is predicted by the proximity forecaster. The forecast meteorological value is for a time or an interval.
A weather forecast system may display to the user a set of one or more forecast weather values. The weather forecast is about a time or an interval.
A user forwards a weather forecast to a person or a machine.
A weather related event is, for example, at least one of hail, a gust of wind, lightning, a temperature change, and the like.
Rain Type (PType): Indicates the type of rainfall. Examples of rainfall types include, but are not limited to, rain, snow, hail, freezing rain, ice beads, ice crystals.
Rain rate (PRate): Indicates the intensity of the rain. Examples of rainfall rates include, but are not limited to, none (ie, no), small, medium, large, and very large. In an embodiment, the rain rate may also be expressed as a range such as one of: no to small, small to medium, medium to large, or any combination of the above.
Rainfall probability: Indicates the probability of rain. Examples of rainfall chances include, but are not limited to, none, unlikely, unlikely, possible, possible, highly probable, and certain.
In an embodiment, the rainfall probability may also be expressed as a range such as one of: no to small, small to medium, medium to large. The probability of rain can also be expressed as a percentage: for example 0%, 25%, 50%, 75%, 100%; or a percentage range: for example 0% to 25%, 25% to 50%, 50% to 75%, 75% to 100%. In an embodiment, the rainfall probability may be taken from a probability distribution.
Rainfall Type and Rain Rate Category (PTypeRate): A PTypeRate category is a combination of rainfall type and rainfall rate associated with the probability of occurrence of one of a given period to indicate the likelihood of receiving a certain type of rainfall at a certain rate. .
Throughout the specification and claims, unless the context clearly indicates otherwise, the following terms are used in the meaning of the context. The phrase "in one embodiment" as used herein does not necessarily mean the same embodiment, but may be the same embodiment. In addition, the phrase "in another embodiment" as used herein does not necessarily mean a different embodiment, but it can be a different embodiment. Therefore, the embodiments of the present invention can be easily combined without departing from the scope or spirit of the invention. The terms "including" and "comprising" shall be interpreted to mean: include but not limited to.
Further, as used herein, the <RTI ID=0.0>"or"</RTI> includes an "or" operator and is equivalent to the term "and/or" unless the context clearly indicates otherwise. The term "based on" is not exclusive and is admitted to be based on additional factors not described, unless the context clearly indicates otherwise.
The features and advantages of the subject matter will be apparent from the following detailed description of the embodiments. As will be realized, the subject matter disclosed and claimed can be modified in various aspects, all of which are not in the scope of the claims. Accordingly, the drawings and description are to be regarded as illustrative in nature

CROSS-REFERENCE TO RELATED APPLICATIONS RELATED APPLICATIONS STATEMENT OF RELATED APPLICATIONS RELATED APPLICATIONS RELATED APPLICATIONS Patent Application Serial No. 13/922,800; U.S. Patent Application Serial No. 13/947,331, filed on Jul. 22, 2013; U.S. Provisional Application No. 61/839,675, filed on Jun. 16, 2013, U.S. Provisional Patent Application U.S. Provisional Application Serial No. 61/836,713, filed on Jun.
The present invention will now be described more fully hereinafter with reference to the accompanying drawings. The embodiments are also described so that the disclosure conveys the scope of the invention to those skilled in the art. However, the embodiments may be embodied in many different forms and should not be construed as being limited to the embodiments set forth herein.
This embodiment can be embodied as a method or apparatus, among other things. Thus, embodiments may take the form of an entirely hardware embodiment, a full software embodiment, a combination of software and a hardware embodiment (and the like). Moreover, although the embodiments have been described with reference to a portable or handheld device, they can be implemented in a desktop computer, laptop computer, tablet device, or any computing operation with sufficient computing resources in the embodiments. On the device.
Briefly, this embodiment describes a computer implementation method for generating and displaying a proximity prediction in selectable time increments. The user of the method selects a time increment and outputs a weather forecast that follows the selected time increment. A weather forecast is generated by a system known as a short-term weather forecaster for preparing forward-looking forecasts or a proximity predictor described more fully below.
1A is a block diagram of one method for generating and displaying a proximity prediction in selectable time increments, in accordance with an embodiment. The method shown in Figure 1 is implemented within the proximity predictor 200. A forecast weather value of 120 is prepared in the proximity predictor 200. The predicted weather value 120 is preset for a given period of time in a given time increment for a given zone. In an embodiment, the given time is a current time. In an embodiment, the preset time increment is the finest time increment, such as one minute. In another embodiment, there may be a selected time increment 100 that is less than a preset time increment, in which case an interpolation is required.
According to an embodiment, the predicted weather value may be prepared within the method, but the weather value may also be prepared by a weather value predictor that is not part of the method. In this case, the method described herein includes receiving the predicted weather value.
FIG. 1A further illustrates that the user selects a time increment of 100. This selection is made through a user interface. The selected time increment 100 is generally (but not necessarily) equal to or greater than the preset of the characterization weather forecast value 120.
According to an embodiment, the time increment can be memorized to subsequently retrieve the memory time increment instead of prompting the user to make a selection, thus allowing one of the time increments to be saved from one of the methods previously used.
According to an embodiment, the selected time increment 100 can include a plurality of selection time increments to allow for the generation of 110 weather forecasts by a variable time increment during a given period of time during which a weather forecast is generated. For example, in the first 5 minutes, it can be incremented by one minute, then one hour in the previous hour, and then become one of 30 minutes in the next few hours. Time increments generate and output weather forecasts.
Once the forecast meteorological value 120 and the selected time increment 100 are known, the method is ready to generate 110 weather forecasts in selected time increments. According to an embodiment, generating 110 weather forecasts may include two steps as illustrated by FIG. 1A. Since the predicted weather values are not all relevant for a user, one of the meteorological values is selected 125 to retain only the relevant values of the weather forecasts ultimately output by the method. A summary 130 can then occur.
Summary 130 is a portion of the method that converts the list of related forecast meteorological values generated for the preset time increments from selection 125 to a list of predicted weather values 120 for one of the selected time increments 100, the selected The time increment 100 is coarser than the preset time increment; that is, the selected time increment 100 is greater than or equal to the preset time increment.
According to an embodiment, the summary 130 is a rainfall guide, meaning that when a summary 130 occurs, it is verified whether a rain is likely to occur within the selected time increment 100, and if the answer is affirmative, the selected time increment 100 will be output. The type and rate of rainfall that may occur during the period. For example, in this embodiment, if the preset time increment of the forecast meteorological value 120 is one minute and if the user selects a five minute time increment of 100, the summary 130 will check the five forecasted weather that has occurred during the time period. A value of 120 and a check for a forecast of rainfall. If the four forecast meteorological values are 120 (for example) "no rain" and one is "possibly light rain", the summary 130 will allow for the possibility of outputting 140 light rain.
In other words, the selected time increment definition is referred to as a series of times of a particular time, which begins at a given time (which may or may not be the current time) and continues for a subsequent time separated by the selected time increment 100. The specific time of the series can be used to generate the time interval for the series of weather forecasts during the separation. The method includes selecting at least one of predicted weather values prepared for each particular time between predicted weather values prepared in preset time increments.
According to another embodiment, the summary 130 may include averaging the forecasted weather values 120 within the selected time increment 100. For example, in this embodiment, if the preset time increment of the forecast meteorological value 120 is one minute, and if the user selects a five minute time increment of 100, the summary 130 will include averaging the same type generated during the time period. The five forecast weather values 120 (eg, five temperature values or five pressure values or five PTypeRate values, etc.), and this average value will be used to display the series of weather forecasts for the time series. Averaging the weather value may include outputting an arithmetic mean or a geometric mean. All weather values will be avoided for averaging.
In other words, this embodiment can still use a specific time and a time interval separated by a particular time and defined by the previous embodiments described above. The series of weather forecasts are generated for time intervals between these specific times. In this embodiment, for each particular time, the method includes selecting between preset weather increments in a predetermined time increment and at a time ranging from one of the specific time periods, and then averaging such The weather value is used to generate a weather forecast within this time interval. During display, this weather forecast can be associated with the closest specific time for the convenience of the user, rather than being associated with the time interval.
In other embodiments, the summary 130 may include other algorithms or selection rules to determine how to aggregate the forecast weather values 120 generated for the fine preset time increments into a more coarse selected time increment 100.
Output 140 can include displaying the series of weather forecasts relative to a given period. According to an embodiment, this given period may vary depending on the selected time increment 100.
The output 140 can be updated at a given frequency to allow the user to learn the most recent series of weather forecasts.
According to other embodiments, the output 140 may include saving the series of weather forecasts or transmitting the series of weather forecasts to another computer.
According to an embodiment, the selected time increment 100 may vary over a given period of time during which the series of weather forecasts are output.
Figure 1B illustrates a different embodiment of the method of embedding the above. The difference from the embodiment presented in Figure 1A is the fact that the selection of a time increment 100 is not made at the beginning of the method. In the present embodiment, once the forecast meteorological value 120 is known, a preset time increment of the meteorological value is considered for generating a weather forecast of 110 in a predetermined time increment. The output 140 of the series of weather forecasts occurs to present 150 to the user. The user can then select a time increment of 100. This selection returns the method to generate a weather forecast of 110 in real time increments, followed by an output 140 in actual time increments and presenting 150 a series of weather forecasts until the user selects a new time increment of 100.
The present invention is a block diagram of a suitable proximity predictor 200, such as described in co-owned and co-invented U.S. Patent Application Serial No. 13/856, 923, filed on Apr. 4, 2013. .
As shown in Figures 2A and 2B, the proximity predictor 200 receives meteorological observations from different sources 201 (such as meteorological observation sources) including, but not limited to, point observations 201-2 (e.g., by the user and Feedback provided by the automatic station), weather radar 201-3, satellite 201-4 and other types of meteorological observations 201-1 and weather forecast sources, such as numerical weather prediction (NWP) model output 201-5 and weather forecast and consultation 201- 6.
The proximity predictor 200 includes a memory 220 and a processor 210. The memory 220 includes instructions for the method and also stores data from the weather source 201, intermediate results, and weather forecasts. The processor 210 allows the proximity predictor 200 to perform calculations.
The proximity predictor 200 can receive information 230 from a user via a communication network 254. According to an embodiment, this information 230 can be a selected time increment of 100.
The proximity predictor 200 outputs a weather forecast or a series of weather forecasts.
In an embodiment, the proximity predictor 200 includes a PType distribution predictor 202 and a PRate distribution predictor 204. The PType predictor 202 receives meteorological observations from different sources 201 and outputs a probability distribution of a given latitude and longitude (and/or position) of the type of rainfall over a time interval. E.g:
a. Snow: 10%
b. Rain: 30%
c. Freezing rain: 60%
d. Hail: 0%
e. Ice beads: 0%
Similarly, the PRate predictor 204 receives meteorological observations of a given latitude and longitude from different sources 201 and indicates one of the probability distributions of the output one rainfall rate (PRate) as one of the expression uncertainties. For example, PRate can be distributed at a given rate of latitude and longitude in a time interval or a rate range. E.g:
f. No rainfall: 30%
g. Light rain: 40%
h. moderate rain: 20%
i. Heavy rain: 10%
The PRate value and the PType value output by the PRate predictor 204 and the PType predictor 202 are sent to a forecast combiner 206 to combine the equal values into a single value PTypeRate representing a rain result. For example, if the value of PType is "snow" and the value of PRate is large, the combined value of PTypeRate can be "grand snow".
For a given latitude and longitude, the system outputs a predicted PTypeRate distribution for a predefined time interval (fixed (eg, 1 minute) or variable (eg, 1 minute, then 5 minutes, then 10 minutes, etc.)). The system can pre-calculate and store the predicted PTypeRate distribution or calculate the PTypeRate distribution in real time over a sequence of time intervals. For each time interval, a PTypeRate distribution indicates that a certainty or uncertainty of a PTypeRate will occur.
Referring to FIG. 2B, the forecast combiner 206 receives the final PRate distribution from the PType predictor 202 and receives the final PRate distribution from the PRate predictor 204 to combine them into one of the PTypeRate distribution values, each PTypeRate distribution value indicating receipt of a certain The probability of a certain type of rainfall at a rate. An example is provided below.
Assume that the PType distribution is as follows: snow 50%, rain 0%, freezing rain 30%, hail 0%, ice beads 20%, and PRate distribution as follows: no 0%, small 10%, medium 20%, large 30%, maximum 40% The PTypeRate distribution can be as follows:

Thus, the forecast combiner 206 multiplies the probability of each type of rainfall by the probability of rainfall at each rate to obtain a probability of receiving a certain type of rainfall at a certain rate (eg, 20% likelihood of heavy snow, or The 12% probability is extremely freezing rain). In one embodiment, the probability range can be associated with textual information to display textual information to the user rather than digitally displaying the probability. For example, a chance between 5% and 15% can be associated with the word "small likelihood", and a chance between 40% and 70% can be related to the word "probability" or "very likely" Union, etc., can show "very likely heavy snow" instead of display: 60% possibility is heavy snow.
In another embodiment, two or more different PTypeRates may be combined along one or more dimensions (the dimensions include: rate, type, or probability). For example, the result of this combination may include: it may be as small as moderate rain, it may be as small as moderate rain or heavy snow; it may rain or snow; it may rain or snow; it may be small to moderate rain or heavy snow or small Hail; may be rainy, snowy, or hail; it may rain, snow, or hail.
Thus, the proximity predictor 200 receives the location requiring the nearcast and the time and/or time interval required for the nearcast, and outputs the PTypeRate distribution for the given location and the particular time.
There may be another embodiment of the proximity predictor 200. In this embodiment, the proximity predictor includes a PType selector/receiver and a PRate distribution predictor. Similar to the embodiment shown in FIG. 2B, the PRate distribution predictor receives meteorological observations of a given latitude and longitude from different sources, and expresses one probability distribution of the output one rainfall rate (PRate) with one of the expression uncertainties. . For example, PRate can output a probability rate of a given rate of latitude and longitude over a time interval or a range of rates. In a non-limiting example, the probability distribution of the rain rate can be:
1) No rainfall: 30%
2) Light rain: 40%
3) Moderate rain: 20%
4) Heavy rain: 10%
One of ordinary skill in the art will appreciate that various other types and numbers of categories exist in addition to the examples provided above.
However, the PType selector/receiver does not output a probability distribution associated with different types of rainfall. Instead, the PType selector/receiver receives meteorological observations of a given latitude and longitude from different sources to select a type of rainfall from a different list of rainfall types. For example, based on inputs received from the sources, the PType selector/receiver selects one of the most likely occurrences of a given latitude and longitude (and/or location) from the following list of rainfall types:
1) Snow
2) Rain
3) Freezing rain
4) Hail
5) Ice beads
6) Mixing (eg a+c, a+d, b+c, a+e, c+e, d+e, etc.)
From the list of rainfall types (such as one of the above), only one type of rainfall is selected for a given location. For example, one of the most likely types of rainfall at a given time can be selected by mixing one of the snow and the freezing rain. The type of rainfall is not associated with a probability value. In fact, because only one rainfall type is selected for any given location and time corresponding to that location, the selected rainfall type will have a 100% effective probability value.
The list of rainfall types that can be used to select a type can include one of a mixture type that represents one of two different rainfall types (eg, snow and freezing rain, hail, ice, etc.). A hybrid type is considered to be useful for selecting one of the distinct rainfall types, and as shown in type (6) of the listing above, there may be many different hybrid types representing different combinations of various rainfall types.
In another embodiment, instead of selecting the type of rainfall by the PType selector/receiver, the rainfall type is received from one of the sources external to the neighboring predictor. In other words, the proximity predictor 200 can request a remote source (eg, a third party weather service) to identify the type of rainfall most likely to occur at a given location at a given time and receive one of the most likely types of rainfall from the source. Respond. In this case, the selection of the type of rainfall is not performed by the proximity predictor. The proximity predictor is only input to the selected type of rainfall and thereby saves the computing power of the proximity predictor required to perform the selection.
The selected rainfall type and PRate value output by the PType selector/receiver and the PRate distribution predictor are combined. For example, if the selected rainfall type is snow and the PRate value is as described above, the combined information will indicate:
1) No snow: 30%
2) Xiaoxue: 40%
3) Zhongxue: 20%
4) Heavy snow: 10%.
Since only one type of rainfall is concerned, performing a combination to output the final weather forecast data requires only a minimal amount of computing power. Since the PType selector/receiver will output a type of rainfall (1) of a given position and time, if the PRate distribution predictor outputs m probability distributions, the final weather forecast data will only include m (m*1) Distribution of weather forecasts.
Similar to the embodiment shown in FIG. 2, when outputting the final weather forecast data, the probability range can be associated with the text information to display the text information to the user instead of displaying the probability in digital. For example, a chance between 5% and 15% can be associated with the word "small likelihood", and a chance between 40% and 70% can be related to the word "probability" or "very likely" In addition, this can show "great possibility is heavy snow" instead of showing: 60% of the possibility is heavy snow. One of ordinary skill in the art will appreciate that many other variations are possible in addition to the examples provided above.
Thus, the proximity predictor receives the location requiring the nearcast and the time and/or time interval required for the nearcast, and outputs the selected PType and PRate distribution for the given location and time.
In some situations where efficiency is desired, the proximity predictor according to this alternative embodiment of the proximity predictor can be superior to the embodiment shown in Figure 2B. This other embodiment can be implemented using processing capabilities that are much smaller than the embodiment of Figure 2B. However, the embodiment of Figure 2B may be more stable than the other embodiment described above in providing a more detailed and accurate snapshot of weather forecast data for any given location and time.
3 is an example of one of the network environments in which embodiments may be practiced. The proximity predictor 200 can be implemented on a server 250 that can be accessed by a plurality of client computers 252 via a communication network 254. Client computer 252 can include, but is not limited to, a laptop, a desktop computer, a portable computing device, a tablet, and the like. Using a client computer 252, each user can select the selected time increment 100 and view the displayed forecast weather value. As discussed in connection with FIG. 2B, the server accesses the weather source 201 via a telecommunications network. The server 250 can store map data on it.
According to an embodiment, the client computer 252 can be used to locate a weather forecast for a given zone, which can be the current location of the user. This positioning can occur through activation of one of the computing devices or through a GPS navigation device.
The client computer 252 should include a user interface, such as a screen, to allow for the output of 140 weather forecasts. At the user interface, the user can select a time increment of 100.
FIG. 5 illustrates a screen capture screen of a user interface of one of a series of weather forecasts displayed by a presentation 150 in a predetermined one-minute increment of time, in accordance with an embodiment. The highlighted white display shows the time increment 100 that has been selected by the user. Since the number 1 is displayed in reverse, the weather forecast shown in Figure 5 ("no rain" in this example) is displayed in increments of one minute. This series of weather forecasts is related to location 500 and time 550.
6 is a screen capture screen of a user interface of one of a series of weather forecasts displayed by the output 140 in a preset five minute time increment according to an embodiment. As shown in Figure 5, the highlighted white display shows the time increment 100 that has been selected by the user. Since the number 5 is displayed in reverse, the weather forecast shown in Figure 6 ("no rain" in this example) is displayed in increments of five minutes. This series of weather forecasts is related to location 500 and time 550.
HARDWARE AND OPERATIONAL ENVIRONMENT FIG. 4 illustrates an exemplary diagram of a suitable computing operating environment in which one embodiment of the present invention may be practiced. The following description is associated with FIG. 4 and is intended to provide a brief general description of one suitable computer hardware and a suitable computing environment that can be combined to implement the embodiments. The implementation of the embodiments is not required to all of the components, and variations in the configuration and type of components may be made without departing from the spirit or scope of the embodiments.
Although not required, the embodiments are described in the general context of computer-executable instructions, such as a computer (such as a personal computer, a handheld or handheld computer, intelligent). Telephone) or an embedded system (such as a consumer device or a computer in a dedicated industrial controller) to perform. In general, program modules contain routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types.
Moreover, those skilled in the art will appreciate that the embodiments can be practiced using other computer system configurations including handheld devices, microprocessor systems, microprocessor-based or programmable consumer electronics, Internet PCS, small computers, large computers, cellular phones, smart phones, display pagers, radio frequency (RF) devices, infrared (IR) devices, personal digital assistants (PDAs), laptops, wearable computers, A tablet, an IPOD device or an IPAD device family manufactured by Apple Computer, an integrated device that combines one or more of the foregoing devices, or any other computing device capable of performing the methods and systems described herein. The embodiments may also be practiced in a decentralized computing environment in which tasks are performed by remote processing devices that are coupled through a communications network. In a distributed computing environment, the program modules can be located in the local and remote memory storage devices.
The exemplary hardware and operating environment of FIG. 4 includes a general purpose computing device in the form of a computer 720. The computer 720 includes a processor unit 721, a system memory 722, and a system bus 723. The system bus 723 will include Various system components of system memory are operatively coupled to processing unit 721. There may be only one processing unit 721 or more than one processing unit 721 such that the processor of computer 720 includes a single central processing unit (CPU) or a plurality of processing units collectively referred to as a parallel processing environment. The computer 720 can be a conventional computer, a decentralized computer, or any other type of computer; these embodiments are not so limited.
System bus 723 can be any of several types of bus bars, including a memory bus or memory controller, a peripheral bus, and a local bus using any of a variety of bus architectures. The system memory may also be referred to simply as a memory, and includes a read only memory (ROM) 724 and a random access memory (RAM) 725. A basic input/output system (BIOS) 726, which contains one of the basic routines, such as facilitating the transfer of information between components within computer 720 during startup, is stored in ROM 724. In an embodiment of the invention, the computer 720 further includes a hard disk drive 727 for reading from or writing to a hard disk (not shown) for reading from a removable disk 729. Read or write to a disk drive 728 of the removable disk 729 and for reading or writing to a removable optical disk from a removable optical disk 731 (such as a CD ROM or other optical medium) One of the 731 disc players 730. In an alternative embodiment of the invention, volatile or non-volatile RAM is used to simulate the functions provided by hard disk drive 727, magnetic disk 729, and optical disk drive 730 to save power and reduce the size of the system. In such alternative embodiments, the RAM can be fixed in a computer system, or it can be a removable RAM device such as a compact flash memory card.
In one embodiment of the present invention, the hard disk drive 727, the magnetic disk drive 728, and the optical disk drive 730 are respectively connected to the system bus 723 by a hard disk drive interface 732, a disk drive interface 733, and a disk drive interface 734. The disk drives and their associated computer readable media provide non-volatile storage of computer readable instructions, data structures, program modules and other data of the computer 720. Those skilled in the art will appreciate that any type of computer readable medium (such as magnetic cymbals, flash memory cards, digital video discs, Bernoulli cassettes, random access) that can store data that can be accessed by a computer. Memory (RAM), read only memory (ROM), etc. can be used in an exemplary operating environment.
A plurality of program modules can be stored on the hard disk, the magnetic disk 729, the optical disk 731, the ROM 724 or the RAM 725. The programming module includes an operating system 735, one or more application programs 736, other program modules 737, and program data 738. . A user can input commands and information into the personal computer 720 through, for example, a keyboard 740 and an indicator device 742. Other input devices (not shown) may include a microphone, joystick, game board, satellite dish, scanner, touch sensitive panel, and the like. These and other input devices are typically coupled to processing unit 721 via a serial port 746 coupled to the system bus, but may be connected by other interfaces such as a parallel port, game cartridge, or a universal serial bus (USB). . Additionally, the input of the system can be provided by a microphone to receive the audio input.
A monitor 747 or other type of display device is also coupled to system bus 723 via an interface, such as a video adapter 748. In one embodiment of the invention, the monitor includes a liquid crystal display (LCD). In addition to monitors, 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 interface with the computer by pressing or touching the surface.
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 made up of a portion of a communication device or computer 720 coupled to one of the portions of computer 720; embodiments are not limited to a particular type of communication device. The remote computer 749 can be another computer, a server, a router, a network PC, a client, a peer device, or other common network node, and although typically includes many of the above described with respect to the computer 720 or All components, but only one memory storage device 750 is shown in FIG. The logical connection depicted in FIG. 6 includes a local area network (LAN) 751 and a wide area network (WAN) 752. These network environments are commonplace in offices, enterprise-wide computer networks, intranets, and the Internet.
When used in a LAN network environment, computer 720 is coupled to regional network 751 via a network interface or adapter 753, which is a type of communication device. When in a WAN network environment, computer 720 typically includes a data machine 754, a type of communication device, or any other type of communication device for establishing communications over a wide area network 752, such as the Internet. Data machine 754, internal or external, can be coupled to system bus 723 via serial port 746. In a networked environment, the program modules described with respect to the personal computer 720 or portions thereof can be stored in the remote memory storage device. It will be appreciated that the network connections shown are exemplary and other components and communication means for establishing a communication link between computers can be used.
The hardware and operating environment in which the embodiments of the present invention may be practiced are described. A computer that can be combined to practice embodiments of the present invention can be a conventional computer, a handheld or palmtop computer, a computer in an embedded system, a distributed computer, or any other type of computer; limit. Such a computer typically includes one or more processing units as its processor and a computer readable medium such as a memory. The computer may also include a communication device such as a network adapter or a data modem such that it can communicatively couple to other computers.
While the preferred embodiment has been described and illustrated in the drawings, it will be understood by those skilled in the art Such modifications are considered to include possible variations in the scope of the present disclosure.

100‧‧‧Selected time increment

110‧‧‧generated

120‧‧‧ forecast weather values

125‧‧‧Select

Summary of 130‧‧

140‧‧‧ Output

150‧‧‧present

200‧‧‧ Proximity forecaster

201‧‧‧Weather source

201-1‧‧‧Weather observation

201-2‧‧ ‧ observation

201-3‧‧‧Weather radar

201-4‧‧‧ Satellite

201-5‧‧‧ Numerical weather prediction model output

201-6‧‧‧Weather Forecasting and Consulting

202‧‧‧Rain type (PType) distribution forecaster

204‧‧‧Rain rate (PRate) distribution predictor

206‧‧‧ Forecast combiner

210‧‧‧ processor

220‧‧‧ memory

230‧‧‧Information

240‧‧‧Weather forecast

250‧‧‧Server

252-1‧‧‧Customer Computer

252-2‧‧‧User computer

252-3‧‧‧User computer

254‧‧‧Communication network

500‧‧‧ position

550‧‧ hours

720‧‧‧ computer

721‧‧‧ processor unit

722‧‧‧ system memory

723‧‧‧System Bus

724‧‧‧Reading Memory (ROM)

725‧‧‧ Random Access Memory (RAM)

726‧‧‧Basic Input/Output System (BIOS)

727‧‧‧ hard disk drive

728‧‧‧Disk machine

729‧‧‧Removable Disk

730‧‧‧CD player

731‧‧‧Removable CD

732‧‧‧hard drive interface

733‧‧‧Disk interface

734‧‧‧CD player interface

735‧‧‧ operating system

736‧‧‧Application

737‧‧‧Program Module

738‧‧‧Program data

740‧‧‧ keyboard

742‧‧‧ indicator device

746‧‧‧Serial interface

747‧‧‧Monitor

748‧‧‧Video Adapter

749‧‧‧ remote computer

750‧‧‧Memory storage device

751‧‧‧Local Network (LAN)

752‧‧‧ Wide Area Network (WAN)

753‧‧‧ Adapter

754‧‧‧Data machine

Further features and advantages of the present invention will become apparent from the Detailed Description of the Drawing.

1A is a block diagram of one method for generating and displaying a proximity prediction in selectable time increments, in accordance with an embodiment;

1B is a block diagram of one method for generating and displaying a proximity prediction in selectable time increments in accordance with another embodiment;

Figure 2A is a block diagram of one of the suitable proximity predictors for implementing one of the embodiments;

Figure 2B is a more detailed block diagram of one of the suitable proximity predictors for implementing one of the embodiments;

Figure 3 is an example of one of the network environments in which embodiments may be practiced;

4 is a diagram showing an example of a suitable operational operating environment in which one embodiment of the present invention may be practiced;

FIG. 5 is a screen capture screen of a user interface of one of the above practical method embodiments, showing a weather forecast displayed in one minute increments;

FIG. 6 is a screen capture screen of a user interface of one of the above practical method embodiments, showing a weather forecast displayed in increments of five minutes.

It should be noted that similar features are identified by like reference numerals throughout the drawings.

Claims (15)

A computer implemented method for outputting a series of weather forecasts for a given period of time and for a given zone starting at a given time, the method comprising: Receiving a predicted weather value prepared by a weather value predictor, the forecast weather value being for a preset time period separated by a preset time increment; Receiving a selection time increment from a user, the selection time increment defining a series of selection time periods separated by the selection time increment, the selection time increment being longer than the preset time increment, such that each selection The time period includes a plurality of preset time periods; Generating a weather value for each of the selected time periods by summarizing the predicted weather values for the plurality of predetermined time periods included in the selected time period; and The predicted weather values for the selected time period for the series are output. The method of claim 1, wherein receiving the predicted weather values comprises receiving a plurality of forecast weather values, the forecast weather values comprising at least one of: a rainfall rate, a rainfall type, a rainfall probability, a temperature, a pressure , a relative humidity, a wind speed, a wind direction, a value relative to a lightning, a value relative to the hail, and a value relative to a micro storm. The method of claim 2, wherein generating a forecast meteorological value comprises using at least one of: in addition to using the forecasted meteorological values: a rainfall rate, a rainfall type, a rainfall probability, a temperature, a pressure, a relative humidity , a wind speed, a wind direction, a value relative to a lightning, a value relative to the hail and a value relative to a micro storm. The method of any one of claims 1 to 3, wherein outputting the predicted weather values for the selected time periods comprises: outputting the forecasts for the selected time periods within a given period of less than 6 hours Meteorological value. The method of any one of clauses 1 to 3, wherein receiving the selection time increment comprises receiving a time increment from a previous usage save. The method of any one of items 1 to 3, wherein the selection time increment is 1 minute, 5 minutes, 15 minutes, or 30 minutes. The method of any one of clauses 1 to 3, wherein the selection time increment is variable during the given period. The method of any one of clauses 1 to 3, wherein the given time is a current time. The method of any one of claims 1 to 3, wherein the given zone is defined as having a minimum of one of a resolution ranging between 5 meters and 1,000 meters. The method of claim 9, wherein the current location of the one of the users is included. The method of claim 10, wherein the outputting the current location of the user is determined by enabling an operation device to be located by a communication network or by a GPS navigation device. The method of claim 1, wherein receiving the selection time increment from a user comprises receiving any positive real number specified by the user. A system for outputting a series of weather forecasts for a given period of time and for a given zone starting at a given time, the system comprising: And an input for receiving one of the predicted meteorological values, wherein the predicted meteorological values are prepared for a preset time period separated by a preset time increment and prepared by a weather value predictor; For receiving a selection time increment from a user, the selection time increment defines a series of selection time periods separated by the selection time increment, the selection time increment being longer than the preset time increment So that each selection time period includes a plurality of preset time periods; a weather forecast generator for generating a weather value for each of the selected time periods by summarizing the predicted weather values for the plurality of predetermined time periods included in the selected time period; and A graphical user interface for outputting the predicted weather values for a selected time period for the series. A device for outputting a series of weather forecasts for a given period of time and for a given zone starting at a given time, the device comprising: One or more processors; a memory storing instructions of the one or more processors, wherein when the one or more processors execute the instructions, causing the apparatus to: Receiving a forecast meteorological value, which is prepared for a preset time period separated by a predetermined time increment and prepared by a weather value predictor; Receiving a selection time increment from a user, the selection time increment defining a series of selection time periods separated by the selection time increment, the selection time increment being longer than the preset time increment, such that each selection The time period includes a plurality of preset time periods; Generating a weather value for each of the selected time periods by summarizing the predicted weather values for the plurality of predetermined time periods included in the selected time period; The predicted weather value for each selected time period is output via a graphical user interface. A non-transitory computer readable medium comprising instructions that, when executed by a processor, cause a computer to perform the following steps: Receiving a plurality of forecast weather values prepared by a weather value predictor, the forecast weather values being for a preset time period separated by a preset time increment; Receiving a selection time increment from a user, the selection time increment defining a series of selection time periods separated by the selection time increment, the selection time increment being longer than the preset time increment, such that each selection The time period includes a plurality of preset time periods; Generating a weather value for one of each selected time period by summarizing the predicted weather values for the plurality of predetermined time periods included in each selected time period; The predicted weather values for the selected time periods are output.