Patent Abstract:
The present invention is directed to system and method of forecasts, displays, and alerts for localized hail activity. An exemplary method comprises the steps of selecting a region to monitor, receiving meteorological data for that region, processing the meteorological data for storm cell and hail activity in order to determine hail risk activity. The system forecasts the direction of an active storm as well a user position. Probability bands of hail risk activity are created for display. Optionally, an alert is generated when the user position is in or proximate a threshold probability band.

Full Description:
PRIORITY 
       [0001]    The present invention claims priority to provisional application 61/975,810, which has a filing date of Apr. 5, 2014 and is incorporated by reference. 
     
    
     FIELD OF THE INVENTION 
       [0002]    The present invention relates to systems and processes for meteorological data processing, and more specifically to systems and methods of forecasts and notification of localized hail activity. 
       DESCRIPTION OF THE RELATED ART 
       [0003]    It is desirable for a person to have hail activity risk data at their specific location. It is even more desirable for that person to have hail activity risk data during travel along their route. Based upon the hail activity risk data, the person may take appropriate action to avoid injury to themselves or damage to their vehicle by taking shelter or altering the travel path in response to possible hail in their current proximity or intended proximity. 
         [0004]    First, current hail activity display and notification typically notify users by broad geographic area, typically over a metropolitan area. Even National Oceanic and Atmospheric Administration (NOAA) radio transmits alerts by broad geographic area or by a station ID. Secondly, the hail activity display and notification fail to include potential severity of the hail condition in a non-distracting format for ready consumption in order to take meaningful action in response to the hail activity risk. 
         [0005]    Moreover, weather forecasts are generally only reliable and valid for some period following their generation. Once a person is en route, the weather conditions may be updated so as to take account changes in weather conditions, and in turn the hail activity risk. Managing the forecast data and maintaining situational awareness, especially while traveling, can decrease attention due to the necessary focus on the data. 
         [0006]    For the above reasons, it would therefore be advantageous to have systems and methods of creation, display, and alert of hail activity risk data for a localized area in a readily perceptible format. 
       SUMMARY 
       [0007]    Exemplary embodiments of the present invention are directed to systems and methods for monitoring and notification of localized hail activity. An embodiment of method of the current invention comprises the steps of receiving contemporaneous user position information, defining a weather monitoring zone encompassing the user position, defining a user travel zone encompassing the user position and within the weather monitoring zone. The system receives meteorological data for the weather monitoring zone from at least one meteorological data source. The system determines hail risk within a storm cell for the user travel zone, projects a subsequent user position, and projects a subsequent storm cell position. The system compares the position data of the user travel zone with the position data of the hail risk in the storm cell for overlap, and determines user hail risk as a function of the projected user position and the projected storm cell position. 
         [0008]    These and other features, aspects, and advantages of the invention will become better understood with reference to the following description, and accompanying drawings. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0009]      FIG. 1  depicts elements of an embodiment of a system according to the current invention; 
           [0010]      FIG. 2  depicts a flowchart of the major steps of a process implemented to an embodiment of a system according to the current invention; 
           [0011]      FIG. 3  depicts a flowchart of a representative subset of the process of  FIG. 1 ; 
           [0012]      FIG. 4  depicts a representative partial database schema for input into the process and system of  FIG. 3 ; 
           [0013]      FIG. 5  depicts representative storm cell activity center and representative hail activity monitoring zone; 
           [0014]      FIG. 6  depicts a representative storm cell activity travel vector; 
           [0015]      FIG. 7  depicts representative storm cell activity travel within a hail activity monitoring zone; 
           [0016]      FIG. 8  depicts a representative historical cell activity travel vector, present cell activity travel vector, and forecast cell activity bands in a storm cell; 
           [0017]      FIG. 9  depicts a representative historical cell activity travel vector, present cell activity travel vector, and forecast cell activity bands within a hail activity monitoring zone; and 
           [0018]      FIG. 10  depicts a representative historical cell activity travel vector, present cell activity travel vector, and forecast cell activity bands within a hail activity monitoring zone. 
       
    
    
     DETAILED DESCRIPTION 
       [0019]    Detailed descriptions of the preferred embodiment are provided herein. It is to be understood, however, that the present invention may be embodied in various forms. Therefore, specific details disclosed herein are not to be interpreted as limiting, but rather as a basis for the claims and as a representative basis for teaching one skilled in the art to employ the present invention in virtually any appropriately detailed system, structure or manner. 
         [0020]    Exemplary embodiments of the present invention are directed to systems and processes for monitoring hail risk activity for a selected local area and presenting a graphical representation or generating a notification based upon the same. Referring to  FIG. 1 , the major components of embodiments of the system  10  are presented. Meteorological data sources  12   13   14   16 , a processor  20  of a computer  21 , and a personal computer  26  having a display  24  are illustrated. 
         [0021]    In certain embodiments, doppler radar  12  in communication with a radar processor  13  as a source of meteorological data is shown. NEXRAD  14 , as an alternate source of meteorological data is shown. Additional data sources  16 , such as alternate online providers, may exist as another source of meteorological data is also shown. One exemplary meteorological data source is the current and historical weather products of NOAA, NEXRAD, or the National Climatic Data Center (NCDC). A computer  21  having a processor  20  compiles, processes, and stores meteorological data. The processor  20  outputs data packets for transmission and presentation on a display  24  of a user computer  26 . 
         [0022]    A computer  21   26  as referred to in this specification generally refers to a system which includes a central processing unit (CPU), memory, a screen, a network interface, and input/output (I/O) components connected by way of a data bus. The I/O components may include for example, a mouse, keyboard, buttons, or a touchscreen. The network interface enables data communications with the computer network. A server is a computer  21  containing various server software programs and preferably contains application server software. A minicomputer is a computer  21   26  such as a smartphone or tablet PC with smaller dimensions, such as iPhone, iPod Touch, iPad, Blackberry, or Android based device. Those skilled in the art will appreciate that computer  21   26  may take a variety of configurations, including personal computers, hand-held devices, multi-processor systems, microprocessor-based electronics, network PCs, minicomputers, mainframe computers, and the like. Additionally, the computer  21   26  may be part of a distributed computer environment where tasks are performed by local and remote processing devices that are linked. Although shown as separate devices, one skilled in the art can understand that the structure of and functionality associated with the aforementioned elements can be optionally partially or completely incorporated within one or the other, such as within one or more processors. As noted above, the processes of this invention, or subsets thereof, may exist in on one or more computers such as a client/server approach. The process, or subsets thereof, may exist in a machine-readable medium. The machine-readable medium may include, but is not limited to, floppy diskettes, optical disks, CD-ROMs, and magneto-optical disks, ROMs, RAMs, EPROMs, EEPROMs, magnetic or optical cards, propagation media or other type of media/machine-readable medium suitable for storing electronic instructions. For example, the present invention or aspects thereof may be downloaded as a computer program or “app” which may be transferred from a remote computer to a requesting computer by way of data signals embodied in a carrier wave or other propagation medium via a communication link. 
         [0023]    Referring to  FIG. 2 , an exemplary process of forecasting and displaying hail activity risk is shown. The system  10  receives a geographic area to monitor  200 . The meteorological data for the selected geographical area for the selected time frame is received  210 . The received meteorological data for the selected geographical area for the subject time frame is processed  220 . The system  10  determines the likelihood of hail activity in the subject geographic area  230 . The system  10  projects active storm cell direction  240 . The system  10  projects user direction  250 . The system  10  displays hail activity information  260 . Each of these steps will be considered in more detail below. 
         [0024]    Now referring to  FIG. 5 , at step  200 , the system  10  receives a geographic area to monitor for hail activity. The system  10  determines position information for a user in order to determine a user position  29 , for example in the form of latitude and longitude coordinates. In one configuration, the user enters an address. The system  10  performs a lookup of the address to retrieve the corresponding latitude and longitude coordinates. In other configurations, the system  10  directly receives user position  29  information in an automated manner from sources such as a personal computer  26  GPS, standalone GPS, vehicle mounted GPS, OnStar, or other devices. It should be appreciated that other forms of geocoding are within the spirit of this invention. Based on the user position  30 , current and past (where available), the system optionally defines a user travel zone  28  representing an area in which the user is likely to be in the near term. Factors influencing the user travel zone  28  size include user velocity, available travel routes, and other factors. For example, where there is only a single or limited corridors for a route or segments thereof, size in the anticipated direction of travel can be elongated in that direction. 
         [0025]    The system  10  defines a weather monitoring zone  30  including the user position  29  and user travel zone  28 . The weather monitoring zone  30  is a system selected distance or range of distances from the user position  29 . Factors influencing the weather monitoring zone  30  size include the storm cell center  32 , the storm sell size, user velocity, storm cell velocity, wind speed, and other factors. Again, as an example, where there is only a single or limited corridors for a route or segments thereof, size in the anticipated direction of travel can be elongated in that direction. 
         [0026]    At step  210 , the system  10  receives meteorological data for the subject weather monitoring zone  30 . Exemplary meteorological data sources  14   16  include the current and historical weather products of NOAA, NEXRAD, or the National Climatic Data Center (NCDC). More specifically, the NOAA Hail Index and nx3hail weather products. Exemplary processed meteorological data includes active storm cells, storm cell identification numbers, storm cell position, storm cell size, storm cell direction, and probability of severe hail. 
         [0027]    At steps  220  and  230 , the meteorological data  14   16  is processed to forecast hail activity risk. In exemplary configuration, the system calculates the probability of hail for a representative sample of the user travel zone  28  or the weather monitoring zone  30 , with the probabilities of hail corresponding to their respective coordinates. 
         [0028]    In one configuration, the available probability of severe hail data is retrieved from the weather product for points within the user travel zone  28  or the weather monitoring zone  30 . 
         [0029]    In certain configurations, the system  10  employs the hail probability calculations disclosed in U.S. patent application Ser. No. 14/071,414 to Sneed, which is hereby incorporated by reference. It is further disclosed below, as necessary. 
         [0030]    Referring to  FIG. 3 , the system  10  receives meteorological data for a selected geographical area for a selected time frame  100 . The system  10  processes and transforms the received meteorological data. The system  10  then generates a data packet representing hail intensity overlay data in the form of a derived hail intensity index. 
         [0031]    Still referring to  FIG. 3 , a more detailed disclosure of the above embodied process is shown. The system  10  receives meteorological data for the weather zone  100 . In one configuration, doppler radar units are C-band or X-band Doppler meteorological surveillance radar with automatic computer processing systems. The system may further include S-band to supplement. These radar units provide measurement of both reflectivity and velocity of liquid and can scan volumetrically to produce detailed data. In a reflectivity mode, the liquid echoes are scaled to correspond directly to values of liquid content. In velocity mode, the radar measures the movement of scattering particles along the radar beam. In one configuration, meteorological data including precipitation, cloud cover data, the bottom and top of cloud formations, and reflectivity and velocity of liquid are acquired from C-band Doppler radar, which is combined with NEXRAD data, and the data is digitized and stored for real-time, near real-time, or historical processing. The full volumetric data of the storm enables the system to “slice” a storm to view cross sections from various angles, and from various vantage points. The meteorological data sources  12   13   14   16 , directly or indirectly, and without exclusion, can include data products such as rainfall intensity, reflectivity, composite reflectivity, clear air mode, precipitation mode, echo tops, vertical integrated liquid, surface rainfall accumulation, radial velocity, velocity azimuth display winds, winds aloft, wind shear, microburst activity, and the like. 
         [0032]      FIG. 4  shows a representative partial database schema for input to the current configuration of the system  10 . It includes series of rows or “slices” having a timestamp for a particular set of data, a latitude and latitude, water particle size, number of water particles, the height of those water particles, and the probability of severe hail (“POSH”). It is to be understood that the input meteorological data can be pre-processed prior to input to the system  10  or post-processed for use by the system  10 . For example, as the basis of the data in this configuration is received from radar incident or at an angle relative to the atmosphere being sampled, latitude and longitudinal data for the ground position of the sampled air column is computed as known in the art. For example, water particle size may represent an average of an array of water particles within the particular data set. In an exemplary configuration, the meteorological data is received from external sources, preferably the National Climactic Data Center NEXRAD Data Inventory  14 . 
         [0033]    In certain embodiments, the system  10  supplements the radar data  12  or NEXRAD data  14  with additional data sources  16 . 
         [0034]    The system  10  processes the meteorological data in plural data channels  110   120   130 . A first data channel is the hail index  110  for use in locating storm cells which have the potential to produce hail. More specifically, the preferred subset of hail index information is the probability of severe hail  110  data, which indicates the probability of severe hail within the area of representing the particular dataset. It is commonly represented by a value between zero and one hundred percent. In a first configuration, it is derived from the input meteorological data. In a second configuration, it is derived from the input meteorological data and provided by a third party. Additional information on the derivation of hail index and probability of severe hail is annexed and incorporated by reference. 
         [0035]    A second data channel is the vertically integrated liquid  120  data, which is useful in determining the amount of precipitation that the radar detects in a vertical column of the atmosphere for an area. It is determined as known in the art. In a first configuration, it is derived from the input meteorological data. In a second configuration, it is derived from the input meteorological data and provided by a third party. Additional disclosure of vertical integrated liquid calculation is annexed and incorporated by reference. 
         [0036]    A third data channel is the enhanced echo tops  130 , which is useful in determining the peak height of an atmospheric area of precipitation. It is determined as known in the art. In a first configuration, it is derived from the input meteorological data. In a second configuration, it is derived from the input meteorological data and provided by a third party. Additional disclosure of enhanced echo tops determination is annexed and incorporated by reference. 
         [0037]    Having the enhanced echo top  130  and the vertically integrated liquid  120  data, the system  10  calculates the vertically integrated liquid (VIL) density  140 . This embodiment calculates the VIL density as known in the art. This embodiment employs the following formula: 
         [0000]      (VIL/Echo Top)*1000 
         [0000]    to yield a value in g/m 3 . 
         [0038]    An optional fourth data channel is the spatial offset  135 , which is useful in determining potential spatial offset of hail position from atmospheric formation to ground level impact. The spatial offset is determined determining the hail potential for a given area. The system starts with the hail&#39;s anticipated position at an enhanced echo top above ground level. A vector is formed applying the gravitational constant from that altitude to ground level. The vector is adjusted based on storm motion and wind direction data. More specifically, vectors from fields such as radial velocity, velocity azimuth display winds, winds aloft, wind shear, and microburst activity at different altitudes between the echo top and ground level are accumulated. An offset value for ground level (or proximate ground level) is calculated and applied. 
         [0039]    Having the VIL density and probability of severe hail data, the system  10  prepares a series of data packets to facilitate display of hail activity. In addition to the visual map data, each data packet contains hail activity overlay data. The data packets represent map data and hail activity overlay for a selected geographic area and a selected time window, each data packet representing a single frame of the same dimension. Each data packet contains hail activity data for the same selected geographic area. That is to say the geographic boundaries represented by each of the data packets is the same. Further, a coordinate, typically an x, y cartesian coordinate or the like, representing a pixel in one data packet corresponds to the same underlying position within the selected geographic area across the series of data packets. 
         [0040]    Each data packet is based on meteorological data from a single time slice, with the series of data packets representing a chronologically ordered sequence of hail activity proximate the currently processed subject time. The data packet is structured for transformation to an image showing hail activity in that time slice or subset thereof. 
         [0041]    As previously mentioned, the data packets include hail activity overlay data corresponding to given coordinates. The hail activity overlay data is based on a derived hail index  150 . In an exemplary configuration, each point or pixel in the geographic area represented by the data packet includes a derived hail index number. In the current embodiment, the derived hail index is a scaled number representing the intensity of the hail activity, indicating how the system  10  should represent the data packet in its transformation for hail risk. In one configuration, a high derived hail index indicates high hail activity. 
         [0042]    In computing the derived hail index  150 , the current embodiment of the system  10  retrieves the probability of severe hail  110  data, the vertically integrated liquid  120  data, the enhanced echo tops  130  data, and VIL density  140  data for an area. The input meteorological data includes probability of severe hail  110  data. This is commonly available for an area within the selected geographic region. However, the area corresponding those input points varies depending on radar processing resolution, gaps due to radar scan intervals, and other factors. The applicable probability of severe hail  110  data of the input meteorological data is retrieved by selecting those points having a latitude &amp; longitude within or adjacent the selected geographic region. VIL density  140  is commonly available as clusters and is retrieved from the meteorological data in a similar manner. 
         [0043]    As previously disclosed, the exemplary embodiment of the system  10  assigns a derived hail index  150  to each data point within the data packet corresponding to a pixel to be displayed. The derived hail index is a number calculated based on the product of VILD and POSH. Optionally, the derived hail index is scaled. Where a probability of severe hail  110  data is available for pixel data representing a latitude/longitude position within the selected geographic region, one configuration of the system  10  for computing the derived hail index  150  employs the following formula: 
         [0000]      Ceiling(VILD*(POSH/2)/100+VILD,max) 
         [0000]    where VILD is vertically integrated liquid digital density for the cluster containing the latitude/longitude position, POSH is probability of severe hail for the latitude/longitude position, and max is the configured upper end of the scale. 
         [0044]    In some cases, probability of severe hail  110  data is unavailable for pixel data representing a latitude/longitude. In such a case, the system will substitute or calculate a suitable probability of severe hail  110  point based on proximate POSH data within a pre-configured maximum distance threshold from available data. The maximum distance threshold is determined by comparing available probability of severe hail  110  data to VIL density  140  clusters, where a suitable proximate probability of severe hail  110  point is available. On one configuration, the system  10  employs the above disclosed formula to that point adjusted by the following distance adjustment formula: 
         [0000]      ((A COS(SIN(posh_lat*PI/180)*SIN(vild_p_lat*PI/180)+COS(posh_lat*PI/180)*COS(vild_p_lat*PI/180)*COS((posh_lon−vild_p_lon)*PI/180))*180/PI)*60*1.1515)
 
         [0000]    where posh_lat is the latitude of the proximate probability of severe hail point, vild_p lat is the latitude for the proximate VIL density cluster, posh_lon is the longitude of the proximate probability of severe hail point, vild_p_lon is the longitude for the proximate VIL density cluster. 
         [0045]    After steps  220  and  230 , hail activity risk values are stored in the form of probability of severe hail or a derived hail index for the respective coordinates for the given time slice. 
         [0046]    At step  240 , the direction of the storm cell is projected.  FIGS. 6 ,  7 ,  8 ,  9  depict representative storm cell center  32   34  travel scenarios.  FIGS. 6 and 7  depict a current storm cell center  32  and a historical storm cell center  34 .  FIGS. 8 and 9  depict a current storm cell center  32  and prior historical storm cell centers. 
         [0047]    The system  10  employs varying approaches to projecting storm cell direction, individually or in combination. In one configuration, the retrieved meteorological data source weather product includes a direction vector for the storm cell, such as the libnexrad weather product. The system  10  projects from the current storm center  32  using the direction and velocity data of that retrieved vector data. 
         [0048]    In an alternate configuration, the system  10  bases the projection on comparison of successive storm cell centers  32   34 . For example, the system  10  uses the time and position data of each storm cell center  32   34  to determine the velocity and direction of the storm cell for that time interval. It can define a current storm cell vector  36  based on the data. The system  10  may define historical storm cell vectors  38  based on other historical storm cell centers  34 . The system  10  then defines a forecast vector  42 , weighting each vector  36   38  accordingly, average, weighted, or otherwise compositing. 
         [0049]    In yet another configuration, storm cell travel from prior storm cell events in the same geography or similar conditions is used to define a forecast storm cell vector  42 . As mentioned, the approaches may be used individually or in combination, weighting each approach to produce the forecast storm cell vector  42 . 
         [0050]    At step  250 , the system  10  projects the user position based on user travel. For example, the updated user position can be received from the prior disclosed real-time or near real-time systems or from projects route systems such as Google Maps. In certain configurations, the projected user position  29  is based on user velocity, direction of travel, prior travel history, likely or available routes to the destination, and other sources. The system  10  optionally updates the hail risk activity based upon the projections at step  240  and  250 . 
         [0051]    Now referring to  FIGS. 8 ,  9 , and  10 , at step  260 , configurations of the system  10  displays the hail risk activity based on the forecast storm cell vector  42 . In certain configurations, the system  10  creates data packets with visual data representing confidence bands. Each confidence band represents a range of certainty of storm hail activity in the forecast storm cell vector  42 . The area closest to the forecast storm cell vector  42  is assigned the highest probability. The area further from the forecast storm cell vector  42  are assigned gradually decreasing probability values. 
         [0052]    The projected storm cell position probability values may be further modified by projected hail risk probability values to calculate a composite probability value. Specifically, a hail probability value is calculated for the same position as a corresponding projected storm cell position probability value. Accordingly, a projected high probability of storm activity input modified by a low probability of hail activity yields a low composite probability value whereas a projected mid-range probability of storm activity input modified by a high probability of hail activity yields a mid-range composite probability value. 
         [0053]    This configuration applies a color gradient from the highest probability value to the lowest probability value. The color gradient overlay is stored as a visual data packet for transmission to the user computer  26  for display  24 . Optionally, where the user position  29  is within or proximate a threshold storm cell probability, hail activity risk probability, and/or hail density probability, the system generates a notification for the user. The threshold can be system generated, user input, or a combination thereof. The notification can be in the form of signal on the display  24 , an email message, an SMS message, instant message, in-app message, or other forms of communication known in the art. 
         [0054]    The system  10  reiterates the steps  200 - 260  while monitoring, as it is activated. 
         [0055]    Insofar as the description above, and the accompanying drawing disclose any additional subject matter that is not within the scope of the single claim below, the inventions are not dedicated to the public and the right to file one or more applications to claim such additional inventions is reserved.

Technology Classification (CPC): 6