# US 9,436,784 B2 · Validating and calibrating a forecast model > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 9,436,784 B2 - Document ID: US9436784B2 - Status: Granted - Kind: B2 - Application number: 14/176,449 - Filed: 2014-02-10 - Published: 2015-01-15 - Granted: 2016-09-06 - Earliest claimed: 2013-02-08 - Assignees: University of Alaska Fairbanks - Applicants: University of Alaska Fairbanks - Inventors: Keith W. Cunningham; Peter Webley - Web page: https://patentgrep.com/patent/US9436784B2 ## CPC - G06F 30/20: Design optimisation, verification or simulation (optimisation, verification or simulation of circuit designs G06F30/30) - G16Z 99/00: Subject matter not provided for in other main groups of this subclass - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G16Z: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS, NOT OTHERWISE PROVIDED FOR ## Abstract Disclosed are methods and systems of rapidly validating accuracy in particle cloud forecast transport and dispersion models. An example method can comprise accessing a forecast model of a volcanic ash cloud. An example method can comprise generating first data based at least on the forecast model. The first data can have at least one fewer spatial dimension than second data associated with the forecast model. An example method can comprise determining accuracy of the forecast model based at least on measurements of at least a portion of the volcanic ash cloud. An example method can comprise refining the forecast model based at least on the determined accuracy, thereby improving a representation of the volcanic ash cloud. ## Claims ### Claim 1 A method, comprising: receiving first data based at least on a forecast model of a volcanic ash cloud, wherein the first data has at least one fewer spatial dimension than second data associated with the forecast model, wherein the first data comprises an image; determining accuracy of the forecast model based on at least the image of the first data and measurements of at least a portion of the volcanic ash cloud; and providing results of the determining the accuracy of the forecast model. ### Claim 2 (depends on 1) The method of claim 1, further comprising accessing the forecast model of the volcanic ash cloud, wherein the accessing the forecast model comprises at least one of generating the forecast model of the volcanic ash cloud and receiving the forecast model of the volcanic ash cloud. ### Claim 3 (depends on 1) The method of claim 1, further comprising: determining one or more ash sizes, wherein the measurements of at least a portion of the volcanic ash cloud include the one or more ash sizes, wherein the one or more ash sizes are determined at one or more altitudes; and determining one or more ash concentrations, wherein the measurements of at least a portion of the volcanic ash cloud include the ash concentrations, wherein the one or more ash concentrations are determined at one or more altitudes. ### Claim 4 (depends on 1) The method of claim 1, further comprising receiving a satellite image of the volcanic ash, wherein the measurements of at least a portion of the volcanic ash cloud comprise at least a portion of the satellite image. ### Claim 5 (depends on 4) The method of claim 4, further comprising determining a satellite look angle. ### Claim 6 (depends on 5) The method of claim 5, further comprising: calculating one or more ash sizes based on the satellite look angle; and calculating one or more ash concentrations based on the satellite look angle. ### Claim 7 (depends on 1) The method of claim 1, wherein refining the forecast model comprises changing at least one of, one or more ash sizes in the forecast model and one or more ash concentrations in the forecast model. ### Claim 8 (depends on 1) The method of claim 1, wherein the first data comprises only two spatial dimensions. ### Claim 9 (depends on 1) The method of claim 1, further comprising refining the forecast model based at least on the results of the determining the accuracy of the forecast model, thereby improving a representation of the volcanic ash cloud. ### Claim 10 (depends on 1) The method of claim 1, wherein the forecast model comprises a volcanic ash forecast transport and dispersion model. ### Claim 11 (depends on 1) The method of claim 1, wherein the measurements of at least a portion of the volcanic ash are obtained from at least one of RADAR, LiDAR, an optical sensor, a dropsonde, an unmanned aircraft, and a weather balloon. ### Claim 12 (depends on 1) The method of claim 1, wherein determining accuracy is based on a comparison of a portion of the first data and the measurements of at least a portion of the volcanic ash. ### Claim 13 (depends on 1) The method of claim 1, further comprising: determining a total mass of the volcanic ash cloud, wherein the measurements of at least a portion of the volcanic ash cloud comprise the one or more ash sizes; and determining a density of one or more particles in the volcanic ash cloud, wherein the measurements of at least a portion of the volcanic ash cloud comprise the one or more ash sizes. ### Claim 14 (depends on 1) The method of claim 1, further comprising determining accuracy of the forecast model based at least on information associated with a volcano. ### Claim 15 (depends on 14) The method of claim 14, wherein the information associated with the volcano comprises at least one of a geochemistry of the volcano and an eruption rate of the volcano. ### Claim 16 A method comprising: accessing a three dimensional forecast model comprising a representation of a particle cloud; generating a two dimensional synthetic image of the three dimensional forecast model based on a time parameter; receiving measurements of the particle cloud, wherein the measurements are associated with a time indicated by the time parameter; determining a difference between the representation of the particle cloud and the particle cloud based on the measurements and the two dimensional synthetic image; and adjusting the three dimensional forecast model based on the difference between the representation of the particle cloud and the particle cloud. ### Claim 17 (depends on 16) The method of claim 16, wherein receiving measurements of the particle cloud comprises using at least one of RADAR, LIDAR, satellite imagery, satellite remote sensing, infrared reverse absorption, brightness temperature differencing, visible spectrometry, infrared spectrometry, a balloon, an unmanned aerial vehicle, a dropsonde, a rocket power aircraft, an electrically powered aircraft, and an air breathing engine aircraft. ### Claim 18 (depends on 16) The method of claim 16, wherein determining a difference between the representation of the particle cloud and the particle cloud based on the measurements of the particle cloud and the two dimensional synthetic image comprises comparing the two-dimensional synthetic image to a two dimensional satellite image made at the time indicated by the time parameter. ### Claim 19 (depends on 16) The method of claim 16, wherein receiving measurements of the particle cloud comprises receiving satellite imaging from a plurality of look-angles and calculating a vertical height of particles of the particle cloud. ### Claim 20 A system, comprising: a memory having computer-executable instructions encoded thereon; and at least one processor functionally coupled to the memory and configured, by the computer-executable instructions, for, receiving first data based at least on a forecast model of a volcanic ash cloud, wherein the first data has at least one fewer spatial dimension than second data associated with the forecast model, wherein the first data comprises an image; determining accuracy of the forecast model based on at least the image of the first data and measurements of at least a portion of the volcanic ash cloud; and providing results of the determining the accuracy of the forecast model.