The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Aug. 08, 2023

Filed:

Nov. 08, 2020
Applicants:

Terrafuse, Inc., San Francisco, CA (US);

Hunter Connell, San Francisco, CA (US);

Inventors:

Adrian Albert, San Francisco, CA (US);

Brian White, San Francisco, CA (US);

Alok Singh, Berkeley, CA (US);

Ashray Manepalli, San Jose, CA (US);

Mayur Mudigonda, Berkeley, CA (US);

Assignee:

Terrafuse, Inc., San Francisco, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 30/27 (2020.01); G06F 111/04 (2020.01); G06F 111/08 (2020.01); G06F 111/10 (2020.01);
U.S. Cl.
CPC ...
G06F 30/27 (2020.01); G06F 2111/04 (2020.01); G06F 2111/08 (2020.01); G06F 2111/10 (2020.01);
Abstract

Apparatuses, methods, and systems for increasing a spatial resolution of gridded spatial-temporal data on weather and climate-related physical variables are disclosed. One method includes obtaining weather and climate data including at least a coarse resolution and a fine resolution, or observational data that includes physical data, pre-processing the weather and climate data, training one or more probabilistic downscaling mapping functions of the at least one of the gridded numeric simulation data or the observational data comprising applying interpolation filters to successively interpolate the pre-processed weather and climate data to generate output data having a resolution that is equal to the fine resolution, and generating high-resolution physical parameters for at least one of a plurality of applications utilizing the trained probabilistic downscaling mapping functions receiving different weather and climate input data that has different times or locations than the pre-processed weather and climate data used in the training.


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