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:
Jun. 23, 2020

Filed:

Apr. 08, 2015
Applicant:

The Climate Corporation, San Francisco, CA (US);

Inventors:

Holly Dail, Seattle, WA (US);

Stephan Hoyer, San Francisco, CA (US);

Assignee:

The Climate Corporation, San Francisco, CA (US);

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
G01V 99/00 (2009.01); G01N 21/359 (2014.01); G01N 33/00 (2006.01); G06Q 10/04 (2012.01); G01W 1/10 (2006.01); G01N 33/24 (2006.01); G01N 21/17 (2006.01); G01N 21/31 (2006.01); A01B 79/00 (2006.01);
U.S. Cl.
CPC ...
G01V 99/005 (2013.01); G01N 21/359 (2013.01); G01N 33/0098 (2013.01); G01N 33/24 (2013.01); G01W 1/10 (2013.01); G06Q 10/04 (2013.01); A01B 79/005 (2013.01); G01N 2021/1793 (2013.01); G01N 2021/3155 (2013.01); G01N 2201/0616 (2013.01); G01N 2201/129 (2013.01);
Abstract

A method for calibrating forecasts involving temperature, precipitation, and other weather related variables is provided. In an embodiment historical ensemble-based forecasts and historical observations are received by an agricultural intelligence computing system. Historical differences are determined between the forecasts and the observations corresponding to the forecasts and stored in the volatile memory of the agricultural intelligence computing system. The agricultural intelligence computing system receives current ensemble-based forecasts and a request for improved forecasts. The agricultural intelligence computing system retrieves the historical differences and uses a combination of the historical differences and the current ensemble-based forecasts to create probability distributions for the weather for each lead day. The agricultural intelligence computing system then samples from the probability distributions to create improved ensemble-based forecasts at the requested location.


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