View source for Google llc (20240184013). OPTIMIZING A PROBABILITY OF PRECIPITATION FORECAST simplified abstract

=OPTIMIZING A PROBABILITY OF PRECIPITATION FORECAST=


==Organization Name==

[[:Category:google llc|google llc]]


[[Category:google llc]]

==Inventor(s)==

[[:Category:William B. Gail of Boulder CO (US)|William B. Gail of Boulder CO (US)]][[Category:William B. Gail of Boulder CO (US)]]

[[:Category:Brett Basarab of Longmont CO (US)|Brett Basarab of Longmont CO (US)]][[Category:Brett Basarab of Longmont CO (US)]]

[[:Category:William Myers of Boulder CO (US)|William Myers of Boulder CO (US)]][[Category:William Myers of Boulder CO (US)]]

==OPTIMIZING A PROBABILITY OF PRECIPITATION FORECAST - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20240184013 titled 'OPTIMIZING A PROBABILITY OF PRECIPITATION FORECAST

==Simplified Explanation==

The patent application describes a method for optimizing a probability of precipitation forecast by associating spatiotemporal subregions with specific forecasting algorithms and calibrating them using climatological data.

* Uniquely associate spatiotemporal subregions with probability of precipitation forecasting algorithms.
* Select climatological data meeting the data segment definition.
* Calibrate the forecasting algorithms using the selected climatological data.

== Potential Applications ==
This technology could be applied in weather forecasting, agriculture, disaster preparedness, and resource management.

== Problems Solved ==
This technology helps improve the accuracy of probability of precipitation forecasts by calibrating algorithms based on specific climatological data for different subregions.

== Benefits ==
The method can lead to more precise and reliable precipitation forecasts, which can help in making informed decisions related to various activities and industries that are sensitive to weather conditions.

== Potential Commercial Applications ==
Potential commercial applications include weather forecasting services, agricultural advisory services, disaster management agencies, and industries reliant on accurate weather predictions.

== Possible Prior Art ==
One possible prior art could be the use of machine learning algorithms to optimize weather forecasts based on historical data and geographical factors.

== What are the limitations of this method in optimizing probability of precipitation forecasts? ==
One limitation of this method could be the availability and quality of climatological data for calibration, which can vary across different regions.

== How does this method compare to traditional methods of probability of precipitation forecasting? ==
This method improves upon traditional forecasting methods by tailoring algorithms to specific subregions and calibrating them using relevant climatological data, leading to more accurate forecasts.


==Original Abstract Submitted==

a method of optimizing a probability of precipitation forecast is provided. the method includes uniquely associating, based on a data segment definition, a spatiotemporal subregion with a probability of precipitation forecasting algorithm of two or more probability of precipitation forecasting algorithms. the method further includes selecting climatological data meeting the data segment definition and calibrating, using the selected climatological data meeting the data segment definition, the probability of precipitation forecasting algorithm associated with the spatiotemporal subregion.



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