Mobile Device Atmospheric Model Using Pressure and Altitude Data
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Solution Overview
Problem
Conventional methods for determining weather conditions using mobile devices often rely on data from distant ground-based weather stations, which can be inaccurate for localized conditions due to differences in weather patterns.
Innovation Solution
A computer-implemented method on mobile devices that collects pressure and location data, including altitude, to generate an atmospheric model using linear regression, enabling accurate estimation of ambient air temperature and weather conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If weather data is obtained from distant ground-based weather stations, then the device complexity is reduced, but the measurement precision of localized weather conditions deteriorates
Solution Approach 1:
The patent creates a virtual copy of the weather station system by enabling mobile devices to function as portable weather measurement nodes. Each mobile device collects local pressure, temperature, and humidity data, effectively copying the weather station's measurement capability to distributed locations throughout the environment.
Solution Approach 2:
The patent segments the traditional centralized weather station system into distributed mobile measurement nodes. Instead of relying on a single distant station, the system divides weather monitoring across multiple mobile devices, each independently collecting and reporting localized atmospheric data to improve overall measurement precision.
2Measurement precision
If mobile devices collect and process local pressure and location data to generate atmospheric models, then the measurement precision of localized weather conditions improves, but the device complexity increases
Solution Approach 1:
The patent makes mobile devices universal by enabling them to perform multiple functions: they serve as both consumer electronics devices and as atmospheric measurement instruments. The pressure sensor and location system, originally designed for other purposes, are repurposed for weather data collection, eliminating the need for dedicated complex weather station infrastructure.
Solution Approach 2:
The system implements self-service by allowing mobile devices to autonomously collect atmospheric data, process it through atmospheric models, and generate localized weather conditions without requiring complex external processing infrastructure. The devices serve themselves as both sensors and computational units.
3Measurement precision
If atmospheric models are generated using data from multiple mobile devices at different locations, then the measurement precision of ambient air temperature improves, but the loss of time for data collection and processing increases
Solution Approach 1:
The patent applies preliminary action by pre-configuring mobile devices with atmospheric models and processing algorithms before data collection begins. The devices are pre-programmed to continuously collect pressure, temperature, and humidity data and perform real-time processing, eliminating delays associated with post-collection analysis and enabling immediate generation of localized weather conditions.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Provides more accurate localized weather and temperature information, allowing for improved user experiences and notifications based on real-time, location-specific conditions.
Implementation Method 1
a pressure sensor for collecting pressure data
Implementation Method 2
a position-determining subsystem for generating location data including altitude data
Data Source
AI summary
A mobile device comprising a pressure sensor for collecting pressure data, a position-determining subsystem for generating location data including altitude data, and a processor operatively coupled to a memory to generate an atmospheric model based on the pressure data and the location data. In one implementation, the processor is configured to determine an Above Mean Sea Level (AMSL) altitude using a position-determining subsystem, determine a pressure altitude using the pressure sensor, calculate a difference between the pressure altitude and the AMSL altitude, and calculate a temperature at sea level based on the AMSL altitude and pressure altitude. In one implementation, the processor performs a linear regression on an equation AMSL altitude=offset+ScaleFactor*PressureAltitude to solve for the offset and the ScaleFactor, and then estimates the temperature at sea level as 1-ScaleFactor=(T−15)/3. The model may be used to estimate ambient air temperature or weather conditions.