GNSS Receiver Snowpack Parameter Determination
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Solution Overview
Problem
Current automated snowpack measurement systems are expensive to install and maintain, and existing methods using Global Navigation Satellite System (GNSS) signals for measuring snowpack parameters like snow water equivalent (SWE), snow liquid water content (LWC), snow depth, and snow density are inaccurate, with large uncertainties and limitations in measuring SWE.
Innovation Solution
A method utilizing GNSS signals received under the snowpack, employing a physical model and non-linear solver to accurately determine SWE, LWC, snow depth, and snow density without requiring assumptions about snow density, using variations in GNSS signals with satellite angle from zenith.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If automated snowpack measurement systems (snow pillows, snow scales, gamma ray sensors) are used, then measurement precision is improved, but device complexity and installation cost increase
Solution Approach 1:
The patent replaces complex mechanical and electronic snowpack measurement systems (snow pillows, snow scales, gamma ray sensors) with a simplified GNSS-based system. The GNSS receiver measures snowpack parameters by detecting signal attenuation and phase changes as signals pass through the snowpack, eliminating the need for expensive and complex hardware while maintaining measurement capability
Solution Approach 2:
The GNSS receiver is a multi-functional device that can determine multiple snowpack parameters (SWE, LWC, snow depth, snow density) simultaneously from a single instrument. This universal approach replaces multiple specialized measurement devices, reducing overall system complexity while providing comprehensive snowpack characterization
2Device complexity
If GNSS signals are used to measure snowpack parameters, then device complexity is reduced, but measurement precision deteriorates due to large uncertainties
Solution Approach 1:
The patent transforms the raw GNSS signal observations (attenuation, phase, amplitude) into multiple snowpack parameters (SWE, LWC, depth, density) by applying a physical model. This parameter transformation process extracts multiple independent pieces of information from the GNSS signals, resolving the uncertainty and enabling precise SWE measurement that overcomes the initial precision limitations
Solution Approach 2:
The patent introduces a physical model as an intermediary between the GNSS signal observations and the snowpack parameters. This model acts as a bridge that translates the indirect signal measurements into accurate snowpack characterizations, resolving the uncertainty inherent in direct signal-to-parameter conversion and enabling precise measurement
3Ease of manufacture
If existing GNSS methods are used, then installation cost is reduced, but measurement reliability worsens due to limitations in measuring SWE
Solution Approach 1:
The patent changes the measurement parameters by simultaneously determining multiple snowpack properties (SWE, LWC, depth, density) from GNSS signals. This multi-parameter approach provides more constraints for the physical model, improving the reliability of SWE measurements and overcoming the limitations of existing single-parameter GNSS methods
Solution Approach 2:
The patent employs a physical model that uses feedback from multiple measured parameters (attenuation, phase, amplitude variations with satellite angle) to iteratively refine the snowpack parameter estimates. This feedback mechanism improves measurement reliability by continuously adjusting the solution to match all observed signal characteristics
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
This method provides precise and economical measurements of snowpack parameters, enabling improved hydrological forecasting and increased spatial sampling of snowpack, reducing installation costs and improving accuracy compared to existing systems.
Implementation Method 1
employing a physical model and non-linear solver to accurately determine SWE, LWC, snow depth, and snow density without requiring assumptions about snow density, using variations in GNSS signals with satellite angle from zenith
Implementation Method 2
using variations in GNSS signals with satellite angle from zenith
Data Source
AI summary
This method calculates snowpack parameters such as: snow water equivalent, snow liquid water content, snow depth and snow density, using Earth orbiting satellites from a GNSS constellation transmitting radio wave frequency carrier signals. An under-snow receiver system and an out-of-snow receiver system are employed are capable of decoding the encoded GNSS data into data products such as a carrier to noise ratios and carrier phases. A computer system receives data products. The Snowpack software programs derive a measured carrier to noise ration reduction from the carrier signals and the carrier to noise ratios. A physical model calculates a modeled excess phase and a modeled carrier noise ratio reduction, calculating measured snowpack parameters, such as: a snow water equivalent, a snow liquid water content, a snow depth, and a snow density, of the at least one snow layer. A non-linear mathematical solver adjusts the plurality of snow parameters.