SLD Detection via Dual-Volume Optical Sampling
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Solution Overview
Problem
Current LIDAR systems struggle to accurately characterize multi-modal distributions of super-cooled water droplet sizes in clouds, leading to potential underdetection of hazardous Super-cooled Large Droplets (SLDs) due to averaging techniques that can attenuate scintillation spikes, resulting in signals falling below noise floors, especially in clouds with sparse distributions.
Innovation Solution
A system that projects both collimated and uncollimated optical pulses into the cloud atmosphere, allowing for comparison of backscattered signals to calculate the size and density of SLDs by distinguishing between smooth continuous and scintillation-spike portions of the backscatter signal, using a processor to determine metrics based on these differences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a collimated laser beam is used to sample cloud atmosphere, then measurement precision of cloud metrics is improved, but the sampling volume is limited and sparse SLD distributions may be missed
Solution Approach 1:
The patent segments the backscatter signal into two distinct components: scintillation spikes (from SLDs) and smooth continuous signal (from small droplets). This segmentation allows the system to process each component separately, enabling precise detection of sparse SLDs without being limited by the small sampling volume of collimated beams.
Solution Approach 2:
The patent uses an intermediary approach by introducing a signal processing mediator that separates and analyzes different signal components. The scintillation spike detection acts as an intermediary mechanism to identify SLDs independently from the continuous backscatter signal, resolving the contradiction between precision and sampling volume.
2Measurement precision
If backscatter signals are averaged over multiple laser pulses, then signal-to-noise ratio is improved, but scintillation spikes from sparse SLDs are attenuated and may fall below noise floor
Solution Approach 1:
The patent extracts scintillation spikes from the averaged backscatter signal by identifying rapid fluctuations that exceed a threshold. This extraction process removes the harmful effect of averaging on SLD detection while maintaining the beneficial noise reduction from signal averaging, as the spikes are identified and processed separately from the smoothed signal.
Solution Approach 2:
The system employs periodic laser pulse emission and uses the temporal periodicity of the signal to distinguish scintillation spikes from noise. By analyzing signal fluctuations at known pulse intervals, the system can identify genuine SLD-related scintillation events while filtering out random noise, thus preserving SLD information while maintaining signal-to-noise ratio.
3Measurement precision
If collimated beam with small field of view is used, then backscatter signal resolution is improved, but the beam encounters few SLDs in sparse distributions
Solution Approach 1:
The patent replaces the mechanical approach of increasing beam divergence (which would reduce spatial resolution) with an optical-signal processing approach. By using scintillation spike detection in the temporal domain, the system achieves both high spatial resolution (through collimated beams) and high statistical sampling (through spike identification), substituting mechanical beam expansion with signal-based detection.
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 approach enhances the detection of SLDs by sampling larger volumes with uncollimated beams to capture sparse droplet distributions and smaller volumes with collimated beams to quantify metrics, improving the accuracy of water droplet characterization and reducing the risk of ice accretion on aircraft surfaces.
Implementation Method 1
Some aircraft are equipped with Light Detection and Ranging (LIDAR) systems to measure cloud metrics. Such systems can characterize clouds that have water droplets that have a size distribution having a single mode.
Implementation Method 2
LIDAR systems project pulses of a collimated laser beam into the cloud atmosphere and then sense the signal backscattered by the cloud atmosphere.
Implementation Method 3
Depending on the size and density of the SLDs in the secondary distribution, the backscatter signal can appear as scintillation spikes superimposed on an otherwise smooth continuous range-resolved backscatter signal characteristic of the primary distribution.
Data Source
Figure 1
Figure 2A~2B
Figure 3
AI summary
Apparatus and associated methods relate to determining a size and/or density of Super-cooled Large Droplets (SLDs) in a cloud atmosphere by comparing detected optical signals reflected from small and large sampling volumes of a cloud atmosphere. In some embodiments, an optical pulse is generated and divergently projected from a first optical fiber. A collimating lens is aligned within the divergently projected optical pulse collimating a portion thereof. The collimated and uncollimated portions of the optical pulse are projected into the small and large sampling volumes of the cloud atmosphere, respectively. The ratio of the collimated to the uncollimated portions can be optically controlled. Signals corresponding to optical pulses having different collimated/uncollimated ratios are backscattered by the cloud atmosphere, detected and compared to one another. A processor is configured to calculate, based on scintillation spike differences between the optical pulses of different collimated/uncollimated ratios, a size and/or density of SLDs.