Leveraging machine learning for weather radar quality control and microphysical retrievals

Data from meteorological radars must undergo an extensive quality control process in order to become useful in research and analysis. A primary part of some quality control procedures is the removal of measurements returned by non-meteorological features such as the earth's surface and biological targets (birds, insects, etc.). Existing methods struggle to achieve acceptable levels of non-meteorological data removal in a timely fashion, resulting in the development of RONIN.jl (Random forest Optimized Nonmeteorological IdentificatioN) - an open-source Julia package for the end-to-end tuning, training, and testing of random forest (RF) models to remove non-meteorological features from radar data. It is shown that Ronin is able to achieve performance that meets or exceeds current operational products while operating at a speed orders of magnitude faster than a prototype experimental machine learning based method. Hydrometeor size distributions (HSDs) are quantities of great interest in a range of meteorological disciplines including cloud microphysics and numerical modeling. Prior literature has shown that these distributions can be succinctly represented through normalization by two or three integral moments of the distribution itself, with effective normalizations drastically reducing variability toward a distribution that does not vary across climactic regimes or precipitation habits. In this study, a novel three-moment normalization is employed to generate an extensive library of simulated HSDs, broadly conditioned on observations, in service of training a retrieval algorithm for the full distribution. Simulated radar variables are also computed for the distributions contained in the synthetic dataset. Subsequently, several Artificial Neural Networks (ANNs) are trained to use the radar variables as input to retrieve the full HSD through a quartet of different mathematical techniques. The four techniques are then evaluated on several real-world datasets. It is shown that all methods examined are at least somewhat effective at retrieving moments, even of lower order, of observed distributions, and produce distributions that match well with both radar and disdrometer observations.

Name thesis:degree_name

Master of Science (M.S.)

Level thesis:degree_level

Masters

Discipline thesis:degree_discipline

Atmospheric Science

Grantor dc:publisher

Colorado State University. Libraries

Year dc:date.issued

2025

Authors dc:creator

• Schluesche, Isaac R., author
• Bell, Michael M., advisor
• Schumacher, Russ, committee member
• Chen, Haonan, committee member

• microphysics
• radar
• precipitation
• machine learning

Statement dc:rights

• Copyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.

Language dc:language.iso

eng, English

Identifier URI

https://doi.org/10.25675/3.025581

OAI record oai:identifier

https://api.mountainscholar.org/server/oai/request?verb=GetRecord&metadataPrefix=dim&identifier=oai%3Amountainscholar.org%3A10217%2F242689