An ensemble machine-learning first-guess approach for physics-based retrieval of ice particle size distributions from multi-frequency radar, validated with CCREST-M aircraft observations

Baran, Anthony J.; Fox, Stuart; Cotton, Richard; Delanoë, Julien; Walden, Christopher J.; McCusker, Karina; Westbrook, Christopher D.; Huggard, Peter G.

doi:10.5194/amt-19-4367-2026

Articles | Volume 19, issue 12

https://doi.org/10.5194/amt-19-4367-2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/amt-19-4367-2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 19, issue 12

Research article

|

01 Jul 2026

Research article |

| 01 Jul 2026

An ensemble machine-learning first-guess approach for physics-based retrieval of ice particle size distributions from multi-frequency radar, validated with CCREST-M aircraft observations

Anthony J. Baran, Stuart Fox, Richard Cotton, Julien Delanoë, Christopher J. Walden, Karina McCusker, Christopher D. Westbrook, and Peter G. Huggard

Data sets

FAAM C374 CCREST-M flight: Airborne atmospheric measurements from core instrument suite on board the BAE-146 aircraft Facility for Airborne Atmospheric Measurements, Natural Environment Research Council https://catalogue.ceda.ac.uk/uuid/7892db5c68104a0c9caf99bc59337647

Airborne atmospheric measurements from core instrument suite on board the BAE-146 aircraft Facility for Airborne Atmospheric Measurements, Natural Environment Research Council https://catalogue.ceda.ac.uk/uuid/7892db5c68104a0c9caf99bc59337647

FAAM C382 CCREST-M flight: Airborne atmospheric measurements from core instrument suite on board the BAE-146 aircraft Facility for Airborne Atmospheric Measurements, Natural Environment Research Council https://catalogue.ceda.ac.uk/uuid/67911f14d0a24d7ea8866eaf4575e9f5

Short summary

We demonstrate how multi-frequency ground-based radars at 3, 35 and 94 GHz can be used to determine vertical profiles of ice-particle size spectra by combining reflectivity with machine-learning prior information on UK wintertime ice clouds. The method is validated using independent profiles from a 200 GHz radar and aircraft-based in-situ observations. It gives a consistent representation to compare with aircraft-based radiometric measurements in future radiative-transfer closure studies.