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Snow transitions to rain : A review of atmospheric melting layer studies using micro rain radar and complementary approaches

Hosseini, Hasan LU ; Hashemi, Hossein LU orcid ; Olsson, Jonas LU ; Thorndahl, Søren Liedtke ; Rydberg, Bengt ; Körnich, Heiner and Berndtsson, Ronny LU orcid (2025) In Atmospheric Research 326.
Abstract

The atmospheric melting layer (AML), where snow transitions into rain, influences satellite signals, precipitation remote sensing, and a range of hydro-meteorological applications. AML has been extensively studied using radar technology, but methods for continuous and consistent monitoring remain limited. This paper presents the first comprehensive review of AML research, with a focus on the Micro Rain Radar (MRR), a specialized precipitation profiler based on K-band frequency-modulated continuous-wave (FMCW) radar. We have organized the literature into four main themes: AML detection using MRR, comparisons with other instruments (e.g., radiosonde, scanning weather radar, satellite, and lidar) and reanalysis data, spatiotemporal... (More)

The atmospheric melting layer (AML), where snow transitions into rain, influences satellite signals, precipitation remote sensing, and a range of hydro-meteorological applications. AML has been extensively studied using radar technology, but methods for continuous and consistent monitoring remain limited. This paper presents the first comprehensive review of AML research, with a focus on the Micro Rain Radar (MRR), a specialized precipitation profiler based on K-band frequency-modulated continuous-wave (FMCW) radar. We have organized the literature into four main themes: AML detection using MRR, comparisons with other instruments (e.g., radiosonde, scanning weather radar, satellite, and lidar) and reanalysis data, spatiotemporal variations in AML height, and methods for classifying precipitation event types (e.g., convective vs. stratiform). The in-depth critical review highlights strengths in the MRR's high temporal and spatial resolution, portability, and ability to continuously monitor precipitation microphysics. MRR provides multiple moments (e.g., reflectivity, and fall velocity) invaluable for AML studies, and their combination can enhance the consistency of AML detection, and event type classification. Nonetheless, limitations such as the single-polarization design, signal attenuation during heavy rainfall, and limited vertical range necessitate complementary datasets and advanced data processing techniques including artificial intelligence. Key findings underscore opportunities for gap filling and regionalization of AML observations, using mobile setups and multi-sensor campaigns. Also, modeling AML using surface weather variables can help link climate change studies and precipitation projections to anticipated AML behavior. The review concludes by outlining future directions and opportunities to advance AML research and applications.

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author
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organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Doppler velocity, Drop size, Hydrometeor, Machine learning, Rayleigh scattering, Reflectivity gradient
in
Atmospheric Research
volume
326
article number
108307
publisher
Elsevier
external identifiers
  • scopus:105008711780
ISSN
0169-8095
DOI
10.1016/j.atmosres.2025.108307
language
English
LU publication?
yes
additional info
Publisher Copyright: © 2025
id
4c5a8d1b-26c6-4756-b7de-49f592a05e4a
date added to LUP
2025-08-13 13:19:07
date last changed
2025-08-15 10:01:28
@article{4c5a8d1b-26c6-4756-b7de-49f592a05e4a,
  abstract     = {{<p>The atmospheric melting layer (AML), where snow transitions into rain, influences satellite signals, precipitation remote sensing, and a range of hydro-meteorological applications. AML has been extensively studied using radar technology, but methods for continuous and consistent monitoring remain limited. This paper presents the first comprehensive review of AML research, with a focus on the Micro Rain Radar (MRR), a specialized precipitation profiler based on K-band frequency-modulated continuous-wave (FMCW) radar. We have organized the literature into four main themes: AML detection using MRR, comparisons with other instruments (e.g., radiosonde, scanning weather radar, satellite, and lidar) and reanalysis data, spatiotemporal variations in AML height, and methods for classifying precipitation event types (e.g., convective vs. stratiform). The in-depth critical review highlights strengths in the MRR's high temporal and spatial resolution, portability, and ability to continuously monitor precipitation microphysics. MRR provides multiple moments (e.g., reflectivity, and fall velocity) invaluable for AML studies, and their combination can enhance the consistency of AML detection, and event type classification. Nonetheless, limitations such as the single-polarization design, signal attenuation during heavy rainfall, and limited vertical range necessitate complementary datasets and advanced data processing techniques including artificial intelligence. Key findings underscore opportunities for gap filling and regionalization of AML observations, using mobile setups and multi-sensor campaigns. Also, modeling AML using surface weather variables can help link climate change studies and precipitation projections to anticipated AML behavior. The review concludes by outlining future directions and opportunities to advance AML research and applications.</p>}},
  author       = {{Hosseini, Hasan and Hashemi, Hossein and Olsson, Jonas and Thorndahl, Søren Liedtke and Rydberg, Bengt and Körnich, Heiner and Berndtsson, Ronny}},
  issn         = {{0169-8095}},
  keywords     = {{Doppler velocity; Drop size; Hydrometeor; Machine learning; Rayleigh scattering; Reflectivity gradient}},
  language     = {{eng}},
  publisher    = {{Elsevier}},
  series       = {{Atmospheric Research}},
  title        = {{Snow transitions to rain : A review of atmospheric melting layer studies using micro rain radar and complementary approaches}},
  url          = {{http://dx.doi.org/10.1016/j.atmosres.2025.108307}},
  doi          = {{10.1016/j.atmosres.2025.108307}},
  volume       = {{326}},
  year         = {{2025}},
}