A Deep-Learning Scheme for Hydrometeor Type Classification Using Passive Microwave Observations Article Swipe
Ruiyao Chen
,
Ralf Bennartz
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/rs15102670
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/rs15102670
This paper proposes a novel approach for hydrometeor classification using passive microwave observations. The use of passive measurements for this purpose has not been extensively explored, despite being available for over four decades. We utilize the Micro-Wave Humidity Sounder-2 (MWHS-2) to relate microwave brightness temperatures to hydrometeor types derived from the global precipitation measurement’s (GPM) dual-frequency precipitation radar (DPR), which are classified into liquid, mixed, and ice phases. To achieve this, we utilize a convolutional neural network model with an attention mechanism that learns feature representations of MWHS-2 observations from spatial and temporal dimensions. The proposed algorithm classified hydrometeors with 84.7% accuracy using testing data and captured the geographical characteristics of hydrometeor types well in most areas, especially for frozen precipitation. We then evaluated our results by comparing predictions from a different year against DPR retrievals seasonally and globally. Our global annual cycles of precipitation occurrences largely agreed with DPR retrievals with biases being 8.4%, −11.8%, and 3.4%, respectively. Our approach provides a promising direction for utilizing passive microwave observations and deep-learning techniques in hydrometeor classification, with potential applications in the time-resolved observations of precipitation structure and storm intensity with a constellation of smallsats (TROPICS) algorithm development.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/rs15102670
- https://www.mdpi.com/2072-4292/15/10/2670/pdf?version=1685071579
- OA Status
- gold
- References
- 55
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4377291644
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4377291644Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/rs15102670Digital Object Identifier
- Title
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A Deep-Learning Scheme for Hydrometeor Type Classification Using Passive Microwave ObservationsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-05-20Full publication date if available
- Authors
-
Ruiyao Chen, Ralf BennartzList of authors in order
- Landing page
-
https://doi.org/10.3390/rs15102670Publisher landing page
- PDF URL
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https://www.mdpi.com/2072-4292/15/10/2670/pdf?version=1685071579Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/2072-4292/15/10/2670/pdf?version=1685071579Direct OA link when available
- Concepts
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Brightness temperature, Precipitation, Environmental science, Microwave, Remote sensing, Meteorology, Radar, Computer science, Advanced Microwave Sounding Unit, Microwave imaging, Brightness, Geology, Telecommunications, Optics, PhysicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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55Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W2031675844, https://openalex.org/W2138644576, https://openalex.org/W2039845302, https://openalex.org/W2097975612, https://openalex.org/W3136421480, https://openalex.org/W2352044181, https://openalex.org/W2040882433, https://openalex.org/W2127969284, https://openalex.org/W2518850026, https://openalex.org/W4214496757, https://openalex.org/W2793177454, https://openalex.org/W2122412480, https://openalex.org/W2781748871, https://openalex.org/W4281783612, https://openalex.org/W2072881174, https://openalex.org/W1649947482, https://openalex.org/W2159780131, https://openalex.org/W2079529265, https://openalex.org/W2157747676, https://openalex.org/W2120985218, https://openalex.org/W2045947288, https://openalex.org/W2101902804, https://openalex.org/W2136550063, https://openalex.org/W2968686375, https://openalex.org/W3036558832, https://openalex.org/W2287049325, https://openalex.org/W2179040032, https://openalex.org/W2158139061, https://openalex.org/W2139661162, https://openalex.org/W2026865765, https://openalex.org/W2098960569, https://openalex.org/W2038268867, https://openalex.org/W2016568524, https://openalex.org/W3102302111, https://openalex.org/W3029752768, https://openalex.org/W1995341919, https://openalex.org/W4290653181, https://openalex.org/W2977666392, https://openalex.org/W2790832316, https://openalex.org/W2073298425, https://openalex.org/W1667819958, https://openalex.org/W3016719260, https://openalex.org/W3172509117, https://openalex.org/W3183430956, https://openalex.org/W6766978945, https://openalex.org/W1836465849, https://openalex.org/W2919115771, https://openalex.org/W6739901393, https://openalex.org/W2963091558, https://openalex.org/W6738893770, https://openalex.org/W2413554747, https://openalex.org/W2980276221, https://openalex.org/W4295312788, https://openalex.org/W4385245566, https://openalex.org/W2963907629 |
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| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5044099297 |
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| institutions_distinct_count | 2 |
| corresponding_institution_ids | https://openalex.org/I200719446 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/14 |
| sustainable_development_goals[0].score | 0.6200000047683716 |
| sustainable_development_goals[0].display_name | Life below water |
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