Capturing the Diversity of Mesoscale Trade Wind Cumuli Using Complementary Approaches From Self‐Supervised Deep Learning
Abstract At mesoscale, trade wind clouds organize with various spatial arrangements, shaping their effect on Earth's energy budget. Representing their fine‐scale dynamics even at 1 km scale climate simulations remains challenging. However, geostationary satellites (GS) offer high‐resolution clo...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-06-01
|
| Series: | Geophysical Research Letters |
| Subjects: | |
| Online Access: | https://doi.org/10.1029/2024GL108889 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850134761944645632 |
|---|---|
| author | Dwaipayan Chatterjee Sabrina Schnitt Paula Bigalke Claudia Acquistapace Susanne Crewell |
| author_facet | Dwaipayan Chatterjee Sabrina Schnitt Paula Bigalke Claudia Acquistapace Susanne Crewell |
| author_sort | Dwaipayan Chatterjee |
| collection | DOAJ |
| description | Abstract At mesoscale, trade wind clouds organize with various spatial arrangements, shaping their effect on Earth's energy budget. Representing their fine‐scale dynamics even at 1 km scale climate simulations remains challenging. However, geostationary satellites (GS) offer high‐resolution cloud observation for gaining insights into trade wind cumuli from long‐term records. To capture the observed organizational variability, this work proposes an integrated framework using a continuous followed by discrete self‐supervised deep learning approach, which exploits cloud optical depth from GS measurements. We aim to simplify the entire mesoscale cloud spectrum by reducing the image complexity in the feature space and meaningfully partitioning it into seven classes whose connection to environmental conditions is illustrated with reanalysis data. Our framework facilitates comparing human‐labeled mesoscale classes with machine‐identified ones, addressing uncertainties in both methods. It advances previous methods by exploring transitions between regimes, a challenge for physical simulations, and illustrates a case study of sugar‐to‐flower transitions. |
| format | Article |
| id | doaj-art-63afd1cfc0334c5fb9aee97fb37b9651 |
| institution | OA Journals |
| issn | 0094-8276 1944-8007 |
| language | English |
| publishDate | 2024-06-01 |
| publisher | Wiley |
| record_format | Article |
| series | Geophysical Research Letters |
| spelling | doaj-art-63afd1cfc0334c5fb9aee97fb37b96512025-08-20T02:31:38ZengWileyGeophysical Research Letters0094-82761944-80072024-06-015112n/an/a10.1029/2024GL108889Capturing the Diversity of Mesoscale Trade Wind Cumuli Using Complementary Approaches From Self‐Supervised Deep LearningDwaipayan Chatterjee0Sabrina Schnitt1Paula Bigalke2Claudia Acquistapace3Susanne Crewell4Institute for Geophysics and Meteorology University of Cologne Cologne GermanyInstitute for Geophysics and Meteorology University of Cologne Cologne GermanyInstitute for Geophysics and Meteorology University of Cologne Cologne GermanyInstitute for Geophysics and Meteorology University of Cologne Cologne GermanyInstitute for Geophysics and Meteorology University of Cologne Cologne GermanyAbstract At mesoscale, trade wind clouds organize with various spatial arrangements, shaping their effect on Earth's energy budget. Representing their fine‐scale dynamics even at 1 km scale climate simulations remains challenging. However, geostationary satellites (GS) offer high‐resolution cloud observation for gaining insights into trade wind cumuli from long‐term records. To capture the observed organizational variability, this work proposes an integrated framework using a continuous followed by discrete self‐supervised deep learning approach, which exploits cloud optical depth from GS measurements. We aim to simplify the entire mesoscale cloud spectrum by reducing the image complexity in the feature space and meaningfully partitioning it into seven classes whose connection to environmental conditions is illustrated with reanalysis data. Our framework facilitates comparing human‐labeled mesoscale classes with machine‐identified ones, addressing uncertainties in both methods. It advances previous methods by exploring transitions between regimes, a challenge for physical simulations, and illustrates a case study of sugar‐to‐flower transitions.https://doi.org/10.1029/2024GL108889mesoscale cloud organizationtropical cloudsartificial intelligenceself‐supervisioncloud variabilitycloud system transition |
| spellingShingle | Dwaipayan Chatterjee Sabrina Schnitt Paula Bigalke Claudia Acquistapace Susanne Crewell Capturing the Diversity of Mesoscale Trade Wind Cumuli Using Complementary Approaches From Self‐Supervised Deep Learning Geophysical Research Letters mesoscale cloud organization tropical clouds artificial intelligence self‐supervision cloud variability cloud system transition |
| title | Capturing the Diversity of Mesoscale Trade Wind Cumuli Using Complementary Approaches From Self‐Supervised Deep Learning |
| title_full | Capturing the Diversity of Mesoscale Trade Wind Cumuli Using Complementary Approaches From Self‐Supervised Deep Learning |
| title_fullStr | Capturing the Diversity of Mesoscale Trade Wind Cumuli Using Complementary Approaches From Self‐Supervised Deep Learning |
| title_full_unstemmed | Capturing the Diversity of Mesoscale Trade Wind Cumuli Using Complementary Approaches From Self‐Supervised Deep Learning |
| title_short | Capturing the Diversity of Mesoscale Trade Wind Cumuli Using Complementary Approaches From Self‐Supervised Deep Learning |
| title_sort | capturing the diversity of mesoscale trade wind cumuli using complementary approaches from self supervised deep learning |
| topic | mesoscale cloud organization tropical clouds artificial intelligence self‐supervision cloud variability cloud system transition |
| url | https://doi.org/10.1029/2024GL108889 |
| work_keys_str_mv | AT dwaipayanchatterjee capturingthediversityofmesoscaletradewindcumuliusingcomplementaryapproachesfromselfsuperviseddeeplearning AT sabrinaschnitt capturingthediversityofmesoscaletradewindcumuliusingcomplementaryapproachesfromselfsuperviseddeeplearning AT paulabigalke capturingthediversityofmesoscaletradewindcumuliusingcomplementaryapproachesfromselfsuperviseddeeplearning AT claudiaacquistapace capturingthediversityofmesoscaletradewindcumuliusingcomplementaryapproachesfromselfsuperviseddeeplearning AT susannecrewell capturingthediversityofmesoscaletradewindcumuliusingcomplementaryapproachesfromselfsuperviseddeeplearning |