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f | 1 | { | f | 1 | { |
2 | "author": "Wellmann, Marie-Constanze", | 2 | "author": "Wellmann, Marie-Constanze", | ||
3 | "author_email": "", | 3 | "author_email": "", | ||
4 | "creator_user_id": "17755db4-395a-4b3b-ac09-e8e3484ca700", | 4 | "creator_user_id": "17755db4-395a-4b3b-ac09-e8e3484ca700", | ||
5 | "doi": "10.35097/1166", | 5 | "doi": "10.35097/1166", | ||
6 | "doi_date_published": "2023", | 6 | "doi_date_published": "2023", | ||
7 | "doi_publisher": "", | 7 | "doi_publisher": "", | ||
8 | "doi_status": "True", | 8 | "doi_status": "True", | ||
9 | "groups": [], | 9 | "groups": [], | ||
10 | "id": "d175d0f9-191c-49a8-918b-b31f646451b9", | 10 | "id": "d175d0f9-191c-49a8-918b-b31f646451b9", | ||
11 | "isopen": false, | 11 | "isopen": false, | ||
12 | "license_id": "CC BY-SA 4.0 Attribution-ShareAlike", | 12 | "license_id": "CC BY-SA 4.0 Attribution-ShareAlike", | ||
13 | "license_title": "CC BY-SA 4.0 Attribution-ShareAlike", | 13 | "license_title": "CC BY-SA 4.0 Attribution-ShareAlike", | ||
14 | "metadata_created": "2023-08-04T08:50:11.090676", | 14 | "metadata_created": "2023-08-04T08:50:11.090676", | ||
t | 15 | "metadata_modified": "2023-08-04T08:51:47.984240", | t | 15 | "metadata_modified": "2023-08-04T08:53:18.686271", |
16 | "name": "rdr-doi-10-35097-1166", | 16 | "name": "rdr-doi-10-35097-1166", | ||
17 | "notes": "Abstract: This study aims to identify whether model | 17 | "notes": "Abstract: This study aims to identify whether model | ||
18 | parameters describing atmospheric conditions such as wind shear or | 18 | parameters describing atmospheric conditions such as wind shear or | ||
19 | model parameters related to cloud microphysics such as the fall | 19 | model parameters related to cloud microphysics such as the fall | ||
20 | velocity of hail lead to larger uncertainties in the prediction of | 20 | velocity of hail lead to larger uncertainties in the prediction of | ||
21 | deep convective clouds.\r\nIn an idealized setup of a cloud-resolving | 21 | deep convective clouds.\r\nIn an idealized setup of a cloud-resolving | ||
22 | model including a two-moment microphysics scheme we use the approach | 22 | model including a two-moment microphysics scheme we use the approach | ||
23 | of statistical emulation to allow for a Monte Carlo sampling of the | 23 | of statistical emulation to allow for a Monte Carlo sampling of the | ||
24 | parameter space, which enables a comprehensive sensitivity analysis. | 24 | parameter space, which enables a comprehensive sensitivity analysis. | ||
25 | We analyze the impact of three sets of input parameters (environmental | 25 | We analyze the impact of three sets of input parameters (environmental | ||
26 | conditions, microphysics, combined) on cloud properties (vertically | 26 | conditions, microphysics, combined) on cloud properties (vertically | ||
27 | integrated content of six hydrometeor classes), precipitation, the | 27 | integrated content of six hydrometeor classes), precipitation, the | ||
28 | size distribution of hail and diabatic heating rates. \r\nThis dataset | 28 | size distribution of hail and diabatic heating rates. \r\nThis dataset | ||
29 | contains the processed model output and the generated emulators when | 29 | contains the processed model output and the generated emulators when | ||
30 | the convection is triggered by a warm bubble.\r\nTechnicalRemarks: The | 30 | the convection is triggered by a warm bubble.\r\nTechnicalRemarks: The | ||
31 | csv-files contain the processed model output (spatio-temporal means or | 31 | csv-files contain the processed model output (spatio-temporal means or | ||
32 | maximum values) for output parameters of interest. This dataset was | 32 | maximum values) for output parameters of interest. This dataset was | ||
33 | used to train the emulators which are also included as R workspaces. | 33 | used to train the emulators which are also included as R workspaces. | ||
34 | The R package \"Sensitivity\" is necessary to perform sensitivity | 34 | The R package \"Sensitivity\" is necessary to perform sensitivity | ||
35 | analyses using the emulators.", | 35 | analyses using the emulators.", | ||
36 | "num_resources": 0, | 36 | "num_resources": 0, | ||
37 | "num_tags": 0, | 37 | "num_tags": 0, | ||
38 | "orcid": "", | 38 | "orcid": "", | ||
39 | "organization": { | 39 | "organization": { | ||
40 | "approval_status": "approved", | 40 | "approval_status": "approved", | ||
41 | "created": "2023-01-12T13:30:23.238233", | 41 | "created": "2023-01-12T13:30:23.238233", | ||
42 | "description": "RADAR (Research Data Repository) is a | 42 | "description": "RADAR (Research Data Repository) is a | ||
43 | cross-disciplinary repository for archiving and publishing research | 43 | cross-disciplinary repository for archiving and publishing research | ||
44 | data from completed scientific studies and projects. The focus is on | 44 | data from completed scientific studies and projects. The focus is on | ||
45 | research data from subjects that do not yet have their own | 45 | research data from subjects that do not yet have their own | ||
46 | discipline-specific infrastructures for research data management. ", | 46 | discipline-specific infrastructures for research data management. ", | ||
47 | "id": "013c89a9-383c-4200-8baa-0f78bf1d91f9", | 47 | "id": "013c89a9-383c-4200-8baa-0f78bf1d91f9", | ||
48 | "image_url": "radar-logo.svg", | 48 | "image_url": "radar-logo.svg", | ||
49 | "is_organization": true, | 49 | "is_organization": true, | ||
50 | "name": "radar", | 50 | "name": "radar", | ||
51 | "state": "active", | 51 | "state": "active", | ||
52 | "title": "RADAR", | 52 | "title": "RADAR", | ||
53 | "type": "organization" | 53 | "type": "organization" | ||
54 | }, | 54 | }, | ||
55 | "owner_org": "013c89a9-383c-4200-8baa-0f78bf1d91f9", | 55 | "owner_org": "013c89a9-383c-4200-8baa-0f78bf1d91f9", | ||
56 | "private": false, | 56 | "private": false, | ||
57 | "production_year": "2019", | 57 | "production_year": "2019", | ||
58 | "publication_year": "2023", | 58 | "publication_year": "2023", | ||
59 | "publishers": [ | 59 | "publishers": [ | ||
60 | { | 60 | { | ||
61 | "publisher": "Karlsruhe Institute of Technology" | 61 | "publisher": "Karlsruhe Institute of Technology" | ||
62 | } | 62 | } | ||
63 | ], | 63 | ], | ||
64 | "relationships_as_object": [], | 64 | "relationships_as_object": [], | ||
65 | "relationships_as_subject": [], | 65 | "relationships_as_subject": [], | ||
66 | "repository_name": "RADAR (Research Data Repository)", | 66 | "repository_name": "RADAR (Research Data Repository)", | ||
67 | "resources": [], | 67 | "resources": [], | ||
68 | "services_used_list": "", | 68 | "services_used_list": "", | ||
69 | "source_metadata_created": "2023", | 69 | "source_metadata_created": "2023", | ||
70 | "source_metadata_modified": "", | 70 | "source_metadata_modified": "", | ||
71 | "state": "active", | 71 | "state": "active", | ||
72 | "subject_areas": [ | 72 | "subject_areas": [ | ||
73 | { | 73 | { | ||
74 | "subject_area_additional": "", | 74 | "subject_area_additional": "", | ||
75 | "subject_area_name": "Geological Science" | 75 | "subject_area_name": "Geological Science" | ||
76 | } | 76 | } | ||
77 | ], | 77 | ], | ||
78 | "tags": [], | 78 | "tags": [], | ||
79 | "title": "Training data and emulators for the analysis of | 79 | "title": "Training data and emulators for the analysis of | ||
80 | sensitivity of deep convective clouds and hail to environmental | 80 | sensitivity of deep convective clouds and hail to environmental | ||
81 | conditions and microphysics", | 81 | conditions and microphysics", | ||
82 | "type": "vdataset", | 82 | "type": "vdataset", | ||
83 | "url": "https://doi.org/10.35097/1166" | 83 | "url": "https://doi.org/10.35097/1166" | ||
84 | } | 84 | } |