Changes

On March 16, 2022 at 3:21:14 PM UTC, Administrator:

Set author of Wind-Topo_model to [{"affiliation": "CRYOS, ENAC, EPFL", "affiliation_02": "SLF / WSL", "affiliation_03": "", "data_credit": ["collection", "validation", "curation", "software", "publication", "supervision"], "email": "jerome.dujardin@slf.ch", "given_name": "J\u00e9r\u00f4me", "identifier": "0000-0001-5404-7734", "name": "Dujardin"}, {"affiliation": "CRYOS, ENAC, EPFL", "affiliation_02": "SLF / WSL", "affiliation_03": "EPFL", "data_credit": ["publication", "supervision"], "email": "lehning@slf.ch", "given_name": "Michael", "identifier": "0000-0002-8442-0875", "name": "Lehning"}] (previously [{"affiliation": "CRYOS, ENAC, EPFL", "affiliation_02": "SLF / WSL", "affiliation_03": "", "email": "jerome.dujardin@slf.ch", "given_name": "J\u00e9r\u00f4me", "identifier": "0000-0001-5404-7734", "name": "Dujardin"}, {"affiliation": "CRYOS, ENAC, EPFL", "affiliation_02": "SLF / WSL", "affiliation_03": "EPFL", "email": "lehning@slf.ch", "given_name": "Michael", "identifier": "0000-0002-8442-0875", "name": "Lehning"}])

Updated description of Wind-Topo_model from
Architecture of Wind-Topo and its optimized parameters, as well as a python script to downscale uniform wind fields with a prescribed vertical profile for any given 53-meter DEM. Accompanies the publication "Wind-Topo: Downscaling near-surface wind fields to high-resolution topography in highly complex terrain with deep learning" published in the Quarterly Journal of the Royal Meteorological Society, 2022.
to
Wind-Topo is a statistical downscaling model for near surface wind fields especially suited for highly complex terrain. It is based on deep learning and was trained (calibrated) using the hourly wind speed and direction from 261 automatic measurement stations (IMIS and SwissMetNet) located in Switzerland. The periods 1st October 2015 to 1st October 2016 and 1st October 2017 to 1st October 2018 were used for training. The model was validated using 60 other stations on the period 1st October 2016 to 1st October 2017. Wind-Topo was trained using COSMO-1 data and a 53-meter Digital Elevation Model as input. This dataset provides all the necessary code to understand, use and incorporate Wind-Topo in a new downscaling scheme. Specifically, the dataset contains the architecture of Wind-Topo and its optimized parameters, as well as a python script to downscale uniform wind fields with a prescribed vertical profile for any given 53-meter DEM. Accompanies the publication "Wind-Topo: Downscaling near-surface wind fields to high-resolution topography in highly complex terrain with deep learning" Dujardin and Lehning, Quarterly Journal of the Royal Meteorological Society, 2022. https://doi.org/10.1002/qj.4265 Please cite this publication if you use Wind-Topo or derive new models from it. The code can be used under the GNU Affero General Public License (AGPL).

Added tag MACHINE LEARNING to Wind-Topo_model

Changed the license of Wind-Topo_model to Other (Specified in the description) (previously Creative Commons Zero - No Rights Reserved (CC0 1.0))

Changed the version of Wind-Topo_model to 0.1.0 (previously 1.0)

Changed value of field related_publications to * "Wind-Topo: Downscaling near-surface wind fields to high-resolution topography in highly complex terrain with deep learning" Dujardin and Lehning, Quarterly Journal of the Royal Meteorological Society, 2022. https://doi.org/10.1002/qj.4265 in Wind-Topo_model

Changed value of field date to [{"date": "2022-03-14", "date_type": "created", "end_date": ""}] in Wind-Topo_model

Changed value of field related_datasets to * Wind-Topo is an ongoing development. New versions can be found at: https://gitlabext.wsl.ch/dujardin/wind-topo * The model and its performance are described in: "Wind-Topo: Downscaling near-surface wind fields to high-resolution topography in highly complex terrain with deep learning" Dujardin and Lehning, Quarterly Journal of the Royal Meteorological Society, 2022. https://doi.org/10.1002/qj.4265 Please cite this publication if you use Wind-Topo or derive new models from it. in Wind-Topo_model

Changed value of field resource_type_general to software in Wind-Topo_model

Changed value of field publication_state to approved in Wind-Topo_model

Changed value of field resource_type to software in Wind-Topo_model

Renamed resource /dataset/d004814d-4376-4e5c-8588-396ddf246669/resource/b7eb1ae7-9e0d-4036-b108-fd7b515f1ea6?activity_id=bad1716c-93af-47cb-af2f-7791e07124e1 to Wind-Topo_v0.1.0 in Wind-Topo_model

Set format of resource Wind-Topo_v0.1.0 to .zip in Wind-Topo_model

Updated description of resource Wind-Topo_v0.1.0 in Wind-Topo_model to
Contains: all codes, installation procedure, technical documentation, an example of Digital Elevation Model of the Swiss Alps, the expected outputs of the code (downscaled wind fields).

Uploaded a new file to resource Wind-Topo_v0.1.0 in Wind-Topo_model

Changed value of field url_type to upload in resource Wind-Topo_v0.1.0 in Wind-Topo_model

              
    
          
          
        
        
            f 1 { f 1 {
            2   "author": "[{\"affiliation\": \"CRYOS, ENAC, EPFL\",  2   "author": "[{\"affiliation\": \"CRYOS, ENAC, EPFL\", 
            3 \"affiliation_02\": \"SLF / WSL\", \"affiliation_03\": \"\",  3 \"affiliation_02\": \"SLF / WSL\", \"affiliation_03\": \"\", 
            n 4 \"email\": \"jerome.dujardin@slf.ch\", \"given_name\":  n 4 \"data_credit\": [\"collection\", \"validation\", \"curation\", 
            5 \"J\\u00e9r\\u00f4me\", \"identifier\": \"0000-0001-5404-7734\",  5 \"software\", \"publication\", \"supervision\"], \"email\": 
            6 \"name\": \"Dujardin\"}, {\"affiliation\": \"CRYOS, ENAC, EPFL\",  6 \"jerome.dujardin@slf.ch\", \"given_name\": \"J\\u00e9r\\u00f4me\", 
            7 \"affiliation_02\": \"SLF / WSL\", \"affiliation_03\": \"EPFL\",  
            8 \"email\": \"lehning@slf.ch\", \"given_name\": \"Michael\",  
            9 \"identifier\": \"0000-0002-8442-0875\", \"name\": \"Lehning\"}]", 7 \"identifier\": \"0000-0001-5404-7734\", \"name\": \"Dujardin\"}, 
            8 {\"affiliation\": \"CRYOS, ENAC, EPFL\", \"affiliation_02\": \"SLF / 
            9 WSL\", \"affiliation_03\": \"EPFL\", \"data_credit\": 
            10 [\"publication\", \"supervision\"], \"email\": \"lehning@slf.ch\", 
            11 \"given_name\": \"Michael\", \"identifier\": \"0000-0002-8442-0875\", 
            12 \"name\": \"Lehning\"}]",
            10   "author_email": null, 13   "author_email": null,
            11   "creator_user_id": "16379126-7832-4ddf-b7b2-4e1f408d532e", 14   "creator_user_id": "16379126-7832-4ddf-b7b2-4e1f408d532e",
            n 12   "date": "[{\"date\": \"2020-01-01\", \"date_type\": \"collected\",  n 15   "date": "[{\"date\": \"2022-03-14\", \"date_type\": \"created\", 
            13 \"end_date\": \"\"}]", 16 \"end_date\": \"\"}]",
            14   "doi": "10.16904/envidat.301", 17   "doi": "10.16904/envidat.301",
            15   "funding": "[{\"grant_number\": \"\", \"institution\": \"SNF\",  18   "funding": "[{\"grant_number\": \"\", \"institution\": \"SNF\", 
            16 \"institution_url\": \"\"}, {\"grant_number\": \"\", \"institution\":  19 \"institution_url\": \"\"}, {\"grant_number\": \"\", \"institution\": 
            17 \"SFOE\", \"institution_url\": \"\"}, {\"grant_number\": \"\",  20 \"SFOE\", \"institution_url\": \"\"}, {\"grant_number\": \"\", 
            18 \"institution\": \"Innosuisse\", \"institution_url\": \"\"}]", 21 \"institution\": \"Innosuisse\", \"institution_url\": \"\"}]",
            19   "groups": [], 22   "groups": [],
            20   "id": "d004814d-4376-4e5c-8588-396ddf246669", 23   "id": "d004814d-4376-4e5c-8588-396ddf246669",
            n 21   "isopen": true, n 24   "isopen": false,
            22   "language": "en", 25   "language": "en",
            n 23   "license_id": "CC0-1.0", n 26   "license_id": "other-undefined",
            24   "license_title": "Creative Commons Zero - No Rights Reserved (CC0  27   "license_title": "Other (Specified in the description)",
            25 1.0)", 
            26   "license_url": "https://creativecommons.org/publicdomain/zero/1.0/", 
            27   "maintainer": "{\"affiliation\": \"CRYOS, ENAC, EPFL\", \"email\":  28   "maintainer": "{\"affiliation\": \"CRYOS, ENAC, EPFL\", \"email\": 
            28 \"jerome.dujardin@slf.ch\", \"given_name\": \"J\u00e9r\u00f4me\",  29 \"jerome.dujardin@slf.ch\", \"given_name\": \"J\u00e9r\u00f4me\", 
            29 \"identifier\": \"0000-0001-5404-7734\", \"name\": \"Dujardin\"}", 30 \"identifier\": \"0000-0001-5404-7734\", \"name\": \"Dujardin\"}",
            30   "maintainer_email": null, 31   "maintainer_email": null,
            31   "metadata_created": "2022-01-07T11:24:01.534922", 32   "metadata_created": "2022-01-07T11:24:01.534922",
            n 32   "metadata_modified": "2022-03-02T16:06:23.292862", n 33   "metadata_modified": "2022-03-16T15:21:14.779671",
            33   "name": "wind-topo_model", 34   "name": "wind-topo_model",
            n n 35   "notes": "Wind-Topo is a statistical downscaling model for near 
            36 surface wind fields especially suited for highly complex 
            37 terrain.\r\n\r\nIt is based on deep learning and was trained 
            38 (calibrated) using the hourly wind speed and direction from 261 
            39 automatic measurement stations (IMIS and SwissMetNet) located in 
            40 Switzerland. The periods 1st October 2015 to 1st October 2016 and 1st 
            41 October 2017 to 1st October 2018 were used for training. The model was 
            42 validated using 60 other stations on the period 1st October 2016 to 
            43 1st October 2017. Wind-Topo was trained using COSMO-1 data and a 
            44 53-meter Digital Elevation Model as input.\r\n\r\nThis dataset 
            45 provides all the necessary code to understand, use and incorporate 
            46 Wind-Topo in a new downscaling scheme. Specifically, the dataset 
            34   "notes": "Architecture of Wind-Topo and its optimized parameters, as  47 contains the architecture of Wind-Topo and its optimized parameters, 
            35 well as a python script to downscale uniform wind fields with a  48 as well as a python script to downscale uniform wind fields with a 
            36 prescribed vertical profile for any given 53-meter DEM.\r\nAccompanies  49 prescribed vertical profile for any given 53-meter 
            37 the publication \"Wind-Topo: Downscaling near-surface wind fields to  50 DEM.\r\n\r\nAccompanies the publication \"Wind-Topo: Downscaling 
            38 high-resolution topography in highly complex terrain with deep  51 near-surface wind fields to high-resolution topography in highly 
            39 learning\" published in the Quarterly Journal of the Royal  52 complex terrain with deep learning\" Dujardin and Lehning, Quarterly 
            40 Meteorological Society, 2022.", 53 Journal of the Royal Meteorological Society, 2022. 
            54 https://doi.org/10.1002/qj.4265\r\nPlease cite this publication if you 
            55 use Wind-Topo or derive new models from it.\r\nThe code can be used 
            56 under the GNU Affero General Public License (AGPL).",
            41   "num_resources": 1, 57   "num_resources": 1,
            n 42   "num_tags": 7, n 58   "num_tags": 8,
            43   "organization": { 59   "organization": {
            44     "approval_status": "approved", 60     "approval_status": "approved",
            45     "created": "2016-11-17T12:24:20.447699", 61     "created": "2016-11-17T12:24:20.447699",
            46     "description": "CRYOS is the EPFL laboratory of the WSL/SLF - EPFL  62     "description": "CRYOS is the EPFL laboratory of the WSL/SLF - EPFL 
            47 joint appointment for Prof. Michael Lehning. At his WSL side, Prof.  63 joint appointment for Prof. Michael Lehning. At his WSL side, Prof. 
            48 Michael Lehning is head of the research unit \"Snow and Permafrost\"  64 Michael Lehning is head of the research unit \"Snow and Permafrost\" 
            49 at SLF in Davos.\r\n \r\n###General Mission\r\nThe laboratory of  65 at SLF in Davos.\r\n \r\n###General Mission\r\nThe laboratory of 
            50 cryospheric sciences investigates the processes that shape snow and  66 cryospheric sciences investigates the processes that shape snow and 
            51 ice in mountains and polar regions. In particular, snow cover  67 ice in mountains and polar regions. In particular, snow cover 
            52 processes, snow-atmosphere interactions and mountain hydrology are in  68 processes, snow-atmosphere interactions and mountain hydrology are in 
            53 the focus of current research. This includes a strive for deeper  69 the focus of current research. This includes a strive for deeper 
            54 understanding of the complicated mass and energy exchange processes  70 understanding of the complicated mass and energy exchange processes 
            55 within, above and below a snow cover but also predictions of future  71 within, above and below a snow cover but also predictions of future 
            56 snow and ice in mountains and high latitudes. A newer work area is the  72 snow and ice in mountains and high latitudes. A newer work area is the 
            57 risk management and optimization in the field of renewable energy  73 risk management and optimization in the field of renewable energy 
            58 production based on our detailed understanding of water, wind and  74 production based on our detailed understanding of water, wind and 
            59 radiation processes in mountains.\r\n\r\nMore information:  75 radiation processes in mountains.\r\n\r\nMore information: 
            60 http://cryos.epfl.ch/", 76 http://cryos.epfl.ch/",
            61     "id": "b2ef52fe-c56a-4973-8309-24837f3848ed", 77     "id": "b2ef52fe-c56a-4973-8309-24837f3848ed",
            62     "image_url": "2019-03-26-151807.125593CRYOSLogoFinal.jpg", 78     "image_url": "2019-03-26-151807.125593CRYOSLogoFinal.jpg",
            63     "is_organization": true, 79     "is_organization": true,
            64     "name": "cryos", 80     "name": "cryos",
            65     "state": "active", 81     "state": "active",
            66     "title": "CRYOS", 82     "title": "CRYOS",
            67     "type": "organization" 83     "type": "organization"
            68   }, 84   },
            69   "owner_org": "b2ef52fe-c56a-4973-8309-24837f3848ed", 85   "owner_org": "b2ef52fe-c56a-4973-8309-24837f3848ed",
            70   "private": false, 86   "private": false,
            71   "publication": "{\"publication_year\": \"2022\", \"publisher\":  87   "publication": "{\"publication_year\": \"2022\", \"publisher\": 
            72 \"EnviDat\"}", 88 \"EnviDat\"}",
            n 73   "publication_state": "pub_pending", n 89   "publication_state": "approved",
            74   "related_datasets": "Wind-Topo is an ongoing development. New  90   "related_datasets": " * Wind-Topo is an ongoing development. New 
            75 versions can be found  91 versions can be found 
            n 76 at:\r\nhttps://gitlabext.wsl.ch/dujardin/wind-topo", n 92 at:\r\nhttps://gitlabext.wsl.ch/dujardin/wind-topo\r\n\r\n * The model 
            77   "related_publications": "", 93 and its performance are described in:\r\n\"Wind-Topo: Downscaling 
            94 near-surface wind fields to high-resolution topography in highly 
            95 complex terrain with deep learning\" Dujardin and Lehning, Quarterly 
            96 Journal of the Royal Meteorological Society, 2022. 
            97 https://doi.org/10.1002/qj.4265\r\nPlease cite this publication if you 
            98 use Wind-Topo or derive new models from it.",
            99   "related_publications": " * \"Wind-Topo: Downscaling near-surface 
            100 wind fields to high-resolution topography in highly complex terrain 
            101 with deep learning\" Dujardin and Lehning, Quarterly Journal of the 
            102 Royal Meteorological Society, 2022. https://doi.org/10.1002/qj.4265",
            78   "relationships_as_object": [], 103   "relationships_as_object": [],
            79   "relationships_as_subject": [], 104   "relationships_as_subject": [],
            n 80   "resource_type": "dataset", n 105   "resource_type": "software",
            81   "resource_type_general": "dataset", 106   "resource_type_general": "software",
            82   "resources": [ 107   "resources": [
            83     { 108     {
            84       "cache_last_updated": null, 109       "cache_last_updated": null,
            85       "cache_url": null, 110       "cache_url": null,
            86       "created": "2022-01-07T11:24:06.974225", 111       "created": "2022-01-07T11:24:06.974225",
            n 87       "description": "", n 112       "description": "Contains: all codes, installation procedure, 
            113 technical documentation, an example of Digital Elevation Model of the 
            114 Swiss Alps, the expected outputs of the code (downscaled wind 
            115 fields).",
            88       "doi": "", 116       "doi": "",
            n 89       "format": "", n 117       "format": ".zip",
            90       "hash": "", 118       "hash": "",
            91       "id": "b7eb1ae7-9e0d-4036-b108-fd7b515f1ea6", 119       "id": "b7eb1ae7-9e0d-4036-b108-fd7b515f1ea6",
            n 92       "last_modified": null, n 120       "last_modified": "2022-03-16T15:42:32.497435",
            93       "metadata_modified": "2022-01-07T11:24:06.957079", 121       "metadata_modified": "2022-03-16T14:44:34.770509",
            94       "mimetype": null, 122       "mimetype": null,
            95       "mimetype_inner": null, 123       "mimetype_inner": null,
            n 96       "name": "", n 124       "name": "Wind-Topo_v0.1.0",
            97       "package_id": "d004814d-4376-4e5c-8588-396ddf246669", 125       "package_id": "d004814d-4376-4e5c-8588-396ddf246669",
            98       "position": 0, 126       "position": 0,
            99       "publication_state": "", 127       "publication_state": "",
            100       "resource_size": "{\"size_units\": \"kb\", \"size_value\":  128       "resource_size": "{\"size_units\": \"kb\", \"size_value\": 
            101 \"\"}", 129 \"\"}",
            102       "resource_type": null, 130       "resource_type": null,
            103       "restricted": "{\"allowed_users\": \"\", \"level\": \"public\",  131       "restricted": "{\"allowed_users\": \"\", \"level\": \"public\", 
            104 \"shared_secret\": \"\"}", 132 \"shared_secret\": \"\"}",
            n 105       "size": null, n 133       "size": 447661935,
            106       "state": "active", 134       "state": "active",
            n 107       "url": "", n 135       "url": 
            136 e/b7eb1ae7-9e0d-4036-b108-fd7b515f1ea6/download/wind-topo_v0.1.0.zip",
            108       "url_type": null 137       "url_type": "upload"
            109     } 138     }
            110   ], 139   ],
            111   "spatial":  140   "spatial": 
            112 80838],[10.49203,47.80838],[10.49203,45.81802],[5.95587,45.81802]]]}", 141 80838],[10.49203,47.80838],[10.49203,45.81802],[5.95587,45.81802]]]}",
            113   "spatial_info": "Switzerland", 142   "spatial_info": "Switzerland",
            114   "state": "active", 143   "state": "active",
            115   "subtitle": "", 144   "subtitle": "",
            116   "tags": [ 145   "tags": [
            117     { 146     {
            118       "display_name": "COMPLEX TERRAIN", 147       "display_name": "COMPLEX TERRAIN",
            119       "id": "4da4b8e9-b59c-4152-8ba6-25ae11657f61", 148       "id": "4da4b8e9-b59c-4152-8ba6-25ae11657f61",
            120       "name": "COMPLEX TERRAIN", 149       "name": "COMPLEX TERRAIN",
            121       "state": "active", 150       "state": "active",
            122       "vocabulary_id": null 151       "vocabulary_id": null
            123     }, 152     },
            124     { 153     {
            125       "display_name": "CONVOLUTIONAL NEURAL NETWORKS", 154       "display_name": "CONVOLUTIONAL NEURAL NETWORKS",
            126       "id": "4c408460-fb63-494c-bfec-acc86e5d1ebc", 155       "id": "4c408460-fb63-494c-bfec-acc86e5d1ebc",
            127       "name": "CONVOLUTIONAL NEURAL NETWORKS", 156       "name": "CONVOLUTIONAL NEURAL NETWORKS",
            128       "state": "active", 157       "state": "active",
            129       "vocabulary_id": null 158       "vocabulary_id": null
            130     }, 159     },
            131     { 160     {
            132       "display_name": "DOWNSCALING", 161       "display_name": "DOWNSCALING",
            133       "id": "469b794e-38f0-4a97-879a-b12d8253a221", 162       "id": "469b794e-38f0-4a97-879a-b12d8253a221",
            134       "name": "DOWNSCALING", 163       "name": "DOWNSCALING",
            135       "state": "active", 164       "state": "active",
            136       "vocabulary_id": null 165       "vocabulary_id": null
            137     }, 166     },
            138     { 167     {
            139       "display_name": "HIGH RESOLUTION", 168       "display_name": "HIGH RESOLUTION",
            140       "id": "485950e8-f269-4960-bbf6-7ae9ad756bcc", 169       "id": "485950e8-f269-4960-bbf6-7ae9ad756bcc",
            141       "name": "HIGH RESOLUTION", 170       "name": "HIGH RESOLUTION",
            142       "state": "active", 171       "state": "active",
            143       "vocabulary_id": null 172       "vocabulary_id": null
            144     }, 173     },
            145     { 174     {
            n n 175       "display_name": "MACHINE LEARNING",
            176       "id": "66c2084e-b683-47a2-878c-48f33c6e2a78",
            177       "name": "MACHINE LEARNING",
            178       "state": "active",
            179       "vocabulary_id": null
            180     },
            181     {
            146       "display_name": "METEOROLOGY", 182       "display_name": "METEOROLOGY",
            147       "id": "1cde4b46-1fd4-4227-b910-ce97d87f99ad", 183       "id": "1cde4b46-1fd4-4227-b910-ce97d87f99ad",
            148       "name": "METEOROLOGY", 184       "name": "METEOROLOGY",
            149       "state": "active", 185       "state": "active",
            150       "vocabulary_id": null 186       "vocabulary_id": null
            151     }, 187     },
            152     { 188     {
            153       "display_name": "SURFACE WINDS", 189       "display_name": "SURFACE WINDS",
            154       "id": "c44e52a7-1709-4f25-928d-97c95f5df31e", 190       "id": "c44e52a7-1709-4f25-928d-97c95f5df31e",
            155       "name": "SURFACE WINDS", 191       "name": "SURFACE WINDS",
            156       "state": "active", 192       "state": "active",
            157       "vocabulary_id": null 193       "vocabulary_id": null
            158     }, 194     },
            159     { 195     {
            160       "display_name": "TOPOGRAPHY", 196       "display_name": "TOPOGRAPHY",
            161       "id": "00a7c594-8c2a-4b5c-9ed9-0ff13cbb6d32", 197       "id": "00a7c594-8c2a-4b5c-9ed9-0ff13cbb6d32",
            162       "name": "TOPOGRAPHY", 198       "name": "TOPOGRAPHY",
            163       "state": "active", 199       "state": "active",
            164       "vocabulary_id": null 200       "vocabulary_id": null
            165     } 201     }
            166   ], 202   ],
            167   "title": "Wind-Topo_model", 203   "title": "Wind-Topo_model",
            168   "type": "dataset", 204   "type": "dataset",
            169   "url": null, 205   "url": null,
            t 170   "version": "1.0" t 206   "version": "0.1.0"
            171 } 207 }