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  "author": "[{\"affiliation\": \"WSL-SLF\", \"affiliation_02\": \"\", 
            
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\"affiliation_03\": \"\", \"email\": \"adam.winstral@slf.ch\", 
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\"affiliation_03\": \"\", \"email\": \"adam.winstral@slf.ch\", 
            
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\"given_name\": \"Adam\", \"identifier\": \"0000-0003-2556-8359\", 
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\"given_name\": \"Adam\", \"identifier\": \"0000-0003-2556-8359\", 
            
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\"name\": \"Winstral\"}]",
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\"name\": \"Winstral\"}]",
            
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  "author_email": null,
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  "date": "[{\"date\": \"2013-10-01\", \"date_type\": \"collected\", 
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  "date": "[{\"date\": \"2013-10-01\", \"date_type\": \"collected\", 
            
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\"end_date\": \"2017-06-30\"}]",
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\"end_date\": \"2017-06-30\"}]",
            
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  "doi": "10.16904/envidat.600",
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  "doi": "10.16904/envidat.600",
            
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  "extras": [
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  "funding": "[{\"grant_number\": \"\", \"institution\": \"Funding 
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  "funding": "[{\"grant_number\": \"\", \"institution\": \"Funding 
            
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information not available.\", \"institution_url\": \"\"}]",
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information not available.\", \"institution_url\": \"\"}]",
            
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  "license_id": "odc-odbl",
            
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  "license_title": "ODbL with Database Contents License (DbCL)",
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  "license_title": "ODbL with Database Contents License (DbCL)",
            
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  "maintainer": "{\"affiliation\": \"WSL-SLF\", \"email\": 
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  "maintainer": "{\"affiliation\": \"WSL-SLF\", \"email\": 
            
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\"adam.winstral@slf.ch\", \"given_name\": \"Adam\", \"identifier\": 
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\"adam.winstral@slf.ch\", \"given_name\": \"Adam\", \"identifier\": 
            
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\"0000-0003-2556-8359\", \"name\": \"Winstral\"}",
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\"0000-0003-2556-8359\", \"name\": \"Winstral\"}",
            
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  "metadata_modified": "2025-03-06T15:14:00.255863",
            
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  "name": 
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  "name": 
            
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al-data-used-to-develop-and-validate-the-bias-detecting-ensemble-bde",
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al-data-used-to-develop-and-validate-the-bias-detecting-ensemble-bde",
            
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  "notes": "These data were used to drive and evaluate Jules 
34
  "notes": "These data were used to drive and evaluate Jules 
            
35
Investigation Model (JIM) snow simulations. The data provided are the 
35
Investigation Model (JIM) snow simulations. The data provided are the 
            
36
forcing data used for the \"deterministic\" runs as described in 
36
forcing data used for the \"deterministic\" runs as described in 
            
37
Winstral et al., 2019. The bias-detecting ensemble (Winstral et al., 
37
Winstral et al., 2019. The bias-detecting ensemble (Winstral et al., 
            
38
2019) used observed snow depths (HS) to detect biases in these 
38
2019) used observed snow depths (HS) to detect biases in these 
            
39
deterministic simulations related to precipitation and energy inputs 
39
deterministic simulations related to precipitation and energy inputs 
            
40
to JIM. Simulations that included the BDE evaluations substantially 
40
to JIM. Simulations that included the BDE evaluations substantially 
            
41
improved JIM simulations.",
41
improved JIM simulations.",
            
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  "organization": {
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  "organization": {
            
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    "approval_status": "approved",
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    "approval_status": "approved",
            
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    "created": "2021-08-23T15:25:48.676190",
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    "created": "2021-08-23T15:25:48.676190",
            
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    "description": "The research group \u00abSnow Hydrology\u00bb 
47
    "description": "The research group \u00abSnow Hydrology\u00bb 
            
48
investigates snow as a component of the hydrological cycle. In the 
48
investigates snow as a component of the hydrological cycle. In the 
            
49
Alps a significant percentage of precipitation comes in the form of 
49
Alps a significant percentage of precipitation comes in the form of 
            
50
snow. The timing of snow melt thus influences the annual dynamics of 
50
snow. The timing of snow melt thus influences the annual dynamics of 
            
51
runoff from alpine watersheds. Of particular interest for our research 
51
runoff from alpine watersheds. Of particular interest for our research 
            
52
is to enhance estimations of snow water resources and subsequent melt 
52
is to enhance estimations of snow water resources and subsequent melt 
            
53
water discharge.\r\n\r\nThe research group covers a broad range of 
53
water discharge.\r\n\r\nThe research group covers a broad range of 
            
54
projects and methods. The latest measuring techniques are used to 
54
projects and methods. The latest measuring techniques are used to 
            
55
investigate snow distribution patterns in alpine terrain, e.g. laser 
55
investigate snow distribution patterns in alpine terrain, e.g. laser 
            
56
scanning or radar technology. We use different types of numerical 
56
scanning or radar technology. We use different types of numerical 
            
57
models to calculate snow water resources based on input data from 
57
models to calculate snow water resources based on input data from 
            
58
meteorological monitoring networks. These models are being used to 
58
meteorological monitoring networks. These models are being used to 
            
59
predict the consequences of climate change on the water balance of 
59
predict the consequences of climate change on the water balance of 
            
60
mountain watersheds. The models also constitute a valuable tool for 
60
mountain watersheds. The models also constitute a valuable tool for 
            
61
our operational services, such as periodic snow hydrological 
61
our operational services, such as periodic snow hydrological 
            
62
bulletins, which contribute to the federal flood prevention and 
62
bulletins, which contribute to the federal flood prevention and 
            
63
forecasting system.\r\n\r\nThe research group \u00abSnow 
63
forecasting system.\r\n\r\nThe research group \u00abSnow 
            
64
Hydrology\u00bb is based in Davos and ensures the link between other 
64
Hydrology\u00bb is based in Davos and ensures the link between other 
            
65
Davosian research groups and the research unit \u201dMountain 
65
Davosian research groups and the research unit \u201dMountain 
            
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Hydrology and Mass Movements\u201d in Birmensdorf.",
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Hydrology and Mass Movements\u201d in Birmensdorf.",
            
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    "state": "active",
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    "title": "Snow Hydrology",
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    "title": "Snow Hydrology",
            
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NWP data to stations and for distributing BDE evaluations at 
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NWP data to stations and for distributing BDE evaluations at 
            
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HS-stations to SWE-stations",
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HS-stations to SWE-stations",
            
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      "description": "All forcing data need to run JIM at HS and SWE 
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      "description": "All forcing data need to run JIM at HS and SWE 
            
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stations. These are data downscaled directly from COSMO NWP data 
146
stations. These are data downscaled directly from COSMO NWP data 
            
147
(WY14, WY15) and further enhanced by additional station observations 
147
(WY14, WY15) and further enhanced by additional station observations 
            
148
of snow depth (HS) and air temperature (Ta) in WY17. Further 
148
of snow depth (HS) and air temperature (Ta) in WY17. Further 
            
149
information can be found in README file. Note that in WY14 and WY15, 
149
information can be found in README file. Note that in WY14 and WY15, 
            
150
shortwave radiation was not split into direct and diffuse (not 
150
shortwave radiation was not split into direct and diffuse (not 
            
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necessary for JIM) and that the default method in JIM for splitting 
151
necessary for JIM) and that the default method in JIM for splitting 
            
152
precipitation phase was used. In WY 17, direct and diffuse were 
152
precipitation phase was used. In WY 17, direct and diffuse were 
            
153
combined as input to JIM and JIM was forced with pre-processed liquid 
153
combined as input to JIM and JIM was forced with pre-processed liquid 
            
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and solid precipitation phases.",
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and solid precipitation phases.",
            
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