From 1st December 2023 to 30th April 2024, this dataset includes air temperature and snow depth data from the Rovaniemi Railway FMI station and the Sodankyla FMI station. Manual measurements include snow density, hardness, temperature, wetness, and ice crust density at both observation sites. The data was authored by Wenli Wang and is hosted on the Harvard Dataverse platform.
Use Cases
- Model snowpack evolution based on manual snow density and hardness measurements.
- Analyze air temperature and snow depth correlations for winter climate studies.
- Study ice crust formation and properties based on density measurements.
- Compare snow parameter variability between the Rovaniemi and Sodankyla observation sites.
Strengths
- Data covers a specific five-month winter period from December 2023 to April 2024.
- Includes manual measurements of multiple snow parameters like density, hardness, and wetness.
- Provides observations from two distinct Finnish Meteorological Institute (FMI) stations.
Limitations
- Column-level documentation is absent; field semantics must be inferred after download.
- Row count is unknown, which may limit suitability assessment for large-scale modeling.
- Description metadata is limited; actual data quality requires manual inspection after download.
Provenance
- Source
- wang, wenli via Harvard Dataverse
- Collection Method
- Manual measurements at FMI observation stations.
- Time Range
- 2023-12-01 to 2024-04-30
- Freshness
- Last updated 2026-07-14 21:53:32; freshness should be verified.
- Geography
- Rovaniemi and Sodankyla, Finland