...
| Info | ||||
|---|---|---|---|---|
| ||||
|
History of modifications
| Expand | ||||||||
|---|---|---|---|---|---|---|---|---|
|
Product change log
Dataset version | Date | Description | Changes as compared to previous version |
1.1.0 | 09/07/2025 | Initial public version | - |
Summary
The present document details the methods used in the generation of the In situ Comprehensive Upper-Air Observation Network dataset (CUON, described here).
...
These steps are described hereafter in more detail.
Detailed documentation of algorithm components
Creation of a Common Station Inventory
The processing begins with the construction of a comprehensive station inventory. Observational datasets from a variety of archives are collected, accompanied by metadata and gridded ERA5 reanalysis data to support later stages of quality control and analysis. The following sources provide input to the CUON dataset:
...
There are multiple station configuration files, generated separately for each input dataset and they serve as the foundational reference for all subsequent data handling. Refer to the Appendix in the PUG for the archived list of station inventories per CDS dataset version.
Harmonisation of Observational Data
In the harmonisation step, observational data from all sources are translated into a common data model and format. Data originally provided in various native formats are transformed into netCDF files with a unified structure. Within these files, all observations are systematically organised by date, report identifier, observed variable, and pressure level (/public/harvest/code_cop2/harvest_convert_to_netCDF_yearSplit.py).
...
The resulting files are fully compatible with the Common Data Model.
Merging of Data from Multiple Sources
To ensure consistency and avoid duplication, a merging process is applied whenever multiple sources provide data for the same station. For each overlapping observation, the algorithm selects the record with the most complete vertical structure—i.e., the highest top level and the greatest number of levels (/public/merge/merging_cdm_netCDF_yearSplit_SEP2023.py).
Following the merge step, a single netCDF file remains for each station identifier, containing the most comprehensive data. As with earlier steps, orphan and mobile stations are treated separately, maintaining consistency and accounting for their unique data characteristics.
Enhancement of Variables and Model-Level Coverage
To complete the station records, missing mandatory pressure levels are filled through interpolation. Variables such as humidity and wind components are calculated where they are not directly available, using transformations based on related observed quantities. This step ensures that all key meteorological variables are represented across all time periods and pressure levels (/public/resort/convert_faster_with_recarray_plus_fb_year.py).
...
After the enhancement procedures, individual yearly files are concatenated into continuous, station-wise time series. These comprehensive datasets form the basis for homogenisation procedures. Further details on the derivation and interpolation methods are provided in accompanying Algorithm Theoretical Basis documents.
Homogeneity Adjustments and Uncertainty Estimates
In the final step - if the processing is not done via the near-real-time updating script - homogeneity adjustments are applied to account for temporal inconsistencies in the observational record.
These adjustments are essential to address biases introduced by changes in instrumentation, observation techniques, or station relocations. Dedicated procedures are used for temperature, humidity, and wind, each described in separate ATBDs:
In parallel, uncertainty estimates are calculated using the Desroziers method. This statistical technique allows for objective quantification of observational error characteristics based on innovation statistics.
| Info | ||
|---|---|---|
| ||
This document has been produced in the context of the Copernicus Climate Change Service (C3S). The activities leading to these results have been contracted by the European Centre for Medium-Range Weather Forecasts, operator of C3S on behalf of the European Union (Delegation Agreement signed on 11/11/2014 and Contribution Agreement signed on 22/07/2021). All information in this document is provided "as is" and no guarantee or warranty is given that the information is fit for any particular purpose. The users thereof use the information at their sole risk and liability. For the avoidance of all doubt , the European Commission and the European Centre for Medium - Range Weather Forecasts have no liability in respect of this document, which is merely representing the author's view. |
Related articles
| Content by Label | ||||
|---|---|---|---|---|
|
...