A global webservice to generate SWAT+ hourly and daily input climate files from ERA5 reanalysis data
Bibliographic record
Abstract
The ERA5 reanalysis dataset by the European Centre for Medium-Range Weather Forecasts (ECMWF) represents one of the latest and most accurate, consistent and globally available weather datasets. The raw data comes in hourly resolution, for the period 1950s to present, on a global grid of approx. 30km resolution. While accessing the data from the Climate Data Store by Copernicus is possible from a simple webpage or from scripts, this is a cumbersome process, and significant effort and multiple downloads are required when requesting long time series for multiple climate variables, which is often required when applying a hydrological model such as SWAT+. We developed a new Webservice that now provide an easy way to download and generate input climate files formatted directly for SWAT+ and a lake and reservoir model called GOTM-WET. The service provides a graphical user interface, which makes it easy to select the area and time period of interest. The motivation for creating the service was that the interface provided by ECMWF was quite slow and unpredictable to use in 2019, when we started working on providing SWAT+ modelling globally. We also found that converting units from ECMWF raw data to SWAT+, while not a hard problem, must be done correctly, and the service we developed ensures this. The service has two operating modes, depending on the way data is stored. The first mode accesses the ERA5 data sorted by time. In this mode it is simple to check the available time period, but somewhat slow. The second mode access the ERA5 data stored by location. In this mode it is quite fast to retrieve the wanted data. The SWAT+ input files is retrieved by downloaded a zip file from a link sent in a mail when the files are generated. The service provides data from 1958-2023, which is the full time span of the 5 Tb ERA5 dataset. We demonstrate the use of the free Webservice, and also give an example of how SWAT+ can perform when using ERA5 data.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.072 | 0.060 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".