Where do you source the data?
To create a consistent product, we need to combine multiple data sources because each source has some of these limitations, or multiple limitations:
- 99% of raw data sources contain only historical data (no forward-looking view).
- other datasets began in the 1980s and stopped 5, 10 or 20 years ago: never updated.
- many sources cover only one country (typically when it’s a national service).
- the majority of open data sources cover just one hazard, for example, only storms, rainfall, or just the wildfire, and nothing else.
- most sources have a coarse time step, for example, certain satellite data are available only once per week, so we combine complementary observations from several satellites.
- almost all official public flood maps aren’t truly digital, they’re just PNG images, not geolocated data in the proper sense.
The primary data sources for the historical period include ERA5-land reanalysis and observations.
ERA5-land weather reanalysis provides reconstructed hourly fields of all weather parameters. It assimilates and integrates weather and satellite observations. This global dataset is at 0.1-degree resolution (~10km) and it is updated several times per month with new weather observations.
For the forward-looking analysis we use three high resolution (HR) global dynamic climate models from CMIP6 run:
- CNRM-CM6-1-HR
- MPI-ESM1-2-LR, and
- EC-Earth3
These are numerical High Resolution global climate models from the most recent CMIP6 run [Seferian et al. 2019].
The CMIP6 abbreviation stands for the Coupled Model Intercomparison Project Phase 6.
A multi-model approach offers more objective and more stable and reliable results while reducing uncertainty.
The raw CMIP6 climate model data can be downloaded from several nodes from the Earth System Grid Federation:
Many alternative raw data sources are not used as ingredients for our risk assessment, but appear to be extremely useful for the calibration of our products, and also for the uncertainty analysis and bias correction. Logically, one can validate the data products only against independent data sources.
| Hazard | Sources |
| River and Pluvial Flood | DEFRA, FEMA, JRC-EU, ESA, NASA, CMIP6, CODEC, catastrophe databases |
| Sea Level Rise, Storm Surge and Coastal Inundation | CMIP6, CODEC, Historical tide-gauges PSMSL, GESLA |
| Wildfire and Burned area | Terra and Aqua MODIS, FEMA, CMIP6, CODEC, catastrophe databases |
| Heat Wave | ECMWF, CMIP6, Synop |
| Drought | MODIS, LandSat, FEMA, CMIP6, SYNOP, catastrophe databases |
| Cold Stress | ECMWF, CMIP6, SYNOP |
| Wind storms, Hurricanes, Snow storms, Sand storms, Thunder and Hail |
FEMA, CMIP6, CODEC, catastrophe databases |
| Extreme Rainfall | Chirps, ECMWF, CMIP6, SYNOP, METAR, WMO, NASA/IMERG |
| Landslides | Global Landslide Catalog (GLC/NASA), TMPA, ECMWF, CMIP6, SYNOP, METAR, WMO, catastrophe databases |
| Change in Temperature Patterns | MODIS, FEMA, CMIP6, CODEC, catastrophe databases |
| Change in Precipitation Patterns | ECMWF, CMIP6, Synop |
High resolution risk rating and probability assessment provided by Weather Trade Net is an in-house commercial data product.
We don't re-distribute raw temperatures, rainfall or wind speed data.
In contrast to the majority of OpenData sources, at WTN all our risk assessment data is quality checked, bias corrected and continuously updated.
Quantile mapping method is used for the bias correction: individually for each calendar month, for each weather parameter and for each geolocation. The reference period for the data bias correction is 1991-2020. Same bias correction is applied to the historical and forward-looking climate model data.
Multi-year and season-specific tendencies are preserved. Trends are unique for each geolocation, each calendar month and each weather parameter.
WTN's blog is another great source of information about the data collection and post-processing.
If you have any questions and suggestions regarding data processing, please don’t hesitate to reach out at contact@weathertrade.net