Data Access
There are several options to download TAMSAT rainfall and soil moisture estimates:
Extract a time-series for a given location or an area-average from January 1983 to present. This tool is recommended for users conducting time-series analysis.
Download individual rainfall files (netCDF format at 0.0375° resolution) and accompanying quicklook files (png format). This is useful if you want to look at rainfall estimates for a single event or over a short time-period.
Download individual soil moisture files (netCDF format at 0.25° resolution) and accompanying quicklook files (png format). This is useful if you want to look at agricultural drought conditions at given point in time.
Download individual rainfall files (1983-present) using HTTP file listing. This can be used with tools such as wget to download multiple files.
Download individual soil moisture files (1983-present) using HTTP file listing. This can be used with tools such as wget to download multiple files.
Rainfall estimates for a given time-step and year compressed into a single zip file. This is useful if you need to download multiple files at once. If you need to download the entire archive, we suggest using a download utility such as wget. An example (bash shell) script to download the entire archive can be found here.
Rainfall estimates interpolated to a common grid (0.25°, 0.5° and 1.0°). These regridded files enable users to handle the data with greater ease. Interpolation has been carried out using the robust interpolation package within cf-python.
Additional TAMSAT products
User-relevant drought and excess rainfall metrics derived from TAMSAT rainfall estimates (netCDF and accompanying PNG quicklooks). We have recently begun releasing user-relevant metrics that can support with the monitoring of drought and excess rainfall conditions. We are still developing these products, so the current version may be updated. Eventually, these metrics will be generated for the full TAMSAT record.
Product release notes
For information about new releases, updates or changes relating to the TAMSAT operational data, please refer to the TAMSAT dataset release notes for rainfall and soil moisture.
Known issues
Through user feedback, we document particular issues with the data that we aim to address for the next version of the data. These issues are documented under here. If you encounter any particular issues, please feel free to contact the TAMSAT Group (tamsat@reading.ac.uk) or the TAMSAT operations lead (Ross Maidment; r.i.maidment@reading.ac.uk).
Citing TAMSAT data
If you make use of TAMSAT data, please observe the TAMSAT Data Policy which applies to both rainfall and soil moisture data. In accordance with our data policy, when using TAMSAT data, you are required to cite the following papers:
Rainfall estimates
- Maidment, R. I., D. Grimes, E. Black, E. Tarnavsky, M. Young, H. Greatrex, R. P. Allan et al. (2017). A new, long-term daily satellite-based rainfall dataset for operational monitoring in Africa Nature Scientific Data 4: 170063 DOI:10.1038/sdata.2017.63.
- Tarnavsky, E., D. Grimes, R. Maidment, E. Black, R. Allan, M. Stringer, R. Chadwick, F. Kayitakire (2014). Extension of the TAMSAT Satellite-based Rainfall Monitoring over Africa and from 1983 to present Journal of Applied Meteorology and Climate DOI 10.1175/JAMC-D-14-0016.1
- Maidment, R., D. Grimes, R.P.Allan, E. Tarnavsky, M. Stringer, T. Hewison, R. Roebeling and E. Black (2014). The 30 year TAMSAT African Rainfall Climatology And Time series (TARCAT) data set Journal of Geophysical Research DOI: 10.1002/2014JD021927
Soil moisture estimates
- Pinnington, E., Quaife, T., and Black, E.: (2018) Impact of remotely sensed soil moisture and precipitation on soil moisture prediction in a data assimilation system with the JULES land surface model, Hydrol. Earth Syst. Sci., 22, 2575–2588, DOI:10.5194/hess-22-2575-2018.
- Pinnington, E., Amezcua, J., Cooper, E., Dadson, S., Ellis, R., Peng, J., Robinson, E., Morrison, R., Osborne, S., and Quaife, T. (2021): Improving soil moisture prediction of a high-resolution land surface model by parameterising pedotransfer functions through assimilation of SMAP satellite data, Hydrol. Earth Syst. Sci., 25, 1617–1641, DOI:10.5194/hess-25-1617-2021.