Summary and Acknowledgements

The SPhR algorithm combines NWP data with satellite observations in order to provide information on convective environmental parameters: total and layer moisture content and atmospheric instability. These are important parameters when studying the potential for later deep convection, or in predicting the further development of already existing convective clouds.

As the SPhR product is retrieved from geostationary satellite data,

  • Its temporal resolution is excellent: 15 or 5 minutes.
  • Its spatial resolution is comparable to or better than that of most NWP data. The default resolution is 9 x 9 km, and at best it reaches 3 x 3 km (both resolutions are valid at nadir).
  • It can only slightly improve NWP humidity profiles. However, this slight improvement might be useful and sometimes even provides the crucial missing piece of information.
  • It can improve the shape of some mesoscale features, such as the exact location of a moisture boundary or local moisture gradient.
  • The retrieval is possible only in cloud-free areas. Undetected clouds cause mistakes.

SPhR products could be used to validate NWP fields by checking for shifts of moisture boundaries or significant differences in values of precipitable water and instability indices.

The accuracy of SPhR products depends on the temporal and spatial resolution of the NWP data. Users are advised to use the best available resolution. This product tutorial is based on the 2012 version of the SAFNWC/MSG program package. The version 2013 (which can uses hybrid level NWP data as well) may provide better SPhR products.

The quality of the cloud mask is very important, as undetected clouds cause mistakes in the retrieval. Unfortunately, certain cloud types (e.g. water clouds smaller than pixel size or thin cirrus clouds) are particularly difficult to detect. An improvement in thin cirrus cloud detection is expected in the 2015 version of the SAFNWC/MSG program package. The Meteosat Third Generation (MTG) satellite's imager instrument (FCI) should make cloud detection more reliable. The spatial resolution will increase and there will be a new channel (NIR1.3) intended specifically for improving thin cirrus detection.

The FCI instrument will have another new channel (VIS0.9) as well, which will provide more information on low-level humidity than the current SEVIRI channels.

MTG satellites will even have an infrared sounder instrument (IRS), which will provide more accurate and reliable temperature and humidity profiles with a good temporal resolution.

 

Acknowledgements

This study was supported by the NWCSAF as a Visiting Scientist Activity. We are grateful to Marianne König from EUMETSAT for her valuable comments, and for improving the text of the product tutorial.