Precipitation rate at ground by MW cross-track scanners AMSU/MHS

Maps of instantaneous precipitation (mm/hr) generated from MW cross-track scanning radiometers in sun synchronous orbit. Currently processing data from AMSU-A/MHS on board European MetOp and U.S. NOAA satellites

• Coverage: Strips of ~ 2250 km swath crossing the MSG 0 Degree Full Disk area [60°S-75°N lat, 60° W-60° E long]
• Cycle: Up to 8 passes/day (if two Met-Op and two NOAA satellites are available) at approximately 09:30, 01:30, 03:00 ECT (descending mode) and 21:30, 13:30, 15:00 ECT (ascending mode)
• Spatial Resolution: Corresponds to the nominal resolution of MHS, varying with the viewing scan angle from 16 x 16 km2 / circular at nadir to 26 x 52 km2 / elliptical at scan edge
• Accuracy:

  Precipitation range	threshold	target	optimal
  > > 10 mm/h	90	80	25
  > 1-10 mm/h	120	105	50
  < 1 mm/h	240	145	90

  • Accuracy requirements for product P-IN-MHS [RMSE (%)]
• Timeliness: 30 min from observing time
• Dissemination: By dedicated lines to centres connected by GTS - By EUMETCast to most other users, especially scientific
• Formats: BUFR with values on grid points corresponding to the MHS orbital projection. Also JPEG or similar for quick-look

The relationship linking passive microwave brightness temperatures to precipitation has a variable degree of complexity depending on background surface, cloud microphysical and macrophysical structure, environmental conditions, channel frequency, viewing geometry and spatial resolution. In P-IN-MHS this relationship is obtained through the use of Cloud Resolving Model (CRM) simulations, which provide the microphysical structure and the meteorological/environmental parameters needed to fully describe a precipitating cloud system, coupled to a Radiative Transfer Model, for calculating simulated satellite TB vectors consistent with the AMSU-A and MHS PMW radiometers' channel frequencies, viewing angles and view-angle dependent IFOV sizes along the scan projections. A large physically-based database is generated and used in the training phase of an artificial neural network algorithm. Window channels and absorption bands channels are used, exploiting the differential effect of liquid drops or ice particles at different frequencies associated to weighting functions peaking in different atmospheric layers.

Sanò, P., Panegrossi, G., Casella, D., Di Paola, F., Milani, L., Mugnai, A., Petracca, M., Dietrich, S. (2015). The Passive microwave Neural network Precipitation Retrieval (PNPR) algorithm for AMSU/MHS observations: description and application to European case studies. Atmospheric Measurement Techniques, 8(2).