Precipitation rate at ground by MW cross-track scanners (with indication of phase)

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 H-SAF area [25-75°N lat, 25°W-45°E long] in direction approx. S-N or N-S
  • Cycle: Up to six passes/day (if one Met-Op-A 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 / ovate 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 PR-OBS-2 [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

Measurements in both window and absorption bands of AMSU-A and MHS

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 PR-OBS-2 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.