P-IN-SSMIS (H01 new rel)
  • Product images (click on image for animation, frame selection and zoom)
Precipitation rate at ground by MW cross track scanners

Maps of instantaneous precipitation (mm/hr) generated from MW conical scanning radiometers in sun synchronous orbit

  • Coverage: Strips of ~ 1700 km swath crossing the MSG 0 Degree Full Disk area [60°S-75°N lat, 60° W-60° E long]
  • Cycle: Up to six passes/day (if three DMSP satellites are available) at approximately 05:30, 06:30, 08:00 ECT (descending mode)
  • Spatial Resolution: Approximately 13.2 km x 15.5 km (SSMIS 91.6 GHz channel resolution).
  • 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-SSMIS [RMSE (%)]
  • Timeliness: 150 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 SSMIS 91.7 GHz channel orbital projection

Short description of the basic principles for product generation

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-SSMIS 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 SSMIS PMW radiometers' channel frequencies, viewing angles and IFOV sizes. A large physically-based database is generated and used in the a-priori knowledge Bayesian retrieval scheme. The scheme works on all types of background surface conditions, and uses synthetic dynamical and thermodynamical variables derived from ECMWF analysis/forecasts as further constraints in the retrieval process to reduce non-uniquess problem affecting the retrieval solution. 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.

Relevant Publications

Sano, P., Casella, D., Mugnai, A., Schiavon, G., Smith, E. A., & Tripoli, G. J. (2013). Transitioning from CRD to CDRD in Bayesian retrieval of rainfall from satellite passive microwave measurements: Part 1. Algorithm description and testing. IEEE transactions on geoscience and remote sensing, 51(7), 4119-4143. Casella, D., Panegrossi, G., Sano, P., Dietrich, S., Mugnai, A., Smith, E. A., ... & Mehta, A. V. (2013). Transitioning from CRD to CDRD in Bayesian retrieval of rainfall from satellite passive microwave measurements: Part 2. Overcoming database profile selection ambiguity by consideration of meteorological control on microphysics. IEEE transactions on geoscience and remote sensing, 51(9), 4650-4671. Smith, E. A., Leung, H. Y., Elsner, J. B., Mehta, A. V., Tripoli, G. J., Casella, D., ... & Sanò, P. (2013). Transitioning from CRD to CDRD in Bayesian retrieval of rainfall from satellite passive microwave measurements: Part 3-Identification of optimal meteorological tags. Natural Hazards and Earth System Sciences, 13(5), 1185.

Case Studies

Belgium

2012

Belgium

2011

Hungary

2011

Hungary

2011

Italy

2012

Italy

2011

Poland

2013

Poland

2011

Slovakia

2012

User Documents

ATBD

2.1

PUM

2.1

PVR

1.1

Operational Reports