Rainfall estimation from infrared data using an improved version of the auto-estimator technique

In order to apply a suitable procedure for now-casting application in the case of heavy precipitation events, we discuss possible improvements to the Auto-Estimator Technique (AET). The AET rain estimation technique identifies precipitating clouds by comparing two successive infrared satellite images. If the pixel in the second image is colder, then upward motion is assumed and the convective system is considered to be a precipitating cloud. If the pixel in the second image is warmer, then the convective system is likely to be in a weakening phase and no precipitation is assigned. In the case of horizontal movement of the convective system, for instance approaching and weakening, the pixel in the second image can be colder without any upward motion or rain. We propose an improvement of AET (IAET) that determines the cloud top temperature variation and the corresponding rain rate estimation, taking into account the effect of horizontal motion of clouds, obtained by optical flow techniques. Precipitation events over Sardinia during November 1999, May 2000 and June 2000 were analysed. Three-hour cumulated precipitation on one pixel size area were determined by infrared Meteosat data, using both AET and IAET. A comparison between satellite estimation and 49 corresponding ground data stations was made. Contingency tables and relative statistical indices (Bias, False Alarm Ratio, Probability of Detection, Hit Rate, Critical Success Index), and errors on 3 h precipitation (mean absolute error (MAE) and Bias) were computed. When no horizontal motion of the convective system is present, AET and IAET have the same performances. In the events of May 2000, with fast movement of the precipitation system, AET significantly overestimates the rain rate while IAET gives much better performances.

@Article{BMG04a,
  author       = {Boi, P. and Marrocu, M. and Giachetti, A.},
  title        = {Rainfall estimation from infrared data using an improved version of the auto-estimator technique},
  journal      = {INT. J. REMOTE SENSING,},
  number       = {21},
  volume       = {25},
  pages        = {4657 4673},
  month        = {november},
  year         = {2004},
  note         = {idxproject: PON COSOT},
  keywords     = {Data assimilation, remote sensing},
  url          = {https://publications.crs4.it/pubdocs/2004/BMG04a},
}