Articles | Volume 8, issue 3
Atmos. Meas. Tech., 8, 1217–1232, 2015
https://doi.org/10.5194/amt-8-1217-2015
Atmos. Meas. Tech., 8, 1217–1232, 2015
https://doi.org/10.5194/amt-8-1217-2015

Research article 12 Mar 2015

Research article | 12 Mar 2015

A novel algorithm for detection of precipitation in tropical regions using PMW radiometers

D. Casella et al.

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Cited articles

Barrett, E. C., Kidd, C., and Bailey, J. O.: A new instrument with rainfall monitoring potential, Int. J. Remote Sens., 9, 1943–1950, 1988.
Bennartz, R.: Optimal convolution of amsu-b to amsu-a, J. Atmos. Ocean. Tech., 17, 1215–1225, 2000.
Casella, D., Panegrossi, G., Sano, P., Dietrich, S., Mugnai, A., Smith, E. A., Tripoli, G. J., Formenton, M., Di Paola, F., and Leung, W.-Y.: 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 T. Geosci. Remote, 51, 4650–4671, 2013.
Chen, F. W. and Staelin, D. H.: Airs/amsu/hsb precipitation estimates, IEEE T. Geosci. Remote, 41, 410–417, 2003.
Desbois, M., Roca, R., Eymar L., Viltard, N., Viollier, M., Srinivasan, J., and Narayanan, S.: The Megha-Tropiques mission, in: Atmospheric and Oceanic Processes, Dynamics, and Climate Change, edited by: Sun, Z., Jin, F.-F., and Iwasaki, T., International Society for Optical Engineering, SPIE P., 4899, 172–183, https://doi.org/10.1117/12.466703, 2003
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Short summary
The CCA algorithm is applicable to any modern passive microwave radiometer on board polar orbiting satellites; it has been developed using a data set of co-located SSMIS and TRMM-PR measurements and AMSU-MHS and TRMM-PR measurements. The algorithm shows a small rate of false alarms and superior detection capability and can efficiently detect (POD between 0.55 and 0.71) minimum rain rate varying from 0.14 mm/h (AMSU over ocean) to 0.41 (SSMIS over coast).