Articles | Volume 8, issue 3
https://doi.org/10.5194/amt-8-1217-2015
https://doi.org/10.5194/amt-8-1217-2015
Research article
 | Highlight paper
 | 
12 Mar 2015
Research article | Highlight paper |  | 12 Mar 2015

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

D. Casella, G. Panegrossi, P. Sanò, L. Milani, M. Petracca, and S. Dietrich

Related authors

The High lAtitude sNowfall Detection and Estimation aLgorithm for ATMS (HANDEL-ATMS): a new algorithm for snowfall retrieval at high latitudes
Andrea Camplani, Daniele Casella, Paolo Sanò, and Giulia Panegrossi
Atmos. Meas. Tech., 17, 2195–2217, https://doi.org/10.5194/amt-17-2195-2024,https://doi.org/10.5194/amt-17-2195-2024, 2024
Short summary
The new Passive microwave Neural network Precipitation Retrieval (PNPR) algorithm for the cross-track scanning ATMS radiometer: description and verification study over Europe and Africa using GPM and TRMM spaceborne radars
Paolo Sanò, Giulia Panegrossi, Daniele Casella, Anna C. Marra, Francesco Di Paola, and Stefano Dietrich
Atmos. Meas. Tech., 9, 5441–5460, https://doi.org/10.5194/amt-9-5441-2016,https://doi.org/10.5194/amt-9-5441-2016, 2016
Short summary
Multi-sensor analysis of convective activity in central Italy during the HyMeX SOP 1.1
N. Roberto, E. Adirosi, L. Baldini, D. Casella, S. Dietrich, P. Gatlin, G. Panegrossi, M. Petracca, P. Sanò, and A. Tokay
Atmos. Meas. Tech., 9, 535–552, https://doi.org/10.5194/amt-9-535-2016,https://doi.org/10.5194/amt-9-535-2016, 2016
Short summary
The Passive microwave Neural network Precipitation Retrieval (PNPR) algorithm for AMSU/MHS observations: description and application to European case studies
P. Sanò, G. Panegrossi, D. Casella, F. Di Paola, L. Milani, A. Mugnai, M. Petracca, and S. Dietrich
Atmos. Meas. Tech., 8, 837–857, https://doi.org/10.5194/amt-8-837-2015,https://doi.org/10.5194/amt-8-837-2015, 2015
Analysis of long-term precipitation pattern over Antarctica derived from satellite-borne radar
L. Milani, F. Porcù, D. Casella, S. Dietrich, G. Panegrossi, M. Petracca, and P. Sanò
The Cryosphere Discuss., https://doi.org/10.5194/tcd-9-141-2015,https://doi.org/10.5194/tcd-9-141-2015, 2015
Revised manuscript not accepted
Short summary

Related subject area

Subject: Others (Wind, Precipitation, Temperature, etc.) | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
Determination of low-level temperature profiles from microwave radiometer observations during rain
Andreas Foth, Moritz Lochmann, Pablo Saavedra Garfias, and Heike Kalesse-Los
Atmos. Meas. Tech., 17, 7169–7181, https://doi.org/10.5194/amt-17-7169-2024,https://doi.org/10.5194/amt-17-7169-2024, 2024
Short summary
Aeolus lidar surface return (LSR) at 355 nm as a new Aeolus Level-2A product
Lev D. Labzovskii, Gerd-Jan van Zadelhoff, David P. Donovan, Jos de Kloe, L. Gijsbert Tilstra, Ad Stoffelen, Damien Josset, and Piet Stammes
Atmos. Meas. Tech., 17, 7183–7208, https://doi.org/10.5194/amt-17-7183-2024,https://doi.org/10.5194/amt-17-7183-2024, 2024
Short summary
Sampling the diurnal and annual cycles of the Earth's energy imbalance with constellations of satellite-borne radiometers
Thomas Hocking, Thorsten Mauritsen, and Linda Megner
Atmos. Meas. Tech., 17, 7077–7095, https://doi.org/10.5194/amt-17-7077-2024,https://doi.org/10.5194/amt-17-7077-2024, 2024
Short summary
Retrieval of top-of-atmosphere fluxes from combined EarthCARE lidar, imager, and broadband radiometer observations: the BMA-FLX product
Almudena Velázquez Blázquez, Carlos Domenech, Edward Baudrez, Nicolas Clerbaux, Carla Salas Molar, and Nils Madenach
Atmos. Meas. Tech., 17, 7007–7026, https://doi.org/10.5194/amt-17-7007-2024,https://doi.org/10.5194/amt-17-7007-2024, 2024
Short summary
Analysis of the measurement uncertainty for a 3D wind lidar
Wolf Knöller, Gholamhossein Bagheri, Philipp von Olshausen, and Michael Wilczek
Atmos. Meas. Tech., 17, 6913–6931, https://doi.org/10.5194/amt-17-6913-2024,https://doi.org/10.5194/amt-17-6913-2024, 2024
Short summary

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
Download
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).