Joint EOL/RAL seminar presented by
Robin J. Hogan,
Department of Meteorology, University of Reading, UK
Increasingly in radar remote sensing, large numbers of instruments are being deployed together, and individual instruments may measure many parameters. It is a real challenge to make optimum use of all the information available to get the best estimate of the state of the atmosphere. In this talk I will show how the variational approach is an ideal framework for doing this, and demonstrate two applications: retrieving rain rate and hail from polarization radar, and retrieving cloud profiles from the CloudSat radar, the Calipso lidar and the MODIS radiometer in the A-train of satellites.
Polarization weather radars provide information on the raindrop size distribution necessary to improve rainfall estimates, but operational implementation of retrieval algorithms is hampered by noise in the polarization parameters and the unstable nature of many attenuation-correction schemes. A new variational scheme is described that overcomes all these problems, and by appropriate use of smoothness constraints is tolerant of random errors in Zdr of up to 1 dB. Hail intensity is also retrieved. The second scheme I will describe combines the A-train radar, lidar and radiometers to retrieve the properties of liquid, ice and mixed- phase clouds. The rigorous treatment of observational errors and careful use of additional constraints enables the retrieval to blend smoothly in the vertical between regions where different instruments are sensitive. The efficiency of the method is facilitated by a new ultra-fast lidar multiple-scattering model. The scheme has been tested on ground-based observations from the Cloudnet project, and is shortly to be applied to global A-train data.
Monday, July 17 at 3:30pm in FL2-1022.
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