Radar Modeling Cloud observation, attenuation case 3 radars data fusion Leonid Tolstoy, UMass-UPRM Collaborative Ph.D. Student Sandra Cruz-Pol, Ph.D.

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Presentation transcript:

Radar Modeling Cloud observation, attenuation case 3 radars data fusion Leonid Tolstoy, UMass-UPRM Collaborative Ph.D. Student Sandra Cruz-Pol, Ph.D.

Approach Modeled area observed by radar with parameters: Area: 100x100 pixels Beamwidth: 2 deg, Range gate width: 2 pixels (1 pixel ≈ 1 km) Range Gate

Cloud and air Cloud is modeled as a circle with radius=20 pixels, Air area, Zair=0.1 [1/mm m3] a=0.01/4.34 dB/pixel Cloud is modeled as a circle with radius=20 pixels, Parameters: reflectivity: Zair=0.1 Zcloud=100 attenuation: aair=0.01 Np/pixel acloud=0.3 Np/pixel Cloud area, Z=100, Alpha=0.3/4.34 dB/pixel

Attenuation Total attenuation is calculated as a cumulative sum from the radar position at every point (range, angle)

Reflectivity in Log view Log view (dBZ) of exact (not averaged by beam) reflectivity

Reflectivity (averaged) Radar Averaged data , Z, within a given range-gate (radar view) Log of averaged data, dBZ

Z seen by 3 radars Radars 3 radars on the corners of the square area look to the same cloud from different sides.

ZdB Data fusion: Average, MAX, MIN Averaged data shown Average, MAX, MIN Log of merged data, ZdB, from all 3 radars. Radar

Max merge Log of merged Max data from all 3 radars: at every point we select just max value. Radar

Min Log of merged MIN data from all 3 radars: at every point we select just min value Radar

Conclusions Averaging data from three radars looking at same cloud provides with better estimate than using max or min for data fusion. Need to add 3rd dimension (elevation angle) Run with different cloud/air parameter conditions.