[1] A method is presented for estimating the calibration error affecting L-band ground-based radiometers, using the sky as a cold source. In a first step, the optimum conditions to perform this calibration are limited by removing sky areas where large radiation contributors (Sun and Moon) are present. In the region thus selected, an accurate computation of the brightness temperature measured by the antenna is performed using available sky background temperature survey charts (radio continuum and hydrogen HI line) and integrating the sky temperature over the directional gain pattern of a representative radiometer L-band antenna. Contributions from rear lobe are not considered. After adding the atmospheric contribution, maps of the noise temperature and its error are produced for the region of interest. The best calibration orientation for a sky-looking radiometer at medium northern latitude would be 0°in azimuth (northward) and an elevation equal to the radiometer's latitude. It is found that the computed total sky noise contribution is 6.6 K, with 24 hour variations of ±0.2 K and a maximum bias of ±0.6 K. The results are valid for the whole year, assuming low to moderate solar activity and no rain.
The data acquisition system for a new type of optical disdrometer is presented. As the device must measure sizes and velocities of raindrops as small as .1 mm diameter in real time in the presence of high noise and a variable baseline, algorithm design has been a challenge. The combining of standard signal processing techniques and machine learning methods (in this case, a neural network) has been essential to obtaining good performance.
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