To compensate radar reflectivity for attenuation effect, a new method for attenuation correction of the radar reflectivity using arbitrary oriented microwave link (referred henceforth to as ACML) is developed and evaluated. Referring to the measurement of arbitrary oriented microwave link, the ACML method optimizes the ratio of specific attenuation to specific differential phase which is a key parameter in attenuation correction schemes. The proposed method was evaluated using real data of a dual-polarization X-band radar and measurements of two microwave links during two rainstorm events. The results showed that the variation range of the optimized ratio was overall consistent with the results of the previous studies. After attenuation correction with the optimal ratios, the radar reflectivity was significantly compensated, especially at long distances. The corrected reflectivity was more intense than the reflectivity corrected by the "self-consistent" (SC) method and closer to the reflectivity of a nearby S-band radar. The effectiveness of the method was also verified by comparing the rain rates estimated by the X-band radar with those derived by rain gauges. It is demonstrated that arbitrary oriented microwave link can be adopted to optimize the attenuation correction of radar reflectivity.
Abstract:In order to monitor nighttime particular matter (PM) air quality in urban area, a back propagation neural network (BP neural network) inversion model is established, using low-light radiation data from the day/night band (DNB) of the Visible Infrared Imaging Radiometer Suite (VIIRS) aboard the Suomi National Polar-orbiting Partnership (S-NPP) satellite. The study focuses on the moonless and cloudless nights in Beijing during March-May 2015. A test is carried out by selecting surface PM 2.5 data from 12 PM 2.5 automatic monitoring stations and the corresponding night city light intensity from DNB. As indicated by the results, the linear correlation coefficient (R) between the results and the corresponding measured surface PM 2.5 concentration is 0.91, and the root-mean-square error (RMSE) is 14.02 µg/m 3 with the average of 59.39 µg/m 3 . Furthermore, the BP neural network model shows better accuracy when air relative humility ranges from 40% to 80% and surface PM 2.5 concentration exceeds 40 µg/m 3 . The study provides a superiority approach for monitoring PM 2.5 air quality from space with visible light remote sensing data at night.
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