In January 2022, ESSIC/CISESS Scientist Hu Yang won a CISESS Seed Grant to develop a low-cost microwave radiometer operating at Ka band (22 GHz) intended for education as well as training materials to support effective use of JPSS microwave products such as TPW from ATMS. Recently, the initial Seeds Grant period ended and Yang reported his results. This Seed Grant also funded the development of the new CISESS Remote Sensing Laboratory.
ESSIC/CISESS Scientists Hu Yang and Jun Dong worked with student interns Samatha Smith, Feng Pei Zhang, Richard Zhou, Chao-Wei Tu and Zhuo-Yu Yang, and with Electrical Engineer Doug Baker, to build a working microwave radiometer. The goal was to let students get hands-on learning of an important satellite instrument. Students learned the skills required for microwave instrument development, including hands-on skills of electronic circuit design and development, software and hardware programming, as well as the instrument performance evaluation and testing.
They developed a dual-mode microwave radiometer and tested it in the Lab and outside. The instrument is designed with center frequency of 22GHz and can be used for atmosphere water vapor observations. The signal is collected by the antenna and turned on and off by the switch. When the switch is on, the detected signal contains background noise and scene signal. When the switch is off, the detected signal contains background noise and a reference signal. We take the difference between the detected signal when the switch is on and when the switch is off to remove the noise and restore the water vapor information. With the support of the 2021 Seed Grant, some details of digital detector based on high-speed (analog to digital converter) ADC and (field-programmable gate array) FPGA have also been investigated.
Recently, the team successfully carried out a field campaign using the Ka-band radiometer. The purpose of this test was to collect the atmosphere down warding brightness temperature of 22GHz for the total column water vapor retrieval by using the Machine learning based algorithm. Three interns, Felix, Matias and Niko helped to carry out the test. See photos of this test below.
