A197-07
IceCube Level 1 Data Calibration and Analysis
Abstract:
In this presentation, the 15-month of calibrated IceCube Level 1 radiance data will be presented. IceCube is an essentially free-running radiometer without onboard calibration targets. It relies on an onboard switching circuitry to calibrate its backend electronics. The end-to-end radiometric calibration is based on periodical space views and the modeled Earth atmospheric clear-sky radiances. Because cloud-induced radiance (Tcir) is the difference between the observed and modeled clear-sky radiances, a small bias in the modeled clear-sky radiances has a minimal impact on Tcir. In this study we will demonstrate an innovative method for IceCube calibration, which uses machine learning/artificial intelligence (ML/AI) to capture the count variations during cold-space observations. For a free-running radiometer, the instrument gain and cold-space background are a strong function of time from power-on, ambient operation temperature, as well as cubesat spin rate. The ML/AL method is able to successfully reproduce the large radiometer count variability and suppress the cold-space measurement noise to ~ 2 counts (1 K in brightness temperature). The gain model is updated on a self-aware timeframe to account for the orbital change and receiver degradation during IceCube’s lifetime. Using collocated CloudSat-IIR-IceCube measurements, an empirical ice water path (IWP) retrieval algorithm is developed, and the first global 883 GHz IWP distribution and statistics will be presented in conjunction with other microwave and infrared measurements of frozen particles.