Abstract
Abstract. A methodology based on quantile regression neural networks (QRNNs) is presented that identifies and corrects the cloud impact on microwave humidity sounder radiances at 183 GHz. This approach estimates the posterior distributions of noise-free clear-sky (NFCS) radiances, providing nearly bias-free estimates of clear-sky radiances with a full posterior error distribution. It is first demonstrated by application to a present sensor, the MicroWave Humidity Sounder 2 (MWHS-2); then the applicability to sub-millimetre (sub-mm) sensors is also analysed. The QRNN results improve upon what operational cloud filtering techniques like a scattering index can achieve but are ultimately imperfect due to limited information content on cirrus impact from traditional microwave channels – the negative departures associated with high cloud impact are successfully corrected, but thin cirrus clouds cannot be fully corrected. In contrast, when sub-mm observations are used, QRNN successfully corrects most cases with cloud impact, with only 2 %–6 % of the cases left partially corrected. The methodology works well even if only one sub-mm channel (325 GHz) is available. When using sub-mm observations, cloud correction usually results in error distributions with a standard deviation less than typical channel noise values. Furthermore, QRNN outputs predicted quantiles for case-specific uncertainty estimates, successfully representing the uncertainty of cloud correction for each observation individually. In comparison to deterministic correction or filtering approaches, the corrected radiances and attendant uncertainty estimates have great potential to be used efficiently in assimilation systems due to being largely unbiased and adding little further uncertainty to the measurements.
Highlights
Satellite observations of humidity inside the troposphere are mainly performed by downward-looking sensors
For MicroWave Humidity Sounder 2 (MWHS-2), multiple experiments using both quantile regression neural networks (QRNNs) configurations are performed. With these we aim to delve into the sensitivity of the method to different input channels: 1. In the first experiment, we examine the performance of QRNN cloud correction with MWHS-2 window channels of 89 and 150 GHz
A methodology based on a quantile regression neural network (QRNN) is used for identifying and correcting the cloud contamination in operational microwave humidity channels
Summary
Satellite observations of humidity inside the troposphere are mainly performed by downward-looking sensors. Among this class of observations, the frequency range around 183 GHz has a special position. Water vapour has a noticeable transition at 22 GHz, but it is relatively weak and only column values can be derived The first transition in the microwave region that can be used to derive altitude information, i.e. At infrared wavelengths, a high number of water vapour transitions are found, including some of high strength. Today, such channels are part of several sensors, such as ATMS (Advanced Technology Microwave Sounder; Weng et al, 2012)
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
