A machine-learning-based marine atmosphere boundary layer (MABL) moisture profile retrieval product from GNSS-RO deep refraction signals

<p>Marine atmosphere boundary layer (MABL) water vapor amount and gradient impact global energy transport through directly affecting the sensible and latent heat exchange between the ocean and atmosphere. Yet, it is a well-known challenge for satellite remote sensing to profile MABL water vapor, especially when cloud or a sharp vertical gradient of water vapor is present. <span class="cit" id="xref_text.1"><a href="#bib1.bibx36">Wu et al.</a> (<a href="#bib1.bibx36">2022</a>)</span> identified good correlations between the Global Navigation Satellite System (GNSS) deep refraction signal-to-noise-ratio (SNR) value and the global MABL water vapor specific humidity when the radio occultation (RO) signal is ducted by the moist planetary boundary layer (PBL), and they laid out the underlying physical mechanisms to explain such a correlation. In this work, we apply a machine learning/artificial intelligence (ML/AI) technique to demonstrate the feasibility of profile-by-profile MABL water vapor retrieval using the SNR signal. Three convolutional neural network (CNN) models are trained using multi-months of global collocated hourly ERA-5 reanalysis and COSMIC-1, Metop-A, and Metop-B 1 Hz SNR observations between 975–850 hPa with 25 hPa vertical resolution. The COSMIC-1 ML model is then applied to both COSMIC-1 and COSMIC-2 in other time ranges for independent retrieval and validation. The Monte Carlo Dropout method was employed for the uncertainty estimation. Comparison against multiple field campaign radiosonde/dropsonde observations globally suggests that SNR-ML-method-retrieved water vapor consistently outperforms the wetPrf/wetPf2 standard retrieval product at all six pressure levels between 975 and 850 hPa and either outperforms or achieves similar performance against ERA-5, indicating real and useful information is gained from the SNR signal, though training was performed against the reanalysis. The climatology and diurnal cycle of MABL structure constructed from the SNR-ML technique are studied and compared to the reanalysis. Disparities of climatology suggest ERA-5 may systematically produce dry biases at high latitudes and wet biases in marine stratocumulus regions. The diurnal cycle amplitudes are too weak and sometimes off phase in ERA-5, especially in the Arctic and stratocumulus regions. These areas are particularly prone to PBL processes, where this GNSS SNR-ML water vapor product may contribute the most.</p>