Are elevated moist layers a blind spot for hyperspectral infrared sounders? A model study

• Published in: Atmospheric measurement techniques 2021-11, Vol.14 (11), p.7025-7044
• Main Authors: Prange, Marc, Brath, Manfred, Buehler, Stefan A
• Format: Article
• Language: English
• Subjects: Analysis; Anomalies; Case studies; Clear sky; Clouds; Convective cells; Datasets; Detrainment; Freezing; Freezing level; Heating; Heating rate; Humidity; Infrared instruments; Instruments; Meteorological satellites; Moisture; Passive satellites; Radiative heating; Remote sensing; Retrieval; Satellite-borne instruments; Satellites; Smoothing; Temperature inversion; Temperature inversions; Tropical climate; Tropical environments; Troposphere
• ISSN: 1867-8548; 1867-1381; 1867-8548
• DOI: 10.5194/amt-14-7025-2021

The ability of the hyperspectral satellite-based passive infrared (IR) instrument IASI to resolve elevated moist layers (EMLs) within the free troposphere is investigated. EMLs are strong moisture anomalies with significant impact on the radiative heating rate profile and typically coupled to freezing level detrainment from convective cells in the tropics. A previous case study by Stevens et al. (2017) indicated inherent deficiencies of passive satellite-based remote sensing instruments in resolving an EML. In this work, we first put the findings of Stevens et al. (2017) into the context of other retrieval case studies of EML-like structures, showing that such structures can in principle be retrieved, but retrievability depends on the retrieval method and the exact retrieval setup. To approach a first more systematic analysis of EML retrievability, we introduce our own basic optimal estimation (OEM) retrieval, which for the purpose of this study is based on forward-modelled (synthetic) clear-sky observations. By applying the OEM retrieval to the same EML case as Stevens et al. (2017), we find that a lack of independent temperature information can significantly deteriorate the humidity retrieval due to a strong temperature inversion at the EML top. However, we show that by employing a wider spectral range of the hyperspectral IR observation, this issue can be avoided and EMLs can generally be resolved. We introduce a new framework for the identification and characterization of moisture anomalies, a subset of which are EMLs, to specifically quantify the retrieval's ability to capture moisture anomalies. The new framework is applied to 1288 synthetic retrievals of tropical ocean short-range forecast model atmospheres, allowing for a direct statistical comparison of moisture anomalies between the retrieval and the reference dataset. With our basic OEM retrieval, we find that retrieved moisture anomalies are on average 17 % weaker and 15 % thicker than their true counterparts. We attribute this to the retrieval smoothing error and the fact that rather weak and narrow moisture anomalies are most frequently missed by the retrieval. Smoothing is found to also constrain the magnitude of local heating rate extremes associated with moisture anomalies, particularly for the strongest anomalies that are found in the lower to mid troposphere. In total, about 80 % of moisture anomalies in the reference dataset are found by the retrieval. Below 5 km altitude, this fraction