Details

Autor(en) / Beteiligte

• Huang, Yi
• Siems, Steven
• Manton, Michael
• Protat, Alain
• Majewski, Leon
• Nguyen, Hanh

Titel

Evaluating Himawari-8 Cloud Products Using Shipborne and CALIPSO Observations: Cloud-Top Height and Cloud-Top Temperature

Ist Teil von

• Journal of atmospheric and oceanic technology, 2019-12, Vol.36 (12), p.2327-2347

Ort / Verlag

Boston: American Meteorological Society

Erscheinungsjahr

2019

Quelle

EZB Electronic Journals Library

Beschreibungen/Notizen

• Abstract Cloud-top height (CTH) and cloud-top temperature (CTT) retrieved from the Himawari-8 observations are evaluated using the active shipborne radar–lidar observations derived from the 31-day Clouds, Aerosols, Precipitation Radiation and Atmospheric Composition over the Southern Ocean (CAPRICORN) experiment in 2016 and 1-yr observations from the spaceborne Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) cloud product over a large sector of the Southern Ocean. The results show that the Himawari-8 CTH (CTT) retrievals agree reasonably well with both the shipborne estimates, with a correlation coefficient of 0.837 (0.820), a mean bias error of 0.226 km (−2.526°C), and an RMSE of 1.684 km (10.069°C). In the comparison with CALIOP, the corresponding quantities are found to be 0.786 (0.480), −0.570 km (1.343°C), and 2.297 km (25.176°C). The Himawari-8 CTH (CTT) generally falls between the physical CTHs observed by CALIOP and the shipborne radar–lidar estimates. However, major systematic biases are also identified. These errors include (i) a low (warm) bias in CTH (CTT) for warm liquid cloud type, (ii) a cold bias in CTT for supercooled liquid water cloud type, (iii) a lack of CTH at ~3 km that does not have a corresponding gap in CTT, (iv) a tendency of misclassifying some low-/mid-top clouds as cirrus and overlap cloud types, and (v) a saturation of CTH (CTT) around 10 km (−40°C), particularly for cirrus and overlap cloud types. Various challenges that underpin these biases are also explored, including the potential of parallax bias, low-level inversion, and cloud heterogeneity.