Evaluation of Radiative Transfer Models With Clouds. Issue 11 (13th June 2018)

• Record Type: Journal Article
• Authors: Aumann, Hartmut H.; Chen, Xiuhong; Fishbein, Evan; Geer, Alan; Havemann, Stephan; Huang, Xianglei; Liu, Xu; Liuzzi, Giuliano; DeSouza‐Machado, Sergio; Manning, Evan M.; Masiello, Guido; Matricardi, Marco; Moradi, Isaac; Natraj, Vijay; Serio, Carmine; Strow, Larrabee; Vidot, Jerome; Chris Wilson, R.; Wu, Wan; Yang, Qiguang; Yung, Yuk L.
• Is Part Of: Journal of geophysical research. Volume 123:Issue 11(2018)
• Page Start: 6142
• Page End: 6157
• Publication Date: 2018-06-13
• Subjects: infrared -- hyperspectral -- cloud -- radiative transfer -- weather forecasting -- climate; Atmospheric physics -- Periodicals; Geophysics -- Periodicals; 551.5
• DOI: 10.1029/2017JD028063
• Languages: English
• ISSNs: 2169-897X

Abstract

Abstract: Data from hyperspectral infrared sounders are routinely ingested worldwide by the National Weather Centers. The cloud‐free fraction of this data is used for initializing forecasts which include temperature, water vapor, water cloud, and ice cloud profiles on a global grid. Although the data from these sounders are sensitive to the vertical distribution of ice and liquid water in clouds, this information is not fully utilized. In the future, this information could be used for validating clouds in National Weather Center models and for initializing forecasts. We evaluate how well the calculated radiances from hyperspectral Radiative Transfer Models (RTMs) compare to cloudy radiances observed by AIRS and to one another. Vertical profiles of the clouds, temperature, and water vapor from the European Center for Medium‐Range Weather Forecasting were used as input for the RTMs. For nonfrozen ocean day and night data, the histograms derived from the calculations by several RTMs at 900 cm −1 have a better than 0.95 correlation with the histogram derived from the AIRS observations, with a bias relative to AIRS of typically less than 2 K. Differences in the cloud physics and cloud overlap assumptions result in little bias between the RTMs, but the standard deviation of the differences ranges from 6 to 12 K. Results at 2, 616 cm −1 at night are reasonably consistent with results at 900 cm −1 . Except for RTMs which use full scattering calculations, the bias and histogram correlations at 2, 616 cm −1 are inferior to those at 900 cm −1 for daytime calculations. Plain Language Summary: Getting the right clouds of the right type, at the right time and location in Global Circulation Models, is key to getting the local energy balance right. This is key to an accurate forecast. If the clouds are of the wrong type or at the wrong location or time, the accuracy of the forecast is degraded. We evaluate the accuracy of the best currently available cloud description (produced by the European Center for Medium‐Range Weather Forecasting) by comparing the radiances calculated using Radiative Transfer Models (RTMs) from six major development teams to cloudy radiances observed by the Atmospheric Infrared Sounder at the same location and time. The better RTMs fit statistically reasonably well in the 11‐μm atmospheric window area, with little latitude (zonal) and day/night cloud‐type related bias. None of the RTMs fit well in the 4‐μm atmospheric window area during daytime, unless the calculations use full scattering. With the current state of art, all major RTMs would be suitable to start the validation of cloud effects in the National Weather Center models using just one 11‐μm atmospheric window channel. Key Points: In the 900‐cm −1 atmospheric window channel several Radiative Transfer Models have a better than 0.95 correlation between the histogram derived from the observations and those derived from the calculations Differences in the bias between observations and calculations for the 2, 616‐cm −1 atmospheric window channel are not inconsistent with results at 900 cm −1 if the daytime calculations use full scattering Differences in the cloud physics and cloud overlap assumptions between Radiative Transfer Models result in a standard deviation of the pairwise difference of between 6 and 12 K; differences due to the cloud overlap assumption alone result in a 3‐K standard deviation