Evaluation of Radiative Transfer Models With Clouds
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.
2018-06-16
Citation
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. (2018). "Evaluation of Radiative Transfer Models With Clouds." Journal of Geophysical Research: Atmospheres 123(11): 6142-6157.
Abstract
Data from hyperspectral infrared sounders are routinely ingested worldwide by the National Weather Centers. The cloudâ 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 Mediumâ 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 cmâ 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 cmâ 1 at night are reasonably consistent with results at 900 cmâ 1. Except for RTMs which use full scattering calculations, the bias and histogram correlations at 2,616 cmâ 1 are inferior to those at 900 cmâ 1 for daytime calculations.Plain Language SummaryGetting 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 Mediumâ 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 11â μm atmospheric window area, with little latitude (zonal) and day/night cloudâ type related bias. None of the RTMs fit well in the 4â 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 11â μm atmospheric window channel.Key PointsIn the 900â cmâ 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 calculationsDifferences in the bias between observations and calculations for the 2,616â cmâ 1 atmospheric window channel are not inconsistent with results at 900 cmâ 1 if the daytime calculations use full scatteringDifferences 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 3â K standard deviationPublisher
Wiley Periodicals, Inc. Kluwer Acad
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2169-897X 2169-8996
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