Abstract
Abstract. Extensive observational and numerical investigations have been performed to better characterize cloud properties. However, due to the large variations in cloud spatiotemporal distributions and physical properties, quantitative depictions of clouds in different atmospheric reanalysis datasets are still highly uncertain. A radiance-based evaluation approach is introduced and performed to evaluate the quality of cloud properties from reanalysis datasets. The China Meteorological Administration reanalysis (CRA); the ECMWF fifth-generation reanalysis (ERA5); and the Modern-Era Retrospective analysis for Applications, Version 2 (MERRA-2), i.e., those reanalyses providing sufficient cloud information, are considered. To avoid the influence of assumptions and uncertainties on satellite retrieval algorithms, forward radiative transfer simulations are used as a bridge to translate the reanalyses to corresponding radiances that are expected to be observed by satellites. The simulated reflectances and brightness temperatures (BTs) are directly compared with observations from the Advanced Himawari Imager onboard the Himawari-8 satellite in the East Asia region. We find that the simulated reflectances and BTs based on CRA and ERA5 are close to each other. CRA represents the total and midlayer cloud cover better than the other two datasets, and ERA5 depicts deep-convection structures more closely than CRA does. Comparisons of the simulated and observed BT differences suggest that water clouds are generally overestimated in ERA5 and MERRA-2, and MERRA-2 also overestimates the ice clouds over cyclone centers. Overall, clouds from CRA, ERA5, and MERRA-2 show their own advantages in different aspects. The ERA5 reanalysis has the best capability to represent the cloudy atmospheres over East Asia, and the CRA representations are close to those in ERA5.
Highlights
As an important element in the Earth’s atmosphere, clouds play a vital role in the global radiation budget, the water cycle, and climate change
This study extends the application of a radiance-based approach to evaluate the cloud properties in three reanalysis datasets: the China Meteorological Administration reanalysis (CRA); the ECMWF fifth-generation reanalysis (ERA5; Hersbach and Dee, 2016); and the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA2; Gelaro et al, 2017)
This study performs an evaluation of cloud properties from three reanalysis datasets (i.e., CRA, ERA5, and MERRA-2) with the Himawari-8 satellite observations by the radiancebased approach
Summary
As an important element in the Earth’s atmosphere, clouds play a vital role in the global radiation budget, the water cycle, and climate change. Cloud formation is governed by the balance between dynamical, thermodynamic, and microphysical processes (Boucher et al, 2013). The representations of clouds and cloud evolution in regional and global models have been significantly improved in the past few decades (Cess et al, 1989; Cotton et al, 2003; Arakawa, 2004), cloud is still one of the dominant uncertainties in the atmosphere and causes difficulties in understanding the energy budget and climate change (Dufresne and Bony, 2008; Boucher et al, 2013). B. Yao et al.: Evaluation of cloud properties
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