Assessment of cloud properties from the reanalysis with satellite observations over East Asia

Abstract

Abstract. Extensive observational and numerical investigations have been performed to better characterize cloud properties. However, due to the large variations of cloud spatiotemporal distributions and physical properties, quantitative depictions of clouds in different atmospheric reanalysis datasets are still highly uncertain, and cloud parameters in the models to produce those datasets remain largely unconstrained. A radiance-based evaluation approach is introduced and performed to assess the quality of cloud properties by directly comparing reanalysis-driven forward radiative transfer results with radiances from satellite observation. The newly developed China Meteorological Administration Reanalysis data (CRA), the ECMWF’s Fifth-generation Reanalysis (ERA5), and the Modern-Era Retrospective Analysis for Applications, Version 2 (MERRA-2) are considered in the present study. To avoid the unrealistic assumptions and uncertainties on satellite retrieval algorithms and products, the radiative transfer model (RTM) is used as a bridge to “translate” the reanalysis to corresponding satellite observations. The simulated reflectance and brightness temperatures (BTs) are directly compared with observations from the Advanced Himawari Imager (AHI) onboard the Himawari-8 satellite in the region from 80° E to 160° W between 60° N and 60° S, especially for results over East Asia. Comparisons of the reflectance in the solar and BTs in the infrared (IR) window channels reveal that CRA reanalysis better represents the total cloud cover than the other two reanalysis datasets. The simulated BTs for CRA and ERA5 are close to each other in many pixels, whereas the vertical distributions of cloud properties are significantly different, and ERA5 depicts a better deep convection structure than CRA reanalysis. Comparisons of the BT differences (BTDs) between the simulations and observations suggest that the water clouds are generally overestimated in ERA5 and MERRA-2, whereas the ice cloud is responsible for the overestimation over the center of cyclones in ERA5. Overall, the cloud from CRA, ERA5, and MERRA-2 show their own advantages in different aspects. The ERA5 reanalysis is found the most capability in representing the cloudy atmosphere over East Asia, and the results in CRA are close to those in ERA5.

Assessing the Surface Downward Longwave Irradiance Models Using ERA5 Input Data in Canada

“…of these DLI models depends on the measured observations for humidity and cloud cover at the multiple layers of the atmosphere. Yao et al (2019) suggest that the water clouds are generally overestimated in ERA5 and MERRA-2 for East Asia. However, our results for simulated DLI using total cloud cover don't show systematic biases for selected models (Fig.…”

“…There are two possible alternative methods of estimation for all sky conditions: First, it is possible to use reanalysis products for cloud cover. The assimilated products of reanalysis climate models provide a reasonably acceptable estimation for cloud cover for largescale studies(Gatien et al 2022; Tang et al 2021;Yao et al 2019; Yao et al 2020). However, different cloud types have a distinct influence on DLI.…”

“…Since different cloud types can be active simultaneously and influence DLI, the total cloud cover is used. Cloud cover is one of the most uncertain variables among reanalysis products since it's purely influenced by the implemented atmospheric models and is model-dependent(Free et al 2016;Yao et al 2019; Yao et al 2020).…”