Evaluation of cloud properties from reanalyses over East Asia with a radiance-based approach

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.

Analysis of cloud effects on long-term global and diffuse photosynthetically active radiation at a Mediterranean site

“…Total column cloud liquid water is the amount of liquid water contained within cloud droplets in a column extending from the surface to the TOA and averaged for the model grid box. These ERA5 cloud variables have been analyzed and compared in several regions against surface and satellite cloud observations with good agreements in both comparisons (Danso et al, 2019;Lei et al, 2020;Yao et al, 2020). Thus, in the analysis performed by Yao et al (2020) for the period 2007-2016, ERA5 shows monthly mean cloud cover with relative errors below 10% with respect to MODIS, with special good behaviour for latitudes between 0-30º.…”

Clouds and Radiation Processes in Regional Climate Models Evaluated Using Observations Over the Ice‐free Arctic Ocean

“…The CAFS successfully reproduces the frequency distribution of the LWD, while the METUM underestimates the higher values of the observed peak (i.e., it has a narrower spread), resulting in a bias of −8 W/m 2 (Table 3). Overall, the performance of ERA5 is superior to that of the RCMs because of the many satellite‐derived cloud products it assimilates (e.g., Yao et al., 2020).…”

“…Despite the many differences between the CCLMi and CCLM5, it was difficult to explain why the choice of boundary conditions (ERA‐Interim or ERA5) might have led to those differences (e.g., SWD in Figure 6a; LTS in Figures 8d and 8e; and cloud base temperature in Figure 9b). The ERA5 originally shows the best performance for most parameters; however, although it assimilates many satellite products related to clouds (Yao et al., 2020), the CCLM5 does not always provide better performance in comparison with the CCLMi. Analysis of the use of different large‐scale dynamic constraints (e.g., grid point nudging) rather than the forecast mode and/or different intervals of updating the lateral boundary conditions (every hour for the CCLM5 and every 6 h for the CCLMi) might provide further understanding. The CAFS is the only model that has its own Arctic coupled system.…”

“…ERA5 data (Hersbach et al., 2020) were also utilized as an additional reference data source (1‐h product) because it assimilates many satellite‐based brightness temperatures for cloud water, column water vapor, and cloud top temperature (Yao et al., 2020). In addition, ERA5 uses a prognostic microphysics scheme that separates cloud water, cloud ice, rain, and snow as prognostic variables (Sotiropoulou et al., 2015).…”

Evaluation of the precipitation of the East Asia regional reanalysis system mainly over mainland China

“…To identify the improvement from ERA‐Interim to EARS‐CMA, 6‐hourly ERA‐Interim with a 0.75° × 0.75° spatial resolution is employed as one of the global analyses compared with the new EARS‐CMA. Another chosen global reanalysis data is the hourly ERA5 reanalysis at a 25 km resolution, which represents the state‐of‐the‐art global assimilation system and has been widely applied and accepted in climatological research over East Asia (Gui et al ., 2020; Lei et al ., 2020; Yao et al ., 2020). For validation and comparison with reanalysis datasets, three observation‐gridded products of precipitation are applied, including the Tropical Rainfall Measuring Mission (TRMM) 3B42V7 product (Huffman et al ., 2007), Climate Prediction Center (CPC) MORPHing technique (CMORPH) bias‐corrected product (CMORPH, Joyce et al ., 2004) and CN05.1 (Wu and Gao, 2013).…”