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Article summary:

1. Parametrization of radiation transfer through clouds is important for accurate weather prediction.

2. A practical parameterization of both liquid droplets and ice optical properties has been developed using an advanced spectral averaging method.

3. The asymmetry factor is parametrized as a function of aspect ratio, which is a first in operational numerical weather prediction models.

Article analysis:

The article discusses the importance of accurately parameterizing the optical properties of clouds in numerical weather prediction models. The authors present a new parameterization for both liquid droplets and ice particles, using an advanced spectral averaging method to calculate extinction coefficient, single scattering albedo, forward scattered fraction, and asymmetry factor. The new parameterization was evaluated using the COSMO model to simulate stratiform ice and water clouds.

Overall, the article provides a thorough analysis of the current state of knowledge regarding cloud optical properties and presents a useful contribution to the field. However, there are some potential biases and limitations that should be considered.

One potential bias is that the study focuses primarily on operational numerical weather prediction models rather than climate models. While these models are important for short-term forecasting, they may not capture long-term climate trends as accurately as climate models. Additionally, the study only evaluates the new parameterization using one model (COSMO), which may limit its generalizability to other models.

Another limitation is that the study assumes randomly oriented hexagonal ice crystals, which may not accurately represent all types of ice particles found in nature. Additionally, while the authors acknowledge that aerosol concentrations can affect cloud properties, they do not explore this relationship in depth or consider potential feedback loops between clouds and aerosols.

Despite these limitations, the article provides valuable insights into cloud optical properties and their impact on weather forecasting and climate modeling. The authors' use of advanced spectral averaging methods represents an important step forward in accurately characterizing cloud properties in numerical models.