The primary purpose of this study is to assess the performance of 1D solar radiative transfer codes that are used currently both for research and in weather and climate models. Emphasis is on interpretation and handling of unresolved clouds. Answers are sought to the following questions: (i) How well do 1D solar codes interpret and handle columns of information pertaining to partly cloudy atmospheres? (ii) Regardless of the adequacy of their assumptions about unresolved clouds, do 1D solar codes perform as intended? One clear-sky and two plane-parallel, homogeneous (PPH) overcast cloud cases serve to elucidate 1D model differences due to varying treatments of gaseous transmittances, cloud optical properties, and basic radiative transfer. The remaining four cases involve 3D distributions of cloud water and water vapor as simulated by cloud-resolving models. Results for 25 1D codes, which included two line-by-line (LBL) models (clear and overcast only) and four 3D Monte Carlo (MC) photon transport algorithms, were submitted by 22 groups. Benchmark, domain-averaged irradiance profiles were computed by the MC codes. For the clear and overcast cases, all MC estimates of top-of-atmosphere albedo, atmospheric absorptance, and surface absorptance agree with one of the LBL codes to within Ϯ2%. Most 1D codes underestimate atmospheric absorptance by typically 15-25 W m Ϫ2 at overhead sun for the standard tropical atmosphere regardless of clouds. Depending on assumptions about unresolved clouds, the 1D codes were partitioned into four genres: (i) horizontal variability, (ii) exact overlap of PPH clouds, (iii) maximum/random overlap of PPH clouds, and (iv) random overlap of PPH clouds. A single MC code was used to establish conditional benchmarks applicable to each genre, and all MC codes were used to establish the full 3D benchmarks. There is a tendency for 1D codes to cluster near their respective conditional benchmarks, though intragenre variances typically exceed those for the clear and overcast cases. The majority of 1D codes fall into the extreme category of maximum/random overlap of PPH clouds and thus generally disagree with full 3D benchmark values. Given the fairly limited scope of these tests and the inability of any one code to perform extremely well for all cases begs the question that a paradigm shift is due for modeling 1D solar fluxes for cloudy atmospheres.

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[1] Observing system impact assessments using atmospheric simulation experiments are conducted to provide an objective quantitative evaluation of future observing systems and instruments. Such simulation experiments using a proxy true atmosphere, Nature Run, are known as observing system simulation experiments (OSSEs). Through OSSEs, future observing systems that effectively use data assimilation systems in order to improve weather forecasts can be designed. Various types of simulation experiments have been performed in the past by many scientists, but the OSSE at the National Centers for Environmental Prediction (NCEP) presented in this paper is the most extensive and complete OSSE. The agreement between data impacts from simulated data and the corresponding real data is satisfactory. The NCEP OSSE is also the first OSSE where radiance data from satellites were simulated and assimilated. Since a Doppler wind lidar (DWL) is a very costly instrument, various simulation experiments have been funded and performed. OSSEs that evaluate the data impact of DWL are demonstrated. The results show a potentially powerful impact from DWL. In spite of the many controversies regarding simulation experiments, this paper demonstrates that carefully constructed OSSEs are able to provide useful information that influences the design of future observing systems. Various factors that affect the assessment of the impact are discussed.

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