期刊
JOURNAL OF THE ATMOSPHERIC SCIENCES
卷 77, 期 3, 页码 1043-1064出版社
AMER METEOROLOGICAL SOC
DOI: 10.1175/JAS-D-19-0071.1
关键词
Atmosphere; Cloud microphysics; Radars; Radar observations; Bayesian methods; Cloud parameterizations; Model errors
资金
- U.S. DOE Atmospheric System Research [DE-SC0016579]
- National Science Foundation [DMS-1619630]
- Office of Naval Research [N00173-17-2-C003]
- U.S. Department of Energy (DOE) [DE-SC0016579] Funding Source: U.S. Department of Energy (DOE)
Observationally informed development of a new framework for bulk rain microphysics, the Bayesian Observationally Constrained Statistical-Physical Scheme (BOSS; described in Part I of this study), is demonstrated. This scheme's development is motivated by large uncertainties in cloud and weather simulations associated with approximations and assumptions in existing microphysics schemes. Here, a proof-of-concept study is presented using a Markov chain Monte Carlo sampling algorithm with BOSS to probabilistically estimate microphysical process rates and parameters directly from a set of synthetically generated rain observations. The framework utilized is an idealized steady-state one-dimensional column rainshaft model with specified column-top rain properties and a fixed thermodynamical profile. Different configurations of BOSS-flexibility being a key feature of this approach-are constrained via synthetic observations generated from a traditional three-moment bulk microphysics scheme. The ability to retrieve correct parameter values when the true parameter values are known is illustrated. For cases when there is no set of true parameter values, the accuracy of configurations of BOSS that have different levels of complexity is compared. It is found that addition of the sixth moment as a prognostic variable improves prediction of the third moment (proportional to bulk rain mass) and rain rate. In contrast, increasing process rate formulation complexity by adding more power terms has little benefit-a result that is explained using further-idealized experiments. BOSS rainshaft simulations are shown to well estimate the true process rates from constraint by bulk rain observations, with the additional benefit of rigorously quantified uncertainty of these estimates.
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