Using machine learning to derive cloud condensation nuclei number concentrations from commonly available measurements

doi:10.5194/acp-2020-509

GSTDTAP > 地球科学

DOI	10.5194/acp-2020-509
	Using machine learning to derive cloud condensation nuclei number concentrations from commonly available measurements
	Arshad Arjunan Nair and Fangqun Yu
	2020-06-10
发表期刊	Atmospheric Chemistry and Physics
出版年	2020
英文摘要	Cloud condensation nuclei (CCN) number concentrations are an important aspect of aerosol–cloud interactions and the subsequent climate effects; however, their measurements are very limited. We use a machine learning tool, random decision forests, to develop a Random Forest Regression Model (RFRM) to derive CCN at 0.4 % supersaturation ([CCN0.4]) from commonly available measurements. The RFRM is trained on the long-term simulations in a global size-resolved particle microphysics model. Using atmospheric state and composition variables as predictors, through associations of their variabilities, the RFRM is able to learn the underlying dependence of [CCN0.4] on these predictors, which are: 8 fractions of PM2.5 (NH₄, SO₄, NO₃, secondary organic aerosol (SOA), black carbon (BC), primary organic carbon (POC), dust, and salt), 7 gaseous species (NO_x, NH₃, O₃, SO₂, OH, isoprene, and monoterpene), and 4 meteorological variables (temperature (T), relative humidity (RH), precipitation, and solar radiation). The RFRM is highly robust: median mean fractional bias (MFB) of 4.4 % with ~ 96.33 % of the derived [CCN0.4] within a good agreement range of −60 % < MFB < +60 % and strong correlation of Kendall's τ coefficient ≈ 0.88. The RFRM demonstrates its robustness over 4 orders of magnitude of [CCN0.4] over varying spatial (such as continental to oceanic, clean to polluted, and near surface to upper troposphere) and temporal (from the hourly to the decadal) scales. At the Atmospheric Radiation Measurement Southern Great Plains observatory (ARM SGP) in Lamont, Oklahoma, United States, long-term measurements for PM2.5 speciation (NH₄, SO₄, NO₃, and organic carbon (OC)), NO_x, O₃, SO₂, T, and RH, as well as [CCN0.4] are available. We modify, optimise, and retrain the developed RFRM to make predictions from 19 → 9 of these available predictors. This retrained RFRM (RFRM-ShortVars) shows a reduction in performance due to the unavailability and sparsity of measurements (predictors); it captures the [CCN0.4] variability and magnitude at SGP with ~ 67.02 % of the derived values in the good agreement range. This work shows the potential of using the more commonly available measurements of PM2.5 speciation to alleviate the sparsity of CCN number concentrations' measurements.
领域	地球科学
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文献类型	期刊论文
条目标识符	http://119.78.100.173/C666/handle/2XK7JSWQ/274334
专题	地球科学
推荐引用方式 GB/T 7714	Arshad Arjunan Nair and Fangqun Yu. Using machine learning to derive cloud condensation nuclei number concentrations from commonly available measurements[J]. Atmospheric Chemistry and Physics,2020.
APA	Arshad Arjunan Nair and Fangqun Yu.(2020).Using machine learning to derive cloud condensation nuclei number concentrations from commonly available measurements.Atmospheric Chemistry and Physics.
MLA	Arshad Arjunan Nair and Fangqun Yu."Using machine learning to derive cloud condensation nuclei number concentrations from commonly available measurements".Atmospheric Chemistry and Physics (2020).