首页> 外文期刊>Journal of Geophysical Research, D. Atmospheres: JGR >Aerosol and cloud microphysics covariability in the northeast Pacific boundary layer estimated with ship-based and satellite remote sensing observations
【24h】

Aerosol and cloud microphysics covariability in the northeast Pacific boundary layer estimated with ship-based and satellite remote sensing observations

机译:基于舰载和卫星遥感观测的东北太平洋边界层气溶胶和云微物理协变性估计

获取原文
获取原文并翻译 | 示例
获取外文期刊封面目录资料

摘要

Ship measurements collected over the northeast Pacific along transects between the port of Los Angeles (33.7°N, 118.2°W) and Honolulu (21.3°N, 157.8°W) during May to August 2013 were utilized to investigate the covariability between marine low cloud microphysical and aerosol properties. Ship-based retrievals of cloud optical depth (τ) from a Sun photometer and liquid water path (LWP) from a microwave radiometer were combined to derive cloud droplet number concentration N_d and compute a cloud-aerosol interaction (ACI) metric defined as ACI_(CCN) = ? ln(N_d)/? ln(CCN), with CCN denoting the cloud condensation nuclei concentration measured at 0.4 (CCN_(0.4)) and 0.3 (CCN_(0.3)) supersaturation. Analysis of CCN_(0.4), accumulation mode aerosol concentration (N_a), and extinction coefficient (σ_(ext)) indicates that N_a and σ_(ext) can be used as CCN_(0.4) proxies for estimating ACI. ACI_(CCN) derived from 10 min averaged N_d and CCN_(0.4) and CCN_(0.3), and CCN_(0.4) regressions using N_a and σ_(ext), produce high ACI_(CCN): near 1.0, that is, a fractional change in aerosols is associated with an equivalent fractional change in N_d. ACI_(CCN) computed in deep boundary layers was small (ACI_(CCN) = 0.60), indicating that surface aerosol measurements inadequately represent the aerosol variability below clouds. Satellite cloud retrievals from MODerate-resolution Imaging Spectroradiometer and GOES-15 data were compared against ship-based retrievals and further analyzed to compute a satellite-based ACI_(CCN). Satellite data correlated well with their ship-based counterparts with linear correlation coefficients equal to or greater than 0.78. Combined satellite N_d and ship-based CCN_(0.4) and N_a yielded a maximum ACI_(CCN) = 0.88–0.92, a value slightly less than the ship-based ACI_(CCN), but still consistent with aircraft-based studies in the eastern Pacific.
机译:利用2013年5月至8月在洛杉矶港(33.7°N,118.2°W)和檀香山(21.3°N,157.8°W)之间的横断面上空收集的船舶测量数据,研究了海洋低云微物理和气溶胶特性之间的协变性。结合来自太阳光度计的云光深度(τ)和微波辐射计的液态水路(LWP)的船载反演,得出云滴数浓度N_d并计算定义为ACI_(CCN)=?ln(N_d)/?ln(CCN),其中CCN表示在0.4%(CCN_(0.4))和0.3%(CCN_(0.3))过饱和度下测得的云凝结核浓度。对CCN_(0.4)、积累模式气溶胶浓度(N_a)和消光系数(σ_(ext))的分析表明,N_a和σ_(ext)可以作为估计ACI的CCN_(0.4)代理。ACI_(CCN) 从 10 min 平均 N_d 和 CCN_(0.4) 和 CCN_(0.3) 以及使用 N_a 和 σ_(ext) 的 CCN_(0.4) 回归得出,产生高 ACI_(CCN):接近 1.0,即气溶胶的分数变化与N_d的等效分数变化相关。 在深边界层中计算的ACI_(CCN)很小(ACI_(CCN) = 0.60), 表明地表气溶胶测量不能充分反映云下的气溶胶变化。将MODerate分辨率成像光谱仪和GOES-15数据的卫星云检索与基于船的检索进行了比较,并进一步分析以计算基于卫星的ACI_(CCN)。卫星数据与线性相关系数等于或大于0.78的舰载卫星数据相关性良好。卫星N_d和舰载CCN_(0.4)和N_a的组合产生了最大ACI_(CCN)= 0.88–0.92,该值略低于舰载ACI_(CCN),但仍与东太平洋的机基研究一致。

著录项

获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号