Quantifying the Cloud Water Resource: Methods Based on Observational Diagnosis and Cloud Model Simulation

• DOI: 10.1007/s13351-020-9126-6
• Publication Date: 2021-01-07T10:34:57Z
• Authors: Miao Cai; Yuquan Zhou; Jianzhao Liu; Chao Tan; Yahui Tang; Qianrong Ma; Qi Li; Jietai Mao; Zhijin Hu
• Keywords: Cloud top

Based on the concepts of cloud water resource (CWR) and related variables proposed in first part this study, paper provides details two methods to quantify CWR. One is diagnostic quantification (CWR-DQ) based satellite observations, precipitation products, atmospheric reanalysis data; other numerical (CWR-NQ) a resolving model developed at Chinese Academy Meteorological Sciences (CAMS). The are applied CWR April August 2017 over North China, results evaluated against all available observations. Main as follows. (1) For CWR-DQ approach, reference profiles firstly derived CloudSat/CALIPSO joint observations for 2007–2010. NCEP/NCAR data 2000–2017 then employed produce three-dimensional fields. budget/balance equations substance lastly used, together with retrieve variables. It found that distribution vertical structure clouds obtained by method consistent (2) CWR-NQ it assumes able describe microphysical processes completely precisely, from which four-dimensional distributions vapor, hydrometeors, wind fields can be obtained. terms/quantities. After one-month continuous integration, mass becomes conserved, tempospatial hydrometeors/cloud water, (3) Diagnostic values difference transition between hydrometeors vapor (Cvh − Chv) surface evaporation (Es) well their values. (4) Correlation bias analyses show contributors correlated match counterparts well, indicating reasonable. (5) Underestimation converted (Chv) shortcoming method, may rectified or use advanced higher spatiotemporal resolutions.