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

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