Analysis and Application of the Relationship between Cumulonimbus (Cb) Cloud Features and Precipitation Based on FY-2C Image

Abstract

Although cumulonimbus (Cb) clouds are the main source of precipitation in south China, the relationship between Cb cloud characteristics and precipitation remains unclear. Accordingly, the primary objective of this study was to thoroughly analyze the relationship between Cb cloud features and precipitation both at the pixel and cloud patch scale, and then to apply it in precipitation estimation in the Huaihe River Basin using China's first operational geostationary meteorological satellite, FengYun-2C (FY-2C), and the hourly precipitation data of 286 gauges from 2007. First, 31 Cb parameters (14 parameters of three pixel features and 17 parameters of four cloud patch features) were extracted based on a Cb tracking method using an artificial neural network (ANN) cloud classification as a pre-processing procedure to identify homogeneous Cb patches. Then, the relationship between Cb cloud properties and precipitation was analyzed and applied in a look-up table algorithm to estimate precipitation. The results were as follows: (1) Precipitation increases first and then declines with increasing values for cold cloud and time evolution parameters, OPEN ACCESSAtmosphere 2014, 5 212 and heavy precipitation may occur not only near the convective center, but also on the front of the Cb clouds on the pixel scale. (2) As for the cloud patch scale, precipitation is typically associated with cold cloud and rough cloud surfaces, whereas the coldest and roughest cloud surfaces do not correspond to the strongest rain. Moreover, rainfall has no obvious relationship with the cloud motion features and varies significantly over different life stages. The involvement of mergers and splits of minor Cb patches is crucial for precipitation processes. (3) The correlation coefficients of the estimated rain rate and gauge rain can reach 0.62 in the cross-validation period and 0.51 in the testing period, which indicates the feasibility of the further application of the relationship in precipitation estimation.

Automatic Tracking and Characterization of Cumulonimbus Clouds from FY-2C Geostationary Meteorological Satellite Images

“…VMCP can be indicated by the ratio of the average TB of a current Cb patch ( mean PD 0 ) to that of a previous one ( mean PD 1 ), for cloud top TB reflects the height of Cb. The life stages of Cb patches can be divided into 8 (Figure 2), which was also shown in Liu et al 's previous work [50].…”

Effects of the Forecasting Methods, Precipitation Character, and Satellite Resolution on the Predictability of Short-Term Quantitative Precipitation Nowcasting (QPN) from a Geostationary Satellite

“…However, the precipitation forecast skill was always analyzed for only a single aspect such as the rainfall pattern, scale dependence, etc. [ 10 – 12 , 18 ] Few studies were performed to systematically analyze the performance of different nowcasting models, although selecting a proper method is notably important to improve the predictability. It is of particular interest to determine the predictability of QPN from a comprehensive prospective.…”

Optimizing deep belief network parameters using grasshopper algorithm for liver disease classification

“…The textural features such as Gabor filters, local binary patterns (LBP), 16 acutance, 17 energy, contrast, inverse difference moment (IDM), 18 Gray level cooccurrence matrix (GLCM)-based features, 19 gray scale features, 18 pyramid histogram of oriented gradient (PHOG) features, 20 shape based features, 21,22 and morphological features. For liver disease classification, the following features are extracted after the preprocessing stage.…”

“…For liver disease classification, the following features are extracted after the preprocessing stage. The textural features such as Gabor filters, local binary patterns (LBP), 16 acutance, 17 energy, contrast, inverse difference moment (IDM), 18 Gray level cooccurrence matrix (GLCM)-based features, 19 gray scale features, 18 pyramid histogram of oriented gradient (PHOG) features, 20 shape based features, 21,22 and morphological features. 23 Then the extracted features with high dimensionality are reduced by utilizing principal component analysis (PCA) 24 method.…”