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Frontiers of Earth Science

ISSN 2095-0195

ISSN 2095-0209(Online)

CN 11-5982/P

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Front. Earth Sci.    2019, Vol. 13 Issue (4) : 829-835    https://doi.org/10.1007/s11707-019-0771-y
RESEARCH ARTICLE
Cloud type identification for a landfalling typhoon based on millimeter-wave radar range-height-indicator data
Zhoujie CHENG1,2, Ming WEI1(), Yaping ZHU3, Jie BAI2, Xiaoguang SUN4, Li GAO5
1. Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing 210001, China
2. Beijing Institute of Aviation Meteorology, Beijing 100085, China
3. Beijing Marine Hydrometeorologic Centre, Beijing 100071, China
4. Beijing Meteorological Centre, Beijing 100038, China
5. Taizhou Meteorological Bureau of Zhejiang Province, Taizhou 317000, China
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Abstract

As a basic property of cloud, accurate identification of cloud type is useful in forecasting the evolution of landfalling typhoons. Millimeter-wave cloud radar is an important means of identifying cloud type. Here, we develop a fuzzy logic algorithm that depends on radar range-height-indicator (RHI) data and takes into account the fundamental physical features of different cloud types. The algorithm is applied to a ground-based Ka-band millimeter-wave cloud radar. The input parameters of the algorithm include average reflectivity factor intensity, ellipse long axis orientation, cloud base height, cloud thickness, presence/absence of precipitation, ratio of horizontal extent to vertical extent, maximum echo intensity, and standard variance of intensities. The identified cloud types are stratus (St), stratocumulus (Sc), cumulus (Cu), cumulonimbus (Cb), nimbostratus (Ns), altostratus (As), altocumulus (Ac) and high cloud. The cloud types identified using the algorithm are in good agreement with those identified by a human observer. As a case study, the algorithm was applied to typhoon Khanun (1720), which made landfall in south-eastern China in October 2017. Sequential identification results from the algorithm clearly reflected changes in cloud type and provided indicative information for forecasting of the typhoon.
Keywords landfalling typhoon      identification of cloud type      millimeter-wave cloud radar      RHI data      fuzzy logic     
Corresponding Author(s): Ming WEI   
Just Accepted Date: 30 July 2019   Online First Date: 23 September 2019    Issue Date: 30 December 2019
 Cite this article:   
Zhoujie CHENG,Ming WEI,Yaping ZHU, et al. Cloud type identification for a landfalling typhoon based on millimeter-wave radar range-height-indicator data[J]. Front. Earth Sci., 2019, 13(4): 829-835.
 URL:  
https://academic.hep.com.cn/fesci/EN/10.1007/s11707-019-0771-y
https://academic.hep.com.cn/fesci/EN/Y2019/V13/I4/829
Parameter Value
Regime dual-polarized pulse Doppler
Polarization slant 45° linear polarized emission, co-polar and cross-polar receiver
Frequency 35 GHz
Scanning mode RHI, PPI, volume scan, stationary direction
Measurements reflectivity, Doppler velocity, spectral width, linear depolarization ratio
Range 0.15–30 km
Azimuth 0–360°
Elevation 0–90°
Reflectivity −50 to+30 dBZ
Velocity −8.5 to+8.5 m·s−1
Spectral width 0–4 m·s−1
Linear depolarization ratio −5 to −30 dB
Tab.1  Main parameters of the millimeter-wave cloud radar used in this study
Variable Physical meaning
Zave The average intensity among all effective grids within cloud cluster
θ The angle between long axis and horizontal direction after fitting cloud cluster boundary points with an ellipse
CB The mean height of the cloud base
CT The mean thickness of cloud cluster
BP Precipitation is present or not according to cloud base height and intensity distribution of reflectivity factors
RHV The ratio of maximum horizontal extent to maximum vertical extent of cloud cluster
Zmax The maximum of reflectivity factors within cloud cluster
Zstd The standard variance of reflectivity factors among all effective grids within cloud cluster
Tab.2  Input variables of the fuzzy logic algorithm
Zave θ CB CT BP RHV Zmax Zstd
m a m a m a m a m a m a m a m a
St −10 5 0 8 500 500 1000 1000 0.5 0.5 15 15 −5 15 3 3
Sc −5 6 0 25 1000 700 1200 900 0.5 0.5 15 5 0 15 5 5
Cu −5 8 90 40 1100 800 1500 1000 0.5 0.5 2 2 5 15 5 5
Cb 5 10 90 20 1100 500 12000 7000 1 0.5 3 3 27 8 12 5
Ns 0 5 0 40 1500 1500 6500 3000 1 0.5 4 2 22 10 5 5
As −10 4 0 10 3500 1500 3000 2500 0 0.5 15 15 −5 10 3 3
Ac −5 5 90 40 4000 2000 1400 700 0 0.5 2 2 0 10 5 5
High cloud 9000 3000
Weight 0.5 0.3 2 1.5 1 1 1.2 0.5
Tab.3  Fuzzification parameters for the eight algorithm inputs
Automatic classification algorithm
St Sc Cu Cb Ns As Ac High
Observer St 158 3 1 51 21
Sc 16 10 8 5 5 2
Cu 49 1 3
Cb 1 12 5
Ns 2 189
As 5 1 2 10 159 9
Ac 3 12 58
High 1 92
Agreement 86.8% 71.4% 80.3% 80.0% 74.7% 80.7% 81.7% 100%
Tab.4  Contingency table comparing cloud type derived from the algorithm with that from a human observer
Fig.1  Radar RHI image and identification results at 00:14:26 UTC (top) and 00:22:56 UTC (bottom) on October 16, 2017. The borders of cloud clusters are indicated by red dotted lines, and the cluster number is provided at the centroid of each cloud cluster.
Fig.2  As in Fig. 1, but for 00:18:05 UTC (top) and 00:59:12 UTC (bottom).
Fig.3  As in Fig. 1, but for 00:20:26 UTC (top) and 01:01:35 UTC (bottom).
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