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Simultaneous observations of rainfall collected by a tipping bucket rain gauge (TBRG), a weighing rain gauge (WRG), an optical rain gauge (ORG), a present weather detector (PWD), a Joss–Waldvogel disdrometer (JWD), and a 2-D video disdrometer (2DVD) during January to October 2012 were analyzed to evaluate how accurately they measure rainfall and drop size distributions (DSDs). For the long-term observations, there were different discrepancies in rain amounts from six instruments on the order of 0% to 27.7%. The TBRG, WRG, and ORG have a good agreement, while the PWD and 2DVD record higher and the JWD lower rain rates when R > 20 mm h⁻¹, the ORG agrees well with JWD and 2DVD, while the TBRG records higher and the WRG lower rain rates when R > 20 mm h⁻¹. Compared with the TBRG and WRG, optical and impact instruments can measure the rain rate accurately in the light rain. The overall DSDs of JWD and 2DVD agree well with each other, except for the small raindrops (D < 1 mm). JWD can measure more moderate-size raindrops (0.3 mm < D < 1.5 mm) than 2DVD, but 2DVD can measure more small-size raindrops (D < 0.3 mm). 2DVD has a larger measurement range; more overall raindrops can be measured by 2DVD than by JWD in different rain rate regimes. But small raindrops might be underestimated by 2DVD when R > 15 mm h⁻¹. The small raindrops tend to be omitted in the more large-size raindrops due to the shadow effect of light. Therefore, the measurement accuracy of small raindrops in the heavy rainfall from 2DVD should be handled carefully

Some cloud structure features that can be extracted from infrared images of the sky are suggested for cloud classification. Both the features and the classifier are developed over zenithal images taken by the whole-sky infrared cloud-measuring system (WSIRCMS), which is placed in Nanjing, China. Before feature extraction, the original infrared image was smoothed to suppress noise. Then, the image was enhanced using top-hat transformation and a high-pass filtering. Edges are detected from the enhanced image after adaptive optimization threshold segmentation and morphological edge detection. Several structural features are extracted from the segment image and edge image, such as cloud gray mean value (ME), cloud fraction (ECF), edge sharpness (ES), and cloud mass and gap distribution parameters, including very small-sized cloud mass and gaps (SMG), middle-sized cloud gaps (MG), medium–small-sized cloud gaps (MSG), and main cloud mass (MM). It is found that these features are useful for distinguishing cirriform, cumuliform, and waveform clouds. A simple but efficient supervised classifier called the rectangle method is used to do cloud classification. The performance of the classifier is assessed with an a priori classification carried out by visual inspection of 277 images. The index of agreement is 90.97%.

The cloud base height (CBH) derived from the whole-sky infrared cloud-measuring system (WSIRCMS) and two ceilometers (Vaisala CL31 and CL51) from November 1, 2011, to June 12, 2012, at the Chinese Meteorological Administration (CMA) Beijing Observatory Station are analysed. Significant differences can be found by comparing the measurements of different instruments. More exactly, the cloud occurrence retrieved from CL31 is 3.8% higher than that from CL51, while WSIRCMS data shows 3.6% higher than ceilometers. More than 75.5% of the two ceilometers' differences are within ±200 m and about 89.5% within ±500 m, while only 30.7% of the differences between WSIRCMS and ceilometers are within ±500 m and about 55.2% within ±1000 m. These differences may be caused by the measurement principles and CBH retrieval algorithm. A combination of a laser ceilometer and an infrared cloud instrument is recommended to improve the capability for determining cloud occurrence and retrieving CBHs.

Cloud properties derived from the whole-sky infrared cloud-measuring system (WSIRCMS) are analyzed in relation to measurements of visual observations and a ceilometer during the period July-August 2010 at the Chinese Meteorological Administration Yangjiang Station, Guangdong Province, China. The comparison focuses on the performance and features of the WSIRCMS as a prototype instrument for automatic cloud observations. Cloud cover derived from the WSIRCMS cloud algorithm compares quite well with cloud cover derived from visual observations. Cloud cover differences between WSIRCMS and visual observations are within ±1 octa in 70.83% and within ±2 octa in 82.44% of the cases. For cloud-base height from WSIRCMS data and Vaisala ceilometer CL51, the comparison shows a generally good correspondence in the lower and midtroposphere up to the height of about 6 km, with some systematic difference due to different detection methods. Differences between the resulting cloud-type classifications derived from the WSIRCMS and from visual observations show that cumulus and cirrus are classified with high accuracy, but that stratocumulus and altocumulus are not. Stratocumulus and altocumulus are suggested to be treated as waveform cloud for classification purposes. In addition, it is considered an intractable problem for automatic cloud-measurement instruments to do cloud classification when the cloud amount is less than 2 octa.

Recently, the oblique earth-space links (OELs) between satellite and earth station have been used for rainfall monitoring as a supplement to existing observation methods. Most present studies achieved the rainfall measurement by OELs based on the empirical method such as power-law (PL) model. In practice, two crucial issues need to be addressed: (1) identification of rain and no-rain periods; and (2) determination of attenuation baseline. To solve these problems, this paper adopts several machine learning algorithms based on the analysis of earth-space link signal characteristics. For the first issue, we choose the support vector machine (SVM) as a classifier and the adaptive synthetic sampling algorithm (ADSYN) is deployed to eliminate the effects caused by data imbalance. For the second issue, the long short-term (LSTM) neural network is selected for the determination of attenuation baseline since it has a good ability to solve time series problem. In terms of the rainfall inversion, we establish a new model by combining the back-propagation (BP) network and genetic algorithm (GA). The PL model is also used as a comparison. To validate the proposed method, we set up an earth-space link that receives the signal from AsiaSat5 in 12.32GHz. The results demonstrate that the two issues are successfully addressed and the inversion of precipitation is also carried out. Compared to disdrometer, the correlation and mean absolute error of GA-BP model are 0.83 and 1.30 mm/h, respectively, indicating a great potential to use densely OELs for global precipitation monitoring. Index Terms-rainfall monitoring, remote sensing, machine learning, earth-space link, Ku-band I. INTRODUCTION 1 CCURATE and real-time rainfall measurement plays an important role in many aspects of human life such as agricultural issues, water resource management and natural disaster warning. Existing rainfall detection method mainly comprises rain gauge, weather radar and weather satellite [1]. Based on the exploitation of existing radio spectrum sources, the opportunistic use of microwave links has become a new approach to detecting precipitation. Messer et al. firstly suggested the application of commercial wireless communication networks (CWCNs) to environmental monitoring [2]. In recent years, the use of horizontal microwave links (HMLs) has been developed rapidly in many fields such as path-average rain intensity inversion [3, 4], radar calibration [5] and regional rainfall monitoring [6].

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[1] The principle of VHF broadband interferometer for lightning observations is to extract the phase differences between a pair of RF signals at different frequency components, and thereafter, to compute the direction of the radiation source. However, the phase difference spectra are usually distorted by random noise, which directly affects the location accuracy and reliability. In order to suppress phase interference effectively, a phase filtering algorithm which combines circular correlation with translation-invariant denoising is proposed. Application of the algorithm to a segment of observational data shows that the proposed algorithm does a much better job in recovering the phase spectra than other techniques. Simulated data distorted by the addition of random noise are also used to illustrate the improvements of the method in resolving incident angle, which is compared with other wavelet-based thresholding techniques and the conventional Fourier transform-based cross-correlation method. Finally, this phase filtering algorithm is applied to mapping two natural lightning processes. Contrast analysis reveals that this algorithm could be utilized to retrieve well-defined paths which are not discerned by conventional method, and depict the branches more clearly and precisely.