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Pak Wai Chan

Bio: Pak Wai Chan is an academic researcher from Hong Kong Polytechnic University. The author has contributed to research in topics: Wind speed & Wind shear. The author has an hindex of 31, co-authored 264 publications receiving 3385 citations.


Papers
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Journal ArticleDOI
TL;DR: Based on 6-year wind data recorded at five meteorological stations with different terrain conditions, the authors presents a statistical analysis of the wind characteristics and wind energy potential at typical sites in Hong Kong by the assistance of Weibull distribution model.

138 citations

Journal ArticleDOI
TL;DR: Li et al. as mentioned in this paper developed an innovative GPScan (GPScan) strategy for the lidar, pointing the laser beam toward the approach and departure glide paths, with the changes in azimuth and elevation angles concerted.
Abstract: In December 2005, operational wind shear alerting at the Hong Kong International Airport (HKIA) reached an important milestone with the launch of the automatic Lidar (light detection and ranging) Windshear Alerting System (LIWAS). This signifies that the anemometer-based and radar-based wind shear detection technologies deployed worldwide in the twentieth century have been further advanced by the addition of the lidar—a step closer to all-weather coverage. Unlike the microburst and gust front, which have a well-defined coherent vertical structure in the lowest several hundred meters of the atmosphere, terrain-induced wind shear tends to have high spatial and temporal variability. To detect the highly changeable winds to be encountered by the aircraft under terraininduced wind shear situations, the Hong Kong Observatory devises an innovative glide path scan (GPScan) strategy for the lidar, pointing the laser beam toward the approach and departure glide paths, with the changes in azimuth and elevation angles concerted. The purpose of the GPScans is to derive the headwind profiles and hence the wind shear along the glide paths. Developed based on these GPScans, LIWAS is able to capture about 76% of the wind shear events reported by pilots over the most-used approach corridor under clear-air conditions. During the past two years, further developments of the lidar took place at HKIA, including the use of runway-specific lidar to further enhance the wind shear detection performance.

130 citations

Journal ArticleDOI
TL;DR: Based on 6-year wind observations from three meteorological stations at three islands in Hong Kong, the authors provides a statistical assessment of the wind characteristics and wind energy potential at offshore locations surrounding Hong Kong.

110 citations

Journal ArticleDOI
TL;DR: In this paper, a new concept of urban cool island degree hours (UCIdh) was proposed to measure the urban heat island intensity and duration in Hong Kong and showed that when anthropogenic heat is small or absent, a high-rise and high-density city experiences a significant daytime UCI effect.
Abstract: The urban heat island (UHI) phenomenon has been studied extensively, but there are relatively fewer reports on the so-called urban cool island (UCI) phenomenon. We reveal here that the UCI phenomenon exists in Hong Kong during the day, and is associated with the UHI at night under all wind and cloud conditions. The possible mechanisms for the UCI phenomenon in such a high-rise compact city have been discovered using a lumped urban air temperature model. A new concept of urban cool island degree hours (UCIdh) to measure the UCI intensity and duration is proposed. Our analyses reveal that when anthropogenic heat is small or absent, a high-rise and high-density city experiences a significant daytime UCI effect. This is explained by an intensified heat storage capacity and the reduced solar radiation gain of urban surfaces. However, if anthropogenic heat in the urban area increases further, the UCI phenomenon still exists, yet UCIdh decrease dramatically in a high-rise compact city. In a low-rise, low-density city, the UCI phenomenon also occurs when there is no anthropogenic heat, but easily disappears when there is little anthropogenic heat, and the UHI phenomenon dominates. This probably explains why the UHI phenomenon is often observed, but the UCI phenomenon is rarely observed. The co-existence of urban heat/cool island phenomena implies reduction of the daily temperature range (DTR) in such cities, and its dependence on urban morphology also implies that urban morphology can be used to control the urban thermal environment.

104 citations

Journal ArticleDOI
TL;DR: In this article, water vapour observations over the last 6 years from an AErosol RObotic NETwork (AERONET) sunphotometer, 9 years from the MODerate resolution Imaging Spectroradiometer (MODIS) TERRA satellite images and 38 years from radiosonde were analyzed and cross-validated.
Abstract: Water vapour is one of green house gases (GHG) and a key parameter affecting weather forecasting. Situated on the edge of the South China Sea and in the path of the wet Asian monsoon, Hong Kong experiences more rainstorms than most other cities. An accurate observation of water vapour has a special significance in severe weather prediction for Hong Kong, one of the cities with the highest density of population in the world. In this paper, water vapour observations over the last 6 years from an AErosol RObotic NETwork (AERONET) sunphotometer, 9 years from the MODerate resolution Imaging Spectroradiometer (MODIS) TERRA satellite images and 38 years from radiosonde were analysed and cross-validated. The operational MODIS water vapour products, namely MOD05 and MOD07, with a spatial resolution of 1 and 5 km, respectively, were compared with both radiosonde and AERONET data. The correlation coefficients between MODIS water vapour products and radiosonde data are r = 0.878 and 0.876 for MOD05 and MOD07 products, and the correlations of those with AERONET data are r = 0.822, r = 0.976, respectively. The results also indicate that radiosonde and AERONET water vapour observations have a good agreement, with a correlation of r = 0.988 and a small mean absolute difference (MAD), and root mean square error (RMS) of 0.197 and 0.289 cm, respectively. Although the satellite data (with a frequency of once per day) represent water vapour coverage of all the territories of Hong Kong, they do not meet short-term weather prediction demand due to their low temporal resolution. Radiosonde observations with a frequency of twice per day are also temporally inadequate. This study demonstrates that the AERONET sunphotometer can provide accurate and high temporal resolution water vapour data which can be used for short-term weather prediction and long-term climate change research. Copyright © 2011 Royal Meteorological Society

93 citations


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[...]

08 Dec 2001-BMJ
TL;DR: There is, I think, something ethereal about i —the square root of minus one, which seems an odd beast at that time—an intruder hovering on the edge of reality.
Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

33,785 citations

Journal ArticleDOI
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

13,246 citations

01 Jan 1989
TL;DR: In this article, a two-dimensional version of the Pennsylvania State University mesoscale model has been applied to Winter Monsoon Experiment data in order to simulate the diurnally occurring convection observed over the South China Sea.
Abstract: Abstract A two-dimensional version of the Pennsylvania State University mesoscale model has been applied to Winter Monsoon Experiment data in order to simulate the diurnally occurring convection observed over the South China Sea. The domain includes a representation of part of Borneo as well as the sea so that the model can simulate the initiation of convection. Also included in the model are parameterizations of mesoscale ice phase and moisture processes and longwave and shortwave radiation with a diurnal cycle. This allows use of the model to test the relative importance of various heating mechanisms to the stratiform cloud deck, which typically occupies several hundred kilometers of the domain. Frank and Cohen's cumulus parameterization scheme is employed to represent vital unresolved vertical transports in the convective area. The major conclusions are: Ice phase processes are important in determining the level of maximum large-scale heating and vertical motion because there is a strong anvil componen...

3,813 citations

01 Apr 2003
TL;DR: The EnKF has a large user group, and numerous publications have discussed applications and theoretical aspects of it as mentioned in this paper, and also presents new ideas and alternative interpretations which further explain the success of the EnkF.
Abstract: The purpose of this paper is to provide a comprehensive presentation and interpretation of the Ensemble Kalman Filter (EnKF) and its numerical implementation. The EnKF has a large user group, and numerous publications have discussed applications and theoretical aspects of it. This paper reviews the important results from these studies and also presents new ideas and alternative interpretations which further explain the success of the EnKF. In addition to providing the theoretical framework needed for using the EnKF, there is also a focus on the algorithmic formulation and optimal numerical implementation. A program listing is given for some of the key subroutines. The paper also touches upon specific issues such as the use of nonlinear measurements, in situ profiles of temperature and salinity, and data which are available with high frequency in time. An ensemble based optimal interpolation (EnOI) scheme is presented as a cost-effective approach which may serve as an alternative to the EnKF in some applications. A fairly extensive discussion is devoted to the use of time correlated model errors and the estimation of model bias.

2,975 citations