This article presents the results of a comparison between several ceilometers. The ceilometers at Hong Kong International Airport are due to be replaced, and this comparison was undertaken to ensure climatological continuity following the installation of a newer model. Cloud base height measurements are examined, and comparisons are made between the results from the different ceilometers, and between the ceilometer data and the data from a human observer. Quantile-quantile (EQQ) plots are also presented. The results provide assurance, through statistical analysis and consideration of case studies, that continuity of measurement can be achieved for clouds that are operationally significant in terms of aviation meteorology. It is also demonstrated that the new model of ceilometer has advantages over the existing models. The use of ceilometers for cloud amount measurement is also studied through comparison with human observations.
The purpose of this study is the selection of an appropriate coupled general circulation model (CGCM) for seasonal weather prediction of winter temperatures in two cities of Pakistan: Islamabad and Lahore. The data used in the study comprises 32 years (1969-2000) of real-time observations from the Pakistan Meteorological Department (PMD) and simulation data from CGCMs, including the European Centre for Medium Range Weather Forecasts (ECMWF) and the UK Met Office (UKMO). This study attempts to establish accurate average seasonal weather predictions using a single-model Ensemble Prediction System (EPS). Accurate seasonal prediction is important for developing an early warning system. The probabilistic forecast is calculated for the lower tercile (i.e. [0, 0.33]) keeping in view the trend of winter temperature for both cities. The initial conditions (ICs) obtained from the CGCMs have been alternatively processed for better results. The probabilistic forecast obtained from the EPS has been further verified against PMD observations and European Reanalysis Data (ERA-40) using two different measures of probabilistic skill: the Brier Skill Score (BSS) and the Relative Operating Characteristic Skill Score (ROCSS). The ECMWF and UKMO models were used for both of the cities under study. The results demonstrate that the ECMWF model is more suitable for Islamabad, whereas the UKMO model is more suitable for Lahore. The results from these models might be generalised for other areas at the same latitudes with similar topographic and climatic conditions.
Simulated satellite imagery offers a direct means for visual evaluation of high-resolution numerical weather prediction (NWP) output, giving potential insights into a model's ability to represent weather systems at various spatial scales. This study explores the feasibility and performance of an experimental, large-domain 200m-resolution configuration of the Hong Kong Observatory's sub-kilometre Aviation Model (AVM) in reproducing cloud features over the geographically complex Pearl River Estuary, as observed by visible and infrared channels of the Advanced Himawari Imager of the Himawari-8 satellite. Simulated radiances using the RTTOV radiative transfer model are able to provide realistic predictions of observed cloud occurrence spatial distribution, even under complex atmospheric conditions involving multiple cloud regimes, highlighting the potential scientific and operational values of photo-realistic satellite imagery products based on very high-resolution NWP.
For the first time, Hong Kong Observatory conducted a full scale dropsonde observation of the circulation of a tropical cyclone, namely, Tropical Storm Aere, on 7 October 2016, over the northern part of the South China Sea. This paper documents the observations and their use in the prediction of the track and strength of the tropical cyclone in a numerical weather prediction model. In addition to the model analysis fields, the dropsonde observations are found to provide further information about the structure of Aere. The quality of the dropsonde data was found to be satisfactory in comparison with the radiosonde data nearby. The dropsonde data were found to have a positive impact on the prediction of the track of the tropical cyclone, improving track location accuracy by about 13%. The prediction of the strength of Aere was found to have a higher dependence on the assimilation of the wind field than the assimilation of the humidity field.
Agriculture contributes around a quarter to Pakistan's economy and is closely linked with the variability of monsoon rainfall. The prediction of monsoon rains with sufficient lead time has immense importance for the planning and management of water resources and agriculture. In this study, Multiple Linear Regression (MLR) and Principal Component Regression (PCR) methods are employed to predict monsoon rainfall, and their performances are compared for June-September (JJAS) for the period 1961-2014 over the monsoon region of Pakistan. Rainfall data of Meteorological stations are used as the predictand. In the MLR method, predictors are carried out from sea level pressure (SLP) and sea surface temperature (SST) of the National Centers for Environmental Prediction (NCEP) reanalysis datasets. The PCR method first calculates principal components (PCs) from SLP and SST data, and these PCs are then combined with the regression technique and used as predictors. The performance of both models is tested using statistical measures such as root mean square error (RMSE), mean absolute error (MAE), bias and the correlation coefficient to evaluate the skill of the forecast. The agreement between actual and predicted rainfall data provides evidence for reasonably accurate predictions from both methods. The MLR and PCR models explained 84.6 and 92.2% of the variation of data, and the multiple correlation coefficients are 0.92 and 0.96 respectively. The correlation coefficient for the verification period (2005-2014) is 0.73 for MLR and 0.89 for PCR. The values of mean bias, MAE and RMSE are -5.5, 20.0 and 25.1mm for MLR, and -0.42, 16.2 and 16.6mm for PCR, respectively. The results indicate that the PCR model forecast is slightly better than that of the MLR model.