A historical re-analysis of the calamitous midget typhoon passing through Hong Kong region on 18 September 1906 and its storm surge impact to Hong Kong region

A typhoon passed through Hong Kong region suddenly and unexpectedly on 18 September 1906(the“Typhoon 1906”)and had a disastrous impact on Victoria Harbour and its surroundings in just a couple of hours.Since the year 1906 was the“Bingwu”year in the Chinese calendar,the typhoon is also known historically as the“Bingwu Typhoon”.Tremendous loss of lives and property resulted,and the shipping and fishing communities were devastated.Two mysteries arising from this calamitous typhoon have existed to date:1)Why the Hong Kong region Observatory was not able to provide any forewarning?2)whether the storm surge reported in some contemporary records is entirely credible?This paper will focus on both of these.In this paper,we re-analyse historical weather observations recorded in various historical documents and estimate the possible storm size,intensity and track of Typhoon 1906 using tropical cyclone models.Based on the re-analyses,the storm surges,storm tides and wave heights in Hong Kong region are also estimated using storm surge and wave models.The results reveal that Typhoon 1906 was a midget typhoon,with a radius of maximum winds of 11 km or smaller,during its passage through Hong Kong region.This explains why it was technically impossible for a forewarning to be given at that time when real-time weather observations from ships,meteorological satellites and radars were non-existent.We also estimate that the maximum storm surges(storm tides)in Hong Kong region were not higher than 0.82 m(2.43 mCD)and 1.98 m(4.15 mCD)in Victoria Harbour and Tolo Harbour respectively.These figures are found to be limited by the intensity and the storm size of the typhoon.Therefore,we conclude that the previously documented storm surge figures are not supported by the present study.

On the Downscaling of Meteorological Fields Using Recurrent Networks for Modelling the Water Balance in a Meso-Scale Catchment Area of Saxony,Germany

In this study, recurrent networks to downscale meteorological fields of the ERA-40 re-analysis dataset with focus on the meso-scale water balance were investigated. Therefore two types of recurrent neural networks were used. The first approach is a coupling between a recurrent neural network and a distributed watershed model and the second a nonlinear autoregressive with exogenous inputs (NARX) network, which directly predicted the component of the water balance. The approaches were deployed for a meso-scale catchment area in the Free State of Saxony, Germany. The results show that the coupled approach did not perform as well as the NARX network. But the meteorological output of the coupled approach already reaches an adequate quality. However the coupled model generates as input for the watershed model insufficient daily precipitation sums and not enough wet days were predicted. Hence the long-term annual cycle of the water balance could not be preserved with acceptable quality in contrary to the NARX approach. The residual storage change term indicates physical restrictions of the plausibility of the neural networks, whereas the physically based correlations among?the components of the water balance were preserved more accurately by the coupled approach.

Wind Power Generation Variations and Aggregations

Climate and weather-propelled wind power is characterized by significant spatial and temporal variability.It has been substantiated that the variability of wind power,in addition to contributing hugely to the instability of power grids,can also send the balancing costs of electricity markets soaring.Existing studies on the same establish that curtailment of such variability can be achieved through the geographic aggregation of various widespread production sites;however,there exists a dearth of comprehensive evaluation concerning different levels/scales of such aggregation,especially from a global perspective.This paper primarily offers a fundamental understanding of the relationship between the wind power variations and aggregations from a systematic viewpoint based on extensive wind power data,thereby enabling the benefits of these aggregations to be quantified from a state scale ranging up to a global scale.Firstly,a meticulous analysis of the wind power variations is undertaken at 6 different levels by converting the 7-year hourly meteorological re-analysis data with a high spatial resolution of 0.25◦×0.25◦(approximate 28 km×28 km)into a wind power series globally.Subsequently,the proposed assessment framework employs a coefficient of variation of wind power as well as a standard deviation of wind power ramping rate to quantify the variations of wind power and wind power ramping rate to exhibit the characteristics and benefits yielded by the wind power aggregation at 6 different levels.A system planning example is adopted to illustrate the correlation between the coefficient of variation reduction of wind power and investment reduction,thereby emphasizing the benefits pertaining to significant investment reduction via aggregation.Furthermore,a wind power duration curve is used to exemplify the availability of wind power aggregated at different levels.Finally,the results provide insights into devising a universal approach towards the deployment of wind power,principally along the lines of Net-Zero.