Saturday, January 25, 2020
Weather Forecasting with Digital Signals INTRODUCTION: Digital signal processing (DSP) is concerned with the representation of the signals by a sequence of numbers or symbols and the processing of these signals. Digital signal processing and analog signal processing are subfields of signal processing. The analog waveform is sliced into equal segments and the waveform amplitude is measured in the middle of each segment. The collection of measurements makes up the digital representation of the waveform. Converting a continuously changing waveform (analog) into a series of discrete levels (digital) Applications of DSP DSP technology is nowadays commonplace in such devices as mobile phones, multimedia computers, video recorders, CD players, hard disc drive controllers and modems, and will soon replace analog circuitry in TV sets and telephones. An important application of DSP is in signal compression and decompression. Signal compression is used in digital cellular phones to allow a greater number of calls to be handled simultaneously within each local cell. DSP signal compression technology allows people not only to talk to one another but also to see one another on their computer screens, using small video cameras mounted on the computer monitors, with only a conventional telephone line linking them together. In audio CD systems, DSP technology is used to perform complex error detection and correction on the raw data as it is read from the CD. some of the mathematical theory underlying DSP techniques, such as Fourier and Hilbert Transforms, digital filter design and signal compression, can be fairly complex, the numerical operations required actually to implement these techniques are very simple, consisting mainly of operations that could be done on a cheap four-function calculator. The architecture of a DSP chip is designed to carry out such operations incredibly fast, processing hundreds of millions of samples every second, to provide real-time performance: that is, the ability to process a signal live as it is sampled and then output the processed signal, for example to a loudspeaker or video display. All of the practical examples of DSP applications mentioned earlier, such as hard disc drives and mobile phones, demand real-time operation. Weather forecasting- is the science of making predictions about general and specific weather phenomenon for a given area based on observations of such weather related factors as atmospheric pressure, wind speed and direction, precipitation, cloud cover, temperature, humidity, frontal movements, etc. Meteorologists use several tools to help them forecast the weather for an area. These fall under two categories: tools for collecting data and tools for coordinating and interpreting data. Weather forecasting- is the science of making predictions about general and specific weather phenomenon for a given area based on observations of such weather related factors as atmospheric pressure, wind speed and direction, precipitation, cloud cover, temperature, humidity, frontal movements, etc. Meteorologists use several tools to help them forecast the weather for an area. These fall under two categories: tools for collecting data and tools for coordinating and interpreting data. In a typical weather-forecasting system, recently collected data are fed into a computer model in a process called assimilation. This ensures that the computer model holds the current weather conditions as accurately as possible before using it to predict how the weather may change over the next few days. Weather forecasting is an exact science of data collecting, but interpretation of the data collected can be difficult because of the chaotic nature of the factors that affect the weather. These factors can follow generally recognized trends, but meteorologists understand that many things can affect these trends. With the advent of computer models and satellite imagery, weather forecasting has improved greatly. Weather forecasting- is the science of making predictions about general and specific weather phenomenon for a given area based on observations of such weather related factors as atmospheric pressure, wind speed and direction, precipitation, cloud cover, temperature, humidity, frontal movements, etc. Meteorologists use several tools to help them forecast the weather for an area. These fall under two categories: tools for collecting data and tools for coordinating and interpreting data. * Tools for collecting data include instruments such as thermometers, barometers, hygrometers, rain gauges, anemometers, wind socks and vanes, Doppler radar and satellite imagery (such as the GOES weather satellite). * Tools for coordinating and interpreting data include weather maps and computer models. In a typical weather-forecasting system, recently collected data are fed into a computer model in a process called assimilation. This ensures that the computer model holds the current weather conditions as accurately as possible before using it to predict how the weather may change over the next few days. Weather forecasting is an exact science of data collecting, but interpretation of the data collected can be difficult because of the chaotic nature of the factors that affect the weather. These factors can follow generally recognized trends, but meteorologists understand that many things can affect these trends. With the advent of computer models and satellite imagery, weather forecasting has improved greatly. Since lives and livelihoods depend on accurate weather forecasting, these improvements have helped not only the understanding of weather, but how it affects living and non living things on Earth. Weather forecasting is the science of making predictions about general and specific weather phenomena for a given area based on observations of such weather related factors as atmospheric pressure, wind speed and direction, precipitation, cloud cover, temperature, humidity, frontal movements, etc. Meteorologists use several tools to help them forecast the weather for an area. These fall under two categories: tools for collecting data and tools for coordinating and interpreting data. Tools for collecting data include instruments such as thermometers, barometers, hygrometers, rain gauges, anemometers, wind socks and vanes, Doppler radar and satellite imagery (such as the GOES weather satellite). Tools for coordinating and interpreting data include weather maps and computer models. In a typical weather-forecasting system, recently collected data are fed into a computer model in a process called assimilation. This ensures that the computer model holds the current weather conditions as accurately as possible before using it to predict how the weather may change over the next few days. Weather forecasting is an exact science of data collecting, but interpretation of the data collected can be difficult because of the chaotic nature of the factors that affect the weather. These factors can follow generally recognized trends, but meteorologists understand that many things can affect these trends. With the advent of computer models and satellite imagery, weather forecasting has improved greatly. Since lives and livelihoods depend on accurate weather forecasting, these improvements have helped not only the understanding of weather, but how it affects living and nonliving things on Earth. Weather forecasting is the application of science and technology to predict the state of the atmosphere for a future time and a given location. Human beings have attempted to predict the weather informally for millennia, and formally since at least the nineteenth century. Weather forecasts are made by collecting quantitative data about the current state of the atmosphere and using scientific understanding of atmospheric processes to project how the atmosphere will evolve. Once an all-human endeavor based mainly upon changes in barometric pressure, current weather conditions, and sky condition, forecast models are now used to determine future conditions. Human input is still required to pick the best possible forecast model to base the forecast upon, which involves pattern recognition skills, teleconnections, knowledge of model performance, and knowledge of model biases. The chaotic nature of the atmosphere, the massive computational power required to solve the equations that describe the atmosphere, error involved in measuring the initial conditions, and an incomplete understanding of atmospheric processes mean that forecasts become less accurate as the difference in current time and the time for which the forecast is being made (the range of the forecast) increases. The use of ensembles and model consensus help narrow the error and pick the most likely outcome. There are a variety of end uses to weather forecasts. Weather warnings are important forecasts because they are used to protect life and property. Forecasts based on temperature and precipitation are important to agriculture, and therefore to traders within commodity markets. Temperature forecasts are used by utility companies to estimate demand over coming days. On an everyday basis, people use weather forecasts to determine what to wear on a given day. Since outdoor activities are severely curtailed by heavy rain, snow and the wind chill, forecasts can be used to plan activities around these events, and to plan ahead and survive them. History of weather control If we dispense with legends, at least Native American Indians had methods which they believed to induce rain. The Finnish people, on the other hand, were believed by others to be able to control all weather. Thus Vikings refused to take Finns on their raids by sea. Remnants of this belief lasted well into the modern age, with many ship crews being reluctant to accept Finnish sailors. The early modern era saw people observe that during battles the firing of cannons and other firearms often precipitated precipitation. The first example of weather control which is still considered workable is probably the lightning conductor. For millennia people have tried to forecast the weather. In 650 BC, the Babylonians predicted the weather from cloud patterns as well as astrology. In about 340 BC, Aristotle described weather patterns in Meteorologica. Later, Theophrastus compiled a book on weather forecasting, called the Book of Signs. Chinese weather prediction lore extends at least as far back as 300 BC. In 904 AD, Ibn Wahshiyyas Nabatean Agriculture discussed the weather forecasting of atmospheric changes and signs from the planetary astral alterations; signs of rain based on observation of the lunar phases; and weather forecasts based on the movement of winds. Ancient weather forecasting methods usually relied on observed patterns of events, also termed pattern recognition. For example, it might be observed that if the sunset was particularly red, the following day often brought fair weather. This experience accumulated over the generations to produce weather lore. However, not all of these predictions prove reliable, and many of them have since been found not to stand up to rigorous statistical testing. It was not until the invention of the electric telegraph in 1835 that the modern age of weather forecasting began. Before this time, it had not been possible to transport information about the current state of the weather any faster than a steam train. The telegraph allowed reports of weather conditions from a wide area to be received almost instantaneously by the late 1840s. This allowed forecasts to be made by knowing what the weather conditions were like further upwind. The two men most credited with the birth of forecasting as a scienc e were Francis Beaufort (remembered chiefly for the Beaufort scale) and his protÃ ©gÃ © Robert FitzRoy (developer of the Fitzroy barometer). Both were influential men in British naval and governmental circles, and though ridiculed in the press at the time, their work gained scientific credence, was accepted by the Royal Navy, and formed the basis for all of todays weather forecasting knowledge. To convey information accurately, it became necessary to have a standard vocabulary describing clouds; this was achieved by means of a series of classifications and, in the 1890s, by pictorial cloud atlases. Great progress was made in the science of meteorology during the 20th century. The possibility of numerical weather prediction was proposed by Lewis Fry Richardson in 1922, though computers did not exist to complete the vast number of calculations required to produce a forecast before the event had occurred. Practical use of numerical weather prediction began in 1955, spurred by the development of programmable electronic computers. * Modern aspirations There are two factors which make weather control extremely difficult if not fundamentally intractable. The first one is the immense quantity of energy contained in the atmosphere. The second is its turbulence. Effective cloud seeding to produce rain has always been some 50 years away. People do utilize even the most expensive and experimental types of it, but more in hope than confidence. Another even more speculative and expensive technique that has been semiseriously discussed is the dissipation of hurricanes by exploding a nuclear bomb in the eye of the storm. It is questionable that it will ever even be tried, because if it failed, the result would be a hurricane bearing radioactive fallout along with the destructive power of its winds and rain. * Modern day weather forecasting system Components of a modern weather forecasting system include: Data collection Data assimilation Numerical weather prediction Model output post-processing Forecast presentation to end-user * Data collection Observations of atmospheric pressure, temperature, wind speed, wind direction, humidity, precipitation are made near the earths surface by trained observers, automatic weather stations or buoys. The World Meteorological Organization acts to standardize the instrumentation, observing practices and timing of these observations worldwide. Stations either report hourly in METAR reports, or every six hours in SYNOP reports. Diurnal (daily) rhythm of air pressure in northern Germany (black curve is air pressure) Atmospheric pressure is the pressure at any point in the Earths atmosphere. For other uses, see Temperature (disambiguation). An AWS in Antarctica An automatic weather station (AWS) is an automated version of the traditional weather station, either to save human labour or to enable measurements from remote areas. Weather buoys are instruments which collect weather and ocean data within the worlds oceans. WMO flag The World Meteorological Organization (WMO, French: , OMM) is an intergovernmental organization with a membership of 188 Member States and Territories. METAR (for METeorological Aerodrome Report) is a format for reporting weather information. SYNOP (surface synoptic observations) is a numerical code (called FM-12 by WMO) used for reporting marine weather observations made by manned and automated weather stations. Measurements of temperature, humidity and wind above the surface are found by launching radiosondes (weather balloon). Data are usually obtained from near the surface to the middle of the stratosphere, about 30,000 m (100,000 ft). In recent years, data transmitted from commercial airplanes through the AMDAR system has also been incorporated into upper air observation, primarily in numerical models. radiosonde with measuring instruments A radiosonde (Sonde is German for probe) is a unit for use in weather balloons that measures various atmospheric parameters and transmits them to a fixed receiver. Rawinsonde weather balloon just after launch. Atmosphere diagram showing stratosphere. Aircraft Meteorological Data Relay (AMDAR) is a program initiated by the World Meteorological Organization. Increasingly, data from weather satellites are being used due to their (almost) global coverage. Although their visible light images are very useful for forecasters to see development of clouds, little of this information can be used by numerical weather prediction models. The infra-red (IR) data however can be used as it gives information on the temperature at the surface and cloud tops. Individual clouds can also be tracked from one time to the next to provide information on wind direction and strength at the clouds steering level. Polar orbiting satellites provide soundings of temperature and moisture throughout the depth of the atmosphere. Compared with similar data from radiosondes, the satellite data has the advantage that coverage is global, however the accuracy and resolution is not as good. A weather satellite is a type of artificial satellite that is primarily used to monitor the weather and/or climate of the Earth. Sounding The historical nautical term for measuring dept h. Meteorological radar provide information on precipitation location and intensity.. Additionally, if a Pulse Doppler weather radar is used then wind speed and direction can be determined.. * Data assimilation Data assimilation (DA) is a method used in the weather forecasting process in which observations of the current (and possibly, past) weather are combined with a previous forecast for that time to produce the meteorological `analysis; the best estimate of the current state of the atmosphere. Weatherman redirects here. Modern weather predictions aid in timely evacuations and potentially save lives and property damage. More generally, Data assimilation is a method to use observations in the forecasting process. In weather forecasting there are 2 main types of data assimilation: 3 dimensional (3DDA) and 4 dimensional (4DDA). In 3DDA only those observations are used available at the time of analyses. In 4DDA the past observations are included (thus, time dimension added). The first data assimilation methods were called the objective analyses (e.g., Cressman algorithm). This was in contrast to the subjective analyses, when (in the past practice) numerical weather predictions (NWP) forecasts were arbitrarily corrected by meteorologists. The objective methods used simple interpolation approaches, and thus were the kind of 3DDA methods. The similar 4DDA methods, called nudging also exist (e.g. in MM5 NWP model). They are based on the simple idea of Newtonian relaxation. The idea is to add in the right part of dynamical equations of the model the term, proportional to the difference of the calculated meteorological variable and the observation value. This term, that has a negative sign keeps the calculated state vector closer to the observations. The first breakdown in the field of data assimilation was introducing by L.Gandin (1963) with the statistical interpolation (or optimal interpolation ) method. It developed the previous ideas of Kolmogorov. That method is the 3DDA method and is the kind of regression analyses, which utilizes the information about the spatial distributions of covariance functions of the errors of the first guess field (previous forecast) and true field. These functions are never known. However, the different approximations were assumed. In fact optimal interpolation algorithm is the reduced version of the Kalman filtering (KF) algorithm, when the covariance matrices are not calculated from the dynamical equations, but are pre-determined in advance. The Kalman filter (named after its inventor, Rudolf Kalman) is an efficient recursive computational solution for tracking a time-dependent state vector with noisy equations of motion in real time by the least-squares method. When this was recognised the attempts to introduce the KF algorithms as a 4DDA tool for NWP models were done. However, this was (and remains) a very difficult task, since the full version of KF algorithm requires solution of the enormous large number of additional equations. In connection with that the special kind of KF algorithms (suboptimal) for NWP models were developed. Another significant advance in the development of the 4DDA methods was utilizing the optimal control theory (variational approach) in the works of Le Dimet and Talagrand, 1986, based on the previous works of G. Marchuk. The significant advantage of the variational approaches is that the meteorological fields satisfy the dynamical equations of the NWP model and at the same time they minimize the functional, characterizing their difference from observations. Thus, the problem of constrained minimization is solved. The 3DDA variational methods also exist (e.g., Sasaki, 1958). Optimal control theory is a mathematical field that is concerned with control policies that can be deduced using optimization algorithms. As it was shown by Lorenc, 1986, the all abovementioned kinds of 4DDA methods are in some limit equivalent. I.e., under some assumptions they minimize the same cost functional. However, these assumptions never fulfill. The rapid development of the various data assimilation methods for NWP is connected to the two main points in the field of numerical weather prediction: 1. Utilizing the observations currently seems to be the most promicing challange to improve the quality of the forecasts at the different scales (from the planetary scale to the local city, or even street scale) 2. The number of different kinds of observations (sodars, radars, sattelite) is rapidly growing. The DA methods are currently used not also in weather forecasting, but in different environmental forecasting problems, e.g. in hydrological forecasting. Basically the same types of DA methods, as those, described above are in use there. Data assimilation is the challange for the every forecasting problem. Numerical weather prediction Numerical weather prediction is the science of predicting the weather using mathematical models of the atmosphere. Manipulating the huge datasets and performing the complex calculations necessary to do this on a resolution fine enough to make the results useful can require some of the most powerful supercomputers in the world. Image File history File links NAM_500_MB.PNGÃ ¢Ã ¢Ã¢â¬Å¡Ã ¬Ãâ¦Ã ½ File links The following pages on the English Wikipedia link to this file (pages on other projects are not listed): Numerical weather prediction Block (meteorology) Image File history File links NAM_500_MB.PNGÃ ¢Ã ¢Ã¢â¬Å¡Ã ¬Ãâ¦Ã ½ File links The following pages on the English Wikipedia link to this file (pages on other projects are not listed): Numerical weather prediction Block (meteorology) A millibar (mbar, also mb) is 1/1000th of a bar, a unit for measurement of pressure. Geopotential height is a vertical coordinate referenced to Earths mean sea level an adjustment to geomet ric height (elevation above mean sea level) using the variation of gravity with latitude and elevation. Weather is a term that encompasses phenomena in the atmosphere of a planet. A mathematical model is an abstract model that uses mathematical language to describe the behaviour of a system. A supercomputer is a computer that leads the world in terms of processing capacity, particularly speed of calculation, at the time of its introduction. An example of 500 mbar geopotential height prediction from a numerical weather prediction model Model output post processing The raw output is often modified before being presented as the forecast. This can be in the form of statistical techniques to remove known biases in the model, or of adjustment to take into account consensus among other numerical weather forecasts. For other senses of this word, see bias (disambiguation). In the past, the human forecaster used to be responsible for generating the entire weather forecast from the observations. However today, for forecasts beyond 24hrs human input is generally confined to post-processing of model data to add value to the forecast. Humans are required to interpret the model data into weather forecasts that are understandable to the end user. Additionally, humans can use knowledge of local effects which may be too small in size to be resolved by the model to add information to the forecast. However, the increasing accuracy of forecast models continues to decrease the need for post-processing and human input. Examples of weather model data can be found on Vigilant Weathers Model Pulse. Presentation of weather forecasts The final stage in the forecasting process is perhaps the most important. Knowledge of what the end user needs from a weather forecast must be taken into account to present the information in a useful and understandable way. * Public information One of the main end users of a forecast is the general public. Thunderstorms can cause strong winds, dangerous lightning strikes leading to power outages, and widespread hail damage. Heavy snow or rain can bring transportation and commerce to a stand-still, as well as cause flooding in low-lying areas. Excessive heat or cold waves can kill or sicken those without adequate utilities. The National Weather Service provides forecasts and watches/warnings/advisories for all areas of the United States to protect life and property and maintain commercial interests. Traditionally, television and radio weather presenters have been the main method of informing the public, however increasingly the internet is being used due to the vast amount of information that can be found. * Air traffic The aviation industry is especially sensitive to the weather. Fog and/or exceptionally low ceilings can prevent many aircraft landing and taking off. Similarly, turbulence and icing can be hazards whilst in flight. Thunderstorms are a problem for all aircraft, due to severe turbulence and icing, as well as large hail , strong winds, and lightning , all of which can cause fatal damage to an aircraft in flight. On a day to day basis airliners are routed to take advantage of the jet stream tailwind to improve fuel efficiency. Air crews are briefed prior to take off on the conditions to expect en route and at their destination. * Utility companies Electricity companies rely on weather forecasts to anticipate demand which can be strongly affected by the weather. In winter, severe cold weather can cause a surge in demand as people turn up their heating. Similarly, in summer a surge in demand can be linked with the increased use of air conditioning systems in hot weather. * Private sector Increasingly, private companies pay for weather forecasts tailored to their needs so that they can increase their profits. For example, supermarket chains may change the stocks on their shelves in anticipation of different consumer spending habits in different weather conditions. a) =Ensemble forecasting= Although a forecast model will predict realistic looking weather features evolving realistically into the distant future, the errors in a forecast will inevitably grow with time due to the chaotic nature of the atmosphere. The detail that can be given in a forecast therefore decreases with time as these errors increase. There becomes a point when the errors are so large that the forecast is completely wrong and the forecasted atmospheric state has no correlation with the actual state of the atmosphere. However, looking at a single forecast gives no indication of how likely that forecast is to be correct. Ensemble forecasting uses lots of forecasts produced to reflect the uncertainty in the initial state of the atmosphere (due to errors in the observations and insufficient sampling). The uncertainty in the forecast can then be assessed by the range of different forecasts produced. They have been shown to be better at detecting the possibility of extreme events at long range. Ensemble forecasts are increasingly being used for operational weather forecasting (for example at ECMWF , NCEP , and the Canadian forecasting center). b) =Nowcasting= The forecasting of the weather in the 0-6 hour timeframe is often referred to as nowcasting . It is in this range that the human forecaster still has an advantage over computer NWP models. In this time range it is possible to forecast smaller features such as individual shower clouds with reasonable accuracy, however these are often too small to be resolved by a computer model. A human given the latest radar, satellite and observational data will be able to make a better analysis of the small scale features present and so will be able to make a more accurate forecast for the following few hours. Signal Processing Generating imagery for forecasting terror threats Intelligence analysts and military planners need predictions about likely terrorist targets in order to better plan the deployment of security forces and sensing equipment. We have addressed this need using Gaussian-based forecasting and uncertainty modeling. Our approach excels at indicating the highest threats expected for each point along a travel path and for a global war on terrorism mission. It also excels at identifying the greatest-likelihood collection areas that would be used to observe a target. 1 on geospatial analysis and asymmetric-threat forecasting in the urban environment. He showed how to extract distinct signatures from associations made between historical event information and contextual information sources such as geospatial and temporal political databases. We have augmented this to include uncertainty estimates associated with historical events and geospatial information layers.2 Event Forecasting Spatial Preferences The notion of spatial preferences has been used to find potential crime1 and threat3 hot spots. The premise is that a terrorist or criminal is directed toward a certain location by a set of qualities, such as geospatial features, demographic and economic information, and recent political events. Focusing on geospatial information, we assume the intended target is associated with features a small distance from the event location. We assign the highest likelihoods to the distances between each key feature and the event, and taper them away from these distances. This behavior is modeled using a kernel function centered at each of these distances. For a Gaussian kernel applied to a discretized map, the probability density function Ã Ã for a given grid cell g and uncertainty estimates u is given by Dig is the distance from feature i to the grid cell, Din is the distance from the feature to event location n, c is a constant, ÃÅ½Ã ¦E and ÃÅ½Ã ¦F are the position uncertainty for event and features respectively, I is the total number of features, and N is the total number of events. Figure 1(a) shows a sample forecast image based on this approach, denoting threat level with colors ranging from blue for lowest threat, through red for highest threat. For the same set of features and events, Figure 1(b) shows a more manageable forecast-in terms of allocating security resources-determined by aggregating feature layers prior to generating the likelihood values. Modeling Uncertainty One of the most important aspects of forecasting is having an estimate of the confidence in the supporting numerical values. In numerical weather prediction, there is always a value of confidence assigned with each forecast. For example, predicting an 80% chance of rain implies that numerical weather models given input parameter variations, predicted eight o
Friday, January 17, 2020
The British were wrong by taking the option of trading opium because by trading opium, they would be jeopardising the wellbeing of an entire country. But they only did it because the Chinese were refusing to trade, so therefore it is only partially Britains fault. The Ã¢â¬Å"Opium WarÃ¢â¬ also known as the Anglo-Chinese war began in 1839. It started as a conflict over trading between Britain and China. China was refusing to trade because they didnÃ¢â¬â¢t need anything. Eventually the British were able to trade opium on the black market. China did nearly everything to stop the opium being traded but nothing could stop it. This eventually caused the war. Was Britain Completely in the wrong? No. Although they were the ones that started the opium trade, China is still partially to blame. The following points will be argued for the fact that both sides contributed and neither were completely wrong: Ã · The introduction of trading opium by Britain Ã · The stupidity of the Chinese stimulating the British and judging them to be bad at war. Ã · And The greedy treaty made by the British But firstly, the refusal for trade and the cruel regulations that China put upon the British traders. There was a demand for Chinese tea, silk and porcelain in the west, though there was practically nothing that the west could offer to trade with China, because of the simple reason that they didnÃ¢â¬â¢t want anything and were refusing to trade for things they didnÃ¢â¬â¢t need. The Chinese didnÃ¢â¬â¢t realise how hard they were making the situation. A British man, Lord William John Nappier was sent to China to try and extend British trading interests. He was told that he could only address himself to the Hong Merchants and that he could only live in Guangzhou during trading season. When he refused to leave, Lu Kun, Governor of Guangzhou prohibited all the buying and selling to the English and then ordered all the withdrawal of all Chinese labour from them. What were the British to do? The regulations were too harsh and the British couldnÃ¢â¬â¢t trade no matter whatÃ they tried. In this situation, The Chinese were obviously in the wrong because they didnÃ¢â¬â¢t consider the needs of the British and they were to stubborn to trade because they thought they were more superior. Secondly, Britain introduced the opium to China because they ran out of choices. Since China ignored BritainÃ¢â¬â¢s proposal to trade, Britain had to find some other way they could get the bits and pieces that they required. They started to illegally export opium on the black market, aware of the consequences. The result was a widespread addiction throughout China causing serious social and economic disruption in China. Britain was most definitely in the wrong with this choice because nothing can make the trading of opium justifiable. The cost is too painful. But it was ChinaÃ¢â¬â¢s fault in the fist place that they didnÃ¢â¬â¢t want to establish trading with Britain. Thirdly, the stupidity of the Chinese stimulating the British and judging them to be hopeless at fighting caused them the loss of the war. The Chinese were ignorant, and they thought that the British were bad compared to them. Lin Zexu says, Ã¢â¬Å"Besides guns, the barbarian soldiers do not know how to use fist or swordsÃ¢â¬ ¦ Therefore, what is called their power can be controlled without difficulty.Ã¢â¬ Unfortunately Lin Zexu was wrong about this. The underestimation of the British made the Chinese disadvantaged because they werenÃ¢â¬â¢t prepared enough and much unorganised. Their weapons were completely useless against those of the British. Chinese cities were then captured and Chinese citizenÃ¢â¬â¢s soldiers were forced to surrender. Therefore ChinaÃ¢â¬â¢s stupidity and bad organisation skills in this case were to blame for the opium war and their loss. So China was, in this case was in the wrong. The last factor is the greedy treaty made by the British. Once the Chinese had lost the war, they had no choice but to sign a treaty written by the British. Many unreasonable discissions were made in favour of the British including many unjust payments. China was completely demoralised and Britain was in the wrong for making them sign such an unfair treaty. They took advantage of China when they shouldnÃ¢â¬â¢t have. To conclude this argument, neither China nor Britain was completely wrong orÃ right with all their decisions. They both contributed to the war and therefore it was both their fault. ChinaÃ¢â¬â¢s refusal for trade was wrong because they were being selfish and stubborn and they werenÃ¢â¬â¢t considering the welfare of others. Britain was wrong in introducing opium because nothing can justify the trading of opium and it shouldnÃ¢â¬â¢t have even been an option to trade it. Britain was also in the wrong by creating a treaty in their favour because China was in a weak position.
Thursday, January 9, 2020
Erica Carter Erica Carter teaches Cultural Studies at the University of Warwick. Recently, she published How German is She? Postwar West German Reconstruction and the consuming Woman (1996), in which she explores how the development of a social market economy after 1949 gave a new centrality to consumers as key players in the economic life of the (German) nation and in that process gave women a new public significance. Carter argues that concepts of nationhood survived in the rhetorics of public policy and in popular culture of the period. Carters (1984) interesting argument regarding young women and their relationship to consumerism and the market owes much to early feminist critique. Carter insists that the image industriesÃ¢â¬ ¦show more contentÃ¢â¬ ¦Girls are written into youth cultural theory in the language of consumption--initially, as objects for consumption by men. At first, British cultural theorists thought of girls as an absence, a silence, a silence which could only be filled in some separate world of autonomous female culture. Feminist researchers turned to the family as the pivotal point. In following working-class girls into the closed arena of the family, researchers of female culture gained insight into the possibilities of specifically female cultural forms. In this way, they thought of so-called bedroom culture as analogous to male subcultures (p. 105). Searching for autonomous female cultural forms in the bedroom hideaways of teenage girls has been problematic--in terms of the creative, productive, and potentially subversive power of this mode of femininity. Researchers thought that studying teeny bopper culture was the key which would unlock the potentialities of specifically female forms. Subculture theory proved to be an inadequate starting point for studies of female culture. The spectacle of working-class subcultures erupted into a gap between class relations as they are lived by working-class youth and the classless categories according to which capitalist markets are structured. Ever since W.L. Warners (1960) classic study of social class in America, the marketing establishment has measured consumers against typological grids on
Wednesday, January 1, 2020
The Women of HomerÃ¢â¬â¢s Odyssey HomerÃ¢â¬â¢s Odyssey, by, is typically seen as a male dominated poem: the hero is male and the majority of the characters are male. We follow the men on their attempt to return to Ithaca. However, even though women are not the main characters, they are omnipresent through much of the story. Women play a very important role in the movement of the story line: they all want to marry, help or hurt Odysseus. During the course of his journey, Odysseus meets three different women who want him to be their husband: Circe, Calypso, Nausicca, and finally one woman who is his true wife: Penelope. Each of these women has a profound effect on Odysseus journey home. Yet, even though these women are much more powerfulÃ¢â¬ ¦show more contentÃ¢â¬ ¦She is not depicted as an ordinary Greek female. She is described in Book 10 in this way: the nymph with lovely braids, . . .lifting her spellbinding voice as she glided back and forth at her great immortal loom (Homer 10: 241-245). She has many characteristics, which are praised in classical Greek society such as beauty, a wonderful voice, and talent at weaving. Yet, Circe is far from the ideal Greek woman who keeps her house and is relatively innocent of the ways of the world. She is far from innocent and tries to seduce Odysseus saying: Come, sheath your sword, lets go to bed together, mount my bed and mix in the magic work of love-well breed deep trust between us. This lengthy description is rather interesting considering Circe is female and females were not even spoken of by name at the time of their marriage (Kebric 140), however, she also has a profound effect on the outcome of the story. Odysseus having been persuaded to share Circes bed at her oath that she not harm him (Homer 0: 383-386) stays on her island for a year while Penelopes suitors ravage his house in Ithaca. It is also Circe who tells Odysseus he must descend to the underworld rather than sailing straight back to Ithaca. The next female who attempts to seduce and keep Odysseus on her island is the nymph Calypso. Again this woman is no ordinaryShow MoreRelatedThe Odyssey : The Role Of Women In Homers Odyssey966 Words Ã |Ã 4 Pagesinteractions. The Odyssey portrays what is right or wrong in relationships between god and mortal, father and son, and man and woman. In the epic poem, the role of women is a vital demonstration of Ancient Greece. The women in the epic are unique in their personality, motives, and relationships towards men. In Homers, The Odyssey, all women are different, but all of them help to represent the role of the ideal woman. Homers epic describes the world of women in Ancient Greece, a time where women were seenRead MoreThe Role Of Women In Homers Odyssey1165 Words Ã |Ã 5 Pages The Role of Women In The Odyssey In literature, are women used as important roles or only used as love interests and for their beauty? This question has been around for a long time and there is still no clear answer. One literary example is the women characters in HomerÃ¢â¬â¢s The Odyssey- the ancient Greek epic of Odysseus and his journey home to Ithaka. When on his journey, Odysseus meets many women who are ultimately used as alluring characters to distract Odysseus on his journey home. ThroughoutRead More Women in Homers The Odyssey Essay627 Words Ã |Ã 3 PagesHomers The Odyssey Women are important to the plot and overall theme of the Odyssey. In fact, without many of the women there would not be a complex plot to this epic poem. In the narrative and in Greek society women played a variety of roles, as mothers, herons, and many other strong roles yet, they were treated as less significant, and were made to be loyal and submissive to men. The women were required to wait on and sulk for love, as Penelope did for 20 years. In Greek society, the women hadRead More Powerful Women of Homers Odyssey Essay516 Words Ã |Ã 3 Pages The Powerful Women of Homers Odyssey nbsp;nbsp;nbsp;nbsp;nbsp; nbsp;nbsp;nbsp;nbsp;nbsp; Homers Odyssey depicts women as strong subjects-they are real substantive characters. Women in this poem are tough, strong-willed and are treated with the respect and seriousness they deserve.nbsp; Homer characterizes the women in his poem as the real counterparts of men-they have real feelings, real plans and are able to accomplish them on their own. nbsp; nbsp;nbsp;nbsp;nbsp;nbsp;Read More The Role of Women in Homers The Odyssey Essay695 Words Ã |Ã 3 PagesThe Role of Women in Homers The Odyssey Women form an important part of the folk epic, written by Homer, The Odyssey. Within the story there are three basic types of women: the goddess, the seductress, and the good hostess/wife. Each role adds a different element and is essential to the telling of the story. The role of the goddess is one of a supernatural being, but more importantly one in a position to pity and help mortals. Athena, the goddess of wisdom,Read More Women in Homers Odyssey Essay1960 Words Ã |Ã 8 Pageswayward King. Odysseus treats all women he encounters with the same caution alluded to by Agamemnon when the shade tells him how his treacherous wife Clytemnestra acted in a way that defiled all women kind. Agamemnon is giving words to the concept of women that existed in Greek times, and still exists today although it is hopefully not expressed as much. Even before Odysseus speaks to his dead friend, he reveals the same attitude in the encounters that he has with women along his journey home. Each andRead More Comparing the Deceitful Women of Homers Odyssey and the Bible1184 Words Ã |Ã 5 PagesThe Deceitful Women of Homers Odyssey and the Bible Across all barriers, women have always brought pain, suffering, and aguish to the men as demonstrated in both Homers Odyssey and the Bible. With their beauty and grace, temptresses like the Sirens and Delilah lure men into their grasps, only to later steer them to their ruin. Other times, they use their cunning abilities and deception, as Circe and Jezebel did, in order to entice men into doing things that they normally would neverRead More The Cunning and Deceitful Women of HomerÃ¢â¬â¢s Odyssey Essay1483 Words Ã |Ã 6 Pages The Cunning and Deceitful Women of HomerÃ¢â¬â¢s Odyssey One of the most famous works from the early Greek era is HomerÃ¢â¬â¢s Odyssey. It details the journey home of a war hero, Odysseus. His homecoming entails many adventures, each presented as a separate episode that he must overcome. Though the varied episodes differ in terms of characters and settings, most are based on similar patterns of plot and theme. The themes that are most emphasized are forgetfulness, a willingness to risk pain for pleasureRead MoreEssay about Portrayal of Women in Homers Odyssey719 Words Ã |Ã 3 PagesThe Portrayal of Women inÃ Homers Odyssey In the first section of Odyssey, mortal women are presented to us as controlled by the stereotypes and expectations of the culture of the day, and it is only within that context that we can consider the examples Homer provides of women to be admired or despised. He provides us with clear contrasts, between Penelope and Eurycleia on the one hand, and Helen and Clytemnaestra on the other. In PenelopeÃ¢â¬â¢s case, it is made clear that her freedom of actionRead More The Role of Men and Women in Homers The Odyssey 1173 Words Ã |Ã 5 PagesWho is the ideal Greek man? It can be argued that through the illustration Homer weaves throughout the Odyssey, Odysseuss character traits cast a lens as a prime example of a man in ancient Greek society. He appears to be brave, intelligent, well-spoken, and clever. Much of his knowledge is discovered by his travels, absorbing the local culture around him and using it as a guide. Aristocratic and a warrior. Perhaps the best warrior of all time. It is the Iliad which presents these attributes initially