Hello and welcome to ExamPundit. Here is a set of English Quiz based on Comprehension for IBPS PO Prelims 2015.
Directions (Q. 1-10):
Read the following passage carefully and answer the questions given below it.
Certain words have been printed in bold
to help you locate them while answering some of the questions.
Read the following passage carefully and answer the questions given below it.
Certain words have been printed in bold
to help you locate them while answering some of the questions.
About once in every seven years,
the ocean surface off the coast of Peru warms up. This cuts the normal
enriching nutrient-rich cold water. Plankton production is drastically reduced.
This phenomenon, known as EL Nino because it starts during the Christmas
season, usually lasts for up to a year. Occasionally it goes on for longer. Three
times this century, it has persisted into a third year. The latest of these
prolonged episodes have been blamed for much more than its effect on the
Peruvian fishery. It certainly seems to have played a part in droughts in
normally humid Indonesia as well as those that brought catastrophic fires to the outskirts of Sydney. It has been blamed
for storms and landslides in coastal regions of Peru and Ecuador and is
associated with drought in north-eastern Brazil. Its influence may stretch as
far as Western Europe where the recent
winter brought heavy rainfall and flooding. El Nino is not the only ocean
phenomenon to affect the weather. To understand how this might be done, it is
useful to think about how the weather is forecast and about what makes it
predictable. Weather forecasting uses two types of techniques, both of which rely on observations of what is going
on at and shortly before the time of the forecast. To supply this information,
national authorities operate extensive
land, sea and air based observation networks. These are coordinated under the
United Nations World Meteorological Organization. There is an effective and
almost instantaneous worldwide exchange of the information gathered. A great
deal of research has been put into this with some success but progress has been
limited by two factors. The first is that coupled ocean-atmosphere models
require enormously greater computing capacity than is provided even by the
super computers used in weather forecasting. The second is that not enough is
known about the state of the ocean at any given time- there is no global
observation network as there is for the atmosphere or about the processes that
govern the interactions. When these limitations have been overcome, it seems
likely that coupled models will permit prediction of such climatological
factors as frequency and intensity of rainfall for seasons and perhaps years
ahead. In the meantime, climatologists have begun to be able to predict the
onset and co – n sequences of phenomena such as El Nino using techniques like
those used in the classical weather forecasting method. These ex – amine the
condition of the ocean at a given instant and by comparison with past
experience, attempt to predict in statistical way ho – w the ocean or the
atmosphere- and hence the climate- are likely to behave for the next few
months. As with the weather forecasts, this depends critically on the existence
of a bank of past experience, in this case, particularly of sea-surface
temperatures. A key contribution to this, recently completed by the U.K. Meteorological
Office, has been the analysis of many millions of sea-surface temperatures.
This has led to the publication of the first globally complete monthly fields
of sea-surface temperatures from 1871 to the pre sent day. This type of
information may be used to predict events such as El Nino. Various scientific
groups have tried to do so with varying degrees of success. In the meantime
meteorological office scientists have also compared the temperature fields
statistically with climatological factors. They have shown that particularly in
tropics there are significant
correlations between sea-surface temperature anomalies and climate statistics.
This does not necessarily mean that one cause the other, though some degree of
direct linkage seems likely. But it does open up the possibility of predicting
short term climate fluctuations. The U.K. meteorological office has app –
roached this by a rigorous comparison between rainfall statistics in the
Nordeste area of Brazil, whose crops can be seriously affected by drought, with
contemporaneous sea-surface temperatures worldwide. This has revealed
significant links, verified over a period 1901-85, between rainfall and
sea-surface temperatures in the north and south tropical Atlantic and western
tropical Pacific, the area most strongly affected by El Nino.
the ocean surface off the coast of Peru warms up. This cuts the normal
enriching nutrient-rich cold water. Plankton production is drastically reduced.
This phenomenon, known as EL Nino because it starts during the Christmas
season, usually lasts for up to a year. Occasionally it goes on for longer. Three
times this century, it has persisted into a third year. The latest of these
prolonged episodes have been blamed for much more than its effect on the
Peruvian fishery. It certainly seems to have played a part in droughts in
normally humid Indonesia as well as those that brought catastrophic fires to the outskirts of Sydney. It has been blamed
for storms and landslides in coastal regions of Peru and Ecuador and is
associated with drought in north-eastern Brazil. Its influence may stretch as
far as Western Europe where the recent
winter brought heavy rainfall and flooding. El Nino is not the only ocean
phenomenon to affect the weather. To understand how this might be done, it is
useful to think about how the weather is forecast and about what makes it
predictable. Weather forecasting uses two types of techniques, both of which rely on observations of what is going
on at and shortly before the time of the forecast. To supply this information,
national authorities operate extensive
land, sea and air based observation networks. These are coordinated under the
United Nations World Meteorological Organization. There is an effective and
almost instantaneous worldwide exchange of the information gathered. A great
deal of research has been put into this with some success but progress has been
limited by two factors. The first is that coupled ocean-atmosphere models
require enormously greater computing capacity than is provided even by the
super computers used in weather forecasting. The second is that not enough is
known about the state of the ocean at any given time- there is no global
observation network as there is for the atmosphere or about the processes that
govern the interactions. When these limitations have been overcome, it seems
likely that coupled models will permit prediction of such climatological
factors as frequency and intensity of rainfall for seasons and perhaps years
ahead. In the meantime, climatologists have begun to be able to predict the
onset and co – n sequences of phenomena such as El Nino using techniques like
those used in the classical weather forecasting method. These ex – amine the
condition of the ocean at a given instant and by comparison with past
experience, attempt to predict in statistical way ho – w the ocean or the
atmosphere- and hence the climate- are likely to behave for the next few
months. As with the weather forecasts, this depends critically on the existence
of a bank of past experience, in this case, particularly of sea-surface
temperatures. A key contribution to this, recently completed by the U.K. Meteorological
Office, has been the analysis of many millions of sea-surface temperatures.
This has led to the publication of the first globally complete monthly fields
of sea-surface temperatures from 1871 to the pre sent day. This type of
information may be used to predict events such as El Nino. Various scientific
groups have tried to do so with varying degrees of success. In the meantime
meteorological office scientists have also compared the temperature fields
statistically with climatological factors. They have shown that particularly in
tropics there are significant
correlations between sea-surface temperature anomalies and climate statistics.
This does not necessarily mean that one cause the other, though some degree of
direct linkage seems likely. But it does open up the possibility of predicting
short term climate fluctuations. The U.K. meteorological office has app –
roached this by a rigorous comparison between rainfall statistics in the
Nordeste area of Brazil, whose crops can be seriously affected by drought, with
contemporaneous sea-surface temperatures worldwide. This has revealed
significant links, verified over a period 1901-85, between rainfall and
sea-surface temperatures in the north and south tropical Atlantic and western
tropical Pacific, the area most strongly affected by El Nino.
1. Which of the
following is not true about El Nino?
following is not true about El Nino?
1) It is the most important ocean phenomenon to affect a
region’s weather pattern.
region’s weather pattern.
2) There seems to be a statistical link between sea-surface
temperatures and the occurrence of El Nino.
temperatures and the occurrence of El Nino.
3) The consequences of El Nino can vary with the
geographical position of the affected area.
geographical position of the affected area.
4) Generally the effects of El Nino persist for about a
year.
year.
5) The consequences of El Nino may vary with the
geographical position of the affected area.
geographical position of the affected area.
2. Which of the
following data will be helpful in the prediction of El Nino?
following data will be helpful in the prediction of El Nino?
1) Daily temperature recorded in coastal areas.
2) Daily atmospheric pressure levels for one previous year.
3) A data bank of sea-surface temperatures.
4) All of the above
5) None of the above
3. The frequency of
occurrence of El Nino, approximately, is
occurrence of El Nino, approximately, is
1) Once every year
2) About three times in a century
3) Once in three years
4) Once every seven years
5) About three times in a decade
4. The passage talks
about
about
1) The factors that can trigger phenomenon like El Nino
2) Phenomenon like El Nino that affect the planet’s climate.
3) The global efforts being done in order to predict El Nino
4) The advances being made in the field of meteorological
predictions through a greater study of ocean-atmosphere interactions.
predictions through a greater study of ocean-atmosphere interactions.
5) Phenomenon like El Nino that do not affect the planet’s
climate.
climate.
5. Long term, weather
forecasting may become a reality one day when
forecasting may become a reality one day when
1) There exists a global databank on the state of the ocean
and its result ant interactions with the atmosphere.
and its result ant interactions with the atmosphere.
2) Computers which are more efficient than supercomputers
come into existence.
come into existence.
3) There is a thorough analysis of sea surface temperatures
and their effects on the oceans.
and their effects on the oceans.
4) 1), 2) and 3) simultaneously fall into place.
5) 1) and 2) are taken care of.
Directions (Q.6-8):
Choose the word which is MOST SIMILAR in meaning to the word printed in bold as
used in the passage.
Choose the word which is MOST SIMILAR in meaning to the word printed in bold as
used in the passage.
6. Catastrophic
1) Disastrous
2) Developing
3) Prosperous
4) Possible
5) Probable
7. Recent
1) Latest
2) Last
3) Previous
4) Final
5) Prior
8. Rely
1) Depend
2) Vary
3) Very
4) Fluctuate
5) Swing
Directions (Q.9-10):
Choose the word which is MOST OPPOSITE in meaning to the word printed in bold
as used in the passage.
Choose the word which is MOST OPPOSITE in meaning to the word printed in bold
as used in the passage.
9. Extensive
1) Wide
2) Spacious
3) Ample
4) Plenty
5) Narrow
10. Significant
1) Important
2) Imperative
3) Trivial
4) Central
5) Chief
Regards
Team ExamPundit
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