Tropical Tropopause

From the CER maps (Figure 9), it can be observed that in 2013 ISMR the entire region had witnessed high CER values while other years were partially cover by cloud drops of different radii. The high rainfall intensity during 2013 ISMR could be the manifestation of the high CER values observed during that year compared to the other years (Figure 12). However, the low CER values in the other years can be seen in conjugation with high aerosol loading during that period. Similar trend is also observed in LWP plots for the study area during 2012-15 ISMR (Fig 13). It is evident from the figure 9 that LWP varies from low to high values over the region while, it is more homogeneous in 2014 and 2015 ISMR. The OLR was considerably

Natural disasters occurring from the climate change could be on the rise. Global warming has been rumored to be causing more hurricanes, typhoons, cyclones, heavier monsoonal rains that cause major flooding, mud slides, and other disasters worldwide. A tropical cyclone, also referred to as hurricanes, typhoons, or cyclones, depending on where in the world the cyclone is occurring, are one of the world’s grandest shows of energy provided by nature. Hurricanes are large, swirling, low pressure storms that have sustained winds of over 74 miles an hour and are formed over warm ocean waters (NASA, n.d.). The purpose of this paper is to discuss hurricanes

Graumann, Axel. Hurricane Katrina. [Electronic Resource] : A Climatological Perspective : Preliminary Report. Asheville, NC : U.S. Dept. of Commerce, National Oceanic and Atmospheric Administration, National Environmental Satellite Data and Information Service, National Climatic Data Center, [2006], 2006. Technical report: no. 2005-01. EBSCOhost,

St. Louis falls into the mellow midlatitude atmosphere gathering, and this is a district loaded with air mass complexities (Hess, 2011). These differentiations cause a mixture of unsettling influences in the air leaving St. Louis with an assortment of climate (Hess, 2011). The summers have a tendency to have more precipitation with the coastal stream and incessant convection (Hess, 2011). However winter can encounter rain and periodic snow due to midlatitude typhoons (Hess, 2011).

Chapter four of our textbook, while making the aspect of physical geography a priority concerning weather and climate, take special care in introducing the topic of hurricanes as well as changes in air pressure and ocean currents. Hurricanes can be described as low-pressure areas which begin over warm waters. As they develop, hot, humid air at the surface rises which aids in the suction of air. This causes cumulonimbus clouds to appear. The energy these clouds release warms the center which contributes to the distinctively calm core commonly referred to as the eye.

Forecasting hurricanes has two components to it: where the hurricane is going (track) and how strong it is going to be (intensity). Hurricane forecasting has improved over time, “In 1992, hurricane forecasts were issued to only three days, but now they are issued to five—and soon they will be given for up to a week” (Main). Predicting hurricanes is extremely difficult, but the development of faster computers and better satellite data has decreased the error percentage of forecasting hurricanes. Meteorologists had been using the limited technology to try and forecast the hurricane before it hit, but the advancement in technology has made forecasting hurricanes more accurate.

On the evening of October 15, 2005 tropical depression number 24 appeared in Atlantic Ocean, its winds at a mere 35 kilo-meters per hour, pressure of about 1004 mb and heading West at a pace of 5 kilo-meters per hour. In the next few hours, the winds of depression number 24 gradually increased to 45 km/h and on the morning of October 16, it had reached speeds of 55 km/h. On the 17nth of October this depression developed into tropical storm “Wilma”, with wind speeds at about 65 km/h and an internal pressure of 1000 mb. At about 10 A.M the Yucatan Peninsula was issued a warning, alerting them of the possible dangers from Wilma. Following an erratic pattern of movement, experts were not sure where Wilma was headed. On the morning of October 18 Wilma was about 810 km from Punta Allen, Mexico. Wilma once again intensified reaching speeds of 120 km/h, by the end of the day it was officially a category II hurricane at 175 km/h. On the 19nth at 4 A.M, about 595 km from Punta Allen, Mexico hurricane Wilma quickly reached wind speeds of 280 km and pression levels of 980 mb, at these speeds Wilma was now classified as a category V. The rate at which

A large area of very cold cloud-tops developed over and just southeast of the center of the storm, and convective banding wrapping into the mass began to form, mainly in the north and east semicircles.[18] Though the cloud pattern became elongated north to south during the pre-dawn hours of the following day,[19] a rapid improvement of the inner core on microwave and the structure on satellite imagery became apparent. A timely AMSU pass indicated an eyewall nearly closed with an accompanying intensity estimate of 77 mph (124 km/h), and a transient eye became visible on early morning satellite images. As a result, the National Hurricane Center upgraded Cosme to a Category 1 hurricane on the Saffir–Simpson hurricane wind scale.[20] Given the light wind shear, warm sea surface temperature, and moist air environment, the agency predicted further intensification for the following hours, and the system attained its peak intensity with winds of 85 mph (140 km/h) and a minimum barometric pressure of 981 mbar (hPa; 28.97 inHg) late on June 25.[21] Thereafter, a result of cooler sea surface temperatures forecast several days in advance, convection on the west side of the circulation center began to erode significantly.[22] The tropical storm wind radii expanded the following morning, but the inner core of Cosme, specifically the

The effect of rainpower is actually a negative feedback loop, as increased sea surface temperatures cause more water vapour, which leads to more rain, and consequently lessens hurricane intensity. Sabuwala et al (2015) used tailored versions of the Carnot-heat-engine

The Hadley cells are thermally driven, direct circulations in the tropical atmosphere between 30N and 30S which conserve

1- most of these stations are unevenly spatially distributed and not enough to extract regional and long-term records of climatic parameters. Rainfall measurements by gages are a good example for the heterogeneity of assessment in space and time (El Kenawy and McCabe 2015); 2- some stations are restricted by topographic influences, nonetheless, their data are used to comprise the climate of the entire region including its plateaus and depressions, which gives biased results. For example, Hereher (2010) found that wind data obtained from meteorological stations in some depressions within the Western Desert of Egypt do not accurately reflect the general wind conditions in the mainland of the desert and there is differences between the main sand drift calculated in these depressions and those extracted from satellite images for the upland desert; and 3- meteorological stations are generally settled in or around cities and, hence, temperature data may be under the influence of the urban heat island (UHI) effect that rises the urban temperature more than surrounding environment. Li et al. (2004) observed that the average surface air temperatures in China were considerably affected for about 50 years due to the UHI. In such cases, it is not possible to point out the

Firstly, even though the datas are likely to present a general weather trend of Tempe city to a certain degree but not scientifically precise enough. [1]The city of Tempe has a total area of 40.2 square miles and 0.1 square miles is water. The purpose data consists of temperatures, evaporations, precipitations and wind speed in this observation, varying from site to site. Like Tempe, the datas of the site in A mountain and the counterpart site in Tempe Town Lake would a great differences. In order to determine temperature

In a new study, published in Global Change Biology, scientists from the Universities of Bristol, James Cook University, and Melbourne University in Australia tested the response of the tropical rainforest fly Drosophila birchii to a changing climate by transplanting flies in hundreds of cages along mountain gradients in north-eastern Australia, and measuring their reproductive success at different elevations.

The benefits of running convection-allowing models (CAMs) for convective storm prediction have been demonstrated in a variety of contexts over the past decade (e.g., Done et al. 2004; Kain et al. 2006; Lean et al. 2008; Kain et al. 2008; Clark et al. 2009; Coniglio et al. 2010; Schwartz et al. 2009; Sobash et al. 2011; Clark et al. 2010, Clark et al. 2011; Weisman et al. 2013). As these studies show, much of the value of running CAM forecasts comes from their ability to provide explicit information about convective properties such as initiation, mode, motion, longevity, and intensity. To effectively use CAM output in operational forecasting settings, novel forms of guidance are needed to summarize these forecast attributes in ways that can be easily understood by forecasters and other end users.

Teng-Yok Lee et. al. [1] and his colleagues present an integrated analysis and also visualization framework for scientists to better understand the (MJO) Madden Julian Oscillation phenomenon from very large scale spatio temporal climate simulation data. In this the authors bring forward how the tight integration of MJO domain knowledge, data analysis techniques such as feature tracking and also visualization methods such as virtual globe can lead to powerful system for climate research.