Cloud Aerosol Lidar And Infrared Pathfinder Satellite Observations )

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

Condensation As the evaporated water vapor gets colder rising into the atmosphere, it changes back into different levels of liquid clouds of different types.

Trp. Burrell was participating in a Lidar project on I-85 north near Popular Creek Road in Vance County. Trp. Burrell was notified of a black SUV traveling north at a high rate of speed. Trp. Burrell activated his blue lights and siren and attempted to make an enforcement stop, but was impeded by a silver Mitsubishi SUV in the passing lane. Trp. Burrell followed behind this vehicle a short distance with his blue lights and siren activated, and the vehicle yielded to the left toward the median. Trp. Burrell proceeded north on I-85 and stopped the black SUV that originally was the target of the Lidar project. This complaint is unfounded based on Trp. Burrell was not traveling at a high rate, and the vehicle in front of him yielded to the

The Ocean Optics does not have this ability and fluctuations that deviate from the accepted value are observed as shown by its residuals plot. The r2 value (see table 12 in Appendix A and table 13 in Appendix B) shows that the data are more linear for the Cary 60 instrument (r2 = 1.00) than for the Ocean Optics instrument (r2 = 0.994).

Since the beggining of time, man has always been curious about its surroundings. But, as time changes so do the techniques and technology used by curious investigators. Weather satellites have proved the most useful technology in the passages because they can gather information about on coming weather, they can save lives, and they are a tool that works well with other weather calculating instruments that allow for a better prediction of approaching weather and storms.

According to NASA clouds are made of water or ice crystals floating in the sky, not gas (add reference). A scientific explanation of how clouds are form is that water vapour evaporates from the earth into the atmosphere than condenses on the earth. There are many different reasons as to why clouds are formed. Some other ways include when dust, smoke or other particles are suspended into the air or when water vapour condenses into particles into the air (Skamp & Preston, 2007, p.

In conclusion, Cloud Seeding is the best solution to mitigate the effects of a drought. Although it has it’s pros and cons, the pros out way the cons. It is more area effective and the most effective way to mitigate the effects of a drought. To conclude our report, our three solutions are all capable of mitigating a drought, but cloud seeding is the best

When the air parcel rises in the troposphere, the more energy transfer. The reason why the energy transfers is because the water vapor must have an equivalent temp to the surrounding air. More energy causes it to have more evaporation transferring more water vapor to the surrounding air. As the energy transfer occurs, the water vapor gets through condensation and turns to liquid water. The higher the parcel gets, the colder it is in the surrounding air which causes the water to condense and come out as rain. This is important because when the temperature of the air parcel is higher, it will rise higher in the troposphere and the higher the cloud is in the troposphere, the more rain that will fall. Also, the modeling tool tells us that storms 2 and 3 had all the same factors ( water vapor, troposphere temperature and surface water) but, the parcel temperature, was higher in storm 3 which caused it to have more rain. Lastly, the reason why the weather has been acting up is the new lake. The lake is causing more evaporation (the process by which a liquid changes into a gas). This is important because the more evaporation there is, the more condensation (the process by which a gas changes into a

where PXIN is the mean monthly precipitation for the Xinjiang province of China; and x is the month in which the average precipitation occurred.

9. All other factors being the same, how does cloud cover affect the day’s minimum air

Outside of my resident the sky is covered with mid-level gray clouds that form during the months of June, July and August. In addition to this, these types of cloud called Nimbostratus forms with separate large dense layers of scattered showers or freezing precipitation and they sink into the bottom of the atmosphere while covering the entire sky with darkness. The Weather Facts article (p., 4) believes, Nimbostratus rise into the planet’s upper atmosphere when warm fronts and depressions develop in damp skies, Weather Facts: Nimbostratus (n.d.).Their base also forms 6,500 feet below the planet’s atmosphere and when they are present in the sky other planets are difficult to spot.Nimbostratus clouds are classified with similar clouds that

Year after year, increased carbon dioxide output is contributing to an increase in Earth’s climate. Carbon dioxide is the main greenhouse gas causing climate change, but many people forget that there are other ones which can amplify the effect of carbon dioxide many times over. In fact, water vapor itself is the most abundant greenhouse gas, and it can increase the amount of warming from carbon dioxide twice over. I want to know how data collected from NASA satellites about the El Niño weather phenomenon and its effect on water vapor in the environment can help to understand long-term trends in Earth’s climate.

Cloud forests are tropical ecosystems scattered throughout Central and South America, Africa, and Oceania (Scatena, Hamilton, & Juvik, 1995). Frequent, low level moisture shrouds the flora and fauna found in these jungles in a constant hazy mist. This fog cover differentiates the cloud forest environment from lowland tropical rainforests or temperate rainforests (Doumenge, 1995). To support an ecosystem, many processes such as photosynthesis, evapotranspiration, nutrient cycling, and predator-prey dynamics must work together to sustain life (Buddle, 2015). The hydrological cycle, specifically the processes of cloud formation and water interception, is the critical factor that shapes the emergence of a cloud forest and its species composition (Gotsch, Nadkarni, and Amici, 2016).

sity, the damping coefficient for the sponge layer applied above the lowest altitude subject to TLC will be varied. Indeed, this damping could be chosen large to remove TLC and observe the ef- fects on TC intensity and structure. The main limitation for this experiment is the proximity of TLC and the top of convective clouds. Damping too close to the cloud top might apply a forc- ing on convection in ways that could modify the TLC-intensity relationship. The characteristics of the tropopause (height, temperature, sharpness) in the background profile will also be varied to determine the relationship between these characteristics and the cooling that develops near the tropopause.

The model calibration results are summarized in Figure 3. The modeled volumes are compared with monitored volumes for all the 67 events at PSW45 and results are shown in the top plot in Figure 3. The slope of linear regression between modeled and monitored volume is about 0.9 with R square error equal to 0.9 as well, indicating a good fit of modeled results to monitored results from a volume point of view. In addition, only 3 events out of 67 events (4.4%) are in the outlier of 95% prediction interval, which is another indicator for a good fit. Modeled peak flow and modeled time to peak results are also compared with the monitored results at PSW45, shown in Figure 3 respectively. For the peak flow comparison, 4 events out of 67 events (6.0%) are in the outlier of 95% prediction interval, and for the time to peak comparison, only 2 events out of 67 events (3.0%) are in the outlier of 95% prediction interval, both indicating a good fit of modeled results to monitored results. The cumulative frequency distribution (CFD) of modeled volume is compared with that of monitored volume and results are shown in the bottom plot in Figure 3. There is a 3% difference between the total modeled volume and the total monitored volume throughout the entire period from 11/10/2011 to 12/31/2012, which once more shows a good fit for all small, median, and large storm events through the entire monitoring period. After calibration, the baseline model is properly set up and ready to model the impact