Comparing Observed Meteorological Data From Two Different Types Of Automatic Weather Stations

Quito is the capital city from Ecuador where is considering a high elevation area with approximately 2800 meters above sea level in its elevation. Also, the city is located in the center of Andean Region and it is influenced by the equatorial line with latitudes nearest to 0 grades and being in a Tropical Region without seasons (Figure 1). Moreover, Quito doesn’t present stations, only the city shows two times considered like a dry and a wet season. The mean temperature during the year has a mean in minimum about 9.0°C and a maximum 25.4 °C20, also presented a high precipitation near to 1126 mm on 2015 that let to have a high density cloud every year.

GEOG 104 – Weather Climate Society: You have gotten 283.5/425 66.7% of the available points thus far. I know that this is your most difficult class; however, I have seen you working with your assigned tutor for this class. Please, come every day to prepare for this class. Your Exam # 2 is going to be given this Thursday, November 12th, contact your tutor and prepare very well for this

Weather is the state of the atmosphere at a given time and place, with respect to variables such as temperature, moisture, wind velocity, and barometric pressure. Weather can be classified as day to day temperature and precipitation activity, where climiate is average atmospheric conditions over longer periods of time. Weather occurs from temperature and moisture differences between one place and another. These differences can occur due to the sun angle at any particular spot. The strong temperature contrast between polar and tropical air gives rise to the jet stream. Weather systems in the mid-latitudes, such as "extratropical cyclones", are caused by instabilities of the jet stream flow. Because the Earth's axis is tilted

Although meteorologist hardly acknowledge that their forecasts are not completely accurate, it is clear that without them the amount of harm would be great. The information of a possible tornado formation helps the public become prepared to the possibility of a tornado. Tornadoes warning systems have come a long way from the days where they were inaccurate and unreliable. Lead times have increased and are becoming more reliable. However, more sensitive technology is needed until forecasts can become more

But not all weathermen are meteorologists, many are broadcast journalists. Broadcast meteorologists are equal to atmospheric scientists. All in all the jobs are alike in many ways but they all do different tasks, even though they help each other to research and predict the climate and weather. Broadcast meteorologist take the data and information given to them from atmospheric scientists, and relay that information to the public, so we can know what the weather will be like for the future, but to also know if severe weather is forthcoming, and the actions needed to avoid serious injury to

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.

Weather is created by the movement of air masses of different temperature and moisture content moving around the Earth’s atmosphere and interacting with each other.

Climate change data was collected from the IPPC AR4 General Circulation Model (GCM). The GCM is regionally dependent and shows the different mixes of greenhouse gas concentration in the atmosphere. (Sheffield & Wood, 2007, p.81) The simple mutlicalar drought index (SPEI) is used to show the climatic water balance. Monthly or weekly difference physiologically equivalent temperature (PET) and precipitation and is calculated at different times. (Vicente-Serrano, Beguería, & López-Moreno, 2009, p. 1699) Streamflow data was obtained from the U.S. Geological Survey Hydrological Climate Date Network (HCDN). (Regonda, Rajagopalan, Clark, & Pitlick, 2004, p.374) Snowfall data is gathered from surveys that were performed by the Natural Resources Conservation Service and precipitation data was gathered from the National Weather Service Cooperative Network. (Regonda, Rajagopalan, Clark, & Pitlick, 2004, p.375)

The highest population which aligned with locality and climate requirements was Buenos Aries, with an urban agglomeration population of nearly 14 million. Situated along the east coast of Argentina, the city shares its boundaries with 24 other districts, forming the Greater Buenos Aries (Figure 6). This region spans over 3880 km2 with a population density of over 3500 persons/km2 (Table 3). However, within the city centre, i.e. in Buenos Aries alone, the population density increases to 14000 persons/km2. The two urban stations are located in Buenos Aries and in the surrounding district of El Parlomar. These stations are situated in central metropolitan areas with population densities of 15296 persons/km2 and 2926 persons/km2 (Table 2). These densities are in contrast with those found in the neighbouring regions of the rural weather stations. In Ministro Pistarini and on the outskirts of La Plata, the population density of the rural stations was estimated from the population density of the total internal province, with density of 20 persons/km2 (Table 2). Similarly, the elevation of the stations had a minimal difference of 7 m (Table

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

The value chain depicted in Figure 3, page 5 (SETT, 2016) was used as a starting point. The Weather/ Climate/Water component, seen in Figure 3 of the Socioeconomic Benefits Tiger Team (SETT) Report, has been altered as depicted in Figure 1 (of this document). An image of an atmospheric bore captured by Suomi-NPP off the North Atlantic coast of the U.S. is displayed in the background (although any meteorological image could be substituted). The list of satellite observations are from the NCEP real time data dump for hourly observations, but the observations could be obtained from any source. The Meteorological and Environmental Models are data models that can be derived from the listed satellite observations using algorithms that are based upon brightness temperature difference techniques or other calculation methodologies. The Generating Warnings, Alerts, and Forecast portion of the value chain is comprised of the entities that issue forecasts, warnings, and alerts i.e.: NWS, The Weather Channel, universities, non-profit organizations, individual citizens, etc. The value or benefit component of the value chain is realized by prevention of accidental encounters with meteorological events resulting in injuries or fatalities, reducing travel delays, and by providing opportunities

Britain experienced extensive flooding in autumn 2000. Various scientific and technical reports (e.g., Hulme and Jenkins, 1998; McKenzie et al., 2000) of high public profile had predicted increased flooding for the U.K. as a result of climate change. When that event occurred, various influential individuals and institutions linked the floods with climate change, even though such heavy precipitation could simply be due to natural climate variability (Desai et al. 2004). Due to recent analytical advancements, climate scientists are now able to more accurately determine how climate change impacts the odds of an individual extreme event occurring, like the 98 inches of snow that has fallen in Boston this season. So what is going on with this extreme weather, and what does it have to do with global climate change? This paper will try to provide an overview of current knowledge of climate change and its effects on extreme weather with a keen focus on attribution to climate change study.

The spatial distributions of accumulated rainfall from gauge measurements, GSMaP_NRT (GSMaP, hereafter), CMORPH, and PERSIANN-CCS (CCS, hereafter) are different from each other in the three storms (Figure 2). GSMaP shows similar spatial patterns of precipitation with gauge observations, but failed to capture the intense rainfall centers indicated by the red letters in all storms. CMORPH has similar spatial patterns with GSMaP in all storms, and successfully captures the rainfall centers in the second and third storms. CMORPH rainfall intensity agrees well with gauge observations in the second storm, but still considerably underestimates the rainfall intensity center in the third storm. CCS also misplaced the intense rainfall areas, and

Herein, an evaluation of the uncertainty of precipitation-type observations and its eect on the validation of forecast precipitation type is undertaken.The forms of uncertainty considered are instrument/observer bias and horizontal/temporal representivity of the observations.Instrument/observer biases are assessed by comparing observations from the Automated Surface Observing Station (ASOS) and meteorological Phenomena Identification Near the Ground (mPING) networks.Relative to the augmented ASOS, mPING observations are biased toward ice pellets (PL) and away from rain (RA).Consequently, when mPING is used to validate precipitation-type forecasts, the Probabilities of Detection (PODs) for RA (PL) are decreased (increased) relative to those

Where pij and EToij are precipitation and ETo of the j-th month of the i-th year and N is the total number of years of the available data. The normalized RDI (RDIn) is estimated as follows: