An Evaluation Of The Uncertainty Of Precipitation Type Observations And Its Eect On The Validation Of Forecast Precipitation

The concept of statistics is useful and necessary in many situations that we encounter on our daily life. Every morning when you walk out from your house do you wonder, what the weather will be like today, for the week or even for the month? Meteorologist use computer models that are built using statistics to help predict the future of the weather. Their main function is to gather and study data from past weather and identify the best indicators that will prognosticate a more accurate weather forecast. Using these statistical procedures are a great way to calculate the maximum temperature that will be on a specific day and place. By gathering together the wind velocity, direction, the amount of humidity, and cloudiness you can use this data

Rain is measured by the amount of liquid contained in the measurement device. Whatever is amount of liquid is in the device, is the measurement. However snow, has various factors that can impact its measurement. First of all snowfall measurement is based on how much liquid there would be if the snow was melted into liquid form. There are several factors that can impact how much snow is measured in a device. Wind can move snow from high points to low points and vice versa. The surface on which the snow is falling onto can also impact a measurement. The length of time the snow has been on the ground and how compact it is can also effect measurements. Measuring in several places and then using an average can give a more accurate total. Meteorologists usually forecast snow amounts giving a range of totals due to these factors which make it challenging to give an exact forecast.

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

The NOAA’s long term plan is to engage in six major areas. The first being continue in conduct experiments for understanding the natural process of weather. Second is to build models to predict the effects and outcomes that may affect the world. Third and Fourth are coherent being use new observing technology for data to feed the models, and develop new forecasting tools for improving weather services. To share information to public, federal, and academic partners, and to prepare scientific assessments to enhance the public's knowledge and to inform if any governmental actions need to happen.(Goldman)

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).

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.

There are already mechanisms in place that are used to predict high and low years for weather temperatures and rainfall. In 1997, prior to the El Nino in 1997-1998 (called El Nino 97/98), the scientific community was able to surveil and predict its occurrence, thus giving weather researchers and their science, credibility by the public and those who make decisions. (Below is a historical table of all recorded El Nino events).

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

In a study of New York City, Hosannah (2014) found that particle size distribution with a high volume of fine mode particles in urban areas can suppress precipitation. Particle size distribution with a high volume of coarse mode particles (with giant cloud condensation nuclei) enhanced precipitation totals. Urban effects also changed spatial precipitation patterns. Jin et al (2005) found weekly signals of aerosol optical thickness for summer months in AERONET measurements, and in MODIS cloud effective radius over New York City. Aerosols were also elevated seasonally, with peaks in summer. This study also found that cloud

In today's time, we don't need to think much about the weather. All we have to do is open up the weather app on our smartphones or turn on the weather channel on the TV and we willl be able to know if we need to put on a sweater or start to prepare our canned food and water bottles for a hurricane coming in a week. What we really don't think about, is how complicated weather prediction realy is. A long time ago, there were no satellites showing detailed weather reports. It wasn't until 1943 when Colonel Joe Duckworth discovered that he could fly through a stom that people began to know when a large storm was coming. Hurricane hunting and weather satellites were a huge advancement in technology that greatly helped researchers find more detailed

In the lakes and rivers basins as mainly accessible inland water resources existing for ecosystems and human life, the area that covered with snow can control surface energy and water resources because of its low thermal conductivity, high albedo, high emissivity and considerable spatial and temporal variability [1, 2]. Knowledge of snow cover conditions is critical for climate change prediction, and hydrological events prediction such as

Technology, O. O. (n.d.). Accuracy of 6-10 Day Precipitation Forecasts and Its Improvement in the Past Six Years. Retrieved January 23, 2017, from http://www.nws.noaa.gov/ost/climate/STIP/RServices/huq_032509.htm

Numerous websites exist providing information on the utilization of weather satellite data. The National Weather Service (NWS) maintains a site that include data dumps from the sensors. The site provides information on the number of observations from the various sensors with hourly counts in some cases. It also includes a monthly average and whether the source information is critical. It is unclear from the data available in the public domain regarding the method used to calculate the monthly average. It is also unknown whether the National Centers for Environmental Prediction’s (NCEP’s) real time observations counts for Suomi-NPP (NOAA, 2017) are included in the counts for Suomi-NPP Yearly

My general areas of interest are numerical modeling and tropical meteorology. For example, numerical simulation models of tropical cyclones is the problem that interest me most in atmospheric science. Based on one or two problems in tropical meteorology or related fields, I hope to work based on the existing models, and make unique contributions. My ultimate goal is to develop skills to become a sophisticated researcher and teacher in the field, while pursuing a doctorate degree in atmospheric sciences.

The daily gridded rainfall data at spatial resolution of 0.25º x 0.25º covering the entire Indian region obtained from National Climate Centre (NCC), Pune, of India Meteorological Department (IMD) is used for the present study. The gridded data is developed by interpolating the point observations from a network of rainguage stations, by using inverse distance weighted interpolation (IDW) scheme (Pai et al., 2014).