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You searched for subject:(Probability of Precipitation). Showing records 1 – 3 of 3 total matches.

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University of Illinois – Urbana-Champaign

1. Kaur, Harpreet. Spatial and temporal variation in precipitation pattern.

Degree: MS, Environ Engr in Civil Engr, 2015, University of Illinois – Urbana-Champaign

The intensification of hydrological cycle and its nature is an important question in hydrology. The human activities are a major cause of change in hydrological cycle. The fluctuations in hydrological cycle can influence the dynamics of ecosystem. The changes in global water cycle and the corresponding redistribution of precipitation is a threat to food availability, stability, access and utilization. The droughts as well as floods have increased in number and scale. The effect of climate change can be seen by understanding the changes in trends of evaporation, precipitation, temperature and increase in level of water as well as salinity in seas as well as oceans. There has been extensive climate change in North America. The major impacts of climate change in North America are sea level rise, wildfire and insect outbreaks, increased risk of deaths due to heat waves and degraded water quality, diminishing snowfields and increase in precipitation. There is increase in intensity as well as frequency of precipitation in North America. Studies have been focused on extreme precipitation and extreme dry days as well as timing of precipitation. In this study the focus is on change in precipitation probability and persistence of wet days and dry spells over contiguous US and south Canada. There is a paucity of empirical evidence for this study. We are studying the pattern of rain and no-rain statistics analyzed over decadal time scale to identify any statistically significant temporal patterns of change using observed data. The year is divided into four seasons and all the comparisons are made seasonal as well as yearly. The day with a precipitation greater than 0.1 mm is considered as a rainy day. Here we show that precipitation probability and persistence of wet days has generally increased throughout the study area with the exception of a few stations. This study also presents the variation of magnitude of precipitation as well as its seasonal distribution in Minnesota River Basin. The motivation for the study is the sediment increment in Minnesota River. We anticipate that the northwestern and north eastern US have undergone a major change. The amount of precipitation received by these areas is already high; this means wet days are getting wetter. Advisors/Committee Members: Kumar, Praveen (advisor), Kumar, Praveen (committee member).

Subjects/Keywords: precipitation probability; Intensification of hydrological cycle

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Kaur, H. (2015). Spatial and temporal variation in precipitation pattern. (Thesis). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/78290

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Kaur, Harpreet. “Spatial and temporal variation in precipitation pattern.” 2015. Thesis, University of Illinois – Urbana-Champaign. Accessed October 31, 2020. http://hdl.handle.net/2142/78290.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Kaur, Harpreet. “Spatial and temporal variation in precipitation pattern.” 2015. Web. 31 Oct 2020.

Vancouver:

Kaur H. Spatial and temporal variation in precipitation pattern. [Internet] [Thesis]. University of Illinois – Urbana-Champaign; 2015. [cited 2020 Oct 31]. Available from: http://hdl.handle.net/2142/78290.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Kaur H. Spatial and temporal variation in precipitation pattern. [Thesis]. University of Illinois – Urbana-Champaign; 2015. Available from: http://hdl.handle.net/2142/78290

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Georgia

2. Crowell, Kevin Lee. Precipitation prediction using artificial neural networks.

Degree: 2014, University of Georgia

Precipitation, in meteorology, is defined as any product, liquid or solid, of atmospheric water vapor that is accumulated onto the earth’s surface. Water, and thus precipitation, has a major impact on our daily livelihood. As such, the uncertainty of both the future occurrence and amount of precipitation can have a negative impact on many sectors of our economy, especially agriculture. There is, therefore, a need to use innovative computer technologies such as artificial intelligence to improve the accuracy of precipitation predictions. Artificial neural networks have been shown to be useful as an aid for the prediction of weather variables. The goal of this study was to develop artificial neural network models for the purpose of predicting both the Probability of Precipitation and quantitative precipitation over a 24-hour period beginning and ending at midnight.

Subjects/Keywords: Artificial Neural Networks; Probabilistic Neural Network; Precipitation; Probability of Precipitation; Quantitative Precipitation; Brier Score

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Crowell, K. L. (2014). Precipitation prediction using artificial neural networks. (Thesis). University of Georgia. Retrieved from http://hdl.handle.net/10724/25157

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Crowell, Kevin Lee. “Precipitation prediction using artificial neural networks.” 2014. Thesis, University of Georgia. Accessed October 31, 2020. http://hdl.handle.net/10724/25157.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Crowell, Kevin Lee. “Precipitation prediction using artificial neural networks.” 2014. Web. 31 Oct 2020.

Vancouver:

Crowell KL. Precipitation prediction using artificial neural networks. [Internet] [Thesis]. University of Georgia; 2014. [cited 2020 Oct 31]. Available from: http://hdl.handle.net/10724/25157.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Crowell KL. Precipitation prediction using artificial neural networks. [Thesis]. University of Georgia; 2014. Available from: http://hdl.handle.net/10724/25157

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Florida

3. Tian, Di. Improving Medium-Range and Seasonal Hydroclimate Forecasts in the Southeast USA.

Degree: PhD, Agricultural and Biological Engineering, 2014, University of Florida

Accurate hydro-climate forecasts are important for decision making by water managers, agricultural producers, and other stake holders. Numerical weather prediction models and general circulation models may have potential for improving hydro-climate forecasts at different scales. In this study, forecast analogs of the Global Forecast System (GFS) and Global Ensemble Forecast System (GEFS) based on different approaches were evaluated for medium-range reference evapotranspiration (ETo), irrigation scheduling, and urban water demand forecasts in the southeast United States; the Climate Forecast System version 2 (CFSv2) and the North American national multi-model ensemble (NMME) were statistically downscaled for seasonal forecasts of ETo, precipitation (P) and 2-m temperature (T2M) at the regional level. Advisors/Committee Members: MARTINEZ,CHRISTOPHER J (committee chair), JONES,JAMES W (committee member), FRAISSE,CLYDE WILLIAM (committee member), ANNABLE,MICHAEL D (committee member).

Subjects/Keywords: Analytical forecasting; Climate models; Climatology; Evapotranspiration; Forecasting models; Mathematical variables; Probability forecasts; Seasons; Statistical models; Weather; climate  – downscaling  – ensemble  – evapotranspiration  – forecast  – gcm  – hydrology  – nwp  – precipitation  – probability  – statistics  – temperature  – water  – weather; City of Lake Alfred ( local )

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Tian, D. (2014). Improving Medium-Range and Seasonal Hydroclimate Forecasts in the Southeast USA. (Doctoral Dissertation). University of Florida. Retrieved from https://ufdc.ufl.edu/UFE0046906

Chicago Manual of Style (16th Edition):

Tian, Di. “Improving Medium-Range and Seasonal Hydroclimate Forecasts in the Southeast USA.” 2014. Doctoral Dissertation, University of Florida. Accessed October 31, 2020. https://ufdc.ufl.edu/UFE0046906.

MLA Handbook (7th Edition):

Tian, Di. “Improving Medium-Range and Seasonal Hydroclimate Forecasts in the Southeast USA.” 2014. Web. 31 Oct 2020.

Vancouver:

Tian D. Improving Medium-Range and Seasonal Hydroclimate Forecasts in the Southeast USA. [Internet] [Doctoral dissertation]. University of Florida; 2014. [cited 2020 Oct 31]. Available from: https://ufdc.ufl.edu/UFE0046906.

Council of Science Editors:

Tian D. Improving Medium-Range and Seasonal Hydroclimate Forecasts in the Southeast USA. [Doctoral Dissertation]. University of Florida; 2014. Available from: https://ufdc.ufl.edu/UFE0046906

.