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Statistical surface wind forecasting at Goodnoe Hills, WashingtonCurtis, Joel C. 09 March 1983 (has links)
Multiple linear regression was used to develop equations for 12-,
24-, and 36-hour surface wind forecasts for the wind energy site at
Goodnoe Hills. Equations were derived separately for warm and cool
seasons. The potential predictors included LFM II model output, MOS
surface wind forecasts extrapolated from surrounding stations, pressure
observations corrected to mean sea level, and two types of climatological
variables.
Forecasts of wind speed and direction were formulated for an independent
sample of predictands and predictors. The forecasts
were evaluated using standard methods of forecast verification and the
results are summarized in terms of several verification scores. Comparisons
of scores were made by season, projection time, and cycle (or
preparation) time, and some patterns were evident in the scores with
respect to these stratifications. The minimum value of the mean absolute
error attained by the forecast system presented here was 5.64 mph
for a 12-hour, cool season forecast equation. The minimum value of the
root mean square error was 7.57 mph for a 12-hour, warm season forecast
equation. Comparison of these results with the results of other
statistical wind forecasting studies indicates that the forecast
equations for Goodnoe Hills are of comparable accuracy to the
equations developed for other wind energy sites. Suggestions for
future investigations of statistical wind forecasting are offered
as well as recommendations concerning ways of improving the
forecasting system described in this study. / Graduation date: 1983
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Utility of tactical environmental processor (TEP) as a Doppler at-sea weather radar /Robinson, Sean D. January 2002 (has links) (PDF)
Thesis (M.S.)--Naval Postgraduate School, 2002. / Thesis advisor(s): Kenneth L. Davidson, John McCarthy. Includes bibliographical references (p. 61-62). Also available online.
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Using statistical downscaling to project the future climate of Hong KongCheung, Chi-shing, Calvin, 張志成 January 2014 (has links)
Climate in Hong Kong is very likely to be modified due to global climate change. In this study the output of General Circulation Models (GCMs) was statistically downscaled to produce future climate projections for the time periods 2046 –2065 and 2081 –2100 for Hong Kong. The future climate projections are based on two emission scenarios provided by the Intergovernmental Panel on Climate Change (IPCC). The emission scenarios, A1B (rapid economic growth with balanced energy technology) and B1 (global environmental sustainability), make assumptions on future human development, and the resulting emissions of greenhouse gases.
This study established a method to evaluate GCMs for use in statistical downscaling and utilised six GCMs, selected from the 3rd phase of the Coupled Model Intercomparison Project (CMIP3). They were evaluated based upon their performance in simulating past climate in the southeast China region on three aspects: 1) monthly mean temperature; 2) sensitivity to greenhouse gases and 3) climate variability. Three GCMs were selected for statistical downscaling and climate projection in this study.
Downscaling was undertaken by relating large scale climate variables, from NCEP/NCAR reanalysis, a gridded data set incorporating observations and climate models, to local scale observations. Temperature, specific humidity and wind speed were downscaled using multiple linear regressions methods. Rain occurrence was determined using logistic regression and rainfall volume from a generalised linear model. The resultant statistical models were subsequently applied to future climate projections.
Overall, all three GCMs, via statistical downscaling, show that daily average, minimum and maximum temperatures, along with specific humidity, will increase under future climate scenarios. Comparing the model ensemble mean projections with current climate (1981 –2010), the annual average temperature in Hong Kong is projected to increase by 1.0 °C (B1) to 1.6 °C (A1B) in 2046 –2065, and by 1.4 °C (B1) to 2.2 °C (A1B) in 2081 –2100. Furthermore, the projections in this study show an increase of high temperature extremes (daily average temperature ≥ 29.6 °C), by three to four times in 2046 –2065 and four to five times in 2081 –2100.
The projections of rainfall indicate that annual rainfall will increase in the future. Total annual rainfall is projected to increase by 4.9% (A1B) to 8% (B1) in 2046 –2065, and by 8.7% (B1) to 21.5% (A1B) in 2081 –2100. However, this change in rainfall is seasonally dependent; summer and autumn exhibit an increase in rainfall whilst spring and winter exhibit decreases.
In order to test one possible impact of this change in climate, the downscaled climate variables were used to estimate how outdoor thermal comfort (using the Universal Thermal Comfort Index) might change under future climate scenarios in Hong Kong. Results showed that there will be a shift from 'No Thermal Stress' towards 'Moderate Heat Stress' and 'Strong Heat Stress' during the period 2046 –2065, becoming more severe for the later period (2081 –2100). The projections of future climate presented in this study will be important when assessing potential climate change impacts, along with adaptation and mitigation options, in Hong Kong. / published_or_final_version / Geography / Doctoral / Doctor of Philosophy
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A comparative study of friction and numerical smoothing in a global model of atmospheric flow /Ibrahim, Mostafa M. January 1977 (has links)
No description available.
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L'impact de l'assimilation directe de taux de précipitation satellitaires dans un modèle météorologiqueRoch, Michel. January 1986 (has links)
No description available.
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Méthode rapide de calcul de la radiation infrarouge dans l'atmosphère et évaluation de son influence dans un modèle de prévision météorologiqueGarand, Louis. January 1980 (has links)
No description available.
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A simple atmospheric model on infinite domains /Bartello, Peter. January 1984 (has links)
No description available.
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Month and year ahead forecasting of monthly precipitation for the southeastern United StatesMarch, William John 08 1900 (has links)
No description available.
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Cloud droplet growth by stochastic coalescence.Chu, Lawrence Dit Fook January 1971 (has links)
No description available.
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Modelling of cloud patterns using satellite photographsWon, Thorne K. January 1974 (has links)
No description available.
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