The tenth IMSC, Beijing, China, 2007 - International Meetings on ...

89% during the 10 days of mid-June, and gradually decreases to 67% during the 10 days of
early October, showing the seasonal decrease of energy closure with the corn growth. <strong>The</strong>
diurnal cycle of the energy utilizing ratios show that the energy closure in afternoons is better
than mornings. <strong>The</strong> instantaneous energy utilizing ratios are mainly concentrated within the
range of 0~1, but there also exist a number of values outside this range even during the
daytime. <strong>The</strong> latent heat is the main energy consumption form on all scales. <strong>The</strong> heat storage
term from the energy balance equation, may exceed the sensible heat in as long as seventy
days, which suggests it should not be omitted from the energy balance analysis of the corn
field. <strong>The</strong>re are phase differences that exist in the diurnal cycle of the energy components. <strong>The</strong>
phase of the heat storage term is often shifted to earlier times with respect to the net radiation.
We explore the lagging effect of turbulent fluxes as a new reason for failing to obtain the
energy closure. Results show that the energy closure ratios are improved on all time scales
when turbulent fluxes are lagged by 30 minutes to the available energy. As well as not only the
ratio points outside the range of 0~1, but also the earlier phase of the heat storage term are
improved in some degree.
A Decision Tree Classification with Correlation-based Feature Selection for Decision Making in
Conducting the Cloud Seeding Operations
Speaker: Lily Ingsrisawang
Lily Ingsrisawang
Department of Statistics, Kasetsart University, Bangkok,Thailand
fscilli@ku.ac.th
Supawadee Ingsrisawang
National Center for Genetic Engineering and Biotechnology, Bangkok, Thailand
Saisuda Somchit
Department of Statistics, Kasetsart University, Bangkok, Thailand
Warawut Khantiyanan
Bureau of the Royal Rainmaking and Agriculture Aviation, Bangkok, Thailand
Prasert Aungsuratana
Bureau of the Royal Rainmaking and Agriculture Aviation, Bangkok, Thailand
<strong>The</strong> northeastern part of Thailand is an arid region with varied rainfall. To enhance the
precipitation in this area, a number of cloud seeding operations have been conducted by the
Royal Rain Making Project. Since there is no assurance for the success of cloud seeding
operations, it is important to determine or forecast the success rate before any operations are
conducted. Several climate factors, precipitation records and prediction results from the cloud
models such as the Great Plains Cumulus Model (GPCM) are normally used in making the
decision on whether the cloud seeding operation will be successful or not. This study presents
a two-step supervised learning framework to improve the forecasting performance on the
success rate of cloud seeding operations. First, we perform a correlation-based feature
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