KEYWORDS: Picosecond phenomena, Climatology, Meteorology, Geographic information systems, Data modeling, Sun, Information science, Information technology, Atmospheric modeling, Agriculture
Based on digital elevation model (DEM), the distributed models for calculating possible sunshine (PS) over rugged
terrains (RT) are established and this model considers the influence of slope, aspect, and terrain inter-shielding. The
spatial distribution of PS with 100m×100m over RT in Guiyang city is calculated by this model. The results show that
the influences of slope, aspect and the terrain inter-shielding on PS are very big and it is greater than the effect of latitude.
The spatial distribution of PS over RT in Guiyang city has apparent terrain feature. The spatial difference of PS over RT
is big. The values of annual PS over RT in Guiyang city are 3233~4368 hours, the difference is large in different regions.
The smaller values lie in shady slope of the mountain. The bigger values lie in sunny slope of the mountain. The values
of PS over RT in the Guiyang city are 154~322 hours in January. The values of PS over RT in Guiyang city are 411~421
hours in July.
Due to local terrain factors such as slope, aspect and terrain inter-shielding, the insolation duration (ID) over rugged
terrain (RT) is different from over horizontal surface. Based on digital elevation model (DEM), the distributed models to
Guizhou Plateau with 100m×100m DEM data for calculating ID over RT are developed. The results are that the
influences of slope, aspect and the terrain inter-shielding on ID are very big. The spatial distribution of ID has apparent
terrain feature over RT. The solar altitude angle is quite low in January, which causes more inter-shielding by the RT, and
the spatial difference of ID is big. The solar altitude angle is quite high in July; which causes less inter-shielding by the
RT, and the spatial difference of ID is corresponding small. But the ID over horizontal surface is much in July, and the
influence of local terrain on the ID is still obvious. The ID varies spatially 16~142 and 133~210 hours, and the maximum
one is about 9 and 1.6 times bigger than the minimum one in January and July, respectively. The annual ID varies
spatially 768~1824 hours, and its local difference is very obvious.
ECMWF daily reanalysis is applied to investigate 1961-2001 heat source/sink and the climate features in relation to the
atmospheric heat distribution over the QTP (Qinghai-Tibetan Plateau) by means of the "inverse algorithm". Results
suggest that 1) in March - September (October - February), the QTP acts as a heat (cold) source, the strongest being in
June (December). For the region as a whole, the heat source feature lasts longer, with its intensity much higher compared
to the cold source; 2) as shown in the heating vertical profile, the maximum heat source layer occurs dominantly between
500-600 hPa, but with the season-dependent heating strength and depth, and, in contrast, the cold source has its
maximum layer and intensity varying as a function of time; 3) the horizontal distribution of the heat sources throughout
the troposphere 1> (from surface to 100 hPa) is complicated, displaying noticeable regionality, i.e., the heat source
changes faster in the western than in the eastern QTP, with the western source considerably stronger in April - August,
and intensified quickly enough to show a 200 W/m2 center in May, one month ahead of the eastern source. When July
comes the regional heat source begins to weaken towards the south, during which the western source weakens faster,
changing to a cold source in October, again one month earlier compared to the eastern counterpart; 4) since 1979 the
seasonal variability of the heat source has shown climate transition signals, as clearly seen in 1990/91.
This paper mainly discusses how to determine the optimized strategy for retrieval of Precipitable Water Vapor (PWV) with high accuracy from tropospheric zenith wet delay using ground-based GPS receivers. GPS analytical network are constructed on the base of two observation sites in Antarctica in 1999 and several IGS sites. Tests are conducted to study the performance of different network sizes and different schemes parameters. A high-accuracy GPS processing software package GAMIT/GLOBK is utilized; multiple schemes are adopted for searching the optimized parameters for accurate PWV. After having running GAMIT/GLOBK of all test combination, the results are analyzed by Baseline Repeatability Rate(BRR) and bias between calculated GPS water vapor and actual water vapor. The primary achievements and conclusion are reached including the optimal IGS sites involved, network configurations, elevation cut-off angles, processing periods, knots position.
Weather Satellite data has great potential for Precipitation forecast which plays an important role in flood disaster monitoring. In this paper, the GMS-5 infrared cloud imagery combined with surface temperature data for two years in Binjiang reaches of Guangdong province in China is used to study the relationship between infrared cloud imagery and surface rainfall rates. First, parameterization estimate of infrared cloud imagery is made one the base of atmospheric probing principle, then some parameterization estimate result have been obtained under different analysis field from 3×3 to 15×15 pixels. The result shows:1 there exist obvious correlation between the probability of rain and parameterization estimate such as average brightness temperature(Tb), brightness temperature variance(fc), equivalent cloudage(CN),brightness temperature area index(A1--the first A5--the fifth grade, A6-the sixth grade );2 The rainfall intensity increase with Tb and f and CN, and that it decrease with Tb and A1.Finally,the prediction empirical formula of rainfall intensity has been established by means of optimized subclass regression under different analysis field. The following formula is made under analysis field of 11×11 pixels. The statistical result shows that the average precision of rainfall intensity is about 80% using infrared cloud imagery parameters and the size of analysis field has slight effect on it. If the rainfall intensity reached the storm standard, the flood alarm would be sent out.
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