Atmosfera Tiempo Y Clima Barry Chorley Pdf. 7/13/2017 0 Comments Preguntale al Ingeniero. Perimitame hacer algunas acotaciones. Porque la Biblia hablar de leviatan en Job 4. Si saliera de poco y con una chispa, podremos lanzar fuego tambi. Las plumas son s.
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The spatial pattern of precipitation is a complex variable that strongly depends on other geographic and topographic factors. As precipitation is usually known only at certain locations, interpolation procedures are needed in order to predict this variable in other regions. The use of multivariate interpolation methods is usually preferred, as secondary variables—generally derived using GIS tools—correlated with precipitation can be included. In this paper, a comparative study on different univariate and multivariate interpolation methodologies is presented.
Our study area is centred in the region of Valencia, located to the eastern Spanish Mediterranean coast. The followed methodology can be divided in three steps.
First, secondary variables having significant correlations with the precipitation were derived, where the hillsides were used as influence areas of certain variables. Secondly, precipitation was interpolated with different methodologies. Finally, the derived models were compared in terms of predicted errors. Models were achieved for seasonal scales, considering a total of 179 raingauges; data of another 45 raingauges were also used to predict errors. Results prove that there is no ideal method for all the cases but it will depend on one hand, on the number of geographical factors that influence the rainfall and, on the other hand, on the major or minor spatial correlation within the rainfall.
Copyright © 2009 Royal Meteorological Society. 2. Study area The study area is centred in the Valencia region (Figure ), a Mediterranean coastal zone located in eastern Spain with an area of 23,255 Km 2. The physical geography of the region of Valencia is quite heterogeneous. It is divided into two main sectors: interior and coast. The first one is a mountainous area, integrated into the Iberic Range and the Prebetic and Subbetic Ranges. The highest altitudes of the studied area are about 1800 m.
The second sector is a littoral plain region, principally constituted by floodplains and alluvial fans, and a coastline formed by smooth beaches and coastal lagoons. Statistics SPRI SUMM AUTU WINT ANNU Average 128.26 66.59 172.77 123.94 491.56 Stnd. Deviation 32.83 26.70 51.83 46.16 134.41 Minimum 60.78 17.71 83.40 54.70 233.30 Maximum 223.14 144.07 305.44 273.81 847.66 Range 162.36 126.35 222.04 219.11 614.36 Skewness 2.05 3.28 2.63 6.30 1.55 Kurtosis 0.07 − 0.43 − 0.99 2.63 − 0.40 For more than a decade, to the data measured by meteorological observatories, complementary information coming from other instruments (e.g., remote sensing from satellite, radar maps and information from lighting detection systems) can be added (see for example, New et al.,; Islam et al.,; Su et al., ). We have considered the possibility to fill the temporary series by means of the TRMM‐based precipitation estimates, which are available for the research community at the following web site:. The global rainfall algorithm (3B43.v6) combines the estimates generated by combined instrument rain calibration and global gridded raingauge data. The output is rainfall for 0.25 × 0.25 degree grid boxes for each month in the intervals of latitude 50°S ‐ 50°N and longitude 180°W ‐ 180°E. The starting date is 1998‐01‐01.
TRMM 3B43.v6 precipitation estimates provide a total of 72 data for our study area. In Figure (a), distribution is observed as compared to the situation of the 224 meteorological raingauges with useful information. It can be seen that—except for three sectors identified by the letters A, B, C—there is information from one or more raingauges at each sector. Comparison of TRMM 3B43.v6 data and raingauges data, where: (a) Raingauges superimposed to TRMM 3B43.v6 data.
(b) Differences between rainfall data and TRMM 3B43.v6 precipitation estimates for spring 1998. This figure is available in colour online at To compare the records measured in observatories and estimates from the TRMM 3B43.v6 data, all raingauges located within each sector have been located, and average rainfall has been obtained. The difference between this value and the corresponding data on the TRMM 3B43.v6 spring 1998 is shown in Figure (b). This figure also shows the amount of rainfall recorded at each raingauge. It can be observed that the greatest differences occur at the edges of the study area where there are fewer raingauges.
This follows the findings reported in Su et al. ( ), which ensures that TRMM 3B43.v6 data tend to provide slightly larger estimates than that provided by the raingauge data; this difference is interpreted as mostly reflecting the climatological undercatch correction applied to TRMM data (Huffman et al., ). Figure shows the scattergram of precipitation from averaged raingauges data and TRMM 3B43.v6 estimates for spring 1998. The best fit—with a determination coefficient of r 2 = 0.72—has been achieved through an equation of the second degree.
This shows that the differences are exaggerated in the highest and lowest TRMM values. Scattergram of precipitation from averaged raingauge data and TRMM 3B43.v6 estimates for spring 1998 Despite this, the difference between the average of the 224 raingauges and the average of the TRMM 3B43.v6 data is about 0.4 mm. On the other hand, although there are 11 raingauges with rainfall exceeding 200 mm, all TRMM data keep below 175 mm. Similarly, there is a raingauge with 10.2 mm, whereas the minimum value of TRMM B43.v6 is 44.8 mm. In this way, data from the raingauges are able to show the differences in precipitation that occur at small spatial scales, which is not true for the TRMM B43.v6 estimates.
Additionally, with regard to temporal scale, data from raingauges cover the period 1960–2005, while the TRMM 3B43.v6 data are only available since 1998. For these reasons, it was therefore decided to complete the time series with the reference stations instead of the TRMM B43.v6 estimates. However, this comparison with satellite data has been used to verify the validity of our data.