Download Improving Infrared-Based Precipitation Retrieval Algorithms by Nasrin Nasrollahi PDF

By Nasrin Nasrollahi

This thesis transforms satellite tv for pc precipitation estimation during the integration of a multi-sensor, multi-channel method of present precipitation estimation algorithms, and offers extra exact readings of precipitation facts from space.

Using satellite tv for pc info to estimate precipitation from house overcomes the quandary of ground-based observations when it comes to availability over distant parts and oceans in addition to spatial insurance. besides the fact that, the accuracy of satellite-based estimates nonetheless must be improved.

The method brought during this thesis takes benefit of the hot NASA satellites in staring at clouds and precipitation. furthermore, machine-learning innovations also are hired to make the easiest use of remotely-sensed "big data." the implications supply an important development in detecting non-precipitating parts and lowering fake id of precipitation.

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Extra resources for Improving Infrared-Based Precipitation Retrieval Algorithms Using Multi-Spectral Satellite Imagery

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5 Training and Validation Datasets, Summer Season 47 by cos(SZA)−1 resulted in a larger portion of visible data that can be used in the analyses. This study uses the same approach to normalize the visible channels. Only pixels with SZA < 60o are considered to minimize the uncertainty associated with a large SZA. 3) Uniform Distribution of Data One of the other methods to increase the accuracy of model’s training is to use a uniform distribution of data. Different cloud types have different distributions and that affects the results of the model outcome.

2011) provides information about the presence of surface precipitation. The determination of surface precipitation occurrence is based on the radar reflectivity data near the surface and the surface reflection characteristics. Higher radar reflectivity near the surface increases the probability of rain near the surface. The CloudSat Precip. flag data set, © Springer International Publishing Switzerland 2015 N. 1007/978-3-319-12081-2_5 33 34 5 Integration of CloudSat Precipitation Profile in Reduction of False Rain Fig.

2011). Applying the current algorithm, one can see an improvement of precipitation estimation in the winter season. During summertime NR pixels associated with high clouds are identified with high accuracy. As discussed earlier, high clouds account for the majority of false rain estimations in satellite rainfall algorithms. Therefore, using the proposed methodology has a significant role in reducing false rain. e. MODIS and GOES observations) could affect the results. 6 Case Study 37 Fig. 6 Case Study Two case studies on summer and winter precipitation events are presented here to show the application of this technique to improving the quality of satellite rain estimation.

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