ENHANCING VERTICAL RESOLUTION OF SATELLITE ATMOSPHERIC PROFILE DATA: A MACHINE LEARNING APPROACH
| Autor: | Venugopal T, Venkatramana K |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Troposphere
Artificial neural network Computer Science::Neural and Evolutionary Computation Resolution (electron density) Geovetenskap och miljövetenskap Environmental science Relative humidity Satellite Earth and Related Environmental Sciences Artificial Neural Networks Relative Humidity GPSRO COSMIC scatter index the correlation coefficient Physics::Atmospheric and Oceanic Physics Atmospheric profile Remote sensing |
| Zdroj: | International Journal of Advanced Research. 6:542-550 |
| ISSN: | 2320-5407 |
| DOI: | 10.21474/ijar01/7836 |
| Popis: | We developed a statistical approach using the Artificial Neural Networks (ANN) to improve the vertical resolution of tropospheric relative humidity profiles (RH) from 20 pressure levels to 171 pressure levels. The model is based on an unconventional method in which we used the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) Global Positioning System Radio Occultation (GPS RO) data and the corresponding observed values of RH data. The model was developed using 3 years COSMIC daily data during 2007-2009 over the north Indian Ocean and produced high vertical resolution RH (171 pressure levels) output data from the coarse resolution inputs (20 pressure levels). We achieved the best performance in generating high vertical resolution data with a Pearson’s correlation coefficient (CC) of greater than 0.94 and scatter index (SI) of less than 0.1 throughout all pressure levels. Thus, the present approach is an efficient method to achieve the better vertical resolution of RH data from geostationary satellites. |
| Databáze: | OpenAIRE |
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