Volume 9, Issue 4, December 2014, Pages 1793–1808
Adoum Mahamat MOUSSA1, Bouya DIOP2, Aboubakary DIAKHABY3, Abdoulaye DEME4, and Abdoulaye SY5
1 Laboratoire des Sciences de l'atmosphère et des Océans (LSAO), Université Gaston Berger, 224, BP 224, Saint-Louis, Senegal
2 Laboratoire des Sciences de l'atmosphère et des Océans (LSAO), Université Gaston Berger, 224, BP 224, Saint-Louis, Senegal
3 Laboratoire d'Etudes et de Recherches en Statistiques et Développement (LERSTAD), Université Gaston Berger, 224, BP 234, Saint-Louis, Senegal
4 Laboratoire des Sciences de l'atmosphère et des Océans (LSAO), Université Gaston Berger, 224, BP 224, Saint-Louis, Senegal
5 Laboratoire des Sciences de l'atmosphère et des Océans (LSAO), Université Gaston Berger, 224, BP 224, Saint-Louis, Senegal
Original language: English
Copyright © 2014 ISSR Journals. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
A multiple linear regression method and a neural network method are performed to retrieve the Precipitable Water Vapor, surface temperature and relative humidity using microwave (AMSU-A, MHS) and infrared (HIRS) ATOVS sounders. Each method is performed using microwave, infrared, and mixed data separately to assess the best. Near nadir ATOVS data of Dakar region (Senegal) at 12:00 AM and 12:00 PM are used for the whole year 2013. Learning data are from radiosonde and in situ measurements. By comparing them with retrieved data, ECMWF reanalysis data help to validate the different methods. The multiple linear regression method provides good results for microwave data with an RMS of 4.65 mm, 2.27
Author Keywords: AMSU-A, MHS, HIRS, PWV, Temperature, Humidity, Neural network, Regression.
Adoum Mahamat MOUSSA1, Bouya DIOP2, Aboubakary DIAKHABY3, Abdoulaye DEME4, and Abdoulaye SY5
1 Laboratoire des Sciences de l'atmosphère et des Océans (LSAO), Université Gaston Berger, 224, BP 224, Saint-Louis, Senegal
2 Laboratoire des Sciences de l'atmosphère et des Océans (LSAO), Université Gaston Berger, 224, BP 224, Saint-Louis, Senegal
3 Laboratoire d'Etudes et de Recherches en Statistiques et Développement (LERSTAD), Université Gaston Berger, 224, BP 234, Saint-Louis, Senegal
4 Laboratoire des Sciences de l'atmosphère et des Océans (LSAO), Université Gaston Berger, 224, BP 224, Saint-Louis, Senegal
5 Laboratoire des Sciences de l'atmosphère et des Océans (LSAO), Université Gaston Berger, 224, BP 224, Saint-Louis, Senegal
Original language: English
Copyright © 2014 ISSR Journals. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
A multiple linear regression method and a neural network method are performed to retrieve the Precipitable Water Vapor, surface temperature and relative humidity using microwave (AMSU-A, MHS) and infrared (HIRS) ATOVS sounders. Each method is performed using microwave, infrared, and mixed data separately to assess the best. Near nadir ATOVS data of Dakar region (Senegal) at 12:00 AM and 12:00 PM are used for the whole year 2013. Learning data are from radiosonde and in situ measurements. By comparing them with retrieved data, ECMWF reanalysis data help to validate the different methods. The multiple linear regression method provides good results for microwave data with an RMS of 4.65 mm, 2.27
Author Keywords: AMSU-A, MHS, HIRS, PWV, Temperature, Humidity, Neural network, Regression.
How to Cite this Article
Adoum Mahamat MOUSSA, Bouya DIOP, Aboubakary DIAKHABY, Abdoulaye DEME, and Abdoulaye SY, “Precipitable water vapor, temperature and humidity retrieval using AMSU-A, MHS and HIRS,” International Journal of Innovation and Applied Studies, vol. 9, no. 4, pp. 1793–1808, December 2014.
