Next Article in Journal
Infrared Absorption Spectra, Radiative Efficiencies, and Global Warming Potentials of Newly-Detected Halogenated Compounds: CFC-113a, CFC-112 and HCFC-133a
Next Article in Special Issue
A Comparative Study of B-, Γ- and Log-Normal Distributions in a Three-Moment Parameterization for Drop Sedimentation
Previous Article in Journal
Characterization of PM10 and PM2.5 and Their Metals Content in Different Typologies of Sites in South-Eastern Italy
Previous Article in Special Issue
Patterns of Precipitation and Convection Occurrence over the Mediterranean Basin Derived from a Decade of Microwave Satellite Observations
Atmosphere 2014, 5(2), 454-472; doi:10.3390/atmos5020454
Article

Performance of Using Cascade Forward Back Propagation Neural Networks for Estimating Rain Parameters with Rain Drop Size Distribution

1,2,3
 and 1,*
Received: 27 December 2013; in revised form: 22 May 2014 / Accepted: 27 May 2014 / Published: 18 June 2014
(This article belongs to the Special Issue Cloud and Precipitation)
View Full-Text   |   Download PDF [965 KB, uploaded 18 June 2014]   |   Browse Figures
Abstract: The aim of our study is to estimate the parameters M (water content), R (rain rate) and Z (radar reflectivity) with raindrop size distribution by using the neural network method. Our investigations have been conducted in five African localities: Abidjan (Côte d’Ivoire), Boyele (Congo-Brazzaville), Debuncha (Cameroon), Dakar (Senegal) and Niamey (Niger). For the first time, we have predicted the values of the various parameters in each locality after using neural models (LANN) which have been developed with locally obtained disdrometer data. We have shown that each LANN can be used under other latitudes to get satisfactory results. Secondly, we have also constructed a model, using as train-data, a combination of data issued from all five localities. With this last model called PANN, we could obtain satisfactory estimates forall localities. Lastly, we have distinguished between stratiform and convective rain while building the neural networks. In fact, using simulation data from stratiform rain situations, we have obtained smaller root mean square errors (RMSE) between neural values and disdrometer values than using data issued from convective situations.
Keywords: reflectivity; radar; water content; rain rate; artificial neural network reflectivity; radar; water content; rain rate; artificial neural network
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.

Export to BibTeX |
EndNote


MDPI and ACS Style

Tengeleng, S.; Armand, N. Performance of Using Cascade Forward Back Propagation Neural Networks for Estimating Rain Parameters with Rain Drop Size Distribution. Atmosphere 2014, 5, 454-472.

AMA Style

Tengeleng S, Armand N. Performance of Using Cascade Forward Back Propagation Neural Networks for Estimating Rain Parameters with Rain Drop Size Distribution. Atmosphere. 2014; 5(2):454-472.

Chicago/Turabian Style

Tengeleng, Siddi; Armand, Nzeukou. 2014. "Performance of Using Cascade Forward Back Propagation Neural Networks for Estimating Rain Parameters with Rain Drop Size Distribution." Atmosphere 5, no. 2: 454-472.


Atmosphere EISSN 2073-4433 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert