16:00 〜 16:15
[AAS04-09] Seasonal Rainfall prediction in Lagos Nigeria Using artificial Neural Network/Quantifying the Role of Aerosols to Precipitation Variability in East Asian Region through Aerosols-Cloud Interaction: Simulation from WRFCHEM
キーワード：Seasonal rainfall prediction, artificial neural network, non-linear techniques
Deliberating the importance of rainfall in determining process such as agriculture, flood and water
management, these study aim at evaluation of non-linear techniques on seasonal rainfall prediction
(SRP). One of the non-linear method widely used is the Artificial Neural Networks (ANN) approach
which has the ability of mapping between input and output patterns. The complexity of the
atmospheric processes that generate rainfall makes quantitative forecasting of rainfall an
extremely, difficult task. The research goal is to train/develop Artificial Neural Network model using
backward propagation algorithm to predict seasonal Rainfall. Using some meteorological variables
like, sea surface temperature (SST), U-wind at (surface, 700, 850 and 1000), air temperature,
specific humidity, ITD and relative humidity. The study adopt monthly June-October (JJASO)
rainfall data and January-May (JFMAM) monthly data of SST, U-wind at (surface, 700, 850 and
1000), air temperature, specific humidity and relative humidity for a period of 31 years (1986-2017)
over Ikeja. The proposed ANN model architecture (9-4-1) in training the network using backpropagation
algorithm indicated that the statistical performance of the model for predicting 2013 to
2017 (JJASO) rainfall amount indicated as follows; MSE, RMSE, and MAE were 7174, 84.7 and
18.6 respectively with a high statistical coefficient of variation of 94% when the ANN model
prediction is validated with the observed rainfall. The result indicated that the propose ANN built
network is reliable in prediction of seasonal rainfall amount in Ikeja with a minimal error.
Using a high resolution regional model weather research and forecasting WRF-Chem, the impact of the East Asian aerosol load on precipitation variability was assessed through sensitivity experiment Based on a monthly climatology, model simulations compare satisfactory with wind field from reanalysis data, could observation, daily average particulate matter (PM), temperature, relative humidity and satellite retrieved CO mixing, Long term impact of aerosols on precipitation are identified over East Asia through the analysis of 10 years measurement of precipitation 2005-2015 WRF-Chem model with a moment bulk microphysical scheme is employed to simulate monsoon rainfall in these area and elucidate the effect of aerosols on cloud process through aerosols to cloud interactions
management, these study aim at evaluation of non-linear techniques on seasonal rainfall prediction
(SRP). One of the non-linear method widely used is the Artificial Neural Networks (ANN) approach
which has the ability of mapping between input and output patterns. The complexity of the
atmospheric processes that generate rainfall makes quantitative forecasting of rainfall an
extremely, difficult task. The research goal is to train/develop Artificial Neural Network model using
backward propagation algorithm to predict seasonal Rainfall. Using some meteorological variables
like, sea surface temperature (SST), U-wind at (surface, 700, 850 and 1000), air temperature,
specific humidity, ITD and relative humidity. The study adopt monthly June-October (JJASO)
rainfall data and January-May (JFMAM) monthly data of SST, U-wind at (surface, 700, 850 and
1000), air temperature, specific humidity and relative humidity for a period of 31 years (1986-2017)
over Ikeja. The proposed ANN model architecture (9-4-1) in training the network using backpropagation
algorithm indicated that the statistical performance of the model for predicting 2013 to
2017 (JJASO) rainfall amount indicated as follows; MSE, RMSE, and MAE were 7174, 84.7 and
18.6 respectively with a high statistical coefficient of variation of 94% when the ANN model
prediction is validated with the observed rainfall. The result indicated that the propose ANN built
network is reliable in prediction of seasonal rainfall amount in Ikeja with a minimal error.
Using a high resolution regional model weather research and forecasting WRF-Chem, the impact of the East Asian aerosol load on precipitation variability was assessed through sensitivity experiment Based on a monthly climatology, model simulations compare satisfactory with wind field from reanalysis data, could observation, daily average particulate matter (PM), temperature, relative humidity and satellite retrieved CO mixing, Long term impact of aerosols on precipitation are identified over East Asia through the analysis of 10 years measurement of precipitation 2005-2015 WRF-Chem model with a moment bulk microphysical scheme is employed to simulate monsoon rainfall in these area and elucidate the effect of aerosols on cloud process through aerosols to cloud interactions