Designing Weather Forecasting Model Using Computational Intelligence Tools

2018 ◽  
Vol 33 (2) ◽  
pp. 137-151 ◽  
Author(s):  
Bashar Muneer Yahya ◽  
Dursun Zafer Seker
Keyword(s):  
Computational Intelligence ◽  
Weather Forecasting ◽  
Forecasting Model

scholarly journals A snowmelt runoff forecasting model coupling WRF and DHSVM

2009 ◽  
Vol 13 (10) ◽  
pp. 1897-1906 ◽  
Author(s):  
Q. Zhao ◽  
Z. Liu ◽  
B. Ye ◽  
Y. Qin ◽  
Z. Wei ◽  
...  
Keyword(s):  
Weather Forecasting ◽  
Atmospheric Model ◽  
Forecasting Model ◽  
Tianshan Mountain ◽  
Mountain Range ◽  
Snowmelt Runoff ◽  
Runoff Simulation ◽  
Limited Region ◽  
Runoff Forecasting ◽  
Forecasting System

Abstract. This study linked the Weather Research and Forecasting (WRF) modelling system and the Distributed Hydrology Soil Vegetation Model (DHSVM) to forecast snowmelt runoff. The study area was the 800 km2 Juntanghu watershed of the northern slopes of Tianshan Mountain Range. This paper investigated snowmelt runoff forecasting models suitable for meso-microscale application. In this study, a limited-region 24-h Numeric Weather Forecasting System was formulated using the new generation atmospheric model system WRF with the initial fields and lateral boundaries forced by Chinese T213L31 model. Using the WRF forecasts, the DHSVM hydrological model was used to predict 24 h snowmelt runoff at the outlet of the Juntanghu watershed. Forecasted results showed a good similarity to the observed data, and the average relative error of maximum runoff simulation was less than 15%. The results demonstrate the potential of using a meso-microscale snowmelt runoff forecasting model for forecasting floods. The model provides a longer forecast period compared with traditional models such as those based on rain gauges or statistical forecasting.

scholarly journals Weather Forecasting Model using Artificial Neural Network

2012 ◽  
Vol 4 ◽  
pp. 311-318 ◽  
Author(s):  
Kumar Abhishek ◽  
M.P. Singh ◽  
Saswata Ghosh ◽  
Abhishek Anand
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Weather Forecasting ◽  
Forecasting Model ◽  
Artificial Neural

scholarly journals GEM/POPs: a global 3-D dynamic model for semi-volatile persistent organic pollutants – Part 1: Model description and evaluations of air concentrations

2007 ◽  
Vol 7 (15) ◽  
pp. 4001-4013 ◽  
Author(s):  
S. L. Gong ◽  
P. Huang ◽  
T. L. Zhao ◽  
L. Sahsuvar ◽  
L. A. Barrie ◽  
...  
Keyword(s):  
Organic Pollutants ◽  
Persistent Organic Pollutants ◽  
Atmospheric Aerosols ◽  
Weather Forecasting ◽  
Model Description ◽  
Forecasting Model ◽  
Anthropogenic Emissions ◽  
Surface Areas ◽  
Aerosol Module ◽  
Year 2000

Abstract. GEM/POPs was developed to simulate the transport, deposition and partitioning of semi-volatile persistent organic pollutants (POPs) in the atmosphere within the framework of Canadian weather forecasting model GEM. In addition to the general processes such as anthropogenic emissions, atmosphere/water and atmosphere/soil exchanges, GEM/POPs incorporates a dynamic aerosol module to provide the aerosol surface areas for the semi-volatile POPs to partition between gaseous and particle phases and a mechanism for particle-bound POPs to be removed. Simulation results of three PCBs (28, 153 and 180) for the year 2000 indicate that the model captured the main features of global atmospheric PCBs when compared with observations from EMEP, IADN and Alert stations. The annual averaged concentrations and the fractionation of the three PCBs as a function of latitudes agreed reasonably well with observations. The impacts of atmospheric aerosols on the transports and partitioning of the three PCBs are reasonably simulated. The ratio of particulate to gaseous PCBs in the atmospheric column ranges from less than 0.1 for PCB28 to as high as 100 for PCB180, increasing from the warm lower latitudes to the cold high latitudes. Application of GEM/POPs in a study of the global transports and budgets of various PCBs accompanies this paper.

scholarly journals Optimizing an MPI weather forecasting model via processor virtualization

2010 ◽  
Author(s):  
Eduardo R. Rodrigues ◽  
Philippe O. A. Navaux ◽  
Jairo Panetta ◽  
Celso L. Mendes ◽  
Laxmikant V. Kale
Keyword(s):  
Weather Forecasting ◽  
Forecasting Model

scholarly journals GEM/POPs: a global 3-D dynamic model for semi-volatile persistent organic pollutants – 1. Model description and evaluations

2007 ◽  
Vol 7 (2) ◽  
pp. 3397-3422 ◽  
Author(s):  
S. L. Gong ◽  
P. Huang ◽  
T. L. Zhao ◽  
L. Sahsuvar ◽  
L. A. Barrie ◽  
...  
Keyword(s):  
Organic Pollutants ◽  
Persistent Organic Pollutants ◽  
Atmospheric Aerosols ◽  
Weather Forecasting ◽  
Model Description ◽  
Forecasting Model ◽  
Anthropogenic Emissions ◽  
Surface Areas ◽  
Aerosol Module ◽  
Year 2000

Abstract. GEM/POPs was developed to simulate the transport, deposition and partitioning of semi-volatile persistent organic pollutants (POPs) in the atmosphere within the framework of Canadian weather forecasting model GEM. In addition to the general processes such as anthropogenic emissions, atmosphere/water and atmosphere/soil exchanges, GEM/POPs incorporates a dynamic aerosol module to provide the aerosol surface areas for the semi-volatile POPs to partition between gaseous and particle phases and a mechanism for particle-bound POPs to be removed. Simulation results of three PCBs (28, 153 and 180) for year 2000 indicate that the model captured the main features of global atmospheric PCBs when compared with observations from EMEP, IADN and Alert stations. The annual averaged concentrations and the fractionation of the three PCBs as a function of latitudes are agreed reasonably well with observations. The impacts of atmospheric aerosols on the transports and partitioning of the three PCBs are reasonably simulated. The ratio of particulate to gaseous PCBs ranges from less than 0.1 for PCB28 to as high as 100 for PCB180, increasing from the warm lower latitudes to the cold high latitudes. Application of GEM/POPs in a study of the global transports and budgets of various PCBs accompanies this paper.

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