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PINE - RT (1.0)

Plasma density in the Inner magnetosphere Neural network-based Empirical (PINE) - Real Time (RT)

Model Description 

This plasmasphere model is a machine-learning model that produces a nowcast of the plasma density in the whole equatorial plane every hour. It uses the time history of the solar wind features and of the Kp index as inputs and it makes the nowcast of the plasma density in a few seconds, making it especially suitable for real-time operations. 

This model has been developed as part of the PAGER (Prediction of Adverse effects of Geomagnetic storms and Energetic Radiation) project (https://www.spacepager.eu/).

Model Figure(s) :

Model Inputs Description 

solar wind, Kp

Model Outputs Description 

plasmaspheric density

Model Caveats 

When there are too many missing values of solar wind and Kp index, the machine learning model misses input features and cannot produce an output. Therefore the plot produced by the post-processing plotting tool will be blank.

Change Log 


	
	 
	
Model Acknowledgement/Publication Policy (if any)
		
	

	

	
	
	
Model Domains:
		
	

	
		Magnetosphere.Inner_Magnetosphere.Plasmasphere

	
	
	
Space Weather Impacts:
		
	

	
		Near-earth radiation and plasma environment (aerospace assets functionality)

	
	
	
Phenomena :

	

	
Simulation Type(s):
		
	

	
		Empirical

	
		Machine-Learning

	
		
	
Temporal Dependence Possible? (whether the code results depend on physical time?)
		
	

		true

		
    
Model is available at? 
    	
	

		CCMC

		
	
Source code of the model is publicly available?

		false

		
	 
CCMC Model Status (e.g. onboarding, use in production, retired, only hosting output, only source is available): 
    	
	

		resultOnly

	
	
Code Language: 
		
	

		


	
Regions (this is automatically mapped based on model domain):

	
		Earth.NearSurface.Plasmasphere

	
	
	

		Contacts
		
		:
	

	
		Stefano.Bianco, ModelDeveloper 
		

	
		Yihua.Zheng, ModelHostContact 
		

	

	

		Acknowledgement/Institution
		
		:
	

	
		
			
				Yuri Shprits/GFZ
			
			
		
		
		

	

	
	
	

		Relevant Links
		
		:
	

	
		PAGER project: https://www.spacepager.eu/
		
		

	

	

		Publications
		
		:
	

	
		
  • 
		
			
				Zhelavskaya I., N. Aseev, Y. Y. Shprits (2021), A combined neural network- and physics-based approach for modeling plasmasphere dynamics.
			
			
			
		
		
    • 
	
		
  • 
		
			
				Zhelavskaya, I., Shprits, Y. Y., & Spasojević, M. (2017), Empirical modeling of the plasmasphere dynamics using neural networks.
			
			
			
		
		
    • 
	
		
  • 
		
			
				S. Bianco, B. Haas and Y. Y. Shprits (2023), PINE-RT: An operational real-time plasmasphere model
			
			
			
		
		
    • 
	

	
    
		Model Access Information
		
		:
	
    
	
		Access URL: http://iswa.gsfc.nasa.gov/IswaSystemWebApp/index.jsp?i_1=677&l_1=147&t_1=375&w_1=500&h_1=520&s_1=0_1_20_3  
    
		
		Access URL Name: Continuous/RT Run (ISWA layout)
    
		
		Repository ID: spase://CCMC/Repository/NASA/GSFC/CCMC
    
		Availability: online
    
		AccessRights: OPEN
    
		Format: HTML
    
		Encoding: None
    		  
		
    
	
		Access URL: https://iswa.gsfc.nasa.gov/iswa_data_tree/model/magnetosphere/PAGER/PINE/plasma_density_nowcast/  
    
		
		Access URL Name: Continuous/RT Run (ISWA data tree)
    
		
		Repository ID: spase://CCMC/Repository/NASA/GSFC/CCMC
    
		Availability: online
    
		AccessRights: OPEN
    
		Format: HTML
    
		Encoding: None
    		  
		
    
	
	
	
    
		Linked to Other Spase Resource(s) (example: another SimulationModel)
		
		:
	
    
	
	
	


            		
    
    
    
    
        
            




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