1. Weather Research and Forecasting (WRF) Portal
Elias Rodriguez
School of Computer Engineering Computer Science and Electrical Engineering Polytechnic University of Puerto Rico
Seychelles Martinez
School of Computing and
Information Sciences
Florida International University
REU Summer Program
8/1/2008

2. Outline Introduction Problem Statement Initial Goals Project schedule
Challenges encountered
Lessons learned
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3. Outline (cont.) Accomplishment Current project status Next week
Future challenges
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4. Introduction What is WRF? Weather Research and Forecasting
Developed by National Center for Atmospheric Research (NCAR)
Widely accepted in meteorology
The Weather Research and Forecasting (WRF) code is the latest numerical model developed by the national Center for Atmospheric Research (NCAR) for both operational forecating and atmospheric research. A measure of its success is that WRF has been adopted by meteorological services and researchers worldwide.
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5. Introduction (cont.) What is Ensemble Forecasting?
Purpose: account for errors in forecasting system due to chaotic nature of atmospheric dynamics
Different models created in parallel with different initial conditions.
Models, then, combined to produce a probabilistic forecast
Ensemble forecasting is a method used by modern operational forecast centers to account for uncertainties and errors in the forecasting system which are crucial for the prediction errors due to the chaotic nature of the atmospheric dynamics (sensitive dependency on initial conditions). Many different models are created in parallel with slightly different initial conditions or configurations. These models are then combined to produce a forecast that can be fully probabilistic or derive some deterministic products such as the ensemble mean.
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6. WRF conf Netcdf file Netcdf file WRF conf WPS or WRF Web Based Portal
Real
Pert
M 1
M 2
M 3
Aggreg
M n
Web Based Portal
WRF conf
Real
Pert
M 1
M 2
M 3
Aggreg
M n
Job-Flow Manager
Netcdf file
Meta-Scheduler
Netcdf file
Walk through idea (literal)
GCB
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GCB Vis
Mind
FSU
UNF
WRF conf
WPS or WRF

7. Problem Statement
Meteorologist are encumbered by CS intensive labor using WRF
Ensemble Forecasting is slow
WRF software is processor heavy
The motivation for creating this portal is to provide a comprehensive, easy to use, high-level tool for meteorologist to simulate and perform ensemble runs using the WRF forecasting model. Likewise the idea is to help scientists visualize the output statically or dynamically via MPEG/GIF or Google maps with relevant information where one can zoom in and out, respectively.
This will help the meteorologists, who are currently occupied with the particulars of the computer science aspect of weather forecasting, concentrate on their own science. With a readily available tool created for them, a meteorologist does not have to be bothered by the business logic of the program; much in the same way a person who uses a telephone does not need to be aware of the circuitry inside to use it to his or her advantage. In this way the science of meteorology will not be hindered by the cumbersome task of creating tools for themselves, but rather can concentrate on their own science and achieve research and other scientific goals quicker and more efficiently.
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8. Initial Goals:
1st Goal: Portal provides an appropriate interface for meteorologists to be able to specify what is in the ensemble that would like to be run
Amount of ensemble members
What input should the ensemble members receive (or, How should they be perturbed?)
Perturbation algorithms: i.e. random, uniformly, normally, log-normally, Monte Carlo, etc.
How the output should be aggregated
Aggregation Algorithms: i.e. Mean, median, max, min, etc.
1 goal: Portal provide appropirate intrefaces for metoriologist to be able to specify what I s the ensemble that he would like to run
How many esnsemble members
Ensemble members: what input they should receive (how it hould be perturen bed)
How output should be aggregated
Goal: web based portal to compose workflow that encapsulates the
Aggregate
3 phases: diamond – 5 -6
Provide interface for meteorologist w/ list for diff option how to create perturbed input file
Define ensemble members
Perturbation algorithm
Agregation algorithm
2 goal: interface w/ the job-flow manager define API to interact w/ job flow manager (receive a work flow document response return result of execution)
Give job flow manager XML that contains pertubation and aggregation algorithm and # of ensemble members
Generate worklfow pass it to job flow mananger
Ask job flow mangaer status of worklfow
Get result of document
3 goal: to be able to keep the profile of user w/ resepct to
what are the ensemble user fiends
When they were run (history)
User logs out, system should still be running
keep results of past results
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9. Initial Goals (cont.):
2nd Goal: Define an API to interact with the job-flow manager.
Action: Send a work flow document
Response: Return result of the execution
Generate workflow, pass it to the job-flow manager
Ask job flow manager status of the workflow
Get results of execution
1 goal: Portal provide appropirate intrefaces for metoriologist to be able to specify what I s the ensemble that he would like to run
How many esnsemble members
Ensemble members: what input they should receive (how it hould be perturen bed)
How output should be aggregated
Goal: web based portal to compose workflow that encapsulates the
Aggregate
3 phases: diamond – 5 -6
Provide interface for meteorologist w/ list for diff option how to create perturbed input file
Define ensemble members
Perturbation algorithm
Agregation algorithm
2 goal: interface w/ the job-flow manager define API to interact w/ job flow manager (receive a work flow document response return result of execution)
Give job flow manager XML that contains pertubation and aggregation algorithm and # of ensemble members
Generate worklfow pass it to job flow mananger
Ask job flow mangaer status of worklfow
Get result of document
3 goal: to be able to keep the profile of user w/ resepct to
what are the ensemble user fiends
When they were run (history)
User logs out, system should still be running
keep results of past results
11/27/2018

10. Initial Goals (cont.):
3rd Goal: To be able to keep the user’s profile with respect to:
What is in the user’s ensemble’s fields
When the ensembles were run (history)
Keep results of past runs
A logged out user’s runs should still be running on the system
1 goal: Portal provide appropirate intrefaces for metoriologist to be able to specify what I s the ensemble that he would like to run
How many esnsemble members
Ensemble members: what input they should receive (how it hould be perturen bed)
How output should be aggregated
Goal: web based portal to compose workflow that encapsulates the
Aggregate
3 phases: diamond – 5 -6
Provide interface for meteorologist w/ list for diff option how to create perturbed input file
Define ensemble members
Perturbation algorithm
Agregation algorithm
2 goal: interface w/ the job-flow manager define API to interact w/ job flow manager (receive a work flow document response return result of execution)
Give job flow manager XML that contains pertubation and aggregation algorithm and # of ensemble members
Generate worklfow pass it to job flow mananger
Ask job flow mangaer status of worklfow
Get result of document
3 goal: to be able to keep the profile of user w/ resepct to
what are the ensemble user fiends
When they were run (history)
User logs out, system should still be running
keep results of past results
11/27/2018

11. Project Schedule Research Documentation (on Wiki page): Implementation
On autonomic computing (Jun 2)
Documentation (on Wiki page):
Initial “Requirements Specification” (Jun 2)
Initial “Software Architecture” (Jun 23)
User Manual
Final “Requirements Specification” (Jul 31)
Final “Software Architecture”
Implementation
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12. Accomplishments Design of portal
Creating, Editing, and Deleting ensembles
Design and implementation of Database
Maintaining user history
Self Generating template for JFM Work-Flow
Working demo 
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13. Accomplishments (cont.)
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14. Accomplishments (cont.)
(BPEL) Business Process Execution Language
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15. Challenges Learning about WRF and meteorology related information
Finding information on ensemble forecasting
Communicating with the other layers of the system (i.e. JFM)
Creating an ensemble run
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16. Lessons Learned WRF and ensemble forecasting
Communication through layers
Grid enablement
Autonomic Computing
Software Architecture
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17. Current project status
Completed documentation:
Requirements Specification
Software Architecture Document
User Manual
Implementation
Create, edit, and delete an ensemble forecast
Generate the BPEL template to communicate with the JFM
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18. Next Week Complete an ensemble run
Send functional workflow to JFM
Calling API method
Organize Wiki page with all documentation
Write technical report
The wiki page will have all the information from the archives, group forums, detail of the design and implementation documents, all the other related documents.
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19. Future Research Challenges
Dynamically allow for multiple met_em files to be uploaded
according to number of domains
Visualizations
Perturbation and aggregation functions implemented
Creating distinct ensemble members
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20. Demo
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21. References
Gargi Dasgupta, Onyeka Ezenwoye, Liana Fong, Selim Kalayci, S. Masoud Sadjadi, and Balaji Viswanathan. Runtime fault-handling for job-flow management in grid environments. In Proceedings of the 5th IEEE International Conference on Autonomic Computing (ICAC-2008), Chicago, IL, June 2008.
Khalid Saleem, S. Masoud Sadjadi, and Shu-Ching Chen. Towards a self-configurable weather research and forecasting system. In Proceedings of the 5th IEEE International Conference on Autonomic Computing (ICAC-2008), Chicago, IL, June 2008.
Lavanya Ramakrishnan1, Brian O. Blanton4, Howard M. Lander1, Richard A. Luettich, Jr.3, Daniel A. Reed1, Steven R. Thorpe2, 1 Renaissance Computing Institute, 2MCNC, 3UNC Chapel Hill Institute of Marine Sciences, 4Science, Applications International Corporation Real-time Storm Surge Ensemble Modeling in a Grid Environment
S. Masoud Sadjadi, Liana Fong, Rosa M. Badia, Javier Figueroa, Javier Delgado, Xabriel J. Collazo-Mojica, Khalid Saleem, Raju Rangaswami, Shu Shimizu, Hector A. Duran Limon, Pat Welsh, Sandeep Pattnaik,`Anthony Praino, David Villegas, Selim Kalayci, Gargi Dasgupta, Onyeka Ezenwoye, Juan Carlos Martinez, Ivan Rodero, Shuyi Chen, Javier Muñoz, Diego Lopez, Julita Corbalan, Hugh Willoughby, Michael McFail,Christine Lisetti, and Malek Adjouadi1 Transparent Grid Enablement of Weather Research and Forecasting
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22. Questions
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