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From Henry Saputra <henry.sapu...@gmail.com>
Subject Re: [VOTE} Climate Model Diagnostic Analyzer
Date Mon, 20 Apr 2015 21:12:42 GMT
+1 (binding)

Good luck guys!

On Sat, Apr 18, 2015 at 10:00 PM, Mattmann, Chris A (3980)
<chris.a.mattmann@jpl.nasa.gov> wrote:
> OK all, discussion has died down, we have 3 mentors, I think it’s
> time to proceed to a VOTE.
>
> I am calling a VOTE now to accept the Climate Model Diagnostic
> Analyzer (CMDA) into the Apache Incubator. The VOTE is open for
> at least the next 72 hours:
>
> [ ] +1 Accept Apache Climate Model Diagnostic Analyzer into the Apache
> Incubator.
> [ ] +0 Abstain.
> [ ] -1 Don’t accept Apache Climate Model Diagnostic Analyzer into the
> Apache Incubator
> because…
>
> I’ll try and close the VOTE out on Friday.
>
> Of course I am +1!
>
> P.S. the text of the latest wiki proposal is pasted below:
>
> Cheers,
> Chris
>
>
> = Apache ClimateModelDiagnosticAnalyzer Proposal =
>
> == Abstract ==
>
> The Climate Model Diagnostic Analyzer (CMDA) provides web services for
> multi-aspect physics-based and phenomenon-oriented climate model
> performance evaluation and diagnosis through the comprehensive and
> synergistic use of multiple observational data, reanalysis data, and model
> outputs.
>
> == Proposal ==
>
> The proposed web-based tools let users display, analyze, and download
> earth science data interactively. These tools help scientists quickly
> examine data to identify specific features, e.g., trends, geographical
> distributions, etc., and determine whether a further study is needed. All
> of the tools are designed and implemented to be general so that data from
> models, observation, and reanalysis are processed and displayed in a
> unified way to facilitate fair comparisons. The services prepare and
> display data as a colored map or an X-Y plot and allow users to download
> the analyzed data. Basic visual capabilities include 1) displaying
> two-dimensional variable as a map, zonal mean, and time series 2)
> displaying three-dimensional variable’s zonal mean, a two-dimensional
> slice at a specific altitude, and a vertical profile. General analysis can
> be done using the difference, scatter plot, and conditional sampling
> services. All the tools support display options for using linear or
> logarithmic scales and allow users to specify a temporal range and months
> in a year. The source/input datasets for these tools are CMIP5 model
> outputs, Obs4MIP observational datasets, and ECMWF reanalysis datasets.
> They are stored on the server and are selectable by a user through the web
> services.
>
> === Service descriptions ===
>
> 1. '''Two dimensional variable services'''
>
> * Map of two-dimensional variable:  This services displays a two
> dimensional variable as a colored longitude and latitude map with values
> represented by a color scheme. Longitude and latitude ranges can be
> specified to magnify a specific region.
>
> * Two dimensional variable zonal mean:  This service plots the zonal mean
> value of a two-dimensional variable as a function of the latitude in terms
> of an X-Y plot.
>
> * Two dimensional variable time series:  This service displays the average
> of a two-dimensional variable over the specific region as function of time
> as an X-Y plot.
>
> 2. '''Three dimensional variable services'''
>
> * Map of a two dimensional slice of a three-dimensional variable:  This
> service displays a two-dimensional slice of a three-dimensional variable
> at a specific altitude as a colored longitude and latitude map with values
> represented by a color scheme.
>
> * Three dimensional zonal mean:  Zonal mean of the specified
> three-dimensional variable is computed and displayed as a colored
> altitude-latitude map.
>
> * Vertical profile of a three-dimensional variable:  Compute the area
> weighted average of a three-dimensional variable over the specified region
> and display the average as function of pressure level (altitude) as an X-Y
> plot.
>
> 3. '''General services'''
>
> * Difference of two variables:  This service displays the differences
> between the two variables, which can be either a two dimensional variable
> or a slice of a three-dimensional variable at a specified altitude as
> colored longitude and latitude maps
>
> * Scatter and histogram plots of two variables:  This service displays the
> scatter plot (X-Y plot) between two specified variables and the histograms
> of the two variables. The number of samples can be specified and the
> correlation is computed. The two variables can be either a two-dimensional
> variable or a slice of a three-dimensional variable at a specific altitude.
>
> * Conditional sampling:  This service lets user to sort a physical
> quantity of two or dimensions according to the values of another variable
> (environmental condition, e.g. SST) which may be a two-dimensional
> variable or a slice of a three-dimensional variable at a specific
> altitude. For a two dimensional quantity, the plot is displayed an X-Y
> plot, and for a two-dimensional quantity, plot is displayed as a
> colored-map.
>
>
> == Background and Rationale ==
>
> The latest Intergovernmental Panel on Climate Change (IPCC) Fourth
> Assessment Report stressed the need for the comprehensive and innovative
> evaluation of climate models with newly available global observations. The
> traditional approach to climate model evaluation, which is the comparison
> of a single parameter at a time, identifies symptomatic model biases and
> errors but fails to diagnose the model problems. The model diagnosis
> process requires physics-based multi-variable comparisons, which typically
> involve large-volume and heterogeneous datasets, and computationally
> demanding and data-intensive operations. We propose to develop a
> computationally efficient information system to enable the physics-based
> multi-variable model performance evaluations and diagnoses through the
> comprehensive and synergistic use of multiple observational data,
> reanalysis data, and model outputs.
>
> Satellite observations have been widely used in model-data
> inter-comparisons and model evaluation studies. These studies normally
> involve the comparison of a single parameter at a time using a time and
> space average. For example, modeling cloud-related processes in global
> climate models requires cloud parameterizations that provide quantitative
> rules for expressing the location, frequency of occurrence, and intensity
> of the clouds in terms of multiple large-scale model-resolved parameters
> such as temperature, pressure, humidity, and wind. One can evaluate the
> performance of the cloud parameterization by comparing the cloud water
> content with satellite data and can identify symptomatic model biases or
> errors. However, in order to understand the cause of the biases and
> errors, one has to simultaneously investigate several parameters that are
> integrated in the cloud parameterization.
>
> Such studies, aimed at a multi-parameter model diagnosis, require
> locating, understanding, and manipulating multi-source observation
> datasets, model outputs, and (re)analysis outputs that are physically
> distributed, massive in volume, heterogeneous in format, and provide
> little information on data quality and production legacy. Additionally,
> these studies involve various data preparation and processing steps that
> can easily become computationally demanding since many datasets have to be
> combined and processed simultaneously. It is notorious that scientists
> spend more than 60% of their research time on just preparing the dataset
> before it can be analyzed for their research.
>
> To address these challenges, we propose to build Climate Model Diagnostic
> Analyzer (CMDA) that will enable a streamlined and structured preparation
> of multiple large-volume and heterogeneous datasets, and provide a
> computationally efficient approach to processing the datasets for model
> diagnosis. We will leverage the existing information technologies and
> scientific tools that we developed in our current NASA ROSES COUND, MAP,
> and AIST projects. We will utilize the open-source Web-service technology.
> We will make CMDA complementary to other climate model analysis tools
> currently available to the research community (e.g., PCMDI’s CDAT and
> NCAR’s CCMVal) by focusing on the missing capabilities such as conditional
> sampling, and probability distribution function and cluster analysis of
> multiple-instrument datasets. The users will be able to use a web browser
> to interface with CMDA.
>
> == Current Status ==
>
> The current version of ClimateModelDiagnosticAnalyzer was developed by a
> team at The Jet Propulsion Laboratory (JPL). The project was initiated as
> a NASA-sponsored project (ROSES-CMAC) in 2011.
>
> == Meritocracy ==
>
> The current developers are not familiar with meritocratic open source
> development at Apache, but would like to encourage this style of
> development for the project.
>
> == Community ==
>
> While ClimateModelDiagnosticAnalyzer started as a JPL research project, it
> has been used in The 2014 Caltech Summer School sponsored by the JPL
> Center for Climate Sciences. Some 23 students from different institutions
> over the world participated. We deployed the tool to the Amazon Cloud and
> let every student each has his or her own virtual machine. Students gave
> positive feedback mostly on the usability and speed of our web services.
> We also collected a number of enhancement requests. We seek to further
> grow the developer and user communities using the Apache open source
> venue. During incubation we will explicitly seek increased academic
> collaborations (e.g., with The Carnegie Mellon University) as well as
> industrial participation.
>
> One instance of our web services can be found at:
> http://cmacws4.jpl.nasa.gov:8080/cmac/
>
> == Core Developers ==
>
> The core developers of the project are JPL scientists and software
> developers.
>
> == Alignment ==
>
> Apache is the most natural home for taking the
> ClimateModelDiagnosticAnalyzer project forward. It is well-aligned with
> some Apache projects such as Apache Open Climate Workbench.
> ClimateModelDiagnosticAnalyzer also seeks to achieve an Apache-style
> development model; it is seeking a broader community of contributors and
> users in order to achieve its full potential and value to the Climate
> Science and Big Data community.
>
> There are also a number of dependencies that will be mentioned below in
> the Relationships with Other Apache products section.
>
>
> == Known Risks ==
>
> === Orphaned products ===
>
> Given the current level of intellectual investment in
> ClimateModelDiagnosticAnalyzer, the risk of the project being abandoned is
> very small. The Carnegie Mellon University and JPL are collaborating
> (2014-2015) to build a service for climate analytics workflow
> recommendation using fund from NASA. A two-year NASA AIST project
> (2015-2016) will soon start to add diagnostic analysis methodologies such
> as conditional sampling method, conditional probability density function,
> data co-location, and random forest. We will also infuse the provenance
> technology into CMDA so that the history of the data products and
> workflows will be automatically collected and saved. This information will
> also be indexed so that the products and workflows can be searchable by
> the community of climate scientists and students.
>
> === Inexperience with Open Source ===
>
> The current developers of ClimateModelDiagnosticAnalyzer are inexperienced
> with Open Source. However, our Champion Chris Mattmann is experienced
> (Champions of ApacheOpenClimateWorkbench and AsterixDB) and will be
> working closely with us, also as the Chief Architect of our JPL section.
>
> === Relationships with Other Apache Products ===
>
> Clearly there is a direct relationship between this project and the Apache
> Open Climate Workbench already a top level Apache project and also brought
> to the ASF by its Champion (and ours) Chris Mattmann. We plan on directly
> collaborating with the Open Climate Workbench community via our Champion
> and we also welcome ASF mentors familiar with the OCW project to help
> mentor our project. In addition our team is extremely welcoming of ASF
> projects and if there are synergies with them we invite participation in
> the proposal and in the discussion.
>
> === Homogeneous Developers ===
>
> The current community is within JPL but we would like to increase the
> heterogeneity.
>
> === Reliance on Salaried Developers ===
>
> The initial committers are full-time JPL staff from 2013 to 2014. The
> other committers from 2014 to 2015 are a mix of CMU faculty, students and
> JPL staff.
>
> === An Excessive Fascination with the Apache Brand ===
>
> We believe in the processes, systems, and framework Apache has put in
> place. Apache is also known to foster a great community around their
> projects and provide exposure. While brand is important, our fascination
> with it is not excessive. We believe that the ASF is the right home for
> ClimateModelDiagnosticAnalyzer and that having
> ClimateModelDiagnosticAnalyzer inside of the ASF will lead to a better
> long-term outcome for the Climate Science and Big Data community.
>
> === Documentation ===
>
> The ClimateModelDiagnosticAnalyzer services and documentation can be found
> at: http://cmacws4.jpl.nasa.gov:8080/cmac/.
>
> === Initial Source ===
>
> Current source resides in ...
>
> === External Dependencies ===
>
> ClimateModelDiagnosticAnalyzer depends on a number of open source projects:
>
>  * Flask
>  * Gunicorn
>  * Tornado Web Server
>  * GNU octave
>  * epd python
>  * NOAA ferret
>  * GNU plot
>
> == Required Resources ==
>
> === Developer and user mailing lists ===
>
>  * private@cmda.incubator.apache.org (with moderated subscriptions)
>  * commits@cmda.incubator.apache.org
>  * dev@cmda.incubator.apache.org
>  * users@cmda.incubator.apache.org
>
> A git repository
>
> https://git-wip-us.apache.org/repos/asf/incubator-cmda.git
>
> A JIRA issue tracker
>
> https://issues.apache.org/jira/browse/CMDA
>
> === Initial Committers ===
>
> The following is a list of the planned initial Apache committers (the
> active subset of the committers for the current repository at Google code).
>
>  * Seungwon Lee (seungwon.lee@jpl.nasa.gov)
>  * Lei Pan (lei.pan@jpl.nasa.gov)
>  * Chengxing Zhai (chengxing.zhai@jpl.nasa.gov)
>  * Benyang Tang (benyang.tang@jpl.nasa.gov)
>  * Jia Zhang (jia.zhang@sv.cmu.edu)
>  * Wei Wang (wei.wang@sv.cmu.edu)
>  * Chris Lee (chris.lee@sv.cmu.edu)
>  * Xing Wei (xing.wei@sv.cmu.edu)
>
>
> === Affiliations ===
>
> JPL
>
>  * Seungwon Lee
>  * Lei Pan
>  * Chengxing Zhai
>  * Benyang Tang
>
> CMU
>
>  * Jia Zhang
>  * Wei Wang
>  * Chris Lee
>  * Xing Wei
>
> == Sponsors ==
>
> NASA
>
> === Champion ===
>
> Chris Mattmann (NASA/JPL)
>
> === Nominated Mentors ===
>
> Greg Reddin<<BR>>
> Chris Mattmann<<BR>>
> Michael Joyce<<BR>>
> James Carman
>
> === Sponsoring Entity ===
>
> The Apache Incubator
>
>
>
>
> ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
> Chris Mattmann, Ph.D.
> Chief Architect
> Instrument Software and Science Data Systems Section (398)
> NASA Jet Propulsion Laboratory Pasadena, CA 91109 USA
> Office: 168-519, Mailstop: 168-527
> Email: chris.a.mattmann@nasa.gov
> WWW:  http://sunset.usc.edu/~mattmann/
> ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
> Adjunct Associate Professor, Computer Science Department
> University of Southern California, Los Angeles, CA 90089 USA
> ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
>
>
>
>
>
>
> -----Original Message-----
> From: <Mattmann>, Chris Mattmann <Chris.A.Mattmann@jpl.nasa.gov>
> Reply-To: "general@incubator.apache.org" <general@incubator.apache.org>
> Date: Monday, March 23, 2015 at 1:55 AM
> To: "general@incubator.apache.org" <general@incubator.apache.org>
> Cc: "Pan, Lei (398K)" <lei.pan@jpl.nasa.gov>, "Lee, Seungwon (398K)"
> <seungwon.lee@jpl.nasa.gov>, "Zhai, Chengxing (398K)"
> <chengxing.zhai@jpl.nasa.gov>, "Tang, Benyang (398J)"
> <Benyang.Tang@jpl.nasa.gov>, "jia.zhang@west.cmu.edu"
> <jia.zhang@west.cmu.edu>
> Subject: [PROPOSAL] Climate Model Diagnostic Analyzer
>
>>Hi Everyone,
>>
>>I am pleased to submit for consideration to the Apache Incubator
>>the Climate Model Diagnostic Analyzer proposal. We are actively
>>soliciting interested mentors in this project related to climate
>>science and analytics and big data.
>>
>>Please find the wiki text of the proposal below and the link up
>>on the wiki here:
>>
>>https://wiki.apache.org/incubator/ClimateModelDiagnosticAnalyzerProposal
>>
>>Thank you for your consideration!
>>
>>Cheers,
>>Chris
>>(on behalf of the Climate Model Diagnostic Analyzer community)
>>
>>= Apache ClimateModelDiagnosticAnalyzer Proposal =
>>
>>== Abstract ==
>>
>>The Climate Model Diagnostic Analyzer (CMDA) provides web services for
>>multi-aspect physics-based and phenomenon-oriented climate model
>>performance evaluation and diagnosis through the comprehensive and
>>synergistic use of multiple observational data, reanalysis data, and model
>>outputs.
>>
>>== Proposal ==
>>
>>The proposed web-based tools let users display, analyze, and download
>>earth science data interactively. These tools help scientists quickly
>>examine data to identify specific features, e.g., trends, geographical
>>distributions, etc., and determine whether a further study is needed. All
>>of the tools are designed and implemented to be general so that data from
>>models, observation, and reanalysis are processed and displayed in a
>>unified way to facilitate fair comparisons. The services prepare and
>>display data as a colored map or an X-Y plot and allow users to download
>>the analyzed data. Basic visual capabilities include 1) displaying
>>two-dimensional variable as a map, zonal mean, and time series 2)
>>displaying three-dimensional variable’s zonal mean, a two-dimensional
>>slice at a specific altitude, and a vertical profile. General analysis can
>>be done using the difference, scatter plot, and conditional sampling
>>services. All the tools support display options for using linear or
>>logarithmic scales and allow users to specify a temporal range and months
>>in a year. The source/input datasets for these tools are CMIP5 model
>>outputs, Obs4MIP observational datasets, and ECMWF reanalysis datasets.
>>They are stored on the server and are selectable by a user through the web
>>services.
>>
>>=== Service descriptions ===
>>
>>1. '''Two dimensional variable services'''
>>
>>* Map of two-dimensional variable:  This services displays a two
>>dimensional variable as a colored longitude and latitude map with values
>>represented by a color scheme. Longitude and latitude ranges can be
>>specified to magnify a specific region.
>>
>>* Two dimensional variable zonal mean:  This service plots the zonal mean
>>value of a two-dimensional variable as a function of the latitude in terms
>>of an X-Y plot.
>>
>>* Two dimensional variable time series:  This service displays the average
>>of a two-dimensional variable over the specific region as function of time
>>as an X-Y plot.
>>
>>2. '''Three dimensional variable services'''
>>
>>* Map of a two dimensional slice of a three-dimensional variable:  This
>>service displays a two-dimensional slice of a three-dimensional variable
>>at a specific altitude as a colored longitude and latitude map with values
>>represented by a color scheme.
>>
>>* Three dimensional zonal mean:  Zonal mean of the specified
>>three-dimensional variable is computed and displayed as a colored
>>altitude-latitude map.
>>
>>* Vertical profile of a three-dimensional variable:  Compute the area
>>weighted average of a three-dimensional variable over the specified region
>>and display the average as function of pressure level (altitude) as an X-Y
>>plot.
>>
>>3. '''General services'''
>>
>>* Difference of two variables:  This service displays the differences
>>between the two variables, which can be either a two dimensional variable
>>or a slice of a three-dimensional variable at a specified altitude as
>>colored longitude and latitude maps
>>
>>* Scatter and histogram plots of two variables:  This service displays the
>>scatter plot (X-Y plot) between two specified variables and the histograms
>>of the two variables. The number of samples can be specified and the
>>correlation is computed. The two variables can be either a two-dimensional
>>variable or a slice of a three-dimensional variable at a specific
>>altitude.
>>
>>* Conditional sampling:  This service lets user to sort a physical
>>quantity of two or dimensions according to the values of another variable
>>(environmental condition, e.g. SST) which may be a two-dimensional
>>variable or a slice of a three-dimensional variable at a specific
>>altitude. For a two dimensional quantity, the plot is displayed an X-Y
>>plot, and for a two-dimensional quantity, plot is displayed as a
>>colored-map.
>>
>>
>>== Background and Rationale ==
>>
>>The latest Intergovernmental Panel on Climate Change (IPCC) Fourth
>>Assessment Report stressed the need for the comprehensive and innovative
>>evaluation of climate models with newly available global observations. The
>>traditional approach to climate model evaluation, which is the comparison
>>of a single parameter at a time, identifies symptomatic model biases and
>>errors but fails to diagnose the model problems. The model diagnosis
>>process requires physics-based multi-variable comparisons, which typically
>>involve large-volume and heterogeneous datasets, and computationally
>>demanding and data-intensive operations. We propose to develop a
>>computationally efficient information system to enable the physics-based
>>multi-variable model performance evaluations and diagnoses through the
>>comprehensive and synergistic use of multiple observational data,
>>reanalysis data, and model outputs.
>>
>>Satellite observations have been widely used in model-data
>>inter-comparisons and model evaluation studies. These studies normally
>>involve the comparison of a single parameter at a time using a time and
>>space average. For example, modeling cloud-related processes in global
>>climate models requires cloud parameterizations that provide quantitative
>>rules for expressing the location, frequency of occurrence, and intensity
>>of the clouds in terms of multiple large-scale model-resolved parameters
>>such as temperature, pressure, humidity, and wind. One can evaluate the
>>performance of the cloud parameterization by comparing the cloud water
>>content with satellite data and can identify symptomatic model biases or
>>errors. However, in order to understand the cause of the biases and
>>errors, one has to simultaneously investigate several parameters that are
>>integrated in the cloud parameterization.
>>
>>Such studies, aimed at a multi-parameter model diagnosis, require
>>locating, understanding, and manipulating multi-source observation
>>datasets, model outputs, and (re)analysis outputs that are physically
>>distributed, massive in volume, heterogeneous in format, and provide
>>little information on data quality and production legacy. Additionally,
>>these studies involve various data preparation and processing steps that
>>can easily become computationally demanding since many datasets have to be
>>combined and processed simultaneously. It is notorious that scientists
>>spend more than 60% of their research time on just preparing the dataset
>>before it can be analyzed for their research.
>>
>>To address these challenges, we propose to build Climate Model Diagnostic
>>Analyzer (CMDA) that will enable a streamlined and structured preparation
>>of multiple large-volume and heterogeneous datasets, and provide a
>>computationally efficient approach to processing the datasets for model
>>diagnosis. We will leverage the existing information technologies and
>>scientific tools that we developed in our current NASA ROSES COUND, MAP,
>>and AIST projects. We will utilize the open-source Web-service technology.
>>We will make CMDA complementary to other climate model analysis tools
>>currently available to the research community (e.g., PCMDI’s CDAT and
>>NCAR’s CCMVal) by focusing on the missing capabilities such as conditional
>>sampling, and probability distribution function and cluster analysis of
>>multiple-instrument datasets. The users will be able to use a web browser
>>to interface with CMDA.
>>
>>== Current Status ==
>>
>>The current version of ClimateModelDiagnosticAnalyzer was developed by a
>>team at The Jet Propulsion Laboratory (JPL). The project was initiated as
>>a NASA-sponsored project (ROSES-CMAC) in 2011.
>>
>>== Meritocracy ==
>>
>>The current developers are not familiar with meritocratic open source
>>development at Apache, but would like to encourage this style of
>>development for the project.
>>
>>== Community ==
>>
>>While ClimateModelDiagnosticAnalyzer started as a JPL research project, it
>>has been used in The 2014 Caltech Summer School sponsored by the JPL
>>Center for Climate Sciences. Some 23 students from different institutions
>>over the world participated. We deployed the tool to the Amazon Cloud and
>>let every student each has his or her own virtual machine. Students gave
>>positive feedback mostly on the usability and speed of our web services.
>>We also collected a number of enhancement requests. We seek to further
>>grow the developer and user communities using the Apache open source
>>venue. During incubation we will explicitly seek increased academic
>>collaborations (e.g., with The Carnegie Mellon University) as well as
>>industrial participation.
>>
>>One instance of our web services can be found at:
>>http://cmacws.jpl.nasa.gov:8080/cmac/
>>
>>== Core Developers ==
>>
>>The core developers of the project are JPL scientists and software
>>developers.
>>
>>== Alignment ==
>>
>>Apache is the most natural home for taking the
>>ClimateModelDiagnosticAnalyzer project forward. It is well-aligned with
>>some Apache projects such as Apache Open Climate Workbench.
>>ClimateModelDiagnosticAnalyzer also seeks to achieve an Apache-style
>>development model; it is seeking a broader community of contributors and
>>users in order to achieve its full potential and value to the Climate
>>Science and Big Data community.
>>
>>There are also a number of dependencies that will be mentioned below in
>>the Relationships with Other Apache products section.
>>
>>
>>== Known Risks ==
>>
>>=== Orphaned products ===
>>
>>Given the current level of intellectual investment in
>>ClimateModelDiagnosticAnalyzer, the risk of the project being abandoned is
>>very small. The Carnegie Mellon University and JPL are collaborating
>>(2014-2015) to build a service for climate analytics workflow
>>recommendation using fund from NASA. A two-year NASA AIST project
>>(2015-2016) will soon start to add diagnostic analysis methodologies such
>>as conditional sampling method, conditional probability density function,
>>data co-location, and random forest. We will also infuse the provenance
>>technology into CMDA so that the history of the data products and
>>workflows will be automatically collected and saved. This information will
>>also be indexed so that the products and workflows can be searchable by
>>the community of climate scientists and students.
>>
>>=== Inexperience with Open Source ===
>>
>>The current developers of ClimateModelDiagnosticAnalyzer are inexperienced
>>with Open Source. However, our Champion Chris Mattmann is experienced
>>(Champions of ApacheOpenClimateWorkbench and AsterixDB) and will be
>>working closely with us, also as the Chief Architect of our JPL section.
>>
>>=== Relationships with Other Apache Products ===
>>
>>Clearly there is a direct relationship between this project and the Apache
>>Open Climate Workbench already a top level Apache project and also brought
>>to the ASF by its Champion (and ours) Chris Mattmann. We plan on directly
>>collaborating with the Open Climate Workbench community via our Champion
>>and we also welcome ASF mentors familiar with the OCW project to help
>>mentor our project. In addition our team is extremely welcoming of ASF
>>projects and if there are synergies with them we invite participation in
>>the proposal and in the discussion.
>>
>>=== Homogeneous Developers ===
>>
>>The current community is within JPL but we would like to increase the
>>heterogeneity.
>>
>>=== Reliance on Salaried Developers ===
>>
>>The initial committers are full-time JPL staff from 2013 to 2014. The
>>other committers from 2014 to 2015 are a mix of CMU faculty, students and
>>JPL staff.
>>
>>=== An Excessive Fascination with the Apache Brand ===
>>
>>We believe in the processes, systems, and framework Apache has put in
>>place. Apache is also known to foster a great community around their
>>projects and provide exposure. While brand is important, our fascination
>>with it is not excessive. We believe that the ASF is the right home for
>>ClimateModelDiagnosticAnalyzer and that having
>>ClimateModelDiagnosticAnalyzer inside of the ASF will lead to a better
>>long-term outcome for the Climate Science and Big Data community.
>>
>>=== Documentation ===
>>
>>The ClimateModelDiagnosticAnalyzer services and documentation can be found
>>at: http://cmacws.jpl.nasa.gov:8080/cmac/.
>>
>>=== Initial Source ===
>>
>>Current source resides in ...
>>
>>=== External Dependencies ===
>>
>>ClimateModelDiagnosticAnalyzer depends on a number of open source
>>projects:
>>
>> * Flask
>> * Gunicorn
>> * Tornado Web Server
>> * GNU octave
>> * epd python
>> * NOAA ferret
>> * GNU plot
>>
>>== Required Resources ==
>>
>>=== Developer and user mailing lists ===
>>
>> * private@cmda.incubator.apache.org (with moderated subscriptions)
>> * commits@cmda.incubator.apache.org
>> * dev@cmda.incubator.apache.org
>> * users@cmda.incubator.apache.org
>>
>>A git repository
>>
>>https://git-wip-us.apache.org/repos/asf/incubator-cmda.git
>>
>>A JIRA issue tracker
>>
>>https://issues.apache.org/jira/browse/CMDA
>>
>>=== Initial Committers ===
>>
>>The following is a list of the planned initial Apache committers (the
>>active subset of the committers for the current repository at Google
>>code).
>>
>> * Seungwon Lee (seungwon.lee@jpl.nasa.gov)
>> * Lei Pan (lei.pan@jpl.nasa.gov)
>> * Chengxing Zhai (chengxing.zhai@jpl.nasa.gov)
>> * Benyang Tang (benyang.tang@jpl.nasa.gov)
>>
>>
>>=== Affiliations ===
>>
>>JPL
>>
>> * Seungwon Lee
>> * Lei Pan
>> * Chengxing Zhai
>> * Benyang Tang
>>
>>CMU
>>
>> * Jia Zhang
>> * Wei Wang
>> * Chris Lee
>> * Xing Wei
>>
>>== Sponsors ==
>>
>>NASA
>>
>>=== Champion ===
>>
>>Chris Mattmann (NASA/JPL)
>>
>>=== Nominated Mentors ===
>>
>>TBD
>>
>>=== Sponsoring Entity ===
>>
>>The Apache Incubator
>>
>>
>>
>>
>>++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
>>Chris Mattmann, Ph.D.
>>Chief Architect
>>Instrument Software and Science Data Systems Section (398)
>>NASA Jet Propulsion Laboratory Pasadena, CA 91109 USA
>>Office: 168-519, Mailstop: 168-527
>>Email: chris.a.mattmann@nasa.gov
>>WWW:  http://sunset.usc.edu/~mattmann/
>>++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
>>Adjunct Associate Professor, Computer Science Department
>>University of Southern California, Los Angeles, CA 90089 USA
>>++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
>>
>>
>>
>>
>>
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