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From Michael Joyce <jo...@apache.org>
Subject Re: [VOTE} Climate Model Diagnostic Analyzer
Date Mon, 20 Apr 2015 19:47:18 GMT
+1 (binding)


-- Jimmy

On Sun, Apr 19, 2015 at 7:30 PM, Suresh Marru <smarru@apache.org> wrote:

> + 1 (binding).
>
> Suresh
>
> > On Apr 19, 2015, at 1:00 AM, 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
> >> ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
> >>
> >>
> >>
> >>
> >>
> >> ---------------------------------------------------------------------
> >> To unsubscribe, e-mail: general-unsubscribe@incubator.apache.org
> >> For additional commands, e-mail: general-help@incubator.apache.org
> >
> >
> > ---------------------------------------------------------------------
> > To unsubscribe, e-mail: general-unsubscribe@incubator.apache.org
> > For additional commands, e-mail: general-help@incubator.apache.org
>
>
> ---------------------------------------------------------------------
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