[RESULT] [VOTE] Climate Model Diagnostic Analyzer

["Mattmann, Chris A (3980)" <ch...@jpl.nasa.gov>]

Hi Everyone,

This VOTE has passed with the following tallies:

+1

Chris Mattmann*
Lei Pan
Louis Suárez-Potts
Jan Iversen*
Kim Whitehall*
Ted Dunning*
John D. Ament*
Jake Farrell*
Alan Cabrera*
Suresh Marru*
Michael Joyce*
Henry Saputra*
Roman Shoposhnik*
Greg Reddin*
Jia Zhang

* -indicates IPMC

I will now get going on bootstrapping the podling. Congrats
everyone! Welcome to the Incubator :)

Cheers,
Chris

++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
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 <Ch...@jpl.nasa.gov>
Reply-To: "general@incubator.apache.org" <ge...@incubator.apache.org>
Date: Saturday, April 18, 2015 at 7:00 PM
To: "general@incubator.apache.org" <ge...@incubator.apache.org>
Cc: "Pan, Lei (398K)" <le...@jpl.nasa.gov>, "Lee, Seungwon (398K)"
<se...@jpl.nasa.gov>, "Zhai, Chengxing (398K)"
<ch...@jpl.nasa.gov>, "Tang, Benyang (398J)"
<Be...@jpl.nasa.gov>, "jia.zhang@west.cmu.edu"
<ji...@west.cmu.edu>
Subject: [VOTE} Climate Model Diagnostic Analyzer

>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 <Ch...@jpl.nasa.gov>
>Reply-To: "general@incubator.apache.org" <ge...@incubator.apache.org>
>Date: Monday, March 23, 2015 at 1:55 AM
>To: "general@incubator.apache.org" <ge...@incubator.apache.org>
>Cc: "Pan, Lei (398K)" <le...@jpl.nasa.gov>, "Lee, Seungwon (398K)"
><se...@jpl.nasa.gov>, "Zhai, Chengxing (398K)"
><ch...@jpl.nasa.gov>, "Tang, Benyang (398J)"
><Be...@jpl.nasa.gov>, "jia.zhang@west.cmu.edu"
><ji...@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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>>For additional commands, e-mail: general-help@incubator.apache.org
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