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.mattm...@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.mattm...@jpl.nasa.gov> Reply-To: "general@incubator.apache.org" <general@incubator.apache.org> Date: Saturday, April 18, 2015 at 7:00 PM To: "general@incubator.apache.org" <general@incubator.apache.org> Cc: "Pan, Lei (398K)" <lei....@jpl.nasa.gov>, "Lee, Seungwon (398K)" <seungwon....@jpl.nasa.gov>, "Zhai, Chengxing (398K)" <chengxing.z...@jpl.nasa.gov>, "Tang, Benyang (398J)" <benyang.t...@jpl.nasa.gov>, "jia.zh...@west.cmu.edu" <jia.zh...@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 === > > * priv...@cmda.incubator.apache.org (with moderated subscriptions) > * comm...@cmda.incubator.apache.org > * d...@cmda.incubator.apache.org > * us...@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....@jpl.nasa.gov) > * Lei Pan (lei....@jpl.nasa.gov) > * Chengxing Zhai (chengxing.z...@jpl.nasa.gov) > * Benyang Tang (benyang.t...@jpl.nasa.gov) > * Jia Zhang (jia.zh...@sv.cmu.edu) > * Wei Wang (wei.w...@sv.cmu.edu) > * Chris Lee (chris....@sv.cmu.edu) > * Xing Wei (xing....@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.mattm...@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.mattm...@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....@jpl.nasa.gov>, "Lee, Seungwon (398K)" ><seungwon....@jpl.nasa.gov>, "Zhai, Chengxing (398K)" ><chengxing.z...@jpl.nasa.gov>, "Tang, Benyang (398J)" ><benyang.t...@jpl.nasa.gov>, "jia.zh...@west.cmu.edu" ><jia.zh...@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 === >> >> * priv...@cmda.incubator.apache.org (with moderated subscriptions) >> * comm...@cmda.incubator.apache.org >> * d...@cmda.incubator.apache.org >> * us...@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....@jpl.nasa.gov) >> * Lei Pan (lei....@jpl.nasa.gov) >> * Chengxing Zhai (chengxing.z...@jpl.nasa.gov) >> * Benyang Tang (benyang.t...@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.mattm...@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-unsubscr...@incubator.apache.org >>For additional commands, e-mail: general-h...@incubator.apache.org > >?Т���������������������������������������������������������������������ХF� >?V�7V'67&�&R�?R��?�â?vV�W&?��V�7V'67&�&T?��7V&?F�"�???6�R��&pФf�"??FF�F��� >?�?6���?�G2�?R��?�â?vV�W&?�ֆV�??��7V&?F�"�???6�R��&p
