+1 Thanks, -Lei Pan JPL
On 4/19/15, 8:46 AM, "jan i" <j...@apache.org> wrote: >On Sunday, April 19, 2015, Louis Suárez-Potts <lui...@gmail.com> wrote: > >> >> > On 19 Apr 2015, at 01:00, Mattmann, Chris A (3980) < >> chris.a.mattm...@jpl.nasa.gov <javascript:;>> wrote: >> > >> > OK all, discussion has died down, we have 3 mentors, I think it¹s >> > 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 Don¹t accept Apache Climate Model Diagnostic Analyzer into the >> > Apache Incubator >> > becauseŠ >> >> +1 > > >+1 (binding) > >rgds >jan i > >> -louis (non-binding) >> PS this came across with double bang priority. Really? >> >> > >> > I¹ll 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 variable¹s 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., PCMDI¹s CDAT and >> > NCAR¹s 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 <javascript:;> (with moderated >> subscriptions) >> > * comm...@cmda.incubator.apache.org <javascript:;> >> > * d...@cmda.incubator.apache.org <javascript:;> >> > * us...@cmda.incubator.apache.org <javascript:;> >> > >> > 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 <javascript:;>) >> > * Lei Pan (lei....@jpl.nasa.gov <javascript:;>) >> > * Chengxing Zhai (chengxing.z...@jpl.nasa.gov <javascript:;>) >> > * Benyang Tang (benyang.t...@jpl.nasa.gov <javascript:;>) >> > * Jia Zhang (jia.zh...@sv.cmu.edu <javascript:;>) >> > * Wei Wang (wei.w...@sv.cmu.edu <javascript:;>) >> > * Chris Lee (chris....@sv.cmu.edu <javascript:;>) >> > * Xing Wei (xing....@sv.cmu.edu <javascript:;>) >> > >> > >> > === 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 <javascript:;> >> > 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 >> <javascript:;>> >> > Reply-To: "general@incubator.apache.org <javascript:;>" < >> general@incubator.apache.org <javascript:;>> >> > Date: Monday, March 23, 2015 at 1:55 AM >> > To: "general@incubator.apache.org <javascript:;>" < >> general@incubator.apache.org <javascript:;>> >> > Cc: "Pan, Lei (398K)" <lei....@jpl.nasa.gov <javascript:;>>, "Lee, >> Seungwon (398K)" >> > <seungwon....@jpl.nasa.gov <javascript:;>>, "Zhai, Chengxing (398K)" >> > <chengxing.z...@jpl.nasa.gov <javascript:;>>, "Tang, Benyang (398J)" >> > <benyang.t...@jpl.nasa.gov <javascript:;>>, "jia.zh...@west.cmu.edu >> <javascript:;>" >> > <jia.zh...@west.cmu.edu <javascript:;>> >> > 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 variable¹s 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., PCMDI¹s CDAT and >> >> NCAR¹s 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 <javascript:;> (with moderated >> subscriptions) >> >> * comm...@cmda.incubator.apache.org <javascript:;> >> >> * d...@cmda.incubator.apache.org <javascript:;> >> >> * us...@cmda.incubator.apache.org <javascript:;> >> >> >> >> 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 <javascript:;>) >> >> * Lei Pan (lei....@jpl.nasa.gov <javascript:;>) >> >> * Chengxing Zhai (chengxing.z...@jpl.nasa.gov <javascript:;>) >> >> * Benyang Tang (benyang.t...@jpl.nasa.gov <javascript:;>) >> >> >> >> >> >> === 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 <javascript:;> >> >> 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 >> <javascript:;> >> >> For additional commands, e-mail: general-h...@incubator.apache.org >> <javascript:;> >> > >> > >> > --------------------------------------------------------------------- >> > To unsubscribe, e-mail: general-unsubscr...@incubator.apache.org >> <javascript:;> >> > For additional commands, e-mail: general-h...@incubator.apache.org >> <javascript:;> >> >> > >-- >Sent from My iPad, sorry for any misspellings. --------------------------------------------------------------------- To unsubscribe, e-mail: general-unsubscr...@incubator.apache.org For additional commands, e-mail: general-h...@incubator.apache.org
