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 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… > > +1 +1 (binding) rgds jan i > -louis (non-binding) > PS this came across with double bang priority. Really? > > > > > 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 <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 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 <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.
