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From "Mattmann, Chris A (3980)" <chris.a.mattm...@jpl.nasa.gov>
Subject [PROPOSAL] Climate Model Diagnostic Analyzer
Date Mon, 23 Mar 2015 05:55:01 GMT
Hi Everyone,

I am pleased to submit for consideration to the Apache Incubator
the Climate Model Diagnostic Analyzer proposal. We are actively
soliciting interested mentors in this project related to climate
science and analytics and big data.

Please find the wiki text of the proposal below and the link up
on the wiki here:

https://wiki.apache.org/incubator/ClimateModelDiagnosticAnalyzerProposal

Thank you for your consideration!

Cheers,
Chris 
(on behalf of the Climate Model Diagnostic Analyzer community)

= Apache ClimateModelDiagnosticAnalyzer Proposal =

== Abstract ==

The Climate Model Diagnostic Analyzer (CMDA) provides web services for
multi-aspect physics-based and phenomenon-oriented climate model
performance evaluation and diagnosis through the comprehensive and
synergistic use of multiple observational data, reanalysis data, and model
outputs.

== Proposal ==

The proposed web-based tools let users display, analyze, and download
earth science data interactively. These tools help scientists quickly
examine data to identify specific features, e.g., trends, geographical
distributions, etc., and determine whether a further study is needed. All
of the tools are designed and implemented to be general so that data from
models, observation, and reanalysis are processed and displayed in a
unified way to facilitate fair comparisons. The services prepare and
display data as a colored map or an X-Y plot and allow users to download
the analyzed data. Basic visual capabilities include 1) displaying
two-dimensional variable as a map, zonal mean, and time series 2)
displaying three-dimensional variable’s zonal mean, a two-dimensional
slice at a specific altitude, and a vertical profile. General analysis can
be done using the difference, scatter plot, and conditional sampling
services. All the tools support display options for using linear or
logarithmic scales and allow users to specify a temporal range and months
in a year. The source/input datasets for these tools are CMIP5 model
outputs, Obs4MIP observational datasets, and ECMWF reanalysis datasets.
They are stored on the server and are selectable by a user through the web
services.

=== Service descriptions ===

1. '''Two dimensional variable services'''

* Map of two-dimensional variable:  This services displays a two
dimensional variable as a colored longitude and latitude map with values
represented by a color scheme. Longitude and latitude ranges can be
specified to magnify a specific region.

* Two dimensional variable zonal mean:  This service plots the zonal mean
value of a two-dimensional variable as a function of the latitude in terms
of an X-Y plot.

* Two dimensional variable time series:  This service displays the average
of a two-dimensional variable over the specific region as function of time
as an X-Y plot.

2. '''Three dimensional variable services'''

* Map of a two dimensional slice of a three-dimensional variable:  This
service displays a two-dimensional slice of a three-dimensional variable
at a specific altitude as a colored longitude and latitude map with values
represented by a color scheme.

* Three dimensional zonal mean:  Zonal mean of the specified
three-dimensional variable is computed and displayed as a colored
altitude-latitude map.

* Vertical profile of a three-dimensional variable:  Compute the area
weighted average of a three-dimensional variable over the specified region
and display the average as function of pressure level (altitude) as an X-Y
plot.

3. '''General services'''

* Difference of two variables:  This service displays the differences
between the two variables, which can be either a two dimensional variable
or a slice of a three-dimensional variable at a specified altitude as
colored longitude and latitude maps

* Scatter and histogram plots of two variables:  This service displays the
scatter plot (X-Y plot) between two specified variables and the histograms
of the two variables. The number of samples can be specified and the
correlation is computed. The two variables can be either a two-dimensional
variable or a slice of a three-dimensional variable at a specific altitude.

* Conditional sampling:  This service lets user to sort a physical
quantity of two or dimensions according to the values of another variable
(environmental condition, e.g. SST) which may be a two-dimensional
variable or a slice of a three-dimensional variable at a specific
altitude. For a two dimensional quantity, the plot is displayed an X-Y
plot, and for a two-dimensional quantity, plot is displayed as a
colored-map.


== Background and Rationale ==

The latest Intergovernmental Panel on Climate Change (IPCC) Fourth
Assessment Report stressed the need for the comprehensive and innovative
evaluation of climate models with newly available global observations. The
traditional approach to climate model evaluation, which is the comparison
of a single parameter at a time, identifies symptomatic model biases and
errors but fails to diagnose the model problems. The model diagnosis
process requires physics-based multi-variable comparisons, which typically
involve large-volume and heterogeneous datasets, and computationally
demanding and data-intensive operations. We propose to develop a
computationally efficient information system to enable the physics-based
multi-variable model performance evaluations and diagnoses through the
comprehensive and synergistic use of multiple observational data,
reanalysis data, and model outputs.

Satellite observations have been widely used in model-data
inter-comparisons and model evaluation studies. These studies normally
involve the comparison of a single parameter at a time using a time and
space average. For example, modeling cloud-related processes in global
climate models requires cloud parameterizations that provide quantitative
rules for expressing the location, frequency of occurrence, and intensity
of the clouds in terms of multiple large-scale model-resolved parameters
such as temperature, pressure, humidity, and wind. One can evaluate the
performance of the cloud parameterization by comparing the cloud water
content with satellite data and can identify symptomatic model biases or
errors. However, in order to understand the cause of the biases and
errors, one has to simultaneously investigate several parameters that are
integrated in the cloud parameterization.

Such studies, aimed at a multi-parameter model diagnosis, require
locating, understanding, and manipulating multi-source observation
datasets, model outputs, and (re)analysis outputs that are physically
distributed, massive in volume, heterogeneous in format, and provide
little information on data quality and production legacy. Additionally,
these studies involve various data preparation and processing steps that
can easily become computationally demanding since many datasets have to be
combined and processed simultaneously. It is notorious that scientists
spend more than 60% of their research time on just preparing the dataset
before it can be analyzed for their research.

To address these challenges, we propose to build Climate Model Diagnostic
Analyzer (CMDA) that will enable a streamlined and structured preparation
of multiple large-volume and heterogeneous datasets, and provide a
computationally efficient approach to processing the datasets for model
diagnosis. We will leverage the existing information technologies and
scientific tools that we developed in our current NASA ROSES COUND, MAP,
and AIST projects. We will utilize the open-source Web-service technology.
We will make CMDA complementary to other climate model analysis tools
currently available to the research community (e.g., PCMDI’s CDAT and
NCAR’s CCMVal) by focusing on the missing capabilities such as conditional
sampling, and probability distribution function and cluster analysis of
multiple-instrument datasets. The users will be able to use a web browser
to interface with CMDA.

== Current Status ==

The current version of ClimateModelDiagnosticAnalyzer was developed by a
team at The Jet Propulsion Laboratory (JPL). The project was initiated as
a NASA-sponsored project (ROSES-CMAC) in 2011.

== Meritocracy ==

The current developers are not familiar with meritocratic open source
development at Apache, but would like to encourage this style of
development for the project.

== Community ==

While ClimateModelDiagnosticAnalyzer started as a JPL research project, it
has been used in The 2014 Caltech Summer School sponsored by the JPL
Center for Climate Sciences. Some 23 students from different institutions
over the world participated. We deployed the tool to the Amazon Cloud and
let every student each has his or her own virtual machine. Students gave
positive feedback mostly on the usability and speed of our web services.
We also collected a number of enhancement requests. We seek to further
grow the developer and user communities using the Apache open source
venue. During incubation we will explicitly seek increased academic
collaborations (e.g., with The Carnegie Mellon University) as well as
industrial participation.

One instance of our web services can be found at:
http://cmacws.jpl.nasa.gov:8080/cmac/

== Core Developers ==

The core developers of the project are JPL scientists and software
developers.

== Alignment ==

Apache is the most natural home for taking the
ClimateModelDiagnosticAnalyzer project forward. It is well-aligned with
some Apache projects such as Apache Open Climate Workbench.
ClimateModelDiagnosticAnalyzer also seeks to achieve an Apache-style
development model; it is seeking a broader community of contributors and
users in order to achieve its full potential and value to the Climate
Science and Big Data community.

There are also a number of dependencies that will be mentioned below in
the Relationships with Other Apache products section.


== Known Risks ==

=== Orphaned products ===

Given the current level of intellectual investment in
ClimateModelDiagnosticAnalyzer, the risk of the project being abandoned is
very small. The Carnegie Mellon University and JPL are collaborating
(2014-2015) to build a service for climate analytics workflow
recommendation using fund from NASA. A two-year NASA AIST project
(2015-2016) will soon start to add diagnostic analysis methodologies such
as conditional sampling method, conditional probability density function,
data co-location, and random forest. We will also infuse the provenance
technology into CMDA so that the history of the data products and
workflows will be automatically collected and saved. This information will
also be indexed so that the products and workflows can be searchable by
the community of climate scientists and students.

=== Inexperience with Open Source ===

The current developers of ClimateModelDiagnosticAnalyzer are inexperienced
with Open Source. However, our Champion Chris Mattmann is experienced
(Champions of ApacheOpenClimateWorkbench and AsterixDB) and will be
working closely with us, also as the Chief Architect of our JPL section.

=== Relationships with Other Apache Products ===

Clearly there is a direct relationship between this project and the Apache
Open Climate Workbench already a top level Apache project and also brought
to the ASF by its Champion (and ours) Chris Mattmann. We plan on directly
collaborating with the Open Climate Workbench community via our Champion
and we also welcome ASF mentors familiar with the OCW project to help
mentor our project. In addition our team is extremely welcoming of ASF
projects and if there are synergies with them we invite participation in
the proposal and in the discussion.

=== Homogeneous Developers ===

The current community is within JPL but we would like to increase the
heterogeneity.

=== Reliance on Salaried Developers ===

The initial committers are full-time JPL staff from 2013 to 2014. The
other committers from 2014 to 2015 are a mix of CMU faculty, students and
JPL staff.

=== An Excessive Fascination with the Apache Brand ===

We believe in the processes, systems, and framework Apache has put in
place. Apache is also known to foster a great community around their
projects and provide exposure. While brand is important, our fascination
with it is not excessive. We believe that the ASF is the right home for
ClimateModelDiagnosticAnalyzer and that having
ClimateModelDiagnosticAnalyzer inside of the ASF will lead to a better
long-term outcome for the Climate Science and Big Data community.

=== Documentation ===

The ClimateModelDiagnosticAnalyzer services and documentation can be found
at: http://cmacws.jpl.nasa.gov:8080/cmac/.

=== Initial Source ===

Current source resides in ...

=== External Dependencies ===

ClimateModelDiagnosticAnalyzer depends on a number of open source projects:

 * Flask
 * Gunicorn
 * Tornado Web Server
 * GNU octave
 * epd python
 * NOAA ferret
 * GNU plot

== Required Resources ==

=== Developer and user mailing lists ===

 * private@cmda.incubator.apache.org (with moderated subscriptions)
 * commits@cmda.incubator.apache.org
 * dev@cmda.incubator.apache.org
 * users@cmda.incubator.apache.org

A git repository

https://git-wip-us.apache.org/repos/asf/incubator-cmda.git

A JIRA issue tracker

https://issues.apache.org/jira/browse/CMDA

=== Initial Committers ===

The following is a list of the planned initial Apache committers (the
active subset of the committers for the current repository at Google code).

 * Seungwon Lee (seungwon.lee@jpl.nasa.gov)
 * Lei Pan (lei.pan@jpl.nasa.gov)
 * Chengxing Zhai (chengxing.zhai@jpl.nasa.gov)
 * Benyang Tang (benyang.tang@jpl.nasa.gov)


=== Affiliations ===

JPL

 * Seungwon Lee
 * Lei Pan
 * Chengxing Zhai
 * Benyang Tang

CMU

 * Jia Zhang
 * Wei Wang
 * Chris Lee
 * Xing Wei

== Sponsors ==

NASA

=== Champion ===

Chris Mattmann (NASA/JPL)

=== Nominated Mentors ===

TBD

=== Sponsoring Entity ===

The Apache Incubator




++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
Chris Mattmann, Ph.D.
Chief Architect
Instrument Software and Science Data Systems Section (398)
NASA Jet Propulsion Laboratory Pasadena, CA 91109 USA
Office: 168-519, Mailstop: 168-527
Email: chris.a.mattmann@nasa.gov
WWW:  http://sunset.usc.edu/~mattmann/
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
Adjunct Associate Professor, Computer Science Department
University of Southern California, Los Angeles, CA 90089 USA
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++





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