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From Apache Wiki <wikidi...@apache.org>
Subject [Incubator Wiki] Update of "ClimateModelDiagnosticAnalyzerProposal" by LeiPan
Date Sun, 01 Mar 2015 08:34:29 GMT
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The "ClimateModelDiagnosticAnalyzerProposal" page has been changed by LeiPan:
https://wiki.apache.org/incubator/ClimateModelDiagnosticAnalyzerProposal?action=diff&rev1=24&rev2=25

  
  == Abstract ==
  
- The Climate Model Diagnostic Analyzer 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.
+ 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 ==
  
@@ -44, +44 @@

  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 ==
  

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