[Go-essp-tech] Global attributes and DRS extensions for downscaled datasets
Karl Taylor
taylor13 at llnl.gov
Mon Mar 25 15:49:41 MDT 2013
Dear all,
I have spent considerable time reviewing the following four documents:
A. The email (copied below) sent by Galia and Aparna, which proposed
attributes, filenames, and directory structures for downscaled data.
B. http://cmip-pcmdi.llnl.gov/cmip5/docs/cmip5_data_reference_syntax.pdf
which describes the corresponding CMIP5 metadata.
C.
http://cordex.dmi.dk/joomla/images/CORDEX/cordex_archive_specifications.pdf
<http://cordex.dmi.dk/joomla/images/CORDEX/cordex_archive_specifications_121022.pdf>
which describes the corresponding CORDEX metadata.
D.http://cmip-pcmdi.llnl.gov/cmip5/docs/CMIP5_output_metadata_requirements.pdf
which specifies all the CMIP5 metadata requirements.
I hope that document A above could be made compatible with the others
and in general could provide a sound basis for establishing more
uniformity moving forward.Toward that end, I have prepared the attached
document describing for downscaled data a minimal set of global
attributes needed to augment those used in CMIP5 and also the extensions
needed to the DRS document to accommodate downscaled data.
I hope at least a few of you will take the time to study this document
and provide feedback.
Best regards,
Karl
Mail sent by Galia Guentchev 3/12/13
**********************************************************************
Details of each element of the proposed directory structure:
Proposed elements -
/projectID/sub-project/product/institution/*predictorModel/experimentID/frequency/realm/MIPtable/Pred**
**ictor_experiment_rip/predictorversion*//downscalingMethod/predictand
(variableName)/region///DownscaledDataversion//file_name.nc
Example:
/ncpp2013/perfectModel/downscaled/NOAA-GFDL/*GFDL-HIRAM-C360-coarsened/amip/day/atmos/day/r1i1p1/v20121024*//GFDL-ARRMv1/tasmax/US48/v20120227//tasmax_day_amip_r1i1p1_downscaled_US48_GFDLARRMv1_19790101-19831231.nc
The new element sub-project (in blue above) gives the opportunity to
indicate to users that in the one case the method was trained on
observations (standard setting), and in the other on model that was
considered to be the truth (perfect model setting);
The options there could be: PerfectModel or Standard - where possibly
there could be a different name instead of 'standard' for the standard
downscaling setting.
For NASA datasets some of the directories could be:
project = NEX
product = downscaled
institution = NASA-Ames
predictorModel - original model value
experimentID = historical
frequency = mon
realm = atmos
Predictor_experiment_rip - original model value
variable = precipitation or temperature
region = CONUS
DownscalingMethod will also be included as a directory to allow for
search on method.
**********************
There are a set of sub-directories that refer to the _PredictorModel_ -
presented in bold -
*/predictorModel/experimentID/frequency/realm/MIPtable/Pred**
**ictor_experiment_rip/predictorversion*
Where:
* predictor model - is the specific GCM which is the source of the
predictor data set - GFDL-HIRAM-C360-coarsened - in the above example
* experimentID - the specific experiment - amip in this case
* frequency - refers to the temporal scale of the predictor fields - daily
* realm - the realm of the predictors - in this case atmos(phere)
* MIPtable - name of the model intercomparison table - daily in this
example, could be amon - for atm monthly data;
* Predictor-Experiment-rip - follows the standard notation from CMIP5
* version - the version date of the global model that provided the
predictor dataset
The elements above follow quite closely the structure for CMIP5 model
output directory elements.
There is a set of sub-directories that refer to the Downscaling method -
presented in italics -
//downscalingMethod/predictand (variableName)/region///DownscaledDataversion
/
/Where:
* downscalingMethod - is the downscaling method abbreviation - in this
case GFDL-ARRMv1 - the GFDL in the name indicates that this is a
setting applied by GFDL where there were two sets of predictors,
based on the ARRM method of K.Hayhoe; also v.1 indicates which
version of the ARRM method was used (the original version) - more
details about the method are given in the global attributes of the file;
* Predictand (variableName) - the specific predictand variable that
was downscaled; tasmax in this case;
* region - indicates that the method was applied to the US48
* DownscaledDataversion - the version of the downscaled dataset
*For the purposes of standardization there are two directions to consider:*
1) One is to have*one standard directory* structure that will be used by
all - for example, following the example of GFDL to have the details of
the predictor model first and then the downscaling method details:
* ProjectID - sub-project - product - Institution - Predictor dataset
details - Downscaling method details - Filename
Having a standardized approach would help any automated service/web
service to detect the directory path for a particular dataset.
2) During our last teleconference there was a proposal to follow the
downscaling practice and describe the downscaling method first and then
the predictor model. This leads to *two paths*:
. ProjectID - _Standard or Perfect Model sub-project facet_-
product - Institution - then see below:
- (if Perfect model setting) Predictor dataset details
- Downscaling method details,
- (if Standard setting) - Downscaling method details -
Predictor dataset details
The NCPP Core team accepts that it may be reasonable to have a directory
structure - where the method description is first; and another directory
structure - where the predictor description is first and then the
methods that are applied are described; *NCPP will support either
approach* (one overall directory structure, or two separate pathways)
and if the second approach is chosen (with two different sub-directory
sequences) - we would like to promote and to support the standardization
of these different directory pathways - meaning - we will support two
standardized directory structures to accommodate two common practices.
******************
Additional details:
*Variable level attributes-*
The published dataset should also conform to CF-standards.
eg-
tasmax:long_name = "Downscaled Daily Maximum
Near-Surface Air Temperature" ;
tasmax:units = "K" ;
tasmax:missing_value = 1.e+20f ;
tasmax:_FillValue = 1.e+20f ;
tasmax:standard_name = "air_temperature" ;
tasmax:original_units = "K" ;
* tasmax:downscaling_method: GFDL-ARRMv1*
*Global attributes- *listing a few here, several CMIP-style attributes
will be inherited.
"predictorModel" will replace "model_id"
For the 'downscaling model', as agreed with Luca on the call it would
be 'downscalingMethod'
:Conventions = "CF-1.4" ;
:references = "info about model, training datasets etc
will be provided here"
:info = "additional info about the downscaling method"
:creation_date = "2011-08-19T21:57:06Z" ;
:institution = "NOAA GFDL(201 Forrestal Rd, Princeton,
NJ, 08540)" ;
:history = "info on file processing. Eg" processed by
toolX." ;
:projectID = ncpp2013
:subprojectID = perfectModel
:product = downscaled
:institution = NOAA-GFDL
:predictorModel = GFDL-HIRAM-C360-coarsened
:experimentID = amip
:frequency = day
:modeling_realm = atmos
:Predictor_experiment_rip = r1i1p1
:region = US48
:table_id = day
:version = v20120227
:downscalingMethod = GFDL-ARRMv1
**************************************************
Best regards,
Galia and Aparna
--
Galia Guentchev, PhD
Project Scientist
National CLimate
Predictions and
Projections
Platform (NCPP)
NCAR RAL CSAP
FL2 3103
3450 Mitchell Lane
Boulder, CO, 80301
phone: 303 497 2743
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