Open efucile opened 4 years ago
It is already changed in 4.64 in the files for validation (after I stole it).
Thanks @SibylleK . FYI I have changed the PDT number to 4.64. Our next steps will be to produce samples as part of validation.
@tomkralidis Hi, Tom, I created a branch for this issue and the new template has been added. Could you please check it? Thanks.
FYI we are not pursuing development/implementation of the PDT at the moment, but leave the PDT proposal for others who wish to collaborate/implement/improve.
@tomkralidis Hi, Tom, when I was double checking the template 4.64 "58-69 Specification of the outermost (or only) time range over which statistical processing is done". Is it right? Because I notice that from Octet 58 to 69, they have specific contents.
not for FT-2021-1
@tomkralidis @jitsukoh -- Is this targeted for FT2021-2?
@amilan17 there is no specific target at this point in time. FYI we are not pursuing development/implementation of the PDT at the moment, but leave the PDT proposal for others who wish to collaborate/implement/improve.
Branch
https://github.com/wmo-im/GRIB2/tree/issue11
Summary and purpose
The Canadian Centre for Meteorological and Environmental Prediction has a requirement for a GRIB2 Product Definition Template that allows the representation of model ensemble climatology statistics for a given time interval, calculated across the full set of ensemble members and years in a reforecast. For instance, one might be interested in the average February temperature as a climate element from the reforecast. This global average would be calculated from the individual February averages of all members and over every year spanning the reforecast period. We propose a new PDT to meet this requirement.
Action proposed
The meeting is requested to consider this PDT as a draft proposal and provide input and eventual validation assistance.
Discussions
The Canadian Centre for Meteorological and Environmental Prediction has a requirement for a GRIB2 Product Definition Template that allows the representation of model ensemble climatology statistics for a given multi-year interval, calculated across the full set of ensemble members and years in a reforecast. For instance, one might be interested in the average February temperature as a climate element from the reforecast. For instance, this global average could be calculated from the individual February averages of all members for every year spanning the reforecast period.
The defining difference with other existing ensemble or reforecast PDTs is that we apply a temporal-ensemble statistic after the statistical processes that are purely over time. We propose a new PDT to meet this requirement, using PDTs 4.12, 4.60 and 4.61 as starting points.
Description of the reforecast and example use case Usually, seasonal forecasts are expressed as an anomaly forecast or more precisely as a normalized anomaly forecast. In order to produce this forecast we need the forecast values and the historical distribution of these values. The historical distribution is computed on a series of historical forecasts, also called reforecasts. For each year in the reforecast period (1971-2010) and for each month in that particular year, the system runs 20 individual members for a period of 12 months. Then a month by month average is computed over the output of these runs. To characterize the distribution, the mean and the standard deviation is computed for one month and one lead time (as example, February, with one month lead time) with these 600 member-year couplets (20 members x 30 years ), with each couplet containing the time average over this month.
Detailed proposal
Add a new template:
Product definition template 4.64 – Statistics over an ensemble reforecast, at a horizontal level or in a horizontal layer in a continuous or non-continuous time interval
Notes: (1) The reference time in section 1 and the forecast time together define the beginning of the overall time interval. (2) Octets 34-40 define a statistical process over both time and ensemble. (3) This is the date to identify the model version that is used to generate the reforecast. (4) An increment of zero means that the statistical processing is the result of a continuous (or near continuous) process, not the processing of a number of discrete samples. Examples of such continuous processes are the temperatures measured by analogue maximum and minimum thermometers or thermographs, and the rainfall measured by a rain gauge. The reference and forecast times are successively set to their initial values plus or minus the increment, as defined by the type of time increment (one of octets 59, 71. 83 ...). For all but the innermost (last) time range, the next inner range is then processed using these reference and forecast times as the initial reference and forecast time.
Reference document: https://wmoomm.sharepoint.com/:w:/s/wmocpdb/Eb_66UfSVf5OvNAUcogYf_8B_1_uelI4TSjJ0gPiYSprbw?e=hKHUrF
Reference meeting page: https://community.wmo.int/activity-areas/wmo-codes/meetings/ipet-drmm-ii