Dear Jonathan,
sorry for the late answer, but the percentile case is not so easy.
Here are my answers:
> Dear Heinke
>
>
>>> 90th percentile of air temperature = percentile_function (air_temperature
>>> values, cumulative_probability = 90%)
>>>
>>>
>> You are right. But I am careful with the word 'probability', because
>> for transient climate model runs
>> we have different probability spaces and it must be clear what we count.
>>
>
> You mean the threshold is a percentile of a climatology, and then you use the
> threshold to evaluate the probability of exceedance in a different climate.
>
I think 'evaluate the probability of exceedance' is a further step. The
data are
'percent' of time per time period or number and not the 'propabiltiy'.
This could be expressed as a propability {'value above
percentile','value not above percentile'},
(with normalisation) but we can't be sure that the number of samples is
sufficient to create a probability.
For example, if we have only two samples we can't name it probability.
We count and make percent. That is all.
>
>> What do you think ?
>>
>
> I think it's quite difficult to describe this systematically and economically.
>
Yes, I agree, because we have constrained extremum for example:
'daily minimum temperature of five day window centered on each calendar day'
The percentile is not only a value for example 0 degC. It is not
enough to say the percentile is a threshold with value '90 th' and period
1961-1990.' It is a field. The percentile depends on lat and lon.
What can we do ?
The 'percentile' statistic indices are part of the IPCC DDC. See below.
Should we use my first proposals with the definitions with the disadvantage
of growing number of standard names or should we give up and say no standard
names are available.
>
>>> Since we are not using "frost days" elsewhere, I would suggest
>>> number_of_days_with_air_temperature_below_threshold_and_zero_surface_snow_amount
>>>
>>> I think it is still worth considering the alternative approach for these
>>> quantities with one or two thresholds of simple standard names such as
>>> number_of_days and fraction_of_days, and rely on the coordinate variables
>>> alone to indicate the conditions that apply.
>>>
>
> What I meant was, perhaps we could have a standard name of number_of_days and
> allow it to be inferred what the conditions are from the presence of
> coordinate variable specifying thresholds. For instance, if there were a
> coordinate variable with a standard name of lower_bound_of_air_temperature
> (say) it could be deduced that we mean the number of days when the air
> temperature exceeds this lower bound. This would move more of the definition
> of the quantity into the coordinate variables.
>
I am not happy with this. The header structure would be very complex with a
first order logic, 'and' and 'or' ...
Searchability is not given.
> I am concerned that these derived quantities are hard to name. I wonder if we
> are doing this the wrong way. Let me ask a hypothetical question: how would
> you identify these various indices etc. in netCDF without trying to use CF
> standard names? Let us consider only those quantities which you actually have
> a present need for, as I am sure we can imagine indefinitely complicated cases
> without trying hard!
>
I am not happy with this. The header structure would be very complex with a
first order logic, 'and' and 'or' ...
Searchability is not given.
> Best wishes
>
> Jonathan
> _______________________________________________
> CF-metadata mailing list
> CF-metadata at cgd.ucar.edu
> http://mailman.cgd.ucar.edu/mailman/listinfo/cf-metadata
>
Best wishes
Heinke
The indices with number of datasets in CERA
*******************************************+
extreme temperature range
476
heat_wave_duration_index_wrt_mean_of_reference_period
476
very_wet_days_wrt_95th_percentile_of_reference_period (annual)
476
snow_days
32
rain_days
32
days_with_frozen_soil
210
days_with_snowcover
32
frost_days_index_per_time_period
509
summer_days_index_per_time_period
33
ice_days_index_per_time_period
33
tropical_nights_index_per_time_period
33
growing_season_length_index
477
highest_value_of_daily_maximum_temperature_per_time_period
1
highest_value_of_daily_minimum_temperature_per_time_period
1
lowest_value_of_daily_maximum_temperature_per_time_period
1
lowest_value_of_daily_minimum_temperature_per_time_period
1
cold_nights_percent_wrt_10th_percentile_of_reference_period
1
cold_days_percent_wrt_10th_percentile_of_reference_period
1
warm_nights_percent_wrt_90th_percentile_of_reference_period
477
warm_days_percent_wrt_90th_percentile_of_reference_period
1
warm_spell_duration_index
1
cold_spell_duration_index
1
diurnal_temperature_range_index
1
highest_one_day_precipitation_amount-per_time_period
1
highest_five_day_precipitation_amount-per_time_period
477
simple_daily_intensity_index
477
heavy_precipitation_days_index_per_time_period
509
very_heavy_precipitation_days_index_per_time_period
33
consecutive_dry_days_index_per_time_period
477
consecutive_wet_days_index_per_time_period
1
very_wet_days_wrt_95th_percentile_of_reference_period
http://www.mpimet.mpg.de/fileadmin/software/cdo/
1
precipitation_percentage_due_to_R95p_days
1
extremely_wet_days_wrt_99th_percentile_of_reference_period
1
total_wet_day_precipitation_per_time_periode
1
IPCC_DDC datasets of observed data in CERA
**************************************
HADEX_TXx: highest_value_of_daily_maximum_temperature_per_time_period
HADEX_TNx: highest_value_of_daily_minimum_temperature_per_time_period
HADEX_TXn: lowest_value_of_daily_maximum_temperature_per_time_period
HADEX_TNn: lowest_value_of_daily_minimum_temperature_per_time_period
HADEX_TN10p: cold_nights_percent_wrt_10th_percentile_of_reference_period
HADEX_TX10p: cold_days_percent_wrt_10th_percentile_of_reference_period
HADEX_TN90p: warm_nights_percent_wrt_90th_percentile_of_reference_period
HADEX_WSDI: warm_spell_duration_index
HADEX_CSDI: cold_spell_duration_index
HADEX_DTR: diurnal_temperature_range_index
HADEX_Rx1day: highest_one_day_prcipitation_amount-per_time_period
HADEX_SDII: simple_daily_intensity_index
HADEX_CWD: consecutive_wet_days_index_per_time_period
HADEX_R95p: very_wet_days_wrt_95th_percentile_of_reference_period
HADEX_R95pT: precipitation_percentage_due_to_R95p_days
HADEX_R99p: extremely_wet_days_wrt_99th_percentile_of_reference_period
HADEX_PRCPTOT: total_wet_day_precipitation_per_time_periode
IPCC_DDC climate simulation runs indices
see list
http://www-pcmdi.llnl.gov/ipcc/standard_output.html#Table_A4-
*******************************************
frost_days_index_per_time_period
extreme temperature range
growing_season_length_index
heat_wave_duration_index_wrt_mean_of_reference_period
warm_nights_percent_wrt_90th_percentile_of_reference_period
heavy_precipitation_days_index_per_time_period
consecutive_dry_days_index_per_time_period
highest_five_day_precipitation_amount-per_time_period
simple daily intensity index
very_wet_days_wrt_95th_percentile_of_reference_period (annual)
Received on Thu Oct 11 2007 - 03:02:42 BST
