@@ -26,16 +26,20 @@ Predefined parameters for the prototype:
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@@ -26,16 +26,20 @@ Predefined parameters for the prototype:
It is still unclear if hourly bias corrected data of vertical air temperature profiles will be available. Therefore, we see three options to start the data anaylsis:
It is still unclear if hourly bias corrected data of vertical air temperature profiles will be available. Therefore, we see three options to start the data anaylsis:
* option 0 (optimal) - bias-corrected data available: bias-corrected model data is used directly for picking out events and outputting data
* option 0 (optimal) - bias-corrected data available: bias-corrected model data is used directly for picking out events and outputting data
* option 1: bias is (proven) not significant: Procedure as variant 0
* option 1: bias is (proven) not significant: Procedure as variant 0
* option 2: bias-corrected data are available but only for daily data (mean, min, max): Difference: bias-corrected maximum - uncorrected maximum --> imprinting of this difference on the hourly values of the day under consideration or other method? Method applicable for all height levels? Method applicable for all variables?
* option 2*: bias-corrected data are available but only for daily data (mean, min, max): Difference: bias-corrected maximum - uncorrected maximum --> imprinting of this difference on the hourly values of the day under consideration or other method? Method applicable for all height levels? Method applicable for all variables?
* option 3 (most unfavorable): bias is unknown, is judged to be significant and no bias-corrected data: The raw model data are used to derive the climate change signal (e.g. 2K-World - reference period). The climate change signal is suitably imposed on measurement data. Challenge: Derivation of vertical profiles (only measurements at a height of 2 m are available for temperature).
* option 3 (most unfavorable): bias is unknown, is judged to be significant and no bias-corrected data: The raw model data are used to derive the climate change signal (e.g. 2K-World - reference period). The climate change signal is suitably imposed on measurement data. Challenge: Derivation of vertical profiles (only measurements at a height of 2 m are available for temperature).
*most plausible and feasible option at the moment
### "Filter out" heat/warm days and create times series of desired length
### "Filter out" heat/warm days and create times series of desired length
We are not looking for a specific heat/warm event or a concrete number of heat or summer days in the data. For the chosen experiment (historical, 2K, or 3K) we would like to know how the temperature stratification typically evolves when it is e.g. 2 days exceptionally hot/warm, or 5 days or 10 days. The exceptionality of the heat/warm period is defined with the input parameter _event_ (percentiles of temperature probability distribution) and the length of the heat period is defined with the input parameter _length_of_event_. To consider also the seasonality of heat periods the user can select different months with the input parameter _months_of_event_.
We are not looking for a specific heat/warm event or a concrete number of heat or summer days in the data. For the chosen experiment (historical, 2K, or 3K) we would like to know how the temperature stratification typically evolves when it is e.g. 2 days exceptionally hot/warm, or 5 days or 10 days. The exceptionality of the heat/warm period is defined with the input parameter _event_ (percentiles of temperature probability distribution) and the length of the heat period is defined with the input parameter _length_of_event_. To consider also the seasonality of heat periods the user can select different months with the input parameter _months_of_event_.
**We still researching how to define realistic time series of heat events using a statistical data analysis or how to develop an artificial timeseries.**
**We still researching how to define realistic time series of heat events using a statistical data analysis or how to develop an artificial timeseries.**
## Output
## Final Plugin Output
As a result we get 9 timeseries (ensemble members) with a frequency of 1hr and a length of 1 to 14 days (selectable) of vertical temperature stratification (representative for selected heat/warm period) for the chosen historical, 2K, or 3K experiment which can be fed directly into an urban climate model.
As a result we get 9 timeseries (ensemble members) with a frequency of 1hr** and a length of 1 to 14 days (selectable via _length_of_event_) of vertical temperature stratification (representative for selected heat/warm period via _event_) for the chosen historical, 2K, or 3K experiment which can be fed directly into an urban climate model (selectable via _impact_model_).
**to provide hourly time series is a must-have. We will select a heat/warm day based on daily tmax data and then go on as described under option 2 (Data basis section).
