Update README.md

README.md

@@ -18,8 +18,8 @@ Predefined parameters for the prototype:
 - project: nukleus
 - product: ceu-3
 - models: all
-- time_frequency: 1hr
-- variable: tas, tasmin, tasmax ???
+- time_frequency: daily and hourly (issue of bias correction)
+- variable: tas, tasmin, tasmax (temperature at different hights are not needed for statistical analysis of heat/warm days, but for the final output)
 
 ## Method --> still to be figured out!
 ### Data basis
@@ -32,14 +32,14 @@ It is still unclear if hourly bias corrected data of vertical air temperature pr
 *most plausible and feasible option at the moment
 
 ### "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/warm periods the user can select different months with the input parameter _months_of_event_.
+
+To provide hourly time series is a must-have. We will select a heat/warm day based on bias corrected daily tmax data and then go on as described under option 2 (Data basis section) to imprint the bias correction to the hourly data and different hight levels.
 
 **We still researching how to define realistic time series of heat events using a statistical data analysis or how to develop an artificial timeseries.**
 
 ## 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 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).
+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_).
 
 ## Open issues:
 * availability of hourly bias corrected data?