I have a question and I hope that somebody can help me.
I am using SDSM model for downscaling global climate data (CanESM2 and NCEP data) to my study area. I need to make a calibration for these data with measurements data for the same period (1961-2005), but I need to use half of these data (1961-1990) for calibration and second half for validation (1991-2005). My question is: when I applied calibration process in SDSM model the software does not work for the period 1961-1990. Whereas, it works when I applied for the whole period (1961-2005) could anybody assist me?
Note: I do not have this problem with Temperature data.
Thanks in advance
Thanks in advance
It is an indication of different rainfall distribution before and after 1990. This difference is neutralized when you chose the full range. I recommend using a different simulation equation for 1961-90 and another for later years. This simulation series which has two different models could be merged as a continuous sequence for further use. What type of distributions are you using to simulate?
I am a bit confused by the question ... are you trying to adjust the model data to gauge data? Are you looking at annual, monthly, daily, or subdaily data? Presumably you are developing the calibration where all the data sets cover the same period. Is the issue that the calibration doesn't work when applied to an independent period? Are the various datasets homogeneous, or do they change character from decade to decade due to changes in input data sources (like more satellite in recent decades) or analysis schemes? Sorry for all the questions, but these might help you understand the variation that you see.
Please, change the question, because the calibration is an independent task but time-series, or other, statistical method of the precipitation parameters determined is also an independent task.