Disaggregating daily precipitation to hourly data involves distributing the total daily precipitation across the individual hours. One common method is using statistical relationships between daily and hourly precipitation patterns. Time series analysis, such as regression models, can help in this process. Additionally, historical hourly precipitation data for the specific location may provide insights into the distribution pattern. We have to keep in mind that the accuracy of disaggregation depends on the available data and the chosen method.
There is no way of disaggregating daily data without access to statistical information regarding the hourly data. Information is required, either regarding hourly data or the relationship between hourly and daily data.
Precipitation is a very irregular weather phenomena that occurs at differents hours, not every day, with differents intensities and durations and depends of the diferents synoptic conditions. You can obtain the numbers of days of precipitation in a year, the mean days with precipitations in a year, the mean distribution of the precipitation during the day and the total amount of precipitation in a day, in a week, in a month and in the year. If you disaggregate dayly precipitation in hourly, it will not be representative information beacuse of it irregularity.
I think one way to do this would be to take help from the hourly satellite data. Maybe you could use the daily observed data and make a comparison with the satellite data. After enforcing the bias correction on the satellite data at the daily level, you could distribute the correction factor at the sub-daily level. I have not tried this, but it was just one of the ideas that popped up after I read your question.
It's always important to understand your reason for doing something.
Are you trying to represent an actual historical event? Perhaps there's a law suit based on determining what would have happened in a particular event if a culvert had been properly maintained and there were no blockages? In that case, using some statistical method based on just daily data will generally not allow you to replicate the past event in hourly terms, unless you can demonstrate that your disaggregated data is sufficiently representative of the what actually happened to satisfy your particular purpose.
However, if you had some other reasonable and concurrent observations such as high resolution & high quality radar based estimates or nearby hourly gages, you might be able to do it. Or even if you had downstream hourly flow values where the runoff attenuation was minimal.
But, as most of the responses above point out, this is a difficult issue. Rainfall is highly variable. In addition to the reasons already given, even the values obtained from gages, say 10 feet apart can be quite different.
However if what you're doing depends on climatology rather than actual historical values, you might find ways of disaggregating that are reasonable.
The key to all this is that using characteristics from other hourly observations doesn't tell you what actually happened at hourly time steps at your daily observations. Unless of course you can demonstrate that the correlation between what was happening with the other hourly data was sufficiently representative of hourly characteristics of your daily data for your particular purpose.
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