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I actually have 34 years on file. I may be willing to sending the 2015-2045 dataset for my dream climate. The native format is an Excel file, and is organized by month. The stats that I keep are morning lows, afternoon highs, precipitation, rainfall, and snowfall. By what means would the information be sent or shared?
In case you're wondering why the period is in the future, I decided to start my climate data in 2011 (at the time in the future), so as to avoid any biases in regards to different years and to avoid direct comparisons with real-world climates. However the timing is arbitrary and can be adjusted to whatever time period one chooses without consequence.
I've got 110 years of data for one of the climates, I could e-mail the spreadsheet to you if you wish to see it all! Each year is on an individual sheet with daily weather data for primary recordings anyway. Data I have is min temp, max temp, mean temp, rain quantity, sunshine hours, snow, occurance of thunder, hail, fog.
I plan on making a lengthy post full of screenshots of my spreadsheet database fairly soon for one of my most recent projects. Gonna have to wait til its done though, and when I can get myself with my laptop to Starbucks as I only have internet through my smartphone right now.
Did people creates 30 years worth of data manually? I'd find it more interesting to create them out of a statistical distribution and see if one could simulate realistic weather.
My idea:
1) Skewed Gaussian distribution for each month
2) Each day is correlated with the previous day
3) Diurnal range based off of sunshine
4) Some way to get random jumps to simulate fronts; size of jumps might depend on season
Did people creates 30 years worth of data manually? I'd find it more interesting to create them out of a statistical distribution and see if one could simulate realistic weather.
My idea:
1) Skewed Gaussian distribution for each month
2) Each day is correlated with the previous day
3) Diurnal range based off of sunshine
4) Some way to get random jumps to simulate fronts; size of jumps might depend on season
I actually did that with both 1) and 3). However 2) and especially 4) are very hard to simulate...But that would be great indeed.
The problem with Gaussian distributions, also, is that if you simulate a very large number of values, you'll end up with extreme, unwanted values.
I once tried with my July values for dozens of thousands of days and got once a low of 14°C (even with a low standard deviation) whereas my average July low is above 25°C and record July low should be well above 20°C (say like Hong Kong)
Did people creates 30 years worth of data manually? I'd find it more interesting to create them out of a statistical distribution and see if one could simulate realistic weather.
My idea:
1) Skewed Gaussian distribution for each month
2) Each day is correlated with the previous day
3) Diurnal range based off of sunshine
4) Some way to get random jumps to simulate fronts; size of jumps might depend on season
Mine is done manually but certainly all three of the last factors are taken into account in all the records I made.
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