Hi,

Is it reasonable to believe, that monthly or yearly air temperatures
fit a Gaussian or more likely a Weibull distribution with 3 parameters?

Have anyone worked with the opposite, namely building a Weibull
distribution based on the local min, max and average?

Thanks in advance!

Best Regards
Jakob Lundholm

I would not recommend Weibull for temperatures. One problem with
Weibull if that it has an absolute lower limit and does not allow a
lower tail that keeps going to infinity.

If you set the minimum life to absolute zero, then it might make sense,
but I don't think that is what you want.
Weibulls are used for wind speed because wind speed cannot be less than
zero.

Would a normal or log normal distribution be a reasonable model?

Dean M. Ford


wrote:
Hi,

Is it reasonable to believe, that monthly or yearly air temperatures
fit a Gaussian or more likely a Weibull distribution with 3 parameters?

Have anyone worked with the opposite, namely building a Weibull
distribution based on the local min, max and average?

Thanks in advance!

Best Regards
Jakob Lundholm

Hello,


I would like to suggest you trying our software product EasyFit which
allows to fit various distributions to sample data and easily select
the best model (more than 40 probability distributions are supported).
To learn more and download a free trial version, please visit our
website at:

http://www.mathwave.com/products/easyfit.html

If you have any questions or suggestions, please feel free to contact
us at


Sincerely,

Antony Drokin
MathWave Technologies
http://www.mathwave.com

wrote:
"""
Hi,

Is it reasonable to believe, that monthly or yearly air temperatures
fit a Gaussian or more likely a Weibull distribution with 3 parameters?

Have anyone worked with the opposite, namely building a Weibull
distribution based on the local min, max and average?

Thanks in advance!

Best Regards
Jakob Lundholm

Thanks for your reply Dean.

Maybe you have suggested better distibution types than the Weibull !!

My mission (impossible) is to reconstruct the set of observations
behind a local min, max and average temperature, without loss of to
much information. However I can live with a minor loss.

During summer and winter the average temperature is often displaced
from the middle, closer to either min or max, so the distribution must
be able to deal with this.

I am aware of the absolute lower limit, however local weather data
always specify a normal minimum temperature which I think of using as
the location parameter.

So if Weibull still is the better distibution, the question is then how
to estimate the shape and scale parameter based on local min, max and
average air temperature and the knowledge that we are dealing with air
temperatures specifically?

Best regard
Jakob Lundholm




dean ford skrev:
I would not recommend Weibull for temperatures. One problem with
Weibull if that it has an absolute lower limit and does not allow a
lower tail that keeps going to infinity.

If you set the minimum life to absolute zero, then it might make sense,
but I don't think that is what you want.
Weibulls are used for wind speed because wind speed cannot be less than
zero.

Would a normal or log normal distribution be a reasonable model?

Dean M. Ford


wrote:
Hi,

Is it reasonable to believe, that monthly or yearly air temperatures
fit a Gaussian or more likely a Weibull distribution with 3 parameters?

Have anyone worked with the opposite, namely building a Weibull
distribution based on the local min, max and average?

Thanks in advance!

Best Regards
Jakob Lundholm