"Knut-Frode" wrote in message ...
Bill Habr wrote:
"Knut-Frode" wrote in message
...
Bill Habr wrote:
"MET" wrote in message
...
How can one estimate the water content of the atmosphere from surface
data such as temperature, dew point temperature, sea level pressure
and relative humidity? (The height of the station is also known.)

Thanks your help.

Regards MET
You can't.



You can not calculate exactly, but you can of course estimate it.
E.g.:


http://ams.allenpress.com/perlserv/?...V%3E2.0.CO%3B2


So estimate the "water content of the atmosphere" and post your answer


The paper gives as best fit:
W = exp(-0.981 + 0.0341*F)
Where W is the water content in cm/m2 and T is the temperature in
Farenheit (old paper...)
So, for example with T = 50F (283 Kelvin), W is 2.06 cm/m2, and with T =
32F (273 Kelvin), W = 1.12 cm/m2.


There is a reason why I said you can't. Given a very small area around a station and no
air movement your answer would be resonable estimate for such a column of air.

"MET" napisal w wiadomosci
...
So estimate the "water content of the atmosphere" and post your answer.

Sorry, it's only now that I checked here again for an answer to my
initial question. (Since my question didn't get a reply for a while, I
actually lost hope to receive some help through this forum.)

@Szczepan: That's interesting. Unfortunately such measurements are not
(yet?) done or at least the results not yet published from the
different meteorological stations.

" Electric forces are responsible for whole liquid water content in clouds,
...." This is from: http://www.springerlink.com/content/e1846k71563j8527/

It is a new approach but the only to have good estimation.
Without electric factors such estimation is impossible.
S*
..

On Thu, 22 Nov 2007 04:15:14 -0800 (PST), MET
wrote:

So estimate the "water content of the atmosphere" and post your answer.

Sorry, it's only now that I checked here again for an answer to my
initial question. (Since my question didn't get a reply for a while, I
actually lost hope to receive some help through this forum.)

@Szczepan: That's interesting. Unfortunately such measurements are not
(yet?) done or at least the results not yet published from the
different meteorological stations.

@Bill: Yes, I was asking for an *estimation*. Thank you for providing
the link to this paper. Will check now how well the results of this
fitted function compare with some extreme cases.


You say you want the water content of the atmosphere but do you want
the total water content of the atmosphere or the precipitable water
contnet?

Precipitable water is just the water vapor portion of the total water
content. It would exclude the liquid and solid water content in the
atmosphere. The JAM paper refers to a relationship between monthly
mean precipitable water and monthly mean dew point which is higher
smoothed average. Note that the daily or hourly relationship will be
much poorer. I would personally not put too much faith in hourly
estimates of precipitable water made from surface dew point or even
daily average dew ponit values.

What I actually would need is an estimation of all the water content
in the atmosphere from surface data. This parameter is of importance
for the diurnal temperature range (DTR) at a location. It's actually
for this parameter for which I try to find an estimation with a fitted
function using only surface data.

Finally I found also some time for checking the suggested estimation
function. Here now the estimated precipitable water vapour values
which were compared to the values reported from weather balloon
soundings of the same day. The selected locations provide some extreme
values. The daily mean value was used where more than one value was
measured at the same day. Note: I'm aware that the suggested (fitted)
function was derived of monthly mean values and that it is therefore
not really correct to use it on daily base. However, as the results
show, the function delivers even half way reasonable estimates for
daily values. The values shown for W correspond to mm/m^2

Location Day W(sounding) W(estimation)
Manaus 01.05.2006 61.01 45.19
Manaus 02.05.2006 62.42 46.76
Manaus 03.05.2006 58.09 45.19
Manaus 04.05.2006 63.19 45.19
Manaus 05.05.2006 60.04 42.21
Kuching 01.12.2006 63.16 48.38
Kuching 02.12.2006 64.13 48.38
Kuching 03.12.2006 61.64 48.38
Kuching 04.12.2006 62.19 48.38
Kuching 05.12.2006 59.61 48.38
San Diego 21.10.2007 13.71 18.00
San Diego 22.10.2007 1.01 7.67
San Diego 23.10.2007 0.00 10.79
San Diego 24.10.2007 3.90 13.24
San Diego 25.10.2007 4.36 18.00
Halley 11.07.2007 1.93 2.03
Halley 12.07.2007 2.37 3.06
Halley 13.07.2007 1.98 2.03
Halley 14.07.2007 0.86 1.03
Halley 17.07.2007 1.32 0.68

These results suggest that the estimation generally underestimates
high W values. Low W values seem to be well represented in case of low
temperature (Antarctica). However, low W values in the context of high
temperatures and very dry air are overestimated.

What I actually would need is an estimation of all the water content
in the atmosphere from surface data. This parameter is of importance
for the diurnal temperature range (DTR) at a location. It's actually
for this parameter for which I try to find an estimation with a fitted
function using only surface data.

Finally I found also some time for checking the suggested estimation
function. Here now the estimated precipitable water vapour values
which were compared to the values reported from weather balloon
soundings of the same day. The selected locations provide some extreme
values. The daily mean value was used where more than one value was
measured at the same day. Note: I'm aware that the suggested (fitted)
function was derived of monthly mean values and that it is therefore
not really correct to use it on daily base. However, as the results
show, the function delivers even half way reasonable estimates for
daily values. The values shown for W correspond to mm/m^2

Location Day W(sounding) W(estimation)
Manaus 01.05.2006 61.01 45.19
Manaus 02.05.2006 62.42 46.76
Manaus 03.05.2006 58.09 45.19
Manaus 04.05.2006 63.19 45.19
Manaus 05.05.2006 60.04 42.21
Kuching 01.12.2006 63.16 48.38
Kuching 02.12.2006 64.13 48.38
Kuching 03.12.2006 61.64 48.38
Kuching 04.12.2006 62.19 48.38
Kuching 05.12.2006 59.61 48.38
San Diego 21.10.2007 13.71 18.00
San Diego 22.10.2007 1.01 7.67
San Diego 23.10.2007 0.00 10.79
San Diego 24.10.2007 3.90 13.24
San Diego 25.10.2007 4.36 18.00
Halley 11.07.2007 1.93 2.03
Halley 12.07.2007 2.37 3.06
Halley 13.07.2007 1.98 2.03
Halley 14.07.2007 0.86 1.03
Halley 17.07.2007 1.32 0.68

These results suggest that the estimation generally underestimates
high W values. Low W values seem to be well represented in case of low
temperature (Antarctica). However, low W values in the context of high
temperatures and very dry air are overestimated.