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- From: srobeson@nickel.ucs.indiana.edu (scott robeson)
- Subject: Re: Re: climate
- Message-ID: <C1KIF9.FMp@usenet.ucs.indiana.edu>
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- Organization: Indiana University
- References: <01GU117LD10U0017JC@EKU.BITNET>
- Date: Thu, 28 Jan 1993 14:18:44 GMT
- Lines: 47
-
- In article <01GU117LD10U0017JC@EKU.BITNET> GEOWASSE@EKU.BITNET writes:
- >>you exactly plan to deal with elevational effects on temperature... I assume
- >>that some algorithim exists for determining rate of change of temperature
- >>based on average slope rise, or somesuch, but I am not quite clear on that.
- >
- > The average environmental lapse rate is 6.5oC / 1000m
- >
- > Adiabatic rates may also prove useful, but accurate data for a
- > given air mass is more difficult to acquire.
-
- As Arco said, 6.5 oC/km is the average _environmental_ lapse rate,
- which includes air that is both saturated and unsaturated (with
- respect to water vapor). The environmental lapse rate describes the
- actual decrease in temperature that is observed. When air is _forced_
- to rise (or sink), it is heated (or cooled) at either the dry
- adiabatic lapse rate (DALR, 9.8 oC/km) or the wet adiabatic lapse rate
- (WALR, variable depending on water content), depending on whether it
- is saturated or not. When bringing air down to some common level, you
- could assume that the env lapse rate is adiabatic, but this is a major
- assumption. An even bigger assumption needs to be made when you take the
- temperatures back up to an elevation (wet, dry, or something else).
- One can imagine that a particular area has an inversion (temperature
- increase with height) and the interpolation algorithm says that the
- air cools at the DALR. This kind of procedure may be better than not
- doing anything to the data before interpolating, but that needs to be
- shown.
-
- One method that Cort Willmott and I have used to get around this problem
- is to use a high-resolution climatology to guide the interpolation (we
- have called this Climatologically Aided Interpolation or CAI). We
- subtract a high-res temperature field from the observations and
- interpolate the anomalies to a grid. At the grid, we then add the
- climatology back onto the interpolated anomalies to get raw air
- temperature. Although this procedure requires the existence of a
- reliable high-res climatology, we have shown this to be very much
- superior to simply interpolating raw air temperatures. An application
- of this appears in a forthcoming volume of the Publications in
- Climatology monograph series entitled "Spatial Interpolation, Network
- Bias, and Terrestrial Air Temperature Variability." It should be out
- in the next few months. We're also working on a journal article on
- the same topic.
-
- --
- Scott Robeson srobeson@indiana.edu
- Department of Geography, 120 Student Building
- Indiana University, Bloomington, IN 47405 Phone: 812-855-7722
- ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-