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Interpolating temperature fields using static and dynamic lapse rates
Chris Lennard
Climate Systems Analysis Group, University of Cape Town, Cape Town, South Africa. email: lennard@csag.uct.ac.za
1. Introduction
Gridded climate data are important in many climate
applications such as the validation of simulated results
and understanding land-atmosphere interactions. Point
source data often forms the basis of the gridded product
and involves the interpolation of these data to a specified
grid size. The nature of point source, observational
temperature data is usually spatially heterogeneous and
often lacks temporal consistency thus many interpolation
techniques have been developed to aid the spatial
generalization of point source data into a gridded product
(e.g. Cressman 1959, Willmott et al. 1985, Biau et al.
1999). Here, we present a novel approach, which uses
empirical and dynamical methods, in the preparation of
station data for interpolation as well a a more traditional
approach. The latter used a static lapse rate field to get
station data to a common level for interpolation whereas
the new approach a dynamic lapse rate field. Each
method is presented as well as the results from both and
the merits of each discussed. We examined 36 years of
station data over the Cape Fynbos region and used the
Cressman interpolation scheme.
2. Data and Methods
The two preparation methods used lapse rates to
reduce/raise station temperature data to a common level
at which the interpolation was performed. The traditional
approach reduced the station data at a constant lapse rate
of 0.6 degrees per 100 m to sea level where the
interpolation was performed. However, this method is
unlikely to capture local scale phenomena such as
temperature inversions, berg winds and localized
orographic effects. In an attempt to capture these
phenomena, the new method used empirical and dynamic
methods to establish lase rate fields. First, self organizing
maps (SOMs) were used to produce 12 characteristic
synoptic circulations over South Africa for the 36 year
time period (Fig. 1).
Then, for each of these states, a regional climate model
was run at a resolution of 3 km to generate 12 high
resolution archetypal lapse rate fields. Figure 2 shows the
lapse rate field for Cape Town International Airport for
each of the synoptic states over 29 sigma levels.
Every day in the station record mapped to one of the 12
characteristic circulations so every station was raised by
the SOM-specified lapse rate to a common level at the
top of the atmosphere. The interpolation was then
performed at this level. The interpolated temperature
fields were then returned to the surface along respective
lapse rates. We present the technique and the interpolated
results and assess the value of using a dynamic lapse rate
field versus a static one.
Fig.1. Sea level pressure maps of the 12
characteristic synoptic states.
Fig. 2. Lapse rate fields for the SOM circulations
above Cape Town International Airport. Sigma
levels are not evenly spaced with height, more
levels are at lower altitudes to capture boundary
dynamics here. Sigma level 15 is at approximately
3000 m, level 21 at approximately 6000 m and level
30 at about 15000 meters.