STATION EXPOSURE METADATA NEEDED FOR JUDGING AND
IMPROVING QUALITY OF OBSERVATIONS OF WIND,
TEMPERATURE AND OTHER PARAMETERS
Jon Wieringa
Department Environmental Sciences, Wageningen Universiteit
Duivendaal 10, 6701 AR Wageningen, Netherlands
Tel.+31-317-484131, fax +31-317-482419, email: jon.wieringa@user.metair.wau.nl
Ernest Rudel
Department of Climatology, Zentralanstalt für Meteorologie und Geodynamik
Hohe Warte 38, 1191 Wien, Austria
Tel.+43-1-36026-2201, fax +43-1-36026-72, email: ernest.rudel@zamg.ac.at
1. Station instrument exposures can vary uncomfortably.
Applied meteorology work, for forecasting, or for climate analysis, or for small-scale studies of agricultural or
environmental problems, nowadays usually means putting data from weather observation stations into some
computer model, and then analysing the model output. The basic assumption of many modellers is that all
official station observations conform closely to the rules of instrumentation, exposure and siting which are
given in the WMO-CIMO Guide. Unfortunately, for logistic reasons such conformity is often quite impossible
with respect to siting. In busy locations (such as airports) space is often scarce, so many stations have to
make their observations in a significantly obstructed location. In regions which are not so crowded, station
location may be primarily based on availability of an observer, or of power supply. Moreover, well calibrated
instruments may have been mounted in undesirable or unexpected positions.
To transform observations made at, say, a dubiously exposed airport station to an agricultural location we
can use nowadays boundary-layer models (e.g. Van Ulden and Holtslag 1985, Brown and Gillespie 1991, Li
and Avissar 1994, Castellvi et al. 2001). However, such models require information input on how
observations were really made at the station -- to begin with, the actual observation heights of temperature,
humidity, wind and precipitation. For thermal transformations some information is needed on e.g. albedo, soil
heat conductivity, soil wetness and vegetation at the station. For wind transformation, surface roughness and
location of obstacles around the station must be known. Such circumstantial facts about the station situation
are called metadata.
Unfortunately, experience (e.g. in making the European Wind Atlas) shows that in most countries no
metadata about station siting and exposure are archived. Such information is often not even available from
station keepers, even where good records on the technical state of the instrumentation exist. Often the only
non-technical specifications of surface stations, which are officially listed, are geographical coordinates
(location names not being sufficient, since there may be several stations in a city or village) and the elevation
above sea level. One cannot then be surprised any more that many mesoscale studies of atmospheric
surface parameters consist mostly of purely mathematical spatial interpolation (e.g. Gozzini et al., 2000).
One reason for ignorance may be that the WMO in its Guides for instrumentation and for observation
practices gives good advice on how measurements SHOULD be made, but gives no clear instructions for
describing how and where they ARE really routinely made. It is then not surprising when observers do not
provide such records (assuming that those who installed the station knew what they did), or when inspectors
restrict their reporting to technical matters. A close-up photo of some instrument does not inform anyone
about the degree of shelter and disturbance in its surroundings.
For evaluating station exposure errors, the CIMO Guide states that they are "best determined by comparing
station data against numerically-analysed fields using neighbouring stations." However, this approach is
unreliable to evaluate site effects for two reasons. The first reason is that compared stations usually each
have local problems of exposure, and then causes of differences are difficult to assign. Second, regional
homogeneity is object of climatological study, and it may not be assumed beforehand for station calibration
reasons -- that is why the Guide suggests numerical analysis. But since the average budget-determined
distance between stations in Europe is 50 km ± 20 km (elsewhere probably more), the errors in numerical
network model results will be too large to provide reliable determinations of exposure errors.
Summarizing, if we have only a set of observations from some station network, it is an illusion that these data
by themselves will furnish enough information on their quality. We really need station site and exposure
metadata to judge the quality of observations and their representativity. Leroy (1998) proposed to give
stations a classified evaluation from 1 (exposed according to CIMO Guide criteria) to 5 (inadmissibly
obstructed), listing specified criteria for temperature, precipitation and wind. However, his approach seems
not optimal because moderate exposure deficiencies are model-corrigible, but only from quantitative
information on soil properties and sheltering roughness, to be discussed below. Class numbers are not
usable as correction input.
2. Exposure dependence of station observations.
Let us take an example where simply knowing the observation height is needed. Evaporation is proportional
to the water vapour pressure gradient between the surface and the overlying air. A gradient is a difference
divided by a distance; this may seem trivial - but if under fully-wet surface conditions a certain (low) vapour
pressure is measured in a Stevenson screen at 2 m height (customary in France or Norway), the evaporation
is likely to be only two-thirds from what it will be if the same vapour pressure is measured in a screen at 1.2 m
height (customary in England) - other conditions, like radiation and wind, being equal. In this context, it would
be downright misleading to compute a map of evaporation in Europe from station data without using the
humidity observation height of individual stations as model input (Wieringa and Lomas, 2001).
For observing the surface heat regime, the importance of soil properties is clearly shown in a model study of
Zdunkowski and Trask (1971) on nighttime temperature development above various soils. Eight hours after
sundown in a clear radiation night, air cooling at thermograph height is estimated to be 4.5 °C over rocky soil,
7° over sandy clay and 13 ° over quartz sand. The degree of surface vegetation will give an additional effect.
Such model results are well supported by various field investigations (e.g. Beljaars and Holtslag, 1991).
Zdunkowski and Trask (1971) also showed that it can make a systematic difference of a factor five, in
nocturnal inversion height if the station subsoil is either sandy or rocky. For data users the problem is that
documentation on the thermometer screen siting is seldom available. Generally also the small-scale elevation
variations of a few meters near the screen are unknown, while occurrence of a "frost hollow" can change Tmin
easily by 5° or more (Hopkins 1977).
Wind observations are most sensitive to shelter by high roughness or by obstacles, because the length of
obstacle wakes is at least 10 obstacle heights -- a much larger distance than seems intuitively plausible.
Many wind masts, said to be in an “open" location, are actually seriously sheltered and underestimate the
wind in really open terrain by 20% or 30%. Therefore many models of vertical turbulent exchange of
atmospheric properties do not use any surface wind speed data, because the shelter error is unknown and
may be large. But an objective shelter correction procedure exists (Wieringa 1986), requiring as input the
effective roughness zo generated by surface vegetation and obstacles in the nearest kilometers upwind. This
zo can be obtained from the gustiness observed by the anemometer itself (Verkaik, 2000), but it can also be
estimated from visual appraisal of available information in maps, surface or aerial photos. For such appraisal
we need classifications of effective terrain roughness, and the most reliable of these (by Davenport) is given
in abbreviated form in Table 1.
3. Feasibility criteria for exposure and siting documentation.
For meso- and toposcale weather data application, at least within a characteristic radius of the order of 5 km,
it is necessary to know how well the weather station is exposed (Wieringa, 1998). lf we want to have sufficient
station site information for most of the meteorological stations in the world within a few years, it may be made
feasible by providing the station keepers with a simple template for describing their station exposure and site.
This must not ask for things which are difficult to measure well, such as the desired soil conductivity - in that
case, ask for specification of the composition of the soil. Or, getting measured roughness for 12 azimuth
sectors may be desirable, but you may actually get something if you ask for zo-class estimates in four
directions.
Such information may well be sufficient for primary operational estimates of site effects. Also, readily
deliverable information is also the kind of information that can be adequately well checked during a hasty
station inspection. Finally, such a one-time request for facts does not carry a quality judgement (which,
anyway, may vary by data user), so collection and archiving may proceed without social blockages. Updating
is of course desirable - also of the description of the station instrumentation, since change to e.g. a new type
of screen and thermometer can cause significant systematic changes in the station data (Quayle 1991),
incorrectly suggesting a change in local climate.
The following summary description of station exposure was discussed at a meeting of the WMO-CIMO
Working Group on Surface Measurements in Geneva, August 2001. It was considered very useful, and
should be developed for inclusion as an Annex to the first chapter of the 7th edition (2003 ?) of the WMOCIMO Guide. A draft template of such a one-page exposure description for a hypothetical station is sketched
below. The desired final result is to be availability of a workable minimum of site and exposure metadata in a
few years for many of the regular weather stations in the world.
METEOROLOGICAL STATION SITE AND EXPOSURE
Identification: name and number, coordinates, elevation. Date of description update.
General: 1:5000 map (100 x 100 mm = 500 x 500 m) with enclosure in map centre and locations of buildings
and trees (with height) and pavement, and of out placed instruments. Contours of 1 m elevation
differences relative to enclosure. At map edges, notes on major distant terrain features (built-up areas,
woods, open water, hills etc.).
Temperature and humidity: sensor height; artificial ventilation ? type of soil (to 1 m depth) and vegetation
below thermometer screen.
Radiation: horizon sketch, high 25 mm = 10 degrees.
Precipitation: gauge rim height.
Wind: anemometer height (it not free-standing, also dimensions of building below); Davenport roughness
classification of terrain to N, E, S and W. (If wind is measured al location outside enclosure, also
height, distance and azimuth of nearest obstacles).
TABLE 1: Effective terrain roughness classification
Class
Nr. Name
1: Sea
Roughness
length (m)
0.0002
2: Smooth
0.005
3: Open
0.03
4: Roughly open
0.10
5: Rough
0.25
6: Very rough
0.5
7: Skimming
1.0
8: Chaotic
≥2
Landscape description
Open water, flat Plain, fetch > 3
km
Obstacle-free land with
negligible vegetation, marsh,
ridge-free ice
Flat open grass, tundra, airport
runway, isolated obstacles
separated by >50 obstacle
heights H;
Low crops or plant cover,
occasional obstacles separated
by ≥20 H
Crops of varying height,
scattered obstacles with
separation x ≈ 12-15 H if porous
(shelterbelts) and x ≈ 8-12 H if
solid (buildings)
Intensively cultivated landscape
(x ≈ 8 H) with large farms,
orchards, bushland; or low wellspaced buildings and no high
trees (x ≈ 3-7 H)
Full similar-height obstacle
cover with interspaces ≈ H, e.g.
mature forests, suburban town
area
Irregular distribution of very
large elements: high-rise city
centre, big irregular forest with
large clearings
References:
Beijaars, A.C.M., and Holtslag, A.A.M. (1991): Flux parameterization over land surfaces for atmospheric models.
J.Appl.Meteor. 30, 327-341.
Brown, R.D., and Gillespie, T.J. (1991). Estimating crop-top microclimates from weather station data.
Atmosphere-Ocean 29, 110-132.
Castelivi, F., Stockle, C.O., Perez, P.J. and lbanez, M. (2001): Comparison of methods for applying the Priestley-Taylor
equation at a regional scale. Hydrol. Process. 15, 1609-1620.
Davenport, A.G., Grimmond, C.S.B., Oke, T.R., and Wieringa, J. (2000): Estimating the roughness of cities and
sheltered country. Prepr.12th AMS Conf. Applied Climatology (Asheville, N.C.), 96-99.
Gozzini, B., et al. (2000): Cooperation and comparison of several interpolation methods of meteorological data
(minimum temperature). EC-COST-Action Report EUR-19545.
Hopkins, J. S. (1977): Spatial variability of daily temperature and sunshine over uniform terrain.
Meteor.Mag.106, 278-292.
Leroy, M. (1998): Meteorological measurement representativity, nearby obstacles influence.
WMO/TD-No.877, lnstr.Obs.Meth.Rep.70,51-54.
Li, B., and Avissar, R. (1994): The impact of spatial variability of land surface characteristics on landsurface heat fluxes.
J.Climate 7, 527-537.
Quayle, R.G., Easterling, D.R., Kan, T.R., and Hughes, P.Y. (1991): Effects of recent thermometer changes in the
Cooperative Station Network. Bull.Am.Meteor.Soc. 72, 1718-1723.
Van Ulden, A.P., and Holtsiag, A.A.M. (1985): Estimation of atmospheric boundary layer parameters for diffusion
application. J.Clim.Appl.Meteor. 24, 1196-1207.
Verkaik, J.W. (2000): Evaluation of two gustiness models for exposure correction calculations.
J.Appl.Meteor. 39, 1613-1626.
Wieringa,
J.
(1986):
Roughness-dependent
geographical
interpolation
of
surface
wind
speed
averages.
Quari.J.Roy.Meteor.Soc. 112, 867-889.
Wieringa, J. (1998): How far can agrometeorological station observations be considered representative?
Prepr. 23rd Am. Meteor. Soc. Conf. on Agricultural and Forest Meteorology (Albuquerque, N.M.) 9-12.
Wieringa, J., and Lomas, J. (2001): Lecture notes for training agricultural meteorological personnel.
WMO-No.551, 2nd ed., 196 pp.
Zdunkowski, W.G., and Trask, D.C. (1971): Application of a radiative-conductive model to the simulation of nocturnal
temperature changes over different soil types. J.Appl.Meteor. 10, 937-948.