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ATMOSPHERIC SCIENCE LETTERS
Atmos. Sci. Let. 6: 171–175 (2005)
Published online 9 December 2005 in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/asl.112
Letter to the Editor
Reflections on the theme of classifying, documenting and
exchanging meteorological data
The weather systems on planet Earth all contain strong
global elements, and the movements of mass and
energy inside the thin envelope of air surrounding
this planet do not respect the borders put up by
the nations. So, the exchange of meteorological data
sets connected to the man-made systems for making
quantitative measurements and models for predicting the global weather as well as the regional and
local weather is a concern of international character and is connected to many temporal and spatial
scales.
Three related subjects will be considered in the
following text. First, general ideas of classification of
meteorological and physical phenomena are presented
and connected to a few actual systems for classifying
weather and climate. Then, an interpretation of the
scientific principle is presented together with a system
for documentation of meteorological and physical
parameters. In the concluding paragraphs, existing
systems for exchanging meteorological data sets is
considered and the possible challenges of exchanging
such data in the future.
The word ‘meteorology’ is of ancient Grecian origin, and etymologically, the word ‘meteoros’ signifies
phenomena observed above the ground. The weather
phenomena observed has different spatial and temporal
dimensions (scale), and they are connected to a great
many processes (the development of phenomena in
time). The main processes considered in meteorology
and in the biological processes involving meteorology are connected to the exchange of heat and water,
the movement of weather systems (dynamics) and the
water cycle in the atmosphere. The lower boundary
of the atmosphere may be covered by vegetation. It
may also be covered with bodies of water or ice; or, it
may consist of barren land with sand or stone or some
artificial man-made constructions.
The studies of the dynamics (the movements of the
air) of the atmosphere are to a great extent studies of
circulations, eddies and waves on very different spatial
and temporal scales. By using more ordinary concepts, we characterise the eddies as tropical cyclones,
typhoon (western Pacific), hurricanes (in the Atlantic
ocean and the Caribbean), extra tropical cyclones, tornados, rainstorms, gusts or turbulence; see (Wallace
and Hobbs, 1977). Some circulations are global, and
we find the Hadley Circulation, The Ferrel cells and
the monsoon circulations; see (Wallace and Hobbs,
Copyright  2005 Royal Meteorological Society
1977). Hills and mountains and the great ridges of the
mountains of the continents are set up of long wave
systems, gravity waves and gravity-inertial waves on
different scales; see (Smith, 1979). We also have the
wind systems connected to local surface effects and
heating differences of local nature; see (Eliassen and
Pedersen, 1980).
There are a multitude of phenomena connected to
the water in the atmosphere, the clouds, the different
types of precipitation, evaporation of water from
vegetation and the bodies of water on the earth.
Classification of the meteorological phenomena
mentioned is an important part of the science of meteorology, and the classification of climate is an important
part of the studies of climate. The English chemist
Luke Howard constructed a classification system of
clouds in the beginning of the nineteenth century,
and this system is still in use. The concepts of ‘air
mass’ and ‘frontal systems’ as well as the basic ideas
of synoptic meteorology and construction of synoptic charts were developed in Norway just after the
first world war; see Petersen (1958) and Wallace and
Hobbs (1977). The synoptic symbols now in use contain both quantitative measurements of meteorological
parameters as well as observations of phenomena with
rather vague quantitative features.
In meteorology, a multitude of weather phenomena have got their names, and classification systems
of the phenomena have been developed in dynamic
meteorology, in physics of clouds and physics of radiation, in micrometeorology and other parts of physical
meteorology, in upper air research, etc through the
decades of the last century. But there is no unanimous agreement on the definition of several important
meteorological phenomena. Examples are the definition of the temporal and spatial scales connected
to the phenomena on the synoptic scale, the mesoscale, the local scale and the micro-scale; see Linacre
(1992).
One of the most widely used systems for classification of climate was developed in Germany
by Wladimir Köppen during the years 1918–1936;
see Köppen (1923). This classification system is
connected to the temperature and the precipitation/evaporation of a region as well as to the natural vegetative cover of each region. Also, Thorntwait’s classification system of climate, connected to
the regimes of precipitation and temperature on the
172
globe, is used in certain contexts; see Linacre (1992).
According to WMO/OMM/BMO-No.182, 1992, the
phrase ‘climatic classification’ is defined in the following way: ‘Division of the Earth’s climates into
a worldwide system of contiguous regions, each of
which is defined by the relative homogeneity of its
climatic elements. Examples are Köppen’s and Thorntwait’s climate classifications.’
I would then like to point at the idea of systematically using the modern tool of object-oriented
analysis when constructing classes of meteorological
phenomena in numerical models of weather and climate; see Sivertsen (2004, 2005a,b). The basic idea
is that in each class or subclass of a phenomenon
quantitative parameters/attributes should be attached
to the phenomenon to describe it. In order to use a
class of a weather phenomenon or a biological phenomenon in a numerical model, one or several numerical attributes have to actually be attached to the class;
these attributes usually are called parameters. The
attached set of attributes defines each phenomenon and
makes it possible to implement it as a submodel of a
numerical weather-modelling system.
According to WMO/OMM/BMO-No.182, 1992, a
‘climatic element’ is defined in this manner: ‘Any
one of the properties or conditions of the atmosphere
which together define the climate of a place (e.g.
temperature, humidity, precipitation).’ Linacre (1992)
makes the following comment on the term ‘element
of climate’: ‘It is not possible to measure the climate,
but only the individual elements which comprise it. A
climate element is any one of the various properties
or conditions of the atmosphere which together specify
the physical state of the weather or climate at a given
place, for any particularly moment or period of time.
On the other hand, a climatic factor like latitude
or shading is a variable which controls a climatic
element.’
I think the examples mentioned support my ideas
that it is worthwhile to go on with this conceptual discussion on classification of meteorological phenomena and looking closer at the idea of
systematically describing phenomena by numerical
attributes/parameters.
When doing scientific research in agrometeorology
and in meteorology or by using the results of scientific research in practical operational applications,
one leans on the scientific principle. In Figure 1, an
interpretation of the scientific principle in the field
of meteorology is presented. One important idea connected to this discussion is to be able to say something
about the temporal and spatial scope of meteorological or agro-meteorological models and other applications used in operational contexts. Sivertsen (2004)
discusses aspects of the scope of a specific application
of agro-meteorology connected to a simple warning
system of late blight in potato, and some general ideas
of classification systems of meteorological parameters
and meteorological phenomena is presented.
Copyright  2005 Royal Meteorological Society
Letter to the Editor
In Figure 1, a scheme containing what is considered
the main steps of applied science is presented. By having the whole scheme in mind, it may sometimes be
possible, when trying to answer some practical question by using an application in an operational context,
to see that this practical question may have implications leading to reconsideration of the parameterisation
of the phenomena, the use of ‘basic physical laws’,
the mathematical and statistical hypotheses or the way
measurements are made.
The word ‘parameter’ is derived from two words
of ancient Grecian origin; ‘para’ is a prefix which
means ‘beside or subsidiary’ and ‘metron’ means
‘measure’ or ‘device for measuring’. The etymological meaning of the word ‘parameter’ then is
‘some element of nature made measurable’. According to the ‘International Meteorological Vocabulary’
(WMO/OMM/BMO-No.182), the concept ‘parameterisation’ is defined in this manner: ‘Approximate representation of subgrid-scale processes in a numerical model in terms of variables which are explicitly
calculated’. The use of the term ‘parameterisation’
in this article is more general. When any physical,
meteorological or biological phenomenon is described
by attaching quantitative attributes to it, it is called
parameterisation.
The idea of parameterising is thus to construct quantitative models of nature by first classifying the phenomena (giving them names) and then attaching some
system of measurable quantities to the phenomena
(see Sivertsen, 2004). One of the most elegant ways
of constructing models is by using modern objectoriented methods of analysing and programming complex numerical models. In an object-oriented system,
the different sub-phenomena of a model are given certain names called classes, and to each class is attached
quantitative attributes as well as formulas connecting
the quantitative attributes. The classes are often constructed as some nested hierarchy; see (Brown, 1997).
The invention, definition and construction of parameters (parameterisation) of meteorological and biological phenomena are then tasks that never end. The
parameters are quantitative attributes attached to a
phenomenon. Sivertsen, 2004 proposes two groups of
meteorological parameters, those being output from
a system for making measurements and those being
parameters attached to a model. These should be considered a different type of parameter. The first type of
parameter is categorised according to the source and
measuring system:
Name of the parameter
Unit
Definition
Method(s) for measuring the parameter
Representativeness for certain phenomena (models)
The attribute above called ‘Representativeness’ will
link the actual measured parameter to the different
Atmos. Sci. Let. 6: 171–175 (2005)
Letter to the Editor
173
Figure 1. Graphic representation of the scientific principle
Copyright  2005 Royal Meteorological Society
Atmos. Sci. Let. 6: 171–175 (2005)
174
models or phenomena, and this attribute together with
the attribute ‘Method(s) for measuring the parameter’
tells us something about the temporal and spatial
resolutions of the parameter.
The second group of parameters, being outcome
from model calculations, is described in a similar
manner:
Name of the parameter
Unit
Definition
Representativeness of the phenomena of the model
considered
Representativeness for certain phenomena in other
models
The attribute ‘unit’ of a meteorological or biological parameter will as a rule contain one of the following units or some combination of the following
units: meter (length), second (time), Ampére (strength
of electrical current), Celsius (unit of temperature
uniquely connected to the Kelvin scale of temperature), mole (amount of matter) and candela (strength
of light). In addition to this, the parameter might be a
pure integer or a pure real number. This system also
could be linked to the CREX, the BUFR or the GRIB
system; see Sivertsen (2004) and Section 6.
In the frame of the WMO standard classification systems for exchange of meteorological data and related
types of about agro-meteorological and biological geophysical data, called CREX and BUFR have been
developed.
These systems consist of combinations of metadata,
classification and documentation of the data as well
as standardisation of the format. Systems for the
exchange of gridded data sets, called GRIB have also
been developed.
Conducted by WMO, there are two complementary
systems for classification and exchange of meteorological data, the CREX/BUFR and the GRIB systems
that have been developed. CREX is an acronym for
‘Character form for the Representation and Exchange
of data’, and this is the former alphanumeric version
of ‘Binary Universal Form for Representation of meteorological data’ with the acronym BUFR. There are
automated methods for conversion of CREX code to
BUFR code and vise versa.
GRIB is an acronym for ‘GRIdded Binary’. GRIB
is an efficient vehicle for transmitting large volumes
of gridded data to automated centers over high-speed
telecommunication lines. The gridded data is then the
data contained in the numerical weather prediction
models. GRIB may also serve as a data storage format.
A definition of the BUFR form is given in WMO
Manual on Codes, Guide to FM-94 BUFR.
The BUFR form is a bit stream made up of a
sequence of octets (one octet is eight bits). A BUFR
message consists of six sections, some of which are
optional. In order to read a BUFR message, it must be
visualized and interpreted by a computer program. A
BUFR message comprises the following sections:
Copyright  2005 Royal Meteorological Society
Letter to the Editor
– Section number ‘0’ ‘Indicator section’ ‘BUFR’
(coded according to the CCITT International Alphabet, No. 5).
– Section number ‘1’ ‘Identification section’ containing length of section, identification of the section
message.
– Section number ‘2’ ‘Optional section’ containing
length of section and any additional items for local
use by data-processing centers.
– Section number ‘3’ ‘Data description section’ containing length of section, number of data section
subsets, data category flag, data compression flag
and a collection of data descriptors which define
the form and content of individual data elements.
– Section number ‘4’ ‘Data section’ containing length
of section and binary data.
– Section number ‘5’ ‘End section’ ‘7777’ (coded in
CCITT International Alphabet, No. 5).
The metadata of the BUFR system is contained in
Sections 1, 2 and 3. Most of the data are observations,
and ‘bit1’ in octet number 7 in Section 3 is set
to ‘1’. If ‘bit1’ is set to ‘0’, this refers to other
data, usually forecast information from some model.
The metadata is contained in several tables, giving
information about the ‘category’ of the data and
descriptions of the types of quantitative information
on the ‘parameter/observation’ considered.
A GRIB record consists of six sections, two of
which are optional:
– Section number ‘0’ ‘Indicator section’ containing
‘GRIB’ (coded according to the CCITT International Alphabet, No. 5), total length of message in
octets and edition number.
– Section number ‘1’ ‘Product definition section
(PDS)’ containing metadata on the parameters considered, description of the grid and its height, temporal information, forecast time unit, identification
of computer center, etc.
– Section number ‘2’ ‘Grid description section’ containing information on the grid used (type projection of mapping used), etc.
– Section number ‘3’ ‘Bit map section (BMS)–
optional’ contains information of parameter fields
not defined in certain subsystems of the gridded
model by a bit-map system.
– Section number ‘4’ ‘Binary data section (BDS)’
contains the numerical data as binary data and the
way the numerical data may be represented.
– Section number ‘5’ ‘End section’ ‘7777’ (coded in
CCITT International Alphabet, No. 5). This is a
human-readable indication of the end of a GRIB
record.
The metadata of the ‘GRIB’ system is mainly
contained in section numbers ‘1’ and ‘2’ and the
interpretation is given in several tables. The ‘GRIB’
system is tailored for representation and exchange of
the content of numerical weather prediction models.
Atmos. Sci. Let. 6: 171–175 (2005)
Letter to the Editor
The metadata contained in the ‘BUFR’ and ‘GRIB’
systems is called meteorological elements. According
to ‘International Meteorological Vocabulary’ (WMO/
OMM/BMO-No.182, 1992), a ‘meteorological element’ is defined in the following manner: ‘Atmospheric variable which characterizes the state of the
weather at a specific place at a particular time (e.g. air
temperature, pressure, wind, humidity, thunderstorm
and fog).’ There are several tables giving the interpretation and codes of the meteorological elements
attached to ‘BUFR’ and ‘GRIB’. According to my
opinion, this classification system consists of a mixture
of phenomena and parameters describing the phenomena. This system (which is very flexible and has great
scope) is absolutely possible to use; but my message
is that this metadata part ought to be reconsidered
according to the ideas discussed in Section 2.
The conclusion is very short: One probably ought to
look at the metadata systems of GRIB and BUFR to
see if the classification systems may be constructed
in a more logical way using methods from objectoriented analysis of the modern IT world?
The methods of meteorology and research on climate are applied and used operationally in many contexts of modern societies, and these methods serves
as tools for decision making in human societies. One
could then ask, what is the scope of these scientific
methods connected to quantitative parameterisation of
natural phenomena and the quantitative prognoses of
these phenomena?
The basic intention of explicitly trying to clarify the
different elements of the scientific method and then
develop a system for documentation of parameters
(measured entities as well as parameters in models)
is to determine the scope of the scientific methods.
Knowing the scope of the methods in spatial and
temporal contexts, one may determine in each case
when to use these methods and when not to apply
them. The idea is that sometimes the methods are not
applied, when they ought to be applied, and sometimes
they are used outside their scope.
The meteorological phenomena appear on many
different temporal and spatial scales; the general
scientific methods may be applied on the weather
Copyright  2005 Royal Meteorological Society
175
systems anywhere on the globe. The exchange of
meteorological sets of data is important to discuss,
especially as the possibilities of real-time exchange of
data sets are increasing.
What is probably possible to develop in the future
are systems for exchange of data and information in
almost real time between the observation systems on
the ground (automated stations, weather radar systems,
etc.) and the information from the satellites and the different working numerical weather prediction systems.
Tor Håkon Sivertsen
The Norwegian Crop Research Institute, Fellesbygget
Høgskoleveien 7 N-1432 Ås Norway
Published online in Wiley InterScience (www.interscience.
wiley.com). DOI: 10.1002/asl.112
References
Brown D. 1997. An Introduction to Object-oriented Analysis. John
Wiley and Sons: New York; 700.
Eliassen A, Pedersen K. 1980. Meteorology An Introductory Course,
Volume II, Universitetsforlaget: Oslo-Bergen-Trondheim, 165.
Köppen WP. 1923. Klassifikation der Klimat der Erde.
Linacre E. 1992. Climate Data and Resources. Routledge: London and
New York; 366.
Petersen S. 1958. Introduction to Meteorology. McGraw-Hill Book
Company: New York-Toronto-London-Tokyo, 327.
Sivertsen TH. 2004. Invitation to conceptual discussions concerning
the scope of the scientific method and classification systems
of meteorological phenomena and meteorological parameters. In
Selected Papers of the International conference “Fluxes and
Structures in Fluids”, St. Petersburg, Russia, June 23–26, 2003;
Moscow. IPM RAS, 6.
Sivertsen TH. 2005a. Discussing the scientific method and a documentation systems of meteorological and biological parameters. In
Physics and Chemistry of the Earth Special Issue: Agrometeorology, 2003, El Selvier Ltd. Orlando-Amsterdam-Tokyo-Singapore vol
30/1-3, 35–43.
Sivertsen TH. 2005b. Implementation of a general documentation
system for web-based administration and use of historical series of
meteorological and biological data. In Physics and Chemistry of the
Earth Special Issue: Agrometeorology, 2003, vol 30/1-3, 217–222.
Smith, Rb. 1979. The influence of mountains on the atmosphere,
Advances in geophysics, Volume 21, Periodicals Service Company:
Georgetown USA.
Wallace JM, Hobbs PV. 1977. Atmospheric Science An Introductory
Survey. Academic Press: Orlando, FL, USA.
Atmos. Sci. Let. 6: 171–175 (2005)
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