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16 Italian air force observatory network for environmental and meteorological monitoring: from data control to quality assurance
Mountains Witnesses of Global Changes R. Baudo, G. Tartari, and E. Vuillermoz (Editors) r 2007 Elsevier B.V. All rights reserved.
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16 Italian air force observatory network for environmental and meteorological monitoring: from data control to quality assurance Fabio Malaspina, Francesco Foti and Emanuele Vuerich Abstract The main purpose of a meteorological observatory network is to completely describe natural phenomena so to record their characteristics forever. Such recordings increase in importance through time, becoming part of the patrimony of unique and unrepeatable knowledge. These records are useful to all humanity for scientific research for making choices that will have effects on us and our planet. Especially, recently, some of this information can verify and initialize mathematical models. Natural phenomena can have different global or local characteristics, and, according to their typology, the right network for measurements is necessary to fix instrumentation specifics and establish appropriate procedures. Study of global-scale natural phenomena requires fundamental performance measures for long periods in places far from human pollution, and high mountains are particularly suitable, for such purposes. In fact in 1654 the world’s first meteorological network was created in Italy by the Grand Duke of Tuscany, who was already worried about climate change. The Meteorological Service of the Italian Air Force continues this job with its own national network that performs special measures and even takes part in Antarctica’s surveys. The answers to problems discovered in the management of such meteorological stations are mainly due to technology changes, measures of small quantities, the necessity of highly specialized personnel, remote and often uncomfortable measurement locations, and heterogeneity of instrumentation, calibration, methods of measure, and exposure. In this epoch of telecommunications observation systems are ever more distant from those that analyze the data. Such systems analyses are commonly received only as encoded messages or numerical files, without anyone knowing the history, the limits, or the different characteristics of instrumentation used in the monitoring stations. The highest quality data are those that guarantee the requisites dictated by the purpose for which they were produced. The complex climatic system is mainly characterized by incessant variability of its configurations that are irreplaceable. A subsequently assessed ‘‘quality control’’ based on past statistical descriptions is not enough, but it is also necessary a ‘‘quality-assurance’’ ISSN: 0928-2025
DOI: 10.1016/S0928-2025(06)10016-4
116
Fabio Malaspina, Francesco Foti, Emanuele Vuerich
system, in order to give to consumers enough information about measurements that were obtained, and in order to establish limits that must be accounted for in data analysis. Only ‘‘well defined’’ information changes uncertainty into measurable risk that can be emphasized in decision-making.
1. Introduction: Department for aeronautical experimentations (Re.S.M.A.) and observation networks of italian air force meteorological service This Italian Air Force Department is located in Vigna di Valle next to Bracciano Lake, in an extinct volcano. This constitutes an historic headquarters that was the first aerologic station in Italy since 1909. At the present moment it is in charge of doing standard meteorological observations, UV and ozone measurements, and meteorological soundings. It works 24 h a day and is equipped with instrumental emplacements where it is possible to make comparisons among different instruments for a long time and in different weather conditions. Lots of tests or special measurement campaigns are carried out in order to assess the performances of various observation devices (Fig. 16.1). Department personnel are able to state the quality of measurements and observations coming from the Service’s network. The Italian Air Force Meteorological Service Network has two different observation sections: the 84 human-controlled stations part and the 110 automatic weather stations part. Some of these Italian stations are situated in uncomfortable but meteorologically interesting places. For example: in the Alps, the Paganella station is at 2129 m a.s.l. and the Plateau Rosa` station at 3480 m a.s.l.; in the Apennine the Cimone station is at 2165 m a.s.l.; and on some occasions the Italian Meteorological Service takes part in important measurement campaigns, as is the case with the Antarctic National Program.
Figure 16.1. ReSMA experimental site that includes six equipped areas under both automatic and manual control every day. ReSMA has been designated as a site for World Meteorology Organization field intercomparisons of rainfall intensity (RI) gauges to be held in Vigna di Valle since August 2007 for two years.
Italian air force observatory network
117
The Italian meteorological network includes also three special measurement networks: the chemical analysis of the precipitation network (7 stations) activated in 1975, the total ozone network (3 stations) activated in 1957, and the solar radiation network (37 stations) activated in 1958. Cimone station also started measures of carbon dioxide concentration in 1979.
2. What would we like to measure? Two different monitoring networks for two different purposes Interest in natural phenomena is an innate part of human nature. Greek thinkers first began the long tradition of stars-meteorology and empiric-meteorology. They collected and ordered in an organic way knowledge of the atmosphere acquired in the past and raised it to a natural philosophy. Almost 2000 years after that, meteorology was changed from qualitative to quantitative. In the 17th century, after the invention of the first principal meteorological instruments by the Italian scientists, meteorology acquired the characteristics of a quantitative science. It was recognized that natural phenomena should be described and recorded in the most complete way for the present and future studies. Galileo Galilei wrote about this (in Italian) in Il Saggiatore in 1623: Natural phenomena are written in this big book which is always opened in front of our eyes. It is written in a mathematical language, and the characters are triangles, circles, and other geometric figures; without these it is a vain wandering in a dark labyrinth.
Later Lord Kelvin (1824–1890) more clearly stated: I affirm that when you can measure and express in numbers what about you are speaking, you know indeed something.
A single station is not enough to study meteorological phenomena, so a wellorganized network is needed. The purpose of observation networks is to produce quality data of atmospheric parameters, which must be selected in the right way to describe the phenomenon in which we are interested. The importance of good recordings increases more and more through the years to make a part of the patrimony of unique and irreplaceable knowledge, useful to the whole of humanity for scientific research, useful to us and to our children for making choices that will have effects on human beings and planet Earth. It is important to understand that every new measure modifies or creates new knowledge. Only ‘‘quality data’’ from the past and the present acquire additional value over time, so only these kinds of long historical series represent a national and human patrimony. Natural phenomena can have different global or local characteristics. According to their typology, it is necessary to design the appropriate network for measurement of them. It is also necessary to fix instrumentation specifications and establish appropriate procedures. In the case of global pollution, which has a climatic impact and which is not ‘‘directly’’ related to human activities, special and meteorological measurements must be performed in stations settled far from polluting sources. These ‘‘global measurements’’ generally monitor very small changes and very small
118
Fabio Malaspina, Francesco Foti, Emanuele Vuerich
concentrations. They also must be carried out for long periods and in apparently uncontaminated areas such as high mountains. In the case of local pollution, which has mainly an impact on human health, measurements must be collected near the polluting sources. These ‘‘local measurements’’ generally monitor high concentrations and local pollution has a short-period impact. In this epoch of advanced telecommunications, observation systems are more and more distant from those that analyze the data. System analysis can often involve receipt of only encoded messages or numerical files, without knowledge of history, limits, or different characteristics of instrumentation used in monitoring stations. It sometimes happens that the measurement accuracy (i.e., the closeness of the agreement between the result of measurement and the true value of the object to be measured – the measurand) is replaced by instrumental resolution (which is a quantitative expression of the ability of an indicating device to distinguish meaningfully between closely adjacent values of the quantity indicated, for display presentation in tenths, hundredths, etc.). Sometimes global and local measurements are confused, and the instrumental uncertainty is not considered. Therefore, it is our experience that it is sometimes possible to see the risk that the network’s goal is no longer describing as completely as possible the natural phenomena of our interest, but it is, for example, becoming more for the initialization of mathematical models or the monitoring of legal threshold concentrations in pollution areas. In this way, instruments are not used at the maximum of their performance and not all possible data are recorded, preventing the best description of nature to leave to future generations for their future strategic decisions and research.
3.
Problems in performing measurements
In the fields of observation and analysis, it may be possible to move toward a progressive automation; but in meteorology, machines cannot yet completely replace human observation, human intuition and human critical mind. About this argument, it has been written (Anonymous, 1993) Although this guide provides a framework for assessing uncertainty, it cannot substitute for critical thinking, intellectual honesty and professional skill. The evaluation of uncertainty is neither a routine task nor a purely mathematical one; it depends on detailed knowledge of the nature of the measurand and of the measurement. The quality and the utility of the uncertainty quoted for the result of the measurement therefore ultimately depends on the understanding, critical analysis, and integrity of those who contribute to the assignment of its value.
The problems occurring in the management of mountain stations, whose purpose is monitoring global changes, are mainly due to: (1) change of technology; (2) measure of small quantities; (3) necessity of high specialized personnel; (4) remote and often uncomfortable location of the site of measurement; (5) no net homogeneity (instrumentation, calibration, methods of measure, exposure). The following examples can show some of these problems. A comparison campaign among several thermometer screens and shields can show what happens when a technological change occurs, or the presence of a nonhomogeneous network. During a continuous daily sampling, temperature differences
Italian air force observatory network
119
Figure 16.2. The new Radiosonde Vaisala RS92 has been recently introduced to substitute for the previous model RS90. Temperature average direct differences were calculated with RS92-SGP as a reference. Also a ‘‘standard deviation’’ line is presented. The impact of future instrumentation change, considering the measurement accuracy required by current working standards, will be negligible whenever atmospheric sounding is used for local monitoring or for model initial analysis. In the climatic field, however, since statistical analysis is performed at precision of tenths to even hundredths of Celsius degrees, the consequences could be relevant should instrument change not be taken into account during analysis. In fact, as new equipment replaces old, indications of gradual and progressive atmospheric heating will result.
of more than 1.51C have occurred. Maximum temperature differences correspond to sunrise, when generally the absolute minimum of temperature is recorded, and the sunset (van der Meulen, 1998). In a comparison campaign among different radiosondes of the same manufacturer, possible changes in aerological data due to instrument change were discovered (Malaspina et al., 2004). Eight launches were performed, and a systematic error of 0.21C was noted (Fig. 16.2). In another case, inhomogeneity among instruments has produced great problems in UV-A radiation measures (Anav et al., 1994). As shown in the Fig. 16.3, different spectral responses for similar instruments produce very different results and no comparable measures. Because of these problems, it has long been recognized that it is important not only to record the meteorological and special-measurement values, but also the circumstances in which the measurements are made, i.e. the metadata. Metadata are particularly important in climate studies. For example, temperature measurements are affected by the state of the surrounding, by vegetation, by the presence of buildings and other objects, by ground cover, by conditions and changes in design of the radiation shield or screen, and by other changes in equipment. Temperature is one of the meteorological quantities whose measurement is particularly sensitive to
120
Fabio Malaspina, Francesco Foti, Emanuele Vuerich
Figure 16.3. Different spectral responses involving similar instruments can produce very different results.
exposure (Anonymous, 1996). In addition, the statistical error in this kind of measure has to be consistent with the measurement accuracy; the same is true for the average and standard deviation. In addition, data analysis must respect reality, at least: RESOLUTIONZACCURACY (Fig. 16.4).
4.
From data control to quality assurance
Only ‘‘well defined’’ information changes uncertainty into measurable risk and allows assessment of decisional processes. When data satisfy the requisites dictated by the purpose for which they have been produced, they are considered of ‘‘good quality’’. These data are not necessarily excellent, but it is essential that their quality is known and demonstrable. Without a quality system, data must be regarded as being of uncertain or unknown quality, and their usefulness is diminished (Anonymous, 1996). The fundamental difference between ‘‘quality control’’ and ‘‘quality assurance’’ must be noted. Quality control is the best-known component of a quality management system (qms), and it is the irreducible minimum of any qms. It consists of data examination in stations or/and in data-analysis centers in order to detect possible measurement errors, so that unreliable data can be either corrected or deleted (Anonymous, 1996). This statistical control is perfect if one believes that a climate system is stationary, that is, when the statistical description of the past is the probability evaluation of the future. But some doubts can rise, as in the case of David Hume (1711–1776) who stated: Being determined by custom to transfer the past to the future, in all our interferences; where the past has been entirely regular and uniform, we expect the event with the greatest assurance, and leave no room for any contrary supposition. But where different effects have been found to follow from causes, which are to appearance exactly similar, all these various effects must occur to the mind in transferring
Italian air force observatory network
121
Figure 16.4. The choice of significant digits in measurement must be consistent with the accuracy of the measuring system (not only of the sensor itself). In this case, uncertainty in measurement is greater or equal to the ‘‘radiation error’’ plus the sensor accuracy. the past to the future, and enter into our consideration, when we determine the probability of the event. Though we give the preference to that which has been found most usual, and believe that this effect will exist, we must not overlook the other effects, but must assignee to each of them a particular weight and authority, in proportion as we have found it to be more or less frequent.
In addition to this important philosophic aspect, which can cause one to delete or to not consider the most meaningful data, other problems are associated with quality control, such as irrecoverable cost of lost data, control sometimes performed after a long time, and an unrepresentative historical series caused by lost data. Quality assurance operates continuously at all points in the whole observation system, from network planning and training, through installation and station operations to data transmission and archiving. The provision of good-quality meteorological data is not a simple matter, and it is impossible without a quality management system.
Fabio Malaspina, Francesco Foti, Emanuele Vuerich
122 5.
Conclusion: ‘‘Not all numbers are data’’
Not all numbers are data and not all stations provide data to study the meteorological phenomena at all space-time scales. The calculators and the new electronic acquisition systems can deceive, but measurement systems have limits. Commonly the enormous consistency of data can reduce statistical error, but, for example, it cannot be less than instrumental error. If measurement-system limits are not considered, a meteorological network does not describe natural phenomena that happen around us, but instead it produces only a computer-mathematical virtual reality. For climatic studies, it is necessary to store both meteorological quantity and metadata. Instruments and networks should be under the continuous control of specific quality management systems. Therefore, according to our experience, we must not only increase the quantity and kind of measurements, but it is also necessary to increase their quality too.
References Anav, A., DiMenno, M., and Moriconi, M. L., 1994. Misure di Radiazione UV a Tropea, I.F.A.R.I. 94/25. Anonymous, 1993. Guide to the expression of uncertainty in measurement. International Organization for Standardization (ISO). Anonymous, 1996. Guide n.8 to meteorological instruments and methods of observation. 1996. World Meteorological Organization. Malaspina, F., Foti, F., Vuerich, E., and Casu, G., 2004. Radiosounding: possible change in aerological data due to instrument change. Il Nuovo Cimento 27C (5), 503–513. van der Meulen, J.P., 1998. A thermometer screen intercomparison. WMO/TD n .877: pp. 319. http://www.dwd.de/EUMETNET/Berichte/TECO98temp.pdf
115
16 Italian air force observatory network for environmental and meteorological monitoring: from data control to quality assurance Fabio Malaspina, Francesco Foti and Emanuele Vuerich Abstract The main purpose of a meteorological observatory network is to completely describe natural phenomena so to record their characteristics forever. Such recordings increase in importance through time, becoming part of the patrimony of unique and unrepeatable knowledge. These records are useful to all humanity for scientific research for making choices that will have effects on us and our planet. Especially, recently, some of this information can verify and initialize mathematical models. Natural phenomena can have different global or local characteristics, and, according to their typology, the right network for measurements is necessary to fix instrumentation specifics and establish appropriate procedures. Study of global-scale natural phenomena requires fundamental performance measures for long periods in places far from human pollution, and high mountains are particularly suitable, for such purposes. In fact in 1654 the world’s first meteorological network was created in Italy by the Grand Duke of Tuscany, who was already worried about climate change. The Meteorological Service of the Italian Air Force continues this job with its own national network that performs special measures and even takes part in Antarctica’s surveys. The answers to problems discovered in the management of such meteorological stations are mainly due to technology changes, measures of small quantities, the necessity of highly specialized personnel, remote and often uncomfortable measurement locations, and heterogeneity of instrumentation, calibration, methods of measure, and exposure. In this epoch of telecommunications observation systems are ever more distant from those that analyze the data. Such systems analyses are commonly received only as encoded messages or numerical files, without anyone knowing the history, the limits, or the different characteristics of instrumentation used in the monitoring stations. The highest quality data are those that guarantee the requisites dictated by the purpose for which they were produced. The complex climatic system is mainly characterized by incessant variability of its configurations that are irreplaceable. A subsequently assessed ‘‘quality control’’ based on past statistical descriptions is not enough, but it is also necessary a ‘‘quality-assurance’’ ISSN: 0928-2025
DOI: 10.1016/S0928-2025(06)10016-4
116
Fabio Malaspina, Francesco Foti, Emanuele Vuerich
system, in order to give to consumers enough information about measurements that were obtained, and in order to establish limits that must be accounted for in data analysis. Only ‘‘well defined’’ information changes uncertainty into measurable risk that can be emphasized in decision-making.
1. Introduction: Department for aeronautical experimentations (Re.S.M.A.) and observation networks of italian air force meteorological service This Italian Air Force Department is located in Vigna di Valle next to Bracciano Lake, in an extinct volcano. This constitutes an historic headquarters that was the first aerologic station in Italy since 1909. At the present moment it is in charge of doing standard meteorological observations, UV and ozone measurements, and meteorological soundings. It works 24 h a day and is equipped with instrumental emplacements where it is possible to make comparisons among different instruments for a long time and in different weather conditions. Lots of tests or special measurement campaigns are carried out in order to assess the performances of various observation devices (Fig. 16.1). Department personnel are able to state the quality of measurements and observations coming from the Service’s network. The Italian Air Force Meteorological Service Network has two different observation sections: the 84 human-controlled stations part and the 110 automatic weather stations part. Some of these Italian stations are situated in uncomfortable but meteorologically interesting places. For example: in the Alps, the Paganella station is at 2129 m a.s.l. and the Plateau Rosa` station at 3480 m a.s.l.; in the Apennine the Cimone station is at 2165 m a.s.l.; and on some occasions the Italian Meteorological Service takes part in important measurement campaigns, as is the case with the Antarctic National Program.
Figure 16.1. ReSMA experimental site that includes six equipped areas under both automatic and manual control every day. ReSMA has been designated as a site for World Meteorology Organization field intercomparisons of rainfall intensity (RI) gauges to be held in Vigna di Valle since August 2007 for two years.
Italian air force observatory network
117
The Italian meteorological network includes also three special measurement networks: the chemical analysis of the precipitation network (7 stations) activated in 1975, the total ozone network (3 stations) activated in 1957, and the solar radiation network (37 stations) activated in 1958. Cimone station also started measures of carbon dioxide concentration in 1979.
2. What would we like to measure? Two different monitoring networks for two different purposes Interest in natural phenomena is an innate part of human nature. Greek thinkers first began the long tradition of stars-meteorology and empiric-meteorology. They collected and ordered in an organic way knowledge of the atmosphere acquired in the past and raised it to a natural philosophy. Almost 2000 years after that, meteorology was changed from qualitative to quantitative. In the 17th century, after the invention of the first principal meteorological instruments by the Italian scientists, meteorology acquired the characteristics of a quantitative science. It was recognized that natural phenomena should be described and recorded in the most complete way for the present and future studies. Galileo Galilei wrote about this (in Italian) in Il Saggiatore in 1623: Natural phenomena are written in this big book which is always opened in front of our eyes. It is written in a mathematical language, and the characters are triangles, circles, and other geometric figures; without these it is a vain wandering in a dark labyrinth.
Later Lord Kelvin (1824–1890) more clearly stated: I affirm that when you can measure and express in numbers what about you are speaking, you know indeed something.
A single station is not enough to study meteorological phenomena, so a wellorganized network is needed. The purpose of observation networks is to produce quality data of atmospheric parameters, which must be selected in the right way to describe the phenomenon in which we are interested. The importance of good recordings increases more and more through the years to make a part of the patrimony of unique and irreplaceable knowledge, useful to the whole of humanity for scientific research, useful to us and to our children for making choices that will have effects on human beings and planet Earth. It is important to understand that every new measure modifies or creates new knowledge. Only ‘‘quality data’’ from the past and the present acquire additional value over time, so only these kinds of long historical series represent a national and human patrimony. Natural phenomena can have different global or local characteristics. According to their typology, it is necessary to design the appropriate network for measurement of them. It is also necessary to fix instrumentation specifications and establish appropriate procedures. In the case of global pollution, which has a climatic impact and which is not ‘‘directly’’ related to human activities, special and meteorological measurements must be performed in stations settled far from polluting sources. These ‘‘global measurements’’ generally monitor very small changes and very small
118
Fabio Malaspina, Francesco Foti, Emanuele Vuerich
concentrations. They also must be carried out for long periods and in apparently uncontaminated areas such as high mountains. In the case of local pollution, which has mainly an impact on human health, measurements must be collected near the polluting sources. These ‘‘local measurements’’ generally monitor high concentrations and local pollution has a short-period impact. In this epoch of advanced telecommunications, observation systems are more and more distant from those that analyze the data. System analysis can often involve receipt of only encoded messages or numerical files, without knowledge of history, limits, or different characteristics of instrumentation used in monitoring stations. It sometimes happens that the measurement accuracy (i.e., the closeness of the agreement between the result of measurement and the true value of the object to be measured – the measurand) is replaced by instrumental resolution (which is a quantitative expression of the ability of an indicating device to distinguish meaningfully between closely adjacent values of the quantity indicated, for display presentation in tenths, hundredths, etc.). Sometimes global and local measurements are confused, and the instrumental uncertainty is not considered. Therefore, it is our experience that it is sometimes possible to see the risk that the network’s goal is no longer describing as completely as possible the natural phenomena of our interest, but it is, for example, becoming more for the initialization of mathematical models or the monitoring of legal threshold concentrations in pollution areas. In this way, instruments are not used at the maximum of their performance and not all possible data are recorded, preventing the best description of nature to leave to future generations for their future strategic decisions and research.
3.
Problems in performing measurements
In the fields of observation and analysis, it may be possible to move toward a progressive automation; but in meteorology, machines cannot yet completely replace human observation, human intuition and human critical mind. About this argument, it has been written (Anonymous, 1993) Although this guide provides a framework for assessing uncertainty, it cannot substitute for critical thinking, intellectual honesty and professional skill. The evaluation of uncertainty is neither a routine task nor a purely mathematical one; it depends on detailed knowledge of the nature of the measurand and of the measurement. The quality and the utility of the uncertainty quoted for the result of the measurement therefore ultimately depends on the understanding, critical analysis, and integrity of those who contribute to the assignment of its value.
The problems occurring in the management of mountain stations, whose purpose is monitoring global changes, are mainly due to: (1) change of technology; (2) measure of small quantities; (3) necessity of high specialized personnel; (4) remote and often uncomfortable location of the site of measurement; (5) no net homogeneity (instrumentation, calibration, methods of measure, exposure). The following examples can show some of these problems. A comparison campaign among several thermometer screens and shields can show what happens when a technological change occurs, or the presence of a nonhomogeneous network. During a continuous daily sampling, temperature differences
Italian air force observatory network
119
Figure 16.2. The new Radiosonde Vaisala RS92 has been recently introduced to substitute for the previous model RS90. Temperature average direct differences were calculated with RS92-SGP as a reference. Also a ‘‘standard deviation’’ line is presented. The impact of future instrumentation change, considering the measurement accuracy required by current working standards, will be negligible whenever atmospheric sounding is used for local monitoring or for model initial analysis. In the climatic field, however, since statistical analysis is performed at precision of tenths to even hundredths of Celsius degrees, the consequences could be relevant should instrument change not be taken into account during analysis. In fact, as new equipment replaces old, indications of gradual and progressive atmospheric heating will result.
of more than 1.51C have occurred. Maximum temperature differences correspond to sunrise, when generally the absolute minimum of temperature is recorded, and the sunset (van der Meulen, 1998). In a comparison campaign among different radiosondes of the same manufacturer, possible changes in aerological data due to instrument change were discovered (Malaspina et al., 2004). Eight launches were performed, and a systematic error of 0.21C was noted (Fig. 16.2). In another case, inhomogeneity among instruments has produced great problems in UV-A radiation measures (Anav et al., 1994). As shown in the Fig. 16.3, different spectral responses for similar instruments produce very different results and no comparable measures. Because of these problems, it has long been recognized that it is important not only to record the meteorological and special-measurement values, but also the circumstances in which the measurements are made, i.e. the metadata. Metadata are particularly important in climate studies. For example, temperature measurements are affected by the state of the surrounding, by vegetation, by the presence of buildings and other objects, by ground cover, by conditions and changes in design of the radiation shield or screen, and by other changes in equipment. Temperature is one of the meteorological quantities whose measurement is particularly sensitive to
120
Fabio Malaspina, Francesco Foti, Emanuele Vuerich
Figure 16.3. Different spectral responses involving similar instruments can produce very different results.
exposure (Anonymous, 1996). In addition, the statistical error in this kind of measure has to be consistent with the measurement accuracy; the same is true for the average and standard deviation. In addition, data analysis must respect reality, at least: RESOLUTIONZACCURACY (Fig. 16.4).
4.
From data control to quality assurance
Only ‘‘well defined’’ information changes uncertainty into measurable risk and allows assessment of decisional processes. When data satisfy the requisites dictated by the purpose for which they have been produced, they are considered of ‘‘good quality’’. These data are not necessarily excellent, but it is essential that their quality is known and demonstrable. Without a quality system, data must be regarded as being of uncertain or unknown quality, and their usefulness is diminished (Anonymous, 1996). The fundamental difference between ‘‘quality control’’ and ‘‘quality assurance’’ must be noted. Quality control is the best-known component of a quality management system (qms), and it is the irreducible minimum of any qms. It consists of data examination in stations or/and in data-analysis centers in order to detect possible measurement errors, so that unreliable data can be either corrected or deleted (Anonymous, 1996). This statistical control is perfect if one believes that a climate system is stationary, that is, when the statistical description of the past is the probability evaluation of the future. But some doubts can rise, as in the case of David Hume (1711–1776) who stated: Being determined by custom to transfer the past to the future, in all our interferences; where the past has been entirely regular and uniform, we expect the event with the greatest assurance, and leave no room for any contrary supposition. But where different effects have been found to follow from causes, which are to appearance exactly similar, all these various effects must occur to the mind in transferring
Italian air force observatory network
121
Figure 16.4. The choice of significant digits in measurement must be consistent with the accuracy of the measuring system (not only of the sensor itself). In this case, uncertainty in measurement is greater or equal to the ‘‘radiation error’’ plus the sensor accuracy. the past to the future, and enter into our consideration, when we determine the probability of the event. Though we give the preference to that which has been found most usual, and believe that this effect will exist, we must not overlook the other effects, but must assignee to each of them a particular weight and authority, in proportion as we have found it to be more or less frequent.
In addition to this important philosophic aspect, which can cause one to delete or to not consider the most meaningful data, other problems are associated with quality control, such as irrecoverable cost of lost data, control sometimes performed after a long time, and an unrepresentative historical series caused by lost data. Quality assurance operates continuously at all points in the whole observation system, from network planning and training, through installation and station operations to data transmission and archiving. The provision of good-quality meteorological data is not a simple matter, and it is impossible without a quality management system.
Fabio Malaspina, Francesco Foti, Emanuele Vuerich
122 5.
Conclusion: ‘‘Not all numbers are data’’
Not all numbers are data and not all stations provide data to study the meteorological phenomena at all space-time scales. The calculators and the new electronic acquisition systems can deceive, but measurement systems have limits. Commonly the enormous consistency of data can reduce statistical error, but, for example, it cannot be less than instrumental error. If measurement-system limits are not considered, a meteorological network does not describe natural phenomena that happen around us, but instead it produces only a computer-mathematical virtual reality. For climatic studies, it is necessary to store both meteorological quantity and metadata. Instruments and networks should be under the continuous control of specific quality management systems. Therefore, according to our experience, we must not only increase the quantity and kind of measurements, but it is also necessary to increase their quality too.
References Anav, A., DiMenno, M., and Moriconi, M. L., 1994. Misure di Radiazione UV a Tropea, I.F.A.R.I. 94/25. Anonymous, 1993. Guide to the expression of uncertainty in measurement. International Organization for Standardization (ISO). Anonymous, 1996. Guide n.8 to meteorological instruments and methods of observation. 1996. World Meteorological Organization. Malaspina, F., Foti, F., Vuerich, E., and Casu, G., 2004. Radiosounding: possible change in aerological data due to instrument change. Il Nuovo Cimento 27C (5), 503–513. van der Meulen, J.P., 1998. A thermometer screen intercomparison. WMO/TD n .877: pp. 319. http://www.dwd.de/EUMETNET/Berichte/TECO98temp.pdf