Validation of Mode-S Meteorological Routine Air Report aircraft observations

Transcription

1 JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 117,, doi: /2012jd018315, 2012 Validation of Mode-S Meteorological Routine Air Report aircraft observations B. Strajnar 1 Received 19 June 2012; revised 16 October 2012; accepted 17 October 2012; published 7 December [1] The success of mesoscale data assimilation depends on the availability of threedimensional observations with high spatial and temporal resolution. This paper describes an example of such observations, available through Mode-S air traffic control system composed of ground radar and transponders on board the aircraft. The meteorological information is provided by interrogation of a dedicated meteorological data register, called Meteorological Routine Air Report (MRAR). MRAR provides direct measurements of temperature and wind, but is only returned by a small fraction of aircraft. The quality of Mode-S MRAR data, collected at the Ljubljana Airport, Slovenia, is assessed by its comparison with AMDAR and high-resolution radiosonde data sets, which enable high- and low-level validation, respectively. The need for temporal smoothing of raw Mode-S MRAR data is also studied. The standard deviation of differences between smoothed Mode-S MRAR and AMDAR is 0.35 C for temperature, 0.8 m/s for wind speed and below 10 degrees for wind direction. The differences with respect to radiosondes are larger, with standard deviations of approximately 1.7 C, 3 m/s and 25 degrees for temperature, wind speed and wind direction, respectively. It is concluded that both wind and temperature observations from Mode-S MRAR are accurate and therefore potentially very useful for data assimilation in numerical weather prediction models. Citation: Strajnar, B. (2012), Validation of Mode-S Meteorological Routine Air Report aircraft observations, J. Geophys. Res., 117,, doi: /2012jd Introduction [2] The quality of the Numerical Weather Prediction (NWP) forecasts relies on sufficient amount of available observations of the atmospheric parameters. Temperatures and winds from radiosondes and aircraft have traditionally been used for analysis of most global and also regional NWP models. Over Europe, aircraft observations are mostly available through the Aircraft Meteorological Data Relay (AMDAR) [Painting, 2003], with typical average resolution of few tens of kilometers. [3] Operational weather forecasting in the midlatitudes however requires improved description of mesoscale processes, such as downslope windstorms, mountain and sea breezes and organized convective systems and storms in general. To be able to achieve such detailed description of atmospheric processes on the smallest spatial and temporal scales, regional NWP models with horizontal resolution of a few kilometers are needed. This is particularly important for regions with complex terrain, such as mountainous and coastal regions. For operational forecasting in Slovenia, a 1 Meteorological Office, Slovenian Environment Agency, Ljubljana, Slovenia. Corresponding author: B. Strajnar, Meteorological Office, Slovenian Environment Agency, Vojkova 1b, SI-1000 Ljubljana, Slovenia. (benedikt.strajnar@gov.si) American Geophysical Union. All Rights Reserved /12/2012JD limited-area NWP model ALADIN (Aire Limitée Adaption Dynamique et dévelopement InterNational) [e.g., Fischer et al., 2005] with approximately 4 km horizontal resolution is applied. The analysis (initial condition) is provided by a three-dimensional variational assimilation system using surface, radiosonde, aircraft and satellite (radiances and feature-track winds) observations. In order to be able to add more details to the analysis for such a model, additional observations with higher spatial and temporal frequency are required. [4] A good example of such data are high-resolution aircraft observations available from the newer generations of aviation surveillance systems. Recently, more and more aircraft and airports have been equipped with Mode-S (mode selective) technology, designed mainly to improve automatic collision avoidance by avoiding the risk of misidentification. It permits the interrogation of all equipped aircraft and two way exchange of digital data between aircraft and the interrogator ( The system consists of on-board transponders and enhanced tracking and ranging (TAR) radars with data processing unit located at the airports. Collected data not only contain information about the position of an aircraft but may contain additional flight information, including meteorological data. While observations originate from the same instruments as those collected by AMDAR, the reporting frequency is much higher (typically 4 s), and also the local coverage is substantially increased because every Mode-S equipped 1of10

2 with mandatory EHS data registers. In order to avoid confusion about different observations available through Mode-S, we will further refer to observations presented by de Haan [2011] as Mode-S EHS. The Mode-S data used in the present study, which are collected at the main Slovenian airport near Ljubljana, will be referred to as Mode-S MRAR, after the name of involved data register. We show that both winds and temperatures from Mode-S MRAR are very accurate and valuable for mesoscale data assimilation. To improve readability, we will further refer to Mode-S MRAR observations as simply Mode-S, but Mode-S EHS will strictly be used when observations from de Haan [2011] are discussed. [7] The paper is organized as follows. The next section brings a description of Mode-S, AMDAR and radiosonde data, used in this study. In section 3, the validation is carried out by means of observation collocations. The conclusions are given in section 4. Figure 1. Location of TAR radar at Ljubljana Airport (circle) and radiosonde stations (squares) with SYNOP identification numbers. aircraft is required to respond to a radar interrogation, at least with mandatory data registers. [5] Observations available through Mode-S were previously studied by de Haan [2011] using data collected at Amsterdam Airport Schiphol. One-year time series was compared to AMDAR, a radiosonde station nearby, and NWP model outputs. This observation method included retrieval of winds from aircraft s vectors and indirect temperature calculation from Mach number and true air speed. Comparable quality of wind derived from Mode-S and AMDAR was observed after aircraft-type dependent calibration of Mode-S which involved correction of magnetic heading. It was concluded that Mode-S temperatures were less accurate than AMDAR or radiosonde temperatures. Both in case of wind and temperature, a temporal smoothing within time intervals of few tens of seconds was considered essential in order to reduce the noise level. First data assimilation experiments with the Mode-S data were performed by de Haan and Stoffelen [2012] over a period of 40 days. The HIRLAM model (HIgh Resolution Limited Area Model) [Undén et al., 2002] with the horizontal resolution of 11 km was used. Improvements were reported in the short range forecasts, up to few hours. [6] In contrast to the study by de Haan [2011], the present study validates temperature and wind observations, directly available through Mode-S directly from sensors. This essential difference is achieved by the interrogation of an additional dedicated data register containing meteorological parameters, called Meteorological Routine Air Report (MRAR). As this register is not a mandatory Mode- S EnHanced Surveillance (EHS) register, only a relatively small fraction of all aircraft return wind and temperature. The number of such observations will therefore be smaller compared to the data used in de Haan [2011], where all Mode-S capable aircraft were interrogated and responded 2. Data [8] This section describes Mode-S observations and observations used as a reference for validation of Mode-S. The AMDAR data are particularly important, because they share with Mode-S the key meteorological sensors on board the aircraft, intended primarily for flight management: a Pitot-static head for static and total air pressure, an immersion thermometer probe for total air temperature and an inertial platform or Global Positioning System (GPS) navigation to determine position. Radiosondes, a classic and very reliable upper-air observations, are used as a second reference at the highest vertical-resolution possible Mode-S [9] Mode-S TAR radar, used in this study, was installed at the Ljubljana Airport airport (circle in Figure 1) in 2009, and is operated by Slovenia Control, the national air traffic control company. As stated above, the reporting frequency of Mode-S is 4 s, and is determined by the rotation of TAR radar. Upon a radar interrogation, the aircraft respond with several so-called Comm-B Data Selector (BDS) registers holding flight data [International Civil Aviation Organization (ICAO), 1995]. All aircraft equipped with a Mode-S transponder are obliged to respond with three prescribed EHS registers: register BDS 4,0 provides vertical intention (e.g. target altitude of an aircraft), BDS 5,0 includes true air speed, ground speed and true track angle with its rate of change, and BDS 6,0 includes Mach number and magnetic heading. This information is enough to derive Mode-S EHS observations, as presented in de Haan [2011]. The additional non-mandatory data register BDS 4,4 (also called MRAR) is included in the present study to directly obtain temperature, wind speed and direction. [10] The radar range is around 250 km in all directions, except toward the north, where the radar beam is blocked by mountains. The range is limited by the Earth s curvature elsewhere. Apart from the upper-air reports by aircraft only passing the Slovenian air space, to some extent also covered by AMDAR network, additional mid- and lowertroposphere data can be obtained from aircraft flying to or from the Ljubljana Airport. Some data are also available 2of10

3 Figure 2. (top) Horizontal and (bottom) vertical coverage of Mode-S and AMDAR on 20 July 2011 between 6 UTC and 12 UTC. close to the airports of Zagreb, Trieste, Klagenfurt, Graz, Rijeka, Sarajevo and some other small airports in the region, but only above approximately 2000 m above mean sea level. Figure 2 shows horizontal and vertical coverage of Mode-S and AMDAR observations for one date between are 6 UTC and 12 UTC. Much higher reporting frequency of Mode-S can be observed. One can also note that there are almost no low-level AMDAR observations over Slovenia and Mode-S can therefore be seen as a complementary observation source in the low- and midtroposphere. Table 1 shows the number of Mode-S data available in the period from 19 May 2011 until 1 March 2012, and the number of available AMDAR observations over the same rectangular region for different altitude layers. On average, the number of Mode-S data far exceeds the number of AMDAR observations. The ratio is close to 300:1 in the layer between 2 and 8 kilometers, and 40:1 in the layer between 10 and 12 kilometers. The number of wind and temperature observations is the same in the AMDAR data set, and there are around 30% more temperature than wind observations in the Mode-S data set. This difference appears to be rather constant in time and must depend either on the configuration of aircraft transponders or aircraft capability of measuring wind, the exact reason being unknown. It is also important to mention that not all aircraft report temperature and wind observations. Over the evaluation period, 6.4% of all interrogated aircraft by the TAR radar reported temperature and 5.2% of aircraft returned wind observations. Again, this percentages were rather constant in time Raw Mode-S Messages [11] The data set used for this study includes all data from the Airport of Ljubljana in the evaluation period between 19 May 2011 and 1 March 2012, with a short break between 4 and 13 January 2012 due to technical problems. In addition to that, an one-month data series of complete Mode-S messages, containing also various flight parameters, was archived for a period of April This period was included in order to provide overview of all available information and to be able to select a subset of variables to be saved over the evaluation period. The messages also include data on aircraft heading with respect to magnetic and geographic north, ground and air speed, the target flight level (for ascents) and therefore enable full reconstruction of flights. An example of a vertical profile for a few variables obtained during a landing at Ljubljana Airport is shown in Figure 3. Note the discrete height reported with resolution of 1 flight level. Magnetic heading is also included in order to verify that an offset from the actual heading, observed as a deviation from the runway angle during landing and reported also by de Haan [2011] exists. The correction of magnetic heading separately for each aircraft type was considered essential in the aforementioned study, but is not relevant in our case because wind is directly available from aircraft. [12] Mode-S data set over the evaluation period includes aircraft identification, timestamp, geographical position, flight level, barometric pressure setting, wind speed and direction, static air temperature and roll angle which indicates Table 1. Number of Wind and Temperature Observations Available Through AMDAR and Mode-S a Pressure Altitude Mode-S AMDAR Layer (km) Wind Temperature Wind Temperature , , , , ,794 1,233, ,580 1,367, ,721 1,097, ,165,717 1,217,553 28,981 28, , , Total 5,560,777 7,206,914 55,587 55,587 a Time period is 19 May March The geographical area is E and N. 3of10

4 some aircraft wrongly report outdated barometric pressure setting even during high-altitude phase of flight. The optional correction given by equation (2) was therefore only applied below the transition altitude defined for Ljubljana and other Slovenian airports. [14] Total air temperature, measured by immersion probes, is significantly affected by air friction due to aircraft movement. Static air temperature must therefore be computed using the relation T tot T air ¼ 1 þ l g 1 ð3þ 2 M 2 Figure 3. Profile of Mode-S parameters during landing at Ljubljana airport at around 12:25 UTC, March Aircraft type is Canadair Regional Jet 900. the maneuvering of an aircraft. At high roll angles, wind errors can be significant, and measurements are typically excluded within AMDAR above a certain threshold [Painting, 2003]. [13] Aircraft s altimeters use static pressure measurement to determine pressure altitude, an approximate height above an agreed baseline pressure setting, usually the mean sea level pressure of p 0 = hpa according to the International Standard Atmosphere (ISA) [ICAO, 1964]. It is reported in hundreds of feet or flight levels. Flight level F is related to pressure altitude h p in meters by a simple relation (h p = 30.48F). Pressure altitude calculation assumes a mean sea level temperature of T 0 =10 C and a linear decrease with height of 6.5 C/km below 11 kilometers. The information on pressure altitude in flight levels, provided by AMDAR or Mode-S, should only be considered as an approximation of the true altitude which depends on the actual pressure (i.e. on the difference between p 0 and the actual mean sea level pressure). In the vicinity of airports and below the so-called transition altitude (typically around feet or FL105 in case of Slovenian airports), flight procedures require setting the aircraft altimeters to another reference pressure, e.g. observed pressure at the airport. In this phase of flight, a better estimate of the true altitude h is given by the equation h ¼ 30:48F h r gr d g T 0 1 p p0 h r ¼ g ð1þ ; ð2þ where p is pressure at the airport or other reference surface pressure, reduced to mean sea level, h r is the height of a reference pressure and R d = J/kgK is the specific heat constant for dry air, g is gravitational acceleration, and g is the ratio of specific heats of dry air (C p and C v ). The agreement between estimated altitude h and the actual altitude is increased in this case. It was found in this study that where T air is static air temperature, T tot measured total air temperature, M is Mach number, and l is the probe recovery factor. The latter describes the viscosity and the effect of incomplete stagnation of air at the sensor. Mach number is computed from the ratio of total and static air pressure [see Painting, 2003]. The measurement error is considered to be around 0.4 C at Mach number 0.8, and is reduced at lower aircraft speeds. The reporting resolution (i.e. the minimum difference between two reported values) for temperature is typically 0.25 C, both in case of Mode-S and AMDAR. [15] Wind speed and direction are computed from vector difference V ¼ V g V a ; where V g is aircraft velocity with respect to ground and V a is velocity of the air with respect to the aircraft. The latter is internally calculated from true heading (i.e. aircraft s direction toward true north) and true airspeed. True airspeed is a function of Mach number and static air temperature. Aircraft heading and V g are provided by the on-board navigation systems. The reporting resolution is 1 knot. Measurement error, which also combines errors originating in Mach number and temperature computations, can be estimated to 2 3 m/s [Painting, 2003]. As the calculation assumes small roll angles, measurements at roll angles of more than 3 degrees are excluded in the forthcoming analysis Smoothed Mode-S Observations [16] Mode-S data, presented in the previous section, are raw data, that is sensor readings at the time of TAR radar request. AMDAR data, on the other hand, are temporally smoothed over predefined time intervals depending on phase of flight. An application of smoothing procedure can therefore be considered in order to improve the data quality and representativeness, depending also on the resolution of the target application. The typical resolution of mesoscale and convective-scale numerical weather prediction models used nowadays is from one to few kilometers, with model-forecast time steps of order of minutes. Direct assimilation of Mode-S could therefore result in rejection of the majority of observations during the observation screening. Instead, creation of super observations containing information inferred from a number of observations, may be preferable. A simple strategy, similar to the one used in de Haan [2011], is proposed: observation are averaged within 12 s (4 observations) for ascending and descending phase of flight, and within 1 minute (16 successive observations) when flying at constant altitude. Again, observations with roll angles higher than 3 degrees are omitted from the calculation. ð4þ 4of10

5 Figure 4. Mode-S profile from descending aircraft on 19 June 2011 at around 15 UTC. Presented are smoothed (points, see legend) and raw (lines) observations. Aircraft type is Canadair Regional Jet 900. [17] Figure 4 shows impact of temporal smoothing on the vertical profile of temperature and wind for a single aircraft. The number of observations is visibly reduced. At the same time, observation profiles are more smooth. This can be observed especially for the wind direction just below the landing (around minute 22:00), where wind speed is low and less accurately measured. In de Haan [2011] and de Haan and Stoffelen [2012], temporal smoothing of Mode-S was considered essential due to rapid fluctuations in temperature observations (2 5 C), originating especially from the limited reporting resolution of Mach number and true air speed, both used in the indirect computation. In our case, however, the differences between two successive raw observations are much smaller, indicating that also raw data are mostly smooth enough to be used for meteorological applications directly AMDAR [18] The AMDAR system was designed to fully automatically collect and transmit such observations for meteorological community through either Very High Frequency (VHF) or satellite radio links. AMDAR observations are typically provided every 7 min at cruise level, and reporting frequency is increased during ascent and descent; observations should be provided at 50 hpa pressure intervals and every 10 hpa below around 700 hpa. [19] A number of studies have been carried out to evaluate the error characteristics of AMDAR. Schwartz and Benjamin [1995] reported that standard deviation of AMDAR minus radiosonde differences was roughly 0.6 C for temperature and 4 m/s for wind when horizontal separation was less than 25 km. They also found differences between ascent and descent profiles, the latter being slightly colder. Benjamin et al. [1999] observed a standard deviation of 0.5 C and 1.1 m/s comparing AMDAR observations with small separation in space and time near Denver airport, US. More recently, a few studies also indicated that AMDAR exhibits systematic errors. Ballish and Kumar [2008] reported a warm bias of AMDAR observations and proposed a bias correction scheme. Drüe et al. [2007] analyzed more than 300 vertical profiles from Frankfurt airport and observed systematic temperature differences of up to 1 C among different aircraft types and also some vertical dependencies of systematic error was observed. AMDAR temperature error is estimated to 0.4 C and wind error to 2 3 m/s [Painting, 2003]. [20] A relatively small percentage of aircraft send out data through the AMDAR. However, AMDAR is considered an essential upper-air observation network for global NWP models and is one of main data sources (apart from satellite data) over the non-populated regions and oceans [e.g., Cardinali et al., 2003]. The availability of AMDAR data depends, due to significant data cost, on the needs of meteorological community, but also on technical constraints of different aircraft types. Over Slovenia and the northern Adriatic, the availability of low-level AMDAR data is relatively poor (see Figure 2). In the study presented in this paper, AMDAR data are the same as those used within operational data assimilation system at Slovenian Environment Agency Radiosondes [21] Radiosonde measurements have traditionally been made every 6 or 12 hours at few tens of stations over Europe. Although these measurements are sparse, they have traditionally been the main source of upper-air information for NWP, and they remain a crucial wind information. A radiosonde system consists of a balloon with measuring platform and ground receivers. The sensors measure temperature and humidity, while wind is inferred from drifts of a radiosonde measured by the GPS. According to World Meteorological Organization (WMO) [2008], the standard 5of10

6 Figure 5. Vertical profile of collocations over time of Mode-S with radiosonde and AMDAR. Note that collocations with radiosondes are more low-level and collocations with AMDAR are more high-level. temperature error is between C for most modern sounding systems and total wind error is between 3 and 5 m/s. [22] Slovenia s only radiosonde station is Ljubljana, with one sounding per day at around 4 UTC. The distance to Mode-S radar at Ljubljana Brnik airport, described below, is around 20 km toward the north. As aircraft usually approach and depart the airport from/toward southeast, this distance is usually smaller in some phases of descents/ ascents. This makes this sounding a good reference for validation of Mode-S observations. In the vicinity of Slovenia and within the range of TAR radar, there are also Udine (Italy), Zagreb and Zadar (Croatia) radiosonde stations. They enable only mid- and high-level comparisons. The locations of the radiosonde stations used in this study are shown in Figure 1. Figure 6. Difference histograms of (top left, top right, and bottom left) AMDAR and raw Mode-S and (bottom right) dependence of wind direction difference on Mode-S roll angle. A fitting Gaussian curve is added to the histograms. 6of10

7 Figure 7. Vertical dependence of differences between AMDAR and (top) raw and (bottom) temporally smoothed Mode-S. Box size represents the percentage of total observations in the layer, and the length of bars corresponds to plus/minus one standard deviation. Summary over all layers is presented at the bottom of each panel: the position of a diamond corresponds to mean difference and its size to plus/minus one standard deviation. [23] The meteorological community exchanges radiosonde data through the Global Telecommunication System in coarser vertical resolution than measured. The WMO reporting regulations require 16 mandatory pressure levels. Levels with significant changes of the profile, such as bases and tops of temperature inversions, should also be reported. However, to be able to use radiosonde observations for validation of Mode-S, finer measurements were collected from above mentioned radiosonde stations. Reporting frequency is 2 s for Udine, Zagreb and Zadar and 1 s for Ljubljana. For the purpose of this study, all data between May 2011 and March 2012 were used. 3. Validation of Mode-S [24] The validation of Mode-S is carried out by using a collocation technique adopted also in de Haan [2011] and numerous other studies dealing with AMDAR [Schwartz and Benjamin, 1995; Benjamin et al., 1999; Schwartz and Benjamin, 1995; Ballish and Kumar, 2008]. The following two sections describe analysis of differences with respect to radiosondes and AMDAR. Since differences combine errors of Mode-S and the reference observations, the absolute error of Mode-S can not be directly addressed. However, when differences are relatively small compared to the estimated errors of the reference data sets, it can be concluded that the quality is similar to the quality of the reference. Figure 5 shows the temporal and vertical distribution of collocated AMDAR and radiosonde observations with raw Mode-S. The procedure of finding collocations is described below. One can note that comparison with AMDAR is performed mainly at high levels, while the comparison with radiosondes will be more low-level. 7of10

8 Figure 8. Same as Figure 6, except that differences are between raw Mode-S and radiosondes Collocation of Mode-S and AMDAR [25] As AMDAR and Mode-S originate from the same observation platform, the criteria for matching can be quite strict. Taking into account the AMDAR reporting frequency, the maximal allowed horizontal distance between Mode-S and AMDAR observations is 5 km. Within this range from AMDAR observation, a few Mode-S reports will be available given the typical aircraft ground speeds of 250 m/s and Mode-S reporting frequency. The maximal vertical separation is 100 m. If there is more than one match for a single AMDAR observation, the closest in space Mode-S observation is selected. [26] All Mode-S and AMDAR observations within the evaluation period of 9 months are scanned for matches, giving roughly 7000 collocated observation pairs. Around 95% of those are displaced by less than 2 kilometers which means that collocated Mode-S and AMDAR observations mostly originate from the same aircraft. Collocated observations are saved and statistics are computed, for raw and smoothed Mode-S observations. Figure 6 presents difference histograms for raw Mode-S data. It can be seen that the difference distributions are close to Gaussian and possess a relatively small spread, indicating a very good agreement of Mode-S and AMDAR. All collocated data are plotted, which means that no obvious gross errors are present is this data subset, except possibly in wind directions. Figure 6 (bottom right) shows the dependence of wind direction differences on the roll angle. The spread is not much increased at (slightly) higher roll angles, justifying the usage of observations at roll angles up to 3 degrees. [27] Figure 7 shows difference statistics for raw and smoothed Mode-S data sets at different altitude layers up to 12 kilometers. The smoothing procedure causes only approximately 20% decrease of number of collocated data. This can be expected as the frequency of smoothed data is generally higher or at least comparable to the frequency of AMDAR. There is a slight total negative temperature bias of Mode-S temperature in both raw and smoothed data set. It ranges from around 0.3 C in the layer between 2 and 4 kilometers to around 0.1 C in the layer above 10 kilometers, where the number of collocations is highest. Wind differences possess no significant biases. The standard deviation decreases with height for temperature and increases for wind speed and direction. The smoothing procedure degrades slightly the standard deviation of temperature and wind speed. The reason of this effect is not clear but is assumed non-significant. Average standard deviation of around 0.35 C, 0.8 m/s and 8 degrees are still relatively small and smaller than estimated errors of AMDAR observations themselves (see above mentioned literature), especially for wind, suggesting that the quality of Mode-S data is not much different from the quality of AMDAR observations Collocation of Mode-S and Radiosondes [28] When Mode-S is validated against relatively sparse radiosonde network, increased horizontal distance between observation pairs has to be allowed to collect a reliable collocated data set. In our study, the separation of 25 km, the approximate distance between Ljubljana radiosonde station and the Ljubljana airport, is selected. Additionally, in order to further increase the number of collocated observations, the maximal time mismatch is extended to 15 min. The vertical separation remains 100 m. The Mode-S heights were in this case corrected taking into account the barometric pressure setting reported by aircraft (equation (2)). Results are shown in Figures 8 and 9, in the same form as in the previous section. It can be seen again from the vertical distribution of the number of collocations (Figure 9, size of squares) that this comparison is also low-level. The differences have more much more spread than those with respect to AMDAR. Again, the collocation is not degraded at 8of10

9 Figure 9. Same as Figure 7, except that differences are between Mode-S and radiosondes. slightly higher roll angles. In this case, the number of collocated observations is much reduced by smoothing, to around one fifth of the number of raw collocations. This can be expected because of the high frequency of radiosonde data. The temperature bias seems to be vertically dependent and ranges from 0.7 to +0.5 C. It is rather small on average but is slightly increased by smoothing between 8 and 12 km. The standard deviation of temperature differences is 1.8 C and reduces slightly with temporal smoothing. Wind speeds are slightly negatively biased. Wind speed standard deviation is 3.3 m/s and is also slightly reduced by smoothing, while standard deviation of wind direction is in both cases around 25 degrees. These values are reasonable and comparable to those found by other studies comparing radiosondes with aircraft observations. 4. Conclusions [29] In this paper, a relatively unknown set of wind and temperature observations available from aircraft through the Mode-S link were described and evaluated. In contrast to the previous validation of Mode-S observations, carried out by de Haan [2011], the present data includes direct measurements of both wind and temperature. The TAR radar used in our study was configured to interrogate MRAR data register. This was not the case in de Haan [2011] where an indirect calculation of temperatures and winds from other flight parameters was needed, together with a pre-processing method and calibration. As a result, it is shown here that both Mode-S wind and temperature are of sufficient accuracy. [30] The direct meteorological observations are reported only by approximately 5% of all aircraft, which means that observation density is substantially decreased compared to Mode-S EHS. However, it was shown by the collocation technique that the quality of Mode-S (MRAR) observations is fully comparable to the quality of AMDAR, used operationally for data assimilation at many NWP centers. [31] A temporal smoothing procedure was considered but did not affect significantly the quality of observations, proving that also the raw Mode-S observations are relatively smooth. The Mode-S root mean square difference against AMDAR was around 0.35 C for temperature, 0.8 m/s for 9of10

10 wind speed and below 10 degrees for wind direction. Differences with respect to radiosondes were larger, with standard deviations after smoothing of 1.7 C, 3 m/s and around 25 degrees for temperature, wind speed and wind direction, respectively. [32] As the Mode-S system has become a standard in aviation, the number of installed Mode-S radars is increasing. The number of such TAR radars over Europe is estimated to a few hundreds. This makes Mode-S a very promising source of upper-air information for high-resolution NWP provided an increased cooperation between air-traffic controls and meteorological institutions. Currently the transmission of Mode-S meteorological observations, presented in this study, is not mandatory for the European air-traffic controls. These data will therefore only be available if radars are configured to ask for additional data register and this may somewhere be achieved only upon a special request from the meteorological community. [33] Future research is motivated by the hypothesis that this high-resolution data type can significantly improve weather analysis and forecast over Slovenia at least in the now casting scale, especially because there are few lowlevel AMDAR reports available in the region. To exploit the full spatial and temporal resolution of Mode-S, a high resolution variational data assimilation system with the ALADIN model will be used. Even though we have shown here that Mode-S data are high-quality observations, their application over the complex terrain of Slovenia and surrounding countries includes challenges related to the quality of the first guess (a short-range forecast) and the representation of the forecast-error covariances. These are subject of current work. [34] Acknowledgments. The author would like to thank Marko Hrastovec from Slovenia Control for access to Mode-S data and technical support. Aereonautica Militare, Italy, is acknowledged for the radiosonde observations from Udine and Meteorological and Hydrological Service of Croatia for the observations from Zagreb and Zadar. The author is grateful to Nedjeljka Žagar (University of Ljubljana), Loïk Berre (Météo France), and Jure Cedilnik (Slovenian Environment Agency) for careful reading of the manuscript and their comments. The feedback from two reviewers improved the clarity of the manuscript. This research is partly supported by the European Union (the European Social Fund), and the Ministry of Education, Science, Culture and Sport of the Republic of Slovenia. References Ballish, B. A., and K. V. Kumar (2008), Systematic differences in aircraft and radiosonde temperatures, Bull. Am. Meteorol. Soc., 89, , doi: /2008bams Benjamin, S. G., B. E. Schwartz, and R. E. Cole (1999), Accuracy of ACARS wind and temperature observations determined by collocation, Weather Forecast., 14(6), Cardinali, C., L. Isaksen, and E. Andersson (2003), Use and impact of automated aircraft data in a global 4DVAR data assimilation system, Mon. Weather Rev., 131, , doi: // de Haan, S. (2011), High-resolution wind and temperature observations from aircraft tracked by Mode-S air traffic control radar, J. Geophys. Res., 116, D10111, doi: /2010jd de Haan, S., and A. Stoffelen (2012), Assimilation of high-resolution Mode-S wind and temperature observations in a regional NWP model for nowcasting applications, Weather Forecast., 27(4), , doi: /waf-d Drüe, C., W. Frey, A. Hoff, and T. Hauf (2007), Aircraft type-specific errors in AMDAR weather reports from commercial aircraft, Q. J. R. Meteorol. Soc., 134(630), , doi: /qj.205. Fischer, C., T. Montmerle, L. Berre, L. Auger, and S. E. Ştefănescu (2005), An overview of the variational assimilation in the ALADIN/France numerical weather-prediction system, Q. J. R. Meteorol. Soc., 131, , doi: /qj International Civil Aviation Organization (ICAO) (1964), Manual of the ICAO Standard Atmosphere, Montreal, Quebec, Canada. International Civil Aviation Organization (ICAO) (1995), Annex 10 to the Convention on International Civil Aviation, Aeronaut. Telecommun. Ser., vol. III, Montreal, Quebec, Canada. Painting, D. J. (2003), AMDAR reference manual, WMO-958, WMO, Geneva, Switzerland. [Available at Schwartz, B. E., and S. G. Benjamin (1995), A comparison of temperature and wind measurements from ACARS-equipped aircraft and rawinsondes, Weather Forecast., 10(3), Undén, P., et al. (2002), The HIRLAM model (version 5.2), HIRLAM scientific report, 144 pp., SMHI, Norköpping, Sweden. World Meteorological Organization (WMO) (2008), Measurement of Meteorological Variables. Part I. Guide to Meteorological Instruments and Methods of Observation, 7th ed., 681 pp., WMO, Geneva, Switzerland. 10 of 10