WMO INTERCOMPARISON OF GPS RADIOSONDES

Transcription

1 WORLD METEOROLOGICAL ORGANIZATION INSTRUMENTS AND OBSERVING METHODS REPORT No. 9 WMO INTERCOMPARISON OF GPS RADIOSONDES Alcântara, Brazil 2 May - 1 June 21 Reinaldo B. da Silveira (Brazil) Gilberto F. Fisch (Brazil) Luiz Augusto T. Machado (Brazil) Alaor M. Dall'Antonia (Brazil) Luiz Fernando Sapucci (Brazil) David Fernandes (Brazil) Rosa Marques (Brazil) John Nash (United Kingdom) WMO/TD-No

2 NOTE The designations employed and the presentation of material in this publication do not imply the expression of any opinion whatsoever on the part of the Secretariat of the World Meteorological Organization concerning the legal status of any country, territory, city or area, or its authorities, or concerning the limitation of the frontiers or boundaries. This report has been produced without editorial revision by the Secretariat. It is not an official WMO publication and its distribution in this form does not imply endorsement by the Organization of the ideas expressed.

3 FOREWORD This report describes the results of the WMO Radiosonde Intercomparison Experiment (WMO RSO), conducted in Alcântara city, State of Maranhão (Brazil), between 2 th of May and 1 th of June, 21. The report provides results from the analysis of available data, as well as the operational conditions experienced during the experiments. The Intercomparison was named Experimento Meteorológico de Alcântara (EMA) as a tribute to this Brazilian region in the tropics. The goals of this RSO experiment are outlined in the Final Report of the Session of the International Organizing Committee for the WMO Intercomparison of GPS Radiosondes (WMO, 2), held in Brasilia, Brazil, from 21 st to 25 th of August, 2. Other than the preparation of this report, the WMO International Organizing Committee had the following goals:! Improvement in the accuracy of radiosonde measurements and the associated methods of observation.! Test the accuracy and availability of data obtained from the GPS wind measuring systems.! Evaluate the performance and usefulness of position measurements obtained from GPS radiosondes; location both in the horizontal and vertical.! Evaluate the performance of the most widely used radiosonde humidity sensors against newly developed higher performance sensors.! Investigate differences between day and night measurements.! Measure the differences between temperature and pressure sensors from widely used radiosondes against newly developed high performance sensors.! Evaluate practices used in launch preparation, operator in-flight interventions, and reporting and coding procedures.! Benefit from conclusions derived from upper-air practices applied during the Intercomparison. The report provides insight into test site location selection, institutions and manufacturer participation, and operational procedures adopted. It describes the assimilated data sets, from the radiosondes and complimentary ground observations, including cloud cover and radar data, the steps taken in data analysis, analysis results, and finally, the derived conclusions and recommendations for future Intercomparisons. It is with sincere appreciation that I acknowledge Dr J. Nash for his professional leadership as project manager and the Brazilian Team, led by Mr da Silveria for their outstanding support. Finally, I also wish want to acknowledge the individuals and institutions, too many to mention, who assisted in making the effort a success. They are identified within this document. (Dr. R.P. Canterford) Acting President Commission for Instruments and Methods of Observation

4 Report of the WMO/INMET Radiosonde Intercomparison Experiment Alcântara - Brazil Reinaldo B. da Silveira INMET Gilberto F. Fisch ACA/IAE/CTA Luiz Augusto T. Machado ACA/IAE/CTA Alaor M. Dall Antonia Jr. INMET Luiz Fernando Sapucci UNESP David Fernandes ITA/CTA Rosa Marques ACA/IAE/CTA John Nash MetOffice, United Kingdom

5 WMO Intercomparison of GPS Radiosondes Alcântara, Brazil, 2 May - 1 June Introduction The report of the WMO Intercomparison of GPS Radiosondes, named the WMO/INMET Radiosonde Intercomparison Experiment is organized as follows: Section 1: introduction; Section 2: overview of the test site, institutions and manufacture participants; Section 3: operational procedures adopted by local operators; Section 4: data available from measurements from radiosondes, ground observations, cloud cover data and radar data; Section 5: discussion on the steps of the data analysis; Section 6: results of analysis for pressure, temperature, height, humidity and wind; Section 7: conclusions and future tasks. The main institutions involved in this campaign were: 1. The National Meteorological Institute in Brazil, INMET, who hosted the Operation EMA; 2. The World Meteorological Organization, WMO, who sponsored the experiment together INMET, and helped in the organization of the experiment, through IOC and CIMO secretariat. 3. The Brazilian Air Force, who participate with operators and infrastructure; 4. The Satellite and Rocket Launching Centre from the Brazilian Air Force, CLA, at Alcântara, which provided the place for the experiment as well as the necessary infrastructure. 5. The Atmospheric Science Division, of The Aerospace Technical Centre, who provided experts for the preparation, operation and data analysis stages of the experiment; 6. The Brazilian Navy, who provided radiosonde operators; 7. The Maranhão State University (UEMA), who sent observers from the Meteorological Department (NEMRH). 8. The US National Weather Service, who provided parachutes for the experiment and for sending a WMO/IOC observer. 9. The UK Met Office, who provided the operators for the humidity sensor Snow- White and helped with data analysis; 1. The Dr. Graw Messgeraete GmbH&CO, from Germany, who participated with radiosonde DFM-97; 11. Modem, previously integrated to GEOLINK, from France, who participated with radiosonde GL-98; 12. InterMet Systems, from USA, who participated with Modem; 13. Sippican, Inc., from USA, who participated with radiosonde MKII; 14. Vaisala, from Finland, who participated with systems RS8 and RS9; 15. Meteolabor, from Switzerland, who offered the Snow-White, humidity sensor, used as the humidity reference. 1

6 2. Test site, institutions and manufacture participants The RSO Intercomparison of GPS Radiosondes was carried out at the Brazilian Air Force Satellite/Rocket Launch Centre (CLA), at Alcântara city, situated at the State of São Luís do Maranhão, Brazil. The CLA is located at the latitude 2 18 South and longitude of West. The total area of the centre is about 62 km 2 though just a portion of this is used for activities of the CLA. The test site was prepared with good facilities, suitable accommodations and adequate work conditions, which eased most of activities during the experiment. The experiment was performed at the meteorological station of CLA, which is divided into the main building, the external area and the balloon building. Many institutions supported the experiment. The Word Meteorological Organization, WMO, and the Brazilian National Meteorological Institute, INMET, were the main support of the RSO intercomparison. INMET kindly offer to host the experiment. Therefore, almost all facilities and operator expenses were directly or indirectly arranged by INMET. FAPESP (Fundação de Amparo à Pesquisa do Estado de São Paulo) also contributed financially to the RSO experiment, through a grant number 2/ Other supporting institutions were The Brazilian Air Force, with representatives of the CLA, which is the already mentioned test site, the Aerospace Technical Centre (CTA), The Atmospheric Science Division (ACA) and the Diretoria de Eletrônica e Proteção ao Vôo (DEPV); The Brazilian Navy, who sent operators from the Hydrology and Navigation Centre; The Maranhão State University (UEMA), who send observers from the Meteorological Department (NEMRH); The National Weather Service from USA, who cordially offered the parachutes; The MeteoFrance, who provided the Magnetron for one of the radars; The UKMO, who provided help on data analysis and operation with the Snow White equipment, which is the reference sensor for humidity. NASA had also contributed, by offering some balloons for tests, prior the experiment, and for the technical contribution of Frank Schmidlin, who provided very useful suggestions for the experiment. The manufactures taking place at the RSO Intercomparison were: Dr. Graw Messgeraete GmbH&CO, from Germany; Modem, from France; InterMet Systems, from USA; Sippican, Inc., from USA Vaisala, from Finland and Meteolabor, from Switzerland Table 2.1 shows the radiosonde types (column 1) used by the manufactures as well as other equipments available during the experiment and its precedence. The table also tells (column 4) which were the measured parameters for each equipment. 2

7 Table 2.1: Description of the available instruments. EQUIPMENT RS8 RS9 MKII GL-98 DFM-97 SNOW WHITE CEILOMETER Laser CT75K RADAR MILOS 5 THYGAN MANU- FACTURER Vaisala Oyj Finland Vaisala Oyj Finland Sippican USA Modem France Dr. Graw Germany MeteoLabor Switzerland Vaisala Oyj Finland Thomson France Vaisala Oyj Finland MeteoLabor Switzerland Radiosonde Radiosonde Radiosonde Radiosonde Radiosonde TYPE SENSOR TYPE PARAMETER Humidity sensor Cloud detector Doppler Radar C-Band (5.8 GHz) Met automatic station Humidity check sensor Pressure : barocap capacitive aneroid Temperature : thermocap capacitive bead RH : H-humicap thin film capacitor GPS : codeless GPS Pressure :barocap silicon sensor Temperature: RH : H-humicap GPS : codeless GPS Pressure:capacitive aneroid Temperature*: chip thermistor sensor RH: hygristor carbon type GPS: differential C/A LOS GPS Pressure: computed from GPS, T and RH. Temperature: thermistor sensor RH: capacitive sensor GPS: differential C/A GPS Pressure: capacitive inverse aneroid capsule Temperature: ceramic thermistor sensor RH: polymer chip -sensor GPS: differential C/A GPS RH: hydrometer SW chilled mirror. P, T, RH, GPS wind P, T, RH, GPS wind T, RH GPS wind and heights T, RH, GPS wind and heights P, T, RH, GPS wind Relative Humidity Cloud height and cover Balloon tracking wind components P,T,RH, wind, solar radiation and rain Relative humidity * Sippican MKII radiosondes with Snow White, white rod thermistor 3

8 3. Operational procedures Besides the normal operational procedures, this Brazilian RSO Intercomparison was an unique opportunity to join radiosonde operators from various institutions in Brazil, who operate different radiosonde equipments. Therefore, this field trial also attempted to standardize the operational procedures. One can divide the tasks of radiosonde measurement as the following: I. Preparation of balloon and meteorological surface observations; II. Preparation of radiosondes and ground stations; III. Radar set up, if it will be used. Therefore, each one of the participants of the experiment was allocated on one of the three items above. The first and third tasks were performed by Brazilian operators, which were indeed a very successful team for operation, comprising operators from various institutions. 3.1 Launching schedule The experiment last for 21 days, commencing at 21 st of May and finishing by 7 th of June, flights were performed during this period. These were scheduled according to Table 3.2. In Table 3.1, 5 days were reserved for unpacking/packing (mount/unmounting); 1 days were allocated for the flights and 3 days were reserved as rest for all participants. 21 Mon 22 Tue 23 Wed 24 Thu 25 Fri Table 3.1: chronology of the experiment 26 Sat 27 Sun 28 Mon 29 Tue 3 Wed 31 Thu 1 Fri 2 Sat 3 Sun 4 Mon 5 Tue 6 Wed 7 Thu : unpacking/packing and mounting/unmounting : flight days. : rest for all participants The official flights occurred 4 times a day, being UTC, 6 UTC, 12 UTC and 18 UTC, as indicated in Table 3.2. Moreover, the Project Leader, in accordance with participants and WMO representatives, offered 4 additional flights during the period of the trial: on 3 th of May at 14 UTC; 31 st of May at 2 UTC; 31st of May, at 14 UTC and 1 st of June at 2 UTC. These flights were considered official flights as well. Thus, discounting 1 bad flight, which was the 1 st one, on 25 th of May, the total number of flights was 43 flights. 4

9 Table 3.2: launching times. (H) 21: (H) 3: (H) 9: (H) 15: Approx. weight (with GPS) RS8 RS8 RS8 RS8 25g MKII & Snow White MKII MKII MKII & Snow White 35g + 35g Dr Graw InterMet/Modem Dr Graw InterMet/Modem Graw (25g) InterMet (4g) Modem (4g) RS9 - - RS9 25g 2,g balloon 1,2g balloon 1,2g balloon 2,g balloon Time schedule during the flights. The reference time was the balloon launching time, which is the local time (H). Thus the following activities were carried. H 6 min: the participating vendors should attend the operation centre. H 5 min: starting of procedures for launching (participating vendors). H 45 min: starting sondes preparation. H 3 min: Brazilian operators should start filling the balloon. Preparation of the radars. H 15 min: Fixing the balloons in the appropriate support. H 1 min: delivery of the sondes to the launching area. Prepare for satellite synchronization. H 8 min: the Site Manager (SM) should evaluate which radiosondes will fly. H 5 min: Fixing the sondes in the appropriate support. Decision go/no go by SM, who will inform to Project Leader (PL). Radar confirmation. H 3 min: Check of frequency lock. H 1 min: last minute checking of frequency lock by SM, who informed the launch. Final radar confirmation. H: launching H + 5 min: the vendors should inform to SM if their sondes are working properly. H + 9 min: end of the flight. H + 15 min: the vendors should give their processed data to the SM. 3.2 Preparation of balloon and supporting structure A difficult task for the operation team was preparing a rig structure, capable of supporting the balloon, unwinder, parachute and 3 or 4 radiosondes, flying all together. Thus, a rig, consisting of 1 or 2 PVC pipes, was built as shown in Figure 3.1. The rig varies according to the launchings with 3 or 4 radiosondes. Thus, the scheme shown in Figure 3.1a shows the rig used for 3 radiosondes and that shown in Figure 3.1b indicates the rig used to sustain 4 radiosondes. The total length of the axis was about 2 meters. The operators had to take care of the length of each stream being attached to the rig, as well as to the distance between each radiosondes and other components that would fly, such as parachutes, unwinder and the small lamp for flights during the night. The position of the components is also shown in Figures 3.2a and 3.2b. 5

10 Figure 3.1a: rig structure for supporting 3 sondes. Figure 3.1b: rig structure for supporting 4 sondes. 6

11 Figure 3.2a: The 3 LST and 9 LST flight preparation. 7

12 Figure 3.2b: 15 and 21 LST flight preparation. 8

13 Table 3.3 indicates the weights applied on each structure of Figure 3. Table 3.3: specifications for the weights according to the flights. FLIGHT CONFIGURATION WEIGHTS (g) LOCAL TIME RS MK-II Snow-White Dr Graw Modem RS Total weight RS ( g ) Parachute Reel Reflector 9 9 Structure -1( cross ) Structure -2 ( 3m pipe ) 6 6 Battery and lamp Ballast 195 Ascencion force Total flight weight ( g ) Balloon Nozzle Weight - 1 ( 1435 g ) Weight - 2 ( 1455 g ) Weight - 3 ( 146 g ) Plate - 1 ( 1 g ) 1 Plate - 2 ( 2 g ) Plate - 3 ( 4 g ) Extra weight ( 5 g ) 5 Total flight weight

14 4. Available data Besides the data provided by the radiosondes, the EMA made possible a great amount of data, such ground observations of temperature, humidity, clouds, pressure and winds. Also available was a C-Band radar data, which will be used to evaluate the GPS data. The data source comprises also satellite images and synoptic mesoscale observations, as well as high-resolution analysis from INMET s numerical weather model. Table 4.1 describes the radiosonde data, radar and cloud observations available during the EMA. The shaded cells in red indicate the number of flights corresponding to a given observation. The cells below the diagonal correspond to the number of flights having a given pair of observation. For example, the table shows in the 5 th row and 1 st column that Modem flew 19 times with RS8, which correspond to 44 % of the total number of flights, as indicated at 1 st row and 5 th column. This table will used to verify operational performance of each sonde. Table 4.1: The available radiosonde, humidity sensor (Snow-White), radar and ceilometer data. RS8 RS9 MKII MKII+SW GL-98 DFM97 RADAR CEILO RS RS MKII MKII+SW GL DFM RADAR CEILO Note from table 4.1 that some pairs were not available at same time during the flights, as for example the combination of GL-98 and DFM-97, which did not fly together. 4.1 Meteorological conditions during RSO The climatic elements during the RSO international experiment were measured with an automatic weather station from Vaisala Oyj (Milos 5). The data was measured by sampling it at 1 observation each 5 min and hourly and daily values were processed after the field data collection. In Figure 4.1 there are the general overview of the meteorological conditions (daily average of the surface observations). The weather situation can be split in two periods: a dry period from May 2 until June 1 (11.4 mm), 21 and a wet period from June 2 until June 1 (76 mm), where this wet period is likely to be associated with a large-scale disturbance in the region. During the dry conditions, there is only one event with rain (May 24 with 8. mm.day -1 ). Considering the whole period, the air temperature showed values 1

15 between 24 and 27 C and relative humidity ranged from 75 up to 9 %. The lowest temperature values and highest moisture conditions were observed during the wet conditions. The integrated solar energy varied from 12 up to 24 MJ.m -2.day -1 and the values around 12 until 15 MJ.m -2.day -1 were observed during the rain. The wind speed values are between 1, and 3,5 m.s -1 pressure (hpa) temperature (C) /5/1 22/5/1 24/5/1 26/5/1 28/5/1 3/5/1 1/6/1 3/6/1 5/6/1 7/6/1 9/6/1 relative humidity (%) /5/1 27/5/1 3/6/1 1/6/1 rain (mm) /5/1 27/5/1 3/6/1 1/6/1 solar radiation (MJ.m-2.day-1) /5/1 22/5/1 24/5/1 26/5/1 28/5/1 3/5/1 1/6/1 3/6/1 5/6/1 7/6/1 9/6/1 windspeed (m.s-1) /5/1 22/5/1 24/5/1 26/5/1 28/5/1 3/5/1 1/6/1 3/6/1 5/6/1 7/6/1 9/6/1 Figure 4.1: Average meteorological conditions during the experiment. Since collected data always need preparation and clean-up procedures, before it is used for analysis, next section describes some of the procedures used in the EMA at the postprocessing stage and being applied on the data analysis. 11

16 5 Data processing Figure 5.1 describes the upper-air climatology, based on 1 years of measurements performed at the same place of the RSO Intercomparison. Moreover, a data archive was then built by sampling all flights at 2 seconds rate. Those observations, which were not at 2 seconds, were then linearly interpolated to this value. Thus, the GL-98 and the MKII observations, which were at a rate of 1. second, were interpolated. Another approach applied to the data was the adjustment of the time, as we merge the observations. This adjustment is described as follows. 4 CLA temperatura average profile to CLA relative humidity average profile to Height (km) Height (km) T (C) RH (%) 4 CLA wind direction average profile to CLA wind velocity average profile to Height (km) Height (km) DD (degrees) VV (m/s) Figure 5.1: Average profiles of radiosonde observations at Alcântara, from 1988 to Time adjustment Procedure The ideal reference would be the elapsed time for the radiosondes, as this is the only common parameter relating to every instrument in any given observation. However, by assembling all flights it would cause errors in the height post-processing analysis, due to different ascension rates. Thus, an objective technique was used to adjust the time setup of all set of radiosondes. This technique consider the following points: 1) Temperature is the radiosonde measurements that better agree among the different types of radiosonde. Then we have decided to use temperature as the parameters to drive the offset time adjustment. 12

17 2) The Vaisala RS-8 was the radiosonde that participated of all flights. Then we have decided to use RS-8 temperature profile as the reference to adjust the time offset of the others radiosondes. Considering that this procedure only adjust the time offset there is no implication in the results of the radiosonde comparison due to the consideration of RS-8 as a reference. 3) The maximum time offset was considered in the time interval of about 2 seconds. This time step was defined larger than the largest time offset occurred during the radiosonde trial to assure the best adjustment. 4) The time offset was adjusted considering only the average time to the radiosonde to cross a layer slight larger than the mixed layer, i.e., 16 seconds. The layer including the mixed layer and few meters higher has a larger temperature dynamics (temperature changes with height). The use of this layer assure to have a good adjustment without to include all the radiosonde patch that probably add time offset due to with specific radiosonde system. Based in the methods described above we have applied a mean squared error algorithm in the temperature profile, with about 2 seconds lag, for each flight between RS-8 and each other radiosonde participating of the flight. The minimum time lag absolute error was considered as the time offset of each sonde with relation to the RS-8 flight. An example of this approach applied to one of the flights is shown in Figure rs8 ' viz' geo rs8 ' viz' geo Tempo(s) Te mp o(s ) Temperatura (C) Temperatura (C) Figure 5.2: Example of temperature profile without time offset adjustment (left side) and after time offset adjustment (right side) Issues about the tracking radar data Another issue was tackling the processing of the tracking radar data, as it is used as the wind reference for GPS measurements. Besides operational problems at the beginning of the experiment, the radar had other problems related to the digital filtering applied to the time series data generated by the radar. The ADOUR radar is commonly used at the CLA for rocket tracking, which is obviously much faster than the radiosonde. Thus, when the data was taken during the experiment, the sampling rate used for tracking the radiosonde was inappropriate. Therefore, a post-processing procedure was necessary. It is explained as follows. 13

18 The trajectory of meteorological balloons can be tracked by a radiosonde system consisting of a transmitter in the balloon, coupled with a GPS navigation receiver, and of a receiver in the ground that provides the target position. The balloon can be also tracked by radar. The radar measures the balloon trajectory, in polar coordinates, with errors due to the inherent noise of the radar system and the inaccuracy of sensors and in the signal processing. Those errors make unfeasible the calculation of the balloon speed by derivation of trajectory, as this process is equal to a high-pass filtering in which the signal-to-noise ratio is drastically reduced. The Kalman filter is used in the tracking to estimate the position of targets. This filtering yields, besides the filtered trajectory, its first and second derivatives, which can be used in the wind intensity and direction estimation. Therefore, the data collected by the radar was pre-processing through a Kalman filter. The filter was applied to the polar data (elevation, azimuth and distance). In order to use the filter it was necessary to consider a dynamical model of the target and the error model of the radar measurement. As described by Singer (197), the model considered in our procedure was developed for moving targets. Thus, it considers that the radial acceleration of a moving target is an autoregressive process of first order. Moreover, the variables elevation, azimuth and distance are taken as uncorrelated, following a white noise processes Gaussian and stationary. Figure 5.3 shows an application of the above procedure to a particular radar profile. We note the that spikes in the original data (in red), due to the inappropriate filtering processing, are smoothed out when using the Kalman-Filter (in blue) HEIGHT (M) Radar Raw Data Radar - Kalman Filter WIND SPEED (M/S) Figure 5.3: The Kalman filter procedure, applied to one of the radar profiles obtained during the RSO experiment. 14

19 6. Results analysis and results of the experiment 6.1 Relative humidity A reference sensor is necessary for the evaluation of the errors in relative humidity. Therefore the Snow-white chilled mirror hygrometer sensor 1 was used in the RSO experiment and flew interfaced to MKII, as way to obtain reference values for the humidity. However, as we shall observe, this sensor presented large dispersion and bias at high levels, when compared to the radiosonde measurements. It is clear from subsequent use of Snow White measurements that some Snow Whites have large errors in the upper troposphere, and users of Snow white must learn to detect these errors and eliminate them from the data sets if reference quality measurements are required. Thus, it is important to stress that unless the erroneous measurements are eliminated, the use of the chosen reference, at high levels cannot be justified. Initially, some problems could be detected by examining individual flights. At low and medium levels of the troposphere (bottom to around 8 m), where the humidity concentration is relatively large (9 % to 3%), the measurements of radiosondes presented low dispersion. This fact is not observed at high levels of the troposphere as the measurements are highly dispersed. Whereas the others radiosondes continually measured the humidity during most of the soundings, the MKII had a large amount of interruptions and it registered null values when the radiosondes did not. This happened in the flights 29, 3 and 32. These flights were recovered by taking out null values. The flights 34 had to be excluded from the analysis due to possible radiosonde failures. On the other hand, flight 37 had to be excluded due to great dispersion from the radiosondes. Figure brings vertical profiles of flights 29, 3 and 32 and Figure brings vertical profiles of flights 34 and 37, which illustrate these problems. At low levels, where there is high concentration of water vapor, the MKII radiosonde humidity values were higher than those measured by the other radiosondes. Contrarily, the DFM-97 radiosonde, showed values lower than the others. In Summary, MKII and DFM-97 overestimates and underestimates, respectively, the humidity with relation to the other radiosondes, at conditions of high concentration of water vapor. The above considerations can be exemplified through Figure that gives the humidity average of the radiosondes versus height. Note the high dispersion between radiosonde type at high levels and low dispersion between radiosonde types at low levels of the atmosphere. A trend and dispersion analysis was applied to verify the accuracy of the radiosonde measurements. We have used the root mean squared () error as measure of dispersion and the bias to account for possible trends. These statistical measurements were computed on a level-to-level basis, for a combination of available radiosondes, at the same flight and time and further convert for pressure of the RS8. The analysis was done using the height (converted from pressure using the average relationship). Moreover, in order to ease the analysis, three layers were defined: 1 The Snow-white post-processing data was performed by the UKMO. 15

20 1. The first layer comprises the low levels of the troposphere (from the surface to 3 km); 2. The second layer comprises the medium levels of the troposphere, between 3 and 8 km; 3. The third layer comprises the high levels of the troposphere and the rest of layers, beginning at 8 km till the end of the vertical profile Relative Humidity - Flight 29 RS8 RS9 MKII GL-98 DFM-97 SW Relative Humidity - Flight 3 RS8 RS9 MKII GL-98 DFM-97 SW Relative Humidity - Flight 32 RS8 RS9 MKII GL-98 DFM-97 SW Time (s) Time (s) Time (s) Relative Humidity (%) Relative Humidity (%) Relative Humidity (%) 1 12 Figure Vertical profiles of relative humidity, for flights 29, 3 and 32 6 Relative Humidity - Flight 34 6 Relative Humidity - Flight RS8 RS9 MKII GL-98 DFM-97 SW 5 4 RS8 RS9 MKII GL-98 DFM-97 SW 3 3 Time (s) Time (s) Relative Humidity (%) Relative Humidity (%) 1 12 Figure Vertical profiles of relative humidity, for flights 34 and

21 25 2 Average Relative Humidity (n=7) Launching time: : (UTC) RS9 RS8 MKII DFM-97 Snow 25 2 Average Relative Humidity (n=9) Launching time: 6: (UTC) RS8 MKII GL-98 Altitude x1 (km) 3 15 Altitude x1 (km) (a) (b) Relative Humidity (%) Relative Humidity (%) Average Relative Humidity (n=7) Launching time: 12: (UTC) RS8 MKII DFM Average Relative Humidity (n=7) Launching time: 18: (UTC) RS9 RS8 MKII GL-98 Snow Altitude x1 (km) 3 15 Altitude x1 (km) (c) (d) Relative Humidity (%) Relative Humidity (%) Figure Average profiles of relative humidity computed from the radiosonde measurements. 17

22 Figures and give values of bias and in relative humidity as function of altitude, for all possible combination of radiosondes. There is not any combination GL-98 and DFM-97, since they did not flight together. Table I gives a quantitative analysis of the mean layer bias and values. We also examined the results of bias and and taking one of the radiosondes as reference performed a crossing analysis. Thus, if the RS9 is taken as reference then the behavior of the others is evaluated, for example, Figures 6.1.4a to 6.1.4e describe the results for bias and for those combinations comprising the RS9. It is important to mention that neither RS8 nor RS9 are reference instruments. We began our analyses of pair comparisons by first examining the radiosondes against RS8 e RS9, as they were present at almost all flights. All possible parameters comparison can be seeing in Table Intercomparison RS8 - RS9 3 Intercomparison MKII - RS9 3 Intercomparison GL-98 - RS9 25 BIAS 25 BIAS 25 BIAS Altitude (km) 2 15 Altitude (km) 2 15 Altitude (km) Bias Bias Bias (a) (b) (c) Intercomparison DFM-97 - RS9 BIAS 25 Intercomparison Snow - RS9 BIAS 3 25 Intercomparison MKII - RS8 BIAS Altitude (km) 2 15 Altitude (km) 2 15 Altitude (km) Bias (d) Bias (e) Bias (f) Intercomparison GL-98 - RS8 BIAS 3 Intercomparison DFM-97 - RS8 BIAS 25 Intercomparison Snow - RS8 BIAS Altitude (km) 2 15 Altitude (km) 2 15 Altitude (km) Bias (g) Bias (h) Bias (i) Figure Relative humidity bias and as function of altitude with RS9 and RS8 as reference. 18

23 The RS9, at the first layer produced values greater than those given by RS8, DFM-97 and GL-98. However the values are close to those from Snow White and lower than MKII measurements. Table brings the average values for each layer. We note that the between RS9 and RS8 is smaller than that computed for other radiosondes. GL-98 also presented a small when compared to RS Intercomparison GL-98 - MKII BIAS 3 25 Intercomparison DFM-97 - MKII BIAS 25 Intercomparison Snow - MKII BIAS Altitude (km) 2 15 Altitude (km) 2 15 Altitude (km) Bias Bias Bias (a) (b) (c) Intercomparison Snow - DFM-97 BIAS Altitude (km) 2 15 Intercomparison Snow - GL-98 BIAS Altitude (km) Bias (d) Bias (e) Figure Relative humidity bias and as function of altitude, showing different comparison between pairs of radiosondes. In the second layer (from 3 to 8 km) the comparisons do not show clear tendencies. The bias oscillates around zero and values with low dispersion are presented in the pairs RS8-RS9 and GL-98/RS9. The minimum bias came from DFM-97 and Snow White, which are only.19% and.8%, respectively, though the dispersions were considerably high. MKII comparison with RS9 gave the main dispersion, with the average of about 14 %. For the third layer the bias indicates that RS8 and GL-98 underestimated the RS9 values, while MKII, DFM-97 and Snow White overestimated the RS9 values. The smallest bias was resulted from GL-98 and RS9 comparison, which was about 2.6 %. The however is the major impact in this layer. Since the water vapor concentration in this layer is low, high dispersion is expected and it would explain bias of 15% and average of 23% for the Snow White sensor, where the erroneous Snow-white observations were not eliminated from this data set. The smallest dispersion in this layer was found for RS8 and GL-98 radiosondes. Now, taking RS8 as reference instead RS9 the following conclusions can be drawn. The values of bias and are given in figures 6.1.4a, 6.1.4f, 6.1.4g, 6.1.4h and 6.1.4i. We note the low dispersion observed for GL-98. If in the first and second layers the values of and bias are low, the proximity in the third layer is remarkable. At this layer the average bias and is about 2% and 7%, respectively. 19

24 Generally, with exception of MKII, the dispersion in the first layer considering RS8 a reference was about 5%. This figure indicates that the RS8 gave humidity values close to the average of the other radiosondes. As with RS9, the strong divergences between MKII and Snow White are also observed with relation to the RS8. The computed for the Snow White reached 28% in the third layer, as the errors in Snow white were not eliminated from the data set. We note that the comparisons regarding MKII, as shown in figures 6.1.4b, 6.1.4f, 6.1.5a, 6.1.5b and 6.1.5c, have the highest bias values in the first layer. This can be verified in Table I. As this layer comprises the major amount of water vapor, this result is significant. In the other layers, the comparisons did not improve and had similar results. Table Average of bias and for the vertical profile of radiosonde relative humidity measurements, at the three selected layers. Comparison Average figures BIAS (%) (%) 1 st layer 2 nd layer 3 rd layer 1 st layer 2 nd layer 3 rd layer RS8-RS MKII-RS GL-98-RS DFM97- RS Snow RS MKII RS GL-98-RS DFM97 RS Snow RS GL-98-MKII DFM97 MKII Snow MKII Snow GL Snow-DFM

25 Intercomparison Specific Humidity RS8 - RS9 Intercomparison Specific Humidity Snow - RS9 Intercomparison Specific Humidity MKII - RS BIAS BIAS BIAS Altitude (km) Altitude (km) Altitude (km) Bias (a) Bias (b) Bias (c) Figure Bias and of specific humidity (g/kg) for the following comparisons: (a) RS8 and RS9; (b) Snow White and RS9 and (c) MKII and RS9. The large dispersion on Table at upper levels is due also to the parameter itself, which depends on saturation pressure of the water vapor, which is small at low temperatures. On the other hand, taking specific humidity parameter, we shall note that such dispersions are of smaller magnitudes than the previous analysis, though the patterns are kept. Figure 6.1.6, brings the bias and of specific humidity for comparisons between RS8 and RS9, which was the best comparison, and between Snow White/MKII and RS9, which were the worst results. The values are around 1.5 g/kg and MKII presents the worst results, which are about 2g/kg for both bias and. 21

26 5 4 3 Day period Averege D ifference (% ) RS9 - RS8 (n=1) MKII - RS8 (n=18) GL-98 - RS8 (n=1) DFM-97 - RS8 (n=9) SW - RS8 (n=8) Night period Averege D ifference (% ) RS9 - RS8 (n=8) MKII - RS8 (n=15) GL-98 - RS8 (n=1) DMF-97 - RS8 (n=7) SW - RS8 (n=8) Relative Humidity (%) Figure Average difference of relative humidity for day and night periods, and considering RS8 as reference. 22

27 In order to evaluate the sensitivity of humidity sensors to solar radiation, we divided the soundings into day and night periods. This analysis was performed by taking RS8 as reference and by computing the average differences among the radiosondes. Figure brings the results of these comparisons. One can note that RS9 during the night overestimate the relative humidity, with relation to the others radiosondes (except to the MKII) for higher values (larger than 75%) Whereas at this condition the bias for the radiosondes is negative, except for MKII, during the day the bias is close to zero. With regards to Snow White, for relative humidity values under 6% the sensor presented high dispersion related to the radiosonde measurements regardless the period of the day. However, for values above 6% its measurements were very close to the two Vaisala systems during the night, and more than 5 per cent higher than these during the day. Subsequent tests [ WMO High Quality Radiosonde test, Mauritius] have shown that the Snow-White measurements usually have small daynight difference, so this result is evidence of significant day-night difference in the quality of Vaisala relative humidity measurements. Averege Difference (%) Day period Temperature greater than C RS8 - RS9 MKII - RS9 GL-98 - RS9 Snow - RS Relative Humidity (%) 8 1 Averege Difference (%) Night period Temperature greater than C RS8 - RS9 MKII - RS9 GL-98 - RS9 DFM-97 - RS9 SW - RS Relative Humidity (%) 8 1 Figure Average difference for day and night periods and considering RS9 as reference at temperatures above C. 23

28 Averege Difference (%) Day period Temperature between -25 C and C RS8 - RS9 MKII - RS9 GL-98 - RS9 SW - RS Relative Humidity (%) 8 1 Averege Difference (%) RS8 - RS9 MKII - RS9 GL-98 - RS9 DFM-97 - RS9 SW - RS9 Night period Temperature between -25 C and C Relative Humidity (%) 8 1 Figure As the previous figure, but for temperatures from 25 C to C. Another analysis was to evaluate the humidity computations considering the variations of temperature. This is important as temperature is related to the maximum amount of humidity that an air parcel contains at the moment of the soundings and can also give additional information about the humidity sensors. Therefore we selected temperatures above C as first analysis interval; temperature between 25 C and C and second interval, and temperature less than -25 C as the third interval. Figures 6.1.8, and show the bias for day and night periods, considering these selected intervals. 24

29 Averege Difference (%) RS8 - RS9 MKII - RS9 GL-98 - RS9 SW - RS9 Day period Temperature smaller -25 C 2 4 Relative Humidity (%) 6 8 Averege Difference (%) Night period Temperature smaller -25 C RS8 - RS9 MKII -RS9 GL-98 - RS9 DFM-97 - RS9 SW Relative Humidity (%) 6 8 Figure As previous figure, but for temperatures under 25 C. Figures and show that for temperatures above -25 C and during the day, the radiosondes RS9, RS8 and GL-98 gave values very close to each other s, where the average bias was close to zero. However, for low temperatures, below 25 C, the RS9 differed from the other radiosondes. During the night, under temperatures above -25 C, RS9, RS8 and DFM-97 measured closely the humidity and, as shown in figure 6.1.8, for temperatures above C the RS9 presented higher values. One reason that these high values did not appear during the day might be related to the radiation factor, which could compensate possible problems in the humidity sensor of RS9. As it was expected, at temperatures under -25 C, the radiosondes diverge more and RS8 measured different from RS9. At low temperatures, several studies have now confirmed that the RS8-A Humicap calibration has significant errors The humidity values computed by MKII and Snow White presented high dispersion regardless the period of the day, and this fact is noticeable, as the temperature gets smaller. 6.2 Height and Pressure The height and pressure variables have been analyzed following the same procedures applied to relative humidity. In this case, there is one additional problem related to these parameters, as some radiosonde systems measured the pressure and then computed the height (RS8, RS9, DFM-97). Others perform the reverse mechanism, i.e., they measured the height with a 3D GPS and then computed the pressure (MKII and GL-98). In comparing height and pressure measurements timing of the samples is critical and errors in time adjustment would cause more errors than in the case of temperature and relative humidity. Nevertheless, the results are show below and can be used to gain the insights about the performance of the use of the radiosondes in operational activities. 25

30 Figure presents the statistical parameter computed for all possible combination of sondes. The agreement between RS8 and RS9 is very good as they use the same procedure d but the pressure sensors are quite different. The pairs of sondes RS8 and DFM97, MKII and GL98 and MKII and DFM97 show height differences less than 1 m for the complete sounding. The others pairs showed and increase of the difference with the height, reaching 3 m when including RS9 in the analysis. Vaisala believe that the RS9 pressure sensor is more accurate than the RS8 pressure sensor and this has been confirmed in subsequent national tests. At Table there are the statistics of the and BIAS for each of the 3 layers. In general, the BIAS is in the range of 5 m up to + 5 m in the 1 st and 2 nd layer, increasing this value to 2 m up to 9 m for the 3 rd layer. The biggest difference occurs with the RS9. The shows the same patterns with the higher values at the 3 rd layer. Figure 6.2.1: The E and BIAS of the height for all combination of sondes. 26

31 Table Average of bias and of height for the vertical profile of radiosonde measurements at the three selected layers. Comparison BIAS (m) (m) 1 st layer 2 nd layer 3 rd layer 1 st layer 2 nd layer 3 rd layer RS8-RS RS8-MKII RS8-GL RS8- DFM RS9-MKII RS9- GL RS9- DFM MKII-GL MKII-DFM Figure presents the statistical parameter computed for all possible combination of sondes considering the pressure as a dependent variable. In all possible pairs of sondes, there are high values at the surface, decreasing these figures with the height. This is due to the time-offset adjustment. In general, differences are in the range of 2.7 hpa up to 2.7 hpa in the first layer, reducing this to.9 hpa up to 1.1 hpa for the 3 rd layer. Table Average of bias and error of pressure for the vertical profile of radiosonde measurements at the three selected layers. Comparison BIAS (hpa) (hpa) 1 st layer 2 nd layer 3 rd layer 1 st layer 2 nd layer 3 rd layer RS8-RS RS8-MKII RS8-GL RS8- DFM RS9-MKII RS9- GL RS9- DFM MKII-GL MKII-DFM

32 Figure 6.2.2: The E and BIAS of the pressure for all combination of radiosondes. 6.3 Temperature The RS8 was chosen as a reference for the temperature analysis. The reason for this choice was the large number of soundings with RS8 (39 flights). Initially for all flights the differences of each radiosonde related to the RS8 were plotted individually in order to identify possible errors associated with bad functioning of the sensors. In Figure there are 2 examples of bad functioning of the equipment. During the flight 9 (Figure 6.2.1a) the GL-98 did not work properly for 3 s (left). This behavior can clearly been observed from the spike at the level of 58 s. This piece of data has been neglected in the construction of the database and the resulting profile is much cleaner (right). Figure 6.3.1b show the same kind of error for another sonde (MK2) in a different flight. In this case, all data above 4 s was deleted. 28

33 Figure 6.3.1a: Example of editing data for GL-98 (Flight 9) Figure 6.3.1b: Example of editing data for MK-II (Flight 5) The Table presents the flights used for the computation of all the statistics derived ( and BIAS). In the flights numbers 2,15,17,2,26 the RS8 presented technical problems. Thus, all the measurements on these flights were excluded from the database. In the flights numbers 13,14, 29,3,32,37, the MKII showed technical problems and it was also excluded from the database. The flight 33 did not occur because of heavy rain. The height considered in this analysis was taken from the RS8 measurements 29

34 Table 6.3.1: The availability of the measurements for each flight. FL RS8 RS9 MKII GL-98 DFM97 1 X X X 2 X X X X 3 X X X 4 X X X 5 X X X X 6 X X X X 7 X X X 8 X X X 9 X X X X 1 X X X X 11 X X X 12 X X X 13 X X X X 14 X X X X 15 X X X 16 X X X 17 X X 18 X X X X 19 X X X X 2 X X X 21 X X 22 X X X 23 X X X 24 X X X X 25 X X X X 26 X X 27 X X X 28 X X 29 X X X X 3 X X X X 31 X X X 32 X X X X 34 X X X X 35 X X X 36 X X X X 37 X X X X 38 X X X 39 X X X 4 X X X X 41 X X X X 42 X X X 43 X X Note: Flights in red were excluded from the database. Fl 33 was canceled. 3

35 Figure 6.3.2: The error and BIAS of the temperature for all combination of sondes. Figure presents the statistical parameter computed for all possible combination of sondes. The BIAS and error between RS8 and RS9 for the first 1 km shows a very good agreement (the differences were less that.5 C) and this was the best correlation between sondes (although RS8 and RS9 had different sensors, they used the same algorithms for correction of radiation errors). The comparison between RS8 and GL98 are also good up to 5 km, then the deviation start to growth with the height. The worst case was the comparison between MKII and GL-98 and MKII and DFM97, which showed error around 2 C at 15 km height (but it should be noticed that the number of profiles are very low - 14 cases). The average statistics of the BIAS and RSM error for the 3 layers are presented at Table

36 Table Average of bias and error for the vertical profile of radiosonde measurements at the three selected layers. Comparison BIAS ( C) ( C) 1 st layer 2 nd layer 3 rd layer 1 st layer 2 nd layer 3 rd layer RS8-RS RS8-MKII RS8-GL RS8- DFM RS9-MKII RS9- GL RS9- DFM MKII-GL MKII-DFM Considering the first layer (measurements up to 3 m, i.e, approximately 6 s 3 m), the BIAS amongst the sondes is very low, roughly around.1 C. The maximum BIAS was between RS8 and DFM97 and between MKII and DFM97. For the second layer ( from 3 m to 8 m), the dispersion is high, with BIAS ranging from.17 (BIAS for RS9- MKII) up to.31 (BIAS for MKII-GL98). In general the BIAS of the 2 nd layer is higher than the results from the 1 st layer. For the third layer (above 16 s or 8 m), the situation is quite complex: the better agreement is between RS8 and DFM97 but this may be due to the lack of measurements to be compared. The difference between RS8 and RS9 is.23 C. The for the DFM97 is high for the pair with RS8 and MKII, especially in the first and second layer. The MKII shows bad performance in the 3 rd layer. The influence of the solar heating in the sensors was analyzed by splitting up the data set for daytime (soundings at 12 and 18 UTC) and nighttime (soundings at and 6 UTC). The results are depicted at Figure 6.3.3a (daytime) and 6.3.3b (nighttime). For daytime conditions, up to an altitude with temperature higher than 3 C, the absolute differences among the radiosondes are within the range from.5 C to +.5 C. For temperatures lower than 35 C, the difference between RS8 and MKII increased substantially to a value around 1.5 C. The others radiosondes also show high differences, although lower than this figure. The radiation corrections for RS8 and RS9 and built-in the processors so it was included in the data processing. This is not the case of the MKII and may explain this big difference for temperature lowers than 35 C. For nighttime period, the differences are mostly reduced to a value close to.3 C up to the height of temperature 3 C. Above this height, the difference is still lower (related to the daytime conditions) in the range from.5 C to +.5 C. The exception is the difference between RS8 and GL97 at 4 C of temperature. 32

37 Figure The absolute difference among the sondes. Daytime (a) and nighttime (b) conditions. 33

38 6.4 GPS wind Some flight statistics The wind vector was computed by using the decoded) Differential GPS Technique. For all the GPS radiosonde measurements apart from Vaisala who were using the codeless technique implemented since The GPS wind was computed during the whole experiment and under different environmental conditions. Most flights happened during the dry period and they were almost equally divided between night and day, as we can see in the histogram of Figure Although it is important to consider the different environmental conditions, we have not stressed the interpretation of the results achieved during the experiment, since the number of flights under rain condition is small and it could not be a good generalization of the radiosonde performance. Therefore, we shall present only few cases when it was raining during the flight RS8 RS9 GL-98 DFM-97 MKII Day/dry Day/wet Night/dry Night/wet Figure 6.4.1: Histogram of number of flights, during the RSO experiment, according to the environmental conditions. Figure shows the duration of the measurements for each flight. The recent WMO review of operational GPS performance indicated that 1 to 15% missing wind data were typical. In this test, RS9, GL-98 and DFM-97 had less than 6% missing wind data up to 5 s of ascent time. 34

39 Figure 6.4.2: Duration of the radiosonde measurements, considering all flights Wind vector analysis The wind vector analysis basically comprises the following: Post-processing of the radiosonde horizontal wind data; Post-processing of the radar data; Computation of the radar horizontal wind data; Computation of the bias and error quantities, by comparing a given radiosonde winds against the other radiosondes and also comparing it with the radar winds. Examination of performance under different weather conditions, i.e, raining and dry, and under situations of day and night. Computation of bias and error among sondes and radar for 3 vertical layers: from surface to 3 m; from 3 m to 8 m and from 8 m up to the top of the sounding. We have considered the horizontal wind as vector comprising the zonal (east-west) and the meridional (north-south) components as depicted in figure

40 N v V W θ u E S Figure 6.4.3: Horizontal wind vector, V. The components are u and v and θ is the direction, taking clockwise from the North (N) Computed parameters Flight radiosonde differences: = ( u ) 1 u2u1 2 u Bias (regarding to a given reference): B N 1 = ( u N u u2u1 2 j 1 j j= 1 ) Root mean squared () error: u = 1 ( u N 2 2 j u1 ) 2u1 j where N is the number of flights for a given pair of radiosondes, u1 and u2 are two different radiosondes at a given level. The bias and are not computed if a pair, or an element of it, is missing. 36

41 6.4.3 Results A summary of the results is presented in the following set of figures, from to , which comprises the examination of bias and considering all flights, four individual flights and the examination of bias and under the above-described conditions. In order to make possible the radiosonde wind intercomparison, the independent variable in the graphs corresponds to the height of either RS8 or RS9 and the wind components, bias and were first scaled to this variable. Thus, by examining figures and we noted that, in general, there is not much difference among the radiosonde wind components. Most of the bias is within the range of 2 m/s to +2 m/s and is less than 2 m/s in most of cases. These figures display bias and, considering all possible pair of radiosondes that flew during the experiment. In spite of some deviation of RS8, regarding other radiosondes, it can be noted that the measurements are very close. This is confirmed by examining the next set of figures, from to 6.4.9, corresponding to the individual flights 4, 2, 18, 36 and 41. The wind components were very similar for all systems. The computed error displayed in Figure and Figures of letter b in Figs to , show variations between pairs of zonal and meridional wind components. Although the error values are not large, the dispersion patterns are noted in the figures, especially when MKII, GL-98 and DFM-97 are compared against RS8 or RS9. It can be noted also that weather and daylight conditions had influenced the measurements. These variations are less pronounced at day and dry conditions and increases during night and wet conditions. This can be observed in the comparison between GL-98 and DFM-97 and the other radiosondes, through the graphs in Figure b. Another analysis was done for layers, as described at previous sections. We have considered three layers. The first one goes from surface to 3 m; the second on extends from 3 m to 8m and last one goes from 8 m up to the top. Table and table show the results for zonal and meridional wind components respectively. Although the averaging process smoothed the bias and error values out, it can be seen that the radiosondes are in excellent agreement as far as GPS wind measurement is concerned. 37

42 Figure 6.4.4: Mean differences among the radiosondes for all flights, by considering the available pairs of radiosondes, which flew together. 38

43 Figure 6.4.5: error for all flights of the comparison among radiosondes, which flew together, and considering all available flights. 39

44 Figure 6.4.6: zonal and meridional wind components of flight 2. 4

45 Figure 6.4.7: zonal and meridional wind components of flight

46 Figure 6.4.8: zonal and meridional wind components of flight

47 Figure 6.4.9: zonal and meridional wind components of flight

48 Figure 6.4.1a: mean differences for zonal and meridional wind components, by considering all flights which flew together during the day. 44

49 Figure 6.4.1b: error for zonal and meridional wind components, by considering all flights which flew together during the day. 45

50 Figure a: mean differences for zonal and meridional wind components, by considering all flights which flew together during the night. 46

51 Figure b: error for zonal and meridional wind components, by considering all flights which flew together during the night. 47

52 Figure a: mean differences for zonal and meridional wind components, by considering all flights which flew together during day without rain. 48

53 Figure b: error for zonal and meridional wind components, by considering all flights which flew together during day without rain. 49

54 Figure a: mean differences for zonal and meridional wind components, by considering all flights which flew together during day with rain. 5

55 Figure b: error for zonal and meridional wind components, by considering all flights which flew together during day with rain. 51

56 Table 6.4.1: Bias and computed for layers I, II and III, for zonal wind component. Comparison BIAS (m/s) Average figures (m/s) 1 st layer 2 nd layer 3 rd layer 1 st layer 2 nd layer 3 rd layer RS8-RS MKII-RS GL-98-RS DFM97- RS MKII RS GL-98-RS DFM97 RS GL-98-MKII DFM97 MKII Table 6.4.2: Bias and computed for layers I, II and III, for meridional wind component. Comparison BIAS (m/s) Average figures (m/s) 1 st layer 2 nd layer 3 rd layer 1 st layer 2 nd layer 3 rd layer RS8-RS MKII-RS GL-98-RS DFM97- RS MKII RS GL-98-RS DFM97 RS GL-98-MKII DFM97 MKII

57 6.4.6 Radar comparison The horizontal wind components were derived from the radar azimuth, range and elevation coordinates as the following. Let us consider the geometry of figure z Balloon N H R θ x φ r E Y Figure : radar geometry when tracking the balloon. N and E indicate the North and East directions, respectively. θ is the radar azimuth, ϕ is the elevation, R is the radial range and r is the horizontal range on a Cartesian coordinate system, with x and y coordinates. Therefore, one can determine the horizontal balloon position, by knowing ϕ (the radar elevation), θ (the radar azimuth) and R (the range). Thus, from figure : (1) H = R sinφ (2) r = R cosφ (3) x = r sinθ = R cosφ sinθ (4) y = r cosθ = R cosφ cosθ 53

58 The balloon velocity is then determined by taking the time derivatives of x and y coordinates: (5) dx dt ( R sinφ sinθ ) + = R cosφ sinθ θ ( R cosφ cosθ ) θ (6) dy dt = R cosφ cosθ θ ( R cosφ sinθ ) θ ( R sinφ cosθ ) but: (7) H H H = cot an = φ R sinφ sinφ φ φ sinφ ( R cot anφ ) Thus from 1 to 7 one can obtain the horizontal wind velocity and direction as: (8) V = 2 + x 2 y (9) DD 1 x = tan y and finally, the horizontal wind components were obtained from the following relations: (1) u = V sindd (11) v = V cos DD 54

59 Results The radar was available at only 14 flights. These flights are 1, 4, 5, 8, 9, 12, 13, 16, 18, 35, 36, 39, 4 and 43. The radar-tracking rate was about every second, but it was re-computed to the rate of 2 seconds to accomplish the same rate as the radiosondes. Figures and , presents the zonal wind component for flights 4 and 9, respectively. The figures comprise the vertical profiles and level differences between the radiosondes and the radar. Flight 4 had MKII, DFM-97 radiosondes and the radar. As the radar had a different start time from the radiosondes, Figure shows only the portion of the radar profile that most agree with the radiosonde sampling. One can note from the graphs that the radar and radiosondes are very close. Figure : Wind component computed by sondes and the radar, which participated in the flight 4. 55

60 Figure : Wind component computed by sondes and the radar, which participated in the flight 9. Flight 9, as depicted at figure had RS8, RS9, MKII, GL-98 and the radar. As it can be noted the profiles of zonal wind components are very close to the radar s one. The bias is very small and presents some discrepancies at only few levels of RS8. Such differences looks more associated to problems of RS8 on this specific flight than the overall performance. 56