ENVISAT VALIDATION CAMPAIGN AT IMAA CNR

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1 ENVISAT VALIDATION CAPAIGN AT IAA CNR Vincenzo Cuomo, Aldo Amodeo, Carmela Cornacchia, Lucia ona, arco Pandolfi, Gelsomina Pappalardo Istituto di etodologie per l Analisi Ambientale, IAA-CNR, C.da S. Loja, Tito Scalo, Potenza, Italy I-855 cornacchia@imaa.cnr.it ABSTRACT In the period July 22 - arch 24, at IAA-CNR, Tito Scalo (Potenza, 4 36 N, E, 82 m above sea level) we have performed more than 5 hours of Raman lidar measurements and 5 radiosonde launches in coincidence with ENVISAT overpasses. This campaign has been carried out by using a Raman lidar system, able to perform measurements of water vapor mixing ratio vertical profiles with high vertical and temporal resolution, and a radiosounding station for PTU measurements. These data have been used to validate IPAS water vapor and temperature products and GOOS water vapor products. In this paper, we present the results of the validation campaign performed at IAA, only for IPAS and GOOS data processed with IPAS software version 4.61 and with GOOS IPF 5. respectively. 1. INTRODUCTION Water vapor is one of the most important constituents of the atmosphere. There is no single technique or instrument platform that is able to perform water vapor measurements at all altitudes, with an adequate spatial and temporal resolution. We want to focus our attention on upper troposphere and lower stratosphere. Water vapor content at this altitude region has a dominant role in the Earth radiation balance. Numerous studies have been carried out about water vapor efficiency like greenhouse gas and the positive feedback that this can cause [1, 2]. However, the sign of the feedback between the water vapor mixing ratio in the upper troposphere and surface temperature is not well defined [3, 4]. oreover, water vapor abundance and distribution in the upper troposphere and lower stratosphere are more variable, spatially and temporally, than in the stratosphere [5]. The considerably different content of water vapor between troposphere and stratosphere, strongly influenced by both large-scale circulation and localized convection, causes the presence of a large gradient around the tropopause. This is the cause of the difficulties for most of the used techniques to evaluate water vapor content and relative humidity in this atmospheric region. Among in situ techniques, radiosondes are widely employed and their data have been utilized for climate studies [6]. Unfortunately, radiosonde performances are characterized by poor quality level at cold temperature and low pressure conditions as in the upper troposphere. Among remote sensing techniques, the lidar technique allows to perform systematic and high quality measurements of water vapor in the free troposphere up to lower stratosphere, allowing to follow the typical large variability of water vapor both in time and space [7,8,9]. A wider use of Raman LIDAR and DIAL techniques and an improvement in radiosondes performances of water vapor observations are suggested by the SPARC Assessment of Upper Tropospheric and Stratospheric Water Vapor [5], where it is also stressed the importance of well designed intercomparison experiments including both in situ instruments and satellite missions with strong validation programs. In this frame, the ENVISAT atmospheric products validation campaign can be considered important. ENVISAT is the ESA environmental satellite launched on the 1 st of arch 22, that gets aboard three atmospheric chemistry sensors: IPAS (ichelson Interferometer for Passive Atmospheric Sounding), GOOS (Global Ozone easurement by the Occultation of Stars) and SCIAACHY (Scanning Imaging Absorption Spectrometer for Atmospheric Cartography). IAA CNR is part of Ground Based easurements and Campaign Dataset (GBCD) for the ENVISAT products validation. We have performed Raman lidar and radiosonde measurements for the validation of IPAS and GOOS atmospheric water vapor and temperature measurements. 2. IAA WATER VAPOR EASUREENTS The Raman lidar system operational at IAA-CNR in Tito Scalo (Potenza, Southern Italy, 4 36 N, E, 82 m above sea level) is devoted to perform water vapor measurements, in terms of water vapor mixing ratio vertical profiles []. The IAA Raman lidar system (Fig. 1) is based on a Nd:YAG laser source with a repetition rate up to Hz and is equipped with third harmonic generator. The radiation at 355 nm is transmitted into the atmosphere in a coaxial mode. This system is able to detect the radiation backscattered by the atmosphere at three wavelengths: 355 nm, signal due to atmospheric elastic backscattering, nm, signal due to Raman inelastic scattering by atmospheric nitrogen molecules and 47.5 nm, Raman shifted signal relative to water vapor. Proceedings of the Second Workshop on the Atmospheric Chemistry Validation of ENVISAT (ACVE-2) ESA-ESRIN, Frascati, Italy, 3-7 ay 24 (ESA SP-562, August 24) EPOENVC

2 The radiation is collected by means of a telescope in Cassegrain configuration and is split into three channels by means of dichroic mirrors. Each wavelength radiation is selected by interferential filters and is detected separately for low and high altitude range by means of photomultiplier tubes. This is obtained by means of further splitting of the radiation at 355 nm, 386 nm and 47 nm in two channels in which there are different neutral density filters to differently attenuate the radiation. The separate detection of each wavelength allows to obtain signals with a good dynamic range over all sounding altitudes. Both low and high range signals are acquired in photon counting mode by using a ulti Channel Scaler (CS) with a minimum dwell time of ns. In Tab. 1, the overall characteristics of the apparatus are reported. 355 nm Nd:YAG Laser P L D = mirror P = pinhole L = collimation lens D = dichroic mirror BS = beam splitter PT = photomultiplier IF IF = interferential filter D BS Fig.1: Block diagram of Raman lidar operational at IAA-CNR. The ratio between water vapor and nitrogen Raman shifted signals is proportional to water vapor atmospheric content. Raw lidar signals have a spatial resolution of 15 m and are acquired over 3 laser shots at 5 Hz frequency repetition rate, corresponding to a time resolution of 1 minute. Lidar signals have been corrected for both after pulsing effect, due to the high voltage of photomultiplier tubes that cause delayed pulses, and pile up effect, that limits at about 2 Hz the maximum counts number because of the dead time of the acquisition system. A Wiener filter is applied to the ratio between the water vapor and nitrogen Raman lidar signals in order to retrieve water vapor mixing ratio profile with low uncertainty over all BS BS IF PT 386 nm IF PT 386 nm IF PT 355 nm IF PT 355 nm IF PT 47 nm IF PT 47 nm sounding altitudes. Starting from the local uncertainty in the input data, this filtering method provides a smoothed estimate of water vapor profile. This is possible because the algorithm distinguishes between the natural fluctuations of water vapor vertical distribution and those due to the statistical uncertainty of the raw lidar signals, reducing this latter [11]. oreover, water vapor vertical profiles have to be corrected for the effect due to the different atmospheric aerosol transmissivity at both water vapor (47 nm) and nitrogen (386.7 nm) Raman wavelengths. This correction is possible by retrieving the aerosol extinction vertical profile from the nitrogen Raman signal [12]. Tab.1: Characteristics of the IAA Raman LIDAR system. LASER SOURCE: Nd:YAG laser (Coherent) Wavelength and pulse energy 355 nm E AX = 17 mj aximum pulse repetition rate Hz Beam divergence.4 mrad Pulse duration 3 4 ns RECEIVER: Cassegrain telescope Primary mirror diameter.5 m Combined focal length 5 m Night time Field of view Day time Field of view 1 mrad.6 mrad SPECTRAL SELECTION: Interference filter (Barr Associates) Wavelengths 355 nm, 387 nm, 47 nm Bandwidth night time operation Bandwidth daytime operation 1. nm.5 nm Out of band rejection -6, -8, - Transmission efficiency 33%, 62%, 6% DETECTORS: Photomultipliers THORN EI 9383 QB 355 nm THORN EI 922 QB 387 nm, 47 nm ACQUISITION: Photon counting EG&G Ortec CS inimum dwell time ns Bandwidth 15 Hz PHILLIPS SCIENTIFIC fast discriminator Bandwidth 3 Hz Water vapor Raman lidar systems need to be calibrated by means of independent measurements. In ay and June 22, an intensive water vapor measurements campaign has been performed at IAA-CNR addressed to calibrate our lidar system by means of a co-located radiosounding system. The Vaisala RS8-A radiosondes are launched simultaneously with the lidar measurements and the calibration procedure is applied in the first two kilometers of altitude above lidar station. In this altitude range, the radiosonde data are reliable and lidar and radiosonde sensor investigate the same atmospheric region. The calibration constant value is altitude independent and is applied to calibrate the water vapor vertical profile over the all investigated altitudes.

3 Long term calibration measurements, performed at IAA, show a calibration factor constant within a few per cent. In summary, our Raman lidar system is able to perform water vapor vertical profiles extend up to the tropopause in night time and clear sky conditions. Typical profile is characterized by minutes of integration time and variable vertical resolution between 15 m and about 7 m in the altitude range of about 9 m km above lidar station. Water vapor lidar measurements are complemented with radiosonde launches in order to measure atmospheric pressure, temperature and relative humdity. Balloonborne sonde is equipped with a pressure sensor of.1 mb precision and of.5 mb accuracy in the measurement range of 3 6 mb, with a temperature sensor of.1 C precision and.2 C accuracy in the measurement range of -9 6 C and with a capacitive sensors of humidity with a 3 % precision in the measurement range of % RH and in the temperature range of C. The radiosonde data have been corrected for temperature dependence error, that increases substantially with decreasing temperature below about 3 C, and for dry bias due to chemical contamination error, and ground check error [13]. 3. CHARACTERISTICS OF IPAS AND GOOS PRODUCTS IPAS performs limb sounding observation of the atmospheric emission spectrum, covering the midinfrared spectral region in both diurnal and nocturnal conditions. From the observed spectra (level 1 products), IPAS derives the concentration profiles of about twenty trace gases (level 2 products), atmospheric temperature, aerosol and ice cloud distribution. All these parameters are generated in near real time (NRT), i.e. processed and disseminated within 3-24 hours after sensing, but these data represent a compromise between time computing and accuracy. All these products are also generated off-line (OFL), in order to improve the retrieval so reducing the retrieval error and the extrapolation error [14]. The latest version of IPAS data processor is IPF In order to fully characterize IPAS volume mixing ratio profiles, it is necessary to take into account the main error sources that affect the final accuracy: noise error, temperature error and systematic error. Systematic error is not considered in the data released by ESA and has been estimated by Oxford University for the near real time products and the off-line products (see The IPAS level 2 data have to be validated by means of comparisons with independent measurements, temporally and spatially coincident, but usually having different altitude grids, different vertical resolutions and using different a priori information. In order to perform a correct comparison, it is necessary to apply the Averaging Kernel atrices (AK) to ESA released near real time and off-line products. The AK have been released by Institute for Applied Physics (IFAC- CNR, Italy). These matrices include the information about error and how the observing system modifies the true state of the atmosphere and allow to obtain a IPAS profile with a closer vertical spacing [15, 16]. IPAS off-line water vapor vertical profiles extend from 5 km up to 6 km of height above sea level, with a vertical resolution of about 3 km and a pre-launch estimate accuracy of 5-% and a pre-launch estimate precision of 5%. For IPAS temperature vertical profiles, the pre-launch estimate precision is 1 K and the pre-launch estimate accuracy is 2 K [14]. The GOOS operating principle is the self-calibrating stellar occultation technique. GOOS is able to monitor global vertical ozone distribution and atmospheric constituent profiles such as aerosols, air density, temperature, NO 2, NO 3, H 2 O and possibly BrO from the upper troposphere to the upper mesosphere. The quality of the retrieval strongly depends on star parameters such as brightness and temperature and on the daytime or night-time (divided into dark and twilight) illumination conditions. The GOOS data retrieval processes are not totally independent on external data, such as meteorological information (ECWF data) that can be used in the retrieval algorithm. The latest version of GOOS data processor is IPF 5.. GOOS nominal water vapor vertical profiles (in molec/cm 3 unit) extend from 2 km up to 35 km of height above sea level, with a vertical resolution of 1.7 km and an accuracy of 3% as target requirement (the best value) and of 7% as threshold requirement (the least acceptable value). For GOOS temperature vertical profiles, the pre-launch estimate precision is 1 K and the pre-launch estimate accuracy is 2 K [17]. 4. VALIDATION CAPAIGN AT IAA From July 22 to December 22, in the IAA validation program (CAL/VAL), 2 radiosoundings and 2 lidar measurements of water vapor mixing ratio per week in coincidence with satellite overpasses have been performed. From January 23 to July 23, 1 radiosounding and 1 lidar measurement per week have been performed. oreover, after the validation campaign we have been continued to perform systematic measurements [18]. A set of criteria was defined to select the overpasses and to decide when IPAS, lidar and radiosounding measurements can be considered as coincident and used for the validation: a) the maximum spatial distance between IPAS overpass and IAA station has to be

4 lower than km; b) the maximum temporal distance between IPAS overpass time and the radiosonde launch time has to be lower than 2h3min; c) the lidar water vapor vertical profile is retrieved by integrating measurements over minutes, with this time interval centered on the overpass time. Concerning this last point, the time integration of lidar measurements can be extended in order to improve the signal to noise ratio, without affecting the comparison validity. Of course, this depends on the atmospheric water vapor temporal variability: lower atmospheric water vapor temporal variability allows a higher time integration. The optimal integration time is chosen by estimating the atmospheric water vapor temporal variability from the lidar measurements continuously performed within a very wide time interval centered on the satellite overpass time. Starting from these selection criteria, until April 24, we have collected about 73 night time lidar measurements and 47 radiosoundings in coincidence with IPAS overpasses. In Fig. 2, the total lidar and radiosonde measurements realized in coincidence with IPAS overpasses are plotted as a function of months of the year. GOOS overpasses have been supplied from EGVC (ENVISAT Ground-based Validation Center) for each ground based validation station and are within 5 km of distance. aximum temporal distances from GOOS overpasses for radiosounding launch is lower than 2 h 3 min while lidar profiles are obtained integrating lidar signal over or 3 minutes time interval centered on satellite overpasses. Until April 24, we have collected about 48 night-time lidar measurements and 28 radiosoundings in coincidence Lidar - ipas Radiosonde - ipas 8 Number of coincidences ago-2 ott-2 dic-2 feb-3 apr-3 giu-3 ago-3 ott-3 dic-3 feb-4 apr-4 onth Fig.2: Raman lidar and radiosonde measurements performed in coincidence with IPAS overpasses at IAA-CNR, from July 22 to April 24. Lidar - Gomos Radiosonde - Gomos 8 Number of coincidences ago-2 ott-2 dic-2 feb-3 apr-3 giu-3 ago-3 ott-3 dic-3 feb-4 apr-4 onth Fig.3: Raman lidar and radiosonde measurements performed in coincidence with GOOS overpasses at IAA-CNR, from July 22 to April 24.

5 with GOOS overpasses. In Fig. 3, the total lidar and radiosonde measurements realized in coincidence with GOOS overpasses are plotted as a function of months of the year. It can be observed that the number of measurements performed until December 22 is higher than in the following months for both IPAS and GOOS coincidence, moreover in the winter period there is lower number of observations than in the other months because of typical bad weather conditions. The number of measurements in coincidence with GOOS is lower than the number of measurements in coincidence with IPAS because of both the limited number of stars, available for the occultation, and their uneven distribution. A lack of observations in June and July 23 is due to technical problems with the laser source of the lidar system. 5. COPARISON RESULTS 5.1 Water vapor validation An analysis of the comparison between IPAS, lidar and radiosonde for H 2 O measurements has been performed in terms of water vapor mixing ratio. This is directly measured by Raman lidar, while for IPAS it is easily obtained converting the ppm unit and for radiosonde is obtained by using relative humidity and simultaneous pressure and temperature profiles. 2 (a) 16 October 22 IPAS data processed with IPF 4.61 are used for the validation purposes. Only a subset of the coincidences has been reprocessed with this software version: 24 are available for lidar comparison and 14 for radiosonde. Among these 24 days, only 16 cases have been compared with lidar measurements because only in these cases IPAS profiles extend down to altitudes lower than 13 km having an overlap altitude range with lidar water vapor profiles. About 7% of the 16 validated IPAS profiles extend down to 9 km above sea level, and thus, only a limited range of altitude overlaps with lidar profiles. oreover, the AK is unstable, and thus not available, for altitude ranges lower than 12 km [15, 16]. Therefore, it is not possible to reduce IPAS data to a closer vertical grid and typically, just two points or three points of IPAS profiles can be compared with lidar profiles. Errors on IPAS data have been calculated considering both the systematic error and the data errors released by ESA. In Fig. 4 (a), it is shown the comparison between IPAS, lidar and radiosonde measurements for the data relative to the 16 th October 22. The agreement between IPAS and lidar seems to be good and this is confirmed by the scatter plot reported in Fig. 4 (b), where IPAS data are compared with the mean values of both lidar data and radiosonde data in the altitude range equal to the IPAS vertical resolution. The same plot shows a disagreement between IPAS and sonde data: in the range 9-13 km, radiosonde data are lower than IPAS data, while for altitudes higher than 13 km (b) Lidar Altitude above sea level (km) ipas LIDAR Lidar 2:23-2:33 UT ipas 2:34 UT Radiosonde started at 2:17 UT 1E-4 1E Water vapor mixing ratio (g/kg).2 RADIOSONDE Radiosonde Fig. 4: (a) Comparison between IPAS and IAA-CNR radiosonde and lidar data for the 16 th October 22 and corresponding scatter plot (b).

6 2 (a) 23 April 23 (b) Lidar Altitude above sea level (km) 15 ipas Lidar 21:22-21:32 UT ipas 21:35 UT Radiosonde started at 21:18 UT 1E-4 1E Water vapor mixing ratio (g/kg) Fig. 5: (a) Comparison between IPAS and co-located IAA-CNR radiosonde and lidar data for the 23 rd April 23 measurements and corresponding scatter plot (b) Radiosonde Lidar Radiosonde an underestimation of IPAS is observed. This is probably due to IPAS difficulties to perform measurements near the tropopause, characterized by a sharp water vapor gradient, and to the inefficient performances of sonde sensors at temperature lower than 6 C, typical of the tropopause region. Another example is shown in Fig. 5 relative to the 23 rd April 23 measurement. This is the only available case in which IPAS profile extends down to 5 km above sea level and three data points can be compared with lidar. For the lowest two points, the agreement is quite good considering the atmospheric variability at these altitudes, while the highest point, at about 11 km, shows an underestimation of IPAS. The same analysis performed for the 16 th October 22 and the 23 rd April 23 measurement has been realized for all the available 16 days. The number of compared points is 24, reported in the scatter plot in Fig. 6. Considering the altitude range between 5 and 9 km (reported in blue), there is a clear disagreement but it is not possible to deduce a conclusion because of the large variability typical of this atmospheric region. For the data relative to the -13 km range (reported in red), an almost constant underestimation for IPAS measurements is observed. On the contrary, lidar data show a certain variability. ore quantitatively, the mean Raman lidar data variability is equal to.2 g/kg over 16 days covering all seasons and weather conditions, while the IPAS variability is equal to.9 g/kg. The mean value of IPAS underestimation of water vapor content is estimated equal to.16 g/kg. ipas km above sea level - 13 km above sea level Lidar Fig. 6: Scatter plot of IPAS and Raman lidar water vapor mixing ratio.

7 For the comparison with radiosonde data, we have new software version (IPF 4.61) data for 14 days. In Fig. 8, the scatter plot of IPAS and radiosonde water vapor mixing ratio is reported. The altitude range investigated is 5-35 km above sea level with 47 compared points. There is a quite good agreement on average with a slight underestimation of IPAS. For the most critical altitude range, -13 km, IPAS water vapor content is.6 g/kg lower those measured respect to what measured by radiosonde. However, it is necessary to consider the bad performances of RS8-A Humicap sensors at these dry cold altitudes. Concerning GOOS water vapor measurements validation, the GOOS reprocessed data with the latest software version IPF 5. are available only for the measurement performed on the 18 th September 22. In this case, GOOS profile extends from 9 km up to 5 km above sea level and it has been compared with radiosonde data in the range of altitude of about 9-18 km. GOOS occultation is performed in twilight condition and covers an area with mean lat and lon In Fig. 8, a direct comparison and a scatter plot of GOOS versus radiosonde are reported. Radiosonde data have been calculated as mean value in the altitude range corresponding the GOOS altitude resolution.it is difficult to deduce some quantitative conclusions from only one profile. oreover, only dark occultation measurements are of sufficient quality and should be considered to validation purposes, but GOOS dark occultation data are not available. This comparison is presented as an example of the ipas possibility to perform validation in next future starting from 24 coincidences with IAA co-located radiosounding E km above sea level - 13 km above sea level >13 km above sea level 1E Radiosonde (g/kg) H2 O Fig. 7: Scatter plot of IPAS and radiosonde water vapor mixing ratio. Radiosonde data have been calculated as mean value in the altitude range corresponding the IPAS altitude resolution GOOS 2:14 UT Radiosonde 2:32 UT.8.7 Altitude above sea level (km) Gomos E-4 1E H 2 O (g/kg) (a) Radiosonde Fig. 8: (a) GOOS and radiosonde water vapor mixing ratio comparison relative to the 18 th September 22 data, and (b) the corresponding scatter plot. (b)

8 5.2 Temperature validation IPAS temperature data have been validated at IAA- CNR by means of a co-located radiosounding system. The total number of coincidences between IPAS overpasses and radiosonde launches is 47 and among these only 12 IPAS data IPF 4.61 are available. The 12 days are distributed in the period 18 July April 23. The covered altitude range is about 5-36 km above sea level. For a first analysis, we estimate the differences between sonde and IPAS without considering the altitude and seasonal variations. This distribution of the differences is plotted in Fig. 9, and the characteristic parameters of the data distribution are also shown. From these data it is possible to draw a first conclusion: IPAS temperature seems to be colder than the radiosonde one, with a slight mean bias of.5 K. ( 4.6 ± 7.7) A = K B =.98 ±.3 A good agreement over all investigated altitude interval is observed. The linear fit coefficients are consistent with and 1 respectively, within the error. However, if there is a IPAS bias in temperature lower than the error on A, it is not possible to estimate the presence of IPAS temperature bias ean.46 K edian 1.9 K Dev.std 2.89 max +8 K min -9 K Altitude above sea level (km) Radiosonde started at 2:32 UT ipas 21:55 UT Number Fig. 9: Number of occurrences of differences between radiosonde temperature and IPAS temperature data. Further analysis has been performed in order to consider the possible variations at different altitudes. In Fig. (a), an example of comparison IPAS-RS8-A sonde relative to the 18 th September 22 case is reported. In order to perform a quantitative comparison, radiosonde data have been calculated as mean value in the interval corresponding to the IPAS investigated altitude range, and a scatter plot has been reported in Fig. (b). The agreement is quite good. The same analysis procedure has been applied to the all 12 days with an overall number of compared points equal to 98. The scatter plot of all available data is reported if Fig. 11. A linear fit has been done also in this case and the equation resulting is: T = A + (1) IPAS BT Sonde Sonde - IPAS (K) IPAS T (K) (a) (b) Temperature (K) Radiosonde T (K) Fig. : (a) IPAS (mean location at lat. 41- lon. 4) and radiosonde (located at IAA-CNR, lat. 4.6, lon ) data are compared for the measurements relative to 18 th September 22. (b) Scatter plot between IPAS and radiosonde data for the 18 th September 22.

9 IPAS T (K) Fig. 11: Scatter plot between IPAS and co-located IAA-CNR radiosonde data of atmospheric temperature. Radiosonde data have been calculated as mean value in the range corresponding to the IPAS altitude resolution. GOOS temperature profiles reprocessed with latest software version (GOOS 5.) are available for 3 days in coincidence with radiosonde launches at IAA- CNR. We did not compare with GOOS temperature data because these GOOS profiles are strongly influenced by external model (ECWF). 5. CONCLUSION Experimental data Linear fit Radiosonde T (K) IPAS water vapor data have been compared with IAA-CNR Raman lidar and co-located radiosounding data. The number of comparisons is lower than the number of coincidences because of the unavailability of the complete IPAS data set reprocessed with IPF The water vapor Averaging Kernel atrices are available only for altitude higher than 12 km above sea level, and it was not possible to utilize the AK for these comparisons. From 16 days of comparisons IPAS-Lidar, for a total amount of 24 compared points, a mean underestimation of IPAS of.16 g/kg has been observed in the altitude range -13 km above sea level. From 14 days of IPAS-Radiosonde comparisons, for a total amount of 47 compared points, a mean underestimation of IPAS of.6 g/kg has been observed also in the altitude range of -13 km above sea level. In this case, the IPAS water vapor underestimation seems to be lower than the underestimation found in the comparison with lidar, but the bad performances of RS8-A sensors at low temperature present in this altitude range should be taken into account. It is evident that IPAS presents several problems in performing water vapor measurements in tropopause because of the sharp gradient in water vapor content typical at these heights. IPAS temperature data have been validated by means of a radiosonding system at IAA-CNR with RS8-A. A total number of 12 coincidences with a 98 temperature compared points has been studied. The agreement is quite good. However, it is not possible to verify and quantify the presence of any temperature bias, because of large IPAS temperature error. Up to now, it is not possible to validate GOOS water vapor and temperature data due to the unavailability of reprocessed data. The availability of more reprocessed data both for IPAS and GOOS will improve validation activity. ACKNOWLEDGENTS The validation activity of the IAA-CNR within the ENVISAT Atmospheric Chemistry Validation Team, was partially supported by the Italian Space Agency and by PON 2-26, isura II.1, IUR. REFERENCES 1. Rind D., et al., Positive water vapor feedback in climate models confirmed by satellite data, Nature, vol. 349, 5-53, Inamdar A. K. and Ramanathan V., Tropical and global scale interactions among water vapor, atmospheric greenhouse effect, and surface temperatures, Journal of Geophysical Research, vol. 3, 32, , Lindzen R. S., Some coolness concerning global warming, Bulletin American eteorological Society, vol. 71, , Lindzen R. S., Response to Greenhouse warming and the tropical water budget, Bulletin American eteorological Society, vol. 71, , SPARC Assessment of Upper Tropospheric and Stratospheric Water Vapour, WCRP - 113, WO/TD - N 43, SPARC report N 2, December Elliott W. P., Gaffen D. J., On the utility of radiosonde humidity archives for climate studies, Bulletin American eteorological Society, vol. 72, , 1991.

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