Comparison of GPS and ERA40 IWV in the Alpine region, including correction of GPS observations at Jungfraujoch (3584 m)

Transcription

1 JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 111,, doi: /2005jd006043, 2006 Comparison of GPS and ERA40 IWV in the Alpine region, including correction of GPS observations at Jungfraujoch (3584 m) J. Morland, 1 M. A. Liniger, 2 H. Kunz, 2 I. Balin, 3 S. Nyeki, 1 C. Mätzler, 1 and N. Kämpfer 1 Received 4 April 2005; revised 28 October 2005; accepted 30 December 2005; published 21 February [1] The 31 stations in the Global Positioning System (GPS) network of Switzerland span an altitude range of 330 to 3584 m. The highest station in the network, Jungfraujoch, suffers from a constant negative bias in the Integrated Water Vapor (IWV) due to a protective radome. We compared Jungfraujoch GPS IWV measurements with coincident Precision Filter Radiometer (PFR) observations and showed that the bias in the GPS is fairly constant with respect to the time of year and to the PFR IWV value. A correction was developed for the GPS data and validated by comparison with coincident Raman lidar observations. The IWV observations from nine GPS stations, including Jungfraujoch, were then compared with the IWV field of the ECMWF 40 year reanalysis (ERA40) data. Altitude differences between the ERA40 surface level and the GPS stations resulted, as expected, in a positive bias in the ERA40 IWV. A fairly linear relationship, with an intercept of 0.3 mm, was found between this bias and the difference between the ERA40 surface pressure and the surface pressure at the GPS station. The ERA40 reanalysis captured water vapor variations on timescales of several days very well, as evidenced by an r 2 correlation greater than 0.9 where the altitude difference between ERA40 and the GPS station was less than 1000 m. A comparison between ERA40 and GPS at Davos showed that the reanalysis underestimates IWV during winter temperature inversions. Citation: Morland, J., M. A. Liniger, H. Kunz, I. Balin, S. Nyeki, C. Mätzler, and N. Kämpfer (2006), Comparison of GPS and ERA40 IWV in the Alpine region, including correction of GPS observations at Jungfraujoch (3584 m), J. Geophys. Res., 111,, doi: /2005jd Introduction [2] Water vapor is a crucial component of the climate system and yet it is one which is difficult to observe due to its high spatial and temporal variability. In a warmer climate, the amount of water vapor is predicted to increase as air temperature rises, with a consequent increase in the natural greenhouse effect [e.g., Schneider et al., 1999]. This water vapor feedback could approximately double the warming expected due to greenhouse gases alone [Houghton et al., 2001], although there is debate over the distribution of water vapor in a warmer atmosphere and the magnitude of the feedback effect [Harvey, 2000]. For these reasons it is extremely important to obtain reliable observations of water vapor and also to validate the modeling tools which will be used to analyze water vapor in the present climate and to predict future changes. [3] In comparison to radiosondes, data from the Global Positioning System (GPS) network can be used to provide 1 Institute of Applied Physics, University of Bern, Bern, Switzerland. 2 Climate Services, MeteoSwiss, Zürich, Switzerland. 3 Air Pollution Laboratory (LPAS), Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland. Copyright 2006 by the American Geophysical Union /06/2005JD Integrated Water Vapor (IWV) estimates at a relatively high spatial and temporal resolution. Their potential application to Numerical Weather Prediction has already been demonstrated [Guerova et al., 2004]. Most GPS networks have been operating for less than a decade, and it is still too early to use them for trend detection. They can, however, provide other services to the climate community, such as the validation of model and reanalysis data. [4] It is particularly important to validate the water vapor component of climate models given the debate over the parameterization of convection and the consequent effect on the distribution of water vapor at different pressure levels [Sun and Held, 1996; Bauer et al., 2002]. Climatological studies are limited by the fact that there are no long term, global measurements from a stable, atmospheric observing system. Reanalysis data make the best use of the available information, and have the potential to be used in climatological studies. The recently available European Center for Medium Range Weather Forecasts (ECMWF) 40 year reanalysis (ERA40) is expected to open many new possibilities for climate research [Uppala et al., 2006]. [5] Hagemann et al. [2003] compared four months of GPS data at 160 globally distributed stations with IWV from the ECMWF operational analyses and found a good agreement. They used a complex interpolation technique 1of 12

2 taking into account the characteristics of the water vapour distribution. Bengtsson et al. [2004a] then applied the methodology to ERA-40 data with respect to trends in IWV. They compared the ERA40 reanalysis with GPS data, with and without satellite data assimilation, and were able to confirm a dry bias in the reanalysis without satellite data. They concluded that, due to changes in the observing system being assimilated into the model, particularly the introduction of satellite data in 1979, great care must be taken in the interpretation of the reanalysis data. A further study examined the hydrological cycle in the reanalysis data sets and found a good agreement with other data sets which is quite robust with respect to the assimilation of direct humidity observations [Bengtsson et al., 2004b]. Simmons et al. [2004] carried out a comparison of trends and long term fluctuations in monthly mean surface temperature. They found a disagreement between ERA40 and observations, but to a smaller extent than that in the NCEP (National Center for Environmental Prediction) reanalysis [Kalnay et al., 1996]. The post 1978 ERA40 data are also able to characterize trends and patterns of climate trends in tropopause height [Santer et al., 2004]. Other comparisons between ERA40 and independent observations were undertaken for stratospheric ozone, and a good correlation was found between total ozone and profiles [Dethof and Hólm, 2004]. [6] Few studies have analyzed the ERA40 quality in mountainous regions. Monthly mean 2 meter temperatures over the Tibetan Plateau were found to have a high correlation with observational records [Frauenfeld et al., 2005]. However, there are inadequacies in the long term trends. A study using radiosondes and mountain stations over the Alps found the ERA40 data to have realistic temperature variability and trends [Haimberger, 2004]. [7] The potential of ERA40 reanalysis data to be used in climate research makes it very important to compare this data set to independent observations. Besides the long term trends, the quality of the high frequency variability has to be assessed, in particular over regions of highly structured topography such as the Alps. The dense network of GPS stations in Switzerland provides a framework to compare high temporal and spatial resolution observations with reanalysis data and thus test the ability to model the distribution of water vapor over mountainous terrain. This paper presents a comparison between IWV calculated from observations made by the Automated GPS Network of Switzerland (AGNES) and the ERA40 IWV field. [8] Measurements from the GPS receiver at the Jungfraujoch scientific station (3584 m) are of particular interest because they are made in the mid-troposphere. In a previous comparison between GPS IWV and that calculated from the MeteoSwiss Local Model, Guerova et al. [2003] noted a strong negative bias in the GPS observations at Jungfraujoch (3584 m). This was found to be due to incorrect modeling of the antenna, which includes a heated dome to prevent snow accumulating on top of the GPS receiver. The first part of the paper describes the development of a correction to the Jungfraujoch data based on a comparison with co-located Precision Filter Radiometer (PFR) observations. The corrected Jungfraujoch GPS observations are cross-validated by comparison with a third instrument capable of measuring water vapor profiles and IWV, the Raman lidar. Observations from nine GPS stations, including the corrected Jungfraujoch data, are then compared with the ERA40 reanalysis data. 2. Data Sets 2.1. GPS Data Set [9] The AGNES GPS network consists of 31 fixed GPS stations located throughout Switzerland between 45.8 and 47.8 N and 6.1 and 10.3 E. IWV was calculated from GPS Zenith Total Delay (ZTD) data using the method described in Bevis et al. [1992] and Emardson et al. [1998]. This involves calculating Zenith Hydrostatic Delay (ZHD) from surface pressure measurements and subtracting it from ZTD to obtain Zenith Wet Delay (ZWD). ZWD is converted to IWV using a relationship based on surface temperature. Pressure and temperature were obtained from the closest stations in the Swiss meteorological network (ANETZ). Where there was a height difference between the meteorological station and the GPS station, the ANETZ station pressure was interpolated to the GPS station height using the hydrostatic relationship. [10] Unrealistically high or low ZTD values occasionally occur. Problematic data were filtered out by requiring that the root mean square (rms) error in the estimate of the ZTD is less than m. This is approximately three times the normal rms error value which lies between and m. It should be noted that the rms error somewhat underestimates the true noise in the GPS data due to the assumption that all data points are independent. The chosen ZTD error threshold of m corresponds to an IWV error of around 0.75 mm and is comparable to the total error expected in the IWV value. Hagemann et al. [2003] estimate, from comparison of co-located GPS stations, a GPS accuracy of better than 0.7 mm. Mattioli et al. [2005] estimate, from consideration of the GPS processing errors, a GPS accuracy of 0.75 to 1 mm. [11] Figure 1 shows examples of the ZTD data acquired at Muttenz and Geneva between July and December The dotted line indicates the measurements rejected by the quality control procedure. Large errors in the ZTD are usually associated with missing GPS measurements. The two events marked by the arrows were caused by software updates which resulted in missing measurements at several stations simultaneously Precision Filter Radiometer [12] The Precision Filter Radiometer (PFR) is operated by MeteoSwiss as part of the CHARM radiation monitoring network [Heimo et al., 2000]. It is a sun tracking instrument which has 18 spectral channels in the visible and nearinfrared, including channels centred on the water vapor absorption bands at 719, 817 and 946 nm. Water vapor transmittance can be calculated from the observations in the water vapor bands after first establishing the aerosol amount from observations in other bands. The water vapor transmittance is converted to IWV using a method based on radiative transfer modelling [Ingold et al., 2000]. PFR observations began on the Jungfraujoch in March The PFR is a particularly suitable technique for water vapor measurement at high altitude stations because the error in the measurement is around 10% of the IWV value [Nyeki et 2of12

3 Figure 1. Zenith Total Delay (ZTD) in meters observed at Muttenz and Geneva GPS stations between July and December The dotted line shows the data rejected by the automatic quality control algorithm because the root mean square error in the ZTD was greater than m. The arrows indicate events where more than one station was affected by errors in the ZTD and the text gives the initials of the affected stations (A, Andermatt; B, Bern; D, Davos; G, Geneva; J, Jungfraujoch; M, Muttenz; P, Payerne; S, Stabio). al., 2005]. At stations such as Jungfraujoch, where the mean annual IWV is around 1.9 mm, the error in the PFR measurement is correspondingly low Raman Lidar [13] The Raman lidar technique has been operating at the Jungfraujoch observatory since August 2000 [Balin et al., 2002], and is part of a multi-wavelength LIDAR system [Larchevêque et al., 2002]. The Raman lidar measurement of the water vapor takes advantage of the spontaneous vibrational Raman scattering of an incident laser beam by atmospheric N 2 and H 2 O molecules. This experiment uses a 355 nm incident laser beam and the back-scattered Raman shifted wavelengths are 387 nm and 408 nm for N 2 and H 2 O, respectively. The water-vapor mixing ratio at a given height is calculated from the backscattered signals according to the method described in Balin et al. [2004]. The calibration is based on the in situ water vapor mixing ratio value, calculated from the relative humidity, temperature and pressure measurements made at the lidar site. [14] Profiles obtained in this way were integrated to obtain IWV and corrected for the IWV in the unmeasured atmospheric layer directly above the lidar [Balin, 2004]. The main limitation of this technique is that the Raman backscatter is relatively small compared to background noise from solar radiation and this method can therefore only be used at night ERA40 IWV Data Set [15] The European Centre for Medium-Range Weather Forecasts (ECMWF) have produced a 40 year reanalysis (ERA40) data set covering the period between 1958 and mid This three-dimensional global data set was generated on the basis of the available past observations using a data assimilation technique as applied in operational numerical weather prediction models [Simmons and Gibson, 2000]. This type of analysis compensates for gaps in the observing system by estimating the full atmospheric state in a physically consistent manner. For the reanalysis, the numerical weather model formulation is kept fixed over the whole period. However, the quality of the reanalysis also depends on the temporally varying amount and quality of the data being assimilated. For ERA40, six hourly analyzes have been produced with a horizontal resolution of T159 (125 km) and a vertical resolution of 60 levels. [16] ERA40 total column water vapor fields as well as 2 meter temperature and surface pressure were extracted four times a day using bilinear interpolation for each station position. Hagemann et al. [2003] uses a more complex horizontal interpolation taking into account the typical length scale of the IWV field and a vertical correction for 3of12

4 Figure 2. Comparison of coincident GPS and PFR IWV values at Jungfraujoch for the November 2000 to January 2004 period. The dashed line shows the 1:1 relationship and the solid line shows the best linear fit to the data. the difference in height between station and model. In general a good agreement was found between the operational analyzes of ECMWF and GPS derived IWV fields. However, larger differences were found in mountainous areas. Within this study the difference in height between the ERA40 model orography and the GPS stations was not corrected but will be analyzed. 3. Correction of Jungfraujoch Data 3.1. Establishing a Correction From PFR Data [17] The GPS receiver at Jungfraujoch began operation in November 2000 and is at 3584 m the highest station in the AGNES network. Its average annual pressure is around 655 hpa. Allan et al. [1999] showed that outgoing longwave radiation (OLR) is most sensitive to humidity changes in the mid-troposphere ( hpa). Water vapor at high altitude stations such as Jungfraujoch is therefore an important factor to monitor. However, Guerova et al. [2003] observed abnormally low and sometimes negative IWV values at this GPS station which were attributed to incorrect modeling of the antenna. [18] We investigated the validity of the GPS IWV by comparing coincident hourly averaged PFR and GPS IWV data over the period Nov 2000 to Jan A scatter plot comparing the two data sets is shown in Figure 2. Equation (1) gives the relationship between GPS IWV (IWV GPS ) and PFR IWV (IWV PFR ) in mm, based on 2269 coincident measurements: IWV GPS ¼ 1:32 þ 0:99 IWV PFR : [19] The square of the Pearson correlation coefficient, the r 2 value, is The bias in GPS relative to PFR is 1.3 mm and the standard deviation of residuals is 0.9 mm. ð1þ [20] It is clear from Figure 2 that there is a problem with the GPS data set in that unphysical negative IWV values appear 23% of the time. There is also a considerable amount of scatter in the relationship between the two data sets. PFR is known to be a relatively low noise technique. Nyeki et al. [2005] compared simultaneous measurements made by two PFR instruments during a two year period at Davos. A bias of 0.06 mm was found between the two data sets, with a standard deviation in the bias of just 0.14 mm. Part of the variability in the PFR-GPS comparison is due to the fact that the GPS observes a relatively large area of sky from 15 above the horizon up to zenith, depending on the available satellites, whereas the PFR observes the sun in clear or almost clear sky conditions. The remaining variability comes from the fact that GPS is inherently a noisier technique than the PFR. [21] To test whether a standard correction, based on the PFR data, can be applied to the GPS data, we checked whether the bias in the GPS is dependent on air temperature or humidity and found no relationship with these factors. [22] We next checked whether the bias is dependent on the magnitude of the IWV measurement. The PFR data were divided into six 1 mm wide bins over the 0 to 6 mm range and one 3 mm wide bin covering the 6 to 9 mm range. Figure 3 shows the histograms representing the distribution of GPS data in the first six bins and Table 1 summarizes the GPS and PFR statistics. There is, as expected, more scatter in the GPS data corresponding to each PFR bin than in the PFR data itself. The mean GPS value is biased by 1.1 to 1.5 mm, depending on the bin. Table 1 also gives the skewness and kurtosis of the GPS data in each PFR bin which should be 0 and 3 respectively for a normal distribution. The Jarque-Bera test for normality was applied to bins with more than 100 points and the Lilliefor test to bins 4of12

5 Figure 3. Histograms showing the distribution of GPS IWV data over the first six 1 mm intervals (indicated by the dashed lines) of coincident PFR IWV data for the period November 2000 to December with less than 100 points. The tests indicated that the normal hypothesis cannot be rejected at the 5% confidence level for the 0 to 1, 2 to 3 and 6 to 9 mm bins. The bins in the 3 to 6 mm range have a more positive skewness (more points above than below the mean) and a higher kurtosis (more proneness to outliers) than a normal distribution. Although coincident hourly mean values were compared, the positive skewness could be caused by the fact that PFR only makes observations in direct sunlight when IWV is usually lower than during overcast conditions. We corrected the GPS data using equation (1) and compared the Cumulative Distribution Function (CDF) of all available GPS and PFR data. For IWV values above 1 mm, the PFR CDF has a negative bias compared to the GPS CDF. The medians of the corrected GPS and PFR measurements are 2.8 and 1.8 mm, respectively. [23] The amount of scatter in the GPS relative to the PFR data at Jungfraujoch is comparable to that observed at stations in the Swiss plains. At Payerne (498 m), where IWV ranges between 3 and 36 mm over the course of a year, the bias in GPS relative to PFR is just 0.2 mm and the standard deviation of the residuals is 1 mm for the best fit linear relationship between the two data sets. Other studies indicate similar results. Mattioli et al. [2005] compared GPS, radiosonde and microwave radiometer data at the Southern Great Plains ARM (Atmospheric Radiation Measurement) site and found a standard deviation in the bias between GPS and other data sets of up to 1 mm. [24] Nyeki et al. [2005] noted a small seasonal dependence in the differences between GPS and PFR IWV measurements at both Jungfraujoch and Davos, which they speculated might be due to the seasonal terms in the GPS mapping function. In Table 2, we show the seasonal bias and standard deviation in the GPS compared to the PFR for the three year period investigated. These indicate that there Table 1. Comparison of GPS and PFR IWV Binned According to PFR IWV PFR Range, mm Number of Points Skewness Kurtosis Mean PFR IWV, mm Standard Deviation PFR IWV, mm Mean GPS IWV, mm Standard Deviation GPS IWV, mm GPS Bias, mm 0 to to to to to to to of12

6 Table 2. Statistics for the Seasonal Comparison of GPS and PFR IWV at Jungfraujoch Over the Period November 2000 to December 2003 Season Number of Points Intercept, mm Slope r 2 of Residuals, mm Bias GPS-PFR, mm Standard Deviation Winter (DJF) Spring (MAM) Summer (JJA) Autumn (SON) is a weak seasonal dependence (amplitude 0.6 mm) with the bias being lowest in winter when IWV is lowest and highest in summer and autumn. The standard deviations of the seasonal biases are 0.7 to 0.9 mm. This is larger than the differences between the seasonal biases, and until more data are available to better model the seasonal component, we recommend that the general relationship given in equation (1) is used to correct the Jungfraujoch data. [25] Due to the noise in the GPS signal, the lowest 8% of the GPS data still have an unphysical IWV of less than zero after the correction. For the year 2004, the minimum IWV calculated from the radiosonde data launched at Payerne, between the altitude of Jungfraujoch and the top of the atmosphere was 0.2 mm, while the lowest value recorded by the Raman lidar at Jungfraujoch was 0.28 mm. Given an estimated error of around 0.7 mm in the GPS IWV data, we would expect negative IWV values to occur in dry atmospheric conditions. The negative GPS IWV values predominantly occurred between September and May when the PFR recorded between 0.15 and 2.5 mm IWV. When negative GPS values occur, the mean IWV recorded by GPS and PFR, respectively, is 0.5 mm and +0.8 mm. [26] In view of the problems with the antenna at Jungfraujoch, a correction was made to the antenna phase center used in the ZTD processing [Haefele et al., 2004]. We examined this alternative data set and found that it had an unrealistic positive bias of 1.6 mm relative to the PFR data set. The correction based on the PFR data remains the best method of obtaining valid IWV values for Jungfraujoch Validation With Lidar Data [27] The corrected GPS data were compared with coincident Raman lidar data from the Jungfraujoch observatory. Over the course of a three-year period, there were 35 coincident valid lidar and GPS measurements. The Raman lidar measurements used in the comparison were mainly obtained between 0:00 and 1:00 local time (i.e. 23:00 to 0:00 UT in summer time and 0:00 to 1:00 UT in winter time). During a lidar measurement, a 7.5 m vertical resolution profile is obtained every 80 seconds. The available profiles were averaged over one hour time intervals and 75 to 150 m vertical resolution. The resulting product was integrated in order to obtain the IWV and compared with the closest hourly GPS IWV value. The best linear fit between the two data sets is given in equation (2), where the IWV is in mm: with low intercept (+0.05 mm) and slope close to 1, strongly supports the correction applied to the GPS data. In addition the residuals analysis shows a normal distribution and a standard deviation of 0.5 mm. [29] Unfortunately only one coincident measurement was obtained for a water vapor value greater than 7 mm. The highest Raman lidar measurements were made during the August 2003 heatwave when a persistent residual Planetary Boundary Layer occurred above the Swiss Alps at night as a consequence of the high PBL elevation during the day [Balin, 2004]. The 98th percentile of the GPS CDF is 9.4 mm and the maximum is 13.7 mm. It is hoped that as more lidar measurements are collected, the lidar and the GPS can be validated in this range. 4. Comparison of GPS and ERA40 Reanalysis Data [30] GPS IWV data were compared with the ERA40 IWV field. GPS observations are an independent test of the quality of reanalysis data since they are not at present assimilated by the meteorological observing system. We wanted to test how well the ERA40 data capture the variability of water vapor in and around the Alps and also to determine whether the magnitude of the ERA40 IWV field is correct. [31] For the comparison between ERA40 and GPS data, stations were chosen to reflect both geographical and altitude extremes. Figure 5 shows the locations of the stations as well as their altitude in meters. The altitude IWV lidar ¼ 0:05 þ 1:014 IWV correctedgps : ð2þ [28] Figure 4 shows the IWV obtained from the Raman lidar measurements plotted against coincident corrected GPS IWV values. The r 2 value is This comparison, Figure 4. IWV measured by the Raman lidar at Jungfraujoch plotted against the corrected GPS IWV values. 6of12

7 Figure 5. Positions of the GPS stations used for comparison with ERA40 reanalysis data. The altitude in meters is given beneath the station name. An upward pointing triangle indicates that the ERA40 reanalysis surface level is above the GPS station altitude while a downward pointing triangle indicates that it is below. differences between the ERA40 surface level and the GPS are listed in Table 3. Muttenz, Geneva, Payerne and Bern are in the Swiss plain north of the Alps and Locarno and Stabio are located at low altitudes in the Southern Alps. These were chosen to see if the reanalysis can capture the climatological differences north and south of the Alps. In addition, three stations in the Alps - Davos, (1580 m), Andermatt (2318 m) and Jungfraujoch (3584 m) - were also compared. The data for Jungfraujoch were corrected as described in section 3. Table 3 shows that the reanalysis data smooth out the Swiss topography, with the ERA40 surface level being lower than the actual surface in the Alps and higher in the plain and the valleys. To avoid information loss, we did not interpolate the reanalysis IWV field to the GPS station height. [32] Six hourly ERA40 reanalysis data were available until the end of August GPS data for the hour before and the hour after the ERA40 analysis time were averaged to match the ERA40 time. Reliable GPS ZTD data from the AGNES network became available in November 2000 for all of the comparison stations apart from Geneva, which was operational from December 2000 and Stabio which began operation in December There are therefore almost two years (2300 to 2600 measurements) of coincident data for all of the stations except Stabio, where there is less than one year (632 measurements) of coincident data. [33] The ERA40 IWV data were regressed against the coincident GPS IWV data for each of the stations and the results are given in Table 3. The ERA40 IWV is plotted against the GPS IWV for Davos in Figure 6. This station was selected as an example because it has the smallest altitude difference between the reanalysis surface and the GPS. Due to the altitude of the reanalysis surface being lower than that of the GPS station, the ERA40 IWV data show, as expected, a positive bias which increases slightly with increasing IWV. Higher biases occur in the summer than in the winter because temperature and IWV are higher in the summer months. [34] Figure 7 shows a time plot of the ERA40 bias at Davos normalized by the sum of the ERA40 and GPS IWV values. There is no obvious seasonal trend in the normalized bias, although the period from December 2001 Table 3. Results of the Regression of ERA40 IWV Values Against GPS IWV Values Station ERA40 Height, m Altitude Difference (ERA40 - GPS), m GPS IWV Standard Deviation Range, mm Intercept Slope r 2 of Residuals, mm Bias (ERA40 - GPS), mm Muttenz to Stabio to Locarno to Geneva to Payerne to Bern to Davos to Andermatt to Jungfraujoch to (corrected) 7of12

8 Figure 6. ERA40 IWV field regressed against coincident GPS IWV measurements for Davos (1580 m). The solid line shows the one to one relationship and the dotted line shows the best linear fit between the two data sets. to January 2002 is noticeable as the normalized bias is often negative. Negative values also tend to occur more frequently in January The mean normalized bias is but 28% of the time it is below zero. All cases where it is less than 0.25 occur between November and January and are often associated with a temperature inversion recorded by the Payerne sonde. In these cases, it is possible that the ERA40 model simulates the surface level (1296 m at Davos) as being in the dry air above the inversion whereas it is actually within the inversion. [35] Figure 8 gives examples of the ERA40 and GPS IWV at Davos for the months of January and June 2002, respectively. Throughout January 2002, the ERA40 IWV is, contrary to what we would expect from the altitude difference, often lower than the GPS IWV. The mean bias in the ERA40 IWV over the whole month is 0.5 mm Figure 7. Normalized bias in the ERA40 IWV field (bias in ERA40 IWV divided by the sum of the ERA40 and GPS IWV) plotted against time for Davos (1580 m). 8of12

9 Figure 8. (top) Comparison between ERA40 and GPS IWV at Davos (1580 m) for the month of January (bottom) Same for month of June (std 0.8 mm). If we look more carefully at the period between the 2nd and the 15th January 2002, we see that it was characterized by a high pressure blocking situation over Switzerland when temperature inversions were observed during all Payerne radiosoundings. The mean difference between the surface temperature and the maximum temperature recorded by the sonde was 5.3 K and the mean height of the inversion was 1605 m. During this period, the mean bias in the ERA40 data was 0.44 mm and could be as low as 2.2 mm, which is an indication that humidity during the inversion is under-estimated by the model. [36] From 16th January 2002 onwards, half of the Payerne radiosoundings showed a temperature inversion of at least 1 K. The mean temperature difference during the inversions was smaller ( 3.7K) and the mean height of the inversion, when it occurred, was just 902 m. During this time the mean bias in the ERA40 IWV was 0.6 mm, although the standard deviation was higher, 1.1 mm. The minimum bias in the ERA40 IWV during this period was 3.2 mm. We conclude that the negative bias in the ERA40 IWV, when a positive bias is expected, is related to the presence of a temperature inversion although there is by no means a strict relationship between the magnitude of the bias and the magnitude of the temperature inversion recorded by the Payerne radiosounding. [37] From January 10th 2002 onwards, the ERA40 IWV captures the general shape of large day to day IWV variations very well, with the exception of an increase in GPS IWV on the 20th January, where the ERA40 IWV field increases one day later. The form and timing of IWV variations are also described well by the reanalysis data during June 2002, apart from a situation on the 17th when the increase in the ERA40 IWV begins sooner and lasts longer than the increase in the GPS IWV. During the month of June 2002, the mean bias in the ERA40 IWV was +2.5 mm, which is higher than the average yearly bias of 1.1 mm due to the fact that the bias increases for larger IWV values. [38] Table 3 shows that the r 2 value varies between 0.91 to 0.94 at all stations where the difference between the model and the GPS surface altitudes is less than 1000 m. The Payerne radiosonde data are assimilated in the ERA40 reanalysis [Onogi, 2000]. It is therefore not surprising that the highest correlation occurs at Payerne, where the sonde is launched. The r 2 value is 0.85 and 0.61 for Andermatt (2318 m) and Jungfraujoch (3584 m), respectively. For statistical reasons, a reduction in the correlation coefficient with altitude is expected because the range of IWV values considered decreases with altitude from 44 mm for Muttenz (330 m) to 13.5 mm for Jungfraujoch (3584 m). The noise in the GPS signal remains, however, roughly constant. To demonstrate this point we looked at the degree of correlation between the GPS stations themselves. The r 2 correlation between GPS IWV measurements at Payerne and Muttenz, both in the Swiss plains, is 0.93 for the year In contrast, the r 2 values for the correlation between the GPS measurements at Payerne and those made at Andermatt and Jungfraujoch are 0.85 and 0.70, respectively. This indicates that the reduction in the correlation between ERA40 and GPS at high altitude stations is largely due to the lower dynamical range in the IWV values considered while the noise in the data remains roughly constant. [39] The stations in the valleys south of the Alps - Stabio and Locarno - have a similar r 2 value to those in the plains north of the Alps - Bern, Payerne, Geneva and Muttenz. However, in the former case, the slopes of the best fit linear relationships are considerably lower. On closer examination, we find that this is simply due to the larger altitude difference between the ERA40 surface level and the stations south of the Alps. There is a close linear relationship (r 2 = 0.99) between the slope of the relationship between ERA40 and GPS and the corresponding altitude difference in meters. This relationship is given in equation (3), where alt diff in meters is the difference between the ERA40 surface altitude and the GPS altitude. We therefore conclude that 9of12

10 Figure 9. The mean bias in ERA40 IWV compared to the ERA40 GPS altitude difference for the November 2000 to August 2002 time period. The line shows the linear relationship fitted to the data. there is no significant difference between the quality of the ERA40 data on the north and south side of the Alps. interpolated to the GPS station height using the hydrostatic equation. slope ¼ 0:997 0:0004 alt diff ð3þ IWV diff ¼ 0:3 þ 0:0446 press diff ð5þ [40] The reanalysis data capture the observed IWV variation very well. Because we made no vertical correction to scale the ERA40 data to the GPS height, there is as expected, an altitude related bias between the reanalysis and the observed data. This is illustrated in Figure 9 where the mean bias in the ERA40 IWV relative to GPS IWV is plotted against the altitude difference (ERA40 minus GPS). The bias is positive where the surface level is higher and negative where it is lower. There is a fairly linear relationship (r 2 = 0.97) between the bias in the ERA40 data and the altitude difference. This is given in equation (4) where IWV diff in mm is the mean IWV difference between ERA40 and GPS over the period considered: IWV diff ¼ 0:8 0:0044 alt diff [41] For the year 2001, surface pressure was also obtained from the reanalysis data and mean bias and mean surface pressure were compared. The mean annual surface pressure difference (ERA40 - GPS) ranges from 88 hpa (Locarno) to +220 hpa (Jungfraujoch). A positive relationship (r 2 = 0.99) was obtained between the bias in the ERA40 IWV and the difference between the ERA40 surface pressure and that at the GPS station. This is given in equation (5) where press diff is the mean surface pressure difference (ERA40 minus GPS) over the period considered. Where there is no pressure measurement at a GPS station, the surface pressure at the closest meteorological station is ð4þ [42] The fact that there is a good linear relationship between IWV difference and pressure difference, with an intercept close to zero, indicates a good quantitative agreement between ERA40 and GPS, in that most of the IWV differences can be explained by the difference in surface height. Equations (4) and (5) both have slightly negative intercepts which suggests that there may be a slight dry bias in the reanalysis data or a wet bias in the GPS data, i.e. if altitude or pressure difference was zero, the IWV difference would still be negative. [43] This comparison indicates that the ERA40 data for Switzerland generally provide a good analysis of the water vapor situation in recent years. However, this may not have been the case before the assimilation of satellite observations in the global observing system in 1979 as pointed out by Bengtsson et al. [2004a]. 5. Conclusions [44] The GPS water vapor observations at the high altitude Jungfraujoch station were compared with PFR data and were shown to have a negative bias. The bias showed a weak seasonal dependence, but we recommend, until further data are available, a constant correction for the GPS data, based on the comparison with PFR. The corrected GPS data were found to agree well with coincident Raman lidar measurements over an IWV range of 0.3 to 10 mm. Due to noise in the GPS data, 8% of the measurements have an unphysical IWV of less than zero after correction and 18% 10 of 12

11 have an IWV of less than 1 mm. The error in the GPS IWV (around 0.7 mm) is therefore frequently as large as the quantity being measured at Jungfraujoch. Following the observation by Ruckstuhl and Philipona [2005], that IWV is related to surface humidity and downward radiation, we are currently developing an estimate of the IWV at Jungfraujoch for low water vapor (<1 mm) situations. The estimate is based on PFR measurements when available and temperature, humidity and longwave downward radiation, when no PFR measurements were made. We believe that this combined product could be used to monitor any long term changes in the IWV at Jungfraujoch, provided the system for processing the raw GPS data remains stable. More years of GPS data will be required to detect a significant trend than if a lower noise measurement technique, such as a microwave radiometer, were available. The Jungfraujoch GPS data have proved useful for developing an altitude correction for the IWV observations made by the Swiss GPS network as well as for monitoring short term changes in water vapor due to meteorological phenomena (J. Morland and C. Mätzler, Spatial interpolation of GPS integrated water vapour measurements made in a mountainous terrain, submitted to Meteorological Applications, 2006). [45] Almost two years of quality controlled GPS IWV data from nine stations in the Swiss AGNES network, including the corrected Jungfraujoch data, were compared with the IWV field from the ERA40 reanalysis data. The GPS data are an independent check on the ERA40 data since they are not assimilated in the meteorological observing system. ERA40 captures water vapor variations on the timescales of several days very well as shown by the fact that the square of the Pearson correlation coefficient is greater than 0.9 for all stations except Jungfraujoch (3584 m) and Andermatt (2318 m) where it is 0.85 and 0.61, respectively, due to the fact that the dynamical range of the IWV decreases with altitude while the noise remains constant. [46] Since we did not vertically scale the ERA40 data to the GPS station height, we found, as expected, a bias in ERA40 relative to the GPS data. We investigated the relationship between the ERA40 IWV bias and the difference between the ERA40 surface pressure and that at the GPS station. The intercept is close to zero, 0.3 mm, and indicates that the IWV differences are mainly due to surface pressure. The fact that the intercept is slightly negative may indicate either a small dry bias in the reanalysis data or a wet bias in the GPS data. Neither possibility can be ruled out by this study. Morland et al. [2005] compared GPS IWV at Locarno, Bern and Davos with PFR or the closest radiosonde and found a wet bias of up to 1 mm. However, the GPS IWV at Payerne was found to have a dry bias of 0.8 mm relative to the PFR. Using two months of data from GPS stations in central Europe, Bengtsson et al. [2004a] found a slight wet bias in the ERA40 in comparison to GPS in the winter month and a small dry bias in the summer month. [47] No significant differences were found between stations in the plains north of the Alps and those in the valleys south of the Alps. A detailed comparison of ERA40 and GPS data at Davos indicated that in December and January, the ERA40 appears to under-estimate IWV during high pressure situations and associated temperature inversions. [48] In general, the findings indicate that both the Swiss GPS network and the ERA40 reanalysis produce good quality water vapor data sets for the Alpine regions. Future work will investigate how much of the IWV variability observed at the AGNES stations is due to altitude differences and how much to spatial variations. This is with a view to produce an altitude corrected product which can be compared with analysis data and used to investigate the factors affecting the spatial variability in the IWV. [49] Acknowledgments. This study was funded by the Swiss National Centre for Competence in Research Climate project (NCCR-Climate). We are grateful to the Federal Swiss Office of Topography for providing the GPS ZTD data set and to Elmar Brockmann and Daniel Ineichen for advice and information on the GPS observations. MeteoSwiss provided the meteorological data from which GPS IWV was calculated. Particular thanks are given to the International Foundation High Altitude Research Stations Jungfraujoch and Gornergrat for providing the logistical support for GPS, PFR, and LIDAR observations. 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