• Behrendt, A., , Nakamura T. , , and Tsuda T. , 2004: Combined temperature lidar for measurements in the troposphere, stratosphere, and mesosphere. Appl. Opt., 43 , 29302939.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chilson, P. B., , Palmer R. D. , , Muschinski A. , , Hooper D. A. , , Schmidt G. , , and Steinhagen H. , 2001: SOMARE-99: A demonstrational field campaign for ultra-high resolution VHF atmospheric profiling with frequency diversity. Radio Sci., 36 , 695707.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Furumoto, J., , Kurimoto K. , , and Tsuda T. , 2003: Continuous observations of humidity profiles with the MU radar-RASS combined with GPS and radiosonde measurements. J. Atmos. Oceanic Technol., 20 , 2341.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gossard, E. E., , Gutman S. , , Stankov B. B. , , and Wolfe D. E. , 1999: Profiles of radio refractive index and humidity derived from radar wind profilers and the Global Positioning System. Radio Sci., 34 , 371383.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hocking, W. K., , and Mu P. K. L. , 1997: Upper and middle tropospheric kinetic energy dissipation rates from measurements of Cn2—Review of theories, in situ investigations and experimental studies using the Buckland Park atmospheric radar in Australia. J. Atmos. Sol. Terr. Phys., 59 , 17791803.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Imura, S., , Furumoto J. , , Tsuda T. , , and Nakamura T. , 2007: Estimation of humidity profiles by combining co-located VHF and UHF wind-profiling radar observation. J. Meteor. Soc. Japan, 85 , 301319.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Klaus, V., , Bianco L. , , Gaffard C. , , Matabuena M. , , and Hewison T. J. , 2006: Combining UHF radar wind profiler and microwave radiometer for the estimation of atmospheric humidity profiles. Meteor. Z., 15 , 8797.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kuo, Y-H., , Zou X. , , and Guo Y-R. , 1996: Variational assimilation of precipitable water using nonhydrostatic mesocale adjoint model. Part I: Moisture retrieval and sensitivity experiments. Mon. Wea. Rev., 124 , 122147.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Luce, H., , Yamamoto M. , , Fukao S. , , Hélal D. , , and Crochet M. , 2001: A frequency radar interferometric imaging technique applied with high resolution methods. J. Atmos. Sol. Terr. Phys., 63 , 221234.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Luce, H., , Hassenpflug G. , , Yamamoto M. , , and Fukao S. , 2006: High-resolution vertical imaging of the troposphere and lower atmosphere using the new MU radar system. Ann. Geophys., 24 , 19571975.

    • Search Google Scholar
    • Export Citation
  • Luce, H., , Hassenpflug G. , , Yamamoto M. , , Crochet M. , , and Fukao S. , 2007a: Range-imaging observations of cumulus convection and Kelvin-Helmholtz instabilities with the MU radar. Radio Sci., 42 , RS1005. doi:10.1029/2005RS003439.

    • Search Google Scholar
    • Export Citation
  • Luce, H., , Hassenpflug G. , , Yamamoto M. , , and Fukao S. , 2007b: Comparisons of refractive index gradient and stability profiles measured by balloons and the MU radar at a high vertical resolution in the lower stratosphere. Ann. Geophys., 25 , 4757.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Palmer, R. D., , Yu T-Y. , , and Chilson P. B. , 1999: Range imaging using frequency diversity. Radio Sci., 34 , 14851496.

  • Stankov, B. B., , Westwater E. R. , , and Gossard E. E. , 1996: Use of wind profiler estimates of significant moisture gradients to improve humidity profile retrieval. J. Atmos. Oceanic Technol., 13 , 12851290.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stankov, B. B., , Gossard E. E. , , Weber B. L. , , Lataitis R. J. , , White A. B. , , Welsh D. E. , , and Strauch R. G. , 2003: Humidity gradient profiles from wind profiling radars using NOAA/ETL advanced signal processing system (SPS). J. Atmos. Oceanic Technol., 20 , 322.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tsuda, T., , May P. T. , , Sato T. , , Kato S. , , and Fukao S. , 1988: Simultaneous observations of reflection echoes and refractive index gradient in the troposphere and lower stratosphere. Radio Sci., 23 , 655665.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tsuda, T., , Miyamoto M. , , and Furumoto J. , 2001: Estimation of a humidity profile using turbulence echo characteristics. J. Atmos. Oceanic Technol., 18 , 12141222.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • View in gallery

    (a) Time–height plot of specific humidity retrieved from the lidar observations from 14 Nov 2005 at 2333 LT in the lower troposphere. (b) The corresponding plot of the vertical humidity gradient. S1, S2, and S3 indicate humidity gradient sheet discussed in the text. (c) Time–height plot of radar echo power after doing the Capon processing and corrected from the distance attenuation effects (i.e., pz2). A minimum threshold of 35 dB has been applied to point out the most intense radar backscattering layers.

  • View in gallery

    Time evolution of the negative humidity gradient altitudes manually selected from Fig. 1b (solid lines) and strong radar echo peak altitudes selected in Fig. 1c using an automatic procedure (dots).

  • View in gallery

    Vertical profiles of power (dB) after doing the Capon processing at vertical incidence (solid line) and squared generalized potential refractive index gradient M2 derived from lidar humidity observations (dashed line).

  • View in gallery

    Height–time cross section of mixing ratio gradient retrieved from lidar data collected on 6 Jun 2006 at 2206 LT. The black dots indicate the positions of the radar echo peaks at (top) the range resolution of 150 m and (bottom) after doing the Capon processing.

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Simultaneous Observations of Thin Humidity Gradients in the Lower Troposphere with a Raman Lidar and the Very High-Frequency Middle- and Upper-Atmosphere Radar

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  • 1 Laboratoire de Sondages Electromagnétiques de l’Environnement Terrestre, CNRS, Université du Sud Toulon-Var, La Garde, France
  • | 2 Denso Corporation, Kariya, Aichi, Japan
  • | 3 Space and Upper Atmospheric Science Group, National Institute of Polar Research, Tokyo, Japan
  • | 4 Research Institute for Sustainable Humanosphere, Kyoto University, Uji, Japan
  • | 5 Department of Space Communication Engineering, Fukui University of Technology, Fukui, Japan
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Abstract

Humidity is, among other things, a key parameter in the evolution of atmospheric dynamics and in the formation of clouds and precipitation through latent heat release. The continuous observation of its vertical distribution is thus important in meteorology. In the absence of convection, humidity in the lower troposphere is distributed into nearly horizontally stratified layers. The thin humidity gradients at the edges of these layers are known to be the main cause of very high-frequency (VHF) stratosphere–troposphere (ST) radar backscatter in the lower troposphere. This property has been experimentally demonstrated many times in the literature from comparisons between balloon measurements and low-resolution radar observations. In the present work, original results of comparisons between Raman lidar measurements of water vapor and middle- and upper-atmosphere (MU) radar measurements of echo power using a range-imaging technique are shown at high spatial and temporal resolutions (∼50 m, ∼20 s). Other tremendous advantages of such comparisons are the simultaneity, time continuity, and colocalization of the lidar and radar measurements. The results show that the radar can be used for continuously monitoring the thin positive and negative gradients of humidity when operated in range-imaging mode. With additional information from balloon measurements, it would be possible to retrieve humidity profiles in the lower troposphere at an unprecedented vertical and time resolution.

Corresponding author address: Hubert Luce, LSEET, CNRS, Université du Sud Toulon-Var, La Garde 83957, France. Email: hubert.luce@lseet.univ-tln.fr

Abstract

Humidity is, among other things, a key parameter in the evolution of atmospheric dynamics and in the formation of clouds and precipitation through latent heat release. The continuous observation of its vertical distribution is thus important in meteorology. In the absence of convection, humidity in the lower troposphere is distributed into nearly horizontally stratified layers. The thin humidity gradients at the edges of these layers are known to be the main cause of very high-frequency (VHF) stratosphere–troposphere (ST) radar backscatter in the lower troposphere. This property has been experimentally demonstrated many times in the literature from comparisons between balloon measurements and low-resolution radar observations. In the present work, original results of comparisons between Raman lidar measurements of water vapor and middle- and upper-atmosphere (MU) radar measurements of echo power using a range-imaging technique are shown at high spatial and temporal resolutions (∼50 m, ∼20 s). Other tremendous advantages of such comparisons are the simultaneity, time continuity, and colocalization of the lidar and radar measurements. The results show that the radar can be used for continuously monitoring the thin positive and negative gradients of humidity when operated in range-imaging mode. With additional information from balloon measurements, it would be possible to retrieve humidity profiles in the lower troposphere at an unprecedented vertical and time resolution.

Corresponding author address: Hubert Luce, LSEET, CNRS, Université du Sud Toulon-Var, La Garde 83957, France. Email: hubert.luce@lseet.univ-tln.fr

1. Introduction

Humidity is, among other things, a key parameter in the evolution of atmospheric dynamics and in the formation of clouds and precipitations. The continuous observation of its vertical distribution is important in meteorology. Indeed, the lack of data and inaccuracies can produce large errors when forecasting short-term precipitation (e.g., Kuo et al. 1996).

Vertical profiles of humidity can be measured in situ by radiosondes hung below balloons. Comparisons with very high-frequency (VHF) stratosphere–troposphere (ST) and ultrahigh-frequency (UHF) radar (or wind profiler) measurements showed that these radars are mainly sensitive to humidity gradients in the lower troposphere in clear air when using a vertical beam (e.g., Tsuda et al. 1988; Hocking and Mu 1997). From this property, some attempts have been made for retrieving humidity from ST radars and wind profilers (e.g., Stankov et al. 1996; Gossard et al. 1999; Tsuda et al. 2001; Stankov et al. 2003; Furumoto et al. 2003; Klaus et al. 2006; Imura et al. 2007). However, these attempts are limited in resolutions, and their validations through comparisons with other instruments are sometimes made difficult by the different nature of the measurements. For example, radiosondes only provide a local value of the measured parameters at different times along an oblique path and sometimes far away from the radar site. An ST radar provides vertical profiles of backscattered power averaged over the acquisition time and the radar resolution volume. Its range resolution (typically 150 m or more) is a limitation because the humidity (and temperature) gradients are frequently shallower.

Obviously, colocalized and continuous observations of humidity would more properly help to validate the radar technique approach. This can be achieved from colocalized Raman lidar observations (Imura et al. 2007). Lidar observations can be performed continuously during favorable conditions (clear nights). Humidity is retrieved from inelastic atmospheric backscatter light by water vapor molecules. At Shigaraki Middle- and Upper-Atmosphere (MU) Observatory, a Rayleigh–Mie–Raman (RMR) lidar has been operating for measuring, in addition to humidity profiles, temperature, optical properties (backscatter and extinction coefficients) of aerosol, and cirrus cloud particle properties in the troposphere (Behrendt et al. 2004). Upon appropriate calibration, the Raman lidar set up at Shigaraki provides vertical profiles of water vapor mixing ratio at a high temporal (several 10 s) and altitude (∼10 m) resolution up to about 3–4 km or more. On the other hand, the VHF (46.5 MHz) MU radar was upgraded in 2004 for improving its range resolution with the frequency domain interferometric imaging (FII or range imaging) mode (e.g., Palmer et al. 1999; Luce et al. 2001, 2006). Experimental studies demonstrated the effectiveness of the FII technique using the Capon processing method to resolve temperature and humidity gradients and turbulent layers much thinner than 150 m by analyzing the consistency of the high-resolution reflectivity profiles and by comparing these profiles with parameter profiles estimated from radiosonde data (e.g., Chilson et al. 2001; Luce et al. 2007b).

In the present work, the results of comparisons between lidar measurements of humidity (mixing ratio) gradients and radar reflectivity measurements at a high vertical resolution in range-imaging mode are thus presented. The tremendous advantages of such comparisons are the simultaneity, time continuity, and colocalization of the lidar and radar measurements at similar time and range resolutions.

Section 2 describes the experimental setup, and the observational results are shown in section 3. Finally, conclusions are given in section 4.

2. Experimental setup

a. Lidar

The RMR lidar has a neodymium-doped yttrium aluminum garnet (Nd:YAG) pulsed laser (532 nm, 600 mJ, 50-Hz output) and a telescope of 82 cm in diameter. The received signals are divided into several channels for different measurements (two elastic channels, two rotational Raman channels, and one water vapor Raman channel at 660 nm; Behrendt et al. 2004). The lidar was operated during night periods on 14–15 November 2005 and on 6–7 June 2006. In November 2005 (6–7 June 2006), observations were conducted at a time resolution of 30 s (20 s) and height resolution of 9 m (18 m). A calibration procedure released a few times a year is necessary for retrieving absolute values of the mixing ratio. It can be made by using a reference profile obtained from collocated and simultaneous radiosonde measurements.

b. Radar

The MU radar was operated in a five-frequency FII mode at ranges between 1.32 and 20.32 km above mean sea level (MSL) with an initial height resolution of 150 m. In November 2005, this mode was applied in two directions (vertical and 10° off zenith toward north) and is described in Luce et al. (2007a). In June 2006, the FII mode was applied in five directions (vertical and four oblique beams aligned north, east, south, and west, 10° off zenith). The collected data are processed with the adaptive filter-bank Capon method. Time resolution is 33 and 20.48 s in November 2005 and June 2006, respectively. A vertical sampling of 5 m has been applied, but this value does not correspond to the vertical resolution achieved by the Capon processing because the vertical resolution depends on a signal-to-noise ratio (SNR). For SNR larger than a few decibels, the technique allows us to resolve refractive index fluctuation layers much shallower than the initial radar range resolution separated by a few tens of meters only (e.g., Palmer et al. 1999; Luce et al. 2001, 2006). It also allows us to monitor vertical displacements as small as a few tens of meters or less (e.g., Chilson et al. 2001). The resolution performances are expected to be similar for both experiments because SNR values were much larger than 0 dB for both cases in the analyzed altitude ranges.

3. Observational results

Figure 1a shows a time–height plot of lidar-derived mixing ratio (g kg−1) at a time resolution of about 30 s and a height sampling of 9 m on 14–15 November 2005 from 2333 to 0233 local time (LT) and from 1.5 to 3.8 km MSL. For smoothing out the short-term noise fluctuations, a seven-point running average has been applied in height. A thick and quite homogeneous humidity layer is observed up to about 2.5 km MSL and the mixing ratio decreases above. Some profiles cannot be used above 2.5 km, mainly before 2340 LT and between 0110 and 0140 LT because of the presence of clouds. Figure 1b shows the corresponding time–height plot of mixing ratio gradient (g kg−1 km−1) by using a two-point centered finite difference scheme. Among the most striking features, three negative humidity gradient sheets (marked by S1, S2, and S3) can be clearly identified near the top of the thick humid layer above 2.5 km MSL at about 0150 LT. Here, S2 corresponds to the strongest gradient after 0150 LT (about −50 g kg−1 km−1), but S1 was the strongest before ∼0150 LT. A fourth strong gradient is also observed about 100 m below S1 but cannot clearly be monitored after 0158 LT. Figure 2 shows their position versus time from 0150 to 0222 LT (solid lines); S1 and S2 are vertically separated by about 150 m or less. S3 shows vertical oscillations and moves progressively from 3.1 km down to 2.8 km MSL. In Fig. 1b, some other sporadic (positive and negative) gradient peaks can also be noticed. They can result either from noise or from weaker gradient sheets not fully resolved because of the lack of sensitivity of the lidar.

Figure 1c shows the corresponding time–height plot of radar echo power (dB) corrected from the distance attenuation effects (or reflectivity in arbitrary units, i.e., pz2, where p is power and z is altitude) observed with the vertical beam between 1.50 and 3.80 km MSL after doing the Capon processing. Vertical white bands correspond to radar stops. An arbitrary minimum threshold of 35 dB has been applied for selecting the most intense echoing layers. The thickness of the radar peaks (defined here as their full width at −3 dB taken from the height of the maximum echo power) does not exceed a few tens of meters. The absence of strong and thin layers in the radar plot below ∼2.5 km may be explained by the nearly homogeneous humidity layer in this altitude range. On the contrary, thin echoing layers are clearly observed at the altitudes of the humidity gradients above ∼2.5 km. The good correspondence between the position of the radar echoing layers and the humidity gradients is better seen in Fig. 2. The small circles show the position of all the radar peaks in the height range are 2.3–3.2 km. Here, S1, S2, and S3 and the short-lived layer below S1 correspond well with the radar peaks and the altitude difference does not usually exceed 20 m. The oscillatory motions of the upper layer of about 50 m in amplitude are very similar to those reported for S3. Thus, the FII technique successfully monitored the thin humidity gradients revealed by the lidar measurements. Some peaks are not associated with the humidity gradients measured by the lidar (e.g., around 0158 LT and between 2.9 and 3.0 km, after 0212 LT below 2.5 km). However, this apparent disagreement, which occurs when the radar echo power is weak, can be largely explained by the lack of sensitivity of the lidar measurements. Another significant discrepancy can also be noted between 0210 and 0216 LT and around 2.8 km, where a pair of humidity gradients separated by less than 100 m is observed. The radar plot does not reproduce a similar feature but rather a single and thin layer at the middle. We did not find suitable explanations for this disagreement.

Figure 3 shows an attempt at a comparison between the squared generalized potential refractive index gradient M2 such that
i1520-0426-27-5-950-e1
derived from the lidar and radar data where p is pressure, T is temperature, q is specific humidity, and g is acceleration of gravity. Here, N2 = g/T[(dT/dz)+ Γ] is the squared Brünt–Vaïsälä frequency, and Γ is the adiabatic lapse rate. On the one hand, because high-resolution humidity profiles are only available, the profile of M2 (hereafter noted ML2) is estimated from lidar observations assuming a standard profile of T and a standard value of N2 ∼ 10−4 s−2 for the troposphere. On the other hand, the profile of M2 (hereafter noted MR2) is given by the range-corrected radar echo power profiles averaged at 0150 and 0200 LT (10 min), using the fact that the radar reflectivity at vertical incidence has been found to be proportional to M2 (e.g., Tsuda et al. 1988). An arbitrary constant of +110 dB has been applied to ML2 for fitting the MR2 values. The position and amplitude of ML2 peaks agree well with the MR2 peaks, indicating that humidity contribution is likely dominant everywhere in this altitude range. The dynamics in amplitude of the MR2 profile is less than the ML2 profile (i.e., the minima are not so well pronounced) because of the limitations of the Capon method.

Figure 4 shows some results of comparisons for a longer period of observations on 6–7 June 2006. Because there was no thick cloud during the experiment, the atmospheric conditions were thus favorable to lidar measurements of humidity. The horizontal stratification was also more pronounced below the altitude of 3.0 km than in November 2005. Figure 4 shows the time–height cross section of the mixing ratio gradient derived from humidity observations by the RMR lidar obtained from 2204 to 0420 LT and between 1.245 and 2.70 km MSL. The results above 2.70 km are not shown because of the low sensitivity of the lidar. Red and blue color levels indicate the positive and negative gradients, respectively. Time resolution is 15 s and the vertical sampling is 18 m, but a three- and five-point running average has been applied in height and time, respectively, for a better monitoring of the gradients. Superimposed are the positions of the radar reflectivity peaks (black circles) obtained at the initial range resolution of 150 m (top panel) and after doing the Capon processing (bottom panel). The positions have been selected from an automatic procedure. The absence of peaks between 0120 and 0135 LT is due to a radar stop.

The agreement when comparing at the radar range resolution of 150 m is extremely poor. On the contrary, the agreement is in general excellent after doing the Capon processing and these results validate the high performances of the FII technique with the Capon processing. With a few exceptions described later, all the most significant humidity gradients are monitored by the radar in range-imaging mode. Contrary to the previous example where negative mixing ratio gradients were only reported, these results showed that the radar tracks both positive and negative humidity gradients. The vertical displacements of these gradients, showing wavy fluctuations likely produced by gravity waves, are also revealed by the radar. They are not detectable at the initial range resolution. When the humidity gradient is not confined within a thin layer but rather scattered (e.g., before 2320 LT in the height range of 1.6–2.0 km or between 0140 and 0200 LT in the height range of 1.7–2.0 km), the radar peaks are also randomly distributed.

However, some rare but significant discrepancies can be noted, for example, between 2340 and 0020 LT below 1.80 km. There are two cases (around 1.40 and 1.65 km) where a negative humidity gradient closely surrounded by two strong positive gradients is not detected by the radar. In these cases, the Capon processing revealed two peaks instead of three peaks. Such a disagreement is expected when the gradients are closely separated (here a few tens of meters) because the Capon method has a limited resolution power even in case of a strong SNR (e.g., Palmer et al. 1999; Luce et al. 2001).

4. Conclusions

In the present work, we showed humidity observations at a high time and vertical resolution with the RMR lidar. The lidar can sometimes provide humidity profiles up to about 3.5 km. Simultaneous MU radar observations have been conducted in range-imaging (FII) mode for obtaining high vertical resolution profiles of radar reflectivity. At some rare exceptions, excellent agreement has been found between the lidar-derived humidity gradients and the radar reflectivity peaks revealed by the Capon processing method. Consequently, the presented results confirm the good performances of the FII technique and constitute a validation of the technique for monitoring thin humidity gradients at a high height and time resolution (on the order of ∼50 m and ∼30 s or even less). Performances of the lidar and radar measurements are thus similar to those obtained with a Vaisala sonde but with the advantage of the verticality and instantaneity of the measurements and of the continuity of the observations. A similar conclusion was obtained by Luce et al. (2007b) when comparing radar and balloon observations of N2 in the stratosphere at a vertical resolution of 50 m.

The present results are also a confirmation that humidity gradients are the main cause of the radar echo peaks observed at high resolution in the lower troposphere (independently of the underlying backscattering mechanisms). Radar observations alone cannot dispel the ambiguity on the sign of the humidity gradients. More elaborated approaches using complementary observations [radio acoustic sounding system (RASS), GPS, microwave radiometers, etc.] are necessary (e.g., Stankov et al. 1996; Gossard et al. 1999; Tsuda et al. 2001; Furumoto et al. 2003; Klaus et al. 2006; Imura et al. 2007). However, the tremendous advantage of the radar measurements in range-imaging mode is its capability to resolve quick variations of humidity disturbances produced by gravity waves, turbulence, and convection independently of the period of the day and during all types of weather.

Acknowledgments

The MU radar is operated by and belongs to Kyoto University. The work provided by the main author was partly supported by Kyoto University.

REFERENCES

  • Behrendt, A., , Nakamura T. , , and Tsuda T. , 2004: Combined temperature lidar for measurements in the troposphere, stratosphere, and mesosphere. Appl. Opt., 43 , 29302939.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chilson, P. B., , Palmer R. D. , , Muschinski A. , , Hooper D. A. , , Schmidt G. , , and Steinhagen H. , 2001: SOMARE-99: A demonstrational field campaign for ultra-high resolution VHF atmospheric profiling with frequency diversity. Radio Sci., 36 , 695707.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Furumoto, J., , Kurimoto K. , , and Tsuda T. , 2003: Continuous observations of humidity profiles with the MU radar-RASS combined with GPS and radiosonde measurements. J. Atmos. Oceanic Technol., 20 , 2341.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gossard, E. E., , Gutman S. , , Stankov B. B. , , and Wolfe D. E. , 1999: Profiles of radio refractive index and humidity derived from radar wind profilers and the Global Positioning System. Radio Sci., 34 , 371383.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hocking, W. K., , and Mu P. K. L. , 1997: Upper and middle tropospheric kinetic energy dissipation rates from measurements of Cn2—Review of theories, in situ investigations and experimental studies using the Buckland Park atmospheric radar in Australia. J. Atmos. Sol. Terr. Phys., 59 , 17791803.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Imura, S., , Furumoto J. , , Tsuda T. , , and Nakamura T. , 2007: Estimation of humidity profiles by combining co-located VHF and UHF wind-profiling radar observation. J. Meteor. Soc. Japan, 85 , 301319.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Klaus, V., , Bianco L. , , Gaffard C. , , Matabuena M. , , and Hewison T. J. , 2006: Combining UHF radar wind profiler and microwave radiometer for the estimation of atmospheric humidity profiles. Meteor. Z., 15 , 8797.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kuo, Y-H., , Zou X. , , and Guo Y-R. , 1996: Variational assimilation of precipitable water using nonhydrostatic mesocale adjoint model. Part I: Moisture retrieval and sensitivity experiments. Mon. Wea. Rev., 124 , 122147.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Luce, H., , Yamamoto M. , , Fukao S. , , Hélal D. , , and Crochet M. , 2001: A frequency radar interferometric imaging technique applied with high resolution methods. J. Atmos. Sol. Terr. Phys., 63 , 221234.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Luce, H., , Hassenpflug G. , , Yamamoto M. , , and Fukao S. , 2006: High-resolution vertical imaging of the troposphere and lower atmosphere using the new MU radar system. Ann. Geophys., 24 , 19571975.

    • Search Google Scholar
    • Export Citation
  • Luce, H., , Hassenpflug G. , , Yamamoto M. , , Crochet M. , , and Fukao S. , 2007a: Range-imaging observations of cumulus convection and Kelvin-Helmholtz instabilities with the MU radar. Radio Sci., 42 , RS1005. doi:10.1029/2005RS003439.

    • Search Google Scholar
    • Export Citation
  • Luce, H., , Hassenpflug G. , , Yamamoto M. , , and Fukao S. , 2007b: Comparisons of refractive index gradient and stability profiles measured by balloons and the MU radar at a high vertical resolution in the lower stratosphere. Ann. Geophys., 25 , 4757.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Palmer, R. D., , Yu T-Y. , , and Chilson P. B. , 1999: Range imaging using frequency diversity. Radio Sci., 34 , 14851496.

  • Stankov, B. B., , Westwater E. R. , , and Gossard E. E. , 1996: Use of wind profiler estimates of significant moisture gradients to improve humidity profile retrieval. J. Atmos. Oceanic Technol., 13 , 12851290.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stankov, B. B., , Gossard E. E. , , Weber B. L. , , Lataitis R. J. , , White A. B. , , Welsh D. E. , , and Strauch R. G. , 2003: Humidity gradient profiles from wind profiling radars using NOAA/ETL advanced signal processing system (SPS). J. Atmos. Oceanic Technol., 20 , 322.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tsuda, T., , May P. T. , , Sato T. , , Kato S. , , and Fukao S. , 1988: Simultaneous observations of reflection echoes and refractive index gradient in the troposphere and lower stratosphere. Radio Sci., 23 , 655665.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tsuda, T., , Miyamoto M. , , and Furumoto J. , 2001: Estimation of a humidity profile using turbulence echo characteristics. J. Atmos. Oceanic Technol., 18 , 12141222.

    • Crossref
    • Search Google Scholar
    • Export Citation

Fig. 1.
Fig. 1.

(a) Time–height plot of specific humidity retrieved from the lidar observations from 14 Nov 2005 at 2333 LT in the lower troposphere. (b) The corresponding plot of the vertical humidity gradient. S1, S2, and S3 indicate humidity gradient sheet discussed in the text. (c) Time–height plot of radar echo power after doing the Capon processing and corrected from the distance attenuation effects (i.e., pz2). A minimum threshold of 35 dB has been applied to point out the most intense radar backscattering layers.

Citation: Journal of Atmospheric and Oceanic Technology 27, 5; 10.1175/2010JTECHA1372.1

Fig. 2.
Fig. 2.

Time evolution of the negative humidity gradient altitudes manually selected from Fig. 1b (solid lines) and strong radar echo peak altitudes selected in Fig. 1c using an automatic procedure (dots).

Citation: Journal of Atmospheric and Oceanic Technology 27, 5; 10.1175/2010JTECHA1372.1

Fig. 3.
Fig. 3.

Vertical profiles of power (dB) after doing the Capon processing at vertical incidence (solid line) and squared generalized potential refractive index gradient M2 derived from lidar humidity observations (dashed line).

Citation: Journal of Atmospheric and Oceanic Technology 27, 5; 10.1175/2010JTECHA1372.1

Fig. 4.
Fig. 4.

Height–time cross section of mixing ratio gradient retrieved from lidar data collected on 6 Jun 2006 at 2206 LT. The black dots indicate the positions of the radar echo peaks at (top) the range resolution of 150 m and (bottom) after doing the Capon processing.

Citation: Journal of Atmospheric and Oceanic Technology 27, 5; 10.1175/2010JTECHA1372.1

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