Abstract

Lidar measurements of the atmospheric water vapor mixing ratio provide an excellent complement to radiosoundings and passive, ground-based remote sensors. Lidars are now routinely used that can make high spatial-temporal resolution measurements of water vapor from the surface to the stratosphere. Many of these systems can operate during the day and night, with operation only limited by clouds thick enough to significantly attenuate the laser beam. To enhance the value of these measurements for weather and climate studies, this paper presents an optimal estimation method (OEM) to retrieve the water vapor mixing ratio, aerosol optical depth profile, Ångstrom exponent, lidar constants, detector dead times, and measurement backgrounds from multichannel vibrational Raman-scatter lidars. The OEM retrieval provides the systematic uncertainties due to the overlap function, calibration factor, air density and Rayleigh-scatter cross sections, in addition to the random uncertainties of the retrieval due to measurement noise. The OEM also gives the vertical resolution of the retrieval as a function of height, as well as the height to which the contribution of the a priori is small. The OEM is applied to measurements made by the Meteoswiss Raman Lidar for Meteorological Observations (RALMO) in the day and night for clear and cloudy conditions. The retrieved water vapor mixing ratio is in excellent agreement with both the traditional lidar retrieval method and coincident radiosoundings.

© 2016 Optical Society of America

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References

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2015 (2)

A. Foth, H. Baars, P. Di Girolamo, and B. Pospichal, “Water vapor profiles from Raman lidar automatically calibrated by microwave radiometer data during HOPE,” Atmos. Chem. Phys. 15, 7753–7763 (2015).
[Crossref]

R. J. Sica and A. Haefele, “Retrieval of temperature from a multiple-channel Rayleigh-scatter lidar using an optimal estimation method,” Appl. Opt. 54, 1872–1889 (2015).
[Crossref]

2014 (2)

A. C. Povey, R. G. Grainger, D. M. Peters, and J. L. Agnew, “Retrieval of aerosol backscatter, extinction, and lidar ratio from Raman lidar with optimal estimation,” Atmos. Meas. Tech. 7, 757–776 (2014).
[Crossref]

T. von Clarmann, “Smoothing error pitfalls,” Atmos. Meas. Tech. 7, 3023–3034 (2014).
[Crossref]

2013 (3)

A. Moss, R. J. Sica, E. McCullough, K. Strawbridge, and J. Drummond, “Calibration and validation of water vapor lidar measurements from Eureka, Nunavut, using radiosondes and the atmospheric chemistry experiment Fourier transform spectrometer,” Atmos. Meas. Tech. 6, 741–749 (2013).
[Crossref]

E. Brocard, R. Philipona, A. Haefele, G. Romanens, A. Mueller, D. Ruffieux, V. Simeonov, and B. Calpini, “Raman lidar for meteorological observations, RALMO–part 2: validation of water vapor measurements,” Atmos. Meas. Tech. 6, 1347–1358 (2013).
[Crossref]

T. Dinoev, V. Simeonov, Y. Arshinov, S. Bobrovnikov, P. Ristori, B. Calpini, M. Parlange, and H. van den Bergh, “Raman lidar for meteorological observations, RALMO–part 1: instrument description,” Atmos. Meas. Tech. 6, 1329–1346 (2013).
[Crossref]

2012 (2)

A. C. Povey, R. G. Grainger, D. M. Peters, J. L. Agnew, and D. Rees, “Estimation of a lidar’s overlap function and its calibration by nonlinear regression,” Appl. Opt. 51, 5130–5143 (2012).
[Crossref]

D. N. Whiteman, M. Cadirola, D. Venable, M. Calhoun, L. Miloshevich, K. Vermeesch, L. Twigg, A. Dirisu, D. Hurst, E. Hall, A. Jordan, and H. Vömel, “Correction technique for Raman water vapor lidar signal-dependent bias and suitability for water vapor trend monitoring in the upper troposphere,” Atmos. Meas. Tech. 5, 2893–2916 (2012).
[Crossref]

2011 (2)

D. N. Whiteman, K. C. Vermeesch, L. D. Oman, and E. C. Weatherhead, “The relative importance of random error and observation frequency in detecting trends in upper tropospheric water vapor,” J. Geophys. Res. 116, D21118 (2011).
[Crossref]

D. D. Venable, D. N. Whiteman, M. N. Calhoun, A. O. Dirisu, R. M. Connell, and E. Landulfo, “Lamp mapping technique for independent determination of the water vapor mixing ratio calibration factor for a Raman lidar system,” Appl. Opt. 50, 4622–4632 (2011).
[Crossref]

2009 (1)

2008 (1)

2006 (2)

D. Whiteman, B. Demoz, P. Di Girolamo, J. Comer, I. Veselovskii, K. Evans, Z. Wang, D. Sabatino, G. Schwemmer, B. Gentry, R. Lin, A. Behrendt, E. Browell, R. Ferrare, S. Ismail, and J. Wang, “Raman lidar measurements during the international H2O project. Part II: case studies,” J. Atmos. Ocean. Technol. 23, 170–183 (2006).
[Crossref]

D. Whiteman, B. Demoz, P. Di Girolamo, J. Comer, I. Veselovskii, K. Evans, Z. Wang, M. Cadirola, K. Rush, G. Schwemmer, B. Gentry, S. H. Melfi, B. Mielke, D. Venable, and T. Van Hove, “Raman lidar measurements during the international H2O project. Part I: instrumentation and analysis techniques,” J. Atmos. Ocean. Technol. 23, 157–169 (2006).
[Crossref]

2005 (2)

K. Stelmaszczyk, M. Dell’Aglio, S. Chudzynski, T. Stacewicz, and L. Wöste, “Analytical function for lidar geometrical compression form-factor calculations,” Appl. Opt. 44, 1323–1331 (2005).
[Crossref]

P. Eriksson, C. Jiménez, and S. A. Buehler, “Qpack, a general tool for instrument simulation and retrieval work,” J. Quant. Spectrosc. Radiat. Transfer 91, 47-–64 (2005).
[Crossref]

2004 (1)

U. Löhnert, S. Crewell, and C. Simmer, “An integrated approach toward retrieving physically consistent profiles of temperature, humidity, and cloud liquid water,” J. Appl. Meteorol. 43, 1295–1307 (2004).

2003 (2)

2002 (1)

P. M. F. Forster and K. P. Shine, “Assessing the climate impact of trends in stratospheric water vapor,” Geophys. Res. Lett. 29, 10–11 (2002).
[Crossref]

1995 (2)

1993 (1)

1992 (1)

1984 (1)

M. Nicolet, “On the molecular scattering in the terrestrial atmosphere: an empirical formula for its calculation in the homosphere,” Planet. Space Sci. 32, 1467–1468 (1984).
[Crossref]

1972 (1)

1969 (1)

S. H. Melfi, J. D. Lawrence, and M. P. McCormick, “Observation of Raman scattering by water vapor in the atmosphere,” Appl. Phys. Lett. 15, 295–297 (1969).
[Crossref]

Agnew, J. L.

A. C. Povey, R. G. Grainger, D. M. Peters, and J. L. Agnew, “Retrieval of aerosol backscatter, extinction, and lidar ratio from Raman lidar with optimal estimation,” Atmos. Meas. Tech. 7, 757–776 (2014).
[Crossref]

A. C. Povey, R. G. Grainger, D. M. Peters, J. L. Agnew, and D. Rees, “Estimation of a lidar’s overlap function and its calibration by nonlinear regression,” Appl. Opt. 51, 5130–5143 (2012).
[Crossref]

Ansmann, A.

A. Ansmann and D. Müller, “Lidar and atmospheric aerosol particles,” in Lidar, C. Weitkamp, ed., Vol. 102 of Springer Series in Optical Sciences (Springer, 2005), pp. 105–141.

Argall, P. S.

Arshinov, Y.

T. Dinoev, V. Simeonov, Y. Arshinov, S. Bobrovnikov, P. Ristori, B. Calpini, M. Parlange, and H. van den Bergh, “Raman lidar for meteorological observations, RALMO–part 1: instrument description,” Atmos. Meas. Tech. 6, 1329–1346 (2013).
[Crossref]

Baars, H.

A. Foth, H. Baars, P. Di Girolamo, and B. Pospichal, “Water vapor profiles from Raman lidar automatically calibrated by microwave radiometer data during HOPE,” Atmos. Chem. Phys. 15, 7753–7763 (2015).
[Crossref]

Behrendt, A.

D. Whiteman, B. Demoz, P. Di Girolamo, J. Comer, I. Veselovskii, K. Evans, Z. Wang, D. Sabatino, G. Schwemmer, B. Gentry, R. Lin, A. Behrendt, E. Browell, R. Ferrare, S. Ismail, and J. Wang, “Raman lidar measurements during the international H2O project. Part II: case studies,” J. Atmos. Ocean. Technol. 23, 170–183 (2006).
[Crossref]

Bobrovnikov, S.

T. Dinoev, V. Simeonov, Y. Arshinov, S. Bobrovnikov, P. Ristori, B. Calpini, M. Parlange, and H. van den Bergh, “Raman lidar for meteorological observations, RALMO–part 1: instrument description,” Atmos. Meas. Tech. 6, 1329–1346 (2013).
[Crossref]

Borra, E. F.

Brocard, E.

E. Brocard, R. Philipona, A. Haefele, G. Romanens, A. Mueller, D. Ruffieux, V. Simeonov, and B. Calpini, “Raman lidar for meteorological observations, RALMO–part 2: validation of water vapor measurements,” Atmos. Meas. Tech. 6, 1347–1358 (2013).
[Crossref]

Browell, E.

D. Whiteman, B. Demoz, P. Di Girolamo, J. Comer, I. Veselovskii, K. Evans, Z. Wang, D. Sabatino, G. Schwemmer, B. Gentry, R. Lin, A. Behrendt, E. Browell, R. Ferrare, S. Ismail, and J. Wang, “Raman lidar measurements during the international H2O project. Part II: case studies,” J. Atmos. Ocean. Technol. 23, 170–183 (2006).
[Crossref]

Buehler, S. A.

P. Eriksson, C. Jiménez, and S. A. Buehler, “Qpack, a general tool for instrument simulation and retrieval work,” J. Quant. Spectrosc. Radiat. Transfer 91, 47-–64 (2005).
[Crossref]

Cadirola, M.

D. N. Whiteman, M. Cadirola, D. Venable, M. Calhoun, L. Miloshevich, K. Vermeesch, L. Twigg, A. Dirisu, D. Hurst, E. Hall, A. Jordan, and H. Vömel, “Correction technique for Raman water vapor lidar signal-dependent bias and suitability for water vapor trend monitoring in the upper troposphere,” Atmos. Meas. Tech. 5, 2893–2916 (2012).
[Crossref]

D. Whiteman, B. Demoz, P. Di Girolamo, J. Comer, I. Veselovskii, K. Evans, Z. Wang, M. Cadirola, K. Rush, G. Schwemmer, B. Gentry, S. H. Melfi, B. Mielke, D. Venable, and T. Van Hove, “Raman lidar measurements during the international H2O project. Part I: instrumentation and analysis techniques,” J. Atmos. Ocean. Technol. 23, 157–169 (2006).
[Crossref]

Calhoun, M.

D. N. Whiteman, M. Cadirola, D. Venable, M. Calhoun, L. Miloshevich, K. Vermeesch, L. Twigg, A. Dirisu, D. Hurst, E. Hall, A. Jordan, and H. Vömel, “Correction technique for Raman water vapor lidar signal-dependent bias and suitability for water vapor trend monitoring in the upper troposphere,” Atmos. Meas. Tech. 5, 2893–2916 (2012).
[Crossref]

Calhoun, M. N.

Calpini, B.

T. Dinoev, V. Simeonov, Y. Arshinov, S. Bobrovnikov, P. Ristori, B. Calpini, M. Parlange, and H. van den Bergh, “Raman lidar for meteorological observations, RALMO–part 1: instrument description,” Atmos. Meas. Tech. 6, 1329–1346 (2013).
[Crossref]

E. Brocard, R. Philipona, A. Haefele, G. Romanens, A. Mueller, D. Ruffieux, V. Simeonov, and B. Calpini, “Raman lidar for meteorological observations, RALMO–part 2: validation of water vapor measurements,” Atmos. Meas. Tech. 6, 1347–1358 (2013).
[Crossref]

T. S. Dinoev, V. B. Simeonov, B. Calpini, and M. Parlange, “Monitoring of Eyjafjallajökull ash layer evolution over payerne Switzerland with a Raman lidar,” in Proceedings of the TECO, Helsinki, Finland, 2010).

Carswell, A. I.

Chudzynski, S.

Clayton, M.

Comer, J.

D. Whiteman, B. Demoz, P. Di Girolamo, J. Comer, I. Veselovskii, K. Evans, Z. Wang, D. Sabatino, G. Schwemmer, B. Gentry, R. Lin, A. Behrendt, E. Browell, R. Ferrare, S. Ismail, and J. Wang, “Raman lidar measurements during the international H2O project. Part II: case studies,” J. Atmos. Ocean. Technol. 23, 170–183 (2006).
[Crossref]

D. Whiteman, B. Demoz, P. Di Girolamo, J. Comer, I. Veselovskii, K. Evans, Z. Wang, M. Cadirola, K. Rush, G. Schwemmer, B. Gentry, S. H. Melfi, B. Mielke, D. Venable, and T. Van Hove, “Raman lidar measurements during the international H2O project. Part I: instrumentation and analysis techniques,” J. Atmos. Ocean. Technol. 23, 157–169 (2006).
[Crossref]

Connell, R. M.

Crewell, S.

U. Löhnert, S. Crewell, and C. Simmer, “An integrated approach toward retrieving physically consistent profiles of temperature, humidity, and cloud liquid water,” J. Appl. Meteorol. 43, 1295–1307 (2004).

Dell’Aglio, M.

Demoz, B.

D. Whiteman, B. Demoz, P. Di Girolamo, J. Comer, I. Veselovskii, K. Evans, Z. Wang, M. Cadirola, K. Rush, G. Schwemmer, B. Gentry, S. H. Melfi, B. Mielke, D. Venable, and T. Van Hove, “Raman lidar measurements during the international H2O project. Part I: instrumentation and analysis techniques,” J. Atmos. Ocean. Technol. 23, 157–169 (2006).
[Crossref]

D. Whiteman, B. Demoz, P. Di Girolamo, J. Comer, I. Veselovskii, K. Evans, Z. Wang, D. Sabatino, G. Schwemmer, B. Gentry, R. Lin, A. Behrendt, E. Browell, R. Ferrare, S. Ismail, and J. Wang, “Raman lidar measurements during the international H2O project. Part II: case studies,” J. Atmos. Ocean. Technol. 23, 170–183 (2006).
[Crossref]

Di Girolamo, P.

A. Foth, H. Baars, P. Di Girolamo, and B. Pospichal, “Water vapor profiles from Raman lidar automatically calibrated by microwave radiometer data during HOPE,” Atmos. Chem. Phys. 15, 7753–7763 (2015).
[Crossref]

D. Whiteman, B. Demoz, P. Di Girolamo, J. Comer, I. Veselovskii, K. Evans, Z. Wang, D. Sabatino, G. Schwemmer, B. Gentry, R. Lin, A. Behrendt, E. Browell, R. Ferrare, S. Ismail, and J. Wang, “Raman lidar measurements during the international H2O project. Part II: case studies,” J. Atmos. Ocean. Technol. 23, 170–183 (2006).
[Crossref]

D. Whiteman, B. Demoz, P. Di Girolamo, J. Comer, I. Veselovskii, K. Evans, Z. Wang, M. Cadirola, K. Rush, G. Schwemmer, B. Gentry, S. H. Melfi, B. Mielke, D. Venable, and T. Van Hove, “Raman lidar measurements during the international H2O project. Part I: instrumentation and analysis techniques,” J. Atmos. Ocean. Technol. 23, 157–169 (2006).
[Crossref]

Dinoev, T.

T. Dinoev, V. Simeonov, Y. Arshinov, S. Bobrovnikov, P. Ristori, B. Calpini, M. Parlange, and H. van den Bergh, “Raman lidar for meteorological observations, RALMO–part 1: instrument description,” Atmos. Meas. Tech. 6, 1329–1346 (2013).
[Crossref]

Dinoev, T. S.

T. S. Dinoev, V. B. Simeonov, B. Calpini, and M. Parlange, “Monitoring of Eyjafjallajökull ash layer evolution over payerne Switzerland with a Raman lidar,” in Proceedings of the TECO, Helsinki, Finland, 2010).

Dirisu, A.

D. N. Whiteman, M. Cadirola, D. Venable, M. Calhoun, L. Miloshevich, K. Vermeesch, L. Twigg, A. Dirisu, D. Hurst, E. Hall, A. Jordan, and H. Vömel, “Correction technique for Raman water vapor lidar signal-dependent bias and suitability for water vapor trend monitoring in the upper troposphere,” Atmos. Meas. Tech. 5, 2893–2916 (2012).
[Crossref]

Dirisu, A. O.

Donovan, D. P.

Douglass, A.

J. Holton, P. Haynes, M. McIntyre, A. Douglass, R. Rood, and L. Pfister, “Stratosphere-troposphere exchange,” Rev. Geophys. 33, 403–439 (1995).
[Crossref]

Drummond, J.

A. Moss, R. J. Sica, E. McCullough, K. Strawbridge, and J. Drummond, “Calibration and validation of water vapor lidar measurements from Eureka, Nunavut, using radiosondes and the atmospheric chemistry experiment Fourier transform spectrometer,” Atmos. Meas. Tech. 6, 741–749 (2013).
[Crossref]

Eriksson, P.

P. Eriksson, C. Jiménez, and S. A. Buehler, “Qpack, a general tool for instrument simulation and retrieval work,” J. Quant. Spectrosc. Radiat. Transfer 91, 47-–64 (2005).
[Crossref]

Evans, K.

D. Whiteman, B. Demoz, P. Di Girolamo, J. Comer, I. Veselovskii, K. Evans, Z. Wang, D. Sabatino, G. Schwemmer, B. Gentry, R. Lin, A. Behrendt, E. Browell, R. Ferrare, S. Ismail, and J. Wang, “Raman lidar measurements during the international H2O project. Part II: case studies,” J. Atmos. Ocean. Technol. 23, 170–183 (2006).
[Crossref]

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D. Whiteman, B. Demoz, P. Di Girolamo, J. Comer, I. Veselovskii, K. Evans, Z. Wang, D. Sabatino, G. Schwemmer, B. Gentry, R. Lin, A. Behrendt, E. Browell, R. Ferrare, S. Ismail, and J. Wang, “Raman lidar measurements during the international H2O project. Part II: case studies,” J. Atmos. Ocean. Technol. 23, 170–183 (2006).
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A. C. Povey, R. G. Grainger, D. M. Peters, and J. L. Agnew, “Retrieval of aerosol backscatter, extinction, and lidar ratio from Raman lidar with optimal estimation,” Atmos. Meas. Tech. 7, 757–776 (2014).
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R. J. Sica and A. Haefele, “Retrieval of temperature from a multiple-channel Rayleigh-scatter lidar using an optimal estimation method,” Appl. Opt. 54, 1872–1889 (2015).
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A. Moss, R. J. Sica, E. McCullough, K. Strawbridge, and J. Drummond, “Calibration and validation of water vapor lidar measurements from Eureka, Nunavut, using radiosondes and the atmospheric chemistry experiment Fourier transform spectrometer,” Atmos. Meas. Tech. 6, 741–749 (2013).
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Figures (21)

Fig. 1.
Fig. 1. Count rate for 30 min of RALMO measurements beginning at 0000 UTC on Sept. 6, 2009. Left panel: analog channels (blue curve, water vapor; red curve, nitrogen). Right panel: digital channels.
Fig. 2.
Fig. 2. Jacobians for the analog (top) and digital (bottom) channels for the logarithm of the water vapor mixing ratio (left) and the aerosol optical depth profile (right) for the measurement shown in Fig. 1.
Fig. 3.
Fig. 3. Averaging kernels for the logarithm of the water vapor mixing ratio (left) and the aerosol optical depth profile (right) for the measurement shown in Fig. 1. The horizontal dashed line shows the height below which the retrieval is due primarily to the measurement and not the a priori. The averaging kernels are only shown every 300 m in altitude for clarity.
Fig. 4.
Fig. 4. Vertical resolution of the water vapor retrieval for the measurements shown in Fig. 1. The horizontal dashed line shows the height below which the retrieval is due primarily to the measurement and not the a priori.
Fig. 5.
Fig. 5. Residuals between the forward model and the measurements for the four channels (blue curves). The red curves show the standard deviation of the measurements.
Fig. 6.
Fig. 6. Retrieved water vapor mixing ratio (red curve) using the OEM. The blue curve is the mixing ratio using the traditional analysis method. The green curve is the radiosonde measurement. The sonde is launched at the start of the 30 min RALMO average. The dot-dashed line is the a priori mixing ratio profile used by the OEM. The horizontal dashed line shows the height below which the retrieval is due primarily to the measurement and not the a priori. The stairstep feature at the top of the traditional retrieval is due to a rounding issue in the central data base of MeteoSwiss and is not caused by the traditional retrieval technique.
Fig. 7.
Fig. 7. Water vapor mixing ratio uncertainties for random uncertainties (blue curve) and systematic uncertainties due to the Rayleigh-scatter cross section (red dashed curve), air density (gold dashed curve), lidar calibration factor (purple dashed curve) and overlap (green dot-dash curve). The horizontal dashed line shows the height below which the retrieval is due primarily to the measurement and not the a priori.
Fig. 8.
Fig. 8. Aerosol extinction coefficient at 355 nm found from the retrieved aerosol optical depth profile. Left panel: aerosol extinction coefficient at all heights. Right panel: aerosol extinction coefficient above the height of full overlap, with the horizontal bars indicating the random uncertainty of the extinction coefficient. In both panels the blue curve is an estimate of the molecular extinction coefficient. The horizontal dashed line shows the height below which the retrieved aerosol optical depth profile is due primarily to the measurement and not the a priori. The quantity retrieved below 2500 m is a combination of effects due to both aerosol optical density and the assumed overlap function.
Fig. 9.
Fig. 9. Aerosol transmission uncertainties for random uncertainties (blue curve) and systematic uncertainties due to the Rayleigh-scatter cross section (red dashed curve), air density (gold dashed curve), lidar calibration factor (purple dashed curve) and overlap (green dot-dash curve). The horizontal dashed line shows the height below which the retrieval is due primarily to the measurement and not the a priori. The sharp spike at 1750 m extends to 795%. Three-quarters of the uncertainty is due to the overlap function. Uncertainty in the air density is about 20% of the total, with a small contribution due to the random uncertainty.
Fig. 10.
Fig. 10. Count rate for 30 min of RALMO measurements beginning at 1200 UTC on Sept. 5, 2009. Left panel: analog channels (blue curve water vapor, red curve nitrogen). Right panel: digital channels.
Fig. 11.
Fig. 11. Averaging kernels for the logarithm of the water vapor mixing ratio (left) and the aerosol optical depth profile (right) for the measurements shown in Fig. 10. The horizontal dashed line shows the height below which the retrieval is due primarily to the measurement and not the a priori. The averaging kernels are only shown every 300 m in altitude for clarity.
Fig. 12.
Fig. 12. Residuals between the forward model and the measurements for the four channels (blue curves). The red curves show the standard deviation of the measurements.
Fig. 13.
Fig. 13. Retrieved water vapor mixing ratio (red curve) using the OEM. The blue curve is the mixing ratio using the traditional analysis method. The green curve is the radiosonde measurement. The sonde is launched at the start of the 30-min RALMO average. The dot-dashed line is the a priori mixing ratio profile used by the OEM. The horizontal dashed line shows the height below which the retrieval is due primarily to the measurement and not the a priori.
Fig. 14.
Fig. 14. Vertical resolution of the water vapor retrieval for the measurements shown in Fig. 10. The horizontal dashed line shows the height below which the retrieval is due primarily to the measurement and not the a priori.
Fig. 15.
Fig. 15. Water vapor mixing ratio uncertainties for random uncertainties (blue curve) and systematic uncertainties due to the Rayleigh-scatter cross section (red dashed curve), air density (gold dashed curve), lidar calibration factor (purple dashed curve) and overlap (green dot-dash curve). The horizontal dashed line shows the height below which the retrieval is due primarily to the measurement and not the a priori.
Fig. 16.
Fig. 16. Cloud base height on March 5, 2015, during the RALMO measurement period as measured by the MeteoSwiss Eliasson CBME80 Ceilometer.
Fig. 17.
Fig. 17. Averaging kernels for the logarithm of the water vapor mixing ratio (left) and the aerosol optical depth profile (right) for a 20-min cloudy period starting at 1210 UTC on March 5, 2015. The horizontal dashed line shows the height below which the retrieval is due primarily to the measurement and not the a priori. The averaging kernels are only shown every 200 m in altitude for clarity.
Fig. 18.
Fig. 18. Vertical resolution of the water vapor retrieval for a 20-min cloudy period starting at 1210 UTC on March 5, 2015. The horizontal dashed line shows the height below which the retrieval is due primarily to the measurement and not the a priori.
Fig. 19.
Fig. 19. Residuals between the forward model and the measurements for the 4 channels (blue curves). The red curves show the standard deviation of the measurements.
Fig. 20.
Fig. 20. Retrieved water vapor mixing ratio (red curve) using the OEM. The blue curve is the mixing ratio using the traditional analysis method. The green curve is the radiosonde measurement. The sonde is launched at 1200 UTC, while the RALMO 20-min measurement window for this retrieval begins at 1210 UT. The dot-dashed line is the a priori mixing ratio profile used by the OEM. The horizontal dashed line shows the height below which the retrieval is due primarily to the measurement and not the a priori.
Fig. 21.
Fig. 21. Water vapor mixing ratio uncertainties for random uncertainties (blue curve) and systematic uncertainties due to the Rayleigh-scatter cross section (red dashed curve), air density (gold dashed curve), lidar calibration factor (purple dashed curve) and overlap (green dot-dash curve). The horizontal dashed line shows the height below which the retrieval is due primarily to the measurement and not the a priori.

Tables (2)

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Table 1. Values and Associated Uncertainties of the Measurements and the a priori Retrieval and Forward Model Parametersa

Tables Icon

Table 2. Degrees of Freedom, Ångstrom Exponent, and Dead Time for the 3 Retrievals

Equations (11)

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S obs ( z ) = ψ ( z ) · n ( z ) z 2 + B ( z ) ,
S obs ( z ) = S true ( z ) e S true ( z ) γ ,
S obs ( z ) = S true ( z ) 1 + S true ( z ) γ ,
ψ ( z ) = C · O ( z ) · σ ram · Γ L · Γ R .
S H = O Γ L Γ H C H n air z 2 e q + B H
S N = O Γ L Γ N C N n N 2 z 2 + B N ,
Γ L , N , H ( z ) = e τ L , N , H ( z ) .
τ ( z ) = τ mol + τ aer = 0 z σ ray n air ( z ) d z + 0 z α d z .
τ aer ( λ ) = τ aer ( λ L ) ( λ λ L ) a ,
α a p = L R · β mol · ( β 1 ) ,
C H = η · C N .

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