Optimal estimation of water vapour profiles using a combination of Raman lidar and microwave radiometer

Abstract

Abstract. In this work, a two-step algorithm to obtain water vapour profiles from a combination of Raman lidar and microwave radiometer is presented. Both instruments were applied during an intensive 2-month measurement campaign (HOPE) close to Jülich, western Germany, during spring 2013. To retrieve reliable water vapour information from inside or above the cloud a two-step algorithm is applied. The first step is a Kalman filter that extends the profiles, truncated at cloud base, to the full height range (up to 10 km) by combining previous information and current measurement. Then the complete water vapour profile serves as input to the one-dimensional variational (1D-VAR) method, also known as optimal estimation. A forward model simulates the brightness temperatures which would be observed by the microwave radiometer for the given atmospheric state. The profile is iteratively modified according to its error bars until the modelled and the actually measured brightness temperatures sufficiently agree. The functionality of the retrieval is presented in detail by means of case studies under different conditions. A statistical analysis shows that the availability of Raman lidar data (night) improves the accuracy of the profiles even under cloudy conditions. During the day, the absence of lidar data results in larger differences in comparison to reference radiosondes. The data availability of the full-height water vapour lidar profiles of 17 % during the 2-month campaign is significantly enhanced to 60 % by applying the retrieval. The bias with respect to radiosonde and the retrieved a posteriori uncertainty of the retrieved profiles clearly show that the application of the Kalman filter considerably improves the accuracy and quality of the retrieved mixing ratio profiles.

Profiling water vapor mixing ratios in Finland by means of a Raman lidar, a satellite and a model

“…The exponential component accounts for the different attenuation of the returned signal with α(r, λ N 2 /H 2 O ), including both the molecular and the particle contribution. In this paper the particle extinction contribution is neglected as the resulting error is less than 1.3 % at 2 km altitude as calculated by Foth et al (2015). Whiteman (2003) concluded that such assumption can introduce an error of approximately 2 % for every 0.5 of aerosol optical depth change below 2 km altitude.…”

“…The second approach determines the calibration factor using simultaneous measurements from a collocated reference instrument. Approaches on this second technique include water vapor comparisons with radiosondes, satellites and microwave radiometers (Ferrare et al, 1995;Mattis et al, 2002;Miloshevich et al, 2004;Madonna et al, 2011;Leblanc et al, 2012;Reichardt et al, 2012;Navas-Guzmán et al, 2014;Foth et al, 2015). The accuracy of the calibration factor derived using these techniques fluctuates between 5 and 10 %.…”

“…However, small changes in the lidar setup such as change of neutral density filters requires the calculation of a new factor. A second method falling into the same calibration category is to take into account the simultaneous total precipitate water (TPW) from a microwave radiometer (Madonna et al, 2011;Foth et al, 2015) or any other instrument capable of delivering an equivalent information. By integrating the lidar's WVMR profiles, the two quantities become comparable and it is then possible to compute the calibration factor.…”

“…Furthermore, the wind-driven drifting of the device can be misleading in terms of geographical location of the vertical information. In contrast, remote sensing techniques such as microwave radiometers (England et al, 1992;Reagan et al, 1995), differential absorption lidars (DIALs) (Bösenberg, 1998), photometers (Barreto et al, 2013) and Raman lidars Ferrare et al, 1995;Turner et al, 2002;Whiteman, 2003;Leblanc et al, 2012;Navas-Guzmán et al, 2014;Foth et al, 2015) have been successfully adopted in water vapor studies. While microwave radiometers and photometers can accurately deliver the total precipitable water vapor (TPW), lidars (DIAL and Raman) are the only instruments available for high temporal and vertical resolution of continuous WVMR measurements.…”

Automated compact mobile Raman lidar for water vapor measurement: instrument description and validation by comparison with radiosonde, GNSS, and high-resolution objective analysis

“…Despite the potential usefulness of the MRL-measured data for weather forecasting, the MRL cannot measure the water vapor inside and above optically thick clouds. To overcome this disadvantage, it is important to use synergistic approaches with different instruments such as GNSS, microwave radiometer, and radiosonde to measure the water vapor distribution even under cloudy conditions (e.g., Foth and Pospichal, 2017).…”