ALBERT

Description: Continuous monitoring of atmospheric humidity profiles is important for many applications, e.g. assessment of atmospheric stability and cloud formation. Nowadays there is a wide variety of ground-based sensors for atmospheric humidity profiling. Unfortunately there is no single instrument able to provide a measurement with complete vertical coverage, high vertical and temporal resolution, and good performance under all weather conditions, simultaneously. For example, Raman lidar (RL) measurements can provide water vapor with a high vertical resolution albeit with limited vertical coverage, due to sunlight contamination and the presence of clouds. Microwave radiometers (MWR) receive water vapor information throughout the troposphere though their vertical resolution is poor. In this work, we present a MWR and RL system synergy, which aims to overcome the specific sensor limitations. The retrieval algorithm combining these two instruments is an Optimal Estimation Method (OEM), which allows for an uncertainty analysis of the retrieved profiles. The OEM combines measurements and a priori information taking the uncertainty of both into account. The measurement vector consists of a set of MWR brightness temperatures and RL water vapor profiles. The method is applied to a two month field campaign around Jülich (Germany), focusing on clear sky periods. Different experiments are performed to analyse the improvements achieved via the synergy compared to the individual retrievals. When applying the combined retrieval, on average the theoretically determined absolute humidity uncertainty can be reduced by 60 % (38 %) with respect to the retrieval using only-MWR (only-RL) data. The analysis in terms of degrees of freedom per signal reveals that most information is gained above the usable lidar range, especially important during daytime when the lidar vertical coverage is limited. The retrieved profiles are further evaluated using radiosounding and GPS water vapor measurements. In general, the benefit of the sensor combination is especially strong in regions where Raman lidar data is not available (i.e. blind region, regions characterized by low signal to noise ratio), whereas if both instruments are available, RL dominates the retrieval. In the future, the method will be extended to cloudy conditions, when the impact of the MWR becomes stronger.

Description: Continuous monitoring of atmospheric humidity profiles is important for many applications, e.g., assessment of atmospheric stability and cloud formation. Nowadays there are a wide variety of ground-based sensors for atmospheric humidity profiling. Unfortunately there is no single instrument able to provide a measurement with complete vertical coverage, high vertical and temporal resolution and good performance under all weather conditions, simultaneously. For example, Raman lidar (RL) measurements can provide water vapor with a high vertical resolution, albeit with limited vertical coverage, due to sunlight contamination and the presence of clouds. Microwave radiometers (MWRs) receive water vapor information throughout the troposphere, though their vertical resolution is poor. In this work, we present an MWR and RL system synergy, which aims to overcome the specific sensor limitations. The retrieval algorithm combining these two instruments is an optimal estimation method (OEM), which allows for an uncertainty analysis of the retrieved profiles. The OEM combines measurements and a priori information, taking the uncertainty of both into account. The measurement vector consists of a set of MWR brightness temperatures and RL water vapor profiles. The method is applied to a 2-month field campaign around Jülich (Germany), focusing on clear sky periods. Different experiments are performed to analyze the improvements achieved via the synergy compared to the individual retrievals. When applying the combined retrieval, on average the theoretically determined absolute humidity uncertainty is reduced above the last usable lidar range by a factor of  ∼  2 with respect to the case where only RL measurements are used. The analysis in terms of degrees of freedom per signal reveal that most information is gained above the usable lidar range, especially important during daytime when the lidar vertical coverage is limited. The retrieved profiles are further evaluated using radiosounding and Global Position Satellite (GPS) water vapor measurements. In general, the benefit of the sensor combination is especially strong in regions where Raman lidar data are not available (i.e., blind regions, regions characterized by low signal-to-noise ratio), whereas if both instruments are available, RL dominates the retrieval. In the future, the method will be extended to cloudy conditions, when the impact of the MWR becomes stronger.

Description: A deeper understanding of how clouds will respond to a warming climate is one of the outstanding challenges in climate science. Uncertainties in the response of clouds, and particularly shallow clouds, have been identified as the dominant source of the discrepancy in model estimates of equilibrium climate sensitivity. As the community gains a deeper understanding of the many processes involved, there is a growing appreciation of the critical role played by fluctuations in water vapor and the coupling of water vapor and atmospheric circulations. Reduction of uncertainties in cloud-climate feedbacks and convection initiation as well as improved understanding of processes governing these effects will result from profiling of water vapor in the lower troposphere with improved accuracy and vertical resolution compared to existing airborne and space-based measurements. This paper highlights new technologies and improved measurement approaches for measuring lower tropospheric water vapor and their expected added value to current observations. Those include differential absorption lidar and radar, microwave occultation between low-Earth orbiters, and hyperspectral microwave remote sensing. Each methodology is briefly explained, and measurement capabilities as well as the current technological readiness for aircraft and satellite implementation are specified. Potential synergies between the technologies are discussed, actual examples hereof are given, and future perspectives are explored. Based on technical maturity and the foreseen near-mid-term development path of the various discussed measurement approaches, we find that improved measurements of water vapor throughout the troposphere would greatly benefit from the combination of differential absorption lidar focusing on the lower troposphere with passive remote sensors constraining the upper-tropospheric humidity.