Abstract. The Finnish Meteorological Institute, in collaboration with the University of Helsinki, has established a new ground-based remote-sensing network in Finland. The network consists of five topographically, ecologically and climatically different sites distributed from southern to northern Finland. The main goal of the network is to monitor air pollution and boundary layer properties in near real time, with a Doppler lidar and ceilometer at each site. In addition to these operational tasks, two sites are members of the Aerosols, Clouds and Trace gases Research InfraStructure Network (ACTRIS); a Ka band cloud radar at Sodankylä will provide cloud retrievals within CloudNet, and a multi-wavelength Raman lidar, PollyXT (POrtabLe Lidar sYstem eXTended), in Kuopio provides optical and microphysical aerosol properties through EARLINET (the European Aerosol Research Lidar Network). Three C-band weather radars are located in the Helsinki metropolitan area and are deployed for operational and research applications. We performed two inter-comparison campaigns to investigate the Doppler lidar performance, compare the backscatter signal and wind profiles, and to optimize the lidar sensitivity through adjusting the telescope focus length and data-integration time to ensure sufficient signal-to-noise ratio (SNR) in low-aerosol-content environments. In terms of statistical characterization, the wind-profile comparison showed good agreement between different lidars. Initially, there was a discrepancy in the SNR and attenuated backscatter coefficient profiles which arose from an incorrectly reported telescope focus setting from one instrument, together with the need to calibrate. After diagnosing the true telescope focus length, calculating a new attenuated backscatter coefficient profile with the new telescope function and taking into account calibration, the resulting attenuated backscatter profiles all showed good agreement with each other. It was thought that harsh Finnish winters could pose problems, but, due to the built-in heating systems, low ambient temperatures had no, or only a minor, impact on the lidar operation – including scanning-head motion. However, accumulation of snow and ice on the lens has been observed, which can lead to the formation of a water/ice layer thus attenuating the signal inconsistently. Thus, care must be taken to ensure continuous snow removal.

The Finnish Meteorological Institute, in collaboration with the University of Helsinki, has established a new ground-based remote-sensing network in Finland. The network consists of five topographically, ecologically and climatically different sites distributed from southern to northern Finland. The main goal of the network is to monitor air pollution and boundary layer properties in near real time, with a Doppler lidar and ceilometer at each site. In addition to these operational tasks, two sites are members of the Aerosols, Clouds, and Trace gases Research InfraStructure Network (ACTRIS); a K_a-band Doppler cloud radar at Sodankylä will provide cloud retrievals within CloudNet, and a multi-wavelength Raman lidar, POLLY^XT (POrtabLe Lidar sYstem eXTended), in Kuopio provides optical and microphysical aerosol properties through EARLINET (European Aerosol Research Lidar Network to Establish an Aerosol Climatology). Three C-band weather radars are located in the Helsinki metropolitan area and are deployed for operational and research applications. We carried out two inter-comparison campaigns to investigate the Doppler lidar performance. The aims of the campaigns were to compare the backscatter coefficient and retrieved wind profiles, and to optimise the lidar sensitivity through adjusting the telescope focus and data-integration time to ensure enough signals in low-aerosol-content environments. The wind profiles showed good agreement between different lidars. However, due to inaccurate telescope focus setting and varying receiver sensitivity, backscatter coefficient profiles showed disagreement between the lidars. Harsh Finnish winters could pose problems, but, due to the built-in heating systems, low ambient temperatures had no, or only a minor, impact on the lidar operation: including scanning-head motion. However, accumulation of snow and ice on the lens has been observed, which can lead to formation of a water/ice layer thus attenuating the signal inconsistently. Thus, care must be taken to ensure continuous snow removal

The explanation for climate change is still searching for an experimental proof and the most important question is whether climate change is anthropogenic. According to the Intergovernmental Panel on Climate Change IPCC global warming is mostly man made due to the increasing CO2 concentration. In this work we study the contributions of humidity and clouds to the surface temperature. We will show that changes of relative humidity or low cloud cover explain the major changes in the global mean temperature. We will present the evidence of this argument using the observed relative humidity between years 1970 and 2011 and the observed low cloud cover between years 1983 and 2008. One percent increase in relative humidity or in low cloud cover decreases the temperature by 0.15 °C and 0.11 °C, respectively. In the time periods mentioned before the contribution of the CO2 increase was less than 10% to the total temperature change.

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