Capsule summary.
Helicopter-borne observations with unprecedented high resolution provide new insights in the fine-scale structure of marine boundary layer clouds and aerosol stratification over the Eastern North Atlantic.
Abstract. In this paper, we present two micro rain radar-based approaches to discriminate between stratiform and convective precipitation. One is based on probability density functions (PDFs) in combination with a confidence function, and the other one is an artificial neural network (ANN) classification. Both methods use the maximum radar reflectivity per profile, the maximum of the observed mean Doppler velocity per profile and the maximum of the temporal standard deviation (±15 min) of the observed mean Doppler velocity per profile from a micro rain radar (MRR). Training and testing of the algorithms were performed using a 2-year data set from the Jülich Observatory for Cloud Evolution (JOYCE). Both methods agree well, giving similar results. However, the results of the ANN are more decisive since it is also able to distinguish an inconclusive class, in turn making the stratiform and convective classes more reliable.
<p>Convective-scale forecasts require more detailed and continuous observational data of thermodynamic profiles and wind profiles in the atmospheric boundary layer (ABL) than currently provided. In order to meet these data requirements in the future, DWD evaluates various surface remote sensing systems targeted on ABL-profiling for routine network operation.</p>
<p>One of the candidate systems in operation at the Observatory Lindenberg is a new pre-production broadband DIAL from Vaisala. DIAL instruments are well-established in research activities, but this instrument is developed for operationally providing water vapor profile observations in the ABL during all weather conditions. We present evaluation results of the DIAL&#8217;s operational performance regarding the quality of the water vapor profiles and report on its ability to monitor sub-grid scale processes, such as convection and associated weather phenomena. This includes comparisons with radiosounding observations (4 per day) over at least one year of continuous observations and additional comparisons with Raman lidar for a three-month period during summer 2021. Furthermore, we provide observation-minus-background statistics between the DIAL and the ICON limited area model (ICON-LAM) to evaluate the model performance, e.g. under convection, and to identify observational error sources.</p>
<p>This contribution provides knowledge regarding the operational viability of the new pre-production broadband DIAL, its value for monitoring water vapour profiles 24/7 and ABL processes for future model applications.</p>
Abstract. RAMSES is the operational spectrometric fluorescence and Raman lidar at the Lindenberg Meteorological Observatory. It employs three spectrometers, with the UVA spectrometer (378–458 nm spectral range) being the latest to be implemented in 2018. The UVA spectrometer extends the fluorescence measurement range to shorter wavelengths than previously accessible, and its water vapor measurements can be corrected for fluorescence effects. First the new experimental setup of the RAMSES near-range receiver, which integrates the UVA spectrometer, is described. Then it is detailed how the fluorescence measurement with the UVA spectrometer is absolutely calibrated and how the fluorescence spectra are merged with those obtained with the second fluorescence spectrometer (440–750 nm spectral range). The second part of this study is dedicated to the effect of aerosol fluorescence on water vapor measurements with Raman lidars. When aerosols are present, a fluorescence-induced error always arises and therefore requires thorough analysis, even though it is particularly significant (in relative terms) only when the atmosphere is dry, the fluorescence signal strong, or the bandwidth of the Raman detection channels wide. For moisture measurements with the UVA spectrometer, a method is introduced that effectively eliminates the systematic fluorescence error. However, the increase in trueness comes at the expense of precision. The investigations further show that an accurate correction for fluorescence is impossible when the Raman lidar is not equipped with a spectrometer but with a single fluorescence receiver channel only, at least for biomass burning aerosol, because for a given fluorescence backscatter coefficient at the wavelength of the auxiliary detection channel the induced error in humidity can vary widely due to the changing shape of the fluorescence spectrum, which depends on aerosol type and atmospheric state and possibly also on other factors.
Abstract. In this paper, we present two micro rain radar-based approaches to discriminate between stratiform and convective precipitation. One is based on probability density functions (PDFs) in combination with a confidence function and the other one is an artificial neural network (ANN) classification. Both methods use the maximum radar reflectivity per profile, the maximum of the observed mean Doppler velocity per profile and the maximum of the temporal standard deviation (±15 min) of the observed 5 mean Doppler velocity per profile from a micro rain radar (MRR). Training and testing of the algorithms were performed using a two year data set from the Jülich Observatory for Cloud Evolution (JOYCE). Both methods agree well giving similar results. However, the results of the artificial neural network are more reasonable since it is also able to distinguish into an inconclusive class, in turn making the stratiform and convective classes more reliable.
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