Two power law relations linking equivalent radar reflectivity factor (Ze) and snowfall rate (S) are derived for a Micro Rain Radar (MRR), which operates at K-band, and a W-band cloud radar. For the development of these Ze-S relationships, a dataset of calculated and measured variables is used. Surface-based video-disdrometer measurements were collected during snowfall events over five winters at the high-latitude site in Hyytiälä, Finland. The data from 2014-2018 includes particle size distributions (PSD) and their fall velocities, from which snowflake masses were derived. K- and W-band Ze values are computed using these surface-based observations and snowflake scattering properties as provided by T-matrix and single-particle scattering tables, respectively. The uncertainty analysis shows that the K-band snowfall rate estimation is significantly improved by including the intercept parameter N0 of the PSD calculated from concurrent disdrometer measurements. If N0 is used to adjust the prefactor of the Ze-S relationship, the RMSE of the snowfall rate estimate can be reduced from 0.37 to around 0.11 mmh−1. For W-band, a Ze-S relationship with constant parameters for all available snow events shows a similar uncertainty compared to the method that includes the PSD intercept parameter. To demonstrate the performance of the proposed Ze-S relationships, they are applied to measurements of the MRR and the W-band Microwave Radar for Arctic Clouds at the AWIPEV Arctic research base in Ny-Ålesund, Svalbard. The resulting snowfall rate estimates show a good agreement to in situ snowfall observations while other Ze-S relationships from literature reveal larger differences.

<p>The motion of clouds at a given location can be detected using ground-based all-sky imagers that frequently acquire images of the sky dome. Motion flow is used for minute-scale forecasting of cloud cover and solar irradiance, for example in the case of forecasting photovoltaic power production. While visible-range sky cameras are often applied for this purpose, they neither allow to detect the altitude of clouds, nor accurately detect clouds at night time. However, thermal-infrared all-sky imagers, such as Reuniwatt’s Sky InSight, retrieve brightness temperatures with constant accuracy at day and night time. This allows for the retrieval of diverse cloud parameters such as cloud base height. Atmospheric wind vectors can be derived and geolocalised by combining cloud motion detection and cloud-base height retrieval. In this study, we evaluate the accuracy of atmospheric wind vector retrievals by the means of the Sky InSight. Radiosoundings and wind profiler observations are used as a reference.</p>

This paper presents an innovational way of assimilating observations of clouds into the ICOsahedral Nonhydrostatic weather forecasting model for regional scale, ICON-D2, which is operated by Deutscher Wetterdienst (DWD). A convolutional neural network (CNN) is trained to detect clouds in camera photographs. The network’s output is a greyscale picture, in which each pixel has a value between 0 and 1, describing the probability of the pixel belonging to a cloud (1) or not (0). By averaging over a certain box of the picture a value for the cloud cover of that region is obtained. A forward operator is built to map an ICON model state into the observation space. A three dimensional grid in the space of the camera’s perspective is constructed and the ICON model variable cloud cover (CLC) is interpolated onto that grid. The maximum CLC along the rays that fabricate the camera grid, is taken as a model equivalent for each pixel. After superobbing, monitoring experiments have been conducted to compare the observations and model equivalents over a longer time period, yielding promising results. Further we show the performance of a single assimilation step as well as a longer assimilation experiment over a time period of six days which also yields good results. These findings are a proof of concept and further research has to be invested before these new innovational observations can be assimilated operationally in any numerical weather prediction (NWP) model.