The HD(CP)² Observational Prototype Experiment (HOPE) – an overview

The HD(CP)2 Observational Prototype Experiment (HOPE) was performed as a major 2-month field experiment in Jülich, Germany, in April and May 2013, followed by a smaller campaign in Melpitz, Germany, in September 2013. HOPE has been designed to provide an observational dataset for a critical evaluation of the new German community atmospheric icosahedral non-hydrostatic (ICON) model at the scale of the model simulations and further to provide information on land-surface–atmospheric boundary layer exchange, cloud and precipitation processes, as well as sub-grid variability and microphysical properties that are subject to parameterizations. HOPE focuses on the onset of clouds and precipitation in the convective atmospheric boundary layer. This paper summarizes the instrument set-ups, the intensive observation periods, and example results from both campaigns. HOPE-Jülich instrumentation included a radio sounding station, 4 Doppler lidars, 4 Raman lidars (3 of them provide temperature, 3 of them water vapour, and all of them particle backscatter data), 1 water vapour differential absorption lidar, 3 cloud radars, 5 microwave radiometers, 3 rain radars, 6 sky imagers, 99 pyranometers, and 5 sun photometers operated at different sites, some of them in synergy. The HOPE-Melpitz campaign combined ground-based remote sensing of aerosols and clouds with helicopter- and balloon-based in situ observations in the atmospheric column and at the surface. HOPE provided an unprecedented collection of atmospheric dynamical, thermodynamical, and micro- and macrophysical properties of aerosols, clouds, and precipitation with high spatial and temporal resolution within a cube of approximately 10  ×  10  ×  10 km3. HOPE data will significantly contribute to our understanding of boundary layer dynamics and the formation of clouds and precipitation. The datasets have been made available through a dedicated data portal. First applications of HOPE data for model evaluation have shown a general agreement between observed and modelled boundary layer height, turbulence characteristics, and cloud coverage, but they also point to significant differences that deserve further investigations from both the observational and the modelling perspective

Cloud height measurement by a network of all-sky-imagers

Cloud base height (CBH) is an important parameter for many applications such as aviation, climatology or solarirradiance nowcasting (forecasting for the next seconds to hours ahead). The latter application is of increasing importance tooperate distribution grids as well as photovoltaic power plants, energy storage systems and flexible consumers.To nowcast solar irradiance, systems based on all-sky-imagers (ASIs), cameras monitoring the entire sky dome above theirpoint of installation, have been demonstrated. Accurate knowledge of CBH is required to nowcast the spatial distribution of5solar irradiance around the ASI’s location at a resolution down to5 m. Two ASIs located at a distance of usually less than6 kmcan be combined into an ASI-pair to measure CBH. However, the accuracy of such systems is limited. We present and validatea method to measure CBH using a network of ASIs to enhance accuracy. To the best of our knowledge, this is the first methodto measure CBH by a network of ASIs which is demonstrated experimentally.In this study, the deviations of 42 ASI-pairs are studied in comparison to a ceilometer and characterized by camera distance.10The ASI-pairs are formed from seven ASIs and feature camera distances of0.8...5.7 km. Each of the 21 ASI-tuples formedfrom seven ASIs yields two independent ASI-pairs as the ASI used as main and auxiliary camera respectively is swapped.Deviations found are compiled into conditional probabilities telling how probable it is to receive a certain reading of CBHfrom an ASI-pair given that true CBH takes on some specific value. Based on such statistical knowledge, in the inference thelikeliest actual CBH is estimated from the readings of all 42 ASI-pairs.15Based on the validation results, ASI-pairs with small camera distance (especially if<1.2 km) are accurate for low clouds(CBH<4 km). In contrast, ASI-pairs with camera distance of more than3 kmprovide smaller deviations for greater CBH.No ASI-pair provides most accurate measurements under all conditions. The presented network of ASIs at different distancesproves that, under all cloud conditions, the measurements of CBH are more accurate than using a single ASI-pair

3D cloud envelope and cloud development velocity from simulated CLOUD (C3IEL) stereo images

A method to derive the 3D cloud envelope and the cloud development velocity from high spatial and temporal resolution satellite imagery is presented. The CLOUD instrument of the recently proposed C3IEL mission lends itself well to observing at high spatial and temporal resolutions the development of convective cells. Space-borne visible cameras simultaneously image, under multiple view angles, the same surface domain every 20 s over a time interval of 200 s. In this paper, we present a method for retrieving cloud development velocity from simulated multi-angular, high-resolution top of the atmosphere (TOA) radiance cloud fields. The latter are obtained via the image renderer Mitsuba for a cumulus case generated via the atmospheric research model SAM and via the radiative transfer model 3DMCPOL, coupled with the outputs of an orbit, attitude, and camera simulator for a deep convective cloud case generated via the atmospheric research model Meso-NH. Matching cloud features are found between simulations via block matching. Image coordinates of tie points are mapped to spatial coordinates via 3D stereo reconstruction of the external cloud envelope for each acquisition. The accuracy of the retrieval of cloud topography is quantified in terms of RMSE and bias that are, respectively, less than 25 and 5 m for the horizontal components and less than 40 and 25 m for the vertical components. The inter-acquisition 3D velocity is then derived for each pair of tie points separated by 20 s. An independent method based on minimising the RMSE for a continuous horizontal shift of the cloud top, issued from the atmospheric research model, allows for the obtainment of a ground estimate of the velocity from two consecutive acquisitions. The mean values of the distributions of the stereo and ground velocities exhibit small biases. The width of the distributions is significantly different, with higher a distribution width for the stereo-retrieved velocity. An alternative way to derive an average velocity over 200 s, which relies on tracking clusters of points via image feature matching over several acquisitions, was also implemented and tested. For each cluster of points, mean stereo and ground positions were derived every 20 s over 200 s. The mean stereo and ground velocities, obtained as the slope of the line of best fit to the mean positions, are in good agreement.</p

Acoustic/Gravity Wave Phenomena in Wide-Field Imaging: From Data Analysis to a Modeling Framework for Observability in the Mlt Region and Beyond

Acoustic waves, gravity waves, and larger-scale tidal and planetary waves are significant drivers of the atmosphere’s dynamics and of the local and global circulation that have direct and indirect impacts on our weather and climate. Their measurements and characterization are fundamental challenges in Aeronomy that require a wide range of instrumentation with distinct operational principles. Most measurements share the common features of integrating optical emissions or effects on radio waves through deep layers of the atmosphere. The geometry of these integrations create line-of-sight effects that must be understood, described, and accounted for to properly present the measured data in traditional georeferenced frames or in thin-layer representations. These effects include intensity enhancements/cancellations, filtering of scales, and apparent phase shifts relative to the underlying wave dynamics. We have designed a simulation framework that uses 2D and 3D input model data to perform these line-of-sight integrations based on ray tracing and geodesic transformations. The primary objective is to characterize these effects, to define quantifiable impacts on measurable parameters, and to create a basis for synthetic data for processes to be revealed in current and future measurements

On the autonomous detection of coronal mass ejections in heliospheric imager data

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/95523/1/jgra21843.pd

Analyzing Spatial Variations of Cloud Attenuation by a Network of All-Sky Imagers

All-sky imagers (ASIs) can be used to model clouds and detect spatial variations of cloud attenuation. Such cloud modeling can support ASI-based nowcasting, upscaling of photovoltaic production and numeric weather predictions. A novel procedure is developed which uses a network of ASIs to model clouds and determine cloud attenuation more accurately over every location in the observed area, at a resolution of 50 m × 50 m. The approach combines images from neighboring ASIs which monitor the cloud scene from different perspectives. Areas covered by optically thick/intermediate/thin clouds are detected in the images of twelve ASIs and are transformed into maps of attenuation index. In areas monitored by multiple ASIs, an accuracy-weighted average combines the maps of attenuation index. An ASI observation’s local weight is calculated from its expected accuracy. Based on radiometer measurements, a probabilistic procedure derives a map of cloud attenuation from the combined map of attenuation index. Using two additional radiometers located 3.8 km west and south of the first radiometer, the ASI network’s estimations of direct normal (DNI) and global horizontal irradiance (GHI) are validated and benchmarked against estimations from an ASI pair and homogeneous persistence which uses a radiometer alone. The validation works without forecasted data, this way excluding sources of error which would be present in forecasting. The ASI network reduces errors notably (RMSD for DNI 136 W/m2, GHI 98 W/m2) compared to the ASI pair (RMSD for DNI 173 W/m2, GHI 119 W/m2 and radiometer alone (RMSD for DNI 213 W/m2), GHI 140 W/m2). A notable reduction is found in all studied conditions, classified by irradiance variability. Thus, the ASI network detects spatial variations of cloud attenuation considerably more accurately than the state-of-the-art approaches in all atmospheric conditions