Mixing layer height derived from radiosoundings and ground-based lidar - comparison and assessment

The inversion on top of the atmospheric boundary layer is a strong barrier for the transport of heat, momentum and matter from or to the earth's surface. Regarding aerosols and gaseous constituents like water vapour which originate from the surface, the concentration of those parameters within the boundary layer strongly depends on the height of the layer, the mixing height. During daytime the mixing height over land increases and reaches a maximum value in situations with constant synoptic conditions. In many applications, e.g. the comparison of model output with observations the mixing height is taken from radiosoundings. Often this value is - due to lack of other measurements - also taken as the height of the fully developed convective boundary layer. Since the mixing height is strongly varying both in time and space an observation along a single line like a radiosonde track represents only an estimate of the mixing height. Quasi-continuous measurements of the backscatter signal with a ground-based lidar from several field campains offer the opportunity to estimate the error associated with a mixing-height determination from radiosoundings. A method to determine the mixing height from the backscattered signal is presented. Data from several field campaigns are used, namely the Nauru99 campaign in the tropical western Pacific in June/July 1999, three campaigns at the ARM-site in Oklahoma (USA) in September/October 1999, September/October 2000 and November/December 2000 and three campaigns in the frame of the German EVA-GRIPS (Regional Evaporation at Grid/Pixel Scale) project near Lindenberg (Brandenburg, Germany) in September 2002, April 2003 and May/June 2003 (LITFASS-2003). From these campaigns measurements simultaneous to approximatly 50 radiosoundings exist and allow a statistical analysis of the results. The comparison of radiosonde mixing heights with lidar mixing heights over 10 min time intervalls reveal a good agreement, the better the shorter the distance between radiosonde launch point and lidar location. Lidar mixing heights averaged over 1 h, which are more representative for an area, may deviate up to 300 m from radiosonde mixing heights. The standard deviation within the averaging interval fairly represents the variability of the mixing layer height. The maximum mixing height which is given by an afternoon radiosounding is also compared with lidar measurements

Toward Sharing Brain Images: Differentially Private TOF-MRA Images With Segmentation Labels Using Generative Adversarial Networks

Sharing labeled data is crucial to acquire large datasets for various Deep Learning applications. In medical imaging, this is often not feasible due to privacy regulations. Whereas anonymization would be a solution, standard techniques have been shown to be partially reversible. Here, synthetic data using a Generative Adversarial Network (GAN) with differential privacy guarantees could be a solution to ensure the patient's privacy while maintaining the predictive properties of the data. In this study, we implemented a Wasserstein GAN (WGAN) with and without differential privacy guarantees to generate privacy-preserving labeled Time-of-Flight Magnetic Resonance Angiography (TOF-MRA) image patches for brain vessel segmentation. The synthesized image-label pairs were used to train a U-net which was evaluated in terms of the segmentation performance on real patient images from two different datasets. Additionally, the Fréchet Inception Distance (FID) was calculated between the generated images and the real images to assess their similarity. During the evaluation using the U-Net and the FID, we explored the effect of different levels of privacy which was represented by the parameter ϵ. With stricter privacy guarantees, the segmentation performance and the similarity to the real patient images in terms of FID decreased. Our best segmentation model, trained on synthetic and private data, achieved a Dice Similarity Coefficient (DSC) of 0.75 for ϵ = 7.4 compared to 0.84 for ϵ = ∞ in a brain vessel segmentation paradigm (DSC of 0.69 and 0.88 on the second test set, respectively). We identified a threshold of ϵ <5 for which the performance (DSC <0.61) became unstable and not usable. Our synthesized labeled TOF-MRA images with strict privacy guarantees retained predictive properties necessary for segmenting the brain vessels. Although further research is warranted regarding generalizability to other imaging modalities and performance improvement, our results mark an encouraging first step for privacy-preserving data sharing in medical imaging

Net precipitation over the Baltic Sea for one year using several methods

Precipitation and evaporation over the Baltic Sea are calculated for a one-year period from September 1998 to August 1999 by four different tools, the two atmospheric regional models HIRLAM and REMO, the oceanographic model PROBE-Baltic in combination with the SMHI (1 × 1)° database and Interpolated Fields, based essentially on ship measurements. The investigated period is slightly warmer and wetter than the climatological mean. Correlation coefficients of the differently calculated latent heat fluxes vary between 0.81 (HIRLAM and REMO) and 0.56 (SMHI/PROBE-Baltic and Interpolated Fields), while the correlation coefficients between model fluxes and measured fluxes range from 0.61 and 0.78. Deviations of simulated and interpolated monthly precipitation over the Baltic Sea are less than ±5 mm in the southern Baltic and up to 20 mm near the Finnish coast for the one-year period. The methods simulate the annual cycle of precipitation and evaporation of the Baltic Proper in a similar manner with a broad maximum of net precipitation in spring and early summer and a minimum in late summer. The annual averages of net precipitation of the Baltic Proper range from 57 mm (REMO) to 262 mm (HIRLAM) and for the Baltic Sea from 96 mm (SMHI/PROBE-Baltic) to 209 mm (HIRLAM). This range is considered to give the uncertainty of present-day determination of the net precipitation over the Baltic Sea

Quantification in cardiac MRI: advances in image acquisition and processing

Cardiac magnetic resonance (CMR) imaging enables accurate and reproducible quantification of measurements of global and regional ventricular function, blood flow, perfusion at rest and stress as well as myocardial injury. Recent advances in MR hardware and software have resulted in significant improvements in image quality and a reduction in imaging time. Methods for automated and robust assessment of the parameters of cardiac function, blood flow and morphology are being developed. This article reviews the recent advances in image acquisition and quantitative image analysis in CMR