Automated Low-Cost LED-Based Sun Photometer for City Scale Distributed Measurements

We propose a monochromatic low-cost automatic sun photometer (LoCo-ASP) to perform distributed aerosol optical depth (AOD) measurements at the city scale. This kind of network could fill the gap between current automatic ground instruments—with good temporal resolution and accuracy, but few devices per city and satellite products—with global coverage, but lower temporal resolution and accuracy-. As a first approach, we consider a single equivalent wavelength around 408 nm. The cost of materials for the instrument is around 220 dollars. Moreover, we propose a calibration transfer for a pattern instrument, and estimate the uncertainties for several units and due to the internal differences and the calibration process. We achieve a max MAE of 0.026 for 38 sensors at 408 nm compared with AERONET Cimel; a mean standard deviation of 0.0062 among our entire sensor for measurement and a calibration uncertainty of 0.01. Finally, we perform city-scale measurements to show the dynamics of AOD. Our instrument can measure unsupervised, with an expected error for AOD between 0.02 and 0.03.

Technical Note: Temporal disaggregation of spatial rainfall fields with generative adversarial networks

Creating spatially coherent rainfall patterns with high temporal resolution from data with lower temporal resolution is necessary in many geoscientific applications. From a statistical perspective, this presents a high- dimensional, highly underdetermined problem. Recent advances in machine learning provide methods for learning such probability distributions. We test the usage of generative adversarial networks (GANs) for estimating the full probability distribution of spatial rainfall patterns with high temporal resolution, conditioned on a field of lower temporal resolution. The GAN is trained on rainfall radar data with hourly resolution. Given a new field of daily precipitation sums, it can sample scenarios of spatiotemporal patterns with sub-daily resolution. While the generated patterns do not perfectly reproduce the statistics of observations, they are visually hardly distinguishable from real patterns. Limitations that we found are that providing additional input (such as geographical information) to the GAN surprisingly leads to worse results, showing that it is not trivial to increase the amount of used input information. Additionally, while in principle the GAN should learn the probability distribution in itself, we still needed expert judgment to determine at which point the training should stop, because longer training leads to worse results.

Defining the optimal temporal and spatial resolution for cardiovascular magnetic resonance imaging feature tracking

Background Myocardial deformation analyses using cardiovascular magnetic resonance (CMR) feature tracking (CMR-FT) have incremental value in the assessment of cardiac function beyond volumetric analyses. Since guidelines do not recommend specific imaging parameters, we aimed to define optimal spatial and temporal resolutions for CMR cine images to enable reliable post-processing. Methods Intra- and inter-observer reproducibility was assessed in 12 healthy subjects and 9 heart failure (HF) patients. Cine images were acquired with different temporal (20, 30, 40 and 50 frames/cardiac cycle) and spatial resolutions (high in-plane 1.5 × 1.5 mm through-plane 5 mm, standard 1.8 × 1.8 x 8mm and low 3.0 × 3.0 x 10mm). CMR-FT comprised left ventricular (LV) global and segmental longitudinal/circumferential strain (GLS/GCS) and associated systolic strain rates (SR), and right ventricular (RV) GLS. Results Temporal but not spatial resolution did impact absolute strain and SR. Maximum absolute changes between lowest and highest temporal resolution were as follows: 1.8% and 0.3%/s for LV GLS and SR, 2.5% and 0.6%/s for GCS and SR as well as 1.4% for RV GLS. Changes of strain values occurred comparing 20 and 30 frames/cardiac cycle including LV and RV GLS and GCS (p < 0.001–0.046). In contrast, SR values (LV GLS/GCS SR) changed significantly comparing all successive temporal resolutions (p < 0.001–0.013). LV strain and SR reproducibility was not affected by either temporal or spatial resolution, whilst RV strain variability decreased with augmentation of temporal resolution. Conclusion Temporal but not spatial resolution significantly affects strain and SR in CMR-FT deformation analyses. Strain analyses require lower temporal resolution and 30 frames/cardiac cycle offer consistent strain assessments, whilst SR measurements gain from further increases in temporal resolution.

Reply to “Comments on ‘Flash Flood Verification: Pondering Precipitation Proxies’”

We applaud Gourley and Vergara for their thorough investigation of the relationship between precipitation and flash flood reports, as well as their inclusion of information from advanced hydrologic model output. We conducted some additional analysis to identify the reasons for the substantial differences between their findings and ours. The primary reason for the differences was found to be temporal sampling. The high temporal resolution of the MRMS dataset, as well as their use of “rolling” accumulation periods, explains most of the discrepancies. For guidance related to real-time warning decisions for flash flooding, Gourley and Vergara’s analyses provide an important new guide and we recommend the use of their results for this purpose. For other applications, including model postprocessing and for precipitation datasets with lower temporal resolution, our results will continue to prove useful.

Technical Note: Temporal Disaggregation of Spatial Rainfall Fields with Generative Adversarial Networks

Creating spatially coherent rainfall patterns with high temporal resolution from data with lower temporal resolution is necessary in many geoscientific applications. From a statistical perspective, this presents a high- dimensional, highly under-determined problem. Recent advances in machine learning provide methods for learning such probability distributions. We test the usage of Generative Adversarial Networks (GANs) for estimating the full probability distribution of spatial rainfall patterns with high temporal resolution, conditioned on a field of lower temporal resolution. The GAN is trained on rainfall radar data with hourly resolution. Given a new field of daily precipitation sums, it can sample scenarios of spatiotemporal patterns with sub-daily resolution. While the generated patterns do not perfectly reproduce the statistics of observations, they are visually hardly distinguishable from real patterns. Limitations that we found are that providing additional input (such as geographical information) to the GAN surprisingly lead to worse results, showing that it is not trivial to increase the amount of used input information. Additionally, while in principle the GAN should learn the probability distribution in itself, we still needed expert judgment to determine at which point the training should stop, because longer training leads to worse results.

Improving the monitoring of deciduous broadleaf phenology using the Geostationary Operational Environmental Satellite (GOES) 16 and 17

Monitoring leaf phenology allows for tracking the progression of climate change and seasonal variations in a variety of organismal and ecosystem processes. Networks of finite-scale remote sensing, such as the PhenoCam Network, provide valuable information on phenological state at high temporal resolution, but have limited coverage. To more broadly remotely sense phenology, satellite-based data that has lower temporal resolution has primarily been used (e.g., 16-day MODIS NDVI product). Recent versions of the Geostationary Operational Environmental Satellites (GOES-16 and -17) allow the monitoring of NDVI at temporal scales comparable to that of PhenoCam throughout most of the western hemisphere. Here we examine the current capacity of this new data to measure the phenology of deciduous broadleaf forests for the first two full calendar years of data (2018 and 2019) by fitting double-logistic Bayesian models and comparing the start, middle, and end of season transition dates to those obtained from PhenoCam and MODIS 16-day NDVI and EVI products. Compared to the MODIS indices, GOES was more correlated with PhenoCam at the start and middle of spring, but had a larger bias (3.35 &amp;pm; 0.03 days later than PhenoCam) at the end of spring. Satellite-based autumn transition dates were mostly uncorrelated with those of PhenoCam. PhenoCam data produced significantly more certain (all p-values

Video-Based Pulse Rate Variability Measurement Using Periodic Variance Maximization and Adaptive Two-Window Peak Detection

Many previous studies have shown that the remote photoplethysmography (rPPG) can measure the Heart Rate (HR) signal with very high accuracy. The remote measurement of the Pulse Rate Variability (PRV) signal is also possible, but this is much more complicated because it is then necessary to detect the peaks on the temporal rPPG signal, which is usually quite noisy and has a lower temporal resolution than PPG signals obtained by contact equipment. Since the PRV signal is vital for various applications such as remote recognition of stress and emotion, the improvement of PRV measurement by rPPG is a critical task. Contact based PRV measurement has already been investigated, but the research on remotely measured PRV is very limited. In this paper, we propose to use the Periodic Variance Maximization (PVM) method to extract the rPPG signal and event-related Two-Window algorithm to improve the peak detection for PRV measurement. We have made several contributions. Firstly, we show that the newly proposed PVM method and Two-Window algorithm can be used for PRV measurement in the non-contact scenario. Secondly, we propose a method to adaptively determine the parameters of the Two-Window method. Thirdly, we compare the algorithm with other attempts for improving the non-contact PRV measurement such as the Slope Sum Function (SSF) method and the Local Maximum method. We calculated several features and compared the accuracy based on the ground truth provided by contact equipment. Our experiments showed that this algorithm performed the best of all the algorithms.

Comparison of different methods for the estimation of aortic pulse wave velocity from 4D flow cardiovascular magnetic resonance

Background Arterial pulse wave velocity (PWV) is associated with increased mortality in aging and disease. Several studies have shown the accuracy of applanation tonometry carotid-femoral PWV (Cf-PWV) and the relevance of evaluating central aorta stiffness using 2D cardiovascular magnetic resonance (CMR) to estimate PWV, and aortic distensibility-derived PWV through the theoretical Bramwell-Hill model (BH-PWV). Our aim was to compare various methods of aortic PWV (aoPWV) estimation from 4D flow CMR, in terms of associations with age, Cf-PWV, BH-PWV and left ventricular (LV) mass-to-volume ratio while evaluating inter-observer reproducibility and robustness to temporal resolution. Methods We studied 47 healthy subjects (49.5 ± 18 years) who underwent Cf-PWV and CMR including aortic 4D flow CMR as well as 2D cine SSFP for BH-PWV and LV mass-to-volume ratio estimation. The aorta was semi-automatically segmented from 4D flow data, and mean velocity waveforms were estimated in 25 planes perpendicular to the aortic centerline. 4D flow CMR aoPWV was calculated: using velocity curves at two locations, namely ascending aorta (AAo) and distal descending aorta (DAo) aorta (S1, 2D-like strategy), or using all velocity curves along the entire aortic centreline (3D-like strategies) with iterative transit time (TT) estimates (S2) or a plane fitting of velocity curves systolic upslope (S3). For S1 and S2, TT was calculated using three approaches: cross-correlation (TTc), wavelets (TTw) and Fourier transforms (TTf). Intra-class correlation coefficients (ICC) and Bland-Altman biases (BA) were used to evaluate inter-observer reproducibility and effect of lower temporal resolution. Results 4D flow CMR aoPWV estimates were significantly (p < 0.05) correlated to the CMR-independent Cf-PWV, BH-PWV, age and LV mass-to-volume ratio, with the strongest correlations for the 3D-like strategy using wavelets TT (S2-TTw) (R = 0.62, 0.65, 0.77 and 0.52, respectively, all p < 0.001). S2-TTw was also highly reproducible (ICC = 0.99, BA = 0.09 m/s) and robust to lower temporal resolution (ICC = 0.97, BA = 0.15 m/s). Conclusions Reproducible 4D flow CMR aoPWV estimates can be obtained using full 3D aortic coverage. Such 4D flow CMR stiffness measures were significantly associated with Cf-PWV, BH-PWV, age and LV mass-to-volume ratio, with a slight superiority of the 3D strategy using wavelets transit time (S2-TTw).

An Evaluation of the GOES-16 Rapid Scan for Nowcasting in Southeastern Brazil: Analysis of a Severe Hailstorm Case

The GOES-16 mesoscale domain sector (MDS) scans with 1-min intervals are used in this study to analyze a severe thunderstorm case that occurred in southeastern Brazil. The main objective is to evaluate the GOES-16 MDS rapid scans against the operational full-disk scans with lower temporal resolution for nowcasting. Data from a C-band radar, observed sounding, and a ground-based lightning network are also used in the analysis. A group of thunderstorms formed in the afternoon of 29 November 2017 in an environment of moderate convective available potential energy (CAPE) and deep-layer shear. The storms presented supercell characteristics and intense lightning activity with peak rates in excess of 150 flashes per 5 min. The satellite-derived trends with 1-min interval were skillful in detecting thunderstorm intensification, mainly in the developing stage. The decrease in cloud-top 10.35-μm brightness temperature was accompanied by increases in ice mass flux, concentration of small ice particles at cloud top, and storm depth. In the mature stage, there is no evident trend in the satellite-derived parameters that could indicate storm intensification, but the cluster area expands suggesting cloud-top divergence. The 1-min rapid scans indicate greater lead time to severe weather relative to 10- and 15-min-resolution imagery, but also presented numerous false alarms (indication of severe weather but no occurrence) due to oscillations in the satellite-derived parameters. The parameters calculated every 5 min presented better skill than 10 and 15 min and fewer false alarms than 1 min.

Niger discharge from radar altimetry: bridging gaps between gauge and altimetry time series

The Niger River represents a challenging target for deriving discharge from spaceborne radar altimeter measurements, particularly since most terrestrial gauges ceased to provide data during the 2000s. Here, we propose deriving altimetric rating curves by “bridging” gaps between time series from gauge and altimeter measurements using hydrological model simulations. We show that classical pulse-limited altimetry (Jason-1 and Jason-2, Envisat, and SARAL/Altika) subsequently reproduces discharge well and enables continuing the gauge time series, albeit at a lower temporal resolution. Also, synthetic aperture radar (SAR) altimetry picks up the signal measured by earlier altimeters quite well and allows the building of extended time series of higher quality. However, radar retracking is necessary for pulse-limited altimetry and needs to be further investigated for SAR. Moreover, forcing data for calibrating and running the hydrological models must be chosen carefully. Furthermore, stage–discharge relations must be fitted empirically and may need to allow for break points.