Observational Analysis of a Wind Gust Event during the Merging of a Bow Echo and Mini-Supercell in Southeastern China

The merging of a fast-moving bow echo with a convective cell of a hook-echo signature was studied by using polarimetric radar detections. Gusts with wind speeds near 35 m s–1 were recorded by the surface station, which caused significant damage. A convective cell with a mesovortex signature, which is hereafter referred to as a mini-supercell, was observed over the northeast of the bow echo before the convective merging. It was found that the mesovortex possessed cyclonic circulation and resembled a supercell-like feature. The merging of the bow echo and the mini-supercell strengthened the updraft near the apex of the bow echo. The enhanced updraft was also demonstrated by the appearance of a differential reflectivity (ZDR) column with a topmost height of 4 km above the melting layer (~4 km). The bow was separated into northern and southern sectors after merging with the mini-supercell, leading to the gusty wind over the surface of the south sector.

RAIN-F+: The Data-Driven Precipitation Prediction Model for Integrated Weather Observations

Quantitative precipitation prediction is essential for managing water-related disasters, including floods, landslides, tsunamis, and droughts. Recent advances in data-driven approaches using deep learning techniques provide improved precipitation nowcasting performance. Moreover, it has been known that multi-modal information from various sources could improve deep learning performance. This study introduces the RAIN-F+ dataset, which is the fusion dataset for rainfall prediction, and proposes the benchmark models for precipitation prediction using the RAIN-F+ dataset. The RAIN-F+ dataset is an integrated weather observation dataset including radar, surface station, and satellite observations covering the land area over the Korean Peninsula. The benchmark model is developed based on the U-Net architecture with residual upsampling and downsampling blocks. We examine the results depending on the number of the integrated dataset for training. Overall, the results show that the fusion dataset outperforms the radar-only dataset over time. Moreover, the results with the radar-only dataset show the limitations in predicting heavy rainfall over 10 mm/h. This suggests that the various information from multi-modality is crucial for precipitation nowcasting when applying the deep learning method.

Contaminants and Where to Find Them: Microbiological Quality Control in Axenic Animal Facilities

The use of axenic animal models in experimental research has exponentially grown in the past few years and the most reliable way for confirming their axenic status remains unclear. It is especially the case when using individual ventilated positive-pressure cages such as the Isocage. This type of cage are at a greater risk of contamination and expose animals to a longer handling process leading to more potential stress when opened compared to isolators. The aim of this study was to propose simple ways to detect microbial contaminants with Isocages type isolator resulting by developing, validating and optimizing three different methods (culture, microscopy, and molecular). These three approaches were also tested in situ by spiking 21 axenic mice with different microorganisms. Our results suggest that the culture method can be used for feces and surface station (IBS) swabs exclusively (in Brain Heart Infusion for 7 days at 25°C and 37°C in aerobic conditions, and at 30°C in anaerobic conditions), while microscopy (wet mounts) and molecular method (quantitative PCR) were only suitable for fecal matter analyses. In situ results suggests that the culture and molecular methods can detect up to 100% of bacterial contamination events while the microscopy approach generates many erroneous results when not performed by a skilled microscopist. In situ results also suggest that when an axenic mouse is contaminated by a microbial agent, the microorganism will colonize the mouse to such an extent that detection is obvious in 4 days, in average. This report validates simple but complimentary tests that can be used for optimal detection of contaminants in axenic animal facilities using Isocage type isolators.

Regression-based Gap-filling Methods Show Air Temperature Reductions and Wind Pattern Changes During the 2019 Total Eclipse in Chile

Singular disruptive events like solar eclipses affect the measured values of meteorological variables at the earth’s surface. To quantify such an impact, it is necessary to estimate what value the parameter would have taken had the event not occurred. We design and compare several methods to perform such an estimate based on longer observational timeseries from individual meteorological surface stations. Our methods are based on regularized regressions (including a Bayesian variant) and provide both a point an associated error estimate of the disruptive event’s impact. With their help, we study the effect of the total solar eclipse of July 2 nd , 2019, in the Coquimbo Region of Chile, on near-surface air temperatures and winds. The observational data used have been collected by the meteorological surface station network of the Centro de Estudios Avanzados en Zonas Áridas (CEAZA). Most stations inside the eclipse’s umbra registered a temperature drop of 1-2 • C, while the most extreme estimated temperature drop surpassed 6 • C. The presence of an ‘eclipse cyclone’ can neither be proven nor refuted. Application of the regression methods to other, comparable problems, like volcanic eruptions, forest fires or simply gap filling of observational data, are conceivable.

Variational regional inverse modeling of reactive species emissions with PYVAR-CHIMERE-v2019

Up-to-date and accurate emission inventories for air pollutants are essential for understanding their role in the formation of tropospheric ozone and particulate matter at various temporal scales, for anticipating pollution peaks and for identifying the key drivers that could help mitigate their concentrations. This paper describes the Bayesian variational inverse system PYVAR-CHIMERE, which is now adapted to the inversion of reactive species. Complementarily with bottom-up inventories, this system aims at updating and improving the knowledge on the high spatiotemporal variability of emissions of air pollutants and their precursors. The system is designed to use any type of observations, such as satellite observations or surface station measurements. The potential of PYVAR-CHIMERE is illustrated with inversions of both carbon monoxide (CO) and nitrogen oxides (NOx) emissions in Europe, using the MOPITT and OMI satellite observations, respectively. In these cases, local increments on CO emissions can reach more than +50 %, with increases located mainly over central and eastern Europe, except in the south of Poland, and decreases located over Spain and Portugal. The illustrative cases for NOx emissions also lead to large local increments (> 50 %), for example over industrial areas (e.g., over the Po Valley) and over the Netherlands. The good behavior of the inversion is shown through statistics on the concentrations: the mean bias, RMSE, standard deviation, and correlation between the simulated and observed concentrations. For CO, the mean bias is reduced by about 27 % when using the posterior emissions, the RMSE and the standard deviation are reduced by about 50 %, and the correlation is strongly improved (0.74 when using the posterior emissions against 0.02); for NOx, the mean bias is reduced by about 24 % and the RMSE and the standard deviation are reduced by about 7 %, but the correlation is not improved. We reported strong non-linear relationships between NOx emissions and satellite NO2 columns, now requiring a fully comprehensive scientific study.

Estimating lockdown-induced European NO₂ changes using satellite and surface observations and air quality models

This study provides a comprehensive assessment of NO2 changes across the main European urban areas induced by COVID-19 lockdowns using satellite retrievals from the Tropospheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5p satellite, surface site measurements, and simulations from the Copernicus Atmosphere Monitoring Service (CAMS) regional ensemble of air quality models. Some recent TROPOMI-based estimates of changes in atmospheric NO2 concentrations have neglected the influence of weather variability between the reference and lockdown periods. Here we provide weather-normalized estimates based on a machine learning method (gradient boosting) along with an assessment of the biases that can be expected from methods that omit the influence of weather. We also compare the weather-normalized satellite-estimated NO2 column changes with weather-normalized surface NO2 concentration changes and the CAMS regional ensemble, composed of 11 models, using recently published estimates of emission reductions induced by the lockdown. All estimates show similar NO2 reductions. Locations where the lockdown measures were stricter show stronger reductions, and, conversely, locations where softer measures were implemented show milder reductions in NO2 pollution levels. Average reduction estimates based on either satellite observations (−23 %), surface stations (−43 %), or models (−32 %) are presented, showing the importance of vertical sampling but also the horizontal representativeness. Surface station estimates are significantly changed when sampled to the TROPOMI overpasses (−37 %), pointing out the importance of the variability in time of such estimates. Observation-based machine learning estimates show a stronger temporal variability than model-based estimates.

First model evaluations of height-resolved diurnal water vapour cycles using lidar observations in an Arctic environment

<p>Atmospheric water vapour is a critical component of both meteorological and climatological processes. It is the dominant gas in the greenhouse effect and its diurnal cycle is an essential component of the hydrological cycle. Diurnal water vapour cycles are complex and are a product of several mechanisms, including (but not necessarily limited to): evapotranspiration, advection, large-scale vertical motion, and precipitation. They are dependent on local geography, as well as latitude. Numerical Weather Prediction (NWP) models rely on high-quality water vapour input to provide accurate forecasts, which is particularly difficult in the Arctic due to its extreme weather and harsh environment. Diurnal water vapour cycle observations are also excellent tools for evaluating NWPs due to their complex nature and dependence on multiple processes. Integrated water vapour (IWV), or total column, diurnal water vapour cycles, usually calculated with Global Navigation Satellite Systems (GNSS) instruments, have been the focus of most previous diurnal WV studies; however, height-resolved diurnal cycles provide a more complete picture of the diurnal mechanisms and include vertical motion, which cannot be discerned via IWV measurements. Differential Absorption Lidars (DIALs) are well suited to providing height-resolved diurnal cycles in the boundary layer due to their high vertical and temporal resolution.</p><p>We use the novel Vaisala pre-production DIAL, installed in Iqaluit, Nunavut (63.75 N, 68.55 W), to calculate seasonal height-resolved diurnal WV cycles from 100 m to 1500 m altitude. We also calculate the surface and total column WV diurnal cycles using co-located surface station and GNSS measurements. We find that the first 250 m of the DIAL diurnal cycle magnitudes agree well with the surface station measurements. The phases of the cycle do shift with altitude, and the amplitudes generally increase with altitude. In the summer, all instruments observe a strong 24 hr cycle. As the amount of solar radiation decreases over the year, the 24 hr cycle weakens and the 12 hr cycle begins to dominate in all instruments. While we find a strong correlation between the 24 hr cycle and the solar cycle, we do not observe any correlation between the 12 hr cycle and the solar cycle. Finally, we also compare the DIAL observations to the Environment and Climate Change Canada (ECCC) NWP model. We evaluate both the assimilation of the humidity input and initial water vapour fields, as well as the diurnal cycle over the 24 hour forecast. Future work will include case study comparisons with the Canadian NWP model to assess the model’s ability to resolve rapid changes in diurnal water vapour.</p>

Estimating lockdown induced European NO₂ changes

This study provides a comprehensive assessment of NO2 changes across the main European urban areas induced by the COVID-19 lockdown using satellite retrievals from the Tropospheric Monitoring Instrument (TROPOMI), surface site measurements and simulations from the Copernicus Atmospheric Monitoring Service (CAMS) regional ensemble of air quality models. Some recent TROPOMI-based estimates of NO2 changes have neglected the influence of weather variability between the reference and lockdown periods. Here we provide weather-normalized estimates based on a machine learning method (gradient boosting) along with an assessment of the biases that can be expected from methods that omit the influence of weather. We also compare the weather-normalized satellite NO2 column changes with both weather-normalized surface NO2 concentration changes and simulated changes by the CAMS regional ensemble, composed of 11 models, using recently published emission reductions induced by the lockdown. We show that all estimates show the same tendency on NO2 reductions. Locations where the lockdown was stricter show stronger reductions and, conversely, locations where softer measures were implemented show milder reductions in NO2 pollution levels. Regarding average reductions, estimates based on either satellite observations (−23 %) surface stations (−43 %) or models (−32 %) are presented, showing the importance of vertical sampling but also the horizontal representativeness. Surface station estimates are significantly changed when sampled to the TROPOMI overpasses (−37 %) pointing out the importance of the variability in time of such estimates. Observation based machine learning estimates show a stronger temporal variability than the model-based estimates.

Efficient Node Deployment Algorithm for Enhancing Connectivity and Coverage in Underwater Sensor Networks

As deployment process is one of the major tasks in underwater sensor network due to its constraint like: acoustic communication, energy, processing speed, cost and memory and dynamic nature of water. As many researchers have proposed many algorithms for the deployment of nodes in underwater sensor network. It was always a great issue in WSN as well as underwater sensor networks. This work proposes a node deployment technique based on depth. This work consists the following major components: (i) sensor nodes to sense the phenomena in underwater sensor networks, (ii) multiple surface station on the water surface. Use of multiple surface station provides better area coverage and connectivity in the networks. This work is divided into three phase like: initialization where nodes are randomly deployed at water surface and from 2D network topology, second phase is depth calculation for all the nodes and third is to distribute the depth to each node and send them to their designated depth to expand the 2D network into the 3D network. The proposed technique is simulated on Matlab for the analysis of area coverage and connectivity. Simulation results show better performance in terms of area coverage and connectivity as compared to ADAN-BC.