scholarly journals A new real-time Lagrangian diagnostic system for stratosphere-troposphere exchange: evaluation during a balloon sonde campaign in eastern Canada

2011 ◽  
Vol 11 (10) ◽  
pp. 27967-28011
Author(s):  
M. S. Bourqui ◽  
A. Yamamoto ◽  
D. Tarasick ◽  
M. D. Moran ◽  
L.-P. Beaudoin ◽  
...  
Keyword(s):  
High Resolution ◽  
Real Time ◽  
Diagnostic System ◽  
Weather Forecast ◽  
Identification Algorithm ◽  
Eastern Canada ◽  
Data Set ◽  
Predictive Skill ◽  
Stratospheric Intrusions ◽  
Archived Data

Abstract. A new real-time Lagrangian diagnostic system for stratosphere-troposphere exchange (STE) developed for Environment Canada (EC) has been delivering daily archived data since 20 July 2010. The STE calculations are performed globally following the Lagrangian approach proposed in Bourqui (2006) using medium-range, high-resolution operational global weather forecasts. Following every weather forecast, trajectories are started from a dense three-dimensional grid covering the globe, and are calculated for six days of the forecast. All trajectories crossing either the dynamical tropopause (±2 PVU) or the 380 K isentrope and having a residence time greater than 12 h are archived, and also used to calculate several diagnostics. This system provides daily global STE forecasts that can be used to guide field campaigns, among other applications. The archived data set offers unique high-resolution information on transport across the tropopause for both extra-tropical hemispheres and the tropics. This will be useful for improving our understanding of STE globally, and as a reference for the evaluation of lower-resolution models. This new data set is evaluated here against measurements taken during a balloon sonde campaign with daily launches from three stations in eastern Canada (Montreal, Egbert, and Walsingham) for the period from 12 July to 4 August 2010. The campaign found an unexpectedly high number of observed stratospheric intrusions: 79% (38%) of the profiles appear to show the presence of stratospheric air below 500 hPa (700 hPa). An objective identification algorithm developed for this study is used to identify layers in the balloon-sonde profiles affected by stratospheric air and to evaluate the Lagrangian STE forecasts. We find that the predictive skill for the overall intrusion depth is excellent for intrusions penetrating down to 300 and 500 hPa, while it becomes negligible for intrusions penetrating below 700 hPa. Nevertheless, the statistical representation of these deep intrusions is reasonable, with an average bias of 24%. Evaluation of the skill at representing the detailed structures of the stratospheric intrusions shows good predictive skill down to 500 hPa, reduced predictive skill between 500 and 700 hPa, and none below. A significant low statistical bias of about 30% is found in the layer between 500 to 700 hPa. However, analysis of missed events at one site, Montreal, shows that 70% of them coincide with candidate clusters of trajectories that pass through Montreal, but that are too dispersed to be detected in the close neighbourhood of the station. This allows us to expect a negligible bias throughout the troposphere in the spatially averaged STE frequency derived from this data set, for example in climatological maps of STE mass fluxes.

scholarly journals A new global real-time Lagrangian diagnostic system for stratosphere-troposphere exchange: evaluation during a balloon sonde campaign in eastern Canada

2012 ◽  
Vol 12 (5) ◽  
pp. 2661-2679 ◽  
Author(s):  
M. S. Bourqui ◽  
A. Yamamoto ◽  
D. Tarasick ◽  
M. D. Moran ◽  
L.-P. Beaudoin ◽  
...  
Keyword(s):  
High Resolution ◽  
Real Time ◽  
Diagnostic System ◽  
Weather Forecast ◽  
Identification Algorithm ◽  
Eastern Canada ◽  
Data Set ◽  
Predictive Skill ◽  
Stratospheric Intrusions ◽  
Archived Data

Abstract. A new global real-time Lagrangian diagnostic system for stratosphere-troposphere exchange (STE) developed for Environment Canada (EC) has been delivering daily archived data since July 2010. The STE calculations are performed following the Lagrangian approach proposed in Bourqui (2006) using medium-range, high-resolution operational global weather forecasts. Following every weather forecast, trajectories are started from a dense three-dimensional grid covering the globe, and are calculated forward in time for six days of the forecast. All trajectories crossing either the dynamical tropopause (±2 PVU) or the 380 K isentrope and having a residence time greater than 12 h are archived, and also used to calculate several diagnostics. This system provides daily global STE forecasts that can be used to guide field campaigns, among other applications. The archived data set offers unique high-resolution information on transport across the tropopause for both extra-tropical hemispheres and the tropics. This will be useful for improving our understanding of STE globally, and as a reference for the evaluation of lower-resolution models. This new data set is evaluated here against measurements taken during a balloon sonde campaign with daily launches from three stations in eastern Canada (Montreal, Egbert, and Walsingham) for the period 12 July to 4 August 2010. The campaign found an unexpectedly high number of observed stratospheric intrusions: 79% (38%) of the profiles appear to show the presence of stratospheric air below than 500 hPa (700 hPa). An objective identification algorithm developed for this study is used to identify layers in the balloon-sonde profiles affected by stratospheric air and to evaluate the Lagrangian STE forecasts. We find that the predictive skill for the overall intrusion depth is very good for intrusions penetrating down to 300 and 500 hPa, while it becomes negligible for intrusions penetrating below 700 hPa. Nevertheless, the statistical representation of these deep intrusions is reasonable, with an average bias of 24%. Evaluation of the skill at representing the detailed structures of the stratospheric intrusions shows good predictive skill down to 500 hPa, reduced predictive skill between 500 and 700 hPa, and none below. A significant low statistical bias of about 30% is found in the layer between 500 to 700 hPa. However, analysis of missed events at one site, Montreal, shows that 70% of them coincide with candidate clusters of trajectories that pass through Montreal, but that are too dispersed to be detected in the close neighbourhood of the station. Within the limits of this study, this allows us to expect a negligible bias throughout the troposphere in the spatially averaged STE frequency derived from this data set, for example in climatological maps of STE mass fluxes. This first evaluation is limited to eastern Canada in one summer month with a high frequency of stratospheric intrusions, and further work is needed to evaluate this STE data set in other months and locations.

scholarly journals Chloroplast Genome Sequence ofArabidopsis thalianaAccession Landsbergerecta, Assembled from Single-Molecule, Real-Time Sequencing Data

2016 ◽  
Vol 4 (5) ◽  
Author(s):  
Kai Bernd Stadermann ◽  
Daniela Holtgräwe ◽  
Bernd Weisshaar
Keyword(s):  
Arabidopsis Thaliana ◽  
High Resolution ◽  
Chloroplast Genome ◽  
Real Time ◽  
Genome Sequence ◽  
Single Molecule ◽  
Sequencing Data ◽  
Data Set ◽  
Chloroplast Genomes ◽  
Chloroplast Genome Sequence

A publicly available data set from Pacific Biosciences was used to create an assembly of the chloroplast genome sequence of theArabidopsis thalianagenotype Landsbergerecta. The assembly is solely based on single-molecule, real-time sequencing data and hence provides high resolution of the two inverted repeat regions typically contained in chloroplast genomes.

EMO-5: Copernicus pan-European high-resolution meteorological data set for large-scale hydrological modelling

2020 ◽  
Author(s):  
Vera Thiemig ◽  
Peter Salamon ◽  
Goncalo N. Gomes ◽  
Jon O. Skøien ◽  
Markus Ziese ◽  
...  
Keyword(s):  
High Resolution ◽  
Real Time ◽  
Large Scale ◽  
Interpolation Method ◽  
Meteorological Data ◽  
Hydrological Modelling ◽  
Data Set ◽  
Average Temperature ◽  
Time Period ◽  
Meteorological Observations

<p>We present EMO-5, a Pan-European high-resolution (5 km), (sub-)daily, multi-variable meteorological data set especially developed to the needs of an operational, pan-European hydrological service (EFAS; European Flood Awareness System). The data set is built on historic and real-time observations coming from 18,964 meteorological in-situ stations, collected from 24 data providers, and 10,632 virtual stations from four high-resolution regional observational grids (CombiPrecip, ZAMG - INCA, EURO4M-APGD and CarpatClim) as well as one global reanalysis product (ERA-Interim-land). This multi-variable data set covers precipitation, temperature (average, min and max), wind speed, solar radiation and vapor pressure; all at daily resolution and in addition 6-hourly resolution for precipitation and average temperature. The original observations were thoroughly quality controlled before we used the Spheremap interpolation method to estimate the variable values for each of the 5 x 5 km grid cells and their affiliated uncertainty. EMO-5 v1 grids covering the time period from 1990 till 2019 will be released as a free and open Copernicus product mid-2020 (with a near real-time release of the latest gridded observations in future). We would like to present the great potential EMO-5 holds for the hydrological modelling community.</p><p> </p><p>footnote: EMO = European Meteorological Observations</p>

scholarly journals Assessing the Use of Satellite-Based Estimates and High-Resolution Precipitation Datasets for the Study of Extreme Precipitation Events over the Iberian Peninsula

Water ◽  
2018 ◽  
Vol 10 (11) ◽  
pp. 1688 ◽  
Author(s):  
Riccardo Hénin ◽  
Margarida Liberato ◽  
Alexandre Ramos ◽  
Célia Gouveia
Keyword(s):  
High Resolution ◽  
Iberian Peninsula ◽  
Real Time ◽  
Extreme Precipitation ◽  
Daily Precipitation ◽  
Weather Forecast ◽  
Winter Period ◽  
Medium Range Weather Forecast ◽  
Extreme Precipitation Events ◽  
Precipitation Events

An assessment of daily accumulated precipitation during extreme precipitation events (EPEs) occurring over the period 2000–2008 in the Iberian Peninsula (IP) is presented. Different sources for precipitation data, namely ERA-Interim and ERA5 reanalysis by the European Centre for Medium-Range Weather Forecast (ECMWF) and Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA), both in near-real-time and post-real-time releases, are compared with the best ground-based high-resolution (0.2° × 0.2°) gridded precipitation dataset available for the IP (IB02). In this study, accuracy metrics are analysed for different quartiles of daily precipitation amounts, and additional insights are provided for a subset of EPEs extracted from an objective ranking of extreme precipitation during the extended winter period (October to March) over the IP. Results show that both reanalysis and multi-satellite datasets overestimate (underestimate) daily precipitation sums for the least (most) extreme events over the IP. In addition, it is shown that the TRMM TMPA precipitation estimates from the near-real-time product may be considered for EPEs assessment over these latitudes. Finally, it is found that the new ERA5 reanalysis accounts for large improvements over ERA-Interim and it also outperforms the satellite-based datasets.

scholarly journals Testing Urban Flood Mapping Approaches from Satellite and In-Situ Data Collected during 2017 and 2019 Events in Eastern Canada

2020 ◽  
Vol 12 (19) ◽  
pp. 3141
Author(s):  
Ian Olthof ◽  
Nicolas Svacina
Keyword(s):  
High Resolution ◽  
Real Time ◽  
Urban Areas ◽  
Cloud Cover ◽  
Data Availability ◽  
Flood Mapping ◽  
Eastern Canada ◽  
Urban Flood ◽  
Water Height

The increasing frequency of flooding worldwide has driven research to improve near real-time flood mapping from remote-sensing data. Improved automation and processing speed to map both open water and vegetated area flooding have resulted from these research efforts. Despite these achievements, flood mapping in urban areas where a significant number of overall impacts are felt remains a challenge. Near real-time data availability, shadowing caused by manmade infrastructure, spatial resolution, and cloud cover inhibiting optical transmission, are all factors that complicate detailed urban flood mapping needed to inform response efforts. This paper uses numerous data sources collected during two major flood events that impacted the same region of Eastern Canada in 2017 and 2019 to test different urban flood mapping approaches presented as case studies in three separate urban boroughs. Cloud-free high-resolution 3 m PlanetLab optical data acquired near peak-flood in 2019 were used to generate a maximum flood extent product for that year. Approaches using new Lidar Digital Elevation Models (DEM)s and water height estimated from nineteen RADARSAT-2 flood maps, point-based flood perimeter observations from citizen geographic information, and simulated traffic camera or other urban sensor network data were tested and verified using independent data. Coherent change detection (CCD) using multi-temporal Interferometric Wide (IW) Sentinel-1 data was also tested. Results indicate that while clear-sky high-resolution optical imagery represents the current gold standard, its availability is not guaranteed due to timely coverage and cloud cover. Water height estimated from 8 to 12.5 m resolution RADARSAT-2 flood perimeters were not sufficiently accurate to flood adjacent urban areas using a Lidar DEM in near real-time, but all nineteen scenes combined captured boroughs that flooded at least once in both flood years. CCD identified flooded boroughs and roughly captured their flood extents, but lacked timeliness and sufficient detail to inform street-level decision-making in near real-time. Point-based flood perimeter observation, whether from in-situ sensors or high-resolution optical satellites combined with Lidar DEMs, can generate accurate full flood extents under certain conditions. Observed point-based flood perimeters on manmade features with low topographic variation produced the most accurate flood extents due to reliable water height estimation from these points.

scholarly journals An Intelligent Weather Monitoring System using Internet of Things

2019 ◽  
Vol 8 (4) ◽  
pp. 4531-4536
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Real Time ◽  
Weather Forecast ◽  
Sensor Data ◽  
Graphical Form ◽  
The Internet ◽  
Time Data ◽  
Data Set ◽  
The People

With a drastic change in climate continuously it is very harmful to the people who are living in the disaster-prone areas. In some areas the people are not warned for the consequences of coming specifically in their areas, they are told about the average temperature and humidity of the city while the humidity and temperature vary at different altitude and changes at short distances. The system is a very cost-effective and efficient method for controlling and monitoring the weather, and it sends the data to the cloud so that it can be visible anywhere through internet. The temperature, humidity, and pressure play a significant role in different fields like agricultural, industrial and Logistical Field. Weather forecast is necessary for the growth and development of these industries. The Internet of Things (IoT) is the technology used in developing the proposed system, which is an efficient and advanced method for connecting the sensors to the cloud which can store real-time sensor data and connect the entire world of things in a network. Here things might be anything like electronic gadgets, sensors, and automotive electronic equipment. The system deals with controlling and monitoring the environmental conditions like Temperature, Pressure, Smoke, Relative humidity level and various other gases with sensors and sends the information to the cloud and then plot the sensor data in graphical form. An Intelligent prediction is to be done using machine learning. Machine learning is a branch of Artificial Intelligence (AI) which is a compelling method of Analyzing and predicting the given data-set. The data collected will be analyzed continuously. The real-time data which has to be sent through the sensor can be accessible throughout the world using the internet

scholarly journals Trend Analysis of Modal Identification based Real-time Power System Oscillations using L1 Trend Filtering

2021 ◽  
Vol 1 (2) ◽  
pp. 116-130
Author(s):  
José Oscullo ◽  
Jaime Cepeda ◽  
Carlos Gallardo ◽  
Lenin Haro
Keyword(s):  
Power Systems ◽  
Trend Analysis ◽  
Real Time ◽  
Dominant Mode ◽  
Oscillatory Behavior ◽  
Modal Identification ◽  
Electric Signal ◽  
Identification Algorithm ◽  
Interconnected System ◽  
Data Set

This paper is looking to show to use of system data collected from wide-area monitoring systems (WAMS). They allow monitoring of the dynamics of power systems. Among the WAMS applications, there is the modal identification algorithm, which identifies critical oscillatory modes from PMU measurements. This application permits using data processors for estimating of frequency, damping, and amplitude of dominant mode oscillations observable in a specific electric signal (e.g., active power, frequency) recorded for the analyzed period. However, since modal identification of real-time measurements is based on an online optimization, the results usually have considerable fluctuations. Thus, it is essential to consider the complementary implementation of trend analysis for acquiring convenient early-warning indicators of oscillatory problems. This consideration allows avoiding erroneous information of the systems oscillatory behavior of the system real-time that modal identification of crude results could deliver. In this paper, the application of a l1 filter for determining the trend analysis of high-dimensional data set resulted from a commercial modal identification is explored. The algorithm is applied to an oscillatory event registered by the WAMS of the Ecuadorian National Interconnected System with promising results.

scholarly journals EMO-5: A high-resolution multi-variable gridded meteorological data set for Europe

2021 ◽  
Author(s):  
Vera Thiemig ◽  
Goncalo N. Gomes ◽  
Jon O. Skøien ◽  
Markus Ziese ◽  
Armin Rauthe-Schöch ◽  
...  
Keyword(s):  
High Resolution ◽  
Real Time ◽  
Meteorological Data ◽  
Heavy Precipitation ◽  
Water Vapour Pressure ◽  
Data Set ◽  
High Resolution Data ◽  
Weather Stations ◽  
Resolution Data ◽  
Precipitation Events

Abstract. In this paper we present EMO-51, a European high-resolution, (sub-)daily, multi-variable meteorological data set built on historical and real-time observations obtained by integrating data from 18,964 ground weather stations, four high-resolution regional observational grids (i.e. CombiPrecip, ZAMG - INCA, EURO4M-APGD and CarpatClim) as well as one global reanalysis (ERA-Interim/Land). EMO-5 includes at daily resolution: total precipitation, temperatures (mean, minimum and maximum), wind speed, solar radiation and water vapour pressure. In addition, EMO-5 also makes available 6-hourly precipitation and mean temperature. The raw observations from the ground weather stations underwent a set of quality controls, before SPHEREMAP and Yamamoto interpolation methods were applied in order to estimate for each 5 x 5 km grid cell the variable value and its affiliated uncertainty, respectively. The quality of the EMO-5 precipitation data was evaluated through (1) comparison with two regional high resolution data sets (i.e. seNorge2 and seNorge2018), (2) analysis of 15 heavy precipitation events, and (3) examination of the interpolation uncertainty. Results show that EMO-5 successfully captured 80 % of the heavy precipitation events, and that it is of comparable quality to a regional high resolution data set. The availability of the uncertainty fields increases the transparency of the data set and hence the possible usage. EMO-5 (release 1) covers the time period from 1990 to 2019, with a near real-time release of the latest gridded observations foreseen soon. As a product of Copernicus, the EU's Earth observation programme, EMO-5 dataset is free and open, and can be accessed at https://doi.org/10.2905/0BD84BE4-CEC8-4180-97A6-8B3ADAAC4D26 (Thiemig et al., 2021).1 EMO stands for “European Meteorological Observations”, whereas the 5 denotes the spatial resolution of 5 km.

Algorithms for automated montage synthesis of images from laser-scanning confocal microscopes

1995 ◽  
Vol 53 ◽  
pp. 650-651
Author(s):  
D. E. Becker
Keyword(s):  
Image Analysis ◽  
High Resolution ◽  
Laser Scanning ◽  
Cell Counting ◽  
Wide Area ◽  
Data Set ◽  
Computational Synthesis ◽  
Automated Cell Counting ◽  
Partial View ◽  
The Individual

An efficient, robust, and widely-applicable technique is presented for computational synthesis of high-resolution, wide-area images of a specimen from a series of overlapping partial views. This technique can also be used to combine the results of various forms of image analysis, such as segmentation, automated cell counting, deblurring, and neuron tracing, to generate representations that are equivalent to processing the large wide-area image, rather than the individual partial views. This can be a first step towards quantitation of the higher-level tissue architecture. The computational approach overcomes mechanical limitations, such as hysterisis and backlash, of microscope stages. It also automates a procedure that is currently done manually. One application is the high-resolution visualization and/or quantitation of large batches of specimens that are much wider than the field of view of the microscope.The automated montage synthesis begins by computing a concise set of landmark points for each partial view. The type of landmarks used can vary greatly depending on the images of interest. In many cases, image analysis performed on each data set can provide useful landmarks. Even when no such “natural” landmarks are available, image processing can often provide useful landmarks.

Sign in / Sign up

Export Citation Format

Share Document