Optimization of Airborne Real-time Cueing Hyperspectral Enhanced Reconnaissance (ARCHER) imagery, in situ data with chemometrics to evaluate nutrients in the Shenandoah River, Virginia

2017 ◽  
Vol 33 (12) ◽  
pp. 1326-1349 ◽  
Author(s):  
Mbongowo J. Mbuh
Keyword(s):  
Real Time ◽  
In Situ Data

Real-Time Analysis Of In-Situ Spectroscopic Ellipsometric Data During Mbe Growth Of III-V Semiconductors

1991 ◽  
Vol 222 ◽  
Author(s):  
B. Johs ◽  
J. L. Edwards ◽  
K. T. Shiralagi ◽  
R. Droopad ◽  
K. Y. Choi ◽  
...  
Keyword(s):  
Real Time ◽  
Optical Constants ◽  
Growth Parameters ◽  
Ex Situ ◽  
Time Analysis ◽  
Mbe Growth ◽  
In Situ Data ◽  
Real Time Analysis

ABSTRACTA modular spectroscopic ellipsometer, capable of both in-situ and ex-situ operation, has been used to measure important growth parameters of GaAs/AIGaAs structures. The ex-situ measurements provided layer thicknesses and compositions of the grown structures. In-situ ellipsometric measurements allowed the determination of growth rates, layer thicknesses, and high temperature optical constants. By performing a regression analysis of the in-situ data in real-time, the thickness and composition of an AIGaAs layer were extracted during the MBE growth of the structure.

scholarly journals On the Quality of Real-Time Altimeter Gridded Fields: Comparison with In Situ Data

2009 ◽  
Vol 26 (3) ◽  
pp. 556-569 ◽  
Author(s):  
Ananda Pascual ◽  
Christine Boone ◽  
Gilles Larnicol ◽  
Pierre-Yves Le Traon
Keyword(s):  
Real Time ◽  
Sea Level ◽  
Tide Gauge ◽  
Deep Ocean ◽  
Velocity Estimation ◽  
Time Data ◽  
In Situ Data ◽  
Delayed Time

Abstract The timeliness of satellite altimeter measurements has a significant effect on their value for operational oceanography. In this paper, an Observing System Experiment (OSE) approach is used to assess the quality of real-time altimeter products, a key issue for robust monitoring and forecasting of the ocean state. In addition, the effect of two improved geophysical corrections and the number of missions that are combined in the altimeter products are also analyzed. The improved tidal and atmospheric corrections have a significant effect in coastal areas (0–100 km from the shore), and a comparison with tide gauge observations shows a slightly better agreement with the gridded delayed-time sea level anomalies (SLAs) with two altimeters [Jason-1 and European Remote Sensing Satellite-2 (ERS-2)/Envisat] using the new geophysical corrections (mean square differences in percent of tide gauge variance of 35.3%) than those with four missions [Jason-1, ERS/Envisat, Ocean Topography Experiment (TOPEX)/Poseidoninterlaced, and Geosat Follow-On] but using the old corrections (36.7%). In the deep ocean, however, the correction improvements have little influence. The performance of fast delivery products versus delayed-time data is compared using independent in situ data (tide gauge and drifter data). It clearly highlights the degradation of real-time SLA maps versus the delayed-time SLA maps: four altimeters are needed in real time to get the similar quality performance as two altimeters in delayed time (sea level error misfit around 36%, and zonal and meridional velocity estimation errors of 27% and 33%, respectively). This study proves that the continuous improvement of geophysical corrections is very important, and that it is essential to stay above a minimum threshold of four available altimetric missions to capture the main space and time oceanic scales in fast delivery products.

Effect of the Wind Drag Estimation Methods on Numerical Storm Surge Modeling

2019 ◽  
Author(s):  
C. Gowri Shankar ◽  
Manasa Ranjan Behera
Keyword(s):  
Real Time ◽  
Storm Surge ◽  
Wind Stress ◽  
Evaluation Method ◽  
Tide Gauge ◽  
Wave Model ◽  
Estimation Methods ◽  
Computational Accuracy ◽  
In Situ Data

Abstract Tropical cyclones have always proved the extent of its catastrophe on several occurrences over the years. In particular, the Bay of Bengal (BoB) basin in the Northern Indian Ocean has produced such historic devastating events, thereby mandating accurate real-time predictions. Numerical modeling of storm surge has always been an arduous task, as it is integrated with various uncertain factors. Among those, the major governing component being the wind forcing or the wind stress — that signifies, the computational accuracy of simulated surge and wave parameters. The present study is aimed at analysing the most suited wind drag evaluation method for real-time predictions of storm surge along the BoB. Cyclone Phailin (2013) was considered for the numerical simulations. To evaluate the wind drag coefficient, three most extensively used linear empirical relations along with the enhanced Wave Boundary Layer Model (e_WBLM) were used. The surge was subsequently simulated (using the coupled hydrodynamic circulation and wave model: ADCIRC and SWAN, respectively), individually for each of the above wind stress methods to obtain the corresponding storm surge (residual) and the storm wave features. The modeled values were further validated with the in-situ data obtained from tide gauge station and buoys respectively. It was quite intuitively observed that, e_WBLM based results correlated well with the in-situ values than its linear counterparts since, the former pragmatically includes the effects of air-sea interaction at high wind speeds in the model. The e_WBLM-based computation of significant wave heights (Hs) in deep as well as shallow water, nevertheless enabled efficient and reasonably-reliable estimations of the peak incidents.

scholarly journals Real-Time Tephra Detection and Dispersal Forecasting by a Ground-Based Weather Radar

2021 ◽  
Author(s):  
Magfira Syarifuddin ◽  
Susanna F. Jenkins ◽  
Ratih Indri Hapsari ◽  
Qingyuan Yang ◽  
Benoit Taisne ◽  
...  
Keyword(s):  
Grain Size ◽  
Real Time ◽  
Ensemble Prediction ◽  
Merapi Volcano ◽  
Ensemble Prediction System ◽  
Multiple Parameters ◽  
In Situ Data ◽  
Detection And Tracking ◽  
X Band

Tephra plumes can cause a significant hazard for surrounding towns, infrastructure, and air traffic. The current work presents the use of a small and compact X-band Multi-Parameter (X-MP) radar for the remote tephra detection and tracking of two eruptive events at Merapi Volcano, Indonesia, in May and June 2018. Tephra detection was done by analysing the multiple parameters of radar: copolar correlation and reflectivity intensity. These parameters were used to cancel unwanted clutter and retrieve tephra properties, which are grain size and concentration. Real-time spatial and temporal forecasting of tephra dispersal was performed by applying an advection scheme (nowcasting) in the manner of Ensemble Prediction System (EPS). Cross-validation was done using field-survey data, radar observations, and Himawari-8 imagery. The nowcasting model computed both the displacement and growth and decaying rate of the plume based on the temporal changes in two-dimensional movement and tephra concentration, respectively. Our results with ground-based data, where the radar-based estimated grain size distribution fell within the range of in-situ data. The uncertainty of real-time forecasted tephra plume depends on the initial condition, which affects the growth-and decaying rate estimation. The EPS improves the predictability rate by reducing the number of missed and false forecasted events. Our findings and the method presented here are suitable for early warning of tephra fall hazard at the local scale.

scholarly journals Brief communication: Hurricane Dorian: automated near-real-time mapping of the unprecedented flooding on the Bahamas using SAR

2019 ◽  
Author(s):  
Diego Cerrai ◽  
Qing Yang ◽  
Xinyi Shen ◽  
Marika Koukoula ◽  
Emmanouil N. Anagnostou
Keyword(s):  
Real Time ◽  
Disaster Response ◽  
European Space Agency ◽  
Sar Images ◽  
The Bahamas ◽  
In Situ Data ◽  
Space Agency ◽  
Continental Area ◽  
Flooded Areas

Abstract. Lack of real-time, in situ data on the extent of flooding in many parts of the world can hinder efficient disaster response. With the advent of satellite-based synthetic aperture radar (SAR) sensors, we can deploy techniques to identify flooded areas worldwide while storms are occurring. In this communication, we present an automated near-real-time (NRT) system called RAdar-Produced Inundation Diary (RAPID), applying it to European Space Agency Sentinel-1 SAR images to produce flooding maps for Hurricane Dorian in the northern Bahamas. Images from RAPID released two days after the event show coastal flooding in the Bahamas reached areas located more than 10 km inland, covering more than 3,000 km2 of continental area. RAPID flood estimates from subsequent SAR images show the recession of the flood across the islands.

scholarly journals A Statistical Method for Correcting Salinity Observations from Autonomous Profiling Floats: An ARGO Perspective

2005 ◽  
Vol 22 (3) ◽  
pp. 292-301 ◽  
Author(s):  
Fabien Durand ◽  
Gilles Reverdin
Keyword(s):  
Real Time ◽  
Time Scales ◽  
Measurement Errors ◽  
Statistical Approach ◽  
In Situ Data ◽  
Least Squares Adjustment ◽  
Time Basis ◽  
Argo Project

Abstract The Profiling Autonomous Lagrangian Circulation Explorer (PALACE) float is used to implement the Array for Real-Time Geostrophic Oceanography (ARGO). This study presents a statistical approach to correct salinity measurement errors of an ARGO-type fleet of PALACE floats. The focus is on slowly evolving drifts (typically with time scales longer than a few weeks). Considered for this case study is an ensemble of about 80 floats in the Irminger and Labrador Seas, during the 1996–97 period. Two different algorithms were implemented and validated based on float-to-float data comparison at depth, where the water masses are relatively stable over the time scales of interest. The first algorithm is based upon objective analysis of the float data, while the second consists of a least squares adjustment of the data of the various floats. The authors’ method exhibits good skills to retrieve the proper hydrological structure of the case study area. It significantly improves the consistency of the PALACE dataset with in situ data as well as with satellite altimetric data. As such, the method is readily usable on a near-real-time basis, as required by the ARGO project.

scholarly journals Producing In Situ Data From a Distance With Mobile Instant Messaging Interviews (MIMIs): Examples From the COVID-19 Pandemic

2021 ◽  
Vol 20 ◽  
pp. 160940692110296
Author(s):  
Katja Kaufmann ◽  
Corinna Peil ◽  
Tabea Bork-Hüffer
Keyword(s):  
Real Time ◽  
Instant Messaging ◽  
Low Cost ◽  
Direct Access ◽  
Communication Studies ◽  
Practical Application ◽  
Short Term ◽  
In Situ Data ◽  
Study Participants

Researching people in their chaotic and complex everyday lives is challenging for researchers at any time but especially during the application of social distancing measures. In this article, we make the case for the methodical potential of mobile messengers such as WhatsApp for qualitative mobile in situ research. We exemplify the productive use of the Mobile Instant Messaging Interview (MIMI), a research method developed by Kaufmann and Peil in 2020, to study participants’ everyday life in real-time. Based on two case studies from geography and communication studies conducted during the COVID-19 pandemic, we expound our experiences in the practical application of the MIMI approach and give recommendations. We conclude that MIMIs are a low-cost, easily feasible and short-term implemented approach for research interests across disciplines and possessing great potential for exceptional circumstances like the COVID-19 pandemic. They allow direct access to the practices and experiences of people in situ and in real-time that would otherwise stay hidden and inaccessible to social sciences. The method is suitable for research projects of any size, and can be applied as part of multi- and mixed methods designs and as well for longitudinal designs. Nonetheless, the MIMIs have to be well prepared, demand smart ways of nudging participants into elaborating their responses and require careful coordination between larger teams of researchers.

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