Limitations to a Geostationary Infrared Sounder due to Diffraction: The Meteosat Third Generation Infrared Sounder (MTG IRS)

Abstract Geostationary infrared sounding missions offer good temporal coverage; however, because of the large distance to the observed earth targets, the effect of diffraction is increased compared to sounding from a low earth orbit (LEO). Because of the wavelength dependence of diffraction, the spectral channels do not sample the same volume of air, as is generally assumed by retrieval algorithms for LEO infrared (IR) sounder data. This additional error introduced in the retrieval by diffraction-limited instruments is called pseudonoise throughout the paper. One such diffraction-limited geostationary system is the candidate Infrared Sounder (IRS) mission on the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Meteosat Third Generation (MTG) satellites with a planned need date in 2015, due to the expected lifetime of the current Meteosat Second Generation (MSG) satellites. A simplified point spread function (PSF) is applied. To represent the channels within natural conditions, measured spectra from the LEO Atmospheric Infrared Sounder (AIRS) are used as an underlying scene when integrating over the PSF. As the AIRS spatial resolution is 15 km, the basic assumption made was that the meteorological and surface features can be regarded as fractal to the extent that they can be downscaled to at least 4 km, making them useful for these investigations. The results show that the pseudonoise is highly dependent on wavelength, and highest in the window and CO2 regions (within the broader 700–1200 cm−1, or 8.3–14.3-μm, region). The worst-case pseudonoise values are approximately 1 K for these regions. A case study of the impact of the obtained pseudonoise values on temperature and water vapor retrievals shows that only with the worst-case assumptions (uncorrelated noise) is there a quantifiable impact on the results. For temperature retrievals, this ranged from 0.1 to 0.3 K in the lower and upper troposphere, respectively.

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Toward More Integrated Utilizations of Geostationary Satellite Data for Disaster Management and Risk Mitigation

Remote Sensing ◽

10.3390/rs13081553 ◽

2021 ◽

Vol 13 (8) ◽

pp. 1553

Author(s):

Atsushi Higuchi

Keyword(s):

Risk Mitigation ◽

Low Earth Orbit ◽

Third Generation ◽

Atmospheric Measurements ◽

Terrestrial Environment ◽

Fine Spatial Resolution ◽

Terrestrial Environments ◽

Disaster Monitoring ◽

Low Earth Orbit Satellites

Third-generation geostationary meteorological satellites (GEOs), such as Himawari-8/9 Advanced Himawari Imager (AHI), Geostationary Operational Environmental Satellites (GOES)-R Series Advanced Baseline Imager (ABI), and Meteosat Third Generation (MTG) Flexible Combined Imager (FCI), provide advanced imagery and atmospheric measurements of the Earth’s weather, oceans, and terrestrial environments at high-frequency intervals. Third-generation GEOs also significantly improve capabilities by increasing the number of observation bands suitable for environmental change detection. This review focuses on the significantly enhanced contribution of third-generation GEOs for disaster monitoring and risk mitigation, focusing on atmospheric and terrestrial environment monitoring. In addition, to demonstrate the collaboration between GEOs and Low Earth orbit satellites (LEOs) as supporting information for fine-spatial-resolution observations required in the event of a disaster, the landfall of Typhoon No. 19 Hagibis in 2019, which caused tremendous damage to Japan, is used as a case study.

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Assessing storm surge hazard and impact of sea level rise in the Lesser Antilles case study of Martinique

Natural Hazards and Earth System Science ◽

10.5194/nhess-17-1559-2017 ◽

2017 ◽

Vol 17 (9) ◽

pp. 1559-1571 ◽

Cited By ~ 11

Author(s):

Yann Krien ◽

Bernard Dudon ◽

Jean Roger ◽

Gael Arnaud ◽

Narcisse Zahibo

Keyword(s):

Sea Level Rise ◽

Sea Level ◽

Storm Surge ◽

Numerical Models ◽

Lesser Antilles ◽

Adaptation Measures ◽

Worst Case ◽

Mitigation And Adaptation ◽

The Impact

Abstract. In the Lesser Antilles, coastal inundations from hurricane-induced storm surges pose a great threat to lives, properties and ecosystems. Assessing current and future storm surge hazards with sufficient spatial resolution is of primary interest to help coastal planners and decision makers develop mitigation and adaptation measures. Here, we use wave–current numerical models and statistical methods to investigate worst case scenarios and 100-year surge levels for the case study of Martinique under present climate or considering a potential sea level rise. Results confirm that the wave setup plays a major role in the Lesser Antilles, where the narrow island shelf impedes the piling-up of large amounts of wind-driven water on the shoreline during extreme events. The radiation stress gradients thus contribute significantly to the total surge – up to 100 % in some cases. The nonlinear interactions of sea level rise (SLR) with bathymetry and topography are generally found to be relatively small in Martinique but can reach several tens of centimeters in low-lying areas where the inundation extent is strongly enhanced compared to present conditions. These findings further emphasize the importance of waves for developing operational storm surge warning systems in the Lesser Antilles and encourage caution when using static methods to assess the impact of sea level rise on storm surge hazard.

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Assessing storm surge hazard and impact of sea level rise in Lesser Antilles-Case study of Martinique

10.5194/nhess-2017-148 ◽

2017 ◽

Author(s):

Yann Krien ◽

Bernard Dudon ◽

Jean Roger ◽

Gaël Arnaud ◽

Narcisse Zahibo

Keyword(s):

Sea Level Rise ◽

Sea Level ◽

Storm Surge ◽

Numerical Models ◽

Lesser Antilles ◽

Adaptation Measures ◽

Worst Case ◽

Mitigation And Adaptation ◽

The Impact

Abstract. In the Lesser Antilles, coastal inundations from hurricane-induced storm surges cause great threats to lives, properties, and ecosystems. Assessing current and future storm surge hazard with sufficient spatial resolution is of primary interest to help coastal planners and decision makers develop mitigation and adaptation measures. Here, we use wave-current numerical models and statistical methods to investigate worst case scenarios and 100-year surge levels for the case study of Martinique, under present climate or considering a potential sea-level rise. Results confirm that the wave setup plays a major role in Lesser Antilles, where the narrow island shelf impedes the piling-up of large amounts of wind-driven water on the shoreline during extreme events. The radiation stress gradients thus contribute significantly to the total surge, up to 100 % in some cases. The non-linear interactions of sea level rise with bathymetry and topography are generally found to be relatively small in Martinique, but can reach several tens of centimeters in low-lying areas where the inundation extent is strongly enhanced compared to present conditions. These findings further emphasize the importance of waves for developing operational storm surge warning systems in the Lesser Antilles, and encourage caution when using static methods to assess the impact of sea level rise on storm surge hazard.

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أثر الكلمة المنطوقة على قرار تبني المستهلك للمنتجات الجديدة : دراسة ميدانية لخدمات الجيل الثالث ( 3G ) لمؤسسة موبيليس بولاية الأغواط = The Impact of Spoken Word on Consumer's Decision to Adopt New Products : Case Study of Third-Generation Services ( 3G ) of Mobilis Company in Laghouat

مجلة أداء المؤسسات الجزائرية ◽

10.12816/0026846 ◽

2015 ◽

pp. 43-58

Author(s):

أمينة طريف

Keyword(s):

New Products ◽

Spoken Word ◽

Third Generation ◽

The Impact

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Understanding the Effects of Optimal Combination of Spectral Bands on Deep Learning Model Predictions: A Case Study Based on Permafrost Tundra Landform Mapping Using High Resolution Multispectral Satellite Imagery

Journal of Imaging ◽

10.3390/jimaging6090097 ◽

2020 ◽

Vol 6 (9) ◽

pp. 97 ◽

Cited By ~ 2

Author(s):

Md Abul Ehsan Bhuiyan ◽

Chandi Witharana ◽

Anna K. Liljedahl ◽

Benjamin M. Jones ◽

Ronald Daanen ◽

...

Keyword(s):

Deep Learning ◽

Object Detection ◽

Satellite Imagery ◽

Statistical Measures ◽

Everyday Object ◽

Model Predictions ◽

The Impact ◽

Band Combinations ◽

Spectral Channels

Deep learning (DL) convolutional neural networks (CNNs) have been rapidly adapted in very high spatial resolution (VHSR) satellite image analysis. DLCNN-based computer visions (CV) applications primarily aim for everyday object detection from standard red, green, blue (RGB) imagery, while earth science remote sensing applications focus on geo object detection and classification from multispectral (MS) imagery. MS imagery includes RGB and narrow spectral channels from near- and/or middle-infrared regions of reflectance spectra. The central objective of this exploratory study is to understand to what degree MS band statistics govern DLCNN model predictions. We scaffold our analysis on a case study that uses Arctic tundra permafrost landform features called ice-wedge polygons (IWPs) as candidate geo objects. We choose Mask RCNN as the DLCNN architecture to detect IWPs from eight-band Worldview-02 VHSR satellite imagery. A systematic experiment was designed to understand the impact on choosing the optimal three-band combination in model prediction. We tasked five cohorts of three-band combinations coupled with statistical measures to gauge the spectral variability of input MS bands. The candidate scenes produced high model detection accuracies for the F1 score, ranging between 0.89 to 0.95, for two different band combinations (coastal blue, blue, green (1,2,3) and green, yellow, red (3,4,5)). The mapping workflow discerned the IWPs by exhibiting low random and systematic error in the order of 0.17–0.19 and 0.20–0.21, respectively, for band combinations (1,2,3). Results suggest that the prediction accuracy of the Mask-RCNN model is significantly influenced by the input MS bands. Overall, our findings accentuate the importance of considering the image statistics of input MS bands and careful selection of optimal bands for DLCNN predictions when DLCNN architectures are restricted to three spectral channels.

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How should hospitals manage the backlog of patients awaiting surgery following the COVID-19 pandemic? A demand modelling simulation case study for carotid endarterectomy

10.1101/2020.04.29.20085183 ◽

2020 ◽

Author(s):

Guy Martin ◽

Jonathan Clarke ◽

Sheraz Markar ◽

Alexander W Carter ◽

Sam Mason ◽

...

Keyword(s):

Carotid Endarterectomy ◽

Quality Care ◽

Surgical Care ◽

Surgical Capacity ◽

Case Scenario ◽

Worst Case ◽

Health Crisis ◽

Demand Modelling ◽

The Impact

Background The COVID-19 pandemic presents unparalleled challenges for the delivery of safe and effective care. In response, many health systems have chosen to restrict access to surgery and reallocate resources; the impact on the provision of surgical services has been profound, with huge numbers of patient now awaiting surgery at the risk of avoidable harm. The challenge now is how do hospitals transition from the current pandemic mode of operation back to business as usual, and ensure that all patients receive equitable, timely and high-quality surgical care during all phases of the public health crisis. Aims and Methods This case study takes carotid endarterectomy as a time-sensitive surgical procedure and simulates 400 compartmental demand modelling scenarios for managing surgical capacity in the UK for two years following the pandemic. Results A total of 7,69 patients will require carotid endarterectomy. In the worst-case scenario, if no additional capacity is provided on resumption of normal service, the waiting list may never be cleared, and no patient will receive surgery within the 2-week target; potentially leading to >1000 avoidable strokes. If surgical capacity is doubled after 1-month of resuming normal service, it will still take more than 6-months to clear the backlog, and 30.8% of patients will not undergo surgery within 2-weeks, with an average wait of 20.3 days for the proceeding 2 years. Conclusions This case study for carotid endarterectomy has shown that every healthcare system is going to have to make difficult decisions for balancing human and capital resources against the needs of patients. It has demonstrated that the timing and size of this effort will critically influence the ability of these systems to return to their baseline and continue to provide the highest quality care for all. The failure to sustainably increase surgical capacity early in the post-COVID-19 period will have significant long-term negative impacts on patients and is likely to result in avoidable harm.

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