Seasonal Analysis of Cloud Objects in the High-Resolution Rapid Refresh (HRRR) Model Using Object-Based Verification

AbstractIn this study, object-based verification using the method for object-based diagnostic evaluation (MODE) is used to assess the accuracy of cloud-cover forecasts from the experimental High-Resolution Rapid Refresh (HRRRx) model during the warm and cool seasons. This is accomplished by comparing cloud objects identified by MODE in observed and simulated Geostationary Operational Environmental Satellite 10.7-μm brightness temperatures for August 2015 and January 2016. The analysis revealed that more cloud objects and a more pronounced diurnal cycle occurred during August, with larger object sizes observed in January because of the prevalence of synoptic-scale cloud features. With the exception of the 0-h analyses, the forecasts contained fewer cloud objects than were observed. HRRRx forecast accuracy is assessed using two methods: traditional verification, which compares the locations of grid points identified as observation and forecast objects, and the MODE composite score, an area-weighted calculation using the object-pair interest values computed by MODE. The 1-h forecasts for both August and January were the most accurate for their respective months. Inspection of the individual MODE attribute interest scores showed that, even though displacement errors between the forecast and observation objects increased between the 0-h analyses and 1-h forecasts, the forecasts were more accurate than the analyses because the sizes of the largest cloud objects more closely matched the observations. The 1-h forecasts from August were found to be more accurate than those during January because the spatial displacement between the cloud objects was smaller and the forecast objects better represented the size of the observation objects.

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Assimilation of Synthetic GOES-R ABI Infrared Brightness Temperatures and WSR-88D Radar Observations in a High-Resolution OSSE

Monthly Weather Review ◽

10.1175/mwr-d-15-0366.1 ◽

2016 ◽

Vol 144 (9) ◽

pp. 3159-3180 ◽

Cited By ~ 14

Author(s):

Rebecca M. Cintineo ◽

Jason A. Otkin ◽

Thomas A. Jones ◽

Steven Koch ◽

David J. Stensrud

Keyword(s):

High Resolution ◽

Data Assimilation ◽

Radial Velocity ◽

Doppler Radar ◽

Forecast Accuracy ◽

Central Plains ◽

Radar Observations ◽

Brightness Temperatures ◽

Observing System Simulation Experiment ◽

The Impact

This study uses an observing system simulation experiment to explore the impact of assimilating GOES-R Advanced Baseline Imager (ABI) 6.95-μm brightness temperatures and Weather Surveillance Radar-1988 Doppler (WSR-88D) reflectivity and radial velocity observations in an ensemble data assimilation system. A high-resolution truth simulation was used to create synthetic radar and satellite observations of a severe weather event that occurred across the U.S. central plains on 4–5 June 2005. The experiment employs the Weather Research and Forecasting Model at 4-km horizontal grid spacing and the ensemble adjustment Kalman filter algorithm in the Data Assimilation Research Testbed system. The ability of GOES-R ABI brightness temperatures to improve the analysis and forecast accuracy when assimilated separately or simultaneously with Doppler radar reflectivity and radial velocity observations was assessed, along with the use of bias correction and different covariance localization radii for the brightness temperatures. Results show that the radar observations accurately capture the structure of a portion of the storm complex by the end of the assimilation period, but that more of the storms and atmospheric features are reproduced and the accuracy of the ensuing forecast improved when the brightness temperatures are also assimilated.

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Are Storm Characteristics the Same When Viewed Using Merged Surface Radars or a Merged Satellite Product?

Journal of Hydrometeorology ◽

10.1175/jhm-d-20-0187.1 ◽

2021 ◽

Vol 22 (1) ◽

pp. 43-62

Author(s):

Hooman Ayat ◽

Jason P. Evans ◽

Steven Sherwood ◽

Ali Behrangi

Keyword(s):

High Resolution ◽

Flash Flood ◽

Saturation Properties ◽

Precipitation Measurement ◽

Object Based ◽

Evaluation Mode ◽

Storm Characteristics ◽

Global Precipitation Measurement ◽

Global Precipitation ◽

Droplet Aggregation

AbstractHigh-resolution datasets offer the potential to improve our understanding of spatial and temporal precipitation patterns and storm structures. The goal of this study is to evaluate the similarities and differences of object-based storm characteristics as observed using space- or land-based sensors. The Method of Object-based Diagnostic Evaluation (MODE) Time Domain (MTD) is used to identify and track storm objects in two high-resolution merged datasets: the Integrated Multisatellite Retrievals for Global Precipitation Measurement (IMERG) final product V06B and gauge-corrected ground-radar-based Multi-Radar Multi-Sensor (MRMS) quantitative precipitation estimations. Characteristics associated with landfalling hurricanes were also examined as a separate category of storm. The results reveal that IMERG and MRMS agree reasonably well across many object-based storm characteristics. However, there are some discrepancies that are statistically significant. MRMS storms are more concentrated, with smaller areas and higher peak intensities, which implies higher flash flood risks associated with the storms. On the other hand, IMERG storms can travel longer distances with a higher volume of precipitation, which implies higher risk of riverine flooding. Agreement between the datasets is higher for faster-moving hurricanes in terms of the averaged intensity. Finally, MRMS indicates a higher average precipitation intensity during the hurricane’s lifetime. However, in non-hurricanes, the opposite result was observed. This is likely related to MRMS having higher resolution; monitoring the hurricanes from many viewing angles, leading to different signal saturation properties compared to IMERG; and/or the dominance of droplet aggregation effects over evaporation effects at lower altitudes.

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

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100139627 ◽

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.

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Assessing High-Resolution Weather Research and Forecasting (WRF) Forecasts Using an Object-Based Diagnostic Evaluation

10.21236/ada595323 ◽

2014 ◽

Author(s):

Gail Vaucher ◽

John Raby

Keyword(s):

High Resolution ◽

Diagnostic Evaluation ◽

Weather Research And Forecasting ◽

Object Based ◽

Weather Research

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High-Resolution Facial Feature Saliency Mapping

Perception ◽

10.1068/p150373 ◽

1986 ◽

Vol 15 (4) ◽

pp. 373-386 ◽

Cited By ~ 20

Author(s):

Nigel D Haig

Keyword(s):

Image Processing ◽

High Resolution ◽

Facial Feature ◽

Human Beings ◽

Comparison Process ◽

Compression Technique ◽

Feature Saliency ◽

Basic Set ◽

Computational Speed ◽

The Individual

For recognition of a target there must be some form of comparison process between the image of that target and a stored representation of that target. In the case of faces there must be a very large number of such stored representations, yet human beings seem able to perform comparisons at phenomenal speed. It is possible that faces are memorised by fitting unusual features or combinations of features onto a bland prototypical face, and such a data-compression technique would help to explain our computational speed. If humans do indeed function in this fashion, it is necessary to ask just what are the features that distinguish one face from another, and also, what are the features that form the basic set of the prototypical face. The distributed apertures technique was further developed in an attempt to answer both questions. Four target faces, stored in an image-processing computer, were each divided up into 162 contiguous squares that could be displayed in their correct positions in any combination of 24 or fewer squares. Each observer was required to judge which of the four target faces was displayed during a 1 s presentation, and the proportion of correct responses for each individual square was computed. The resultant response distributions, displayed as brightness maps, give a vivid impression of the relative saliency of each feature square, both for the individual targets and for all of them combined. The results, while broadly confirming previous work, contain some very interesting and surprising details about the differences between the target faces.

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Mask R-CNN and OBIA Fusion Improves the Segmentation of Scattered Vegetation in Very High-Resolution Optical Sensors

Sensors ◽

10.3390/s21010320 ◽

2021 ◽

Vol 21 (1) ◽

pp. 320

Author(s):

Emilio Guirado ◽

Javier Blanco-Sacristán ◽

Emilio Rodríguez-Caballero ◽

Siham Tabik ◽

Domingo Alcaraz-Segura ◽

...

Keyword(s):

High Resolution ◽

Optical Sensors ◽

Biotic Interactions ◽

Relevant Information ◽

Vegetation Monitoring ◽

Object Based Image Analysis ◽

Object Based ◽

Segmentation Methods ◽

High Resolution Images ◽

Very High

Vegetation generally appears scattered in drylands. Its structure, composition and spatial patterns are key controls of biotic interactions, water, and nutrient cycles. Applying segmentation methods to very high-resolution images for monitoring changes in vegetation cover can provide relevant information for dryland conservation ecology. For this reason, improving segmentation methods and understanding the effect of spatial resolution on segmentation results is key to improve dryland vegetation monitoring. We explored and analyzed the accuracy of Object-Based Image Analysis (OBIA) and Mask Region-based Convolutional Neural Networks (Mask R-CNN) and the fusion of both methods in the segmentation of scattered vegetation in a dryland ecosystem. As a case study, we mapped Ziziphus lotus, the dominant shrub of a habitat of conservation priority in one of the driest areas of Europe. Our results show for the first time that the fusion of the results from OBIA and Mask R-CNN increases the accuracy of the segmentation of scattered shrubs up to 25% compared to both methods separately. Hence, by fusing OBIA and Mask R-CNNs on very high-resolution images, the improved segmentation accuracy of vegetation mapping would lead to more precise and sensitive monitoring of changes in biodiversity and ecosystem services in drylands.

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Object-based urban land cover classification using rule inheritance over very high-resolution multisensor and multitemporal data

GIScience & Remote Sensing ◽

10.1080/15481603.2015.1122923 ◽

2015 ◽

Vol 53 (2) ◽

pp. 164-182 ◽

Cited By ~ 18

Author(s):

Ejaz Hussain ◽

Jie Shan

Keyword(s):

High Resolution ◽

Land Cover ◽

Land Cover Classification ◽

Urban Land ◽

Urban Land Cover ◽

Object Based ◽

Multitemporal Data ◽

Very High

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Integrating Magnetic Resonance Imaging Postprocessing Results into Neuronavigation for Electrode Implantation and Resection of Subtle Focal Cortical Dysplasia in Previously Cryptogenic Epilepsy

Neurosurgery ◽

10.1227/01.neu.0000359329.92781.b7 ◽

2010 ◽

Vol 66 (1) ◽

pp. 187-195 ◽

Cited By ~ 31

Author(s):

Jörg Wellmer ◽

Yaroslav Parpaley ◽

Marec von Lehe ◽

Hans-Jürgen Huppertz

Keyword(s):

Magnetic Resonance Imaging ◽

Magnetic Resonance ◽

High Resolution ◽

Morphometric Analysis ◽

Region Of Interest ◽

Normal Database ◽

Resonance Imaging ◽

Electrode Implantation ◽

Neuronavigation System ◽

The Individual

Abstract OBJECTIVE Focal cortical dysplasias (FCDs) are highly epileptogenic lesions. Surgical removal is frequently the best treatment option for pharmacoresistant epilepsy. However, subtle FCDs may remain undetected even after high-resolution magnetic resonance imaging (MRI). Morphometric MRI analysis, which compares the individual brain with a normal database, can facilitate the detection of FCDs. We describe how the results of normal database–based MRI postprocessing can be used to guide stereotactic electrode implantation and subsequent resection of lesions that are suspected to be FCDs. METHODS A presurgical evaluation was conducted on a 19-year-old woman with pharmacoresistant hypermotor seizures. Conventional high-resolution MRI was classified as negative for epileptogenic lesions. However, morphometric analysis of the spatially normalized MRI revealed abnormal gyration and blurring of the gray-white matter junction, which was suggestive of a small and deeply seated FCD in the left frontal lobe. RESULTS The brain region highlighted by morphometric analysis was marked as a region of interest, transferred back to the original dimension of the individual MRI, and imported into a neuronavigation system. This allowed the region of interest–targeted stereotactic implantation of 2 depth electrodes, by which seizure onset was confirmed in the lesion. The electrodes also guided the final resection, which rendered the patient seizure-free. The lesion was histologically classified as FCD Palmini and Lüders IIB. CONCLUSION Transferring normal database–based MRI postprocessing results into a neuronavigation system is a new and worthwhile extension of multimodal neuronavigation. The combination of resulting regions of interest with functional and anatomic data may facilitate planning of electrode implantation for invasive electroencephalographic recordings and the final resection of small or deeply seated FCDs.

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