scholarly journals Spatially consistent postprocessing of precipitation over complex topography 

2021 ◽  
Author(s):  
Stephan Hemri ◽  
Jonas Bhend ◽  
Christoph Spirig ◽  
Reinhard Furrer ◽  
Lionel Moret ◽  
...  
Keyword(s):  
High Resolution ◽  
Spatial Resolution ◽  
Spatial Information ◽  
Weather Prediction ◽  
Dependence Structure ◽  
Complex Topography ◽  
Grid Data ◽  
Hourly Precipitation ◽  
Spatial Consistency ◽  
Ensemble Statistics

<p>Over the last decade statistical postprocessing has become a standard tool to reduce biases and dispersion errors of probabilistic numerical weather prediction (NWP) ensemble forecasts. Most established postprocessing approaches train a statistical model using raw ensemble statistics on a typically small set of stations.  While raw ensemble statistics are available from high resolution NWP grid data, observations are missing at most grid points. Hence, the generation of spatial fields of forecast scenarios requires both some kind of interpolation and reshuffling of forecast quantiles based on a dependence template. The most widely used reshuffling approach, ensemble copula coupling (ECC), applies a reordering based on the raw ensemble rank order structure. ECC relies on the assumption that the spatial dependence structure of the raw ensemble is spatially consistent with the observed fields. This assumption may not always hold for hourly precipitation in particular over complex topography, since even high resolution models do not achieve a perfect representation of the real topography.</p><p>In this study, hourly CombiPrecip fields, which are a blend of precipitation observations from station and radar data, at a spatial resolution of 1 km over Switzerland serve as observations. Hourly precipitation raw ensemble forecast fields covering lead times up to 120 hours with a spatial resolution of 2 km are provided by COSMO-E. This enables us to postprocess hourly  COSMO-E ensemble precipitation forecasts over Switzerland at different spatial scales, from a single global ensemble model output statistics type model, over regional quantile regression  models up to grid point-wise local analog models. The mismatch in spatial resolution between COSMO-E and CombiPrecip as well as  the general issue of non-representative model topography over Switzerland’s complex topography may affect the spatial consistency of the (postprocessed) forecast fields. Starting with an analysis of systematic errors and spatial consistency of COSMO-E precipitation forecasts , we assess the potential for spatially multivariate postprocessing approaches, which are able to incorporate the spatial information from CombiPrecip and are yet simple and computationally efficient. To this end, we analyse the effects of using standard and new postprocessing model designs that vary in the (analog-based) selection of training data, spatial aggregation, postprocessing model parametrizations, and methods to obtain physically realistic forecast scenarios in space. </p>

scholarly journals Long-Term and High-Resolution Global Time Series of Brightness Temperature from Copula-Based Fusion of SMAP Enhanced and SMOS Data

2018 ◽  
Vol 10 (11) ◽  
pp. 1842 ◽  
Author(s):  
Christof Lorenz ◽  
Carsten Montzka ◽  
Thomas Jagdhuber ◽  
Patrick Laux ◽  
Harald Kunstmann
Keyword(s):  
Time Series ◽  
Soil Moisture ◽  
High Resolution ◽  
Brightness Temperature ◽  
Spatial Resolution ◽  
Prediction Models ◽  
Weather Prediction ◽  
Brightness Temperatures ◽  
Climate Analysis

Long and consistent soil moisture time series at adequate spatial resolution are key to foster the application of soil moisture observations and remotely-sensed products in climate and numerical weather prediction models. The two L-band soil moisture satellite missions SMAP (Soil Moisture Active Passive) and SMOS (Soil Moisture and Ocean Salinity) are able to provide soil moisture estimates on global scales and in kilometer accuracy. However, the SMOS data record has an appropriate length of 7.5 years since late 2009, but with a coarse resolution of ∼25 km only. In contrast, a spatially-enhanced SMAP product is available at a higher resolution of 9 km, but for a shorter time period (since March 2015 only). Being the fundamental observable from passive microwave sensors, reliable brightness temperatures (Tbs) are a mandatory precondition for satellite-based soil moisture products. We therefore develop, evaluate and apply a copula-based data fusion approach for combining SMAP Enhanced (SMAP_E) and SMOS brightness Temperature (Tb) data. The approach exploits both linear and non-linear dependencies between the two satellite-based Tb products and allows one to generate conditional SMAP_E-like random samples during the pre-SMAP period. Our resulting global Copula-combined SMOS-SMAP_E (CoSMOP) Tbs are statistically consistent with SMAP_E brightness temperatures, have a spatial resolution of 9 km and cover the period from 2010 to 2018. A comparison with Service Soil Climate Analysis Network (SCAN)-sites over the Contiguous United States (CONUS) domain shows that the approach successfully reduces the average RMSE of the original SMOS data by 15%. At certain locations, improvements of 40% and more can be observed. Moreover, the median NSE can be enhanced from zero to almost 0.5. Hence, CoSMOP, which will be made freely available to the public, provides a first step towards a global, long-term, high-resolution and multi-sensor brightness temperature product, and thereby, also soil moisture.

Three Dimensional materials characterisation at ultrahigh resolution using a position-sensitive atom probe

1990 ◽  
Vol 48 (4) ◽  
pp. 408-409
Author(s):  
R A D Mackenzie ◽  
G D W Smith ◽  
A Cerezo ◽  
T J Godfrey ◽  
J.E. Brown
Keyword(s):  
High Resolution ◽  
Spatial Resolution ◽  
Spatial Information ◽  
Three Dimensional ◽  
Atom Probe ◽  
Sensitive Detector ◽  
Channel Plate ◽  
Position Sensitive ◽  
20 Nm ◽  
Very High

The conventional atom probe field ion microscope permits very high resolution chemical information to be determined with a lateral spatial resolution of typically 2 nm. This spatial resolution is determined by the need to define the analysis area using an aperture. A recent development, the position sensitive atom probe (POSAP), has largely removed this limitation. In a conventional atom probe the ions passing through the aperture, which have come from a circular area of the order of 2 nm in diameter, travel along a long flight path where the mass to charge ratios are determined with high precision. In the position sensitive atom probe the aperture assembly, long flight tube and ion detector (a channel plate) are replaced with a position sensitive detector held at a known distance from the specimen surface. This detector consists of two parts, a channel plate component which permits the flight times (and hence mass to charge ratios) to be determined, and a wedge and strip anode which permits the position of the incoming ion to be calculated. This arrival position corresponds directly to the position on the specimen from which the ion came. The total field of view of the POSAP is a disc approximately 20 nm in diameter. With a conventional atom probe the data acquired during the evaporation sequence can be considered as a core extracted from the specimen, where the average composition as a function of depth is known. The position sensitive atom probe permits us to record data from a much wider core (20 nm rather than 2 nm in diameter), and also to retain the spatial information within the core. As the evaporation proceeds the two dimensional information yielded by the position sensitive detector builds up into a three dimensional block of data. We have, therefore, both chemical and spatial information in three dimensions at very high resolution from the sampled volume of material.

Mapping and Monitoring of the Land Use/Cover Changes in the Wider Area of ltanos, Crete, Using Very High Resolution EO Imagery With Specific Interest in Archaeological Sites

2019 ◽  
Author(s):  
Sawyer Reid stippa ◽  
George Petropoulos ◽  
Leonidas Toulios ◽  
Prashant K. Srivastava
Keyword(s):  
High Resolution ◽  
Spatial Resolution ◽  
Satellite Images ◽  
Archaeological Site ◽  
Digital Photography ◽  
Archaeological Sites ◽  
Large Area ◽  
High Resolution Imagery ◽  
Site Mapping ◽  
Rule Sets

Archaeological site mapping is important for both understanding the history as well as protecting them from excavation during the developmental activities. As archaeological sites generally spread over a large area, use of high spatial resolution remote sensing imagery is becoming increasingly applicable in the world. The main objective of this study was to map the land cover of the Itanos area of Crete and of its changes, with specific focus on the detection of the landscape’s archaeological features. Six satellite images were acquired from the Pleiades and WorldView-2 satellites over a period of 3 years. In addition, digital photography of two known archaeological sites was used for validation. An Object Based Image Analysis (OBIA) classification was subsequently developed using the five acquired satellite images. Two rule-sets were created, one using the standard four bands which both satellites have and another for the two WorldView-2 images their four extra bands included. Validation of the thematic maps produced from the classification scenarios confirmed a difference in accuracy amongst the five images. Comparing the results of a 4-band rule-set versus the 8-band showed a slight increase in classification accuracy using extra bands. The resultant classifications showed a good level of accuracy exceeding 70%. Yet, separating the archaeological sites from the open spaces with little or no vegetation proved challenging. This was mainly due to the high spectral similarity between rocks and the archaeological ruins. The satellite data spatial resolution allowed for the accuracy in defining larger archaeological sites, but still was a difficulty in distinguishing smaller areas of interest. The digital photography data provided a very good 3D representation for the archaeological sites, assisting as well in validating the satellite-derived classification maps. All in all, our study provided further evidence that use of high resolution imagery may allow for archaeological sites to be located, but only where they are of a suitable size archaeological features.

High Resolution Current Imaging by Direct Magnetic Field Sensing

2003 ◽  
Author(s):  
S.I. Woods ◽  
Nesco M. Lettsome ◽  
A.B. Cawthorne ◽  
L.A. Knauss ◽  
R.H. Koch
Keyword(s):  
Magnetic Field ◽  
High Resolution ◽  
Spatial Resolution ◽  
Great Promise ◽  
Magnetoresistive Sensor ◽  
Magnetic Sensitivity ◽  
Current Lines ◽  
Scanning Squid ◽  
Scanning Fiber ◽  
Ferromagnetic Probe

Abstract Two types of magnetic microscopes have been investigated for use in high resolution current mapping. The scanning fiber/SQUID microscope uses a SQUID sensor coupled to a nanoscale ferromagnetic probe, and the GMR microscope employs a nanoscale giant magnetoresistive sensor. Initial scans demonstrate that these microscopes can resolve current lines less than 10 µm apart with edge resolution of 1 µm. These types of microscopes are compared with the performance of a standard scanning SQUID microscope and with each other with respect to spatial resolution and magnetic sensitivity. Both microscopes show great promise for identifying current defects in die level devices.

scholarly journals HRCNet: High-Resolution Context Extraction Network for Semantic Segmentation of Remote Sensing Images

2020 ◽  
Vol 13 (1) ◽  
pp. 71
Author(s):  
Zhiyong Xu ◽  
Weicun Zhang ◽  
Tianxiang Zhang ◽  
Jiangyun Li
Keyword(s):  
Remote Sensing ◽  
Feature Extraction ◽  
High Resolution ◽  
Spatial Information ◽  
Semantic Segmentation ◽  
Context Information ◽  
Remote Sensing Images ◽  
Global Context ◽  
Boundary Information ◽  
Extraction Stage

Semantic segmentation is a significant method in remote sensing image (RSIs) processing and has been widely used in various applications. Conventional convolutional neural network (CNN)-based semantic segmentation methods are likely to lose the spatial information in the feature extraction stage and usually pay little attention to global context information. Moreover, the imbalance of category scale and uncertain boundary information meanwhile exists in RSIs, which also brings a challenging problem to the semantic segmentation task. To overcome these problems, a high-resolution context extraction network (HRCNet) based on a high-resolution network (HRNet) is proposed in this paper. In this approach, the HRNet structure is adopted to keep the spatial information. Moreover, the light-weight dual attention (LDA) module is designed to obtain global context information in the feature extraction stage and the feature enhancement feature pyramid (FEFP) structure is promoted and employed to fuse the contextual information of different scales. In addition, to achieve the boundary information, we design the boundary aware (BA) module combined with the boundary aware loss (BAloss) function. The experimental results evaluated on Potsdam and Vaihingen datasets show that the proposed approach can significantly improve the boundary and segmentation performance up to 92.0% and 92.3% on overall accuracy scores, respectively. As a consequence, it is envisaged that the proposed HRCNet model will be an advantage in remote sensing images segmentation.

scholarly journals Deriving VIIRS High-Spatial Resolution Water Property Data over Coastal and Inland Waters Using Deep Convolutional Neural Network

2021 ◽  
Vol 13 (10) ◽  
pp. 1944
Author(s):  
Xiaoming Liu ◽  
Menghua Wang
Keyword(s):  
Neural Network ◽  
High Resolution ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Ocean Color ◽  
Super Resolution ◽  
Inland Waters ◽  
Coastal Oceans ◽  
Chl A ◽  
Color Data

The Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (SNPP) satellite has been a reliable source of ocean color data products, including five moderate (M) bands and one imagery (I) band normalized water-leaving radiance spectra nLw(λ). The spatial resolutions of the M-band and I-band nLw(λ) are 750 m and 375 m, respectively. With the technique of convolutional neural network (CNN), the M-band nLw(λ) imagery can be super-resolved from 750 m to 375 m spatial resolution by leveraging the high spatial resolution features of I1-band nLw(λ) data. However, it is also important to enhance the spatial resolution of VIIRS-derived chlorophyll-a (Chl-a) concentration and the water diffuse attenuation coefficient at the wavelength of 490 nm (Kd(490)), as well as other biological and biogeochemical products. In this study, we describe our effort to derive high-resolution Kd(490) and Chl-a data based on super-resolved nLw(λ) images at the VIIRS five M-bands. To improve the network performance over extremely turbid coastal oceans and inland waters, the networks are retrained with a training dataset including ocean color data from the Bohai Sea, Baltic Sea, and La Plata River Estuary, covering water types from clear open oceans to moderately turbid and highly turbid waters. The evaluation results show that the super-resolved Kd(490) image is much sharper than the original one, and has more detailed fine spatial structures. A similar enhancement of finer structures is also found in the super-resolved Chl-a images. Chl-a filaments are much sharper and thinner in the super-resolved image, and some of the very fine spatial features that are not shown in the original images appear in the super-resolved Chl-a imageries. The networks are also applied to four other coastal and inland water regions. The results show that super-resolution occurs mainly on pixels of Chl-a and Kd(490) features, especially on the feature edges and locations with a large spatial gradient. The biases between the original M-band images and super-resolved high-resolution images are small for both Chl-a and Kd(490) in moderately to extremely turbid coastal oceans and inland waters, indicating that the super-resolution process does not change the mean values of the original images.

scholarly journals ZnWO4 Nanoparticle Scintillators for High Resolution X-ray Imaging

Nanomaterials ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. 1721
Author(s):  
Heon Yong Jeong ◽  
Hyung San Lim ◽  
Ju Hyuk Lee ◽  
Jun Heo ◽  
Hyun Nam Kim ◽  
...  
Keyword(s):  
Particle Size ◽  
Zinc Oxide ◽  
High Resolution ◽  
Tungsten Oxide ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
X Ray ◽  
X Ray Imaging ◽  
Average Size ◽  
And Tungsten

The effect of scintillator particle size on high-resolution X-ray imaging was studied using zinc tungstate (ZnWO4) particles. The ZnWO4 particles were fabricated through a solid-state reaction between zinc oxide and tungsten oxide at various temperatures, producing particles with average sizes of 176.4 nm, 626.7 nm, and 2.127 μm; the zinc oxide and tungsten oxide were created using anodization. The spatial resolutions of high-resolution X-ray images, obtained from utilizing the fabricated particles, were determined: particles with the average size of 176.4 nm produced the highest spatial resolution. The results demonstrate that high spatial resolution can be obtained from ZnWO4 nanoparticle scintillators that minimize optical diffusion by having a particle size that is smaller than the emission wavelength.

scholarly journals Super Resolution Digital Image Correlation (SR-DIC): an Alternative to Image Stitching at High Magnifications

2021 ◽  
Author(s):  
R. S. Hansen ◽  
D. W. Waldram ◽  
T. Q. Thai ◽  
R. B. Berke
Keyword(s):  
High Resolution ◽  
Digital Image Correlation ◽  
Spatial Resolution ◽  
Digital Image ◽  
Super Resolution ◽  
Image Correlation ◽  
Low Resolution ◽  
Strain Measurements ◽  
Resolution Image ◽  
High Resolution Image

Abstract Background High-resolution Digital Image Correlation (DIC) measurements have previously been produced by stitching of neighboring images, which often requires short working distances. Separately, the image processing community has developed super resolution (SR) imaging techniques, which improve resolution by combining multiple overlapping images. Objective This work investigates the novel pairing of super resolution with digital image correlation, as an alternative method to produce high-resolution full-field strain measurements. Methods First, an image reconstruction test is performed, comparing the ability of three previously published SR algorithms to replicate a high-resolution image. Second, an applied translation is compared against DIC measurement using both low- and super-resolution images. Third, a ring sample is mechanically deformed and DIC strain measurements from low- and super-resolution images are compared. Results SR measurements show improvements compared to low-resolution images, although they do not perfectly replicate the high-resolution image. SR-DIC demonstrates reduced error and improved confidence in measuring rigid body translation when compared to low resolution alternatives, and it also shows improvement in spatial resolution for strain measurements of ring deformation. Conclusions Super resolution imaging can be effectively paired with Digital Image Correlation, offering improved spatial resolution, reduced error, and increased measurement confidence.

Sign in / Sign up

Export Citation Format

Share Document