Mosaicking Weather Radar Retrievals from an Operational Heterogeneous Network at C and X Band for Precipitation Monitoring in Italian Central Apennines

Meteorological radar networks are suited to remotely provide atmospheric precipitation retrieval over a wide geographic area for severe weather monitoring and near-real-time nowcasting. However, blockage due to buildings, hills, and mountains can hamper the potential of an operational weather radar system. The Abruzzo region in central Italy’s Apennines, whose hydro-geological risks are further enhanced by its complex orography, is monitored by a heterogeneous system of three microwave radars at the C and X bands with different features. This work shows a systematic intercomparison of operational radar mosaicking methods, based on bi-dimensional rainfall products and dealing with both C and X bands as well as single- and dual-polarization systems. The considered mosaicking methods can take into account spatial radar-gauge adjustment as well as different spatial combination approaches. A data set of 16 precipitation events during the years 2018–2020 in the central Apennines is collected (with a total number of 32,750 samples) to show the potentials and limitations of the considered operational mosaicking approaches, using a geospatially-interpolated dense network of regional rain gauges as a benchmark. Results show that the radar-network pattern mosaicking, based on the anisotropic radar-gauge adjustment and spatial averaging of composite data, is better than the conventional maximum-value merging approach. The overall analysis confirms that heterogeneous weather radar mosaicking can overcome the issues of single-frequency fixed radars in mountainous areas, guaranteeing a better spatial coverage and a more uniform rainfall estimation accuracy over the area of interest.

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OPTIMIZING RADIOMETRIC PROCESSING AND FEATURE EXTRACTION OF DRONE BASED HYPERSPECTRAL FRAME FORMAT IMAGERY FOR ESTIMATION OF YIELD QUANTITY AND QUALITY OF A GRASS SWARD

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-3-1305-2018 ◽

2018 ◽

Vol XLII-3 ◽

pp. 1305-1310 ◽

Cited By ~ 5

Author(s):

R. Näsi ◽

N. Viljanen ◽

R. Oliveira ◽

J. Kaivosoja ◽

O. Niemeläinen ◽

...

Keyword(s):

Feature Extraction ◽

3D Structure ◽

Point Clouds ◽

Estimation Accuracy ◽

Spectral Features ◽

Data Set ◽

Area Of Interest ◽

Evaluation Task ◽

Corrected Data ◽

Grass Sward

Light-weight 2D format hyperspectral imagers operable from unmanned aerial vehicles (UAV) have become common in various remote sensing tasks in recent years. Using these technologies, the area of interest is covered by multiple overlapping hypercubes, in other words multiview hyperspectral photogrammetric imagery, and each object point appears in many, even tens of individual hypercubes. The common practice is to calculate hyperspectral orthomosaics utilizing only the most nadir areas of the images. However, the redundancy of the data gives potential for much more versatile and thorough feature extraction. We investigated various options of extracting spectral features in the grass sward quantity evaluation task. In addition to the various sets of spectral features, we used photogrammetry-based ultra-high density point clouds to extract features describing the canopy 3D structure. Machine learning technique based on the Random Forest algorithm was used to estimate the fresh biomass. Results showed high accuracies for all investigated features sets. The estimation results using multiview data provided approximately 10&thinsp;% better results than the most nadir orthophotos. The utilization of the photogrammetric 3D features improved estimation accuracy by approximately 40&thinsp;% compared to approaches where only spectral features were applied. The best estimation RMSE of 239&thinsp;kg/ha (6.0&thinsp;%) was obtained with multiview anisotropy corrected data set and the 3D features.

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High resolution LST climatology in an urban area using Landsat 8 thermal data

10.5194/egusphere-egu2020-13555 ◽

2020 ◽

Author(s):

Sorin Cheval ◽

Alexandru Dumitrescu ◽

Vlad Amihăesei

Keyword(s):

Missing Data ◽

High Resolution ◽

Urban Area ◽

Land Surface ◽

Missing Values ◽

Urban Climate ◽

Landsat 8 ◽

Data Set ◽

Area Of Interest ◽

Spatial Coverage

The Landsat 8 satellites retrieve land surface temperature (LST) values at 30-m spatial resolution since 2013, but the urban climate studies frequently use a limited number of images due to the problems related to missing data over the area of interest. This paper proposes a procedure for building a long-term LST data set in an urban area using the high-resolution Landsat 8 imagery. The methodology is demonstrated on 94 images available through 2013-2018 over Bucharest (Romania). The raw images contain between 1.1% and 58.4% missing data. Based on an Empirical Orthogonal Filling (EOF) procedure, the LST missing values were reconstructed by means of the function dineof implemented in sinkr R packages. The output was used for exploring the LST climatology in the area of interest. The gap filling procedure was validated by comparing artificial gaps created in the real data sets. At the best of our knowledge, this is the first study using full spatial coverage high resolution remote sensing data for investigating the urban climate. The validation pursued the comparison between LST and Ta at 3 WMO stations monitoring the climate of Bucharest, and returned strong correlation coefficients (R2 > 0.9). Further research may be envisaged aiming to update the data set with more recent LST information and to combine data from various sources in order to build a more robust urban LST climatology.This work was supported by a grant of the Romanian National Authority for Scientific Research and Innovation, CCCDI - UEFISCDI, project number COFUND-SUSCROP-SUSCAP-2, within PNCDI III.

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Hail storm hazard in urban areas: identification and probability of occurrence by using a single-polarization X-band weather radar

10.5194/hess-2016-177 ◽

2016 ◽

Cited By ~ 4

Author(s):

Vincenzo Capozzi ◽

Errico Picciotti ◽

Vincenzo Mazzarella ◽

Giorgio Budillon ◽

Frank Silvio Marzano

Keyword(s):

Urban Areas ◽

Low Cost ◽

Weather Radar ◽

Ground Truth ◽

Liquid Density ◽

Data Set ◽

Area Of Interest ◽

X Band ◽

Radar Measurements ◽

Single Polarization

Abstract. This work exploits the potentiality of hail warning, based on single-polarization X-band weather radar measurements and tested on a large and well-documented data set of thunderstorm events in southern Italy near Naples. Even though X-band radars may suffer of two-way path attenuation especially at long ranges, due to their relatively low cost their use is rapidly increasing for short-range applications such as urban environments. To identify hail through radar measurements, two different methodologies have been selected and adapted to X-band data within the study area: one uses the Waldvogel (WAL) approach, whereas the other one uses the Vertically-Integrated Liquid Density (VIL-Density) product. The study aims at developing a Probability-of-Hail (POH) index in order to support hail risk management at urban scales. In order to find the optimal threshold values to discriminate between hail and severe rain, an extensive intercomparison between outcomes of the two methodologies and ground truth observations of hail has been performed, using a 2 x 2 contingency table and statistical scores. The results show that both methods are accurate for hail detection in the area of interest, although VIL-Density product is less satisfactory than WAL method in terms of false alarm ratio. The relationship between the output of these two methodologies and POH has been derived through a heuristic approach, using a third-order polynomial fitting curve. As an example, the POH indexes have been applied for the thunderstorm event occurred on 21 July 2014, proving to be reliable for hail core detection.

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Effect of snow microstructure variability on Ku-band radar snow water equivalent retrievals

The Cryosphere ◽

10.5194/tc-13-3045-2019 ◽

2019 ◽

Vol 13 (11) ◽

pp. 3045-3059 ◽

Cited By ~ 3

Author(s):

Nick Rutter ◽

Melody J. Sandells ◽

Chris Derksen ◽

Joshua King ◽

Peter Toose ◽

...

Keyword(s):

Spatial Variability ◽

Layer Thickness ◽

Snow Water Equivalent ◽

Estimation Accuracy ◽

Snow Layer ◽

Data Set ◽

Snow Water ◽

Retrieval Errors ◽

Snow Microstructure ◽

The Impact

Abstract. Spatial variability in snowpack properties negatively impacts our capacity to make direct measurements of snow water equivalent (SWE) using satellites. A comprehensive data set of snow microstructure (94 profiles at 36 sites) and snow layer thickness (9000 vertical profiles across nine trenches) collected over two winters at Trail Valley Creek, NWT, Canada, was applied in synthetic radiative transfer experiments. This allowed for robust assessment of the impact of estimation accuracy of unknown snow microstructural characteristics on the viability of SWE retrievals. Depth hoar layer thickness varied over the shortest horizontal distances, controlled by subnivean vegetation and topography, while variability in total snowpack thickness approximated that of wind slab layers. Mean horizontal correlation lengths of layer thickness were less than a metre for all layers. Depth hoar was consistently ∼30 % of total depth, and with increasing total depth the proportion of wind slab increased at the expense of the decreasing surface snow layer. Distinct differences were evident between distributions of layer properties; a single median value represented density and specific surface area (SSA) of each layer well. Spatial variability in microstructure of depth hoar layers dominated SWE retrieval errors. A depth hoar SSA estimate of around 7 % under the median value was needed to accurately retrieve SWE. In shallow snowpacks <0.6 m, depth hoar SSA estimates of ±5 %–10 % around the optimal retrieval SSA allowed SWE retrievals within a tolerance of ±30 mm. Where snowpacks were deeper than ∼30 cm, accurate values of representative SSA for depth hoar became critical as retrieval errors were exceeded if the median depth hoar SSA was applied.

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Retrievals of Atmospheric Carbonyl Sulfide from IASI

10.5194/egusphere-egu21-10373 ◽

2021 ◽

Author(s):

Michael P. Cartwright ◽

Jeremy J. Harrison ◽

David P. Moore

Keyword(s):

Gross Primary Production ◽

Carbonyl Sulfide ◽

Optimal Estimation ◽

Stratospheric Aerosol ◽

Data Set ◽

Spatial Coverage ◽

Retrieval Scheme ◽

The University ◽

Fresh Insight

Carbonyl sulfide (OCS) is the most abundant sulfur containing gas in the atmosphere and is an important source of stratospheric aerosol. Furthermore, it has been shown that OCS can be used as a proxy for photosynthesis, which is a powerful tool in quantifying global gross primary production. While considerable improvements have been made in our understanding of the location and magnitude of OCS fluxes over the past few decades, recent studies highlight the need for a new satellite dataset to help reduce the uncertainties in current estimations. The Infrared Atmospheric Sounding Interferometer (IASI) instruments on-board the MetOp satellites offer over 14 years of nadir viewing radiance measurements with excellent spatial coverage. Given that there are currently three IASI instruments in operation, there is the potential for a significantly larger OCS dataset than is currently available elsewhere. Retrievals of OCS from these IASI radiances have been made using an adapted version of the University of Leicester IASI Retrieval Scheme (ULIRS). OCS total column amounts are calculated from profiles retrieved on a 31-layer equidistant pressure grid, using an optimal estimation approach for microwindows in the range 2000 &#8211; 2100 cm-1 wavenumbers. Sensitivity of the measurements peak in the mid-troposphere, between 5 &#8211; 10 km.The outlook of this work is to produce a long-term OCS satellite observational data set that provides fresh insight to the spatial distribution and trend of atmospheric OCS. Here, we present subsets of data in the form of case studies for different geographic regions and time periods.

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A combined local and global structure module for human pose estimation

Journal of Computational Methods in Sciences and Engineering ◽

10.3233/jcm-215210 ◽

2021 ◽

pp. 1-11

Author(s):

Zhihui Yang ◽

Xiangyu Tang ◽

Lijuan Zhang ◽

Zhiling Yang

Keyword(s):

Pose Estimation ◽

Computational Cost ◽

Hybrid Structure ◽

Estimation Accuracy ◽

Structure Model ◽

Data Set ◽

Human Pose ◽

Human Joints ◽

Residual Block ◽

Keypoints Detection

Human pose estimate can be used in action recognition, video surveillance and other fields, which has received a lot of attentions. Since the flexibility of human joints and environmental factors greatly influence pose estimation accuracy, related research is confronted with many challenges. In this paper, we incorporate the pyramid convolution and attention mechanism into the residual block, and introduce a hybrid structure model which synthetically applies the local and global information of the image for the analysis of keypoints detection. In addition, our improved structure model adopts grouped convolution, and the attention module used is lightweight, which will reduce the computational cost of the network. Simulation experiments based on the MS COCO human body keypoints detection data set show that, compared with the Simple Baseline model, our model is similar in parameters and GFLOPs (giga floating-point operations per second), but the performance is better on the detection of accuracy under the multi-person scenes.

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Kriging with Unknown Variance Components for Regional Ionospheric Reconstruction

10.20944/preprints201612.0018.v1 ◽

2016 ◽

Author(s):

Ling Huang ◽

Hongping Zhang ◽

Peiliang Xu ◽

Jianghui Geng ◽

Cheng Wang ◽

...

Keyword(s):

Ordinary Kriging ◽

Variance Components ◽

Harmonic Functions ◽

Measurement Errors ◽

Spatial Interpolation ◽

Interpolation Method ◽

Single Frequency ◽

Estimation Accuracy ◽

Spherical Cap ◽

Unknown Variance

Ionospheric delay has been a critical issue that limits the accuracy of GNSS precise positioning and navigation for single-frequency users, especially in mid- and low-latitude regions where irregularity of ionosphere is often significant. Kriging spatial interpolation techniques have been recently introduced to model the spatial correlation and variability of ionosphere, which intrinsically assume that the ionosphere field is stochastically stationary but does not take the random observational errors into account. In this paper, by treating the spatial statistical information on ionosphere as prior knowledge and based on TEC semivariogram analysis, we use Kriging techniques to spatially interpolate TEC values. By assuming that the stochastic models of both the ionospheric signals and measurement errors are only known up to some unknown factors, we propose a new Kriging spatial interpolation method with unknown variance components for both the signals of ionosphere and TEC measurements. Variance component estimation has been integrated with Kriging to reconstruct regional ionospherical delay. The method has been applied to data from the Crustal Movement Observation Network of China (CMONOC) and compared with the ordinary Kriging and polynomial interpolations with spherical cap harmonic functions, polynomial functions and low-degree spherical harmonic functions. The results have shown that the interpolation accuracy of the new proposed method is better than the ordinary Kriging and polynomial interpolation by about 1.2 TECU and 0.7 TECU, respectively. The root mean squared error of the proposed new Kriging with variance components is within 1.5 TECU and is smaller than those from other methods under comparison by about 1 TECU. When compared with ionospheric grid points, the mean squared error of the proposed method is within 6 TECU and smaller than Kriging, indicating that the proposed method can produce more accurate ionospheric delays and better estimation accuracy.

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Photosynthesis-irradiance parameters of marine phytoplankton: synthesis of a global data set

10.5194/essd-2017-40 ◽

2017 ◽

Author(s):

Heather A. Bouman ◽

Trevor Platt ◽

Martina Doblin ◽

Francisco G. Figueiras ◽

Kristinn Gudmudsson ◽

...

Keyword(s):

Primary Production ◽

Broad Band ◽

Marine Phytoplankton ◽

Production Model ◽

Initial Slope ◽

Model Parameters ◽

Data Set ◽

Spatial Coverage ◽

Basin Scale ◽

Marine Primary Production

Abstract. The photosynthetic performance of marine phytoplankton varies in response to a variety of factors, environmental and taxonomic. One of the aims of the MArine primary Production: model Parameters from Space (MAPPS) project of the European Space Agency is to assemble a global database of photosynthesis-irradiance (P-E) parameters from a range of oceanographic regimes as an aid to examining the basin-scale variability in the photophysiological response of marine phytoplankton and to use this information to improve the assignment of P-E parameters in the estimation of global marine primary production using satellite data. The MAPPS P-E Database, which consists of over 5000 P-E experiments, provides information on the spatio-temporal variability in the two P-E parameters (the assimilation number, PmB, and the initial slope, αB, where the superscripts B indicate normalisation to concentration of chlorophyll) that are fundamental inputs for models (satellite-based and otherwise) of marine primary production that use chlorophyll as the state variable. Quality-control measures consisted of removing samples with abnormally-high parameter values and flags were added to denote whether the spectral quality of the incubator lamp was used to calculate a broad-band value of αB. The MAPPS database provides a photophysiological dataset that is unprecedented in number of observations and in spatial coverage. The database would be useful to a variety of research communities, including marine ecologists, biogeochemical modellers, remote-sensing scientists and algal physiologists. The compiled data are available at https://doi.org/10.1594/PANGAEA.874087 (Bouman et al., 2017).

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DUACS DT2014: the new multi-mission altimeter data set reprocessed over 20 years

Ocean Science ◽

10.5194/os-12-1067-2016 ◽

2016 ◽

Vol 12 (5) ◽

pp. 1067-1090 ◽

Cited By ~ 180

Author(s):

Marie-Isabelle Pujol ◽

Yannice Faugère ◽

Guillaume Taburet ◽

Stéphanie Dupuy ◽

Camille Pelloquin ◽

...

Keyword(s):

Measurement Errors ◽

Large Scale ◽

Temporal Correlation ◽

Error Reduction ◽

Internal Tides ◽

Global Ocean ◽

Altimeter Data ◽

Data Set ◽

Error Budget ◽

Spatial Coverage

Abstract. The new DUACS DT2014 reprocessed products have been available since April 2014. Numerous innovative changes have been introduced at each step of an extensively revised data processing protocol. The use of a new 20-year altimeter reference period in place of the previous 7-year reference significantly changes the sea level anomaly (SLA) patterns and thus has a strong user impact. The use of up-to-date altimeter standards and geophysical corrections, reduced smoothing of the along-track data, and refined mapping parameters, including spatial and temporal correlation-scale refinement and measurement errors, all contribute to an improved high-quality DT2014 SLA data set. Although all of the DUACS products have been upgraded, this paper focuses on the enhancements to the gridded SLA products over the global ocean. As part of this exercise, 21 years of data have been homogenized, allowing us to retrieve accurate large-scale climate signals such as global and regional MSL trends, interannual signals, and better refined mesoscale features.An extensive assessment exercise has been carried out on this data set, which allows us to establish a consolidated error budget. The errors at mesoscale are about 1.4 cm2 in low-variability areas, increase to an average of 8.9 cm2 in coastal regions, and reach nearly 32.5 cm2 in high mesoscale activity areas. The DT2014 products, compared to the previous DT2010 version, retain signals for wavelengths lower than ∼  250 km, inducing SLA variance and mean EKE increases of, respectively, +5.1 and +15 %. Comparisons with independent measurements highlight the improved mesoscale representation within this new data set. The error reduction at the mesoscale reaches nearly 10 % of the error observed with DT2010. DT2014 also presents an improved coastal signal with a nearly 2 to 4 % mean error reduction. High-latitude areas are also more accurately represented in DT2014, with an improved consistency between spatial coverage and sea ice edge position. An error budget is used to highlight the limitations of the new gridded products, with notable errors in areas with strong internal tides.

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Variability in rainfall information derived from collocated microwave links

10.5194/egusphere-egu21-8409 ◽

2021 ◽

Author(s):

Anna Špačková ◽

Vojtěch Bareš ◽

Martin Fencl

Keyword(s):

Electromagnetic Waves ◽

Rainfall Intensity ◽

Weather Condition ◽

Estimation Accuracy ◽

Response Patterns ◽

Spatial Coverage ◽

Telecommunications Network ◽

Path Lengths ◽

Point To Point ◽

The Individual

In the field of hydrology, there is a significant demand for high spatial-temporal resolution of rainfall information that can be met by commercial microwave links (CMLs). CMLs are commonly used as a backhaul of telecommunications network with favourable spatial coverage especially in urbanized areas. CMLs are point-to-point radio connections operating at frequencies where attenuation of electromagnetic waves can be related to the rainfall intensity.The ability of CMLs to assess rainfall intensity is determined by hardware parameters and path lengths of CMLs. The CML operates at various frequencies with horizontal or vertical polarization, moreover, link paths have lengths ranging from hundreds of meters up to kilometres. The characteristics of the rainfall needs to be reflected as they have impact on the errors (de Vos et al., 2019). Even collocated CMLs can detect considerably dissimilar rainfall information. To increase effectivity of rainfall information retrieval it is crucial to understand uncertainties arising from diversity of CML characteristics.This study evaluates collocated CMLs that are assumed to be affected by the same weather condition. Having identical CML characteristics (as well as the propagations of the signals), it is expected to observe the same response patterns in the attenuated signals. Any disagreement could be caused by random error, sensitivity to the rainfall intensities, and/or hardware reaction to the condition (e.g. sensitivity of the antenna radome to the rainfall splash). Therefore, the role of arrangement of the direction of rainfall field advection and position of the collocated link paths is considered. The magnitude of disagreement between different groups of collocated links could be specified based on their characteristics. Oppositely, for collocated links under the same conditions but with different characteristics, the attributes of the individual CMLs are suspected for the disagreement.&#160;Referencesde Vos, L. W., Overeem, A., Leijnse, H., and Uijlenhoet, R. (2019). Rainfall Estimation Accuracy of a Nationwide Instantaneously Sampling Commercial Microwave Link Network: Error Dependency on Known Characteristics. Journal of Atmospheric and Oceanic Technology 36, 7, 1267-1283. https://doi.org/10.1175/JTECH-D-18-0197.1&#160;This study was supported by the project SpraiLINK 20-14151J of the Czech Science Foundation.

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