The effect of different contributing sensors in IMERG-Final precipitation estimates

2021 ◽  
Author(s):  
Hooman Ayat ◽  
Jason Evans ◽  
Ali Behrangi
Keyword(s):  
Time Lag ◽  
Bias Reduction ◽  
Average Intensity ◽  
Eastern United States ◽  
Object Based ◽  
Precipitation Estimates ◽  
Precipitation Area ◽  
Time Distance ◽  
Light Precipitation

<p>Ground observation absence in many parts of the world highlights the importance of merged satellite precipitation products. In this study, we aim to evaluate the effect of different sources of data in the uncertainties of a merged satellite product, by comparing the Integrated Multi-satellitE Retrievals for GPM (IMERG) final-product V06B with a ground-radar product, Multi-Radar Multi-Sensor (MRMS), over eastern United-States during the hurricane days that occurred in 2016-2018 using both pixel-based and object-based approaches. The results showed that IMERG had better agreement in terms of the average precipitation intensity and area when the passive microwave (PMW) sensor overpass is matched instantaneously with MRMS in comparison with the temporally averaged MRMS data (MRMS-Averaged) with a bias reduction of 75% and 65%, respectively. PMW observations tend to show storms with smaller areas in the IMERG final product in comparison with MRMS, possibly due to the effect of light precipitation not detected properly by PMW sensors. However, by removing the light precipitation (less than 1mm/hr) in the object-based approach, hurricane objects in the IMERG final product tend to be larger during the PMW observations, which might be related to different viewing angles of sensors contributing to MRMS and IMERG products. Precipitation estimates in the IMERG final product have smaller areas with higher average intensity during the PMW observations compared to data estimated by Morph or IR (morph/IR) observations. It is probably related to the effect of morphing technique, leading to homogenization of the varying rainstorm characteristics. The quality of IMERG data changes with the longer absence of the PMW observations. IMERG data estimated by morph/IR observations, with a 30-minute time-distance to the nearest PMW observation, showed the best agreement with MRMS-Averaged even in comparison with PMW estimates, possibly due to the time-lag in recording the precipitation between satellites and ground-radars. It is also possible to be related to the homogenizing nature of morphing technique in IMERG and averaging MRMS data in time in MRMS-Averaged, relaxing the differences between PMW observations and MRMS. However, the morph/IR data quality deteriorates with the longer absence of PMW sensors. The inter-comparison of PMW sensors showed the priority of imagers over sounders with GMI as the best among imagers and MHS as the best among sounders in terms of correlation and average intensity compared to MRMS; however, SSMIS was the best in capturing the precipitation area.</p>

scholarly journals Performance Evaluation of IMERG GPM Products during Tropical Storm Imelda

Atmosphere ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 687
Author(s):  
Salman Sakib ◽  
Dawit Ghebreyesus ◽  
Hatim O. Sharif
Keyword(s):  
Stage Iv ◽  
Tropical Storm ◽  
Average Correlation ◽  
Acceptable Range ◽  
Precipitation Estimates ◽  
Overall Performance ◽  
Early Late ◽  
Statistical Metrics ◽  
Better Than

Tropical Storm Imelda struck the southeast coastal regions of Texas from 17–19 September, 2019, and delivered precipitation above 500 mm over about 6000 km2. The performance of the three IMERG (Early-, Late-, and Final-run) GPM satellite-based precipitation products was evaluated against Stage-IV radar precipitation estimates. Basic and probabilistic statistical metrics, such as CC, RSME, RBIAS, POD, FAR, CSI, and PSS were employed to assess the performance of the IMERG products. The products captured the event adequately, with a fairly high POD value of 0.9. The best product (Early-run) showed an average correlation coefficient of 0.60. The algorithm used to produce the Final-run improved the quality of the data by removing systematic errors that occurred in the near-real-time products. Less than 5 mm RMSE error was experienced in over three-quarters (ranging from 73% to 76%) of the area by all three IMERG products in estimating the Tropical Storm Imelda. The Early-run product showed a much better RBIAS relatively to the Final-run product. The overall performance was poor, as areas with an acceptable range of RBIAS (i.e., between −10% and 10%) in all the three IMERG products were only 16% to 17% of the total area. Overall, the Early-run product was found to be better than Late- and Final-run.

scholarly journals Detecting Beam Blockage in Radar-Based Precipitation Estimates

2017 ◽  
Vol 34 (7) ◽  
pp. 1407-1422 ◽  
Author(s):  
D. Brent McRoberts ◽  
John W. Nielsen-Gammon
Keyword(s):  
Hydrological Modeling ◽  
Tall Buildings ◽  
Eastern United States ◽  
Spatiotemporal Resolution ◽  
Transmitted Beam ◽  
Geographical Regions ◽  
Precipitation Estimates ◽  
Low Pass ◽  
Prerequisite Knowledge ◽  
Quantitative Precipitation Estimates

AbstractGridded radar-based quantitative precipitation estimates (QPEs) are potentially ideal inputs for hydrological modeling and monitoring because of their high spatiotemporal resolution. Beam blockage is a common type of bias in radar QPEs related to the blockage of the radar beam by an obstruction, such as topography or tall buildings. This leads to a diminishment in the power of the transmitted beam beyond the range of obstruction and a systematic underestimation of reflectivity return to the radar site. A new spatial analysis technique for objectively identifying regions in which precipitation estimates are contaminated by beam blockage was developed. The methodology requires only a long-term precipitation climatology with no prerequisite knowledge of topography or known obstructions needed. For each radar domain, the QPEs are normalized by climatology and a low-pass Fourier series fit captures the expected precipitation as a function of azimuth angle. Beam blockage signatures are identified as radially coherent regions with normalized values that are systematically lower than the Fourier fit. Precipitation estimates sufficiently affected by beam blockage can be replaced by values estimated using neighboring unblocked estimates. The methodology is applied to the correction of the National Weather Service radar-based QPE dataset, whose estimates originate from the NEXRAD network in the central and eastern United States. The methodology is flexible enough to be useful for most radar installations and geographical regions with at least a few years of data.

scholarly journals Synergetic Use of Sentinel-1 and Sentinel-2 Data for Soil Moisture Mapping at Plot Scale

2018 ◽  
Vol 10 (8) ◽  
pp. 1285 ◽  
Author(s):  
Reza Attarzadeh ◽  
Jalal Amini ◽  
Claudia Notarnicola ◽  
Felix Greifeneder
Keyword(s):  
Soil Moisture ◽  
Optical Data ◽  
Estimation Accuracy ◽  
Support Vector ◽  
Practical Applications ◽  
Initial Stage ◽  
Object Based ◽  
Single Polarization ◽  
Selection Of

This paper presents an approach for retrieval of soil moisture content (SMC) by coupling single polarization C-band synthetic aperture radar (SAR) and optical data at the plot scale in vegetated areas. The study was carried out at five different sites with dominant vegetation cover located in Kenya. In the initial stage of the process, different features are extracted from single polarization mode (VV polarization) SAR and optical data. Subsequently, proper selection of the relevant features is conducted on the extracted features. An advanced state-of-the-art machine learning regression approach, the support vector regression (SVR) technique, is used to retrieve soil moisture. This paper takes a new look at soil moisture retrieval in vegetated areas considering the needs of practical applications. In this context, we tried to work at the object level instead of the pixel level. Accordingly, a group of pixels (an image object) represents the reality of the land cover at the plot scale. Three approaches, a pixel-based approach, an object-based approach, and a combination of pixel- and object-based approaches, were used to estimate soil moisture. The results show that the combined approach outperforms the other approaches in terms of estimation accuracy (4.94% and 0.89 compared to 6.41% and 0.62 in terms of root mean square error (RMSE) and R2), flexibility on retrieving the level of soil moisture, and better quality of visual representation of the SMC map.

scholarly journals X-rays Image reconstruction using Proximal Algorithm and adapted TV Regularization

2021 ◽  
Vol 348 ◽  
pp. 01011
Author(s):  
Aicha Allag ◽  
Redouane Drai ◽  
Tarek Boutkedjirt ◽  
Abdessalam Benammar ◽  
Wahiba Djerir
Keyword(s):  
Structural Information ◽  
X Rays ◽  
Reconstruction Algorithms ◽  
Reconstruction Problem ◽  
Tv Regularization ◽  
Object Based ◽  
Suggested Technique ◽  
Reconstruction Image ◽  
Ill Posed

Computed tomography (CT) aims to reconstruct an internal distribution of an object based on projection measurements. In the case of a limited number of projections, the reconstruction problem becomes significantly ill-posed. Practically, reconstruction algorithms play a crucial role in overcoming this problem. In the case of missing or incomplete data, and in order to improve the quality of the reconstruction image, the choice of a sparse regularisation by adding l1 norm is needed. The reconstruction problem is then based on using proximal operators. We are interested in the Douglas-Rachford method and employ total variation (TV) regularization. An efficient technique based on these concepts is proposed in this study. The primary goal is to achieve high-quality reconstructed images in terms of PSNR parameter and relative error. The numerical simulation results demonstrate that the suggested technique minimizes noise and artifacts while preserving structural information. The results are encouraging and indicate the effectiveness of the proposed strategy.

scholarly journals RESULTS OF TESTING OF MODERN INDIVIDUAL MILKING INSTALLATION

2021 ◽  
Author(s):  
O. Kryshtal ◽  
Keyword(s):  
Technological Process ◽  
Mass Fraction ◽  
Total Duration ◽  
Small Diameter ◽  
First Grade ◽  
Vacuum Pump ◽  
Average Intensity ◽  
Milking Machine ◽  
The Individual

The purpose of the research: comprehensive assessment of the individual milking unit of the company "Kurtsan" (Turkey) during operation. Methods of research: Analysis of the structural features of the individual milking unit performed by the observation method given to test sample, the quality of the machine was evaluated by standardized methods: the quality of the technological process and operational-technological indicators in accordance with the SOU 74.3-37-273, energy indices according to DSTU 2331, economic Indicators according to DSTU 4397, safety indicators and ergonomics according to DSTU IEES 60335-1, DSTU EN 60335-2-70. Research Results: The conducted research confirms a sufficiently high quality of the technological process of selection of milk in cows in the conditions of use of milking installation in a personal economy, which provides favorable conditions for the milking of the cow, taking into account its physiological features. Performance per hour of basic time is 10 heads. Milking installation works on the principle of a closed milking system, thanks to which milk does not contact the environment and immediately from the basin enters a sealed can. Such system protects milk from the possibility of bacterial and physical contamination. Milk obtained during milking by milking installation according to quality indicators (acidity, density, content of somatic cells, mass fraction of dry matter, mass fraction of fat) meets the requirements for the first grade according to DSTU 3662. Milking installation is equipped with a dry vacuum pump. Power consumption during installation does not exceed 0.54 kW. Specific electricity consumption for milking of one cow is 0.05 kWh / head. Annual operating expenses for milking of two cows in the farm are 1591.90 UAH / head. Conclusions. According to the testing of the individual milking plant manufacturing company "KURTSAN", it has been established that this installation reliably performs the technological process of machine milking of cows in milking can for their tethered maintenance and allows you to get milk of the first grade. The total duration of visiting one cow is 5.75 minutes. The average intensity of milk is 1.0 kg / min. Milking machine provides complete bodies of cows. The magnitude of the control manual feed is 50 ml. The milking machine is equipped with an adjustable pulsator of pairwise milking, which creates a manual milking process and works for a working vacuum of 40 ± 1 kPa, which prevents injury to dies and diseases of mastitis. In the cover the "Stop-Milk" system is installed, which prevents milk from entering a vacuum pump during the overflow of the poor, or water while washing All items are compactly assembled on a single cart. However, a small diameter of wheels on an unequal surface creates some inconvenience to the operator during the transportation of the machine with a filled milk capacity. The application of the installation increases the amount of milk received. Its gentle work does not harm the emotional and physical health of the cow: the dysfunctions during operation are not pushed, and light vibration creates a massage effect. Milking installation allows you to significantly reduce the labor of service personnel in an economy with a maintenance of 1 to 10 cows.

scholarly journals Ground Clutter Mitigation for Weather Radars Using the Autocorrelation Spectral Density

2014 ◽  
Vol 31 (10) ◽  
pp. 2049-2066 ◽  
Author(s):  
Sebastián M. Torres ◽  
David A. Warde
Keyword(s):  
Spectral Density ◽  
Meteorological Data ◽  
Meteorological Variable ◽  
Clutter Suppression ◽  
Environment Analysis ◽  
Weather Radars ◽  
Ground Clutter ◽  
Precipitation Estimates ◽  
Quantitative Precipitation Estimates

Abstract Radar returns from the ground, known as ground clutter, can contaminate weather signals, often resulting in severely biased meteorological estimates. If not removed, these contaminants may artificially inflate quantitative precipitation estimates and obscure polarimetric and Doppler signatures of weather. A ground-clutter filter is typically employed to mitigate this contamination and provide less biased meteorological-variable estimates. This paper introduces a novel adaptive filter based on the autocorrelation spectral density, which is capable of mitigating the adverse effects of ground clutter without unnecessarily degrading the quality of the meteorological data. The so-called Clutter Environment Analysis using Adaptive Processing (CLEAN-AP) filter adjusts its suppression characteristics in real time to match dynamic atmospheric environments and meets Next Generation Weather Radar (NEXRAD) clutter-suppression requirements.

THE SARS-CoV-2 PANDEMIC IN BULGARIA AND THE CHALLENGES OF REAL-TIME DISTANCE LEARNING FOR TOURISM UNIVERSITY STUDENTS

2020 ◽  
Author(s):  
Ivan Obreshkov ◽  
Keyword(s):  
Distance Learning ◽  
University Students ◽  
Real Time ◽  
Full Time ◽  
Starting Point ◽  
Part Time ◽  
Time Distance ◽  
First Time ◽  
Educational Field

The severe acute respiratory syndrome coronavirus SARS-CoV-2 pandemic brought changes in various aspects of life, including educational field. The present study reveals some of the challenges related to real-time distance learning for university students majoring in tourism in Plovdiv, Bulgaria. The study includes Bulgarian and international students in full-time and part-time bachelor's and master's tourism programs, in which real-time distance education was introduced for the first time. The current study could be a starting point for improving the organization and quality of education of Tourism students, as well as for faster overcoming of related difficulties in communication with students.

scholarly journals Comparing quality of reporting between preprints and peer-reviewed articles in the biomedical literature

2020 ◽  
Vol 5 (1) ◽  
Author(s):  
Clarissa F. D. Carneiro ◽  
Victor G. S. Queiroz ◽  
Thiago C. Moulin ◽  
Carlos A. M. Carvalho ◽  
Clarissa B. Haas ◽  
...  
Keyword(s):  
Time Lag ◽  
Life Sciences ◽  
Biomedical Literature ◽  
Quality Of Reporting ◽  
Journal Publication ◽  
Similar Range ◽  
The Difference ◽  
The Impact ◽  
Completeness Of Reporting

Abstract Background Preprint usage is growing rapidly in the life sciences; however, questions remain on the relative quality of preprints when compared to published articles. An objective dimension of quality that is readily measurable is completeness of reporting, as transparency can improve the reader’s ability to independently interpret data and reproduce findings. Methods In this observational study, we initially compared independent samples of articles published in bioRxiv and in PubMed-indexed journals in 2016 using a quality of reporting questionnaire. After that, we performed paired comparisons between preprints from bioRxiv to their own peer-reviewed versions in journals. Results Peer-reviewed articles had, on average, higher quality of reporting than preprints, although the difference was small, with absolute differences of 5.0% [95% CI 1.4, 8.6] and 4.7% [95% CI 2.4, 7.0] of reported items in the independent samples and paired sample comparison, respectively. There were larger differences favoring peer-reviewed articles in subjective ratings of how clearly titles and abstracts presented the main findings and how easy it was to locate relevant reporting information. Changes in reporting from preprints to peer-reviewed versions did not correlate with the impact factor of the publication venue or with the time lag from bioRxiv to journal publication. Conclusions Our results suggest that, on average, publication in a peer-reviewed journal is associated with improvement in quality of reporting. They also show that quality of reporting in preprints in the life sciences is within a similar range as that of peer-reviewed articles, albeit slightly lower on average, supporting the idea that preprints should be considered valid scientific contributions.

scholarly journals Super-Resolution-Based Snake Model—An Unsupervised Method for Large-Scale Building Extraction Using Airborne LiDAR Data and Optical Image

2020 ◽  
Vol 12 (11) ◽  
pp. 1702 ◽  
Author(s):  
Thanh Huy Nguyen ◽  
Sylvie Daniel ◽  
Didier Guériot ◽  
Christophe Sintès ◽  
Jean-Marc Le Caillec
Keyword(s):  
Large Scale ◽  
Active Contour Model ◽  
Super Resolution ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Force Model ◽  
Building Extraction ◽  
Snake Model ◽  
Object Based

Automatic extraction of buildings in urban and residential scenes has become a subject of growing interest in the domain of photogrammetry and remote sensing, particularly since the mid-1990s. Active contour model, colloquially known as snake model, has been studied to extract buildings from aerial and satellite imagery. However, this task is still very challenging due to the complexity of building size, shape, and its surrounding environment. This complexity leads to a major obstacle for carrying out a reliable large-scale building extraction, since the involved prior information and assumptions on building such as shape, size, and color cannot be generalized over large areas. This paper presents an efficient snake model to overcome such a challenge, called Super-Resolution-based Snake Model (SRSM). The SRSM operates on high-resolution Light Detection and Ranging (LiDAR)-based elevation images—called z-images—generated by a super-resolution process applied to LiDAR data. The involved balloon force model is also improved to shrink or inflate adaptively, instead of inflating continuously. This method is applicable for a large scale such as city scale and even larger, while having a high level of automation and not requiring any prior knowledge nor training data from the urban scenes (hence unsupervised). It achieves high overall accuracy when tested on various datasets. For instance, the proposed SRSM yields an average area-based Quality of 86.57% and object-based Quality of 81.60% on the ISPRS Vaihingen benchmark datasets. Compared to other methods using this benchmark dataset, this level of accuracy is highly desirable even for a supervised method. Similarly desirable outcomes are obtained when carrying out the proposed SRSM on the whole City of Quebec (total area of 656 km2), yielding an area-based Quality of 62.37% and an object-based Quality of 63.21%.

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