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Journal articles on the topic 'Near real-time estimation'

Author: Grafiati

Published: 4 June 2021

Last updated: 14 February 2022

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1

Kim, Hyeoneui, Marcelline R. Harris, Guergana K. Savova, Stuart M. Speedie, and Christopher G. Chute. "Toward Near Real-Time Acuity Estimation." Nursing Research 56, no. 4 (July 2007): 288–94. http://dx.doi.org/10.1097/01.nnr.0000280617.21189.c3.

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2

Gallo, A., G. Costa, and P. Suhadolc. "Near real-time automatic moment magnitude estimation." Bulletin of Earthquake Engineering 12, no. 1 (January 24, 2014): 185–202. http://dx.doi.org/10.1007/s10518-013-9565-x.

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3

Porter, Keith, Judith Mitrani-Reiser, and James L. Beck. "Near-real-time loss estimation for instrumented buildings." Structural Design of Tall and Special Buildings 15, no. 1 (March 2006): 3–20. http://dx.doi.org/10.1002/tal.340.

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4

Ohta, Yusaku, Takuya Inoue, Shunichi Koshimura, Satoshi Kawamoto, and Ryota Hino. "Role of Real-Time GNSS in Near-Field Tsunami Forecasting." Journal of Disaster Research 13, no. 3 (June 1, 2018): 453–59. http://dx.doi.org/10.20965/jdr.2018.p0453.

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This short paper reviews the role of real-time global navigation satellite system (GNSS) in near-field tsunami forecasting. Recent efforts highlight that coseismic fault model estimation based on real-time GNSS has contributed substantially to our understanding of large magnitude earthquakes and their fault expansions. We briefly introduce the history of use of real-time GNSS processing in the rapid estimation of the coseismic finite fault model. Additionally, we discuss our recent trials on the estimation of quasi real-time tsunami inundation based on real-time GNSS data. Obtained results clearly suggest the effectiveness of real-time GNSS for tsunami inundation estimation as the GNSS can capture fault expansion and its slip amount in a relatively accurate manner within a short time period. We also discuss the future prospects of using real-time GNSS data for tsunami warning including effective combination of different methods for more reliable forecasting.

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Mitrescu, Cristian, Steven Miller, Jeffrey Hawkins, Tristan L’Ecuyer, Joseph Turk, Philip Partain, and Graeme Stephens. "Near-Real-Time Applications of CloudSat Data." Journal of Applied Meteorology and Climatology 47, no. 7 (July 1, 2008): 1982–94. http://dx.doi.org/10.1175/2007jamc1794.1.

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Abstract Within 2 months of its launch in April 2006 as part of the Earth Observing System A-Train satellite constellation, the National Aeronautics and Space Administration Earth System Science Pathfinder (ESSP) CloudSat mission began making significant contributions toward broadening the understanding of detailed cloud vertical structures around the earth. Realizing the potential benefit of CloudSat to both the research objectives and operational requirements of the U.S. Navy, the Naval Research Laboratory coordinated early on with the CloudSat Data Processing Center to receive and process first-look 94-GHz Cloud Profiling Radar datasets in near–real time (4–8 h latency), thereby making the observations more relevant to the operational community. Applications leveraging these unique data, described herein, include 1) analysis/validation of cloud structure and properties derived from conventional passive radiometers, 2) tropical cyclone vertical structure analysis, 3) support of research field programs, 4) validation of numerical weather prediction model cloud fields, and 5) quantitative precipitation estimation in light rainfall regimes.

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Douša, J. "Towards an operational near real-time precipitable water vapor estimation." Physics and Chemistry of the Earth, Part A: Solid Earth and Geodesy 26, no. 3 (January 2001): 189–94. http://dx.doi.org/10.1016/s1464-1895(01)00045-x.

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Abate, Andrea F., Paola Barra, Carmen Bisogni, Michele Nappi, and Stefano Ricciardi. "Near Real-Time Three Axis Head Pose Estimation Without Training." IEEE Access 7 (2019): 64256–65. http://dx.doi.org/10.1109/access.2019.2917451.

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Abdalla, Saleh, Peter A. E. M. Janssen, and Jean-Raymond Bidlot. "Altimeter Near Real Time Wind and Wave Products: Random Error Estimation." Marine Geodesy 34, no. 3-4 (July 1, 2011): 393–406. http://dx.doi.org/10.1080/01490419.2011.585113.

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9

Joo, Kyungdon, Tae-Hyun Oh, Junsik Kim, and In So Kweon. "Robust and Globally Optimal Manhattan Frame Estimation in Near Real Time." IEEE Transactions on Pattern Analysis and Machine Intelligence 41, no. 3 (March 1, 2019): 682–96. http://dx.doi.org/10.1109/tpami.2018.2799944.

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10

Sezen, U., F. Arikan, O. Arikan, O. Ugurlu, and A. Sadeghimorad. "Online, automatic, near-real time estimation of GPS-TEC: IONOLAB-TEC." Space Weather 11, no. 5 (May 2013): 297–305. http://dx.doi.org/10.1002/swe.20054.

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11

Hauschild, André, and Oliver Montenbruck. "Kalman-filter-based GPS clock estimation for near real-time positioning." GPS Solutions 13, no. 3 (November 16, 2008): 173–82. http://dx.doi.org/10.1007/s10291-008-0110-3.

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12

Bock, H., R. Dach, Y. Yoon, and O. Montenbruck. "GPS clock correction estimation for near real-time orbit determination applications." Aerospace Science and Technology 13, no. 7 (October 2009): 415–22. http://dx.doi.org/10.1016/j.ast.2009.08.003.

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Zawadzka-Manko, Olga, Iwona S. Stachlewska, and Krzysztof M. Markowicz. "Near-Real-Time Application of SEVIRI Aerosol Optical Depth Algorithm." Remote Sensing 12, no. 9 (May 7, 2020): 1481. http://dx.doi.org/10.3390/rs12091481.

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Within the framework of the Satellite-based Monitoring Initiative for Regional Air quality (SAMIRA) project, the near-real-time (NRT) operation has been documented for an in-house developed algorithm used for the retrieval of aerosol optical depth (AOD) maps from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) sensor onboard the Meteosat Second Generation (MSG). With the frequency of 15 min at a spatial resolution of roughly 5.5 × 5.5 km the AOD maps are provided for the country domains of Poland, the Czech Republic, Romania, and Southern Norway. A significant improvement has been reported in terms of modification of the existing prototype algorithm that it suits the operational NRT AOD retrieval for an extended area. This is mainly due to the application of the optimal interpolation method for the AOD estimation on reference days with the use of ground-based measurements of the Aerosol Robotic Network (AERONET) and the Aerosol Research Network (PolandAOD-NET) as well as simulations of the Copernicus Atmosphere Monitoring Service (CAMS). The main issues that have been addressed regarding surface reflectance estimation, cloud screening and uncertainty calculation. Exemplary maps of the NRT retrieval have been presented.

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Sasaki, Kenzo, Selene Piantanida, André V. G. Cavalieri, and Peter Jordan. "Real-time modelling of wavepackets in turbulent jets." Journal of Fluid Mechanics 821 (May 25, 2017): 458–81. http://dx.doi.org/10.1017/jfm.2017.201.

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Three methods are considered for estimating the downstream evolution of wavepackets in turbulent jets based on upstream measurements. The parabolised stability equations are used to compute a transfer function between axially and radially separated points in the flow, and the performance of this theoretical model is compared with that of two empirical approaches, direct transfer function calculation and autoregressive moving-average exogenous system identification, both of which require unsteady experimental data. The three approaches, which perform equally well, prove suitable for estimation of the downstream evolution of wavepackets using pressure data measured in the near-nozzle region. Over distances of the order of a couple of jet diameters, correlations of up to 80 % are observed between estimation and measurement. The performance deteriorates as axial separation between input and output is increased. While the two empirical approaches are limited in terms of both the number of input–output pairs and the number of flow variables that can be reasonably considered, the parabolised stability equations-based approach has no such limitation and can be used to perform full-field estimates comprising all of the dependent variables; in this it constitutes a potentially formidable means by which to perform single-input–multiple-output estimation. It has the further advantage of not requiring unsteady data for its construction, the only necessary ingredients being the mean flow and the linearised equations of motion.

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Belabid, Nasreddine, Feng Zhao, Luca Brocca, Yanbo Huang, and Yumin Tan. "Near-Real-Time Flood Forecasting Based on Satellite Precipitation Products." Remote Sensing 11, no. 3 (January 27, 2019): 252. http://dx.doi.org/10.3390/rs11030252.

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Floods, storms and hurricanes are devastating for human life and agricultural cropland. Near-real-time (NRT) discharge estimation is crucial to avoid the damages from flood disasters. The key input for the discharge estimation is precipitation. Directly using the ground stations to measure precipitation is not efficient, especially during a severe rainstorm, because precipitation varies even in the same region. This uncertainty might result in much less robust flood discharge estimation and forecasting models. The use of satellite precipitation products (SPPs) provides a larger area of coverage of rainstorms and a higher frequency of precipitation data compared to using the ground stations. In this paper, based on SPPs, a new NRT flood forecasting approach is proposed to reduce the time of the emergency response to flood disasters to minimize disaster damage. The proposed method allows us to forecast floods using a discharge hydrograph and to use the results to map flood extent by introducing SPPs into the rainfall–runoff model. In this study, we first evaluated the capacity of SPPs to estimate flood discharge and their accuracy in flood extent mapping. Two high temporal resolution SPPs were compared, integrated multi-satellite retrievals for global precipitation measurement (IMERG) and tropical rainfall measurement mission multi-satellite precipitation analysis (TMPA). The two products are evaluated over the Ottawa watershed in Canada during the period from 10 April 2017 to 10 May 2017. With TMPA, the results showed that the difference between the observed and modeled discharges was significant with a Nash–Sutcliffe efficiency (NSE) of −0.9241 and an adapted NSE (ANSE) of −1.0048 under high flow conditions. The TMPA-based model did not reproduce the shape of the observed hydrographs. However, with IMERG, the difference between the observed and modeled discharges was improved with an NSE equal to 0.80387 and an ANSE of 0.82874. Also, the IMERG-based model could reproduce the shape of the observed hydrographs, mainly under high flow conditions. Since IMERG products provide better accuracy, they were used for flood extent mapping in this study. Flood mapping results showed that the error was mostly within one pixel compared with the observed flood benchmark data of the Ottawa River acquired by RadarSat-2 during the flood event. The newly developed flood forecasting approach based on SPPs offers a solution for flood disaster management for poorly or totally ungauged watersheds regarding precipitation measurement. These findings could be referred to by others for NRT flood forecasting research and applications.

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Wang, Peng, Xiaotong Zhang, Zhiyang Liu, Liyuan Xu, Jie He, and Jinwu Xu. "FPGA implementation of adaptive time delay estimation for real-time near-field electromagnetic ranging." International Journal of Circuit Theory and Applications 46, no. 11 (July 17, 2018): 1940–52. http://dx.doi.org/10.1002/cta.2513.

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Convertito, V., M. Caccavale, R. De Matteis, A. Emolo, D. Wald, and A. Zollo. "Fault Extent Estimation for Near-Real-Time Ground-Shaking Map Computation Purposes." Bulletin of the Seismological Society of America 102, no. 2 (March 29, 2012): 661–79. http://dx.doi.org/10.1785/0120100306.

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18

., Agilan V. "EVALUATION OF SATELLITE BASED NEAR-REAL TIME PRECIPITATION ESTIMATION OVER URBAN AREA." International Journal of Research in Engineering and Technology 04, no. 23 (October 25, 2015): 22–25. http://dx.doi.org/10.15623/ijret.2015.0423005.

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19

Lu, Xinzheng, Xiang Zeng, Zhen Xu, and Hong Guan. "Improving the Accuracy of near Real-Time Seismic Loss Estimation using Post-Earthquake Remote Sensing Images." Earthquake Spectra 34, no. 3 (August 2018): 1219–45. http://dx.doi.org/10.1193/041417eqs072m.

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With the rapid development of remote sensing technology, satellite or aerial images from the disaster area become available within 24 hours after an earthquake. The collapsed buildings can be easily identified from these images. In this work, a framework for near real-time seismic loss estimation for regional buildings is proposed, which improves the accuracy of nonlinear time-history analysis (THA)-based loss estimations by taking advantage of the identified building collapse scene of the disaster area. Specifically, a series of THA are performed for the target regional buildings, thereby generating a number of simulation results. Those simulation results that bear strong similarities to the identified collapse scene are identified as the optimal solutions, which will be used to estimate the seismic loss. The simulation results of the case studies signify that the use of the identified building collapse scene leads to much closer estimations to actual economic losses.

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20

Ge, M., E. Calais, and J. Haase. "Automatic orbit quality control for near real-time GPS zenith tropospheric delay estimation." Physics and Chemistry of the Earth, Part A: Solid Earth and Geodesy 26, no. 3 (January 2001): 177–81. http://dx.doi.org/10.1016/s1464-1895(01)00043-6.

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21

Chaudhuri, Nilanjan Ray, and Balarko Chaudhuri. "Damping and Relative Mode-Shape Estimation in Near Real-Time Through Phasor Approach." IEEE Transactions on Power Systems 26, no. 1 (February 2011): 364–73. http://dx.doi.org/10.1109/tpwrs.2010.2049386.

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22

Kwak, Youngjoo, Jonggeol Park, and Kazuhiko Fukami. "Near Real-Time Flood Volume Estimation From MODIS Time-Series Imagery in the Indus River Basin." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 7, no. 2 (February 2014): 578–86. http://dx.doi.org/10.1109/jstars.2013.2284607.

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23

Nainar, Karthikeyan, and Florin Iov. "Smart Meter Measurement-Based State Estimation for Monitoring of Low-Voltage Distribution Grids." Energies 13, no. 20 (October 15, 2020): 5367. http://dx.doi.org/10.3390/en13205367.

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The installation of smart meters at customer premises provides opportunities for the monitoring of distribution grids. This paper addresses the problem of improving the observability of low-voltage distribution grids using smart metering infrastructure. In particular, this paper deals with the application of state estimation algorithm using smart meter measurements for near-real-time monitoring of low-voltage distribution grids. This application is proposed to use a nonlinear weighted least squares method-based algorithm for estimating the node voltages from minimum number of smart meter measurements. This paper mainly deals with sensitivity analysis of the state estimation algorithm with respect to multiple uncertainties for, e.g., measurements errors, line parameter errors, and pseudo-measurements. Simulation studies are conducted to estimate the accuracy of the DSSE under various operating scenarios of a real-life low-voltage grid, and cost-effective ways to improve the accuracy of the state estimation algorithm are also evaluated. The paper concludes that by using smart meter measurements from few locations, voltage profiles of the low-voltage grid can be estimated with reasonable accuracy in near-real-time.

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24

Dragos, Toma-Danila, Cioflan Carmen Ortanza, Balan Stefan Florin, and Manea Elena Florinela. "Characteristics And Results Of The Near Real-Time System For Estimating The Seismic Damage In Romania." Mathematical Modelling in Civil Engineering 11, no. 1 (March 1, 2015): 11–19. http://dx.doi.org/10.1515/mmce-2015-0002.

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Abstract The Near Real-Time System for Estimating the Seismic Damage in Romania, implemented in 2012 at the National Institute for Earth Physics, is one of the automated systems that can directly contribute to saving many lives right after a major earthquake, by translating earthquake parameters into damage probabilities for different areas within Romanian counties and showing emergency intervention necessities, and can also lead to mitigation actions before an earthquake, through raising awareness and highlighting vulnerable aspects of the building stock and economic and social impacts. This paper aims to present the scientific background of this constantly upgrading system, and to show different results for relevant scenarios, for intermediate-depth Vrancea earthquakes and other crustal earthquakes. Several important questions are tried to be answered, like: “How credible are the estimated losses?”, “What are the most vulnerable aspects?” or “How can the damage maps be useful for authorities?”. Currently, the system uses for building loss estimation the analytical methods (as the Improved-Displacement Capacity Method - I-DCM) implemented within the open-source software SELENA (SEismic Loss EstimatioN using a logic tree Approach), together with HAZUS methods for estimating the human casualties. The building stock is defined through 48 different capacity and fragility curves, depending on construction material, height and age. As hazard data, PGA and SA values obtained through the ShakeMap System and based on real recordings and attenuation relations are used. The area currently analyzed by the system consists of 19 Romanian Counties, capital Bucharest and 9 regions in northern Bulgaria; resolution of the data is at administrative unit (commune or city) level. We aim to provide an insight of each part of this system, justify the choices made and also discuss the improvement possibilities.

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Prasad, Abhnil Amtesh, and Merlinde Kay. "Prediction of Solar Power Using Near-Real Time Satellite Data." Energies 14, no. 18 (September 16, 2021): 5865. http://dx.doi.org/10.3390/en14185865.

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Solar energy production is affected by the attenuation of incoming irradiance from underlying clouds. Often, improvements in the short-term predictability of irradiance using satellite irradiance models can assist grid operators in managing intermittent solar-generated electricity. In this paper, we develop and test a satellite irradiance model with short-term prediction capabilities using cloud motion vectors. Near-real time visible images from Himawari-8 satellite are used to derive cloud motion vectors using optical flow estimation techniques. The cloud motion vectors are used for the advection of pixels at future time horizons for predictions of irradiance at the surface. Firstly, the pixels are converted to cloud index using the historical satellite data accounting for clear, cloudy and cloud shadow pixels. Secondly, the cloud index is mapped to the clear sky index using a historical fitting function from the respective sites. Thirdly, the predicated all-sky irradiance is derived by scaling the clear sky irradiance with a clear sky index. Finally, a power conversion model trained at each site converts irradiance to power. The prediction of solar power tested at four sites in Australia using a one-month benchmark period with 5 min ahead prediction showed that errors were less than 10% at almost 34–60% of predicted times, decreasing to 18–26% of times under live predictions, but it outperformed persistence by >50% of the days with errors <10% for all sites. Results show that increased latency in satellite images and errors resulting from the conversion of cloud index to irradiance and power can significantly affect the forecasts.

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Connolly, Francis T., and Giorgio Rizzoni. "Real Time Estimation of Engine Torque for the Detection of Engine Misfires." Journal of Dynamic Systems, Measurement, and Control 116, no. 4 (December 1, 1994): 675–86. http://dx.doi.org/10.1115/1.2899267.

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The need for improvements in the on-line estimation of engine performance variables is greater nowadays as a result of more stringent emission control legislation. There is also a concurrent requirement for improved on-board diagnostics to detect different types of malfunctions. For example, recent California Air Resources Board (CARB) regulations mandate continuous monitoring of misfires, a problem which, short of an expensive measurement of combustion pressure in each cylinder, is most directly approached by estimating individual cylinder torque. This paper describes the theory and experimental results of a method for the estimation of individual cylinder torque in automative engines, with the intent of satisfying the CARB misfire detection requirements. Estimation, control, and diagnostic functions associated with automotive engines involve near periodic processes, due to the nature of multi-cylinder engines. The model of the engine dynamics used in this study fully exploits the inherent periodicity of the combustion process in the crank angle domain in order to obtain a simple deconvolution method for the estimation of the mean torque produced by each cylinder during each stroke from a measurement of crankshaft angular velocity. The deconvolution is actually performed in the spatial frequency domain, recognizing that the combustion energy is concentrated at discrete spatial frequencies, which are harmonics of the frequency of rotation of the crankshaft. Thus, the resulting deconvolution algorithm is independent of engine speed, and reduces to an algebraic operation in the frequency domain. It is necessary to perform a Discrete Fourier Transform (DFT) on the measured angular velocity signal, sampled at fixed uniform crank angle intervals. The paper discusses the model used in the study, and the experimental validation of the algorithm, which has been implemented in real time using a portable computer and has been tested extensively on different production vehicles on a chassis dynamometer and on the road.

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Yamada, M., T. Heaton, and J. Beck. "Real-Time Estimation of Fault Rupture Extent Using Near-Source versus Far-Source Classification." Bulletin of the Seismological Society of America 97, no. 6 (December 1, 2007): 1890–910. http://dx.doi.org/10.1785/0120060243.

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Dick, Galina, Gerd Gendt, and Christoph Reigber. "First experience with near real-time water vapor estimation in a German GPS network." Journal of Atmospheric and Solar-Terrestrial Physics 63, no. 12 (August 2001): 1295–304. http://dx.doi.org/10.1016/s1364-6826(00)00248-0.

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Tondaś, D., J. Kapłon, and W. Rohm. "Ultra-fast near real-time estimation of troposphere parameters and coordinates from GPS data." Measurement 162 (October 2020): 107849. http://dx.doi.org/10.1016/j.measurement.2020.107849.

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30

Bernal, Alvaro, Matias Richart, Marc Ruiz, Alberto Castro, and Luis Velasco. "Near real-time estimation of end-to-end performance in converged fixed-mobile networks." Computer Communications 150 (January 2020): 393–404. http://dx.doi.org/10.1016/j.comcom.2019.11.052.

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Sobrino, José A., Yves Julien, Juan-Carlos Jiménez-Muñoz, Drazen Skokovic, and Guillem Sòria. "Near real-time estimation of Sea and Land surface temperature for MSG SEVIRI sensors." International Journal of Applied Earth Observation and Geoinformation 89 (July 2020): 102096. http://dx.doi.org/10.1016/j.jag.2020.102096.

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32

Nguyen, Phu, Eric J. Shearer, Mohammed Ombadi, Vesta Afzali Gorooh, Kuolin Hsu, Soroosh Sorooshian, William S. Logan, and Marty Ralph. "PERSIANN Dynamic Infrared–Rain Rate Model (PDIR) for High-Resolution, Real-Time Satellite Precipitation Estimation." Bulletin of the American Meteorological Society 101, no. 3 (March 1, 2020): E286—E302. http://dx.doi.org/10.1175/bams-d-19-0118.1.

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Abstract Precipitation measurements with high spatiotemporal resolution are a vital input for hydrometeorological and water resources studies; decision-making in disaster management; and weather, climate, and hydrological forecasting. Moreover, real-time precipitation estimation with high precision is pivotal for the monitoring and managing of catastrophic hydroclimate disasters such as flash floods, which frequently transpire after extreme rainfall. While algorithms that exclusively use satellite infrared data as input are attractive owing to their rich spatiotemporal resolution and near-instantaneous availability, their sole reliance on cloud-top brightness temperature (Tb) readings causes underestimates in wet regions and overestimates in dry regions—this is especially evident over the western contiguous United States (CONUS). We introduce an algorithm, the Precipitation Estimations from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN) Dynamic Infrared–Rain rate model (PDIR), which utilizes climatological data to construct a dynamic (i.e., laterally shifting) Tb–rain rate relationship that has several notable advantages over other quantitative precipitation-estimation algorithms and noteworthy skill over the western CONUS. Validation of PDIR over the western CONUS shows a promising degree of skill, notably at the annual scale, where it performs well in comparison to other satellite-based products. Analysis of two extreme landfalling atmospheric rivers show that solely IR-based PDIR performs reasonably well compared to other IR- and PMW-based satellite rainfall products, marking its potential to be effective in real-time monitoring of extreme storms. This research suggests that IR-based algorithms that contain the spatiotemporal richness and near-instantaneous availability needed for rapid natural hazards response may soon contain the skill needed for hydrologic and water resource applications.

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Zhang, Hong, Shan Xu, Xuan Liu, and Chengliang Liu. "Near “real-time” estimation of excess commuting from open-source data: Evidence from China's megacities." Journal of Transport Geography 91 (February 2021): 102929. http://dx.doi.org/10.1016/j.jtrangeo.2020.102929.

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Lang, Dominik H., Sergio Molina-Palacios, and Conrad D. Lindholm. "Towards Near-Real-Time Damage Estimation Using a CSM-Based Tool for Seismic Risk Assessment." Journal of Earthquake Engineering 12, sup2 (May 14, 2008): 199–210. http://dx.doi.org/10.1080/13632460802014055.

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Liu, Yanxiong, and Yongqi Chen. "Improving accuracy of near real-time Precipitable Water Vapor estimation with the IGS predicted orbits." Geophysical Research Letters 29, no. 16 (August 15, 2002): 49–1. http://dx.doi.org/10.1029/2002gl015131.

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36

Lozin, D. V., I. V. Balashov, and E. A. Loupian. "Possibilities of near real-time forest cover damage estimation based on fires radiative power data." IOP Conference Series: Earth and Environmental Science 806, no. 1 (August 1, 2021): 012019. http://dx.doi.org/10.1088/1755-1315/806/1/012019.

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Erdogan, Eren, Michael Schmidt, Florian Seitz, and Murat Durmaz. "Near real-time estimation of ionosphere vertical total electron content from GNSS satellites using B-splines in a Kalman filter." Annales Geophysicae 35, no. 2 (February 27, 2017): 263–77. http://dx.doi.org/10.5194/angeo-35-263-2017.

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Abstract. Although the number of terrestrial global navigation satellite system (GNSS) receivers supported by the International GNSS Service (IGS) is rapidly growing, the worldwide rather inhomogeneously distributed observation sites do not allow the generation of high-resolution global ionosphere products. Conversely, with the regionally enormous increase in highly precise GNSS data, the demands on (near) real-time ionosphere products, necessary in many applications such as navigation, are growing very fast. Consequently, many analysis centers accepted the responsibility of generating such products. In this regard, the primary objective of our work is to develop a near real-time processing framework for the estimation of the vertical total electron content (VTEC) of the ionosphere using proper models that are capable of a global representation adapted to the real data distribution. The global VTEC representation developed in this work is based on a series expansion in terms of compactly supported B-spline functions, which allow for an appropriate handling of the heterogeneous data distribution, including data gaps. The corresponding series coefficients and additional parameters such as differential code biases of the GNSS satellites and receivers constitute the set of unknown parameters. The Kalman filter (KF), as a popular recursive estimator, allows processing of the data immediately after acquisition and paves the way of sequential (near) real-time estimation of the unknown parameters. To exploit the advantages of the chosen data representation and the estimation procedure, the B-spline model is incorporated into the KF under the consideration of necessary constraints. Based on a preprocessing strategy, the developed approach utilizes hourly batches of GPS and GLONASS observations provided by the IGS data centers with a latency of 1 h in its current realization. Two methods for validation of the results are performed, namely the self consistency analysis and a comparison with Jason-2 altimetry data. The highly promising validation results allow the conclusion that under the investigated conditions our derived near real-time product is of the same accuracy level as the so-called final post-processed products provided by the IGS with a latency of several days or even weeks.

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Wang, Wenguan, Henry Shu-Hung Chung, and Jun Zhang. "Near-Real-Time Parameter Estimation of an Electrical Battery Model With Multiple Time Constants and SOC-Dependent Capacitance." IEEE Transactions on Power Electronics 29, no. 11 (November 2014): 5905–20. http://dx.doi.org/10.1109/tpel.2014.2300143.

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Virtanen, Juho-Pekka, Kaisa Jaalama, Tuulia Puustinen, Arttu Julin, Juha Hyyppä, and Hannu Hyyppä. "Near Real-Time Semantic View Analysis of 3D City Models in Web Browser." ISPRS International Journal of Geo-Information 10, no. 3 (March 4, 2021): 138. http://dx.doi.org/10.3390/ijgi10030138.

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3D city models and their browser-based applications have become an increasingly applied tool in the cities. One of their applications is the analysis views and visibility, applicable to property valuation and evaluation of urban green infrastructure. We present a near real-time semantic view analysis relying on a 3D city model, implemented in a web browser. The analysis is tested in two alternative use cases: property valuation and evaluation of the urban green infrastructure. The results describe the elements visible from a given location, and can also be applied to object type specific analysis, such as green view index estimation, with the main benefit being the freedom of choosing the point-of-view obtained with the 3D model. Several promising development directions can be identified based on the current implementation and experiment results, including the integration of the semantic view analysis with virtual reality immersive visualization or 3D city model application development platforms.

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Nguyen, Phu, Mohammed Ombadi, Vesta Afzali Gorooh, Eric J. Shearer, Mojtaba Sadeghi, Soroosh Sorooshian, Kuolin Hsu, David Bolvin, and Martin F. Ralph. "PERSIANN Dynamic Infrared–Rain Rate (PDIR-Now): A Near-Real-Time, Quasi-Global Satellite Precipitation Dataset." Journal of Hydrometeorology 21, no. 12 (December 2020): 2893–906. http://dx.doi.org/10.1175/jhm-d-20-0177.1.

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AbstractThis study presents the Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Dynamic Infrared Rain Rate (PDIR-Now) near-real-time precipitation dataset. This dataset provides hourly, quasi-global, infrared-based precipitation estimates at 0.04° × 0.04° spatial resolution with a short latency (15–60 min). It is intended to supersede the PERSIANN–Cloud Classification System (PERSIANN-CCS) dataset previously produced as the near-real-time product of the PERSIANN family. We first provide a brief description of the algorithm’s fundamentals and the input data used for deriving precipitation estimates. Second, we provide an extensive evaluation of the PDIR-Now dataset over annual, monthly, daily, and subdaily scales. Last, the article presents information on the dissemination of the dataset through the Center for Hydrometeorology and Remote Sensing (CHRS) web-based interfaces. The evaluation, conducted over the period 2017–18, demonstrates the utility of PDIR-Now and its improvement over PERSIANN-CCS at all temporal scales. Specifically, PDIR-Now improves the estimation of rain/no-rain days as demonstrated by a critical success index (CSI) of 0.53 compared to 0.47 of PERSIANN-CCS. In addition, PDIR-Now improves the estimation of seasonal and diurnal cycles of precipitation as well as regional precipitation patterns erroneously estimated by PERSIANN-CCS. Finally, an evaluation is carried out to examine the performance of PDIR-Now in capturing two extreme events, Hurricane Harvey and a cluster of summer thunderstorms that occurred over the Netherlands, where it is shown that PDIR-Now adequately represents spatial precipitation patterns as well as subdaily precipitation rates with a correlation coefficient (CORR) of 0.64 for Hurricane Harvey and 0.76 for the Netherlands thunderstorms.

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Hayatbini, Negin, Bailey Kong, Kuo-lin Hsu, Phu Nguyen, Soroosh Sorooshian, Graeme Stephens, Charless Fowlkes, and Ramakrishna Nemani. "Conditional Generative Adversarial Networks (cGANs) for Near Real-Time Precipitation Estimation from Multispectral GOES-16 Satellite Imageries—PERSIANN-cGAN." Remote Sensing 11, no. 19 (September 20, 2019): 2193. http://dx.doi.org/10.3390/rs11192193.

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In this paper, we present a state-of-the-art precipitation estimation framework which leverages advances in satellite remote sensing as well as Deep Learning (DL). The framework takes advantage of the improvements in spatial, spectral and temporal resolutions of the Advanced Baseline Imager (ABI) onboard the GOES-16 platform along with elevation information to improve the precipitation estimates. The procedure begins by first deriving a Rain/No Rain (R/NR) binary mask through classification of the pixels and then applying regression to estimate the amount of rainfall for rainy pixels. A Fully Convolutional Network is used as a regressor to predict precipitation estimates. The network is trained using the non-saturating conditional Generative Adversarial Network (cGAN) and Mean Squared Error (MSE) loss terms to generate results that better learn the complex distribution of precipitation in the observed data. Common verification metrics such as Probability Of Detection (POD), False Alarm Ratio (FAR), Critical Success Index (CSI), Bias, Correlation and MSE are used to evaluate the accuracy of both R/NR classification and real-valued precipitation estimates. Statistics and visualizations of the evaluation measures show improvements in the precipitation retrieval accuracy in the proposed framework compared to the baseline models trained using conventional MSE loss terms. This framework is proposed as an augmentation for PERSIANN-CCS (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network- Cloud Classification System) algorithm for estimating global precipitation.

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Prakash, Satya, and Jayaraman Srinivasan. "A Comprehensive Evaluation of Near-Real-Time and Research Products of IMERG Precipitation over India for the Southwest Monsoon Period." Remote Sensing 13, no. 18 (September 15, 2021): 3676. http://dx.doi.org/10.3390/rs13183676.

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Precipitation is one of the integral components of the global hydrological cycle. Accurate estimation of precipitation is vital for numerous applications ranging from hydrology to climatology. Following the launch of the Global Precipitation Measurement (GPM) Core Observatory, the Integrated Multi-satellite Retrievals for GPM (IMERG) precipitation product was released. The IMERG provides global precipitation estimates at finer spatiotemporal resolution (e.g., 0.1°/half-hourly) and has shown to be better than other contemporary multi-satellite precipitation products over most parts of the globe. In this study, near-real-time and research products of IMERG have been extensively evaluated against a daily rain-gauge-based precipitation dataset over India for the southwest monsoon period. In addition, the current version 6 of the IMERG research product or Final Run (IMERG-F V6) has been compared with its predecessor, version 5, and error characteristics of IMERG-F V6 for pre-GPM and GPM periods have been assessed. The spatial distributions of different error metrics over the country show that both near-real-time IMERG products (e.g., Early and Late Runs) have similar error characteristics in precipitation estimation. However, near-real-time products have larger errors than IMERG-F V6, as expected. Bias in all-India daily mean rainfall in the near-real-time IMERG products is about 3–4 times larger than research product. Both V5 and V6 IMERG-F estimates show similar error characteristics in daily precipitation estimation over the country. Similarly, both near-real-time and research products show similar characteristics in the detection of rainy days. However, IMERG-F V6 exhibits better performance in precipitation estimation and detection of rainy days during the GPM period (2014–2017) than the pre-GPM period (2010–2013). The contribution of different rainfall intensity intervals to total monsoon rainfall is captured well by the IMERG estimates. Furthermore, results reveal that IMERG estimates under-detect and overestimate light rainfall intensity of 2.5–7.5 mm day−1, which needs to be improved in the next release. The results of this study would be beneficial for end-users to integrate this multi-satellite product in any specific application.

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Zhang, Kexin, Mitchell D. Goldberg, Fengying Sun, Lihang Zhou, Walter W. Wolf, Changyi Tan, Nicholas R. Nalli, and Quanhua Liu. "Estimation of Near-Real-Time Outgoing Longwave Radiation from Cross-Track Infrared Sounder (CrIS) Radiance Measurements." Journal of Atmospheric and Oceanic Technology 34, no. 3 (March 2017): 643–55. http://dx.doi.org/10.1175/jtech-d-15-0238.1.

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AbstractThis study describes the algorithm for deriving near-real-time outgoing longwave radiation (OLR) from Cross-Track Infrared Sounder (CrIS) hyperspectral infrared sounder radiance measurements. The estimation of OLR on a near-real-time basis provides a unique perspective for studying the variability of Earth’s current atmospheric radiation budget. CrIS-derived OLR values are estimated as a weighted linear combination of CrIS-adjusted “pseudochannel” radiances. The algorithm uses the Atmospheric Infrared Sounder (AIRS) as the transfer instrument, and a least squares regression algorithm is applied to generate two sets of regression coefficients. The first set of regression coefficients is derived from collocated Clouds and the Earth’s Radiant Energy System (CERES) OLR on Aqua and pseudochannel radiances calculated from AIRS radiances. The second set of coefficients is derived to adjust the CrIS pseudochannel radiance to account for the differences in pseudochannel radiances between AIRS and CrIS. The CrIS-derived OLR is then validated by using a limited set of available CERES SNPP OLR observations over 1° × 1° global grids, as well as monthly OLR mean and interannual differences against CERES OLR datasets from SNPP and Aqua. The results show that the bias of global CrIS OLR estimation is within ±2 W m−2 and that the standard deviation is within 5 W m−2 for all conditions, and ±1 and 3 W m−2 for homogeneous scenes. The interannual CrIS-derived OLR differences agree well with Aqua CERES interannual OLR differences on a 1° × 1° spatial scale, with only a small drift of the global mean of these two datasets of around 0.004 W m−2.

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Krishnan, S., E. Casarotti, J. Goltz, C. Ji, D. Komatitsch, R. Mourhatch, M. Muto, J. H. Shaw, C. Tape, and J. Tromp. "Rapid Estimation of Damage to Tall Buildings Using Near Real-Time Earthquake and Archived Structural Simulations." Bulletin of the Seismological Society of America 102, no. 6 (December 1, 2012): 2646–66. http://dx.doi.org/10.1785/0120110339.

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Gendt, G., G. Dick, A. Rius, and P. Sedo. "Comparison of software and techniques for water vapor estimation using German near real-time GPS data." Physics and Chemistry of the Earth, Part A: Solid Earth and Geodesy 26, no. 6-8 (January 2001): 417–20. http://dx.doi.org/10.1016/s1464-1895(01)00076-x.

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van der Marel, H. "COST-716 demonstration project for the near real-time estimation of integrated water vapour from GPS." Physics and Chemistry of the Earth, Parts A/B/C 29, no. 2-3 (January 2004): 187–99. http://dx.doi.org/10.1016/j.pce.2004.01.001.

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Sadeghi, Mojtaba, Phu Nguyen, Kuolin Hsu, and Soroosh Sorooshian. "Improving near real-time precipitation estimation using a U-Net convolutional neural network and geographical information." Environmental Modelling & Software 134 (December 2020): 104856. http://dx.doi.org/10.1016/j.envsoft.2020.104856.

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Ge, Maorong, Eric Calais, and Jennifer Haase. "Reducing satellite orbit error effects in near real-time GPS zenith tropospheric delay estimation for meteorology." Geophysical Research Letters 27, no. 13 (July 1, 2000): 1915–18. http://dx.doi.org/10.1029/1999gl011256.

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Kandasamy, Sivasathivel, Aleixandre Verger, and Frederic Baret. "Assessment of Three Methods for Near Real-Time Estimation of Leaf Area Index From AVHRR Data." IEEE Transactions on Geoscience and Remote Sensing 55, no. 3 (March 2017): 1489–97. http://dx.doi.org/10.1109/tgrs.2016.2626307.

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Zhang, Zhi, Dagang Wang, Guiling Wang, Jianxiu Qiu, and Weilin Liao. "Use of SMAP Soil Moisture and Fitting Methods in Improving GPM Estimation in Near Real Time." Remote Sensing 11, no. 3 (February 12, 2019): 368. http://dx.doi.org/10.3390/rs11030368.

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Satellite-based precipitation products have been widely used in a variety of fields. However, near real time products still contain substantial biases compared with the ground data. Recent studies showed that surface soil moisture can be utilized in improving rainfall estimation as it reflects recent precipitation. In this study, soil moisture data from Soil Moisture Active Passive (SMAP) satellite and observation-based fitting are used to correct near real time satellite-based precipitation product Global Precipitation Measurement (GPM) in mainland China. The particle filter is adopted to assimilate the SMAP soil moisture into a simple hydrological model, the antecedent precipitation index (API) model; three fitting methods—i.e., linear, nonlinear, and cumulative distribution function (CDF) fitting corrections—both separately and in combination with the SMAP soil moisture data, are then used to correct GPM. The results show that the soil moisture-based correction significantly reduces the root mean square error (RMSE) and mean absolute errors (BIAS) of the original GPM product in most areas of China. The median RMSE value for daily precipitation over China is decreased by approximately 18% from 5.25 mm/day for the GPM estimates to 4.32 mm/day for the soil moisture corrected estimates, and the median BIAS value is decreased by approximately 13% from 2.03 mm/day to 1.76 mm/day. The fitting correction method alone also improves GPM, although to a lesser extent. The best performance is found when the SMAP soil moisture assimilation is combined with the linear fitting of observed precipitation, with a median RMSE of 4.00 mm/day and a BIAS of 1.69 mm/day. Despite significant reductions to the biases of the satellite precipitation product, none of these methods is effective in improving the correlation between the satellite product and observational reference. Leaf area index and the frequency of the SMAP overpasses are among the potential factors influencing the correction effect. This study highlights that combining soil moisture and historical precipitation information can effectively improve satellite-based precipitation products in near real time.

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