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Artículos de revistas sobre el tema "Radar quantique"

Autor: Grafiati

Publicado: 13 de diciembre de 2023

Última modificación: 31 de julio de 2025

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1

Blanchard, Sophie, and Caroline Schickelé. "La pédagogie par l’image. Entretien avec Ève Barlier." Radar, no. 8 (May 1, 2023): 103–21. http://dx.doi.org/10.57086/radar.638.

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Illustratrice, autrice et graphiste, Ève Barlier réalise des supports visuels divers et variés sur des concepts biologiques, de la physique quantique à l’anatomie. En collaborant avec des organismes et des institutions, elle œuvre à la diffusion des connaissances scientifiques. Ève Barlier adapte son trait, de l’illustration à la bande dessinée, selon le type de public auquel elle destine ses ouvrages, afin de favoriser une meilleure compréhension des notions scientifiques. En évoquant les enjeux contemporains de l’illustration scientifique et le travail d’Ève Barlier, cet entretien met en évi

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2

Muravev, A. V., A. Yu Bundel, D. B. Kiktev, and A. V. Smirnov. "Expertise in spatial verification of radar precipitation nowcasting: identification and statistics of objects, situations and conditional samples." Hydrometeorological research and forecasting 2 (June 16, 2022): 6–52. http://dx.doi.org/10.37162/2618-9631-2022-2-6-52.

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Statistical analysis was performed using methods of the extreme value theory for spatial objects and specified situations identified for object-oriented verification of precipitation regions with substantial and maximal areas. We made an estimation of the effect of missing values at field points and of different observation-forecast pairs construction on volumes and on statistical characteristics of samples retrieved for spatial verification purposes. We used spatial quantile functions and geographical representations in regular coordinates to illustrate particular aspects of composite fields

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3

Song, Linye, Shangfeng Chen, Yun Li, et al. "The Quantile-Matching Approach to Improving Radar Quantitative Precipitation Estimation in South China." Remote Sensing 13, no. 23 (2021): 4956. http://dx.doi.org/10.3390/rs13234956.

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Weather radar provides regional rainfall information with a very high spatial and temporal resolution. Because the radar data suffer from errors from various sources, an accurate quantitative precipitation estimation (QPE) from a weather radar system is crucial for meteorological forecasts and hydrological applications. In the South China region, multiple weather radar networks are widely used, but the accuracy of radar QPE products remains to be analyzed and improved. Based on hourly radar QPE and rain gauge observation data, this study first analyzed the QPE error in South China and then app

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4

Wu, Shiang-Jen, Ho-Cheng Lien, Chih-Tsung Hsu, Che-Hao Chang, and Jhih-Cyuan Shen. "Modeling probabilistic radar rainfall estimation at ungauged locations based on spatiotemporal errors which correspond to gauged data." Hydrology Research 46, no. 1 (2013): 39–59. http://dx.doi.org/10.2166/nh.2013.197.

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This study presents a probabilistic radar rainfall estimation (PRRE) model to quantify the reliability and accuracy of the resulting radar rainfall estimates at ungauged locations from a radar-based quantitative precipitation estimation (QPE) model. This model primarily estimates the quantiles of the radar rainfall errors at ungauged locations by incorporating seven spatiotemporal variogram models with a nonparametric sample quantile estimate method based on the radar rainfall errors at rain gauges. Then, by adding the resulting error quantiles to the radar rainfall estimates, the correspondin

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5

Gyasi-Agyei, Yeboah. "Identification of the Optimum Rain Gauge Network Density for Hydrological Modelling Based on Radar Rainfall Analysis." Water 12, no. 7 (2020): 1906. http://dx.doi.org/10.3390/w12071906.

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Rain gauges continue to be sources of rainfall data despite progress made in precipitation measurements using radar and satellite technology. There has been some work done on assessing the optimum rain gauge network density required for hydrological modelling, but without consensus. This paper contributes to the identification of the optimum rain gauge network density, using scaling laws and bias-corrected 1 km × 1 km grid radar rainfall records, covering an area of 28,371 km2 that hosts 315 rain gauges in south-east Queensland, Australia. Varying numbers of radar pixels (rain gauges) were rep

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6

Zahiri, Eric-Pascal, Modeste Kacou, Marielle Gosset, and Sahouarizié Adama Ouattara. "Modeling the Interdependence Structure between Rain and Radar Variables Using Copulas: Applications to Heavy Rainfall Estimation by Weather Radar." Atmosphere 13, no. 8 (2022): 1298. http://dx.doi.org/10.3390/atmos13081298.

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In radar quantitative precipitation estimates (QPE), the progressive evolution of rainfall algorithms has been guided by attempts to reduce the uncertainties in rainfall retrieval. However, because most of the algorithms are based on the linear dependence between radar and rain variables and designed for rain rates ranging from light to moderate rainfall, they result in misleading estimations of intense or strong rainfall rates. In this paper, based on extensive data gathered during the AMMA and Megha-Tropiques data campaigns, we provided a way to improve the estimation of intense rainfall rat

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7

Brommundt, J., and A. Bárdossy. "Spatial correlation of radar and gauge precipitation data in high temporal resolution." Advances in Geosciences 10 (April 26, 2007): 103–9. http://dx.doi.org/10.5194/adgeo-10-103-2007.

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Abstract. A multi-sites precipitation time series generator for engineering designs is currently being developed. The objective is to generate several time series' simultaneously with correct inter-station relationships. Therefore, a model to estimate correlation between stations for arbitrary points in a project area is needed, using rain gauge data as well as radar data. Two methods are applied to compare the spatial behaviour of precipitation in both the rain gauge data and the radar data. The first approach is to calculate precipitation intensities from radar reflectivity and use it as gau

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8

Yang, Yuyao, and Chunbo Xiu. "Adaptive Constant False Alarm Detector Based on Composite Fuzzy Fusion Rules." Applied Sciences 15, no. 2 (2025): 942. https://doi.org/10.3390/app15020942.

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In order to improve the detection performance of the radar constant false alarm detector in a multiple-target environment, a Kaigh–Lachenbruch Quantile constant false alarm rate detector based on composite fuzzy fusion rules (CFKLQ-CFAR) is designed by combining fuzzy fusion rules and the Kaigh–Lachenbruch Quantile constant false alarm rate detector. Two sensors are used to collect environmental information, and the membership function value is calculated based on the collected information. Furthermore, the presence or absence of the target is judged compositely by four fuzzy fusion rules. CFK

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9

Rudolph, James V., Katja Friedrich, and Urs Germann. "Relationship between Radar-Estimated Precipitation and Synoptic Weather Patterns in the European Alps." Journal of Applied Meteorology and Climatology 50, no. 5 (2011): 944–57. http://dx.doi.org/10.1175/2010jamc2570.1.

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AbstractA 9-yr (2000–08) analysis of precipitation characteristics for the central and western European Alps has been generated from ground-based operational weather radar data provided by the Swiss radar network. The radar-based precipitation analysis focuses on the relationship between synoptic-scale weather patterns and mesoscale precipitation distribution over complex alpine terrain. The analysis divides the Alps into six regions (each approximately 200 × 200 km2 in size)—one on the northern side, two each on the western and southern sides of the Alps, and one in the Massif Central—represe

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10

Ding, Rong, Haiming Jin, Dong Xiang, et al. "Soil Moisture Sensing with UAV-Mounted IR-UWB Radar and Deep Learning." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 7, no. 1 (2022): 1–25. http://dx.doi.org/10.1145/3580867.

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Wide-area soil moisture sensing is a key element for smart irrigation systems. However, existing soil moisture sensing methods usually fail to achieve both satisfactory mobility and high moisture estimation accuracy. In this paper, we present the design and implementation of a novel soil moisture sensing system, named as SoilId, that combines a UAV and a COTS IR-UWB radar for wide-area soil moisture sensing without the need of burying any battery-powered in-ground device. Specifically, we design a series of novel methods to help SoilId extract soil moisture related features from the received r

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11

Tinoy, M. M., A. U. Novero, K. P. Landicho, A. B. Baloloy, and A. C. Blanco. "URBAN EFFECTS ON LAND SURFACE TEMPERATURE IN DAVAO CITY, PHILIPPINES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W19 (December 23, 2019): 433–40. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w19-433-2019.

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Abstract. This study produced spatiotemporal hot and cold spot occurrence maps for Davao City for the period 1994-2019 using land surface temperature (LST) images. Urban heat is theorized to have been affected by some, if not all, of the following impact factors: air pollutant concentrations/particulate matter (PM10), vegetation “abundance” (using EVI), building “density” (NDBI), albedo, topography, and population density. A mobile traverse sampling was performed in the morning and afternoon of 15 April 2019 to measure PM10 in the city’s identified hot spots. The remaining factors were generat

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12

Sumargo, Bagus, Dian Handayani, Alvi Pauziah Lubis, Irman Firmasyah, and Ika Yuni Wulansari. "Detection of Factors Affecting Rainfall Intensity in Jakarta." Jurnal Ilmu Lingkungan 23, no. 1 (2024): 133–40. https://doi.org/10.14710/jil.23.1.133-140.

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The increased intensity of rainfall is becoming one of the most pressing climate-related issues in many parts of the world. Detecting the factors that affect rainfall intensity requires a combination of modern technologies, such as weather satellites, radar systems, and advanced atmospheric models. Extreme conditions (outliers) often occur. This study aims to model data that is not symmetric or contains outliers. This study examines and models quantile regression on daily rainfall intensity in Jakarta which has extreme rainfall events. The results of the study found that the extreme values in

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13

Rudolph, James V., Katja Friedrich, and Urs Germann. "Model-Based Estimation of Dynamic Effect on Twenty-First-Century Precipitation for Swiss River Basins." Journal of Climate 25, no. 8 (2012): 2897–913. http://dx.doi.org/10.1175/jcli-d-11-00191.1.

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Abstract Projections of twenty-first-century precipitation for seven Swiss river basins are generated by linking high-resolution (2 km × 2 km) radar-estimated precipitation observations to a global climate model (GCM) via synoptic weather patterns. The use of synoptic patterns characterizes the effect of changes in large-scale circulation, or dynamic effects, on precipitation. In each basin observed total daily precipitation received during advective synoptic patterns is shown to be dependent on the basin’s general topographic aspect. Across all basins convective synoptic patterns follow the s

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14

Woody, Jonathan, Robert Lund, and Mekonnen Gebremichael. "Tuning Extreme NEXRAD and CMORPH Precipitation Estimates." Journal of Hydrometeorology 15, no. 3 (2014): 1070–77. http://dx.doi.org/10.1175/jhm-d-13-0146.1.

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Abstract High-resolution satellite precipitation estimates, such as the Climate Prediction Center morphing technique (CMORPH), provide alternative sources of precipitation data for hydrological applications, especially in regions where adequate ground-based instruments are unavailable. These estimates are, however, subject to large errors, especially at times of heavy precipitation. This paper presents a method to distributionally convert a set of CMORPH estimates into ground-based Next Generation Weather Radar (NEXRAD) estimates. As our concern lies with floods and extreme precipitation event

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15

Chen, Yinwen, Yuyan Du, Haoyuan Yin, et al. "Radar remote sensing-based inversion model of soil salt content at different depths under vegetation." PeerJ 10 (April 26, 2022): e13306. http://dx.doi.org/10.7717/peerj.13306.

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Excessive soil salt content (SSC) seriously affects the crop growth and economic benefits in the agricultural production area. Prior research mainly focused on estimating the salinity in the top bare soil rather than in deep soil that is vital to crop growth. For this end, an experiment was carried out in the Hetao Irrigation District, Inner Mongolia, China. In the experiment, the SSC at different depths under vegetation was measured, and the Sentinel-1 radar images were obtained synchronously. The radar backscattering coefficients (VV and VH) were combined to construct multiple indices, whose

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16

Chen, Yinwen, Yuyan Du, Haoyuan Yin, et al. "Radar remote sensing-based inversion model of soil salt content at different depths under vegetation." PeerJ 10 (April 26, 2022): e13306. http://dx.doi.org/10.7717/peerj.13306.

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Excessive soil salt content (SSC) seriously affects the crop growth and economic benefits in the agricultural production area. Prior research mainly focused on estimating the salinity in the top bare soil rather than in deep soil that is vital to crop growth. For this end, an experiment was carried out in the Hetao Irrigation District, Inner Mongolia, China. In the experiment, the SSC at different depths under vegetation was measured, and the Sentinel-1 radar images were obtained synchronously. The radar backscattering coefficients (VV and VH) were combined to construct multiple indices, whose

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17

Chen, Yinwen, Yuyan Du, Haoyuan Yin, et al. "Radar remote sensing-based inversion model of soil salt content at different depths under vegetation." PeerJ 10 (April 26, 2022): e13306. http://dx.doi.org/10.7717/peerj.13306.

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Excessive soil salt content (SSC) seriously affects the crop growth and economic benefits in the agricultural production area. Prior research mainly focused on estimating the salinity in the top bare soil rather than in deep soil that is vital to crop growth. For this end, an experiment was carried out in the Hetao Irrigation District, Inner Mongolia, China. In the experiment, the SSC at different depths under vegetation was measured, and the Sentinel-1 radar images were obtained synchronously. The radar backscattering coefficients (VV and VH) were combined to construct multiple indices, whose

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18

El Hachem, Abbas, Jochen Seidel, and András Bárdossy. "Probabilistic downscaling of EURO-CORDEX precipitation data for the assessment of future areal precipitation extremes for hourly to daily durations." Hydrology and Earth System Sciences 29, no. 5 (2025): 1335–57. https://doi.org/10.5194/hess-29-1335-2025.

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Abstract. This work presents a methodology to inspect the changing statistical properties of precipitation extremes with climate change. Data from regional climate models for the European continent (EURO-CORDEX 11) were used. The use of climate model data first requires an inspection of the data and a correction of the biases of the meteorological model. Corrections to the biases of the point precipitation data and those of the spatial structure were both performed. For this purpose, a quantile–quantile transformation of the point precipitation data and a spatial recorrelation method were used

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19

Buhaiov, M. V. "Method of Complex Envelope Processing for Signal Edges Detection." Visnyk NTUU KPI Seriia - Radiotekhnika Radioaparatobuduvannia, no. 92 (June 30, 2023): 54–59. https://doi.org/10.20535/radap.2023.92.54-59.

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Problem statement. The need of information processing automation in modern radio monitoring systems stimulates development of flexible methods for signal detection and its parameters estimation in time domain. A priori uncertainty of signal time-frequency structure complicates the automatic determination of signals edges. Purpose. The purpose of the article is subsequent automation of radio frequency spectrum analysis process by developing and implementing a method for determining signals time edges under conditions of a known noise power and signal-to-noise ratio. Method. To determine signal

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20

Smadi, Mahmoud M., and Mahmoud H. Alrefaei. "New extensions of Rayleigh distribution based on inverted-Weibull and Weibull distributions." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 6 (2021): 5107. http://dx.doi.org/10.11591/ijece.v11i6.pp5107-5118.

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The Rayleigh distribution was proposed in the fields of acoustics and optics by lord Rayleigh. It has wide applications in communication theory, such as description of instantaneous peak power of received radio signals, i.e. study of vibrations and waves. It has also been used for modeling of wave propagation, radiation, synthetic aperture radar images, and lifetime data in engineering and clinical studies. This work proposes two new extensions of the Rayleigh distribution, namely the Rayleigh inverted-Weibull (RIW) and the Rayleigh Weibull (RW) distributions. Several fundamental properties ar

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21

Mahmoud, M. Smadi, and H. Alrefaei Mahmoud. "New extensions of Rayleigh distribution based on invertedWeibull and Weibull distributions." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 6 (2021): 5107–18. https://doi.org/10.11591/ijece.v11i6.pp5107-5118.

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The Rayleigh distribution was proposed in the fields of acoustics and optics by lord Rayleigh. It has wide applications in communication theory, such as description of instantaneous peak power of received radio signals, i.e. study of vibrations and waves. It has also been used for modeling of wave propagation, radiation, synthetic aperture radar images, and lifetime data in engineering and clinical studies. This work proposes two new extensions of the Rayleigh distribution, namely the Rayleigh inverted-Weibull (RIW) and the Rayleigh Weibull (RW) distributions. Several fundamental properties ar

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22

Kosovic, Branko, Sue Ellen Haupt, Daniel Adriaansen, et al. "A Comprehensive Wind Power Forecasting System Integrating Artificial Intelligence and Numerical Weather Prediction." Energies 13, no. 6 (2020): 1372. http://dx.doi.org/10.3390/en13061372.

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The National Center for Atmospheric Research (NCAR) recently updated the comprehensive wind power forecasting system in collaboration with Xcel Energy addressing users’ needs and requirements by enhancing and expanding integration between numerical weather prediction and machine-learning methods. While the original system was designed with the primary focus on day-ahead power prediction in support of power trading, the enhanced system provides short-term forecasting for unit commitment and economic dispatch, uncertainty quantification in wind speed prediction with probabilistic forecasting, an

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23

Marchesini, I., F. Ardizzone, M. Alvioli, M. Rossi, and F. Guzzetti. "Non-susceptible landslide areas in Italy and in the Mediterranean region." Natural Hazards and Earth System Sciences 14, no. 8 (2014): 2215–31. http://dx.doi.org/10.5194/nhess-14-2215-2014.

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Abstract. We used landslide information for 13 study areas in Italy and morphometric information obtained from the 3-arcseconds shuttle radar topography mission digital elevation model (SRTM DEM) to determine areas where landslide susceptibility is expected to be negligible in Italy and in the landmasses surrounding the Mediterranean Sea. The morphometric information consisted of the local terrain slope which was computed in a square 3 × 3-cell moving window, and in the regional relative relief computed in a circular 15 × 15-cell moving window. We tested three different models to classify the

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24

Foresti, Loris, Ioannis V. Sideris, Daniele Nerini, Lea Beusch, and Urs Germann. "Using a 10-Year Radar Archive for Nowcasting Precipitation Growth and Decay: A Probabilistic Machine Learning Approach." Weather and Forecasting 34, no. 5 (2019): 1547–69. http://dx.doi.org/10.1175/waf-d-18-0206.1.

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Abstract Machine learning algorithms are trained on a 10-yr archive of composite weather radar images in the Swiss Alps to nowcast precipitation growth and decay in the next few hours in moving coordinates (Lagrangian frame). The hypothesis of this study is that growth and decay is more predictable in mountainous regions, which represent a potential source of practical predictability by machine learning methods. In this paper, artificial neural networks (ANN) are employed to learn the complex nonlinear dependence relating the growth and decay to the input predictors, which are geographical loc

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25

Amell, Adrià, Patrick Eriksson, and Simon Pfreundschuh. "Ice water path retrievals from Meteosat-9 using quantile regression neural networks." Atmospheric Measurement Techniques 15, no. 19 (2022): 5701–17. http://dx.doi.org/10.5194/amt-15-5701-2022.

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Abstract. The relationship between geostationary radiances and ice water path (IWP) is complex, and traditional retrieval approaches are not optimal. This work applies machine learning to improve the IWP retrieval from Meteosat-9 observations, with a focus on low latitudes, training the models against retrievals based on CloudSat. Advantages of machine learning include avoiding explicit physical assumptions on the data, an efficient use of information from all channels, and easily leveraging spatial information. Thermal infrared (IR) retrievals are used as input to achieve a performance indepe

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Bobbiti, B. K. R., S. Maragatham, R. Santhi, et al. "Spatial variability of soil organic carbon in Tamil Nadu: Integrating remote sensing and machine learning approaches." Journal of Environmental Biology 46, no. 1 (2025): 103–13. https://doi.org/10.22438/jeb/46/1/mrn-5410.

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Aim: The experiment was carried out to estimate the geographical distribution of soil organic carbon (SOC) in Tamil Nadu, India, using advanced machine learning models. Methodology: High-resolution remote sensing data from Landsat 8, climate indices from TerraClimate, and terrain indices from the Shuttle Radar Topography Mission (SRTM) were integrated to construct reliable SOC estimation models. The models employed in this study included Random Forest (RF), Quantile Neural Network (QNN), Cubist, and Bootstrapped Random Forest (BRF). These models were selected for their ability to capture compl

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Saemian, Peyman, Omid Elmi, Molly Stroud, et al. "Satellite Altimetry-based Extension of global-scale in situ river discharge Measurements (SAEM)." Earth System Science Data 17, no. 5 (2025): 2063–85. https://doi.org/10.5194/essd-17-2063-2025.

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Abstract. River discharge is a crucial measurement, indicating the volume of water flowing through a river cross-section at any given time. However, the existing network of river discharge gauges faces significant issues, largely due to the declining number of active gauges and temporal gaps. Remote sensing, especially radar-based techniques, offers an effective means to this issue. This study introduces the Satellite Altimetry-based Extension of the global-scale in situ river discharge Measurements (SAEM) data set, which utilizes multiple satellite altimetry missions and estimates discharge u

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28

Ramezani Ziarani, Maryam, Bodo Bookhagen, Torsten Schmidt, et al. "A Model for the Relationship between Rainfall, GNSS-Derived Integrated Water Vapour, and CAPE in the Eastern Central Andes." Remote Sensing 13, no. 18 (2021): 3788. http://dx.doi.org/10.3390/rs13183788.

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Atmospheric water vapour content is a key variable that controls the development of deep convective storms and rainfall extremes over the central Andes. Direct measurements of water vapour are challenging; however, recent developments in microwave processing allow the use of phase delays from L-band radar to measure the water vapour content throughout the atmosphere: Global Navigation Satellite System (GNSS)-based integrated water vapour (IWV) monitoring shows promising results to measure vertically integrated water vapour at high temporal resolutions. Previous works also identified convective

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29

Wang, Fang, Di Tian, and Mark Carroll. "Customized deep learning for precipitation bias correction and downscaling." Geoscientific Model Development 16, no. 2 (2023): 535–56. http://dx.doi.org/10.5194/gmd-16-535-2023.

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Abstract. Systematic biases and coarse resolutions are major limitations of current precipitation datasets. Many deep learning (DL)-based studies have been conducted for precipitation bias correction and downscaling. However, it is still challenging for the current approaches to handle complex features of hourly precipitation, resulting in the incapability of reproducing small-scale features, such as extreme events. This study developed a customized DL model by incorporating customized loss functions, multitask learning and physically relevant covariates to bias correct and downscale hourly pr

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Han, Haijiao, Qiming Zeng, and Jian Jiao. "Quality Assessment of TanDEM-X DEMs, SRTM and ASTER GDEM on Selected Chinese Sites." Remote Sensing 13, no. 7 (2021): 1304. http://dx.doi.org/10.3390/rs13071304.

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Digital elevation models (DEMs) are the basic data of science and engineering technology research. SRTM and ASTER GDEM are currently widely used global DEMs, and TanDEM-X DEM, released in 2016, has attracted users’ attention due to its unprecedented accuracy. These global datasets are often used for local applications and the quality of DEMs affects the results of applications. Many researchers have assessed and compared the quality of global DEMs on a local scale. To provide some additional insights on quality assessment of 12- and 30-m resolution TanDEM-X DEMs, 30-m resolution ASTER GDEM and

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Seyyedi, H., E. N. Anagnostou, E. Beighley, and J. McCollum. "Satellite-driven downscaling of global reanalysis precipitation products for hydrological applications." Hydrology and Earth System Sciences Discussions 11, no. 7 (2014): 9067–112. http://dx.doi.org/10.5194/hessd-11-9067-2014.

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Abstract. Deriving flood hazard maps for ungauged basins typically requires simulating a long record of annual maximum discharges. To improve this approach, precipitation from global reanalysis systems must be downscaled to a spatial and temporal resolution applicable for flood modeling. This study evaluates such downscaling and error correction approaches for improving hydrologic applications using a combination of NASA's Global Land Data Assimilation System (GLDAS) precipitation dataset and a higher resolution multi-satellite precipitation product (TRMM). The study focuses on 437 flood-induc

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32

van Straaten, Chiem, Kirien Whan, and Maurice Schmeits. "Statistical Postprocessing and Multivariate Structuring of High-Resolution Ensemble Precipitation Forecasts." Journal of Hydrometeorology 19, no. 11 (2018): 1815–33. http://dx.doi.org/10.1175/jhm-d-18-0105.1.

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A comparison of statistical postprocessing methods is performed for high-resolution precipitation forecasts. We keep hydrological end users in mind and thus require that the systematic errors of probabilistic forecasts are corrected and that they show a realistic high-dimensional spatial structure. The most skillful forecasts of 3-h accumulated precipitation in 3 × 3 km2 grid cells covering the land surface of the Netherlands were made with a nonparametric method [quantile regression forests (QRF)]. A parametric alternative [zero-adjusted gamma distribution (ZAGA)] corrected the precipitation

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Seyyedi, H., E. N. Anagnostou, E. Beighley, and J. McCollum. "Satellite-driven downscaling of global reanalysis precipitation products for hydrological applications." Hydrology and Earth System Sciences 18, no. 12 (2014): 5077–91. http://dx.doi.org/10.5194/hess-18-5077-2014.

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Abstract. Deriving flood hazard maps for ungauged basins typically requires simulating a long record of annual maximum discharges. To improve this approach, precipitation from global reanalysis systems must be downscaled to a spatial and temporal resolution applicable for flood modeling. This study evaluates such downscaling and error correction approaches for improving hydrologic applications using a combination of NASA's Global Land Data Assimilation System (GLDAS) precipitation data set and a higher resolution multi-satellite precipitation product (TRMM). The study focuses on 437 flood-indu

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34

Acharya, Suwash Chandra, Rory Nathan, Quan J. Wang, Chun-Hsu Su, and Nathan Eizenberg. "Ability of an Australian reanalysis dataset to characterise sub-daily precipitation." Hydrology and Earth System Sciences 24, no. 6 (2020): 2951–62. http://dx.doi.org/10.5194/hess-24-2951-2020.

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Abstract. The high spatio-temporal variability of precipitation is often difficult to characterise due to limited measurements. The available low-resolution global reanalysis datasets inadequately represent the spatio-temporal variability of precipitation relevant to catchment hydrology. The Bureau of Meteorology Atmospheric high-resolution Regional Reanalysis for Australia (BARRA) provides a high-resolution atmospheric reanalysis dataset across the Australasian region. For hydrometeorological applications, however, it is essential to properly evaluate the sub-daily precipitation from this rea

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Jakob, D., D. J. Karoly, and A. Seed. "Non-stationarity in daily and sub-daily intense rainfall – Part 2: Regional assessment for sites in south-east Australia." Natural Hazards and Earth System Sciences 11, no. 8 (2011): 2273–84. http://dx.doi.org/10.5194/nhess-11-2273-2011.

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Abstract. Using data for a common period (1976–2005) for a set of 31 sites located in south-east Australia, variations in frequency and magnitude of intense rainfall events across durations from 6 min to 72 h were assessed. This study was driven by a need to clarify how variations in climate might affect intense rainfall and the potential for flooding. Sub-daily durations are of particular interest for urban applications. Worldwide, few such observation-based studies exist, which is mainly due to limitations in data. Analysis of seasonality in frequency and magnitude of events revealed conside

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Nguyen, Ngoc Thi, Tien Le Thuy Du, Hyunkyu Park, et al. "Estimating the Impacts of Ungauged Reservoirs Using Publicly Available Streamflow Simulations and Satellite Remote Sensing." Remote Sensing 15, no. 18 (2023): 4563. http://dx.doi.org/10.3390/rs15184563.

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On the Korean Peninsula, the Imjin River is a transboundary river that flows from North Korea into South Korea. Therefore, human intervention activities in the upstream region can have a substantial impact on the downstream region of South Korea. In addition to climate impacts, there are increasing concerns regarding upstream man-made activities, particularly the operation of the Hwanggang dam located in the territory of North Korea. This study explored the feasibility of using the publicly available global hydrological model and satellite remote sensing imagery for monitoring reservoir dynami

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Amell, Adrià, Simon Pfreundschuh, and Patrick Eriksson. "The Chalmers Cloud Ice Climatology: retrieval implementation and validation." Atmospheric Measurement Techniques 17, no. 14 (2024): 4337–68. http://dx.doi.org/10.5194/amt-17-4337-2024.

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Abstract. Ice clouds are a crucial component of the Earth's weather system, and their representation remains a principal challenge for current weather and climate models. Several past and future satellite missions were explicitly designed to provide observations offering new insights into cloud processes, but these specialized cloud sensors are limited in their spatial and temporal coverage. Geostationary satellites have been observing clouds for several decades and can ideally complement the sparse measurements from specialized cloud sensors. However, the geostationary observations that are c

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Movahedi, M., A. Cesta, X. Li, E. Keystone, and C. Bombardier. "POS0445 PHYSICIAN AND PATIENT REPORTED EFFECTIVENESS OUTCOMES ARE SIMILAR IN TOFACITINIB AND TNF INHIBITORS IN RHEUMATOID ARTHRITIS PATIENTS: DATA FROM A RHEUMATOID ARTHRITIS REGISTRY IN CANADA." Annals of the Rheumatic Diseases 80, Suppl 1 (2021): 452.1–452. http://dx.doi.org/10.1136/annrheumdis-2021-eular.787.

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Background:Tofacitinib (TOFA) is an oral, small molecule drug used for rheumatoid arthritis (RA) treatment as an alternative option to biologic disease modifying antirheumatic drugs (bDMARDs) including tumor necrosis factor inhibitors (TNFi).Objectives:We aimed to evaluate physician and patient reported effectivness outcomes in TNFi compared to TOFA, using real-world data from the Ontario Best Practices Research Initiative (OBRI).Methods:RA patients enrolled in the OBRI initiating their TOFA or TNFi (Adalimumab, Certolizumab, Etanercept, Golimumab, Infliximab, and Biosimilars) between 1st June

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White, Phoebe, and Peter A. Nelson. "Evaluation of Sub‐Hourly MRMS Quantitative Precipitation Estimates in Mountainous Terrain Using Machine Learning." Water Resources Research 60, no. 12 (2024). https://doi.org/10.1029/2024wr037437.

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AbstractThe Multi‐Radar Multi‐Sensor (MRMS) product incorporates radar, quantitative precipitation forecasts, and gage data at a high spatiotemporal resolution for the United States and southern Canada. MRMS is subject to various sources of measurement error, especially in complex terrain. The goal of this study is to provide a framework for understanding the uncertainty of MRMS in mountainous areas with limited observations. We evaluate 8‐hr time series samples of MRMS 15‐min intensity through a comparison to 204 gages located in the mountains of Colorado. This analysis shows that the MRMS su

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Movahedi, Mohammad, Angela Cesta, Xiyuing Li, Edward C. Keystone, and Claire Bombardier. "Physician- and Patient-reported Effectiveness Are Similar for Tofacitinib and TNFi in Rheumatoid Arthritis: Data From a Rheumatoid Arthritis Registry." Journal of Rheumatology, February 15, 2022, jrheum.211066. http://dx.doi.org/10.3899/jrheum.211066.

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Objective Tofacitinib (TOF) is an oral, small-molecule drug used for rheumatoid arthritis (RA) treatment and is one of several alternative treatments to tumor necrosis factor inhibitors (TNFi). We evaluated physician- and patient-reported effectiveness of TNFi compared to TOF, using real-world data from the Ontario Best Practices Research Initiative (OBRI). Methods Patients enrolled in the OBRI initiating TOF or TNFi between 2014 and 2019 were included. Patients were required to have physician- and patient-reported effectiveness outcome data, including Clinical Disease Activity Index (CDAI) an

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