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Статті в журналах з теми "Radar quantique"
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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.
Повний текст джерелаАнотація:
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 évidence les liens étroits entre les arts et les sciences.
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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.
Повний текст джерелаАнотація:
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 built on about three dozen radars' data over the European territory of Russia. Keywords: spatial forecast verification, radar precipitation nowcasting, extreme value theory, missing data, conditional verification sampling, spatial quantiles
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Song, Linye, Shangfeng Chen, Yun Li, Duo Qi, Jiankun Wu, Mingxuan Chen, and Weihua Cao. "The Quantile-Matching Approach to Improving Radar Quantitative Precipitation Estimation in South China." Remote Sensing 13, no. 23 (December 6, 2021): 4956. http://dx.doi.org/10.3390/rs13234956.
Повний текст джерелаАнотація:
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 applied the Quantile Matching (Q-matching) method to improve the radar QPE accuracy. The results show that the rainfall intensity of the radar QPE is generally larger than that determined from rain gauge observations but that it usually underestimates the intensity of the observed heavy rainfall. After the Q-matching method was applied to correct the QPE, the accuracy improved by a significant amount and was in good agreement with the rain gauge observations. Specifically, the Q-matching method was able to reduce the QPE error from 39–44%, demonstrating performance that is much better than that of the traditional climatological scaling method, which was shown to be able to reduce the QPE error from 3–15% in South China. Moreover, after the Q-matching correction, the QPE values were closer to the rainfall values that were observed from the automatic weather stations in terms of having a smaller mean absolute error and a higher correlation coefficient. Therefore, the Q-matching method can improve the QPE accuracy as well as estimate the surface precipitation better. This method provides a promising prospect for radar QPE in the study region.
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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 (December 26, 2013): 39–59. http://dx.doi.org/10.2166/nh.2013.197.
Повний текст джерелаАнотація:
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 corresponding radar rainfall quantiles can be obtained. The QPE system Quantitative Precipitation Estimation Using Multiple Sensors (QPESUMS) provides hourly observed and radar precipitation for three typhoons in the Shinmen reservoir watershed in Northern Taiwan, which are used in the model development and validation. The results indicate that the proposed PRRE model can quantify the spatial and temporal variations of radar rainfall estimates at ungauged locations provided by the QPESUMS system. Also, its reliability and accuracy could be evaluated based on a 95% confidence interval and occurrence probability resulting from the cumulative probability distribution established by the proposed PRRE model.
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Gyasi-Agyei, Yeboah. "Identification of the Optimum Rain Gauge Network Density for Hydrological Modelling Based on Radar Rainfall Analysis." Water 12, no. 7 (July 3, 2020): 1906. http://dx.doi.org/10.3390/w12071906.
Повний текст джерелаАнотація:
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 repeatedly sampled using a unique stratified sampling technique. For each set of rainfall sampled data, a two-dimensional correlogram was developed from the normal scores obtained through quantile-quantile transformation for ordinary kriging which is a stochastic interpolation. Leave-one-out cross validation was carried out, and the simulated quantiles were evaluated using the performance statistics of root-mean-square-error and mean-absolute-bias, as well as their rates of change. A break in the scaling of the plots of these performance statistics against the number of rain gauges was used to infer the optimum rain gauge network density. The optimum rain gauge network density varied from 14 km2/gauge to 38 km2/gauge, with an average of 25 km2/gauge.
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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 (August 15, 2022): 1298. http://dx.doi.org/10.3390/atmos13081298.
Повний текст джерелаАнотація:
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 rates from radar measurements. To this end, we designed a formulation of the QPE algorithm that accounts for the co-dependency between radar observables and rainfall rate using copula simulation synthetic datasets and using the quantile regression features for a more complete picture of covariate effects. The results show a clear improvement in heavy rainfall retrieval from radar data using copula-based R(KDP) algorithms derived from a realistic simulated dataset. For a better performance, Gaussian copula-derived algorithms require a 0.8 percentile distribution to be considered. Conversely, lower percentiles are better for Student’s, Gumbel and HRT copula estimators when retrieving heavy rainfall rates (R > 30). This highlights the need to investigate the entire conditional distribution to determine the performance of radar rainfall estimators.
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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.
Повний текст джерелаАнотація:
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 gauge data. The results show that the spatial structure in both data sets is similar, but cross correlation varies too much to use radar derived spatial correlation to describe gauge inter-station relationship. Thus, a second approach was tested to account for the differences in the spatial correlation associated to the distribution. Using the indicator time series, cross correlations for different quantiles were calculated from both the rain gauge and radar data. This approach shows that cross correlation varies depending on the chosen quantile. In the lower quantiles, the correlation is very similar in rain gauge and radar data, hence a transfer is possible. This insight is useful to derive cross correlations of rain gauges from radar images. Correlation data for rain gauges thus obtained contains all the information about heterogeneity and anisotropy of the spatial structure of rainfall, which is in the radar data.
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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 (May 2011): 944–57. http://dx.doi.org/10.1175/2010jamc2570.1.
Повний текст джерелаАнотація:
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—representing various orographic aspects and localized climates within the radar coverage area. For each region, estimated precipitation rate derived from radar data is analyzed on a seasonal basis for total daily precipitation and frequency of high-precipitation-rate events. The summer season has the highest total daily precipitation for all regions in the study, whereas median values of daily precipitation in winter are less than one-half of median daily precipitation for summer. For all regions, high-precipitation-rate events occur most frequently in the summer. Daily synoptic-scale weather patterns are associated with total daily precipitation and frequency of high precipitation rate to show that an advective synoptic-scale pattern with southerly midtropospheric flow results in the highest median and 90th-quantile values for total daily precipitation and that a convective synoptic-scale pattern results in elevated frequency of extreme-precipitation-rate events.
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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.
Повний текст джерелаАнотація:
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 generated from imagery (i.e., Landsat 8, Synthetic Aperture Radar) and obtained from the Philippine Statistics Authority. These factors were analyzed against the LST which was obtained through Project GUHeat’s methodology. The relationships between the factors and LST were studied through multiple and quantile regression models (MRM & QRM). Results showed that variable PM10 does not have any significance in the MRM. Meanwhile, QRM were fitted to different quantile values, namely: 10th, 25th, 50th, 75th, and 90th. It is only at the 90th quantile where all the independent variables are good predictors for the LST. Albedo is the most important predictor for the LST at 10th quantile whereas Elev for the 25th quantile. However, when LST is at the 50th, 75th, and 90th quantiles NDBI is the most significant variable at predicting LST. Reliable spatiotemporal assessment and modelling of surface temperature are essential for urban planning and management to formulate sustainable strategies for the welfare of people and environment.
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Ding, Rong, Haiming Jin, Dong Xiang, Xiaocheng Wang, Yongkui Zhang, Dingman Shen, Lu Su, 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 (March 27, 2022): 1–25. http://dx.doi.org/10.1145/3580867.
Повний текст джерелаАнотація:
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 radar signals, and automatically detect and discard the data contaminated by the UAV's uncontrollable motion and the multipath interference. Furthermore, we leverage the powerful representation ability of deep neural networks and carefully design a neural network model to accurately map the extracted radar signal features to soil moisture estimations. We have extensively evaluated SoilId against a variety of real-world factors, including the UAV's uncontrollable motion, the multipath interference, soil surface coverages, and many others. Specifically, the experimental results carried out by our UAV-based system validate that SoilId can push the accuracy limits of RF-based soil moisture sensing techniques to a 50% quantile MAE of 0.23%.
Дисертації з теми "Radar quantique"
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Borderieux, Sylvain. "Apport de la théorie de l’information quantique dans la perspective du radar quantique." Electronic Thesis or Diss., Brest, École nationale supérieure de techniques avancées Bretagne, 2022. http://www.theses.fr/2022ENTA0011.
Повний текст джерелаАнотація:
Cette thèse propose une approche originale de la thématique du radar à illumination quantique en recourant à la théorie de l’information quantique pour étudier l’évolution des corrélations quantiques le long d’une chaîne radar. Ce mémoire propose d’abord un parallèle des différences et similitudes entre les théories du radar classique et du radar quantique en insistant sur les principes propres aux deux théories. Le radar à illumination quantique étudié utilise des paires de photons intriqués pour établir la présence ou l’absence d’un objet faiblement réfléchissant baigné dans un bruit thermique parasitant la détection. À partir de la mise en parallèle, les travaux se sont concentrés sur l’influence de l’environnement atmosphérique dans l’évolution de l’intrication du système de photons du radar et dans l’évolution des corrélations quantiques représentées par la discorde quantique. L’objectif des recherches était de montrer un lien entre la discorde quantique et la stratégie de détection binaire du radar quantique. Les résultats tendent à montrer ce lien même si des améliorations aux modèles composés pour l’étude seraient bienvenues. Cela permettrait notamment d’orienter la recherche vers des cas concrets pouvant bénéficier d’une application expérimentale du procédé d’illumination quantiqueThis thesis provides an original approach of the quantum illumination radar using the quantum information theory to study the evolution of quantum correlations in a radar system. We first propose a parallel between the classical radar theory and the quantum radar theory to determine similarities anf differences insisting on the last point. The quantum illumination radar uses pairs of entangled photons to detect the absence of the presence of a low-reflecting object into a bright thermal background that disturbs the detection. Using the parallel between the radar theories, research has been done on the atmospheric influence on the evolution of entanglement of the system of photons in the radar, and on the evolution of quantum correlations quantified by the quantum discord. The objective of research was to show a link between the quantum discord and the binary decision strategy of the quantum radar. Results suggest this link even if improvements should be required on the tested models. It should permit to study practical situations particularly if we think about a possible experiment on a quantum illumination protocol
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Baili, Ghaya. "Contribution à la réduction du bruit d'intensité relatif des lasers à semiconducteurs pour des applications aux radars." Paris 11, 2008. http://www.theses.fr/2008PA112362.
Повний текст джерелаАнотація:
L’objectif du travail de thèse présenté est d’étudier deux techniques originales de réduction du bruit d’intensité relatif (RIN) des lasers à semiconducteur pour le transport des signaux radar par voie optique. La première technique consiste à exploiter, dans une fibre à compensation de dispersion et à faibles pertes, les mécanismes de conversion du bruit d’amplitude en bruit de phase et du bruit de phase en bruit d’amplitude avec une bonne dynamique sur une bande fréquence égale à 20 GHz. La deuxième technique consiste à supprimer les oscillations de relaxation dans le laser SC en recherchant un fonctionnement de type classe-A. Deux architectures de lasers classe-A sont proposées, analysées théoriquement et validées expérimentalement. La première architecture est basée sur un amplificateur optique à SC dans une cavité fibrée longue. La deuxième architecture utilise un milieu à gain à émission par la surface (un demi-VSCEL) dans une cavité étendue à surtension élevée. Au moyen de ces deux architectures, nous montrons que le comportement dynamique de classe-A permet de ramener le RIN du laser SC à la limite quantique standard (-155 dB/Hz pour 1 mA détecté) sur une large bande de fréquence, typiquement de 100 MHZ à 18 GHzThe objective of the following thesis is to study two original techniques aiming at reducing the Relative Intensity Noise (RIN) of semiconductor lasers used in optical links for transmission of radar signals. Within the first technique, a dispersion compensating fiber exhibiting low losses is used to study the phase to amplitude noise and amplitude to phase noise conversion mechanisms with a very good signal-to-noise ratio over à 20 GHz bandwidth. The second technique consists in increasing the photon lifetime well above the carrier lifetime in order to eliminate adiabatically the carrier population effects, leading to a relaxation oscillation free class-A laser operation. Two laser architectures have been proposed, theoretically analyzed and experimentally validated. The first configuration is based on a semiconductor optical amplifier in a long fibred cavity. The second one uses a ½-VCSEL in a high-Q external cavity. For both configurations, we demonstrated that class-A laser operation leads to a shot-noise-limited RIN (at -155 dB/Hz for 1 mA detected) over a frequency bandwidth from 100 MHz to 18 GHz
Частини книг з теми "Radar quantique"
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Marghany, Maged. "Quantize of scattering theory." In Synthetic Aperture Radar Imaging Mechanism for Oil Spills, 73–92. Elsevier, 2020. http://dx.doi.org/10.1016/b978-0-12-818111-9.00005-7.
Повний текст джерелаТези доповідей конференцій з теми "Radar quantique"
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Pavy, Anne M., and Brian D. Rigling. "Phase modulated radar waveform classification using quantile one-class SVMs." In 2015 IEEE International Radar Conference (RadarCon). IEEE, 2015. http://dx.doi.org/10.1109/radar.2015.7131095.
Повний текст джерела