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Journal articles on the topic 'Radar quantique'
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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 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.
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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 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.
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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 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.
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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 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.
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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 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.
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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 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.
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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 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.
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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—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.
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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 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.
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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 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%.
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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 (April 10, 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 same trend in total daily precipitation with cyclonic patterns consistently producing a larger amount of precipitation than anticyclonic patterns. Identification of synoptic patterns from a GCM for the twenty-first century [Community Climate System Model, version 3.0, (CCSM3)] shows increasing frequency of anticyclonic synoptic patterns, decreasing frequency of cyclonic patterns, and constant frequency of advective patterns over Switzerland. When coupled with observed radar-estimated precipitation for each synoptic pattern, the changes in synoptic pattern frequencies result in an approximately 10%–15% decrease in decadal precipitation over the course of the twenty-first century for seven Swiss river basins. The study results also show an insignificant change in the future (twenty-first century) probability of exceeding the current (2000–08) 95th quantile of total precipitation. The lack of a trend in exceeding the 95th quantile of precipitation in combination with a decreasing trend in total precipitation provides evidence that dynamic effects will not result in increased frequency of heavy precipitation events, but that heavy precipitation will account for a greater proportion of total precipitation in Swiss river basins by the end of the twenty-first century.
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Woody, Jonathan, Robert Lund, and Mekonnen Gebremichael. "Tuning Extreme NEXRAD and CMORPH Precipitation Estimates." Journal of Hydrometeorology 15, no. 3 (June 1, 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 events, a peaks-over-threshold extreme value approach is adopted that fits a generalized Pareto distribution to the large precipitation estimates. A quantile matching transformation is then used to convert CMORPH values into NEXRAD values. The methods are applied in the analysis of 6 yr of precipitation observations from 625 pixels centered around eastern Oklahoma.
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Chen, Yinwen, Yuyan Du, Haoyuan Yin, Huiyun Wang, Haiying Chen, Xianwen Li, Zhitao Zhang, and Junying Chen. "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 sensitivity was then analyzed using the best subset selection (BSS). Meanwhile, four most commonly used algorithms, partial least squares regression (PLSR), quantile regression (QR), support vector machine (SVM), and extreme learning machine (ELM), were utilized to construct estimation models of salinity at the depths of 0–10, 10–20, 0–20, 20–40, 0–40, 40–60 and 0–60 cm before and after BSS, respectively. The results showed: (a) radar remote sensing can be used to estimate the salinity in the root zone of vegetation (0-30 cm); (b) after BSS, the correlation coefficients and estimation accuracy of the four monitoring models were all improved significantly; (c) the estimation accuracy of the four regression models was: SVM > QR > ELM > PLSR; and (d) among the seven sampling depths, 10–20 cm was the optimal inversion depth for all the four models, followed by 20–40 and 0–40 cm. Among the four models, SVM was higher in accuracy than the other three at 10–20 cm (RP2 = 0.67, RMSEP = 0.12%). These findings can provide valuable guidance for soil salinity monitoring and agricultural production in the arid or semi-arid areas under vegetation.
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Chen, Yinwen, Yuyan Du, Haoyuan Yin, Huiyun Wang, Haiying Chen, Xianwen Li, Zhitao Zhang, and Junying Chen. "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.
Full textAbstract:
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 sensitivity was then analyzed using the best subset selection (BSS). Meanwhile, four most commonly used algorithms, partial least squares regression (PLSR), quantile regression (QR), support vector machine (SVM), and extreme learning machine (ELM), were utilized to construct estimation models of salinity at the depths of 0–10, 10–20, 0–20, 20–40, 0–40, 40–60 and 0–60 cm before and after BSS, respectively. The results showed: (a) radar remote sensing can be used to estimate the salinity in the root zone of vegetation (0-30 cm); (b) after BSS, the correlation coefficients and estimation accuracy of the four monitoring models were all improved significantly; (c) the estimation accuracy of the four regression models was: SVM > QR > ELM > PLSR; and (d) among the seven sampling depths, 10–20 cm was the optimal inversion depth for all the four models, followed by 20–40 and 0–40 cm. Among the four models, SVM was higher in accuracy than the other three at 10–20 cm (RP2 = 0.67, RMSEP = 0.12%). These findings can provide valuable guidance for soil salinity monitoring and agricultural production in the arid or semi-arid areas under vegetation.
15
Chen, Yinwen, Yuyan Du, Haoyuan Yin, Huiyun Wang, Haiying Chen, Xianwen Li, Zhitao Zhang, and Junying Chen. "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.
Full textAbstract:
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 sensitivity was then analyzed using the best subset selection (BSS). Meanwhile, four most commonly used algorithms, partial least squares regression (PLSR), quantile regression (QR), support vector machine (SVM), and extreme learning machine (ELM), were utilized to construct estimation models of salinity at the depths of 0–10, 10–20, 0–20, 20–40, 0–40, 40–60 and 0–60 cm before and after BSS, respectively. The results showed: (a) radar remote sensing can be used to estimate the salinity in the root zone of vegetation (0-30 cm); (b) after BSS, the correlation coefficients and estimation accuracy of the four monitoring models were all improved significantly; (c) the estimation accuracy of the four regression models was: SVM > QR > ELM > PLSR; and (d) among the seven sampling depths, 10–20 cm was the optimal inversion depth for all the four models, followed by 20–40 and 0–40 cm. Among the four models, SVM was higher in accuracy than the other three at 10–20 cm (RP2 = 0.67, RMSEP = 0.12%). These findings can provide valuable guidance for soil salinity monitoring and agricultural production in the arid or semi-arid areas under vegetation.
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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 (December 1, 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 are derived in this study, these include reliability and hazard functions, moments, quantile function, random number generation, skewness, and kurtosis. The maximum likelihood estimators for the model parameters of the two proposed models are also derived along with the asymptotic confidence intervals. Two real data sets in communication systems and clinical trials are analyzed to illustrate the concept of the proposed extensions. The results demonstrated that the proposed extensions showed better fitting than other extensions and competing models.
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Kosovic, Branko, Sue Ellen Haupt, Daniel Adriaansen, Stefano Alessandrini, Gerry Wiener, Luca Delle Monache, Yubao Liu, et al. "A Comprehensive Wind Power Forecasting System Integrating Artificial Intelligence and Numerical Weather Prediction." Energies 13, no. 6 (March 16, 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, and prediction of extreme events such as icing. Furthermore, the empirical power conversion machine-learning algorithms now use a quantile approach to data quality control that has improved the accuracy of the methods. Forecast uncertainty is quantified using an analog ensemble approach. Two methods of providing short-range ramp forecasts are blended: the variational doppler radar analysis system and an observation-based expert system. Extreme events, specifically changes in wind power due to high winds and icing, are now forecasted by combining numerical weather prediction and a fuzzy logic artificial intelligence system. These systems and their recent advances are described and assessed.
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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 (August 27, 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 "non-susceptible" landslide areas, including a linear model (LNR), a quantile linear model (QLR), and a quantile, non-linear model (QNL). We tested the performance of the three models using independent landslide information presented by the Italian Landslide Inventory (Inventario Fenomeni Franosi in Italia – IFFI). Best results were obtained using the QNL model. The corresponding zonation of non-susceptible landslide areas was intersected in a geographic information system (GIS) with geographical census data for Italy. The result determined that 57.5% of the population of Italy (in 2001) was located in areas where landslide susceptibility is expected to be negligible. We applied the QNL model to the landmasses surrounding the Mediterranean Sea, and we tested the synoptic non-susceptibility zonation using independent landslide information for three study areas in Spain. Results showed that the QNL model was capable of determining where landslide susceptibility is expected to be negligible in the validation areas in Spain. We expect our results to be applicable in similar study areas, facilitating the identification of non-susceptible landslide areas, at the synoptic scale.
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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 (October 1, 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 location, mesoscale motion vectors, freezing level height, and time of the day. The average long-term growth and decay patterns are effectively reproduced by the ANN, which allows exploring their climatology for any combination of predictors. Due to the low intrinsic predictability of growth and decay, its prediction in real time is more challenging, but is substantially improved when adding persistence information to the predictors, more precisely the growth and decay and precipitation intensity in the immediate past. The improvement is considerable in mountainous regions, where, depending on flow direction, the root-mean-square error of ANN predictions can be 20%–30% lower compared with persistence. Because large uncertainty is associated with precipitation forecasting, deterministic machine learning predictions should be coupled with a model for the predictive uncertainty. Therefore, we consider a probabilistic perspective by estimating prediction intervals based on a combination of quantile decision trees and ANNs. The probabilistic framework is an attempt to address the problem of conditional bias, which often characterizes deterministic machine learning predictions obtained by error minimization.
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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 (October 12, 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 independent of the solar angle. They are compared with retrievals including solar reflectances as well as a subset of IR channels for compatibility with historical sensors. The retrievals are accomplished with quantile regression neural networks. This network type provides case-specific uncertainty estimates, compatible with non-Gaussian errors, and is flexible enough to be applied to different network architectures. Spatial information is incorporated into the network through a convolutional neural network (CNN) architecture. This choice outperforms architectures that only work pixelwise. In fact, the CNN shows a good retrieval performance by using only IR channels. This makes it possible to compute diurnal cycles, a problem that CloudSat cannot resolve due to its limited temporal and spatial sampling. These retrievals compare favourably with IWP retrievals in CLAAS, a dataset based on a traditional approach. These results highlight the possibilities to overcome limitations from physics-based approaches using machine learning while providing efficient, probabilistic IWP retrieval methods. Moreover, they suggest this first work can be extended to higher latitudes as well as that geostationary data can be considered as a complement to the upcoming Ice Cloud Imager mission, for example, to bridge the gap in temporal sampling with respect to space-based radars.
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Ramezani Ziarani, Maryam, Bodo Bookhagen, Torsten Schmidt, Jens Wickert, Alejandro de la Torre, Zhiguo Deng, and Andrea Calori. "A Model for the Relationship between Rainfall, GNSS-Derived Integrated Water Vapour, and CAPE in the Eastern Central Andes." Remote Sensing 13, no. 18 (September 21, 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 available potential energy (CAPE) as a key climatic variable for the formation of deep convective storms and rainfall in the central Andes. Our analysis relies on GNSS data from the Argentine Continuous Satellite Monitoring Network, Red Argentina de Monitoreo Satelital Continuo (RAMSAC) network from 1999 to 2013. CAPE is derived from version 2.0 of the ECMWF’s (European Centre for Medium-Range Weather Forecasts) Re-Analysis (ERA-interim) and rainfall from the TRMM (Tropical Rainfall Measuring Mission) product. In this study, we first analyse the rainfall characteristics of two GNSS-IWV stations by comparing their complementary cumulative distribution function (CCDF). Second, we separately derive the relation between rainfall vs. CAPE and GNSS-IWV. Based on our distribution fitting analysis, we observe an exponential relation of rainfall to GNSS-IWV. In contrast, we report a power-law relationship between the daily mean value of rainfall and CAPE at the GNSS-IWV station locations in the eastern central Andes that is close to the theoretical relationship based on parcel theory. Third, we generate a joint regression model through a multivariable regression analysis using CAPE and GNSS-IWV to explain the contribution of both variables in the presence of each other to extreme rainfall during the austral summer season. We found that rainfall can be characterised with a higher statistical significance for higher rainfall quantiles, e.g., the 0.9 quantile based on goodness-of-fit criterion for quantile regression. We observed different contributions of CAPE and GNSS-IWV to rainfall for each station for the 0.9 quantile. Fourth, we identify the temporal relation between extreme rainfall (the 90th, 95th, and 99th percentiles) and both GNSS-IWV and CAPE at 6 h time steps. We observed an increase before the rainfall event and at the time of peak rainfall—both for GNSS-integrated water vapour and CAPE. We show higher values of CAPE and GNSS-IWV for higher rainfall percentiles (99th and 95th percentiles) compared to the 90th percentile at a 6-h temporal scale. Based on our correlation analyses and the dynamics of the time series, we show that both GNSS-IWV and CAPE had comparable magnitudes, and we argue to consider both climatic variables when investigating their effect on rainfall extremes.
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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 (March 29, 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 30-m resolution SRTM, this study assessed differences’ performance in relation to not only geographical features but also the ways in which DEMs have been created on selected Chinese sites, taking ICESat/GLAS points with 14-cm absolute vertical accuracy but size of 70-m diameter and 12-m resolution TanDEM-X DEM with less than 10-m absolute vertical accuracy as the reference data for comprehensive quality evaluation. When comparing the three 30-m DEMs with the reference DEM, an improved Least Z-Difference (LZD) method was applied for co-registration between models, and Quantile–Quantile (Q-Q) plot was used to identify if the DEM errors follow a normal distribution to help choose proper statistical indicators accordingly. The results show that: (1) TanDEM-X DEMs have the best overall quality, followed by SRTM. ASTER GDEM has the worst quality. The 12-m TanDEM-X DEM has significant advantages in describing terrain details. (2) The quality of DEM has a strong relationship with slope, aspect and land cover. However, the relationship between aspect and vertical quality weakens after data co-registration. The quality of DEMs gets higher with the increasing number of images used in the fusion process. The quality in where slopes opposite to the radar beam is the worst for SRTM, which could provide a new perspective for quality assessment of SRTM and other DEMs whose incidence angle files are available. (3) Systematic deviations can reduce the vertical quality of DEM. The differences have non-normal distribution even after co-registration. For researchers who want to know the quality of a DEM in order to use it in further applications, they should pay more attention to the terrain factors and land cover in their study areas and the ways in which the DEM has been created.
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Wang, Fang, Di Tian, and Mark Carroll. "Customized deep learning for precipitation bias correction and downscaling." Geoscientific Model Development 16, no. 2 (January 25, 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 precipitation data. We designed six scenarios to systematically evaluate the added values of weighted loss functions, multitask learning, and atmospheric covariates compared to the regular DL and statistical approaches. The models were trained and tested using the Modern-era Retrospective Analysis for Research and Applications version 2 (MERRA2) reanalysis and the Stage IV radar observations over the northern coastal region of the Gulf of Mexico on an hourly time scale. We found that all the scenarios with weighted loss functions performed notably better than the other scenarios with conventional loss functions and a quantile mapping-based approach at hourly, daily, and monthly time scales as well as extremes. Multitask learning showed improved performance on capturing fine features of extreme events and accounting for atmospheric covariates highly improved model performance at hourly and aggregated time scales, while the improvement is not as large as from weighted loss functions. We show that the customized DL model can better downscale and bias correct hourly precipitation datasets and provide improved precipitation estimates at fine spatial and temporal resolutions where regular DL and statistical methods experience challenges.
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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 (July 31, 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-inducing storm events that occurred over a period of ten years (2002–2011) in the Susquehanna River basin located in the northeast US. A validation strategy was devised for assessing error metrics in rainfall and simulated runoff as function of basin area, storm severity and season. The WSR-88D gauge-adjusted radar-rainfall (stage IV) product was used as the reference rainfall dataset, while runoff simulations forced with the stage IV precipitation dataset were considered as the runoff reference. Results show that the generated rainfall ensembles from the downscaled reanalysis products encapsulate the reference rainfall. The statistical analysis, including frequency and quantile plots plus mean relative error and root mean square error statistics, demonstrated improvements in the precipitation and runoff simulation error statistics of the satellite-driven downscaled reanalysis dataset compared to the original reanalysis precipitation product. Results vary by season and less by basin scale. In the fall season specifically, the downscaled product has three times lower mean relative error than the original product; this ratio increases to four times for the simulated runoff values. The proposed downscaling scheme is modular in design and can be applied on gridded satellite and reanalysis dataset.
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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 (November 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 forecasts of the short-range Grand Limited Area Model Ensemble Prediction System (GLAMEPS) up to +60 h less well, particularly at high quantiles, as verified against calibrated precipitation radar observations. For the subsequent multivariate restructuring, three empirical methods, namely, ensemble copula coupling (ECC), the Schaake shuffle (SSh), and a recent minimum-divergence sophistication of the Schaake shuffle (MDSSh), were tested and verified using both the multivariate variogram skill score (VSS) and the continuous ranked probability score (CRPS), the latter after aggregating the forecasts spatially. ECC and MDSSh were more skillful than SSh in terms of the CRPS and the VSS. ECC performed somewhat worse than MDSSh for summer afternoon and evening periods, probably due to the worse representation of deep convection in the hydrostatic GLAMEPS compared to reality. Overall, the high-resolution postprocessing comparison shows that skill for local precipitation amounts improves up to the 98th percentile in both the summer and winter season and that the high-dimensional joint distribution can successfully be restructured. Forecasting products like this enable multiple end users to derive their own desired aggregations.
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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 (December 11, 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-inducing storm events that occurred over a period of ten years (2002–2011) in the Susquehanna River basin located in the northeastern United States. A validation strategy was devised for assessing error metrics in rainfall and simulated runoff as function of basin area, storm severity, and season. The WSR-88D gauge-adjusted radar-rainfall (stage IV) product was used as the reference rainfall data set, while runoff simulations forced with the stage IV precipitation data set were considered as the runoff reference. Results show that the generated rainfall ensembles from the downscaled reanalysis product encapsulate the reference rainfall. The statistical analysis consists of frequency and quantile plots plus mean relative error and root-mean-square error statistics. The results demonstrated improvements in the precipitation and runoff simulation error statistics of the satellite-driven downscaled reanalysis data set compared to the original reanalysis precipitation product. Results vary by season and less by basin scale. In the fall season specifically, the downscaled product has 3 times lower mean relative error than the original product; this ratio increases to 4 times for the simulated runoff values. The proposed downscaling scheme is modular in design and can be applied on any gridded satellite and reanalysis data set.
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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 (June 4, 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 reanalysis. In this regard, this paper evaluates the sub-daily precipitation from BARRA for a period of 6 years (2010–2015) over Australia against point observations and blended radar products. We utilise a range of existing and bespoke metrics for evaluation at point and spatial scales. We examine bias in quantile estimates and spatial displacement of sub-daily rainfall at a point scale. At a spatial scale, we use the fractions skill score as a spatial evaluation metric. The results show that the performance of BARRA precipitation depends on spatial location, with poorer performance in tropical relative to temperate regions. A possible spatial displacement during large rainfall is also found at point locations. This displacement, evaluated by comparing the distribution of rainfall within a day, could be quantified by considering the neighbourhood grids. On spatial evaluation, hourly precipitation from BARRA is found to be skilful at a spatial scale of less than 100 km (150 km) for a threshold of 75th percentile (90th percentile) at most of the locations. The performance across all the metrics improves significantly at time resolutions higher than 3 h. Our evaluations illustrate that the BARRA precipitation, despite discernible spatial displacements, serves as a useful dataset for Australia, especially at sub-daily resolutions. Users of BARRA are recommended to properly account for possible spatio-temporal displacement errors, especially for applications where the spatial and temporal characteristics of rainfall are deemed very important.
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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 (August 19, 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 considerable variation across the set of sites, implying different dominating rainfall-producing mechanisms and/or interactions with local topography. Both these factors are relevant when assessing the potential effects of climate variations on intense rainfall events. The set of sites was therefore split into groups ("north cluster" and "south cluster") according to the characteristics of intense rainfall events. There is a strong polarisation in the nature of changes found for the north cluster and south cluster. While sites in the north cluster typically exhibit decrease in frequency of events, particularly in autumn and at durations of 1 h and longer; sites in the south cluster experience an increase in frequency of events, particularly for summer and sub-hourly durations. Non-stationarity found in historical records has the potential to significantly affect design rainfall estimates. An assessment of quantile estimates derived using a standard regionalisation technique and periods representative of record lengths available for practical applications show that such estimates may not be representative of long-term conditions, so alternative approaches need to be considered, particularly where short records are concerned. Additional rainfall information, in particular radar data, could be used for an in-depth spatial analysis of intense rainfall events.
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Nguyen, Ngoc Thi, Tien Le Thuy Du, Hyunkyu Park, Chi-Hung Chang, Sunghwa Choi, Hyosok Chae, E. James Nelson, Faisal Hossain, Donghwan Kim, and Hyongki Lee. "Estimating the Impacts of Ungauged Reservoirs Using Publicly Available Streamflow Simulations and Satellite Remote Sensing." Remote Sensing 15, no. 18 (September 16, 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 dynamics and assessing their impacts on downstream hydrology. “Naturalized” streamflow simulation was obtained from the Group on Earth Observation (GEO) Global Water Sustainability (GEOGloWS) European Centre for Medium-Range Weather Forecasts (ECMWF) Streamflow Services (GESS) model. To correct the biases of the GESS-based streamflow simulations, we employed quantile mapping using the observed streamflow from a nearby location. This method significantly reduced volume and variability biases by up to 5 times on both daily and monthly scales. Nevertheless, its effectiveness in improving temporal correlation on a daily scale in small catchments remained constrained. For the reservoir storage changes in the Hwanggang dam, we combined multiple remote sensing imagery, particularly cloud-free optical images of Landsat-8, Sentinel-2, and snow-free Sentinel-1, with the area–elevation–volume (AEV) curves derived from the Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM). In assessing its hydrological impacts, the study found that overall impacts within the downstream catchment in Pilseung bridge of South Korea were generally less significant compared to the upstream Hwanggang catchment. However, there was a higher probability of experiencing water shortages during wet months due to the upstream dam’s operations. The study highlights the potential benefits of utilizing the publicly available hydrological model and satellite remote sensing imagery to supplement decision makers with important information for the effective management of the transboundary river basin in ungauged regions.
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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 (May 19, 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 2014 (TOFA approval date in Canada) and 31st Dec 2019 were included. Patients were required to have physician and patient reported effectivness outcomes data available at treatment initiation and 6-month (± 2 months) follow-up. These included clinical disease activity index (CDAI), rheumatoid arthritis disease activity index (RADAI), HAQ-DI, sleep problem, and anxiety/depression scores. Multiple imputation (Imputation Chained Equation, N=20) was used to deal with missing data for covaraites at treatment initiation. To deal with confounding by indication, we estimated propensity scores for covariates with an absolute standard difference greater than 0.1 between the two treatment groups.Results:A total of 419 patients were included. Of those, 226 were initiating a TNFi and 193 TOFA, and had a mean (SD) disease duration of 8.0 (8.7) and 12.6 (9.6) years, respectively. In the TNFi group, 81.9% were female and mean age (SD) at treatment initiation was 56.6 (13.4) years. In the TOFA group, 85% were female and mean (SD) age at treatment initiation was 60.3 (11.2) years. The TNFi group was less likely to have prior biologic use (21.7%) compared to the TOFA group (67.9%). At treatment initiation, physical function measured by HAQ-DI was significantly lower in TNFi compared to the TOFA group (1.2 vs.1.4).The rate of CDAI LDA/remission at 6 months was 36.7% and 33.2% in TNFi and TOFA group, respectively. The generalized linear mixed models (GLMM) adjusting for propensity score quantile, showed that there was no significant difference in CDAI LDA/remission (ORs: 0.85, 95% CI: 0.51, 1.43), RADAI (coefficient: 0.48, 95% CI: -0.18, 1.14), HAQ-DI (coefficient: -0.01, 95% CI: -0.18, 0.16), sleep problems (coefficient: -0.25, 95% CI: -0.95, 0.45), and anxiety/depression scores (coefficient: 0.12, 95% CI: -0.35, 0.58) between the two treatment groups (TOFA used as reference).Conclusion:In this real-world data study, we found that, physician and patient reported effectivness outcomes are similar in the TNFi and TOFA groups 6 months after treatment initiation in patients with RA.Disclosure of Interests:Mohammad Movahedi: None declared, Angela Cesta: None declared, Xiuying Li: None declared, Edward Keystone Grant/research support from: Amgen, Merck, Pfizer Pharmaceuticals, PuraPharm. Speaker Honoraria Agreements: AbbVie, Amgen, Bristol-Myers Squibb Company, Celltrion, Myriad Autoimmune, F. Hoffmann-La Roche Inc, Gilead, Janssen Inc, Lilly Pharmaceuticals, Merck, Pfizer Pharmaceuticals, Sandoz, Sanofi-Genzyme, Samsung Bioepsis. Consulting Agreements/Advisory Board Membership: AbbVie, Amgen, Bristol-Myers Squibb Company, Celltrion, Myriad Autoimmune, F. Hoffmann-La Roche Inc, Gilead, Janssen Inc, Lilly Pharmaceuticals, Merck, Pfizer Pharmaceuticals, Sandoz, Sanofi-Genzyme, Samsung Bioepsis, Claire Bombardier Grant/research support from: OBRI was funded by peer reviewed grants from CIHR (Canadian Institute for Health Research), Ontario Ministry of Health and Long-Term Care (MOHLTC), Canadian Arthritis Network (CAN) and unrestricted grants from: Abbvie, Amgen, Aurora, Bristol-Meyers Squibb, Celgene, Hospira, Janssen, Lilly, Medexus, Merck, Novartis, Pfizer, Roche, Sanofi, & UCB. Dr. Bombardier held a Canada Research Chair in Knowledge Transfer for Musculoskeletal Care and a Pfizer Research Chair in Rheumatology
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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) and RA Disease Activity Index (RADAI), available at treatment initiation and 6 (± 2) months later. To deal with confounding by indication, we estimated propensity scores (PS) for covariates. Results Four hundred nineteen patients were included. Of those, 226 initiated a TNFi and 193 TOF, and had a mean (SD) disease duration of 8.0 (8.7) and 12.6 (9.6) years, respectively. In addition, the TNFi group was less likely to have prior biologic use (21.7%) compared to the TOF group (67.9%). The proportion of patients in CDAI low disease activity (LDA)/remission (REM) at 6 months was 36.7% and 33.2% in the TNFi and TOF groups, respectively. The generalized linear mixed models adjusting for PS quantile showed that there was no significant difference in CDAI LDA/REM (odds ratio [OR] 0.85, 95% CI 0.51–1.43) and RADAI coefficient (OR 0.48, 95% CI –0.18 to 1.14) between the 2 groups (ref: TOF). Conclusion In patients with RA, physician- and patient-reported effectiveness are similar in the TNFi and TOF groups 6 months after treatment.