The Experts below are selected from a list of 252 Experts worldwide ranked by ideXlab platform
Pierre Tandeo - One of the best experts on this subject based on the ideXlab platform.
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Nowcasting solar irradiance using an Analog Method and geostationary satellite images
Solar Energy, 2018Co-Authors: Alex Ayet, Pierre TandeoAbstract:Accurate forecasting of Global Horizontal Irradiance (GHI) is essential for the integration of the solar resource in an electrical grid. We present a novel data-driven Method aimed at delivering up to 6 h hourly probabilistic forecasts of GHI on top of a localized solar energy source. The Method does not require calibration to adapt to regional di ff erences in cloud dynamics, and uses only one type of data, covering Europe and Africa. It is thus suited for applications that require a GHI forecast for solar energy sources at di ff erent locations with few ground measurements. Cloud dynamics are emulated using an Analog Method based on 5 years of hourly images of geostationary satellite-derived irradiance, without using any numerical prediction model. This database contains both the images to be compared to the current atmospheric observation and their successors at one or more hours of interval. The physics of the system is emulated statistically, and no numerical prediction model is used. The Method is tested on one year of data and fi ve locations in Europe with di ff erent climatic conditions. It is compared to persistence (keeping the last observation frozen), ensemble persistence (generating a probabilistic forecast using the last observations) and an adaptive fi rst order vector autoregressive model. As an application, the model is downscaled using ground measurements. In both cases, the Analog Method outperforms the classical statistical approaches. Results demonstrate the skill of the Method in emulating cloud dynamics, and its potential to be coupled with a forecasting algorithm using ground measurements for operational applications.
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Analog nowcasting of solar irradiance from geostationary satellite images
2017Co-Authors: Alex Ayet, Pierre TandeoAbstract:Accurate forecasting of Global Horizontal Ir- radiance (GHI) is essential for the integration of the solar resource in an electrical grid. We implement a novel data-driven model for up to 6h probabilistic forecasting of GHI. Cloud dynamics are emulated using an Analog Method on a geostationary satellite database (herein 5 years of hourly images). It contains both the images to be compared to the current meteorological conditions and their successors at one or more hours of interval. No approximation is thus made on the physics of the system, unlike numerical weather forecast. The algorithm is computationally efficient and requires no tuning. It is designed to be easily used on different locations, requiring only GHI satellite images.
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Combining Analog Method and Ensemble Data Assimilation: Application to the Lorenz-63 Chaotic System
Machine Learning and Data Mining Approaches to Climate Science, 2015Co-Authors: Pierre Tandeo, Pierre Ailliot, Juan Ruiz, Alexis Hannart, Bertrand Chapron, Anne Cuzol, Valérie Monbet, Robert W. Easton, Ronan FabletAbstract:Nowadays, ocean and atmosphere sciences face a deluge of data from space, in situ monitoring as well as numerical simulations. The availability of these different data sources offers new opportunities, still largely underexploited, to improve the understanding, modeling, and reconstruction of geophysical dynamics. The classical way to reconstruct the space-time variations of a geophysical system from observations relies on data assimilation Methods using multiple runs of the known dynamical model. This classical framework may have severe limitations including its computational cost, the lack of adequacy of the model with observed data, and modeling uncertainties. In this paper, we explore an alternative approach and develop a fully data-driven framework, which combines machine learning and statistical sampling to simulate the dynamics of complex system. As a proof concept, we address the assimilation of the chaotic Lorenz-63 model. We demonstrate that a nonparametric sampler from a catalog of historical datasets, namely, a nearest neighbor or Analog sampler, combined with a classical stochastic data assimilation scheme, the ensemble Kalman filter and smoother, reaches state-of-the-art performances, without online evaluations of the physical model.
Wendy D. Graham - One of the best experts on this subject based on the ideXlab platform.
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Development and comparative evaluation of a stochastic Analog Method to downscale daily GCM precipitation
Hydrology and Earth System Sciences, 2013Co-Authors: Syewoon Hwang, Wendy D. GrahamAbstract:Abstract. There are a number of statistical techniques that downscale coarse climate information from general circulation models (GCMs). However, many of them do not reproduce the small-scale spatial variability of precipitation exhibited by the observed meteorological data, which is an important factor for predicting hydrologic response to climatic forcing. In this study a new downscaling technique (Bias-Correction and Stochastic Analog Method; BCSA) was developed to produce stochastic realizations of bias-corrected daily GCM precipitation fields that preserve both the spatial autocorrelation structure of observed daily precipitation sequences and the observed temporal frequency distribution of daily rainfall over space. We used the BCSA Method to downscale 4 different daily GCM precipitation predictions from 1961 to 1999 over the state of Florida, and compared the skill of the Method to results obtained with the commonly used bias-correction and spatial disaggregation (BCSD) approach, a modified version of BCSD which reverses the order of spatial disaggregation and bias-correction (SDBC), and the bias-correction and constructed Analog (BCCA) Method. Spatial and temporal statistics, transition probabilities, wet/dry spell lengths, spatial correlation indices, and variograms for wet (June through September) and dry (October through May) seasons were calculated for each Method. Results showed that (1) BCCA underestimated mean daily precipitation for both wet and dry seasons while the BCSD, SDBC and BCSA Methods accurately reproduced these characteristics, (2) the BCSD and BCCA Methods underestimated temporal variability of daily precipitation and thus did not reproduce daily precipitation standard deviations, transition probabilities or wet/dry spell lengths as well as the SDBC and BCSA Methods, and (3) the BCSD, BCCA and SDBC Methods underestimated spatial variability in daily precipitation resulting in underprediction of spatial variance and overprediction of spatial correlation, whereas the new stochastic technique (BCSA) replicated observed spatial statistics for both the wet and dry seasons. This study underscores the need to carefully select a downscaling Method that reproduces all precipitation characteristics important for the hydrologic system under consideration if local hydrologic impacts of climate variability and change are going to be reasonably predicted. For low-relief, rainfall-dominated watersheds, where reproducing small-scale spatiotemporal precipitation variability is important, the BCSA Method is recommended for use over the BCSD, BCCA, or SDBC Methods.
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Development and comparative evaluation of a stochastic Analog Method to downscale daily GCM precipitation
2013Co-Authors: Syewoon Hwang, Wendy D. GrahamAbstract:Abstract. There are a number of statistical techniques that downscale coarse climate information from global circulation models (GCM). However, many of them do not reproduce the small-scale spatial variability of precipitation exhibited by the observed meteorological data which can be an important factor for predicting hydrologic response to climatic forcing. In this study a new downscaling technique (bias-correction and stochastic Analog Method, BCSA) was developed to produce stochastic realizations of bias-corrected daily GCM precipitation fields that preserve the spatial autocorrelation structure of observed daily precipitation sequences. This approach was designed to reproduce observed spatial and temporal variability as well as mean climatology. We used the BCSA Method to downscale 4 GCM precipitation predictions from 1961 to 1999 over the state of Florida and compared the skill of the Method to the results obtained with the commonly used bias-correction and spatial disaggregation (BCSD) approach, bias-correction and constructed Analog (BCCA) Method, and a modified version of BCSD which reverses the order of spatial disaggregation and bias-correction (SDBC). Spatial and temporal statistics, transition probabilities, wet/dry spell lengths, spatial correlation indices, and variograms for wet (June through September) and dry (October through May) seasons were calculated for each Method. Results showed that (1) BCCA underestimated mean climatology of daily precipitation while the BCSD, SDBC and BCSA Methods accurately reproduced it, (2) the BCSD and BCCA Methods underestimated temporal variability because of the interpolation and regression schemes used for downscaling and thus, did not reproduce daily precipitation standard deviations, transition probabilities or wet/dry spell lengths as well as the SDBC and BCSA Methods, and (3) the BCSD, BCCA and SDBC Methods underestimated spatial variability in precipitation resulting in under-prediction of spatial variance and over-prediction of spatial correlation, whereas the new stochastic technique (BCSA) accurately reproduces observed spatial statistics for both the wet and dry seasons. This study underscores the need to carefully select a downscaling Method that reproduces all precipitation characteristics important for the hydrologic system under consideration if local hydrologic impacts of climate variability and change are going to be accurately predicted. For low-relief, rainfall-dominated watersheds where reproducing small-scale spatiotemporal precipitation variability is important, the BCSA Method is recommended for use over the BCSD, BCCA, or SDBC Methods.
Alex Ayet - One of the best experts on this subject based on the ideXlab platform.
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Nowcasting solar irradiance using an Analog Method and geostationary satellite images
Solar Energy, 2018Co-Authors: Alex Ayet, Pierre TandeoAbstract:Accurate forecasting of Global Horizontal Irradiance (GHI) is essential for the integration of the solar resource in an electrical grid. We present a novel data-driven Method aimed at delivering up to 6 h hourly probabilistic forecasts of GHI on top of a localized solar energy source. The Method does not require calibration to adapt to regional di ff erences in cloud dynamics, and uses only one type of data, covering Europe and Africa. It is thus suited for applications that require a GHI forecast for solar energy sources at di ff erent locations with few ground measurements. Cloud dynamics are emulated using an Analog Method based on 5 years of hourly images of geostationary satellite-derived irradiance, without using any numerical prediction model. This database contains both the images to be compared to the current atmospheric observation and their successors at one or more hours of interval. The physics of the system is emulated statistically, and no numerical prediction model is used. The Method is tested on one year of data and fi ve locations in Europe with di ff erent climatic conditions. It is compared to persistence (keeping the last observation frozen), ensemble persistence (generating a probabilistic forecast using the last observations) and an adaptive fi rst order vector autoregressive model. As an application, the model is downscaled using ground measurements. In both cases, the Analog Method outperforms the classical statistical approaches. Results demonstrate the skill of the Method in emulating cloud dynamics, and its potential to be coupled with a forecasting algorithm using ground measurements for operational applications.
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Analog nowcasting of solar irradiance from geostationary satellite images
2017Co-Authors: Alex Ayet, Pierre TandeoAbstract:Accurate forecasting of Global Horizontal Ir- radiance (GHI) is essential for the integration of the solar resource in an electrical grid. We implement a novel data-driven model for up to 6h probabilistic forecasting of GHI. Cloud dynamics are emulated using an Analog Method on a geostationary satellite database (herein 5 years of hourly images). It contains both the images to be compared to the current meteorological conditions and their successors at one or more hours of interval. No approximation is thus made on the physics of the system, unlike numerical weather forecast. The algorithm is computationally efficient and requires no tuning. It is designed to be easily used on different locations, requiring only GHI satellite images.
H. Storch - One of the best experts on this subject based on the ideXlab platform.
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Influence of similarity measures on the performance of the Analog Method for downscaling daily precipitation
Climate Dynamics, 2008Co-Authors: C. Matulla, X. Zhang, X. L. Wang, J. Wang, E. Zorita, S. Wagner, H. StorchAbstract:This study examines the performance of the Analog Method for downscaling daily precipitation. The evaluation is performed for (1) a number of similarity measures for searching Analogs, (2) various ways to include the past atmospheric evolution, and (3) different truncations in EOF space. It is carried out for two regions with complex topographic structures, and with distinct climatic characteristics, namely, California’s Central Valley (together with the Sierra Nevada) and the European Alps. NCEP/NCAR reanalysis data are used to represent the large scale state of the atmosphere over the regions. The assessment is based on simulating daily precipitation for 103 stations for the month of January, for the years 1950–2004 in the California region, and for 70 stations in the European Alps (January 1948–2004). Generally, simulated precipitation is in better agreement with observations in the California region than in the European Alps. Similarity measures such as the Euclidean norm, the sum of absolute differences and the angle between two atmospheric states perform better than measures which introduce additional weightings to principal components (e.g., the Mahalanobis distance). The best choice seems dependent upon the target variable. Lengths of wet spells, for instance, are best simulated by using the angular similarity measure. Overall, the Euclidean norm performs satisfactorily in most cases and hence is a reasonable first choice, whereas the use of Mahalanobis distance is less advisable. The performance of the Analog Method improves by including large-scale information for bygone days, particularly, for the simulation of wet and dry spells. Optimal performance is obtained when about 85–90% of the total predictor variability is retained.
F Lefevre - One of the best experts on this subject based on the ideXlab platform.
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SSB Phase Noise Evaluation of Analog/IF Signals on Standard Digital ATE
Journal of Electronic Testing, 2016Co-Authors: Florence Azaïs, Laurent Latorre, Stéphane David-grignot, F LefevreAbstract:This paper presents a low-cost solution for the evaluation of frequency-domain phase noise characteristics for Analog/IF signals. The technique is based on 1-bit signal acquisition with a standard digital channel of an Automated Test Equipment (ATE) and a dedicated post-processing algorithm that permits to reconstruct the time-domain phase fluctuations of the Analog/RF signal from the captured binary vector. Single SideBand (SSB) phase noise is then obtained based on FFT applied on the reconstructed phase fluctuations. Simulation results demonstrate a very good agreement between SSB phase noise obtained using the proposed digital Method and the conventional Analog Method on a large range of measurement frequency offset. The digital Method also permits spur detection and exhibits similar performance than the conventional Method in terms of measurement variability. The technique is also validated through hardware measurements on a practical case study, i.e. SSB phase noise evaluation on the 1.3125 MHz sinusoidal signal delivered by the transceiver of a JN5168 wireless microcontroller.
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A digital technique for the evaluation of SSB phase noise of Analog/RF signals
2015 16th Latin-American Test Symposium (LATS), 2015Co-Authors: Florence Azaïs, Laurent Latorre, Stéphane David-grignot, F LefevreAbstract:This paper introduces a digital Method for the evaluation of SSB (Single SideBand) phase noise of Analog/RF signals. The technique is based on 1-bit signal acquisition with a digital Automated Test Equipment (ATE) and a dedicated postprocessing algorithm that permits to reconstruct the timedomain phase fluctuations of the Analog/RF signal from the captured binary vector. SSB phase noise is then obtained by applying an FFT on the reconstructed signal corresponding to phase fluctuations. The proposed Method is validated through simulation and results demonstrate an excellent agreement with SSB phase noise obtained using conventional Analog Method. The proposed Method therefore permits to achieve low-cost evaluation of phase noise frequency-domain characteristics for Analog/RF signals.