The Experts below are selected from a list of 303 Experts worldwide ranked by ideXlab platform
C Piani - One of the best experts on this subject based on the ideXlab platform.
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Statistical precipitation Bias correction of gridded model data using point measurements
Geophysical Research Letters, 2015Co-Authors: Jan O Haerter, C Piani, Christopher Moseley, Bastian Eggert, Peter BergAbstract:It is well known that climate model output data cannot be used directly as input to impact models, e.g., hydrology models, due to climate model errors. Recently, it has become customary to apply Statistical Bias correction to achieve better Statistical correspondence to observational data. As climate model output should be interpreted as the space-time average over a given model grid box and output time step, the status quo in Bias correction is to employ matching gridded observational data to yield optimal results. Here we show that when gridded observational data are not available, Statistical Bias correction can be carried out using point measurements, e.g., rain gauges. Our nonparametric method, which we call scale-adapted Statistical Bias correction (SABC), is achieved by data aggregation of either the available modeled or gauge data. SABC is a straightforward application of the well-known Taylor hypothesis of frozen turbulence. Using climate model and rain gauge data, we show that SABC performs significantly better than equal-time period Statistical Bias correction.
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on the contribution of Statistical Bias correction to the uncertainty in the projected hydrological cycle
Geophysical Research Letters, 2011Co-Authors: C D Chen, C Piani, Jan O Haerter, Stefan HagemannAbstract:[1] Global hydrological modeling is affected by three sources of uncertainty: (i) the choice of the global climate model (GCM) used to provide meteorological forcing data; (ii) the choice of future greenhouse gas concentration scenario; and (iii) the choice of the decade used to derive the Bias correction parameters. We present a comparative analysis of these uncertainties and compare them to the inter-annual variability. The analysis focuses on discharge, integrated runoff and total precipitation over ten large catchments, representative of different climatic areas of the globe. Results are similar for all catchments, all hydrological variables and throughout the year with few exceptions. We find that the choice of different decadal periods over which to derive the Bias correction parameters is a source of comparatively minor uncertainty, while other sources play larger and similarly significant roles. This is true for both the means and the extremes of the studied hydrological variables.
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impact of a Statistical Bias correction on the projected hydrological changes obtained from three gcms and two hydrology models
Journal of Hydrometeorology, 2011Co-Authors: Stefan Hagemann, Jan O Haerter, C D Chen, Jens Heinke, Dieter Gerten, C PianiAbstract:AbstractFuture climate model scenarios depend crucially on the models’ adequate representation of the hydrological cycle. Within the EU integrated project Water and Global Change (WATCH), special care is taken to use state-of-the-art climate model output for impacts assessments with a suite of hydrological models. This coupling is expected to lead to a better assessment of changes in the hydrological cycle. However, given the systematic errors of climate models, their output is often not directly applicable as input for hydrological models. Thus, the methodology of a Statistical Bias correction has been developed for correcting climate model output to produce long-term time series with a Statistical intensity distribution close to that of the observations. As observations, global reanalyzed daily data of precipitation and temperature were used that were obtained in the WATCH project. Daily time series from three GCMs (GCMs) ECHAM5/Max Planck Institute Ocean Model (MPI-OM), Centre National de Recherches Me...
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Statistical Bias correction of global simulated daily precipitation and temperature for the application of hydrological models
Journal of Hydrology, 2010Co-Authors: C Piani, Graham P Weedon, M J Best, S Gomes, Pedro Viterbo, Stefan Hagemann, Jan O HaerterAbstract:Summary A Statistical Bias correction methodology for global climate simulations is developed and applied to daily land precipitation and mean, minimum and maximum daily land temperatures. The Bias correction is based on a fitted histogram equalization function. This function is defined daily, as opposed to earlier published versions in which they were derived yearly or seasonally at best, while conserving properties of robustness and eliminating unrealistic jumps at seasonal or monthly transitions. The methodology is tested using the newly available global dataset of observed hydrological forcing data of the last 50 years from the EU project WATCH (WATer and global CHange) and an initial conditions ensemble of simulations performed with the ECHAM5 global climate model for the same period. Bias corrections are derived from 1960 to 1969 observed and simulated data and then applied to 1990–1999 simulations. Results confirm the effectiveness of the methodology for all tested variables. Bias corrections are also derived using three other non-overlapping decades from 1970 to 1999 and all members of the ECHAM5 initial conditions ensemble. A methodology is proposed to use the resulting “ensemble of Bias corrections” to quantify the error in simulated scenario projections of components of the hydrological cycle.
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climate model Bias correction and the role of timescales
Hydrology and Earth System Sciences, 2010Co-Authors: Jan O Haerter, Stefan Hagemann, Christopher Moseley, C PianiAbstract:Abstract. It is well known that output from climate models cannot be used to force hydrological simulations without some form of preprocessing to remove the existing Biases. In principle, Statistical Bias correction methodologies act on model output so the Statistical properties of the corrected data match those of the observations. However, the improvements to the Statistical properties of the data are limited to the specific timescale of the fluctuations that are considered. For example, a Statistical Bias correction methodology for mean daily temperature values might be detrimental to monthly statistics. Also, in applying Bias corrections derived from present day to scenario simulations, an assumption is made on the stationarity of the Bias over the largest timescales. First, we point out several conditions that have to be fulfilled by model data to make the application of a Statistical Bias correction meaningful. We then examine the effects of mixing fluctuations on different timescales and suggest an alternative Statistical methodology, referred to here as a cascade Bias correction method, that eliminates, or greatly reduces, the negative effects.
Jan O Haerter - One of the best experts on this subject based on the ideXlab platform.
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Statistical precipitation Bias correction of gridded model data using point measurements
Geophysical Research Letters, 2015Co-Authors: Jan O Haerter, C Piani, Christopher Moseley, Bastian Eggert, Peter BergAbstract:It is well known that climate model output data cannot be used directly as input to impact models, e.g., hydrology models, due to climate model errors. Recently, it has become customary to apply Statistical Bias correction to achieve better Statistical correspondence to observational data. As climate model output should be interpreted as the space-time average over a given model grid box and output time step, the status quo in Bias correction is to employ matching gridded observational data to yield optimal results. Here we show that when gridded observational data are not available, Statistical Bias correction can be carried out using point measurements, e.g., rain gauges. Our nonparametric method, which we call scale-adapted Statistical Bias correction (SABC), is achieved by data aggregation of either the available modeled or gauge data. SABC is a straightforward application of the well-known Taylor hypothesis of frozen turbulence. Using climate model and rain gauge data, we show that SABC performs significantly better than equal-time period Statistical Bias correction.
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on the contribution of Statistical Bias correction to the uncertainty in the projected hydrological cycle
Geophysical Research Letters, 2011Co-Authors: C D Chen, C Piani, Jan O Haerter, Stefan HagemannAbstract:[1] Global hydrological modeling is affected by three sources of uncertainty: (i) the choice of the global climate model (GCM) used to provide meteorological forcing data; (ii) the choice of future greenhouse gas concentration scenario; and (iii) the choice of the decade used to derive the Bias correction parameters. We present a comparative analysis of these uncertainties and compare them to the inter-annual variability. The analysis focuses on discharge, integrated runoff and total precipitation over ten large catchments, representative of different climatic areas of the globe. Results are similar for all catchments, all hydrological variables and throughout the year with few exceptions. We find that the choice of different decadal periods over which to derive the Bias correction parameters is a source of comparatively minor uncertainty, while other sources play larger and similarly significant roles. This is true for both the means and the extremes of the studied hydrological variables.
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impact of a Statistical Bias correction on the projected hydrological changes obtained from three gcms and two hydrology models
Journal of Hydrometeorology, 2011Co-Authors: Stefan Hagemann, Jan O Haerter, C D Chen, Jens Heinke, Dieter Gerten, C PianiAbstract:AbstractFuture climate model scenarios depend crucially on the models’ adequate representation of the hydrological cycle. Within the EU integrated project Water and Global Change (WATCH), special care is taken to use state-of-the-art climate model output for impacts assessments with a suite of hydrological models. This coupling is expected to lead to a better assessment of changes in the hydrological cycle. However, given the systematic errors of climate models, their output is often not directly applicable as input for hydrological models. Thus, the methodology of a Statistical Bias correction has been developed for correcting climate model output to produce long-term time series with a Statistical intensity distribution close to that of the observations. As observations, global reanalyzed daily data of precipitation and temperature were used that were obtained in the WATCH project. Daily time series from three GCMs (GCMs) ECHAM5/Max Planck Institute Ocean Model (MPI-OM), Centre National de Recherches Me...
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Statistical Bias correction of global simulated daily precipitation and temperature for the application of hydrological models
Journal of Hydrology, 2010Co-Authors: C Piani, Graham P Weedon, M J Best, S Gomes, Pedro Viterbo, Stefan Hagemann, Jan O HaerterAbstract:Summary A Statistical Bias correction methodology for global climate simulations is developed and applied to daily land precipitation and mean, minimum and maximum daily land temperatures. The Bias correction is based on a fitted histogram equalization function. This function is defined daily, as opposed to earlier published versions in which they were derived yearly or seasonally at best, while conserving properties of robustness and eliminating unrealistic jumps at seasonal or monthly transitions. The methodology is tested using the newly available global dataset of observed hydrological forcing data of the last 50 years from the EU project WATCH (WATer and global CHange) and an initial conditions ensemble of simulations performed with the ECHAM5 global climate model for the same period. Bias corrections are derived from 1960 to 1969 observed and simulated data and then applied to 1990–1999 simulations. Results confirm the effectiveness of the methodology for all tested variables. Bias corrections are also derived using three other non-overlapping decades from 1970 to 1999 and all members of the ECHAM5 initial conditions ensemble. A methodology is proposed to use the resulting “ensemble of Bias corrections” to quantify the error in simulated scenario projections of components of the hydrological cycle.
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climate model Bias correction and the role of timescales
Hydrology and Earth System Sciences, 2010Co-Authors: Jan O Haerter, Stefan Hagemann, Christopher Moseley, C PianiAbstract:Abstract. It is well known that output from climate models cannot be used to force hydrological simulations without some form of preprocessing to remove the existing Biases. In principle, Statistical Bias correction methodologies act on model output so the Statistical properties of the corrected data match those of the observations. However, the improvements to the Statistical properties of the data are limited to the specific timescale of the fluctuations that are considered. For example, a Statistical Bias correction methodology for mean daily temperature values might be detrimental to monthly statistics. Also, in applying Bias corrections derived from present day to scenario simulations, an assumption is made on the stationarity of the Bias over the largest timescales. First, we point out several conditions that have to be fulfilled by model data to make the application of a Statistical Bias correction meaningful. We then examine the effects of mixing fluctuations on different timescales and suggest an alternative Statistical methodology, referred to here as a cascade Bias correction method, that eliminates, or greatly reduces, the negative effects.
Stefan Hagemann - One of the best experts on this subject based on the ideXlab platform.
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Impact of Statistical Bias correction on the projected climate change signals of the regional climate model REMO over the Senegal River Basin
International Journal of Climatology, 2015Co-Authors: Mamadou Lamine Mbaye, Stefan Hagemann, Andreas Haensler, Amadou T. Gaye, Christopher Moseley, Abel AfoudaAbstract:We assess the impact of a Statistical Bias correction method based on histogram equalization functions on the projected climate change signals of regional climate model (RCM) simulations over the Senegal River Basin in West Africa. Focus is given to projected changes in precipitation, temperature, and potential water balance (P − PET) following the RCP4.5 and RCP8.5 emission scenario pathways by the end of the 21st century (2071–2100) compared to the 1971–2000 reference period. We found that applying the Bias correction substantially improved the simulations of present day climate for both temporal and spatial variations of the analysed climate parameters when compared to gridded observations data sets and station data. For the future, the non-corrected RCM projections show a general decrease of precipitation by the end of 21st century for both scenarios over the majority of the basin, except the Guinean highlands where a slight increase is found. The reduction in mean precipitation is accompanied by a projected increase in the annual number of dry days, most pronounced in the northern basin. Furthermore, a general temperature increase is projected over the entire basin for both scenarios, but more pronounced under the RCP8.5 scenario. In addition, the deficit in the water balance (P − PET) above 12°N is projected to increase in the future. Applying the Bias correction to the RCM projections leads to a general dampening of the projected change signals, strongest in the case of heavy precipitation events. However, for all analysed parameters the general directions of change as well as the predominant large-scale change patterns are conserved after applying the Bias correction.
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on the contribution of Statistical Bias correction to the uncertainty in the projected hydrological cycle
Geophysical Research Letters, 2011Co-Authors: C D Chen, C Piani, Jan O Haerter, Stefan HagemannAbstract:[1] Global hydrological modeling is affected by three sources of uncertainty: (i) the choice of the global climate model (GCM) used to provide meteorological forcing data; (ii) the choice of future greenhouse gas concentration scenario; and (iii) the choice of the decade used to derive the Bias correction parameters. We present a comparative analysis of these uncertainties and compare them to the inter-annual variability. The analysis focuses on discharge, integrated runoff and total precipitation over ten large catchments, representative of different climatic areas of the globe. Results are similar for all catchments, all hydrological variables and throughout the year with few exceptions. We find that the choice of different decadal periods over which to derive the Bias correction parameters is a source of comparatively minor uncertainty, while other sources play larger and similarly significant roles. This is true for both the means and the extremes of the studied hydrological variables.
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impact of a Statistical Bias correction on the projected hydrological changes obtained from three gcms and two hydrology models
Journal of Hydrometeorology, 2011Co-Authors: Stefan Hagemann, Jan O Haerter, C D Chen, Jens Heinke, Dieter Gerten, C PianiAbstract:AbstractFuture climate model scenarios depend crucially on the models’ adequate representation of the hydrological cycle. Within the EU integrated project Water and Global Change (WATCH), special care is taken to use state-of-the-art climate model output for impacts assessments with a suite of hydrological models. This coupling is expected to lead to a better assessment of changes in the hydrological cycle. However, given the systematic errors of climate models, their output is often not directly applicable as input for hydrological models. Thus, the methodology of a Statistical Bias correction has been developed for correcting climate model output to produce long-term time series with a Statistical intensity distribution close to that of the observations. As observations, global reanalyzed daily data of precipitation and temperature were used that were obtained in the WATCH project. Daily time series from three GCMs (GCMs) ECHAM5/Max Planck Institute Ocean Model (MPI-OM), Centre National de Recherches Me...
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Statistical Bias correction of global simulated daily precipitation and temperature for the application of hydrological models
Journal of Hydrology, 2010Co-Authors: C Piani, Graham P Weedon, M J Best, S Gomes, Pedro Viterbo, Stefan Hagemann, Jan O HaerterAbstract:Summary A Statistical Bias correction methodology for global climate simulations is developed and applied to daily land precipitation and mean, minimum and maximum daily land temperatures. The Bias correction is based on a fitted histogram equalization function. This function is defined daily, as opposed to earlier published versions in which they were derived yearly or seasonally at best, while conserving properties of robustness and eliminating unrealistic jumps at seasonal or monthly transitions. The methodology is tested using the newly available global dataset of observed hydrological forcing data of the last 50 years from the EU project WATCH (WATer and global CHange) and an initial conditions ensemble of simulations performed with the ECHAM5 global climate model for the same period. Bias corrections are derived from 1960 to 1969 observed and simulated data and then applied to 1990–1999 simulations. Results confirm the effectiveness of the methodology for all tested variables. Bias corrections are also derived using three other non-overlapping decades from 1970 to 1999 and all members of the ECHAM5 initial conditions ensemble. A methodology is proposed to use the resulting “ensemble of Bias corrections” to quantify the error in simulated scenario projections of components of the hydrological cycle.
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climate model Bias correction and the role of timescales
Hydrology and Earth System Sciences, 2010Co-Authors: Jan O Haerter, Stefan Hagemann, Christopher Moseley, C PianiAbstract:Abstract. It is well known that output from climate models cannot be used to force hydrological simulations without some form of preprocessing to remove the existing Biases. In principle, Statistical Bias correction methodologies act on model output so the Statistical properties of the corrected data match those of the observations. However, the improvements to the Statistical properties of the data are limited to the specific timescale of the fluctuations that are considered. For example, a Statistical Bias correction methodology for mean daily temperature values might be detrimental to monthly statistics. Also, in applying Bias corrections derived from present day to scenario simulations, an assumption is made on the stationarity of the Bias over the largest timescales. First, we point out several conditions that have to be fulfilled by model data to make the application of a Statistical Bias correction meaningful. We then examine the effects of mixing fluctuations on different timescales and suggest an alternative Statistical methodology, referred to here as a cascade Bias correction method, that eliminates, or greatly reduces, the negative effects.
Christopher Moseley - One of the best experts on this subject based on the ideXlab platform.
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Impact of Statistical Bias correction on the projected climate change signals of the regional climate model REMO over the Senegal River Basin
International Journal of Climatology, 2015Co-Authors: Mamadou Lamine Mbaye, Stefan Hagemann, Andreas Haensler, Amadou T. Gaye, Christopher Moseley, Abel AfoudaAbstract:We assess the impact of a Statistical Bias correction method based on histogram equalization functions on the projected climate change signals of regional climate model (RCM) simulations over the Senegal River Basin in West Africa. Focus is given to projected changes in precipitation, temperature, and potential water balance (P − PET) following the RCP4.5 and RCP8.5 emission scenario pathways by the end of the 21st century (2071–2100) compared to the 1971–2000 reference period. We found that applying the Bias correction substantially improved the simulations of present day climate for both temporal and spatial variations of the analysed climate parameters when compared to gridded observations data sets and station data. For the future, the non-corrected RCM projections show a general decrease of precipitation by the end of 21st century for both scenarios over the majority of the basin, except the Guinean highlands where a slight increase is found. The reduction in mean precipitation is accompanied by a projected increase in the annual number of dry days, most pronounced in the northern basin. Furthermore, a general temperature increase is projected over the entire basin for both scenarios, but more pronounced under the RCP8.5 scenario. In addition, the deficit in the water balance (P − PET) above 12°N is projected to increase in the future. Applying the Bias correction to the RCM projections leads to a general dampening of the projected change signals, strongest in the case of heavy precipitation events. However, for all analysed parameters the general directions of change as well as the predominant large-scale change patterns are conserved after applying the Bias correction.
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Statistical precipitation Bias correction of gridded model data using point measurements
Geophysical Research Letters, 2015Co-Authors: Jan O Haerter, C Piani, Christopher Moseley, Bastian Eggert, Peter BergAbstract:It is well known that climate model output data cannot be used directly as input to impact models, e.g., hydrology models, due to climate model errors. Recently, it has become customary to apply Statistical Bias correction to achieve better Statistical correspondence to observational data. As climate model output should be interpreted as the space-time average over a given model grid box and output time step, the status quo in Bias correction is to employ matching gridded observational data to yield optimal results. Here we show that when gridded observational data are not available, Statistical Bias correction can be carried out using point measurements, e.g., rain gauges. Our nonparametric method, which we call scale-adapted Statistical Bias correction (SABC), is achieved by data aggregation of either the available modeled or gauge data. SABC is a straightforward application of the well-known Taylor hypothesis of frozen turbulence. Using climate model and rain gauge data, we show that SABC performs significantly better than equal-time period Statistical Bias correction.
-
climate model Bias correction and the role of timescales
Hydrology and Earth System Sciences, 2010Co-Authors: Jan O Haerter, Stefan Hagemann, Christopher Moseley, C PianiAbstract:Abstract. It is well known that output from climate models cannot be used to force hydrological simulations without some form of preprocessing to remove the existing Biases. In principle, Statistical Bias correction methodologies act on model output so the Statistical properties of the corrected data match those of the observations. However, the improvements to the Statistical properties of the data are limited to the specific timescale of the fluctuations that are considered. For example, a Statistical Bias correction methodology for mean daily temperature values might be detrimental to monthly statistics. Also, in applying Bias corrections derived from present day to scenario simulations, an assumption is made on the stationarity of the Bias over the largest timescales. First, we point out several conditions that have to be fulfilled by model data to make the application of a Statistical Bias correction meaningful. We then examine the effects of mixing fluctuations on different timescales and suggest an alternative Statistical methodology, referred to here as a cascade Bias correction method, that eliminates, or greatly reduces, the negative effects.
Kenichi Kanatani - One of the best experts on this subject based on the ideXlab platform.
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Statistical Bias of conic fitting and renormalization
IEEE Transactions on Pattern Analysis and Machine Intelligence, 1994Co-Authors: Kenichi KanataniAbstract:Introducing a Statistical model of noise in terms of the covariance matrix of the N-vector, we point out that the least-squares conic fitting is Statistically Biased. We present a new fitting scheme called renormalization for computing an unBiased estimate by automatically adjusting to noise. Relationships to existing methods are discussed, and our method is tested using real and synthetic data. >
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3-D interpretation of optical flow by renormalization
International Journal of Computer Vision, 1993Co-Authors: Kenichi KanataniAbstract:This article studies 3-D interpretation of optical flow induced by a general camera motion relative to a surface of general shape. First, we describe, using the “image sphere representation,” an analytical procedure that yields an exact solution when the data are exact: we solve theepipolar equation written in terms of theessential parameters and thetwisted optical flow. Introducing a simple model of noise, we then show that the solution is “Statistically Biased.” In order to remove the Statistical Bias, we propose an algorithm calledrenormalization, which automatically adjusts to unknown image noise. A brief discussion is also given to thecritical surface that yields ambiguous 3-D interpretations and the use of theimage plane representation.
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ICCV - Renormalization for unBiased estimation
1993 (4th) International Conference on Computer Vision, 1Co-Authors: Kenichi KanataniAbstract:In many computer vision problems, it is necessary to robustly estimate parameter values from a large quantity of image data. In such problems, least-squares minimization is computationally the most convenient and practical solution method. The author shows that the least-squares solution is in general Statistically Biased in the presence of noise. A scheme called renormalization that iteratively removes the Statistical Bias by automatically adjusting to the image noise is presented. It is applied to the problem of estimating vanishing points and focuses of expansion and conic fitting. >
