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1
Yano, J. I. "Downscaling, parameterization, decomposition, compression: a perspective from the multiresolution analysis". Advances in Geosciences 23 (22 giugno 2010): 65–71. http://dx.doi.org/10.5194/adgeo-23-65-2010.
Testo completoAbstract (sommario):
Abstract. Geophysical models in general, and atmospheric models more specifically, are always limited in spatial resolutions. Due to this limitation, we face with two different needs. The first is a need for knowing (or "downscaling") more spatial details (e.g., precipitation distribution) than having model simulations for practical applications, such as hydrological modelling. The second is a need for "parameterizing" the subgrid-scale physical processes in order to represent the feedbacks of these processes on to the resolved scales (e.g., the convective heating rate). The present article begins by remarking that it is essential to consider the downscaling and parametrization as an "inverse" of each other: downscaling seeks a detail of the subgrid-scale processes, then the parameterization seeks an integrated effect of the former into the resolved scales. A consideration on why those two closely-related operations are traditionally treated separately, gives insights of the fundamental limitations of the current downscalings and parameterizations. The multiresolution analysis (such as those based on wavelet) provides an important conceptual framework for developing a unified formulation for the downscaling and parameterization. In the vocabulary of multiresolution analysis, these two operations may be considered as types of decompression and compression. A new type of a subgrid-scale representation scheme, NAM-SCA (nonhydrostatic anelastic model with segmentally-constant approximation), is introduced under this framework.
2
Yhang, Yoo-Bin, Soo-Jin Sohn e Il-Won Jung. "Application of Dynamical and Statistical Downscaling to East Asian Summer Precipitation for Finely Resolved Datasets". Advances in Meteorology 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/2956373.
Testo completoAbstract (sommario):
Various downscaling approaches have been developed to overcome the limitation of the coarse spatial resolution of general circulation models (GCMs). Such techniques can be grouped into two approaches of dynamical and statistical downscaling. In this study, we investigated the performances of different downscaling methods, focusing on East Asian summer monsoon precipitation to obtain more finely resolved and value added datasets. The dynamical downscaling was conducted by the Regional Model Program (RMP) of the Global/Regional Integrated Model system (GRIMs), while the statistical downscaling was performed through coupled pattern-based simple linear regression. The dynamical downscaling resulted in a better representation of the spatial distribution and long-term trend than the GCM produced; however, it tended to overestimate precipitation over East Asia. In contrast, the application of the statistical downscaling resulted in a bias in the amount of precipitation, due to low variance that is inherent in regression-based downscaling. A combination of dynamical and statistical downscaling produced the best results in time and space. This study provides a guideline for determining the most effective and robust downscaling method in the hydrometeorological applications, which are quite sensitive to the accuracy of downscaled precipitation.
3
Zhang, Xunchang, Mingxi Shen, Jie Chen, Joel W. Homan e Phillip R. Busteed. "Evaluation of Statistical Downscaling Methods for Simulating Daily Precipitation Distribution, Frequency, and Temporal Sequence". Transactions of the ASABE 64, n. 3 (2021): 771–84. http://dx.doi.org/10.13031/trans.14097.
Testo completoAbstract (sommario):
HighlightsNine statistical downscaling methods from three downscaling categories were evaluated.Weather generator-based methods had advantages in simulating non-stationary precipitation.Differences in downscaling performance were smaller within each category than between categories.The performance of each downscaling method varied with climate conditions.Abstract. Spatial discrepancy between global climate model (GCM) projections and the climate data input required by hydrological models is a major limitation for assessing the impact of climate change on soil erosion and crop production at local scales. Statistical downscaling techniques are widely used to correct biases of GCM projections. The objective of this study was to evaluate the ability of nine statistical downscaling methods from three available statistical downscaling categories to simulate daily precipitation distribution, frequency, and temporal sequence at four Oklahoma weather stations representing arid to humid climate regions. The three downscaling categories included perfect prognosis (PP), model output statistics (MOS), and stochastic weather generator (SWG). To minimize the effect of GCM projection error on downscaling quality, the National Centers for Environmental Prediction (NCEP) Reanalysis 1 data at a 2.5° grid spacing (treated as observed grid data) were downscaled to the four weather stations (representing arid, semi-arid, sub humid, and humid regions) using the nine downscaling methods. The station observations were divided into calibration and validation periods in a way that maximized the differences in annual precipitation means between the two periods for assessing the ability of each method in downscaling non-stationary climate changes. All methods were ranked with three metrics (Euclidean distance, sum of absolute relative error, and absolute error) for their ability in simulating precipitation amounts at daily, monthly, yearly, and annual maximum scales. After eliminating the poorest two performers in simulating precipitation mean, distribution, frequency, and temporal sequence, the top four remaining methods in ascending order were Distribution-based Bias Correction (DBC), Generator for Point Climate Change (GPCC), SYNthetic weather generaTOR (SYNTOR), and LOCal Intensity scaling (LOCI). DBC and LOCI are bias-correction methods, and GPCC and SYNTOR are generator-based methods. The differences in performances among the downscaling methods were smaller within each downscaling category than between the categories. The performance of each method varied with the climate conditions of each station. Overall results indicated that the SWG methods had certain advantages in simulating daily precipitation distribution, frequency, and temporal sequence for non-stationary climate changes. Keywords: Climate change, Climate downscaling, Downscaling method evaluation, Statistical downscaling.
4
Njuki, Sammy M., Chris M. Mannaerts e Zhongbo Su. "An Improved Approach for Downscaling Coarse-Resolution Thermal Data by Minimizing the Spatial Averaging Biases in Random Forest". Remote Sensing 12, n. 21 (25 ottobre 2020): 3507. http://dx.doi.org/10.3390/rs12213507.
Testo completoAbstract (sommario):
Land surface temperature (LST) plays a fundamental role in various geophysical processes at varying spatial and temporal scales. Satellite-based observations of LST provide a viable option for monitoring the spatial-temporal evolution of these processes. Downscaling is a widely adopted approach for solving the spatial-temporal trade-off associated with satellite-based observations of LST. However, despite the advances made in the field of LST downscaling, issues related to spatial averaging in the downscaling methodologies greatly hamper the utility of coarse-resolution thermal data for downscaling applications in complex environments. In this study, an improved LST downscaling approach based on random forest (RF) regression is presented. The proposed approach addresses issues related to spatial averaging biases associated with the downscaling model developed at the coarse resolution. The approach was applied to downscale the coarse-resolution Satellite Application Facility on Land Surface Analysis (LSA-SAF) LST product derived from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) sensor aboard the Meteosat Second Generation (MSG) weather satellite. The LSA-SAF product was downscaled to a spatial resolution of ~30 m, based on predictor variables derived from Sentinel 2, and the Advanced Land Observing Satellite (ALOS) digital elevation model (DEM). Quantitatively and qualitatively, better downscaling results were obtained using the proposed approach in comparison to the conventional approach of downscaling LST using RF widely adopted in LST downscaling studies. The enhanced performance indicates that the proposed approach has the ability to reduce the spatial averaging biases inherent in the LST downscaling methodology and thus is more suitable for downscaling applications in complex environments.
5
Wu, Yichen, Zhihua Zhang, M. James C. Crabbe e Lipon Chandra Das. "Statistical Learning-Based Spatial Downscaling Models for Precipitation Distribution". Advances in Meteorology 2022 (7 giugno 2022): 1–12. http://dx.doi.org/10.1155/2022/3140872.
Testo completoAbstract (sommario):
The downscaling technique produces high spatial resolution precipitation distribution in order to analyze impacts of climate change in data-scarce regions or local scales. In this study, based on three statistical learning algorithms, such as support vector machine (SVM), random forest regression (RF), and gradient boosting regressor (GBR), we proposed an efficient downscaling approach to produce high spatial resolution precipitation. In order to demonstrate efficiency and accuracy of our models over traditional multilinear regression (MLR) downscaling models, we did a downscaling analysis for daily observed precipitation data from 34 monitoring sites in Bangladesh. Validation revealed that R 2 of GBR could reach 0.98, compared with RF (0.94), SVM (0.88), and multilinear regression (MLR) (0.69) models, so the GBR-based downscaling model had the best performance among all four downscaling models. We suggest that the GBR-based downscaling models should be used to replace traditional MLR downscaling models to produce a more accurate map of high-resolution precipitation for flood disaster management, drought forecasting, and long-term planning of land and water resources.
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Liu, X., P. Coulibaly e N. Evora. "Comparison of data-driven methods for downscaling ensemble weather forecasts". Hydrology and Earth System Sciences Discussions 4, n. 1 (1 febbraio 2007): 189–210. http://dx.doi.org/10.5194/hessd-4-189-2007.
Testo completoAbstract (sommario):
Abstract. This study investigates dynamically different data-driven methods, specifically a statistical downscaling model (SDSM), a time lagged feedforward neural network (TLFN), and an evolutionary polynomial regression (EPR) technique for downscaling numerical weather ensemble forecasts generated by a medium range forecast (MRF) model. Given the coarse resolution (about 200-km grid spacing) of the MRF model, an optimal use of the weather forecasts at the local or watershed scale, requires appropriate downscaling techniques. The selected methods are applied for downscaling ensemble daily precipitation and temperature series for the Chute-du-Diable basin located in northeastern Canada. The downscaling results show that the TLFN and EPR have similar performance in downscaling ensemble daily precipitation as well as daily maximum and minimum temperature series whatever the season. Both the TLFN and EPR are more efficient downscaling techniques than SDSM for both the ensemble daily precipitation and temperature.
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Coulibaly, P., e N. Evora. "Comparison of data-driven methods for downscaling ensemble weather forecasts". Hydrology and Earth System Sciences 12, n. 2 (20 marzo 2008): 615–24. http://dx.doi.org/10.5194/hess-12-615-2008.
Testo completoAbstract (sommario):
Abstract. This study investigates dynamically different data-driven methods, specifically a statistical downscaling model (SDSM), a time lagged feedforward neural network (TLFN), and an evolutionary polynomial regression (EPR) technique for downscaling numerical weather ensemble forecasts generated by a medium range forecast (MRF) model. Given the coarse resolution (about 200-km grid spacing) of the MRF model, an optimal use of the weather forecasts at the local or watershed scale, requires appropriate downscaling techniques. The selected methods are applied for downscaling ensemble daily precipitation and temperature series for the Chute-du-Diable basin located in northeastern Canada. The downscaling results show that the TLFN and EPR have similar performance in downscaling ensemble daily precipitation as well as daily maximum and minimum temperature series whatever the season. Both the TLFN and EPR are more efficient downscaling techniques than SDSM for both the ensemble daily precipitation and temperature.
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Coulibaly, Paulin, Yonas B. Dibike e François Anctil. "Downscaling Precipitation and Temperature with Temporal Neural Networks". Journal of Hydrometeorology 6, n. 4 (1 agosto 2005): 483–96. http://dx.doi.org/10.1175/jhm409.1.
Testo completoAbstract (sommario):
Abstract The issues of downscaling the outputs of a global climate model (GCM) to a scale that is appropriate to hydrological impact studies are investigated using a temporal neural network approach. The time-lagged feed-forward neural network (TLFN) is proposed for downscaling daily total precipitation and daily maximum and minimum temperature series for the Serpent River watershed in northern Quebec (Canada). The downscaling models are developed and validated using large-scale predictor variables derived from the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis dataset. Atmospheric predictors such as specific humidity, wind velocity, and geopotential height are identified as the most relevant inputs to the downscaling models. The performance of the TLFN downscaling model is also compared to a statistical downscaling model (SDSM). The downscaling results suggest that the TLFN is an efficient method for downscaling both daily precipitation and temperature series. The best downscaling models were then applied to the outputs of the Canadian Global Climate Model (CGCM1), forced with the Intergovernmental Panel on Climate Change (IPCC) IS92a scenario. Changes in average precipitation between the current and the future scenarios predicted by the TLFN are generally found to be smaller than those predicted by the SDSM model. Furthermore, application of the downscaled data for hydrologic impact analysis in the Serpent River resulted in an overall increasing trend in mean annual flow as well as earlier spring peak flow. The results also demonstrate the emphasis that should be given in identifying the appropriate downscaling tools for impact studies by showing how a future climate scenario downscaled with different downscaling methods could result in significantly different hydrologic impact simulation results for the same watershed.
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Xu, Mengchao, Qian Liu, Dexuan Sha, Manzhu Yu, Daniel Q. Duffy, William M. Putman, Mark Carroll, Tsengdar Lee e Chaowei Yang. "PreciPatch: A Dictionary-based Precipitation Downscaling Method". Remote Sensing 12, n. 6 (23 marzo 2020): 1030. http://dx.doi.org/10.3390/rs12061030.
Testo completoAbstract (sommario):
Climate and weather data such as precipitation derived from Global Climate Models (GCMs) and satellite observations are essential for the global and local hydrological assessment. However, most climatic popular precipitation products (with spatial resolutions coarser than 10km) are too coarse for local impact studies and require “downscaling” to obtain higher resolutions. Traditional precipitation downscaling methods such as statistical and dynamic downscaling require an input of additional meteorological variables, and very few are applicable for downscaling hourly precipitation for higher spatial resolution. Based on dynamic dictionary learning, we propose a new downscaling method, PreciPatch, to address this challenge by producing spatially distributed higher resolution precipitation fields with only precipitation input from GCMs at hourly temporal resolution and a large geographical extent. Using aggregated Integrated Multi-satellitE Retrievals for GPM (IMERG) data, an experiment was conducted to evaluate the performance of PreciPatch, in comparison with bicubic interpolation using RainFARM—a stochastic downscaling method, and DeepSD—a Super-Resolution Convolutional Neural Network (SRCNN) based downscaling method. PreciPatch demonstrates better performance than other methods for downscaling short-duration precipitation events (used historical data from 2014 to 2017 as the training set to estimate high-resolution hourly events in 2018).
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Sachindra, D. A., F. Huang, A. Barton e B. J. C. Perera. "Statistical downscaling of general circulation model outputs to catchment scale hydroclimatic variables: issues, challenges and possible solutions". Journal of Water and Climate Change 5, n. 4 (15 luglio 2014): 496–525. http://dx.doi.org/10.2166/wcc.2014.056.
Testo completoAbstract (sommario):
The aim of this paper is to discuss the issues and challenges associated with statistical downscaling of general circulation model (GCM) outputs to hydroclimatic variables at catchment scale and also to discuss potential solutions to address these issues and challenges. Outputs of GCMs (predictors of statistical downscaling models) suffer a considerable degree of uncertainty, mainly due to the lack of theoretical robustness caused by the limited understanding of various physical processes of the atmosphere and the incomplete mathematical representation of those processes in GCMs. The presence of several future GHG emission scenarios with equal likelihood of occurrence leads to scenario uncertainty. Outputs of a downscaling study are dependent on the quality and the length of the record of field observations, as statistical downscaling models are calibrated and validated against these observations of the hydroclimatic variables (predictands of statistical downscaling models). The downscaled results vary from one statistical downscaling technique to another due to different representations of the predictor–predictand relationships. Also different techniques used in selecting the predictors for statistical downscaling models influence the model outputs. Although statistical downscaling faces these issues, it is still considered as a potential method of predicting the catchment scale hydroclimatology from GCM outputs.
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Liu, Jiaming, Di Yuan, Liping Zhang, Xia Zou e Xingyuan Song. "Comparison of Three Statistical Downscaling Methods and Ensemble Downscaling Method Based on Bayesian Model Averaging in Upper Hanjiang River Basin, China". Advances in Meteorology 2016 (2016): 1–12. http://dx.doi.org/10.1155/2016/7463963.
Testo completoAbstract (sommario):
Many downscaling techniques have been developed in the past few years for projection of station-scale hydrological variables from large-scale atmospheric variables to assess the hydrological impacts of climate change. To improve the simulation accuracy of downscaling methods, the Bayesian Model Averaging (BMA) method combined with three statistical downscaling methods, which are support vector machine (SVM), BCC/RCG-Weather Generators (BCC/RCG-WG), and Statistics Downscaling Model (SDSM), is proposed in this study, based on the statistical relationship between the larger scale climate predictors and observed precipitation in upper Hanjiang River Basin (HRB). The statistical analysis of three performance criteria (the Nash-Sutcliffe coefficient of efficiency, the coefficient of correlation, and the relative error) shows that the performance of ensemble downscaling method based on BMA for rainfall is better than that of each single statistical downscaling method. Moreover, the performance for the runoff modelled by the SWAT rainfall-runoff model using the downscaled daily rainfall by four methods is also compared, and the ensemble downscaling method has better simulation accuracy. The ensemble downscaling technology based on BMA can provide scientific basis for the study of runoff response to climate change.
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Hashmi, M. Z., A. Y. Shamseldin e B. W. Melville. "Statistical downscaling of precipitation: state-of-the-art and application of bayesian multi-model approach for uncertainty assessment". Hydrology and Earth System Sciences Discussions 6, n. 5 (27 ottobre 2009): 6535–79. http://dx.doi.org/10.5194/hessd-6-6535-2009.
Testo completoAbstract (sommario):
Abstract. Global Circulation Models (GCMs) are a major tool used for future projections of climate change using different emission scenarios. However, for assessing the hydrological impacts of climate change at the watershed and the regional scale, the GCM outputs cannot be used directly due to the mismatch in the spatial resolution between the GCMs and hydrological models. In order to use the output of a GCM for conducting hydrological impact studies, downscaling is used. However, the downscaling results may contain considerable uncertainty which needs to be quantified before making the results available. Among the variables usually downscaled, precipitation downscaling is quite challenging and is more prone to uncertainty issues than other climatological variables. This paper addresses the uncertainty analysis associated with statistical downscaling of a watershed precipitation (Clutha River above Balclutha, New Zealand) using results from three well reputed downscaling methods and Bayesian weighted multi-model ensemble approach. The downscaling methods used for this study belong to the following downscaling categories; (1) Multiple linear regression; (2) Multiple non-linear regression; and (3) Stochastic weather generator. The results obtained in this study have shown that this ensemble strategy is very efficient in combining the results from multiple downscaling methods on the basis of their performance and quantifying the uncertainty contained in this ensemble output. This will encourage any future attempts on quantifying downscaling uncertainties using the multi-model ensemble framework.
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Mao, Taoning, Wei Shangguan, Qingliang Li, Lu Li, Ye Zhang, Feini Huang, Jianduo Li, Wei Liu e Ruqing Zhang. "A Spatial Downscaling Method for Remote Sensing Soil Moisture Based on Random Forest Considering Soil Moisture Memory and Mass Conservation". Remote Sensing 14, n. 16 (9 agosto 2022): 3858. http://dx.doi.org/10.3390/rs14163858.
Testo completoAbstract (sommario):
Remote sensing soil moisture (SM) has been widely used in various earth science studies and applications, but their low resolution limits their usage and downscaling of them is needed. In this study, we proposed a spatial downscaling method for SM based on random forest considering soil moisture memory and mass conservation to improve downscaling performance. The lagged SM was added as a predictor to represent soil moisture memory, in addition to the regular predictors in previous downscaling studies. The Soil Moisture Active Passive (SMAP) SM data of the Pearl River Basin were used to test our downscaling method. The results show that the downscaling model obtained good performance on the test set (R2 = 0.848, ubRMSE = 0.034 m3/m3 and Bias = 0.008 m3/m3). The spatial and temporal performance of the RF downscaling model can be improved by adding lagged SM variables. Downscaled data obtained can retain the information of the original SMAP SM data well and show more spatial details, and mass conservation correction is considered to be useful to eliminate systematic bias of the downscaling model. Downscaled SM achieved acceptable performance in in situ validation, though it was inevitably limited by the performance of the original SMAP data. The proposed downscaling method can serve as a powerful tool for the development of high-resolution SM information.
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Wright, Stephen. "Downscaling our fear". Nursing Standard 28, n. 22 (29 gennaio 2014): 26–27. http://dx.doi.org/10.7748/ns2014.01.28.22.26.s30.
Testo completo
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Benestad, Rasmus E. "Downscaling precipitation extremes". Theoretical and Applied Climatology 100, n. 1-2 (1 luglio 2009): 1–21. http://dx.doi.org/10.1007/s00704-009-0158-1.
Testo completo
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Mu, Bin, Bo Qin, Shijin Yuan e Xiaoyun Qin. "A Climate Downscaling Deep Learning Model considering the Multiscale Spatial Correlations and Chaos of Meteorological Events". Mathematical Problems in Engineering 2020 (15 novembre 2020): 1–17. http://dx.doi.org/10.1155/2020/7897824.
Testo completoAbstract (sommario):
Climate downscaling is a way to provide finer resolution data at local scales, which has been widely used in meteorological research. The two main approaches for climate downscaling are dynamical and statistical. The traditional dynamical downscaling methods are quite time- and resource-consuming based on general circulation models (GCMs). Recently, more and more researchers construct a statistical deep learning model for climate downscaling motivated by the single-image superresolution (SISR) process in computer vision (CV). This is an approach that uses historical climate observations to learn a low-resolution to high-resolution mapping and produces great enhancements in terms of efficiency and effectiveness. Therefore, it has provided an appreciable new insight and successful downscaling solution to multiple climate phenomena. However, most existing models only make a simple analogy between climate downscaling and SISR and ignore the underlying dynamical mechanisms, which leads to the overaveraged downscaling results lacking crucial physical details. In this paper, we incorporate the a priori meteorological knowledge into a deep learning formalization for climate downscaling. More specifically, we consider the multiscale spatial correlations and the chaos in multiple climate events. Depending on two characteristics, we build up a two-stage deep learning model containing a stepwise reconstruction process and ensemble inference, which is named climate downscaling network (CDN). It can extract more local/remote spatial dependencies and provide more comprehensive captures of extreme conditions. We evaluate our model based on two datasets: climate science dataset (CSD) and benchmark image dataset (BID). The results demonstrate that our model shows the effectiveness and superiority in downscaling daily precipitation data from 2.5 degrees to 0.5 degrees over Asia and Europe. In addition, our model exhibits better performance than the other traditional approaches and state-of-the-art deep learning models.
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Herath, H. M. S. M., P. R. Sarukkalige e V. T. V. Nguyen. "Downscaling approach to develop future sub-daily IDF relations for Canberra Airport Region, Australia". Proceedings of the International Association of Hydrological Sciences 369 (11 giugno 2015): 147–55. http://dx.doi.org/10.5194/piahs-369-147-2015.
Testo completoAbstract (sommario):
Abstract. Downscaling of climate projections is the most adopted method to assess the impacts of climate change at regional and local scale. In the last decade, downscaling techniques which provide reasonable improvement to resolution of General Circulation Models' (GCMs) output are developed in notable manner. Most of these techniques are limited to spatial downscaling of GCMs' output and still there is a high demand to develop temporal downscaling approaches. As the main objective of this study, combined approach of spatial and temporal downscaling is developed to improve the resolution of rainfall predicted by GCMs. Canberra airport region is subjected to this study and the applicability of proposed downscaling approach is evaluated for Sydney, Melbourne, Brisbane, Adelaide, Perth and Darwin regions. Statistical Downscaling Model (SDSM) is used to spatial downscaling and numerical model based on scaling invariant concept is used to temporal downscaling of rainfalls. National Centre of Environmental Prediction (NCEP) data is used in SDSM model calibration and validation. Regression based bias correction function is used to improve the accuracy of downscaled annual maximum rainfalls using HadCM3-A2. By analysing the non-central moments of observed rainfalls, single time regime (from 30 min to 24 h) is identified which exist scaling behaviour and it is used to estimate the sub daily extreme rainfall depths from daily downscaled rainfalls. Finally, as the major output of this study, Intensity Duration Frequency (IDF) relations are developed for the future periods of 2020s, 2050s and 2080s in the context of climate change.
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Sun, Hao, e Yajing Cui. "Evaluating Downscaling Factors of Microwave Satellite Soil Moisture Based on Machine Learning Method". Remote Sensing 13, n. 1 (2 gennaio 2021): 133. http://dx.doi.org/10.3390/rs13010133.
Testo completoAbstract (sommario):
Downscaling microwave remotely sensed soil moisture (SM) is an effective way to obtain spatial continuous SM with fine resolution for hydrological and agricultural applications on a regional scale. Downscaling factors and functions are two basic components of SM downscaling where the former is particularly important in the era of big data. Based on machine learning method, this study evaluated Land Surface Temperature (LST), Land surface Evaporative Efficiency (LEE), and geographical factors from Moderate Resolution Imaging Spectroradiometer (MODIS) products for downscaling SMAP (Soil Moisture Active and Passive) SM products. This study spans from 2015 to the end of 2018 and locates in the central United States. Original SMAP SM and in-situ SM at sparse networks and core validation sites were used as reference. Experiment results indicated that (1) LEE presented comparative performance with LST as downscaling factors; (2) adding geographical factors can significantly improve the performance of SM downscaling; (3) integrating LST, LEE, and geographical factors got the best performance; (4) using Z-score normalization or hyperbolic-tangent normalization methods did not change the above conclusions, neither did using support vector regression nor feed forward neural network methods. This study demonstrates the possibility of LEE as an alternative of LST for downscaling SM when there is no available LST due to cloud contamination. It also provides experimental evidence for adding geographical factors in the downscaling process.
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Zhu, Honglin, Huizeng Liu, Qiming Zhou e Aihong Cui. "Towards an Accurate and Reliable Downscaling Scheme for High-Spatial-Resolution Precipitation Data". Remote Sensing 15, n. 10 (18 maggio 2023): 2640. http://dx.doi.org/10.3390/rs15102640.
Testo completoAbstract (sommario):
Accurate high-spatial-resolution precipitation is significantly important in hydrological and meteorological modelling, especially in rain-gauge-sparse areas. Some methods and strategies have been applied for satellite-based precipitation downscaling, residual correction and precipitation calibration. However, which downscaling scheme can provide reliable high-resolution precipitation efficiently remains unanswered. To address this issue, this study aimed to present a framework combining the machine learning downscaling algorithm and post-process procedures. Firstly, four ML-based models, namely support vector regression, random forest, spatial random forest (SRF) and eXtreme gradient boosting (XGBoost), were tested for downscaling and compared with conventional downscaling methods. Then, the effectiveness of the residual correction process using ordinary Kriging and the calibration process using the geographical difference analysis (GDA) method was investigated. The results showed that the ML-based methods had better performance than the conventional regression and interpolation approaches. The SRF and XGBoost outperformed others in generating accurate precipitation estimation with a high resolution. The GDA calibration process significantly improved the downscaled results. However, the residual correction process decreased the downscaling performance of the ML-based models. Combining the SRF or XGBoost downscaling algorithm with the GDA calibration method could be a promising downscaling scheme for precipitation data. The scheme could be used to generate high-resolution precipitation, especially in areas urgently requiring data, which would benefit regional water resource management and hydrological disaster prevention.
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Wang, Q., V. Rodriguez-Galiano e P. M. Atkinson. "GEOSTATISTICAL SOLUTIONS FOR DOWNSCALING REMOTELY SENSED LAND SURFACE TEMPERATURE". ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W7 (13 settembre 2017): 913–17. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w7-913-2017.
Testo completoAbstract (sommario):
Remotely sensed land surface temperature (LST) downscaling is an important issue in remote sensing. Geostatistical methods have shown their applicability in downscaling multi/hyperspectral images. In this paper, four geostatistical solutions, including regression kriging (RK), downscaling cokriging (DSCK), kriging with external drift (KED) and area-to-point regression kriging (ATPRK), are applied for downscaling remotely sensed LST. Their differences are analyzed theoretically and the performances are compared experimentally using a Landsat 7 ETM+ dataset. They are also compared to the classical TsHARP method.
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Kim, Seon-Ho, Jeong-Bae Kim e Deg-Hyo Bae. "Optimizing Parameters for the Downscaling of Daily Precipitation in Normal and Drought Periods in South Korea". Water 14, n. 7 (30 marzo 2022): 1108. http://dx.doi.org/10.3390/w14071108.
Testo completoAbstract (sommario):
One important factor that affects the performance of statistical downscaling methods is the selection of appropriate parameters. However, no research on the optimization of downscaling parameters has been conducted in South Korea to date, and existing parameter selection methods are dependent on studies conducted in other regions. Moreover, several large-scale predictors have been used to predict abnormal phenomena such as droughts, but in the field of downscaling, parameter optimization methods that are suitable for drought conditions have not yet been developed. In this study, by using the K-nearest analog methodology, suitable daily precipitation downscaling parameters for normal and drought periods were derived. The predictor variables, predictor domain, analog date size, time dependence parameters, and parameter sensitivity values that are representative of South Korea were presented quantitatively. The predictor variables, predictor domain, and analog date size were sensitive to the downscaling performance in that order, but the time dependency did not affect the downscaling process. Regarding calibration, the downscaling results obtained based on the drought parameters returned smaller root mean square errors of 1.3–28.4% at approximately 70% of the stations compared to those of the results derived based on normal parameters, confirming that drought parameter-based downscaling methods are reasonable. However, as a result of the validation process, the drought parameter stability was lower than the normal parameter stability. In the future, further studies are needed to improve the stability of drought parameters.
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Liu, Jia, Yongjian Sun, Kaijun Ren, Yanlai Zhao, Kefeng Deng e Lizhe Wang. "A Spatial Downscaling Approach for WindSat Satellite Sea Surface Wind Based on Generative Adversarial Networks and Dual Learning Scheme". Remote Sensing 14, n. 3 (7 febbraio 2022): 769. http://dx.doi.org/10.3390/rs14030769.
Testo completoAbstract (sommario):
Sea surface wind (SSW) is a crucial parameter for meteorological and oceanographic research, and accurate observation of SSW is valuable for a wide range of applications. However, most existing SSW data products are at a coarse spatial resolution, which is insufficient, especially for regional or local studies. Therefore, in this paper, to derive finer-resolution estimates of SSW, we present a novel statistical downscaling approach for satellite SSW based on generative adversarial networks and dual learning scheme, taking WindSat as a typical example. The dual learning scheme performs a primal task to reconstruct high resolution SSW, and a dual task to estimate the degradation kernels, which form a closed loop and are simultaneously learned, thus introducing an additional constraint to reduce the solution space. The integration of a dual learning scheme as the generator into the generative adversarial network structure further yield better downscaling performance by fine-tuning the generated SSW closer to high-resolution SSW. Besides, a model adaptation strategy was exploited to enhance the capacity for downscaling from low-resolution SSW without high-resolution ground truth. Comprehensive experiments were conducted on both the synthetic paired and unpaired SSW data. In the study areas of the East Coast of North America and the North Indian Ocean, in this work, the downscaling results to 0.25° (high resolution on the synthetic dataset), 0.03125° (8× downscaling), and 0.015625° (16× downscaling) of the proposed approach achieve the highest accuracy in terms of root mean square error and R-Square. The downscaling resolution can be enhanced by increasing the basic blocks in the generator. The highest downscaling reconstruction quality in terms of peak signal-to-noise ratio and structural similarity index was also achieved on the synthetic dataset with high-resolution ground truth. The experimental results demonstrate the effectiveness of the proposed downscaling network and the superior performance compared with the other typical advanced downscaling methods, including bicubic interpolation, DeepSD, dual regression networks, and adversarial DeepSD.
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Arthur, Frank, Didier M. Roche, Ralph Fyfe, Aurélien Quiquet e Hans Renssen. "Simulations of the Holocene climate in Europe using an interactive downscaling within the iLOVECLIM model (version 1.1)". Climate of the Past 19, n. 1 (10 gennaio 2023): 87–106. http://dx.doi.org/10.5194/cp-19-87-2023.
Testo completoAbstract (sommario):
Abstract. This study presents the application of an interactive downscaling in Europe using iLOVECLIM (a model of intermediate complexity), increasing its atmospheric resolution from 5.56 to 0.25∘ kilometric. A transient simulation using the appropriate climate forcings for the entire Holocene (11.5–0 ka BP) was done for both the standard version of the model and with an interactive downscaling applied. Our results show that simulations from downscaling present spatial variability that agrees better with proxy-based reconstructions and other climate models as compared to the standard model. The downscaling scheme simulates much higher (by at least a factor of 2) precipitation maxima and provides detailed information in mountainous regions. We focus on examples from the Scandes mountains, the Alps, the Scottish Highlands, and the Mediterranean. The higher spatial resolution of the downscaling provides a more realistic overview of the topography and gives local climate information, such as precipitation and temperature gradient, that is important for paleoclimate studies. With downscaling, we simulate similar trends and spatial patterns of the precipitation changes reconstructed by other proxy studies (for example in the Alps) as compared to the standard version. Our downscaling tool is numerically cheap, implying that our model can perform kilometric, multi-millennial simulations and is suitable for future studies.
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Kwak, Geun-Ho, Sungwook Hong e No-Wook Park. "Sensitivity Analysis of Regression-Based Trend Estimates to Input Errors in Spatial Downscaling of Coarse Resolution Remote Sensing Data". Applied Sciences 13, n. 18 (12 settembre 2023): 10233. http://dx.doi.org/10.3390/app131810233.
Testo completoAbstract (sommario):
This paper compared the predictive performance of different regression models for trend component estimation in the spatial downscaling of coarse resolution satellite data using area-to-point regression kriging in the context of the sensitivity to input data errors. Three regression models, linear regression, random forest, and support vector regression, were applied to trend component estimation. An experiment on downscaling synthetic Landsat data with different noise levels demonstrated that a regression model with higher explanatory power and residual correction led to the highest predictive performance only when the input coarse resolution data were assumed to be error-free. Through an experiment on spatial downscaling of coarse resolution monthly Advanced Microwave Scanning Radiometer-2 soil moisture products with significant errors, we found that the higher explanatory power of regression models did not always lead to better predictive performance. The residual correction and normalization of trend components also degraded the predictive performance. Using trend components as a final downscaling result showed the best performance in both experiments as the input errors increased. As the predictive performance of spatial downscaling results is susceptible to input errors, the findings of this study should be considered to evaluate downscaling results and develop advanced spatial downscaling methods.
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Zhang, Huihui, Hugo A. Loáiciga, Da Ha e Qingyun Du. "Spatial and Temporal Downscaling of TRMM Precipitation with Novel Algorithms". Journal of Hydrometeorology 21, n. 6 (giugno 2020): 1259–78. http://dx.doi.org/10.1175/jhm-d-19-0289.1.
Testo completoAbstract (sommario):
AbstractTropical Rainfall Measuring Mission (TRMM) satellite products constitute valuable precipitation datasets over regions with sparse rain gauge networks. Downscaling is an effective approach to estimating the precipitation over ungauged areas with high spatial resolution. However, a large bias and low resolution of original TRMM satellite images constitute constraints for practical hydrologic applications of TRMM precipitation products. This study contributes two precipitation downscaling algorithms by exploring the nonstationarity relations between precipitation and various environment factors [daytime surface temperature (LTD), terrain slope, normalized difference vegetation index (NDVI), altitude, longitude, and latitude] to overcome bias and low-resolution constraints of TRMM precipitation. Downscaling of precipitation is achieved with the geographically weighted regression model (GWR) and the backward-propagation artificial neural networks (BP_ANN). The probability density function (PDF) algorithm corrects the bias of satellite precipitation data with respect to spatial and temporal scales prior to downscaling. The principal component analysis algorithm (PCA) provides an alternative method of obtaining accurate monthly rainfall estimates during the wet rainfall season that minimizes the temporal uncertainties and upscaling effects introduced by direct accumulation (DA) of precipitation. The performances of the proposed downscaling algorithms are assessed by downscaling the latest version of TRMM3B42 V7 datasets within Hubei Province from 0.25° (about 25 km) to 1-km spatial resolution at the monthly scale. The downscaled datasets are systematically evaluated with in situ observations at 27 rain gauges from the years 2005 through 2010. This paper’s results demonstrate the bias correction is necessary before downscaling. The high-resolution precipitation datasets obtained with the proposed downscaling model with GWR relying on the NDVI and slope are shown to improve the accuracy of precipitation estimates. GWR exhibits more accurate downscaling results than BP_ANN coupled with the genetic algorithm (GA) in most dry and wet seasons.
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Nagasato, T., K. Ishida, K. Yokoo, M. Kiyama e M. Amagasaki. "Effects of the spatial and temporal resolution of meteorological data on the accuracy of precipitation estimation by means of CNN". IOP Conference Series: Earth and Environmental Science 851, n. 1 (1 ottobre 2021): 012033. http://dx.doi.org/10.1088/1755-1315/851/1/012033.
Testo completoAbstract (sommario):
Abstract Future climate projections are valuable datasets to investigate the impacts of future climate changes on natural disasters such as intense precipitation, severe flood, and drought. However, they are too coarse depending on the purpose, and downscaling is required in such a case. There is nowadays a downscaling technique using deep learning such as CNN. Atmospheric information can be used as an input for precipitation downscaling by means of Convolutional Neural Network (CNN). For such precipitation downscaling, the spatial and temporal resolution of the atmospheric information may be important. This study obtained atmospheric information from a coarser reanalysis dataset and a finer reanalysis dataset as input for precipitation downscaling. As a coarser reanalysis dataset, ERA-Interim was selected. As a finer reanalysis dataset, ERA5 was utilized. Then, this study investigated the effect of spatial and temporal resolution of input data on the estimation accuracy of precipitation downscaling by CNN. For simplification, daily average precipitation at a watershed was used as the target data. The results show advantage of the use of a higher resolution as input can improve the model accuracy.
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Kirchmeier-Young, Megan C., David J. Lorenz e Daniel J. Vimont. "Extreme Event Verification for Probabilistic Downscaling". Journal of Applied Meteorology and Climatology 55, n. 11 (novembre 2016): 2411–30. http://dx.doi.org/10.1175/jamc-d-16-0043.1.
Testo completoAbstract (sommario):
AbstractExtreme events are important to many studying regional climate impacts but provide a challenge for many “deterministic” downscaling methodologies. The University of Wisconsin Probabilistic Downscaling (UWPD) dataset applies a “probabilistic” approach to downscaling that may be advantageous in a number of situations, including realistic representation of extreme events. The probabilistic approach to downscaling, however, presents some unique challenges for verification, especially when comparing a full probability density function with a single observed value for each day. Furthermore, because of the wide range of specific climatic information needed in climate impacts assessment, any single verification metric will be useful to only a limited set of practitioners. The intent of this study, then, is (i) to identify verification metrics appropriate for probabilistic downscaling of climate data; (ii) to apply, within the UWPD, those metrics to a suite of extreme event statistics that may be of use in climate impacts assessments; and (iii) in applying these metrics, to demonstrate the utility of a probabilistic approach to downscaling climate data, especially for representing extreme events.
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Liang, Minggao, Laifu Zhang, Sensen Wu, Yilin Zhu, Zhen Dai, Yuanyuan Wang, Jin Qi, Yijun Chen e Zhenhong Du. "A High-Resolution Land Surface Temperature Downscaling Method Based on Geographically Weighted Neural Network Regression". Remote Sensing 15, n. 7 (23 marzo 2023): 1740. http://dx.doi.org/10.3390/rs15071740.
Testo completoAbstract (sommario):
Spatial downscaling is an important approach to obtain high-resolution land surface temperature (LST) for thermal environment research. However, existing downscaling methods are unable to sufficiently address both spatial heterogeneity and complex nonlinearity, especially in high-resolution scenes (<120 m), and accordingly limit the representation of regional details and accuracy of temperature inversion. In this study, by integrating normalized difference vegetation index (NDVI), normalized difference building index (NDBI), digital elevation model (DEM), and slope data, a high-resolution surface temperature downscaling method based on geographically neural network weighted regression (GNNWR) was developed to effectively handle the problem of surface temperature downscaling. The results show that the proposed GNNWR model achieved superior downscaling accuracy (maximum R2 of 0.974 and minimum RMSE of 0.896 °C) compared to widely used methods in four test areas with large differences in topography, landforms, and seasons. We also achieved the best extracted and most detailed spatial textures. Our findings suggest that GNNWR is a practical method for surface temperature downscaling considering its high accuracy and model performance.
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Ning, Liang, Michael E. Mann, Robert Crane, Thorsten Wagener, Raymond G. Najjar e Riddhi Singh. "Probabilistic Projections of Anthropogenic Climate Change Impacts on Precipitation for the Mid-Atlantic Region of the United States*". Journal of Climate 25, n. 15 (1 agosto 2012): 5273–91. http://dx.doi.org/10.1175/jcli-d-11-00565.1.
Testo completoAbstract (sommario):
Abstract This study uses an empirical downscaling method based on self-organizing maps (SOMs) to produce high-resolution, downscaled precipitation projections over the state of Pennsylvania in the mid-Atlantic region of the United States for the future period 2046–65. To examine the sensitivity of precipitation change to the water vapor increase brought by global warming, the authors test the following two approaches to downscaling: one uses the specific humidity in the downscaling algorithm and the other does not. Application of the downscaling procedure to the general circulation model (GCM) projections reveals changes in the relative occupancy, but not the fundamental nature, of the simulated synoptic circulation states. Both downscaling approaches predict increases in annual and winter precipitation, consistent in sign with the “raw” output from the GCMs but considerably smaller in magnitude. For summer precipitation, larger discrepancies are seen between raw and downscaled GCM projections, with a substantial dependence on the downscaling version used (downscaled precipitation changes employing specific humidity are smaller than those without it). Application of downscaling generally reduces the inter-GCM uncertainties, suggesting that some of the spread among models in the raw projected precipitation may result from differences in precipitation parameterization schemes rather than fundamentally different climate responses. Projected changes in the North Atlantic Oscillation (NAO) are found to be significantly related to changes in winter precipitation in the downscaled results, but not for the raw GCM results, suggesting that the downscaling more effectively captures the influence of climate dynamics on projected changes in winter precipitation.
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Hou, Yu-Kun, Hua Chen, Chong-Yu Xu, Jie Chen e Sheng-Lian Guo. "Coupling a Markov Chain and Support Vector Machine for At-Site Downscaling of Daily Precipitation". Journal of Hydrometeorology 18, n. 9 (22 agosto 2017): 2385–406. http://dx.doi.org/10.1175/jhm-d-16-0130.1.
Testo completoAbstract (sommario):
Abstract Statistical downscaling is useful for managing scale and resolution problems in outputs from global climate models (GCMs) for climate change impact studies. To improve downscaling of precipitation occurrence, this study proposes a revised regression-based statistical downscaling method that couples a support vector classifier (SVC) and first-order two-state Markov chain to generate the occurrence and a support vector regression (SVR) to simulate the amount. The proposed method is compared to the Statistical Downscaling Model (SDSM) for reproducing the temporal and quantitative distribution of observed precipitation using 10 meteorological indicators. Two types of calibration and validation methods were compared. The first method used sequential split sampling of calibration and validation periods, while the second used odd years for calibration and even years for validation. The proposed coupled approach outperformed the other methods in downscaling daily precipitation in all study periods using both calibration methods. Using odd years for calibration and even years for validation can reduce the influence of possible climate change–induced nonstationary data series. The study shows that it is necessary to combine different types of precipitation state classifiers with a method of regression or distribution to improve the performance of traditional statistical downscaling. These methods were applied to simulate future precipitation change from 2031 to 2100 with the CMIP5 climate variables. The results indicated increasing tendencies in both mean and maximum future precipitation predicted using all the downscaling methods evaluated. However, the proposed method is an at-site statistical downscaling method, and therefore this method will need to be modified for extension into a multisite domain.
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Li, Xiaoyuan, Xiufeng He e Xin Pan. "Application of Gaofen-6 Images in the Downscaling of Land Surface Temperatures". Remote Sensing 14, n. 10 (10 maggio 2022): 2307. http://dx.doi.org/10.3390/rs14102307.
Testo completoAbstract (sommario):
The coarse resolution of land surface temperatures (LSTs) retrieved from thermal-infrared (TIR) satellite images restricts their usage. One way to improve the resolution of such LSTs is downscaling using high-resolution remote sensing images. Herein, Gaofen-6 (GF-6) and Landsat-8 images were used to obtain original and retrieved LSTs (Landsat-8- and GF-6-retrieved-LSTs) to perform LST downscaling in the Ebinur Lake Watershed. Downscaling model was constructed, and the regression kernel was explored. The results of downscaling LST using the GF-6 normalized difference vegetation index with red-edge band 2, ratio built-up index, normalized difference sand index, and normalized difference water index as multi-remote sensing indices with multiple remote sensing indices with random forest regression method provided optimal downscaling results, with R2 of 0.836, 0.918, and 0.941, root mean square difference of 1.04 K, 2.06 K, and 1.80 K, and the number of pixels with LST errors between −1 K and +1 K of 87.2%, 76.4%, and 81.9%, respectively. The expression of spatial distribution of 16 m-LST downscaling results corresponded with that of Landsat-8- and GF-6-retrieved-LST, and provided additional details spatial description of LST variations, which was absent in the Landsat-8- and GF-6-retrieved LSTs. The results of downscaling LST could satisfy the application requirements of LST spatial resolution.
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Zhang, Hanbin, Min Chen e Shuiyong Fan. "Study on the Construction of Initial Condition Perturbations for the Regional Ensemble Prediction System of North China". Atmosphere 10, n. 2 (19 febbraio 2019): 87. http://dx.doi.org/10.3390/atmos10020087.
Testo completoAbstract (sommario):
The regional ensemble prediction system (REPS) of North China is currently under development at the Institute of Urban Meteorology, China Meteorological Administration, with initial condition perturbations provided by global ensemble dynamical downscaling. To improve the performance of the REPS, a comparison of two initial condition perturbation methods is conducted in this paper: (i) Breeding, which was specifically designed for the REPS, and (ii) Dynamical downscaling. Consecutive tests were implemented to evaluate the performances of both methods in the operational REPS environment. The perturbation characteristics were analyzed, and ensemble forecast verifications were conducted. Furthermore, a heavy precipitation case was investigated. The main conclusions are as follows: the Breeding perturbations were more powerful at small scales, while the downscaling perturbations were more powerful at large scales; the difference between the two perturbation types gradually decreased with the forecast lead time. The downscaling perturbation growth was more remarkable than that of the Breeding perturbations at short forecast lead times, while the perturbation magnitudes of both schemes were similar for long-range forecasts. However, the Breeding perturbations contained more abundant small-scale components than downscaling for the short-range forecasts. The ensemble forecast verification indicated a slightly better downscaling ensemble performance than that of the Breeding ensemble. A precipitation case study indicated that the Breeding ensemble performance was better than that of downscaling, particularly in terms of location and strength of the precipitation forecast.
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Pascal, Claire, Sylvain Ferrant, Adrien Selles, Jean-Christophe Maréchal, Abhilash Paswan e Olivier Merlin. "Evaluating downscaling methods of GRACE (Gravity Recovery and Climate Experiment) data: a case study over a fractured crystalline aquifer in southern India". Hydrology and Earth System Sciences 26, n. 15 (10 agosto 2022): 4169–86. http://dx.doi.org/10.5194/hess-26-4169-2022.
Testo completoAbstract (sommario):
Abstract. GRACE (Gravity Recovery and Climate Experiment) and its follow-on mission have provided since 2002 monthly anomalies of total water storage (TWS), which are very relevant to assess the evolution of groundwater storage (GWS) at global and regional scales. However, the use of GRACE data for groundwater irrigation management is limited by their coarse (≃300 km) resolution. The last decade has thus seen numerous attempts to downscale GRACE data at higher – typically several tens of kilometres – resolution and to compare the downscaled GWS data with in situ measurements. Such comparison has been classically made in time, offering an estimate of the static performance of downscaling (classic validation). The point is that the performance of GWS downscaling methods may vary in time due to changes in the dominant hydrological processes through the seasons. To fill the gap, this study investigates the dynamic performance of GWS downscaling by developing a new metric for estimating the downscaling gain (new validation) against non-downscaled GWS. The new validation approach is tested over a 113 000 km2 fractured granitic aquifer in southern India. GRACE TWS data are downscaled at 0.5∘ (≃50 km) resolution with a data-driven method based on random forest. The downscaling performance is evaluated by comparing the downscaled versus in situ GWS data over a total of 38 pixels at 0.5∘ resolution. The spatial mean of the temporal Pearson correlation coefficient (R) and the root mean square error (RMSE) are 0.79 and 7.9 cm respectively (classic validation). Confronting the downscaled results with the non-downscaling case indicates that the downscaling method allows a general improvement in terms of temporal agreement with in situ measurements (R=0.76 and RMSE = 8.2 cm for the non-downscaling case). However, the downscaling gain (new validation) is not static. The mean downscaling gain in R is about +30 % or larger from August to March, including both the wet and dry (irrigated) agricultural seasons, and falls to about +10 % from April to July during a transition period including the driest months (April–May) and the beginning of monsoon (June–July). The new validation approach hence offers for the first time a standardized and comprehensive framework to interpret spatially and temporally the quality and uncertainty of the downscaled GRACE-derived GWS products, supporting future efforts in GRACE downscaling methods in various hydrological contexts.
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Taye, M. T., e P. Willems. "Influence of downscaling methods in projecting climate change impact on hydrological extremes of upper Blue Nile basin". Hydrology and Earth System Sciences Discussions 10, n. 6 (20 giugno 2013): 7857–96. http://dx.doi.org/10.5194/hessd-10-7857-2013.
Testo completoAbstract (sommario):
Abstract. Methods from two statistical downscaling categories were used to investigate the impact of climate change on high rainfall and flow extremes of the upper Blue Nile basin. The main downscaling differences considered were on the rainfall variable while a generally similar method was applied for temperature. The applied downscaling methods are a stochastic weather generator, LARS-WG, and an advanced change factor method, the Quantile Perturbation Method (QPM). These were applied on 10 GCM runs and two emission scenarios (A1B and B1). The downscaled rainfall and evapotranspiration were input into a calibrated and validated lumped conceptual model. The future simulations were conducted for 2050s and 2090s horizon and were compared with 1980–2000 control period. From the results all downscaling methods agree in projecting increase in temperature for both periods. Nevertheless, the change signal on the rainfall was dependent on the climate model and the downscaling method applied. LARS weather generator was good for monthly statistics although caution has to be taken when it is applied for impact analysis dealing with extremes, as it showed a deviation from the extreme value distribution's tail shape. Contrary, the QPM method was good for extreme cases but only for good quality daily climate model data. The study showed the choice of downscaling method is an important factor to be considered and results based on one downscaling method may not give the full picture. Regardless, the projections on the extreme high flows and the mean main rainy season flow mostly showed a decreasing change signal for both periods. This is either by decreasing rainfall or increasing evapotranspiration depending on the downscaling method.
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Van Uytven, Els, Jan De Niel e Patrick Willems. "Uncovering the shortcomings of a weather typing method". Hydrology and Earth System Sciences 24, n. 5 (26 maggio 2020): 2671–86. http://dx.doi.org/10.5194/hess-24-2671-2020.
Testo completoAbstract (sommario):
Abstract. In recent years many methods for statistical downscaling of the precipitation climate model outputs have been developed. Statistical downscaling is performed under general and method-specific (structural) assumptions but those are rarely evaluated simultaneously. This paper illustrates the verification and evaluation of the downscaling assumptions for a weather typing method. Using the observations and outputs of a global climate model ensemble, the skill of the method is evaluated for precipitation downscaling in central Belgium during the winter season (December to February). Shortcomings of the studied method have been uncovered and are identified as biases and a time-variant predictor–predictand relationship. The predictor–predictand relationship is found to be informative for historical observations but becomes inaccurate for the projected climate model output. The latter inaccuracy is explained by the increased importance of the thermodynamic processes in the precipitation changes. The results therefore question the applicability of the weather typing method for the case study location. Besides the shortcomings, the results also demonstrate the added value of the Clausius–Clapeyron relationship for precipitation amount scaling. The verification and evaluation of the downscaling assumptions are a tool to design a statistical downscaling ensemble tailored to end-user needs.
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Mehrotra, R., J. P. Evans, A. Sharma e B. Sivakumar. "Evaluation of downscaled daily rainfall hindcasts over Sydney, Australia using statistical and dynamical downscaling approaches". Hydrology Research 45, n. 2 (3 luglio 2013): 226–49. http://dx.doi.org/10.2166/nh.2013.094.
Testo completoAbstract (sommario):
The assessment of local and regional impacts of climate change often requires downscaling of general circulation model (GCM) projections from coarser GCM-scale to finer local- or catchment-scale spatial resolution. This paper provides an assessment of two downscaling approaches for simulation of daily rainfall over Sydney, Australia. The two downscaling alternatives compared include a multivariate multisite statistical downscaling model based on semi-parametric conditional simulation and a dynamical downscaling approach that uses the National Center for Atmospheric Research (NCAR) weather research and forecasting (WRF) model. The two approaches are evaluated for their ability to reproduce important at-site rainfall statistics at a network of 45 raingauge stations and regional statistics over the catchment area of the Warragamba Dam (9,050 km2). The results indicate that the simulations from these approaches capture many regionally observed climate features, including the simulated seasonal and annual means and daily extreme rainfall values. Further analyses suggest that the statistical downscaling approach provides improved simulations of attributes related to point rainfall, spell lengths and amounts, whereas the dynamical approach is well-suited for applications where regionally averaged rainfall is of primary concern.
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Sun, Fengyun, Alfonso Mejia, Sanjib Sharma, Peng Zeng e Yue Che. "Evaluating the Credibility of Downscaling: Integrating Scale, Trend, Extreme, and Climate Event into a Diagnostic Framework". Journal of Applied Meteorology and Climatology 59, n. 9 (1 settembre 2020): 1453–67. http://dx.doi.org/10.1175/jamc-d-20-0078.1.
Testo completoAbstract (sommario):
AbstractBecause downscaling tools are needed to support climate change mitigation and adaptation practices, the guarantee of their credibility is of vital importance. To evaluate downscaling results, one needs to select a set of effective and nonoverlapping indices that reflect key system attributes. However, this subject is still insufficiently researched. With this study, we propose a diagnostic framework that evaluates the credibility of precipitation downscaling using five different attributes: spatial, temporal, trend, extreme, and climate event. A daily variant of the bias-corrected spatial downscaling approach is used to downscale daily precipitation from the GFDL-ESM2G climate model at 148 stations in the Yangtze River basin in China. Results prove that this framework is effective in systematically evaluating the performance of downscaling across the Yangtze River basin in the context of climate change and exacerbating climate extremes. Moreover, results also indicate that the downscaling approach adopted in this study yields good performance in correcting spatiotemporal bias, preserving trends, approximating extremes, and characterizing climate events across the Yangtze River basin. The proposed framework can be beneficial to planners and engineers facing issues relevant to climate change assessment.
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Chen, Shaodan, Dunxian She, Liping Zhang, Mengyao Guo e Xin Liu. "Spatial Downscaling Methods of Soil Moisture Based on Multisource Remote Sensing Data and Its Application". Water 11, n. 7 (8 luglio 2019): 1401. http://dx.doi.org/10.3390/w11071401.
Testo completoAbstract (sommario):
Soil moisture is an important indicator that is widely used in meteorology, hydrology, and agriculture. Two key problems must be addressed in the process of downscaling soil moisture: the selection of the downscaling method and the determination of the environmental variables, namely, the influencing factors of soil moisture. This study attempted to utilize machine learning and data mining algorithms to downscale the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) soil moisture data from 25 km to 1 km and compared the advantages and disadvantages of the random forest model and the Cubist algorithm to determine the more suitable soil moisture downscaling method for the middle and lower reaches of the Yangtze River Basin (MLRYRB). At present, either the normalized difference vegetation index (NDVI) or a digital elevation model (DEM) is selected as the environmental variable for the downscaling models. In contrast, variables, such as albedo and evapotranspiration, are infrequently applied; nevertheless, this study selected these two environmental variables, which have a considerable impact on soil moisture. Thus, the selected environmental variables in the downscaling process included the longitude, latitude, elevation, slope, NDVI, daytime and nighttime land surface temperature (LST_D and LST_N, respectively), albedo, evapotranspiration (ET), land cover (LC) type, and aspect. This study achieved downscaling on a 16-day timescale based on Moderate Resolution Imaging Spectroradiometer (MODIS) data. A comparison of the random forest model with the Cubist algorithm revealed that the R2 of the random forest-based downscaling method is higher than that of the Cubist algorithm-based method by 0.0161; moreover, the root-mean-square error (RMSE) is reduced by 0.0006 and the mean absolute error (MAE) is reduced by 0.0014. Testing the accuracies of these two downscaling methods showed that the random forest model is more suitable than the Cubist algorithm for downscaling AMSR-E soil moisture data from 25 km to 1 km in the MLRYRB, which provides a theoretical basis for obtaining high spatial resolution soil moisture data.
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Chen, Jie, Xunchang John Zhang e Xiangquan Li. "A Weather Generator-Based Statistical Downscaling Tool for Site-Specific Assessment of Climate Change Impacts". Transactions of the ASABE 61, n. 3 (2018): 977–93. http://dx.doi.org/10.13031/trans.12601.
Testo completoAbstract (sommario):
Abstract. Statistical downscaling approaches are usually used to bridge the gap between climate model outputs and data requirements of impact models such as crop and soil erosion models. This study synthesizes, integrates, and standardizes a statistical downscaling method that was initially developed in 2005 and subsequently evaluated and improved during the last decade. A new downscaling software program, Generator for Point Climate Change (GPCC), has been developed to automate and visualize the method to assist end users with detailed technical and user documentation. GPCC readily generates daily time series of climate change scenarios for local and site-specific climate change impact studies using monthly projections from global climate models or regional climate models. The downscaled variables include precipitation and maximum and minimum temperatures. This software provides a simple but effective climate downscaling tool for assessing the impacts of climate change on crop production, soil hydrology, and soil erosion at a field scale. The tool can also provide an alternative downscaling method to facilitate the international collaborative efforts of the Agricultural Model Intercomparison and Improvement Project (AgMIP) for simulation of world food production and food security assessment. The detailed downscaling methods, their scientific bases, and the advantages of GPCC over other commonly used downscaling methods are presented. GPCC is written in the Matlab language, and a standalone version can be run on Windows XP or above without Matlab software. The tool has a graphical user interface that is simple and easy to generate downscaled climates as well as to visualize downscaled outputs. Each interface tab and key button and their functions are described to facilitate its widespread application. Keywords: Agricultural system models, Climate change impacts, Generator for point climate change, Interface, Statistical downscaling, Stochastic weather generator.
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Gutowski Jr., William J., Filippo Giorgi, Bertrand Timbal, Anne Frigon, Daniela Jacob, Hyun-Suk Kang, Krishnan Raghavan et al. "WCRP COordinated Regional Downscaling EXperiment (CORDEX): a diagnostic MIP for CMIP6". Geoscientific Model Development 9, n. 11 (17 novembre 2016): 4087–95. http://dx.doi.org/10.5194/gmd-9-4087-2016.
Testo completoAbstract (sommario):
Abstract. The COordinated Regional Downscaling EXperiment (CORDEX) is a diagnostic model intercomparison project (MIP) in CMIP6. CORDEX builds on a foundation of previous downscaling intercomparison projects to provide a common framework for downscaling activities around the world. The CORDEX Regional Challenges provide a focus for downscaling research and a basis for making use of CMIP6 global climate model (GCM) output to produce downscaled projected changes in regional climates and assess sources of uncertainties in the projections, all of which can potentially be distilled into climate change information for vulnerability, impacts and adaptation studies. CORDEX Flagship Pilot Studies advance regional downscaling by targeting one or more of the CORDEX Regional Challenges. A CORDEX-CORE framework is planned that will produce a baseline set of homogeneous high-resolution, downscaled projections for regions worldwide. In CMIP6, CORDEX coordinates with ScenarioMIP and is structured to allow cross comparisons with HighResMIP and interaction with the CMIP6 VIACS Advisory Board.
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Wilby, R. L., e T. M. L. Wigley. "Downscaling general circulation model output: a review of methods and limitations". Progress in Physical Geography: Earth and Environment 21, n. 4 (dicembre 1997): 530–48. http://dx.doi.org/10.1177/030913339702100403.
Testo completoAbstract (sommario):
General circulation models (GCMs) suggest that rising concentrations of greenhouse gases may have significant consequences for the global climate. What is less clear is the extent to which local (subgrid) scale meteorological processes will be affected. So-called 'downscaling' techniques have subsequently emerged as a means of bridging the gap between what climate modellers are currently able to provide and what impact assessors require. This article reviews the present generation of downscaling tools under four main headings: regression methods; weather pattern (circulation)-based approaches; stochastic weather generators; and limited-area climate models. The penultimate section summarizes the results of an international experiment to intercompare several precipitation models used for downscaling. It shows that circulation-based downscaling methods perform well in simulating present observed and model-generated daily precipitation characteristics, but are able to capture only part of the daily precipitation variability changes associated with model-derived changes in climate. The final section examines a number of ongoing challenges to the future development of climate downscaling.
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Schoof, Justin T. "Statistical Downscaling in Climatology". Geography Compass 7, n. 4 (aprile 2013): 249–65. http://dx.doi.org/10.1111/gec3.12036.
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Huang, Wen, e Xiuling Li. "Downscaling inductors with graphene". Nature Electronics 1, n. 1 (gennaio 2018): 6–7. http://dx.doi.org/10.1038/s41928-017-0015-7.
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Kopf, Johannes, Ariel Shamir e Pieter Peers. "Content-adaptive image downscaling". ACM Transactions on Graphics 32, n. 6 (novembre 2013): 1–8. http://dx.doi.org/10.1145/2508363.2508370.
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Atkinson, Peter M. "Downscaling in remote sensing". International Journal of Applied Earth Observation and Geoinformation 22 (giugno 2013): 106–14. http://dx.doi.org/10.1016/j.jag.2012.04.012.
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Zhang, Feng, e Aris P. Georgakakos. "Joint variable spatial downscaling". Climatic Change 111, n. 3-4 (29 luglio 2011): 945–72. http://dx.doi.org/10.1007/s10584-011-0167-9.
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Hewitson, B. C., J. Daron, R. G. Crane, M. F. Zermoglio e C. Jack. "Interrogating empirical-statistical downscaling". Climatic Change 122, n. 4 (17 dicembre 2013): 539–54. http://dx.doi.org/10.1007/s10584-013-1021-z.
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Ebrahim, Girma Yimer, Andreja Jonoski, Ann van Griensven e Giuliano Di Baldassarre. "Downscaling technique uncertainty in assessing hydrological impact of climate change in the Upper Beles River Basin, Ethiopia". Hydrology Research 44, n. 2 (30 luglio 2012): 377–98. http://dx.doi.org/10.2166/nh.2012.037.
Testo completoAbstract (sommario):
We investigate the uncertainty associated with downscaling techniques in climate impact studies, using the Upper Beles River Basin (Upper Blue Nile) in Ethiopia as an example. The main aim of the study is to estimate the two sources of uncertainty in downscaling models: (1) epistemic uncertainty and (2) stochastic uncertainty due to inherent variability. The first aim was achieved by driving a Hydrologic Engineering Centre-Hydrological Modelling System (HEC-HMS) model with downscaled daily precipitation and temperature using three downscaling models: Statistical Downscaling Model (SDSM), the Long Ashton Research Station Weather Generator (LARS-WG) and an artificial neural network (ANN). The second objective was achieved by driving the hydrological model with individual downscaled daily precipitation and temperature ensemble members, generated by using the stochastic component of the SDSM. Results of the study showed that the downscaled precipitation and temperature time series are sensitive to the downscaling techniques. More specifically, the percentage change in mean annual flow ranges from 5% reduction to 18% increase. By analyzing the uncertainty of the SDSM model ensembles, it was found that the percentage change in mean annual flow ranges from 6% increase to 8% decrease. This study demonstrates the need for extreme caution in interpreting and using the output of a single downscaling model.
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Schmidli, Jürg, Christoph Frei e Pier Luigi Vidale. "Downscaling from GCM precipitation: a benchmark for dynamical and statistical downscaling methods". International Journal of Climatology 26, n. 5 (2006): 679–89. http://dx.doi.org/10.1002/joc.1287.
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Stoll, S., H. J. Hendricks Franssen, M. Butts e W. Kinzelbach. "Analysis of the impact of climate change on groundwater related hydrological fluxes: a multi-model approach including different downscaling methods". Hydrology and Earth System Sciences 15, n. 1 (3 gennaio 2011): 21–38. http://dx.doi.org/10.5194/hess-15-21-2011.
Testo completoAbstract (sommario):
Abstract. Climate change related modifications in the spatio-temporal distribution of precipitation and evapotranspiration will have an impact on groundwater resources. This study presents a modelling approach exploiting the advantages of integrated hydrological modelling and a broad climate model basis. We applied the integrated MIKE SHE model on a perialpine, small catchment in northern Switzerland near Zurich. To examine the impact of climate change we forced the hydrological model with data from eight GCM-RCM combinations showing systematic biases which are corrected by three different statistical downscaling methods, not only for precipitation but also for the variables that govern potential evapotranspiration. The downscaling methods are evaluated in a split sample test and the sensitivity of the downscaling procedure on the hydrological fluxes is analyzed. The RCMs resulted in very different projections of potential evapotranspiration and, especially, precipitation. All three downscaling methods reduced the differences between the predictions of the RCMs and all corrected predictions showed no future groundwater stress which can be related to an expected increase in precipitation during winter. It turned out that especially the timing of the precipitation and thus recharge is very important for the future development of the groundwater levels. However, the simulation experiments revealed the weaknesses of the downscaling methods which directly influence the predicted hydrological fluxes, and thus also the predicted groundwater levels. The downscaling process is identified as an important source of uncertainty in hydrological impact studies, which has to be accounted for. Therefore it is strongly recommended to test different downscaling methods by using verification data before applying them to climate model data.