A new statistical precipitation downscaling method with. Statistical and dynamical mesoscale climate downscaling. Jul 28, 2014 a talk entitled downscaling and uncertainty by hayley flowler, newcastle university and linda mearns, ncar, at the 2014 asp summer colloquium. Statistical downscaling of general circulation model. This method provides a physically based link between global and local scales that. In other words, we add value to global scale data by placing it in the context of observed local climatological conditions, improving the spatial and temporal resolutions along the way. Here youll find current best sellers in books, new releases in books, deals in books, kindle ebooks, audible audiobooks, and so much more. First, the deltachange method was applied to the mean annual precipitation as well as the 5, 10, 20, 50 and 100year return period daily precipitation events. The term downscaling usually refers to an increase in spatial resolution, but it is often also used for temporal resolution. What is the best image downscaling algorithm qualitywise. Title page abstract introduction conclusions references tables figures back close. The references focus on european research, which isnt a drawback as such, but it does mean that some important research from north america and. The gcm configuration and its mean regional climatology are described in section 2. During the last two decades, extensive research has been conducted on downscaling methods and their applications.
In this thesis an original physical scaling sp method for downscaling global circulation model gcm based climatic projections has been developed, tested and applied over a study region. The fundamental basis of spatial downscaling is the assumption that significant relationships exist between local and largescale climate. May 26, 2016 one of the most intensely debated aspects in relation to downscaling of operations is the interrelationship between the provisions of section 52 of the mineral and petroleum resources development act 28 of 2002 mprda and the provisions of section 189189a of the labour relations act 66 of 1995 lra, particularly where the holder of the. Dynamical downscaling, in particular, is an expensive technique, and most studies that perform it have only applied it to a few global models. Downscaling is any procedure to infer highresolution information from lowresolution variables. The third method, biascorrected spatial disaggregation bcsd, relates quantiles of the gcm predictions to historical patterns to produce daily time series to construct the downscaled grid. Statistical downscaling using analogs and classification. The downscaling method here we apply a downscaling method based on thin plate spline spatial interpolation of anomalies deltas of original gcm outputs. While downscaling of a large ensemble is desirable to compute most likely outcomes and fully characterize uncertainty, this can be impractical because of the high computational cost. Alternatively, you might use a statistical downscaling method by establishing relations between large scale predictors from gcms and local scale predictands from insitu observations.
Gmdd 6, 25172549, 20 downscaling a global climate model m. The main objective is to improve downscaling rainfall prediction by combining predictions from different statistical downscaling models. Downscaling is achieved by allowing the markovian transition probabilities between the states to vary nonhomogeneously over time according a set of predictors. Alternatively, you might use a statistical downscaling method by establishing relations between large scale predictors from gcms and local scale predictands from in. Downscaling a global climate model to simulate climate change impacts on us regional and urban air quality m. Teutschbein et al evaluation of different downscaling. Upscaling and downscaling methods for environmental research. The portal can support research in a variety of fields related to. Physical scaling method of downscaling by abhishek gaur. I would like to perform a hydrological analysis using rcm cordex data that need to be downscaled and bias corrected prior to application. Their analyses were based on changes in surface air. Comparison of downscaling methods for mean and extreme. Generally, there are two steps involved in statistical downscaling.
For these examples, all four methods of downscaling will be used for the city of ho chi minh, vietnam for the summer season. Statistical downscaling methods such as multiple linear regression, nonlinear regression e. Two different statistical downscaling techniques were applied to the outputs of four regional climate models at six selected precipitation stations in senegal. Although a large published literature exists on the. The goal of downscaling is to create locally accurate climate information starting from global scale data. This technique is based on dynamical or statistical approaches commonly used in several disciplines, especially meteorology, climatology and remote sensing. Estimates of future greenhouse gas emissions released into the atmosphere.
Statistical downscaling using quantile mapping in this method, biases are calculated for each percentile in the cumulative distribution function from present simulation blue. Statistical downscaling is the most widely used method in downscaling climate variables from gcms. Climate model downscaling geophysical fluid dynamics. This package has been conceived to work in the framework of. An r package for bias correction and statistical downscaling. The exact downscaling locations used for our study in sync with kannan and ghosh 20s work are depicted in table 2. Statistical downscaling of general circulation model output.
In addition, different downscaling techniques exist, ranging from statistical methods deltachange, regressionbased, weather typing, neural networks, etc. A regional climate model rcm solving the same equation set as a gcm is driven at the lateral and surface boundaries by temporally evolving fields simulated by the gcm. Support vector regression, bayesian neural networks, probabilistic graphical models, cart to downscale coarse resolution datasets to local scales. Derivation of local to regionalscale 10100 kilometers information from larger scale modeled or observed data. Statistical downscaling of temperature with the random. Please note the image in this listing is a stock photo and may not match the covers of the actual item. While it is possible to downscale all available gcm datasets, snap provides climate data for the top 5 models for the alaska and arctic regions from the coupled model intercomparison project cmip for. Climate model downscaling contacts, for more information. Statistical downscaling of precipitation using long short. A thorough verification of the method and its extension. Statistical and dynamical mesoscale climate downscaling background climate downscaling is a practice whereby coarseresolution climate data which comes from global atmospheric reanalyses e. Synopsis of statistical downscaling methodologies for user applications. Nonetheless humidity is not available in some widelyused archives of statistically downscaled climate projections for the western u. Correia2, 3 1 pacific northwest national laboratory 2 noaastorm prediction center 3 cooperative institute for mesoscale meteorological studies, university of oklahoma norman, ok 37th noaa climate diagnostics and prediction.
In that particular case area method generally blurs the image, but nearest neighbor can preform surprisingly well. Sep 30, 2015 humidity is important to climate impacts in hydrology, agriculture, ecology, energy demand, and human health and comfort. As the focus is on downscaling, section iv also presents the types of institutions entering microfinance and their downscaling strategies. A new statistical downscaling method was developed and applied to downscale monthly total precipitation from 583 stations in china. The method used for downscaling is linear regression between the 7 coarsescale to finescale but when the climate largescale and local parameters follow. Statistical downscaling can be applied to rcms in an attempt to correct their biases.
Uncertainty analysis of statistical downscaling methods. Statistical downscaling of temperature with the random forest. Anomalies are interpolated between gcm cell centroids and are then applied to a baseline climate given by a high resolution surface worldclim. For the statistical downscaling method, observed relationships between local synoptic situations and the largescale climate are used to build the statistical model. Hydrological validation of statistical downscaling methods. The models used to downscale rainfall in the studied site are the multiple linear regression mlr, the generalized linear model glm, the sdsm wilby et al. Statistical delta downscaling adds the difference delta between a historical period and a modeled value to a known historical climatology. With usual stamps and markings, in good all round condition.
Humidity is important to climate impacts in hydrology, agriculture, ecology, energy demand, and human health and comfort. The model formulation can take into account regional physical characteristics like landcover and elevation into the model formulation. A hybrid dynamicalstatistical downscaling technique. Downscaling definition of downscaling by the free dictionary. This method is a simple downscaling scheme widely used in the field of. Then the calculated biases are added to the future simulation to correct the biases of each percentile. Correia2, 3 1 pacific northwest national laboratory 2 noaastorm prediction center 3 cooperative institute for mesoscale meteorological studies, university of oklahoma norman, ok. Each downscaling method has its advantages and drawbacks, which have been described in great detail in the literature. Evaluation of different downscaling techniques for. Climate model downscaling geophysical fluid dynamics laboratory. This paper evaluates the improvement in statistical downscaling sd predictive power when using predictors from a regional climate model rcm over a. Some type of downscaling is usually required to transform global climate model simulation data into something that can be applied to impacts assessments empiricalstatistical downscaling. The rf is proposed for downscaling daily mean temperature in the pearl river basin. Guidelines for use of climate scenarios developed from.
Downscaling humidity with localized constructed analogs loca. The delta method decision and policy analysis working paper no. The rcm tries to represent the transfer function f through the same 1st principle solution methods as the gcm using higher resolution over a limited geographical region. This paper compares different statistical methods for downscaling daily precipitation from numerical weather prediction model output. Keith dixon lucas harris tom knutson related areas of research. Analog methods can be classified in the second category. Sep 26, 2008 this is a good monograph on statistical downscaling. This is an exlibrary book and may have the usual libraryusedbook markings inside. Statistical downscaling of precipitation using long shortterm memory recurrent neural networks fig.
The guidelines were prepared for consideration by the. Mar 03, 2011 synopsis of statistical downscaling methodologies for user applications. Statistical downscaling is a twostep process consisting of i the development of statistical relationships between local climate variables e. One of the most intensely debated aspects in relation to downscaling of operations is the interrelationship between the provisions of section 52 of the mineral and petroleum resources development act 28 of 2002 mprda and the provisions of section 189189a of the labour relations act 66 of 1995 lra, particularly where the holder of the. The method used for downscaling is linear regression between the 7 coarsescale to finescale but when the climate largescale and local parameters follow different 8 probability laws nonlinear. The california basin characterization model uses several stages of downscaling to produce climate projection maps at a 270meter resolution. In particular, im interested in the study of the often overlooked stationarity assumption, common to all statistical downscaling methods, and its impact on future local projections. Section v synthesizes the thoughts and ideas of those who have seen the downscaling trend from a crosscountry and latin american perspective. There is still no universal single method, or group of methods, that is clearly superior, particularly for downscaling daily precipitation. Beside five years hindcast control run using ncepncar reanalysis data and five years projection during 2070s by pgwm, directly nesting runs driven by daily gcm products are also carried out for the. Comparison of statistical methods for downscaling daily. Meteorological office, hadley centres coupled oceanatmosphere model hadcm2 forced by combined co 2 and sulfate aerosol changes. The random forest rf method is an enhanced classification and regression tree cart method proposed by breiman in 2001, which consists of an ensemble of unpruned decision trees generated through bootstrap samples of the training data and random variable subset selection. In this work the localized constructed analogs loca statistical downscaling method is used to downscale specific humidity to a 116.
Improving accuracy of downscaling rainfall by combining. Downscaling humidity with localized constructed analogs. In this work the localized constructed analogs loca statistical downscaling method is used to downscale. This usually leads to a very close representation of the observed climate in the statistical downscaling model, almost independent of the choice of host global climate model.
Downscaling institutions and competitive microfinance. Statistical downscaling regional climate model evaluation. This book provides a classification of upscaling and downscaling methods that have been developed within the environmental sciences. It now contains about 14 terabytes of easy to use, open access datasets of climate projections that will help researchers make climate change impact assessments. Numerous techniques and methods have been proposed and used which can be broadly divided into statistical and dynamical methods. Meteorological research institute, japan meteorological agency. May 2010 downscaling global circulation model outputs. The one situation where the result is not satisfying is when downscaling an image by a small factor. Jan 31, 2015 a new statistical downscaling method was developed and applied to downscale monthly total precipitation from 583 stations in china. Having investigated best looking downscaling methods i also found area method to produce the best results. Coupling statistical and dynamical methods for spatial. My research involves the use of machinelearning methods e. Questions about empirical downscaling bruce hewitson1, rob wilby2, rob crane3 1 university of cape town, south africa. Which tools to use for downscaling rcm cordex and bias.
Refers to the methods used to derive climate information at finer spatial resolution from coarser spatial resolution gcm output. Its origin as lecture notes to a shortcourse on the subject is fairly evident, as it doesnt quite come together like a really stellar textbook. Each method is used to downscale from an ensemble of gcm simulations made with the echam 4. In particular, im interested in the study of the often overlooked stationarity assumption, common to all statistical downscaling methods, and its impact on. A range of different statistical downscaling models was calibrated using both observed and general circulation model gcm generated daily precipitation time series and intercompared. Climate impacts weather and climate extremes global climate models gcms continue to yield important scientific insights into the dynamics and evolution of the climate system o. The issues with downscaling the outputs of a global climate model gcm to a regional scale that are appropriate to hydrological impact studies are investigated using the random forest rf model, which has been shown to be superior for large dataset analysis and variable importance evaluation. Supporting a wide variety of research created in 2008, the portals immediate objective was to produce and make highresolution climate data available to the public. The books homepage helps you explore earths biggest bookstore without ever leaving the comfort of your couch. A talk entitled downscaling and uncertainty by hayley flowler, newcastle university and linda mearns, ncar, at the 2014 asp summer colloquium. Evaluation of different downscaling techniques for hydrological climatechange impact studies at the catchment scale claudia teutschbein fredrik wetterhall jan seibert received. A spiralbased downscaling method for generating 30 m time series image data boyu liu a, b, jun chen a, huaqiao xing a, c, hao wu a, jun zhang a.