Assessment of climate change for precipitation and. Statistical downscaling methods describe a statistical relationship between largescale atmospheric variables such as temperature, humidity, precipitation, etc. Pdf spatial downscaling of trmm precipitation product using. The basic approach is that of delta change, developed for both continuous and eventbased applications. Stochastic models of daily precipitation are useful both for characterizing different pre. An improved climate dynamical downscaling at regional scales. Downscaling future precipitation extremes to urban hydrology. Introduction precipitation is a key component of the hydrological cycle and one of the most important parameters for a range of natural and socioeconomic systems. This paper integrates perspectives from meteorologists, climatologists, statisticians, and hydrologists to identify generic end user in particular, impact modeler needs and to discuss downscaling capabilities and gaps. This downscaling is traditionally done in one of two ways, either statistical rescaling of climate model precipitation gutmann et al. As shown in table 4, imerg has lower and rmse values than does trmm 3b43 before downscaling, which indicates that the imerg product is more accurate than the trmm 3b43 product. A statistical spatial downscaling algorithm of trmm. Nonparametric statistical downscaling of precipitation from global.
Multisite precipitation downscaling using a stochastic. Daily precipitation and temperature fields over the united states. Most statistical methods for precipitation downscaling are based on a largescale predictor other than precipitation. Downscaling rcm precipitation to the station scale using quantile mapping a comparison of methods. Downscaling precipitation and temperature with temporal. Precipitation simulation has been a challenge in the western u. The scatter of raw precipitation data obtained from both ncepncar reanalysis and 20cr database shown in fig. End up with continuous pseudoprecipitation at 5 km 1 for downscaling biascorrection c.
This is of special interest in dry mountainous areas, which are particularly vulnerable to climate change due to risks of reduced freshwater availability. Wigleya a national center for atmospheric research, boulder co, 80303, usa b di6ision of geography, uni6ersity of derby, kedleston road, derby, de22 1gb, uk recei6ed 18august 1998 re6ised 28august 1999 accepted 9september. The deltachange technique was selected because it is most widely used with rcm outputs maraun et al. Downscaling precipitation using regional climate models and. Highresolution precipitation and temperature downscaling. Observers are consistent when rating image conspicuity vision research. Two global climate models gcms, cgcm3 and hadcm3, have been used to project future maximum temperature tmax, minimum temperature tmin and precipitation.
The stateoftheart in dynamical and statistical downscaling is presented in sections 3 and 4, respectively, and in section 5, validation techniques are introduced. Comparison of quantile regression and standard regression statistical downscaling models for simulating the percentage of summer days with more than 20 mm precipitation left and the percentage of winter days with more than 5 mm precipitation right. Statistical downscaling of monthly precipitation using. Given a set of features and an associated attribute, it evaluates whether the pattern expressed is clustered, dispersed, or random. The two red dotted vertical lines in the insert mark the 95 and the 97. Wilks atmospheric science group, cornell university, ithaca, new york 14853, usa abstract. As a fundamental component in material and energy circulation, precipitation with high resolution and accuracy is of great significance for hydrological, meteorological, and ecological studies. Downscaling climate data for hydrologic applications is a key component of any climate change projection. Precipitation, as a fundamental component of the global water cycle, is a key parameter in ecology, hydrology and meteorology. An improved climate dynamical downscaling at regional scales jiming jin, ripley c.
Daily precipitation and average temperature patterns for the contiguous united states were downscaled from a 2. Statistical precipitation downscaling over the northwestern united. Extreme precipitation and flooding can have profound impacts on public infrastructure, agriculture, and human health. In section 2 we identify a set of generic end user needs. Transform negative pseudoprecipitation with the saved transform coef. Anomalies are interpolated between gcm cell centroids and are then applied to a baseline climate given by a high resolution surface worldclim 1.
In this paper, we present a new technique for downscaling precipitation. Rosendahl3, and lei qiao5 1center for analysis and prediction of storms, university of oklahoma, norman, oklahoma, usa, 2school of meteorology. Statistical and dynamical downscaling of precipitation. Climate model downscaling contacts, for more information. Statistical precipitation downscaling for smallscale hydrological impact investigations of climate change. Extend downscaling of selected cmip5 simulations to north america for daily precipitation and temperature at a 10 km resolution over north america. Dec 15, 2010 the spatial resolution gap between global or regional climate models and the requirements for local impact studies motivates the need for climate downscaling. Empiricalstatistical downscaling and error correction of. Comparison of downscaling methods for mean and extreme. Gmdd 6, 25172549, 20 downscaling a global climate model m. From the downscaling experiments with timeseries tropical rainfall measuring mission trmm 3b43 precipitation data, mlrbased downscaling showed the similar or even better predictive performance. Spatial downscaling of trmm precipitation using geostatistics and. A new precipitation dataset for bias correction and downscaling. Downscaling of shortterm precipitation from regional.
Downscaling future precipitation extremes to urban hydrology scales using a spatiotemporal neymanscott weather generator hjalte jomo danielsen sorup 1,2, ole bossing christensen 2, karsten arnbjergnielsen 1, and peter steen mikkelsen 1. Precipitation simulations for the 19911995 period, for the regcm 11km run lefttop degraded to 50 km leftmiddle and 200 km. A wide range of statistical models have been applied to downscaling gcms but recent advances in machine learning have not been explored. Multisite downscaling of daily precipitation with a. In both cases, delta change factors dcfs are calculated which represent the. Statistical downscaling is a twostep process consisting of i the development of statistical relationships between local climate variables e. Comparison of various precipitation downscaling methods. A comparison of precipitation downscaling procedures to. Timestep grid box mean precipitation saved as convective and resolved components and normalized by respectively convective or resolved cloud fractions convective cloud fractions associated with parameterized convective precipitation generally precipitation. Statistical downscaling of precipitation using quantile. Downscaling of shortterm precipitation time series for. These areas play a key role in hydrology, since they usually receive. Statistical downscaling techniques for global climate model simulations of temperature and precipitation with application to water resources planning. Mujumdar2,3 1 center for climate change research, indian institute of tropical meteorology, pune 411008, india 2 department of civil engineering, indian institute of.
A framework for downscaling precipitation from rcm projections to the high resolutions in time and space required in the urban hydrological climate change impact assessment is outlined and demonstrated. The qaidam basin is an arid and semiarid area, and is a typical example of an ungauged basin. Climate downscaling effects on predictive ecological models. 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. Here we apply a downscaling method based on thin plate spline spatial interpolation of anomalies deltas of original gcm outputs. Changes in precipitation between the present and future scenarios produced. Improved statistical downscaling of daily precipitation. Downscaling precipitation using regional climate models and circulation patterns toward hydrology andras bardossy1 and geoffrey pegram2 received 25 june 2010. A read is counted each time someone views a publication summary such as the title, abstract, and list of authors, clicks on a figure, or views or downloads the fulltext. Climate model downscaling geophysical fluid dynamics laboratory. Dynamical downscaling precis method description a regional climate model rcm is a high resolution climate model that covers a limited area of the globe, typically 5,000 km x 5,000 km, with a typical horizontal resolution of 50 km. Statistical precipitation downscaling over the northwestern united states using numerically simulated precipitation as a predictor martin widmann and christopher s.
Moran, wy precipitation forecast weather underground. Statistical downscaling of precipitation using machine. Guidelines for use of climate scenarios developed from. Pdf spatial downscaling of trmm 3b43 precipitation. Abstract downscaling of atmosphereocean general circulation models aogcms is necessary to transfer predictions of potential climate change scenarios to local levels. Now treat adjusted pseudoprecipitation prior as any other continuous variable. Can we downscale sub daily precipitation data using sdsm. Statistical downscaling daily rainfall statistics from.
Climate downscaling effects on predictive ecological. Precipitation dynamical downscaling over the great plains. The downscaling techniques are discussed in terms of model performance, comparison of techniques and possible model improvements. A circulation parameter is the most common predictor, and often atmospheric moisture is considered as well see wilby and wigley, 2000, for an overview of. In this paper, we compare four fundamental statistical methods, bias correction spatial disaggregation bcsd. For impact studies that involve glacier modelling, the sparsity or complete absence of climate monitoring activities within the regions of interest presents a substantial additional challenge. Also, irrespective of the climate regime and the machine learning technique, at the majority of stations downscaling models showed an overestimating trend of low to mid percentiles i. Rcms are based on physical laws represented by mathematical equations that are solved using a threedimensional. Spatial downscaling of trmm precipitation data using an. Downscaling is any procedure to infer highresolution information from lowresolution variables. Statistical downscaling of precipitation and temperature. Pdf spatial downscaling of precipitation using adaptable. Cerf is also the president and cofounder of the human single neuron society. Pdf a geostatistical downscaling scheme is presented and can generate fine scale precipitation information from coarse scale tropical rainfall.
Statistical downscaling based on regression models requires one to sample from the conditional distribution to preserve the variance of observed precipitation. Statistical downscaling delta method ccafs climate. Moran cerf is a frenchisraeli neuroscientist, assistant professor of business at the kellogg school of management, investor and a former hacker he is the founder of thinkalike and bcube and the host and curator of poptech, one of the top 5 leading conferences in the world. The correlation coefficient for predicted versus observed daily precipitation totals was less than 0. Downscaling precipitation using regional climate models. Comparisons between trmm 3b43 precipitation, imerg precipitation, and observed precipitation a before downscaling and b after downscaling for 2014. The spatial autocorrelation global morans i tool measures spatial autocorrelation based on both feature locations and feature values simultaneously. Downscaling precipitation to river basin in india for ipcc. A gisbased procedure for downscaling climate data for west. An improved dynamical downscaling for the western united states. Statistical precipitation downscaling over the northwestern.
A climate graph showing rainfall, temperatures and normals. Impact of two conceptual precipitation downscaling schemes. In this study, a novel approach that integrates precipitation zoning with random forest regression is proposed for the spatial downscaling of satellite. The precipitation time series of each station is divided into 30 odd years for calibration and 29 even years for validation. Comparison of statistical methods for downscaling daily. This paper integrates perspectives from meteorologists, climatologists, statisticians, and hydrologists to identify generic end user in particular, impact modeler needs and to discuss.
Melville department of civil and environmental engineering, school of engineering, the university of auckland, new zealand. A comparison of techniques for downscaling extreme. The approach of dynamical downscaling nesting regional climate models. Several metrics, including the frequencies of wet and dry spells and statistics of the daily, monthly and annual precipitation are used as criteria to evaluate the multisite downscaling approach. Know whats coming with accuweather s extended daily forecasts for moran, ks.
Bretherton department of atmospheric sciences, university of washington, seattle, washington eric p. On average, there are 211 sunny days per year in moran. Although much progress on downscaling precipitation predictions has been. Precipitation dynamical downscaling over the great plains xiaoming hu 1,2, ming xue, renee a.
Multisite downscaling of daily precipitation with a stochastic weather generator d. Highresolution precipitation and temperature downscaling for. Statistical downscaling model sdsm and automated statistical downscaling asd are among the. Water resources management, agriculture and forestry, tourism, ood protection, to name just a few. Downscaling extreme precipitation from cmip5 simulations. Statistical downscaling techniques are used to assess mediterranean precipitation changes for the.
Impact of two conceptual precipitation downscaling schemes on massbalance modeling of gran campo nevado ice cap, patagonia stephanie weidemann, 1tobias sauter. Statistical precipitation downscaling for smallscale. Statistical downscaling of global climate models gcms allows researchers to study local climate change effects decades into the future. Moran cerf, paxon frady, christof koch using semantic content as cues for better scanpath prediction proceedings of the symposium on eyetracking research and applications 2007 moran cerf, dan cleary, rob j. Assessments of mediterranean precipitation changes for the 21st century using statistical downscaling techniques e. The downscaling techniques were applied to estimate the mean daily precipitation and 5, 10, 20, 50 and 100year daily precipitation events at the six locations in addition to the direction of change.
The dashed blue lines and blue text describe the pdf for a future climate 3 c warmer than present and with 2. While the generally good correlation between downscaled pgw precipitation and 2km wrf pgw precipitation r 2 0. Advantages and disadvantages of statistical downscaling methods have been discussed by hessami et al. Nonparametric statistical downscaling of precipitation from global climate models. A comparison of three methods for downscaling daily precipitation in the punjab region deepashree raje1 and p.
Up to 90 days of daily highs, lows, and precipitation chances. Statistical downscaling of precipitation is required as part of many climate change studies. Title page abstract introduction conclusions references tables figures back close. A comparison of three methods for downscaling daily. This technique is based on dynamical or statistical approaches commonly used in several disciplines, especially meteorology, climatology and remote sensing. A geostatistical downscaling scheme is presented and can generate fine scale precipitation information from coarse scale tropical rainfall measuring mission. The cgcm3 simulations are run through the calibrated and validated svm downscaling model to obtain future projections of predictand for each of the four emission scenarios considered. A comparison of two statistical downscaling methods for daily maximum and minimum surface air temperature, total daily precipitation and total monthly precipitation at indianapolis, in, usa, is presented. Significant differences in the level of skill were found amongst the downscaling methods. The results show that the precipitation is projected to increase in future for almost all the scenarios considered. Experiments with statistical downscaling of precipitation. Abstract this study investigates whether gcmsimulated precipitation is a good predictor for regional precipitation over washington and oregon. Statistical downscaling and bias correction for climate. Spatial downscaling of trmm precipitation using geostatistics.
Statistical downscaling and bias correction are becoming standard tools in climate impact studies. Weather conditions with updates on temperature, humidity, wind speed, snow, pressure, etc. Downscaling is any procedure to infer highresolution information from low resolution variables. A comparison of statistical and dynamical downscaling of. Statistical downscaling of precipitation and temperature for a lake basin by manish kumar goyal. Pdf spatial downscaling of trmm precipitation using. 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. Assessments of mediterranean precipitation changes for the. Monthly temperature, precipitation and hours of sunshine. Teegavarapu aneesh goly hydrosystems research laboratory hrl department of civil, environmental and geomatics engineering, florida atlantic university, boca raton, fl, 33431. A list of predictors used for precipitation downscaling is given by wilby and wigley, along with a comparison of observed and simulated predictors and a stationarity assessment.
There is still no universal single method, or group of methods, that is clearly superior, particularly for downscaling daily precipitation. Experiments with statistical downscaling of precipitation for south florida region. The analysis is conducted for two seasons, the growing season and the nongrowing season, defined based on variability of surface air. Spatial downscaling of gpm annual and monthly precipitation. As this region has a climate where the majority of heavy precipitation events are associated with largescale circulation features, statistical downscaling may potentially be more reliable than in other regions where heavy precipitation is associated with isolated convection. Usually it is best to collect detailed precipitation data from the beginning, but i recognize that many historic. Flint and flint 2012, downscaling future climate scenarios to fine scales for hydrologic and ecological modeling and analysis. Within the geostatistical framework, the trmm precipitation data are first decomposed into trend and residual components. Use downscaled daily temperature and precipitation time series to produce projections of etccdi indices of extremes expert team on climate change detection and indices, see klein. A downscalingmerging method for highresolution daily.
I would estimate that the success at downscaling may vary with type of storms. The downscaling results suggest that the tlfn is an efficient method for downscaling both daily precipitation and temperature series. A geostatistical downscaling scheme is presented and can generate fine scale precipitation information from coarse scale tropical rainfall measuring mission trmm data by incorporating auxiliary fine scale environmental variables. The downscaled daily precipitation series and the extreme precipitation features including maximum. The spatial resolution gap between global or regional climate models and the requirements for local impact studies motivates the need for climate downscaling. This book provides a comprehensive reference to widelyused approaches, and additionally covers the relevant user context and technical background, as well as a synthesis and guidelines for practitioners. Moran gets some kind of precipitation, on average, 84 days per year. This paper compares different statistical methods for downscaling daily precipitation from numerical weather prediction model output. Empiricalstatistical downscaling and error correction of rcms 10 11 12 14 15 16 17 degrees east degrees north 46. Does dynamical downscaling introduce novel information in. Keith dixon lucas harris tom knutson related areas of research. Precipitation data with accurate, high spatial resolution are crucial for improving our understanding of basinscale hydrology. Precipitation is a vital part of the water cycle and plays a critical role in global surface energy exchange and material circulation donohoe, 2016, huntington, 2006, trenberth et al.
The tool calculates the morans i index value and both a a zscore and pvalue to evaluate the significance of. Precipitation has strong spatiotemporal heterogeneity, which is of great importance not only for investigating the spatiotemporal distribution of regional precipitation, but also for the. In the present study sdsm downscaling model was used as a tool for downscaling weather data statistically in upper godavari river basin. Since satellite measured precipitation is often too coarse for practical applications, it is essential to develop spatial downscaling algorithms. Nov 28, 2017 wilks d s 1999 multisite downscaling of daily precipitation with a stochastic weather generator. Precipitation variables tended to show greater discrepancies between downscaling techniques than temperature variables, and for one gcm, there was evidence that more poorly resolved. The primary objective of the modelling framework, which consists of a precipitation weather generator, a hydrologic model and a lake model for alamo lake, is to assess the hydrologic impact of projected climatic changes.
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