Transform negative pseudoprecipitation with the saved transform coef. Therefore, downscaling local precipitation is necessary. Thus, for downscaling of extreme precipitation at subdaily level and subsequent application of climate change signals from climate models. Intense precipitation of short duration that characterizes most of the rainfall in the tropics. Buy new statistical downscaling methods and applications on. Bretherton department of atmospheric sciences, university of washington, seattle, washington eric p. This downscaling is traditionally done in one of two ways, either statistical rescaling of climate model precipitation gutmann et al. Abstract 1 statistical downscaling provides a technique for deriving localscale information of precipitation and temperature from numerical weather prediction model output. A new precipitation dataset for bias correction and downscaling.
Precipitation dynamical downscaling over the great plains. 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. Statistical downscaling techniques combine the information of retrospective gcm analysisforecasts databases with simultaneous local historical observations to infer statistical relationships between the lowresolution gcm fields and the highresolution observed records usually surface variables such as precipitation or temperature. Abstractan approach for downscaling daily precipitation extremes using historical analogs is applied to simulations from phase 5 of the coupled model intercomparison project cmip5. 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. Downscaling future precipitation extremes to urban hydrology scales. Highresolution multisite daily rainfall projections in india. For instance, hydrologists ask for daily total precipitation with spatial. Water resources management, agriculture and forestry, tourism, ood protection, to name just a few. 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. Unfortunately, these models inherit some of the biases of the gcms. The exact downscaling locations used for our study in sync with kannan and ghosh 20s work are depicted in table 2. For this purpose, svdbased statistical downscaling schemes for predicting local precipitation over china were developed. 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.
There appears to be a paucity of studies focussing on the downscaling of autumn precipitation over china. There is still no universal single method, or group of methods, that is clearly superior, particularly for downscaling daily precipitation. This technique is based on dynamical or statistical approaches commonly used in several disciplines, especially meteorology, climatology and remote sensing. Climate change may affect the spatial and temporal distribution of water resources, as well as the intensities and frequencies of extreme hydrological events huntington 2006. Downscaling precipitation using regional climate models and. New statistical downscaling methods and applications. Pdf spatial downscaling of precipitation using adaptable. Downscaling future precipitation extremes to urban.
First results from version 7 trmm 3b43 precipitation. 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. However, the commonly sparse meteorological networks in complex terrains are unable to provide the information needed for many applications. Statistical downscaling of general circulation model. Downscaling extreme precipitation from cmip5 simulations. A new precipitation dataset for bias correction and. The downscaling results suggest that the tlfn is an efficient method for downscaling both daily precipitation and temperature series. Downscaling global circulation model projections of.
Statistical downscaling multimodel forecasts for seasonal. A comparison of techniques for downscaling extreme. Statistical precipitation downscaling over the northwestern united states using numerically simulated precipitation as a predictor martin widmann and christopher s. Here we apply a downscaling method based on thin plate spline spatial interpolation of anomalies deltas of original gcm outputs. In order to distinguish this application from perfect prog downscaling which does. Therefore, the focus of this study is the application of statistical downscaling to predict autumn precipitation in china. The downscaling results, corresponding to the businessasusual climate change scenario, show that while the tlfn model has estimated an increase in average annual precipitation by about 27. Statistical precipitation downscaling over the northwestern. Climate model downscaling contacts, for more information. Statistical downscaling of daily precipitation over greece ioannis kioutsioukis,a dimitrios melasa and prodromos zanisb a laboratory of atmospheric physics, physics department, aristotle university of thessaloniki, thessaloniki, greece b department of meteorologyclimatology, school of geology, aristotle university of thessaloniki. 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. Statistical downscaling of daily precipitation over greece.
The output of the annual downscaling models was then disaggregated into monthly values using four different methods based on the method of fragments. Downscaling rcm precipitation to the station scale using quantile mapping a comparison of methods. The deltachange technique was selected because it is most widely used with rcm outputs maraun et al. Comparison of downscaling methods for mean and extreme. Downscaling daily precipitation time series using a. Annual statistical downscaling of precipitation and. Statistical downscaling of general circulation model outputs to precipitation accounting for nonstationarities in predictorpredic tand relationships d. 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. A downscalingmerging method for highresolution daily.
According to the ipcc 20, which periodically publishes assessment reports on world climate change research, the global sea temperature is expected to rise by approximately 0. 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. Especially for extreme rainfall events, the model is thought to better reflect rainfall variability compared to the pure cpbased downscaling approach. In the case study, annual and monthly downscaling models were developed for precipitation and evaporation at two stations located in victoria, australia. Evaluation of a dynamic downscaling of norwegian precipitation. Downscaling of global climate change estimates to regional scales. All methods are applied to rcm results instead of their usual application to gcms compare figure 1. This paper integrates perspectives from meteorologists, climatologists, statisticians, and hydrologists to identify generic end user in particular, impact. Three statistical downscaling models are used to test the multimodel ensemble on the clutha catchment in new zealand.
Downscaling global circulation model projections of climate. Nonparametric statistical downscaling of precipitation from global climate models. Extreme precipitation and flooding can have profound impacts on public infrastructure, agriculture, and human health. Ndvibased downscaling method was extended to a humid area. Oct 22, 2016 in the case study, annual and monthly downscaling models were developed for precipitation and evaporation at two stations located in victoria, australia. Review a new statistical downscaling model for autumn. Precipitation is an essential input parameter for land surface models because it controls a large variety of environmental processes. This study compared two types of approaches to downscale seasonal precipitation p and 2 meter air temperature t2m forecasts from the north american national multimodel ensemble nmme over the states of alabama, georgia, and florida in the southeastern united states seus.
Statistical downscaling techniques for global climate model simulations of temperature and precipitation with application to water resources planning. Take the model construction for february 2015 as an. Daily precipitation and temperature fields over the united states. T1 downscaling global circulation model projections of climate change for the united arab emirates. The downscaling captures well the spatial distribution of monthly climatology precipitation and the monthlyyearly variability, showing improvement over at least two previously published precipitation downscaling studies. Statistical downscaling based on regression models requires one to sample from the conditional distribution to preserve the variance of observed precipitation.
The approach of dynamical downscaling nesting regional climate models. Anomalies are interpolated between gcm cell centroids and are then applied to a baseline climate given by a high resolution surface worldclim 1. The impact of wrf resolution on hydrology is primarily due to differences in precipitation among wrf simulations, although differences in the spatial resolution of the hydrology model are still important. Since satellite measured precipitation is often too coarse for practical applications, it is essential to develop spatial downscaling algorithms. Bretherton department of atmospheric sciences, university of washington, seattle. Precipitation simulation has been a challenge in the western u. The downscaling algorithm has proven to add valuable information for water resources planning and climate. Highresolution multisite daily rainfall projections in. Conus downscaling ncar research applications laboratory.
In section 2 we identify a set of generic end user needs. Empiricalstatistical downscaling and error correction of. Nearest neighborgenetic algorithm for downscaling of. To further test the performance of the proposed downscaling scheme, we constructed a series of spatial downscaling models for the generalized trmm seasonal precipitation data cumulative precipitation for any three consecutive months during 20152016.
Downscaling a global climate model to simulate climate change impacts on us regional and urban air quality. Downscaling of gcmsimulated precipitation using model output. This paper o ers a fresh comparison of the distributions of downscaled era40 precipitation data with triangulated precipitation observations, over 40 years of norwegian weather. Atmospheric aerosols are solid and liquid particles suspended in air that influence the amount of solar radiation that reaches the surface of the earth. Highlights version 7 trmm 3b43 precipitation product was used. Downscaling precipitation using regional climate models. Nonparametric statistical downscaling of precipitation from global. The knearest neighbor knn is a new analogtype approach that is used in this paper to downscale the national centers for environmental prediction 1998 mediumrange forecast model output. The term downscaling usually refers to an increase in spatial resolution, but it is often also used for temporal resolution.
Interannual variation of statistically downscaled february precipitation from 19822009 red bar and the corresponding observations blue bar at elon, israel. A new satellitebased monthly precipitation downscaling. Guidelines for use of climate scenarios developed from. Comparison of statistical methods for downscaling daily. The results show that the model can well reproduce statistical properties of daily precipitation time series. With the improved precipitation downscaling, a better. Spatial downscaling of precipitation using adaptable random forests. Statistical downscaling of precipitation is required as part of many climate change studies. Calibration and disaggregation was conducted to the downscaled annual precipitation. Downscaling is any procedure to infer highresolution information from low resolution variables. Keith dixon lucas harris tom knutson related areas of research. Statistical downscaling delta method ccafs climate. Downscaling is any procedure to infer highresolution information from lowresolution variables. A gisbased procedure for downscaling climate data for.
Statistical downscaling of precipitation using long shortterm memory recurrent neural networks fig. For this purpose downscaling methods have been developed. A statistical downscaling scheme to improve global. There are several statistical downscaling methods available for generating localscale meteorological variables from largescale model outputs.
Downscaling precipitation and temperature with temporal. In this paper, we present a new technique for downscaling precipitation. Downscaling climate data for hydrologic applications is a key component of any climate change projection. The stateoftheart in dynamical and statistical downscaling is presented in sections 3 and 4, respectively, and in section 5, validation techniques are introduced. The key idea of this sd scheme is to select the optimal predictors that are best forecast by coupled. The key idea of this sd scheme is to select the optimal predictors that are best forecast by coupled general circulation models cgcms. Application to seasonal precipitation in china chen lijuan beijing climate center, china meteorological administration, beijing, p. Perera institute for sustainability and innovation, college of engineering and science victoria university, melbourne, victoria, australia. Statistical downscaling develops empirical relations between climate variables. Regional climate models rcms use the output of the gcms and provide climate variables, including daily rainfall, at a finer spatial resolution. Based on hindcasts obtained from the development of a european multimodel ensemble system for seasonal to interannual prediction demeter project, this study proposes a statistical downscaling sd scheme suitable for global precipitation forecasting.
The guidelines were prepared for consideration by the. Statistical downscaling of precipitation using quantile. Statistical downscaling and bias correction are becoming standard tools in climate impact studies. Now treat adjusted pseudoprecipitation prior as any other continuous variable. Discover delightful childrens books with prime book box, a subscription that. China 81 bpcca is used to build the statistical downscaling model between large scale circulation over east asia and precipitation over china in boreal. This paper integrates perspectives from meteorologists, climatologists, statisticians, and hydrologists to identify generic end user in. Climate model downscaling geophysical fluid dynamics laboratory. Statistical and dynamical downscaling of precipitation. Climate model downscaling geophysical fluid dynamics.
May 22, 2012 based on hindcasts obtained from the development of a european multimodel ensemble system for seasonal to interannual prediction demeter project, this study proposes a statistical downscaling sd scheme suitable for global precipitation forecasting. A comparison of techniques for downscaling extreme precipitation over the northeastern united states lee tryhorna,b,c and art degaetanoa,c a northeast regional climate center, cornell university, ithaca, ny 14853, usa b new york state water resources institute, cornell university, ithaca, ny 14853, usa. Statistical downscaling is a twostep process consisting of i the development of statistical relationships between local climate variables e. Because the scale of a global circulation model gcm output is coarse a grid cell from a typical gcm model run being 2.
Monthly fractions from trmm 3b43 data can disaggregate annual precipitation. For example, regional climate models rcms are nested in gcms to produce higher resolution outputs in a dynamic downscaling fashion with. Spatial downscaling of gpm annual and monthly precipitation. Statistical downscaling and local weather forecast. We created 1 km monthly precipitation for a humid area and a semiarid area. Statistical downscaling of precipitation using long short. Downscaling seasonal precipitation prediction ncar. An improved dynamical downscaling for the western united states.
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