Many downscaling techniques have been developed in the past few years for projection of stationscale hydrological variables from largescale atmospheric variables to assess the hydrological impacts of climate change. Comparison of three statistical downscaling methods and. Therefore, downscaling local precipitation is necessary. Statistical and machine learning techniques applied to local weather forecast by adapting the prediction of numerical models using statistical relationships obtained from historical records. Statistical downscaling of precipitation using long shortterm memory recurrent neural networks fig.
Melville department of civil and environmental engineering, school of engineering, the university of auckland, new zealand. 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. Dec 11, 20 the increasing availability of precipitation observations from space, e. Statistical downscaling based on multiple linear regression analysis for temperature and international organization of scientific research 60 p a g e figure 5. Derivation of local to regionalscale 10100 kilometers information from larger scale modeled or observed data. The emission pathway is representative for scenarios in the literature leading to very low greenhouse gas concentration levels. Statistical downscaling to predict monthly rainfall using. Statistical downscaling of erainterim forecast precipitation data in complex terrain using lasso algorithm lugao,1,2,3 karstenschulz,4 andmatthiasbernhardt3. Statistical precipitation downscaling over the northwestern. For this purpose, svdbased statistical downscaling schemes for predicting local precipitation over china were developed. Statistical downscaling of precipitation using long short. Bretherton department of atmospheric sciences, university of washington, seattle. Sparse regularization for precipitation downscaling nasaads. Aug 08, 2011 also, the study uses several downscaling techniques to develop an ensemble of precipitation time series that can be used in hydrologic climate impact assessment.
A geostatistical downscaling scheme is presented and can generate fine scale precipitation. Precipitation downscaling also acts as an underpinning. Downscaling is any procedure to infer highresolution information from low resolution variables. 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.
The downscaling algorithm performs really well in some years, such as 1983, 2004, 2005, 2009 and the extreme wet situation in 2003. Since satellite measured precipitation is often too coarse for practical applications, it is essential to develop spatial downscaling algorithms. All methods are applied to rcm results instead of their usual application to gcms compare figure 1. Here we apply a downscaling method based on thin plate spline spatial interpolation of anomalies deltas of original gcm outputs. Spatial downscaling of trmm precipitation using geostatistics and. There is still no universal single method, or group of methods, that is clearly superior, particularly for downscaling daily precipitation. Downscaling satellite precipitation with emphasis on extremes. In order to distinguish this application from perfect prog downscaling which does. Section describes the four downscaling methods as well as the evaluation criteria. There are assumptions of stationarity between the large and small scale dynamics when using statistical downscaling. Spatial downscaling of precipitation using adaptable. This technique is based on dynamical or statistical approaches commonly used in several disciplines, especially meteorology, climatology and remote sensing.
Research article statistical downscaling of erainterim. Statistical delta downscaling adds the difference delta between a historical period and a modeled value to a known historical climatology. Downscaling of precipitation for climate change scenarios. Downscaling precipitation and temperature with temporal.
The basic concept of the developed downscaling method is to consider the spatial correlation pattern of precipitation, and to estimate the amount of a certain 20km gridded precipitation for example, r 1 in fig. The concept of fuzzy sets has the advantage for dealing with imprecision and ambiguity makropoulos and butler, 2004. Transform negative pseudoprecipitation with the saved transform coef. Research article spatial downscaling of trmm precipitation. 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. Sparse regularization for precipitation downscaling article pdf available in journal of geophysical research atmospheres 117d8. Sparse regularization for precipitation downscaling ebtehaj. Lasso is an alternative regularized version of least squares, which is useful for. Comparison of statistical methods for downscaling daily. 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. The model was calibrated in 19701979, validated in 19801990 and applied for the whole period 19701990. To improve the simulation accuracy of downscaling methods, the bayesian model averaging bma method combined with three statistical downscaling methods, which are support vector. Validation of a precipitation b mean monthly temperature at pahalgam station for the period 2010 2014. Statistical and dynamical downscaling of precipitation.
The term downscaling usually refers to an increase in spatial resolution, but it is often also used for temporal resolution. To improve the simulation accuracy of downscaling methods, the bayesian model averaging bma method combined with three statistical downscaling. Statistical downscaling of general circulation model. 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. Coupling statistical and dynamical methods for spatial. The probabilistic multisite precipitation downscaling method developed in the current study extends the deterministic expanded downscaling model of burger 1996, 2002. Precipitation is one of the most important variables for a large variety of environmental processes and its spatial. Figure 3shows the initial and trained atoms of the learned high. Projections of future global average annual precipitation and temperature for rcp 2. Statistical downscaling of erainterim forecast precipitation.
Water resources management, agriculture and forestry, tourism, ood protection, to name just a few. Vrac et al nonlinear statistical downscaling over europe 671 insolation, pco2 and icesheet forcing are imposed an in berger 1978, petit et al. Lerman 2012, sparse regularization for precipitation downscaling, j. Anomalies are interpolated between gcm cell centroids and are then applied to a baseline climate given by a high resolution surface worldclim 1.
Global climate models gcms are our best tools for providing climate projections. Sparse regularization for precipitation downscaling ebtehaj 2012 journal of geophysical research. Bretherton department of atmospheric sciences, university of washington, seattle, washington eric p. Does dynamical downscaling introduce novel information in climate model simulations of precipitation change over a complex topography region. Erainterim forecast precipitation data and meteorological observations in the study area. 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.
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 precipitation using machine. Nonparametric statistical downscaling of precipitation from global. Does dynamical downscaling introduce novel information in. The increasing availability of precipitation observations from space, e. Trmm precipitation values when the downscaling results at a ne scale are upscaled or aggregated to the coarse scale. The performance assessment of the results indicates that the procedure is successful in choosing the predictors for downscaling the gcm data both in monthly and seasonal timescales. Statistical downscaling delta method ccafs climate. Downscaling of remotely sensed precipitation images and outputs of general circulation models has been a subject of intense interest in hydrometeorology. Downscaling is any procedure to infer highresolution information from lowresolution variables. Impactrelevant variables not simulated by climate models can be downscaled using statistical downscaling. Sparse regularization for precipitation downscaling.
To quantify the uncertainty of downscaling, stochastic sim. The dictionary learningbased method, or sparse regularization 35, and the superresolution convolutional neural networks srcnn based method 36 are. Ebtehaj am, foufoulageorgiou e, lerman g 2012 sparse regularization for precipitation downscaling. Downscaling seasonal precipitation prediction ncar. Sparse regularization for precipitation downscaling a. Expanded downscaling is based on the multivariate linear regression model.
Statistical downscaling and local weather forecast. A comparison of techniques for downscaling extreme. Reassessing model uncertainty for regional projections of precipitation with an ensemble of statistical downscaling methods. Pryor atmospheric science program, department of geography, indiana uni ersity student building, 701east kirkwood a e. Perera institute for sustainability and innovation, college of engineering and science victoria university, melbourne, victoria, australia.
Nonparametric statistical downscaling of precipitation from global climate models. Sparse regularization for precipitation downscaling by. Review a new statistical downscaling model for autumn. A combined statistical bias correction and stochastic. The problem of downscaling is basically one of resolution enhancement, that is, appropriately adding details or high frequency features onto a lowresolution observation or simulated rainfall field. 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. Highresolution products generated from coarsescale precipitation fields climate. The downscaling results suggest that the tlfn is an efficient method for downscaling both daily precipitation and temperature series. Statistical downscaling based on regression models requires one to sample from the conditional distribution to preserve the variance of observed precipitation. For calibration and validation of the downscaling model the observed and simulated daily precipitation amounts from eight precipitation stations spread over germany were used.
A climate model is a mathematical representation of the climate system. The downscaling and linking of precipitation and temperature to observed cps as well as to echam4 gcmgenerated cps for present day 1xco 2 and perturbed 2xco. On the other hand, the test also exposes unsatisfactory performance in years of 1984, 1996, 1997 and 1999. Pdf spatial downscaling of precipitation using adaptable. The precipitation of two gcms were used as covariates i. George tselioudis,a,b costas douvisa and christos zerefosa a center for atmospheric physics and climatology of.
Atmospheres wiley online library skip to article content. Statistical downscaling of general circulation model outputs to precipitation accounting for nonstationarities in predictorpredic tand relationships d. Researcharticle statistical downscaling of erainterim forecast precipitation data in complex terrain using lasso algorithm lugao,1,2,3 karstenschulz,4 andmatthiasbernhardt3 1collegeofgeographicalsciences,fujiannormaluniversity,fuzhou350007,china. Multivariate stochastic downscaling model for generating.
Multivariate stochastic downscaling model for generating daily precipitation series based on atmospheric circulation. A new precipitation dataset for bias correction and. Within the geostatistical framework, the trmm precipitation data are first. Apr 21, 2012 figure 3shows the initial and trained atoms of the learned high. Also, the study uses several downscaling techniques to develop an ensemble of precipitation time series that can be used in hydrologic climate impact assessment. Statistical downscaling techniques for global climate model simulations of temperature and precipitation with application to water resources planning. Nonlinear statistical downscaling of present and lgm. Spatial downscaling of gpm annual and monthly precipitation. In this paper, we present a new technique for downscaling precipitation. Section 2 introduces the datasets used and the method of downscaling. Statistical downscaling is relatively easy to produce. Statistical precipitation downscaling over the northwestern united states using numerically simulated precipitation as a predictor martin widmann and christopher s. The exact downscaling locations used for our study in sync with kannan and ghosh 20s work are depicted in table 2. Estimates of future greenhouse gas emissions released into the atmosphere.
Statistical downscaling of general circulation model outputs. Reassessing model uncertainty for regional projections of. Statistical downscaling of precipitation is required as part of many climate change studies. N2 downscaling of remotely sensed precipitation images and outputs of general circulation models has been a subject of intense interest in hydrometeorology. Now treat adjusted pseudoprecipitation prior as any other continuous variable. Uncertainty analysis of statistical downscaling methods. A geostatistical downscaling scheme is presented and can generate ne scale precipitation information from coarse scale tropical rainfall measuring mission trmm data by incorporating auxiliary ne scale environmental variables. Application to seasonal precipitation in china chen lijuan beijing climate center, china meteorological administration, beijing, p. Downscaling satellite precipitation with emphasis on.
Pdf sparse regularization for precipitation downscaling. Machine learning based methods for satellite precipitation downscaling xiaoganghegpm downscaling. Statistical downscaling of erainterim forecast precipitation data in. Probabilistic multisite precipitation downscaling by an. Empiricalstatistical downscaling and error correction of. Spatial downscaling of trmm precipitation using geostatistics and fine scale environmental variables. This paper compares different statistical methods for downscaling daily precipitation from numerical weather prediction model output.
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