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. 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. Validation and statistical downscaling of erainterim reanalysis. Statistical and dynamical downscaling of precipitation. A novel approach used in one of the neural network models to output the. Statistical downscaling is a twostep process consisting of i the development of statistical relationships between local climate variables e. In typical downscaling applications, the set of potential predictors comprises. Jacobeit, a novel approach to statistical downscaling considering. The method is exemplarily applied on precipitation observations in the subtropical mountain environment of the high atlas in morocco. In this type of mathematical problem a functional cost is minimized geophysical inverse theory and regularization problems. Using sparse regularization for multiresolution tomography.
Research article statistical downscaling of erainterim. Downscaling satellite precipitation with emphasis on extremes. 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. Therefore, downscaling local precipitation is necessary. Structured sparsity regularization is a class of methods, and an area of research in statistical learning theory, that extend and generalize sparsity regularization learning methods. Using sparse regularization for cit for the global navigation satellite system gnss satellites to cross the sky.
To this end, a new machine learning method, lasso algorithm. Here we apply a downscaling method based on thin plate spline spatial interpolation of anomalies deltas of original gcm outputs. Anomalies are interpolated between gcm cell centroids and are then applied to a baseline climate given by a high resolution surface worldclim 1. Statistical downscaling is relatively easy to produce. In this paper, we present a new technique for downscaling precipitation. In this study, a twostep probabilistic downscaling approach is introduced and evaluated. Siam journal on applied mathematics society for industrial. Amiaux laboratoire aim, umr ceacnrsparis 7, irfu, service dastrophysique, cea saclay, f91191 gifsuryvette cedex, france received 11 june 2014. Sparse regularization for precipitation downscaling by. Statistical downscaling of erainterim forecast precipitation data in. Statistical downscaling of precipitation is required as part of many climate change studies. However, traditional precipitation data usually come from meteorological stations and rely heavily on observation data. A novel temperature downscaling model was developed using the erainterim. The term downscaling usually refers to an increase in spatial resolution, but it is often also used for temporal resolution.
High mountains and basins widely distribute in northwest china, which. Statistical downscaling sd is a model that uses satellite data from general circulation models gcm, which in climatology are. Statistical downscaling of precipitation using long shortterm memory recurrent neural networks fig. The objective of this study was to develop a reliable statistical downscaling algorithm to produce high quality, high spatial resolution precipitation products from tropical rainfall monitoring mission trmm 3b43 data. Water resources management, agriculture and forestry, tourism, ood protection, to. Downscaling satellite precipitation with emphasis on. Evaluation of two statistical downscaling models for daily. However, the commonly sparse meteorological networks in complex terrains are unable to provide the information needed for many applications. Superresolution method using sparse regularization for pointspread function recovery f. High accuracy, high spatial resolution precipitation data is important for understanding basinscale hydrology and the spatiotemporal distributions of regional precipitation.
Statistical downscaling delta method ccafs climate. The precipitation of two gcms were used as covariates i. T1 sparse regularization for precipitation downscaling. This paper illustrates a statistical downscaling technique considering the spatial correlation structure of precipitation. Sparse regularization for precipitation downscaling nasaads. Subdaily statistical downscaling of meteorological variables using. Downscaling of remotely sensed precipitation images and outputs of general circulation models has been a subject of intense interest in hydrometeorology.
Intercomparison of machine learning methods for statistical. In order to distinguish this application from perfect prog downscaling which does. Statistical downscaling regional climate model evaluation. Siam journal on applied mathematics siam society for. Projections of future global average annual precipitation and temperature for rcp 2. This variational approach belongs to the class of methodologies presented and extended in this paper. Dec 11, 20 the increasing availability of precipitation observations from space, e. Groupbased sparse representation for image compressive sensing reconstruction with nonconvex regularization zhiyuan zha a, xinggan zhang, qiong wanga, lan tanga. Statistical downscaling of precipitation using long short.
Precipitation estimation from space using l1regularization, digital technology center, university of minnesota, april 20. We plan to fill this gap by proposing a novel downscaling method, called precipatch, to. Pdf sparse downscaling and adaptive fusion of multi. Structured sparsity regularization methods allow to impose such structure by adding structure to the norms defining the regularization term. Downscaling is any procedure to infer highresolution information from lowresolution variables. Sparse regularization for precipitation downscaling article pdf available in journal of geophysical research atmospheres 117d8. Derivation of local to regionalscale 10100 kilometers information from larger scale modeled or observed data. Spatial downscaling of trmm precipitation using geostatistics and fine scale environmental variables.
Sparse downscaling and adaptive fusion of multisensor precipitation. Highresolution probabilistic projections of precipitation and temperature under climate. However, the vast area of northwest china, its complex geographical environment, and its scarce observation data make it difficult to deeply understand the temporal and spatial variation of precipitation. Harmonized evaluation of daily precipitation downscaled using. Statistical downscaling of precipitation using quantile. Precipitation is a key climatic variable that connects the processes of atmosphere and land surface, and it plays a leading role in the water cycle. Pdf sparse regularization for precipitation downscaling. Lerman 2012,sparse regularization for precipitation downscaling. Quantile regression with elasticnet in statistical.
Atmosphere free fulltext downscaling precipitation in. Pdf spatial downscaling of trmm precipitation using. Lerman 2012, sparse regularization for precipitation downscaling, j. Statistical downscaling of erainterim forecast precipitation. A combined statistical bias correction and stochastic.
The exact downscaling locations used for our study in sync with kannan and ghosh 20s work are depicted in table 2. Two statistical downscaling sd models, the nonhomogeneous hidden markov model nhmm and the statistical downscaling model sdsm, which have been widely applied and proved skillful in terms of downscaling precipitation, were evaluated based on observed daily precipitation over the tarim river basin, an arid basin located in china. Ebtehaj am, foufoulageorgiou e, lerman g 2012 sparse regularization for precipitation downscaling. A conventional approach is based on tikhonov regularization tikhonov and arsenin, 1977 and aims to bal. China 81 bpcca is used to build the statistical downscaling model between large scale circulation over east asia and precipitation over china in boreal. Estimates of future greenhouse gas emissions released into the atmosphere. 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. The 32 use of statistical methods for this purpose is rooted in both operational weather forecasting and 33 synoptic climatology and has become a. Application to seasonal precipitation in china chen lijuan beijing climate center, china meteorological administration, beijing, p. Statistical downscaling of erainterim forecast precipitation data in complex terrain using lasso algorithm lugao,1,2,3 karstenschulz,4 andmatthiasbernhardt3. The emission pathway is representative for scenarios in the literature leading to very low greenhouse gas concentration levels. Impactrelevant variables not simulated by climate models can be downscaled using statistical downscaling. Statistical downscaling based on multiple linear regression analysis for temperature and international organization of scientific research 60 p a g e figure 5.
Empiricalstatistical downscaling and error correction of. Superresolution method using sparse regularization for. Statistical downscaling based on regression models requires one to sample from the conditional distribution to preserve the variance of observed precipitation. The major problems with these projections are both climate model biases and the gap between gridbox and point scale. 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. Pdf sparse downscaling and adaptive fusion of multisensor. Extreme rainfall prediction using bayesian quantile regression in. However, the commonly sparse meteorological networks in complex terrains are. 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. A complex geographical environment, limited meteorological stations, and sparse observation data have made it hard to further investigate the variation of precipitation in northwest china 2,12.
Sparse regularization for precipitation downscaling a. Sparse linear regression is widely used in biomedical data analysis. Statistical downscaling to predict monthly rainfall using. Lasso is an alternative regularized version of least squares, which is useful for. Statistical downscaling of rainfall data using sparse. Groupbased sparse representation for image compressive. Work flows for geostatistical downscaling of coarse scale trmm precipitation data. Conceptual framework 20 the proposed sparse restoration of a 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. 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.
Spatial downscaling of precipitation using adaptable random forests. Sparse regularization for precipitation downscaling ebtehaj. Downscaling heavy precipitation over the united kingdom. The challenge is to deal with a complex terrain, heavily skewed precipitation distributions and a sparse amount of data. Zhangadaptive lasso for sparse highdimensional regression models. We have considered a window having 3x60kmx3x60km of area, and the downscaling target is the. The analysis is conducted for two seasons, the growing season and the nongrowing season, defined based on variability of surface air. There are assumptions of stationarity between the large and small scale dynamics when using statistical downscaling. Precipitation is an essential input parameter for land surface models because it controls a large variety of environmental processes.
The increasing availability of precipitation observations from space, e. Spatial downscaling of trmm precipitation data using an. Section describes the four downscaling methods as well as the evaluation criteria. Statistical downscaling techniques for global climate model simulations of temperature and precipitation with application to water resources planning. On variational downscaling, fusion, and assimilation of. Research article spatial downscaling of trmm precipitation. Both sparsity and structured sparsity regularization methods seek to exploit the assumption that the output variable i. Validation of a precipitation b mean monthly temperature at pahalgam station for the period 2010 2014. Probabilistic downscaling of precipitation data in a. All methods are applied to rcm results instead of their usual application to gcms compare figure 1.
This technique is based on dynamical or statistical approaches commonly used in several disciplines, especially meteorology, climatology and remote sensing. The purpose of this study is to develop a statistical downscaling model to predict extreme rainfall with elasticnet regularized quantile regression. Trmm precipitation values when the downscaling results at a ne scale are upscaled or aggregated to the coarse scale. This regularization capitalizes on two main observations. The true spirit of delight, the exaltation, the sense of being more than man, which is the touchstone of the highest excellence, is to be found in mathematics as surely as poetry. Precipitation downscaling also acts as an underpinning. Coupling statistical and dynamical methods for spatial. Downscaling target is 60km resolution of daily precipitation for 20km resolution data. Sparse regularization for precipitation downscaling. Santri 7 applied lasso regularization in quantile regression.