Sparse regularization for precipitation downscaling book pdf

Extreme rainfall prediction using bayesian quantile regression in. Pdf development and evaluation of statistical downscaling. A combined statistical bias correction and stochastic. Coupling statistical and dynamical methods for spatial.

Intense precipitation of short duration that characterizes most of the rainfall in the tropics. Statistical downscaling of precipitation using extreme value theory petra friederichs meteorological institute university of bonn e2c2giacs summer school, romania, september 312, 2007. Sparse regularization for precipitation downscaling ebtehaj 2012. On variational downscaling, fusion, and assimilation of. Spatial downscaling of precipitation using adaptable.

Statistical downscaling to predict monthly rainfall using. Sparse linear regression is widely used in biomedical data analysis. We have considered a window having 3x60kmx3x60km of area, and the downscaling target is the. Sparse regularization for precipitation downscaling, j. Probabilistic multisite statistical downscaling for daily. 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. Statistical downscaling of precipitation using extreme. A comparison of statistical and dynamical downscaling of. The 32 use of statistical methods for this purpose is rooted in both operational weather forecasting and 33 synoptic climatology and has become a.

However, the commonly sparse meteorological networks in complex terrains are. Statistical downscaling delta method ccafs climate. Research article statistical downscaling of erainterim. Statistical downscaling of rainfall data using sparse variable selection methods. Probabilistic downscaling of precipitation data in a. 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 work, we recast the rainfall downscaling into an illposed inverse problem and introduce a class of nonlinear estimators to properly regularize it and obtain the best highresolution estimate in an optimal sense. Probabilistic downscaling of precipitation data in a subtropical mountain area.

However, the commonly sparse meteorological networks in complex terrains are unable to provide the information needed for many applications. 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. A statistical downscaling method for daily air temperature in data. Quantile regression with elasticnet in statistical. Statistical downscaling of precipitation using long shortterm memory recurrent neural networks fig.

Using sparse regularization for multiresolution tomography. Water resources management, agriculture and forestry, tourism, ood protection, to. Sparse regularization for precipitation downscaling ebtehaj. 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. This regularization capitalizes on twomain observations. The increasing availability of precipitation observations from space, e. Application to seasonal precipitation in china chen lijuan beijing climate center, china meteorological administration, beijing, p. Dec 11, 20 the increasing availability of precipitation observations from space, e. Therefore, downscaling local precipitation is necessary. We analyze the variational method for sparse regression using. Water resources management, agriculture and forestry, tourism, ood protection, to name just a few. Zhangadaptive lasso for sparse highdimensional regression models. Statistical downscaling of precipitation using extreme value. Trmm precipitation values when the downscaling results at a ne scale are upscaled or aggregated to the coarse scale.

This downscaling is important because climate models are spatially coarse 50200 km and often misrepresent extremes in important meteorological variables, such as temperature and precipitation. Pdf this paper introduces precdwarf precipitation downscaling with adaptable random forests, a novel machinelearning based. Statistical downscaling sd is a model that uses satellite data from general. Projections of future global average annual precipitation and temperature for rcp 2. Statistical downscaling of rainfall data using sparse. Pdf sparse regularization for precipitation downscaling. The term downscaling usually refers to an increase in spatial resolution, but it is often also used for temporal resolution. Sparse regularization for precipitation downscaling. It provides the advantage of better control on the estimated model in comparison with traditional regularization methods and, in some cases, leads to a faster iterative convergence. Pdf, nrc committee on progress and priorities of us weather research and. Pdf spatial downscaling of precipitation using adaptable random.

Statistical downscaling of precipitation using quantile. The guidelines were prepared for consideration by the. A conventional approach is based on tikhonov regularization tikhonov and arsenin, 1977 and aims to bal. Pdf sparse downscaling and adaptive fusion of multi. The precipitation of two gcms were used as covariates i. Sparse regularization for precipitation downscaling by. Intercomparison of machine learning methods for statistical.

The area is highly urbanized and has a dense but irregular network of rain gauges designed and used for urban hydrol ogy applications. Within this context, the aim of the present paper is to propose a multisite probabilistic regressionbased model that adapts this approach for daily precipitation downscaling. Three statistical downscaling models are used to test the multimodel ensemble on the clutha catchment in new zealand. 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. An evaluation of statistical and dynamical techniques for. Pdf several statistical downscaling models have been developed in the past. This paper illustrates a statistical downscaling technique considering the spatial correlation structure of precipitation. In this type of mathematical problem a functional cost is minimized geophysical inverse theory and regularization problems. Sparse downscaling and adaptive fusion of multisensor precipitation. 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. Downscaling is any procedure to infer highresolution information from lowresolution variables. The gcm output is a climate data precipitation, temperature, etc. Downscaling of remotely sensed precipitation images and outputs of general circulation models has been a subject of intense interest in. Statistical downscaling based on regression models requires one to sample from the conditional distribution to preserve the variance of observed precipitation.

China 81 bpcca is used to build the statistical downscaling model between large scale circulation over east asia and precipitation over china in boreal. Downscaling of remotely sensed precipitation images and outputs of general circulation models has been a subject of intense interest in hydrometeorology. Statistical downscaling of rainfall data using sparse variable selection methods article in environmental modelling and software 2611. However, these downscaling methods rely on current estimates of the spatial distributions of these variables and largely assume that the smallscale. This technique is based on dynamical or statistical approaches commonly used in several disciplines, especially meteorology, climatology and remote sensing. 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. Here we use output from the highresolution 2 km weather research and forecasting wrf model, version 3. Statistical downscaling is a twostep process consisting of i the development of statistical relationships between local climate variables e. Research article spatial downscaling of trmm precipitation. For instance, the first applications of neural networks date back to the.

Lasso is an alternative regularized version of least squares, which is useful for. The emission pathway is representative for scenarios in the literature leading to very low greenhouse gas concentration levels. Pdf sparse downscaling and adaptive fusion of multisensor. The problem of downscaling is basically one of resolution enhancement, that is, appropriately adding details or high. Downscaling satellite precipitation with emphasis on. Statistical downscaling r package and application to snow. Trmm precipitation values when the downscaling results at. Conceptual framework 20 the proposed sparse restoration of a high. An evaluation of statistical and dynamical techniques for downscaling local climate james murphy.

Statistical downscaling of erainterim forecast precipitation data in. Shaping regularization is integrated in a conjugategradient algorithm for iterative leastsquares estimation. The purpose of this study is to develop a statistical downscaling model to predict extreme rainfall with elasticnet regularized quantile regression. A statistical downscaling technique, in which local values are inferred from. Properly validated, these data provide a new method of estimating the spatial distribution of precipitation over complex terrain. Downscaling target is 60km resolution of daily precipitation for 20km resolution data. Statistical downscaling of precipitation is required as part of many climate change studies.

Santri 7 applied lasso regularization in quantile regression. Typically, gcms have a resolution of 150300 km by 150300 km. Evaluation of two statistical downscaling models for daily. In typical downscaling applications, the set of potential predictors comprises. 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. Abstract an assessment is made of downscaling estimates of screen temperature and precipitation observed at 976 european stations during 198394.

Pryor atmospheric science program, department of geography, indiana uni ersity student building, 701east kirkwood a e. Since satellite measured precipitation is often too coarse for practical applications, it is essential to develop spatial downscaling algorithms. Precipitation is an essential input parameter for land surface models because it controls a large variety of environmental processes. The exact downscaling locations used for our study in sync with kannan and ghosh 20s work are depicted in table 2. 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 future precipitation extremes to urban hydrology scales. The major problems with these projections are both climate model biases and the gap between gridbox and point scale. Atmospheres wiley online library skip to article content. Sparse regularization for precipitation downscaling ebtehaj 2012 journal of geophysical research.

Uncertainty analysis of statistical downscaling methods. A comparison of techniques for downscaling extreme. Furthermore, we explore the use of huber regularization in a. Statistical downscaling of erainterim forecast precipitation. Using sparse regularization for cit for the global navigation satellite system gnss satellites to cross the sky.

R packagesnow modelconclusionsfuture work statistical downscaling r package and application to snow lee richardson 1 2 rachel mccrary 1 doug nychka 1 1national center for atmospheric research 2carnegie mellon university july 30, 2015 125. Interpolation of these anomalies using centroids of gcm cells as points for interpolation. Statistical downscaling of precipitation using long short. Downscaling techniques have been developed to refine atmosphereocean global climate models aogcms data and to provide information at more relevant scales. To this end, a new machine learning method, lasso algorithm. Spatial downscaling of gpm annual and monthly precipitation.

Empiricalstatistical downscaling and error correction of. In order to distinguish this application from perfect prog downscaling which does. Uncertainty analysis of statistical downscaling methods mohammad sajjad khan, paulin coulibaly, yonas dibike department of civil engineeringschool of geography and geology, mcmaster university, hamilton, ont. Statistical and dynamical downscaling of precipitation. Regression with l1 regularization lasso method was compared to principal component regression pcr in statistical downscaling sds modeling to predict monthly rainfall. On variational downscaling, fusion, and assimilation of hydrometeorological states. 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. Downscaling satellite precipitation with emphasis on extremes. For more info see the downscaling global circulation model outputs. Hofer et al statistical downscaling for datasparse, glaciated mountain regions. Spatial downscaling of trmm precipitation using geostatistics and fine scale environmental variables.

Here we apply a downscaling method based on thin plate spline spatial interpolation of anomalies deltas of original gcm outputs. In this paper, we present a new technique for downscaling precipitation. Pdf statistical downscaling to predict monthly rainfall. For example, local intensity scaling loci and quantilemapping qm are the standard. Statistical downscaling description downscaling is a method for obtaining highresolution climate or climate change information from relatively coarseresolution global climate models gcms. Configuration and intercomparison of deep learning neural models.