Simulated changes due to global warming in the variability of precipitation, and their interpretation using a gamma-distributed stochastic model

The interannual variability of monthly mean January and July precipitation and its possible change due to global warming are assessed using a five-member ensemble of climate for the period 1871–2100, simulated by the CSIRO Mark 2 global coupled atmosphere–ocean model. In the 1961–1990 climate, for much of the middle to high latitudes the standard deviation of precipitation for both months is roughly proportional to the mean, with the coefficient of variation (C) typically 0.3–0.5. The variability there is shown to be largely consistent with that from a first-order Markov chain model of the daily rainfall occurrence, with the distribution of wet-day amounts approximated by a gamma distribution. Global distributions of Mark 2-based parameters of this stochastic model, commonly used in weather generators, are presented. In low latitudes, however, the variability from the coupled model is typically double that anticipated by the stochastic model, as quantified by an ‘overdispersion ratio’. C often exceeds one at subtropical locations, where rain is less frequent, but sometimes relatively heavy.The standard deviation of monthly mean precipitation S generally increases as the global model warms, with the global mean S in 2071–2100 in January (July) being 9.0% (11.5%) larger than in 1961–1990. Decreases in some subtropical locations occur, particularly where mean precipitation decreases. The global pattern of overdispersion is largely unchanged, however, and the changes in S can be related to those in the stochastic model parameters. Much of the increase in S is associated with increases in the scale parameter of the gamma distribution of wet-day amounts. Changes in C, which is unaffected by this parameter, are generally small. Increases in C in several subtropical bands and over northern midlatitude land in July are related to a decreased frequency of precipitation, and (to a lesser degree) changes in the gamma shape parameter. Some potential applications of the results to downscaling are discussed, and illustrated using observed rainfall from southeast Australia.     

Conditional resampling of hydrologic time series using multiple predictor variables: A K-nearest neighbour approach

Unlike parametric alternatives for time series generation, non-parametric approaches generate new values by conditionally resampling past observations using a probability rationale. Observations lying ‘close’ to the conditioning vector are resampled with higher probability, ‘closeness’ is defined using a Euclidean or Mahalanobis distance formulation. A common problem with these approaches is the difficulty in distinguishing the importance of each predictor in the estimation of the distance. As a consequence, the conditional probability and hence the resampled series, can offer a biased representation of the true population it aims to simulate. This paper presents a variation of the K-nearest neighbour resampler designed for use with multiple predictor variables. In the modification proposed, an influence weight is assigned to each predictor in the conditioning set with the aim of identifying nearest neighbours that represent the conditional dependence in an improved manner. The workability of the proposed modification is tested using synthetic data from known linear and non-linear models and its applicability is illustrated through an example where daily rainfall is downscaled over 15 stations near Sydney, Australia using a predictor set consisting of selected large-scale atmospheric circulation variables.     

Area-to-point Kriging with inequality-type data

In practical applications of area-to-point spatial interpolation, inequality constraints, such as non-negativity or more general constraints on the maximum and/or minimum attribute value, should be taken into account. The geostatistical framework proposed in this paper deals with the spatial interpolation problem of downscaling areal data under such constraints, while: (1) explicitly accounting for support differences between sample data and unknown values, (2) guaranteeing coherent (mass-preserving) predictions, and (3) providing a measure of reliability (uncertainty) for the resulting predictions. The formal equivalence between Kriging and spline interpolation allows solving constrained area-to-point interpolation problems via quadratic programming (QP) algorithms, after accounting for the support differences between various constraints involved in the problem formulation. In addition, if inequality constraints are enforced on the entire set of points discretizing the study domain, the numerical algorithms for QP problems are applied only to selected locations where the corresponding predictions violate such constraints. The application of the proposed method of area-to-point spatial interpolation with inequality constraints in one and two dimension is demonstrated using realistically simulated data.     

Application of Bias Correction and Spatial Disaggregation in Removing Model Biases and Downscaling over China

Global Climate Models (GCM) are the primary tools for studying past climate change and evaluating the projected future response of climate system to changing atmospheric composition. However, the state of art GCMs contain large biases in regional or local scales and are often characterized by low resolution which is too coarse to provide the regional scale information required for regional climate change impact assessment. A popular technique, Bias Correction and Spatial Disaggregation (BCSD), are widespreadly employed to improve the quality of the raw model output and downscaling throughout the world. Unfortunately, this method has not been applied in China. Consequently, the detailed principle and procedure of BCSD are introduced systematically in this study. Furthermore, the applicability of BCSD over China is also examined based on an ensemble of climate models from phase five of the Coupled Model Intercomparison Project (CMIP5), though the excellent performance of it has been validated for other parts of the world in many works. The result shows that BCSD is an effective, model independent approach to removing biases of model and downscaling. Finally, application scope of BCSD is discussed, and a suite of fine resolution multimodel climate projections over China is developed based on 34 climate models and two emissions scenarios (RCP4.5 and RCP8.5) from CMIP5.     

Stochastic downscaling method: application to wind refinement

In this article, we propose a new stochastic downscaling method: provided a numerical prediction of wind at large scale, we aim to improve the approximation at small scales thanks to a local stochastic model. We first recall the framework of a Lagrangian stochastic model borrowed from Pope. Then, we adapt it to our meteorological framework, both from the theoretical and numerical viewpoints. Finally, we present some promising numerical results corresponding to the simulation of wind over the Mediterranean Sea.     

Study on Precipitation Image Downscaling Based on the Method of Ill-posed Problems Solving

Downscaling of remote sensing precipitation products and the forecasting of circulation model are always the intense interests in hydrology and meteorology. The essence of downscaling is primarily to enhance resolution of observation or simulated rainfall field, and to appropriately increase its details or high frequency characteristics. Precipitation, as the main driving factors of the earth’s hydrologic cycle, not only affects the moisture and heat condition of a certain river basin, but also affects the global water and heat circulation. Based on the properties of rainfall self-similarity structure, the mathematically ill-posed precipitation problem solving method was used in low resolution downscaling precipitation for high resolution reconstruction. When solving the downscaling ill-posed problem, the greedy method of orthogonal matching pursuit was introduced so as to get the best high-resolution estimation in an optimal sense. It is hard to imagine that we might be able to find very similar (in mathematical norms) precipitation patterns over relatively large storm-scales. However, finding similar features over sufficiently small sub-storm scales seems more feasible. Based on the characteristics that small scale organized precipitation features tend to recur across different storm environments, the precipitation of both high and low resolution was obtained by training, which could be used to reconstruct the desired high-resolution precipitation field. Multi-source merged precipitation products were used in this experiment. Given the consideration of incompleteness of merged precipitation dataset, it was firstly interpolated based on the method of Fields of Experts (FoEs), which could solve the problem that common interpolation method could hardly work on the interpolation for dataset where consecutive missing data exists. Secondly, ideal experiments of precipitation products downscaling were carried out, where smooth coupling sampling and resampling operator were adopted respectively. Assessment based on the metrics of fidelity and spatial structural similarity demonstrates that the method used in this paper is feasible.     

降尺度方法在安徽省月降水量预测中的试用

基于NCEP/NCAR 500 hPa高度场、T63月动力延伸预报500 hPa高度场和安徽省降水资料,依据动力预报产品释用方法中所建立的月降水距平百分率预报方程,从月和旬两种不同时间尺度以及固定资料和选择资料来反演方程系数共4种降尺度方法来预报安徽省20个代表站月降水。1995—2005年11 a的回报检验表明了4种方法都具有较好的预报能力,从旬时间尺度较月尺度来预报月降水具有优势,在汛期和汛期降水偏多年更为明显。     

基于月动力延伸预报最优信息的中国降水降尺度预测模型

利用国家气候中心月动力延伸预报结果、NCEP/NCAR再分析资料和中国160个站观测资料,通过计算两次相关的方法,获取最优预报信息作为建立降尺度预测模型的预测因子,提取的最优预测因子同时满足既是观测环流要素场影响降水的关键区域,又是模式要素场预报的高技巧区域两个条件.结合挑选出的最优预测因子,利用最优子集回归建立月平均降水的降尺度预测模型.文中设计了消除预测因子和预测量的线性趋势值后建立预测模型(方案1)和直接利用原始资料建立预测模型(方案2)两种方案.经过独立样本检验,发现这两种方案建立的预测模型都能够提高月尺度降水预测,方案1对月尺度降水预测的距平相关系数平均可达0.35.利用该方案对超前时间分别为0、5、10 d的月动力延伸预报产品进行月降水的降尺度预测表明,模式初值信息不仅影响月动力延伸预报结果,也影响降尺度应用效果,利用超前时间为0和5 d的月动力延伸预报结果进行降水降尺度预测可在业务中参考.此外,降尺度预测模型中选取的预测因子不仪在统计上是显著的,同时也具有清楚的物理意义.     

基于海气耦合模式的山西省夏季降水统计降尺度预测研究

基于中国气象局国国家气候中心海气耦合模式(CGCM/NCC)预测产品和山西省50站夏季降水资料,利用典型因子回归的方法(CCA),建立了山西省夏季降水的统计降尺度预测模型。该预测模型选取了CGCM/NCC模式夏季500 h Pa高度场和海平面气压作为预测因子,分别选取了长江中下游地区和热带中东太平洋作为预报关键区。统计降尺度模型对2007~2014年山西省夏季降水的回算较模式原始结果有显著提高,除2008年外,空间距平相似系数(ACC)均通过了0.01的显著性检验,时间相关系数(TCC)在山西省大部分地区都有显著提高,最大可达0.6,降水预测(PS)评分在70分以上。检验结果显示,基于CCA降尺度方法建立的预测模型对山西省夏季降水模态预测的准确率较高且比较稳定,其预测效果远高于CGCM/NCC直接输出降水结果。     

Downscaling of precipitation for climate change scenarios: A support vector machine approach

The Climate impact studies in hydrology often rely on climate change information at fine spatial resolution. However, general circulation models (GCMs), which are among the most advanced tools for estimating future climate change scenarios, operate on a coarse scale. Therefore the output from a GCM has to be downscaled to obtain the information relevant to hydrologic studies. In this paper, a support vector machine (SVM) approach is proposed for statistical downscaling of precipitation at monthly time scale. The effectiveness of this approach is illustrated through its application to meteorological sub-divisions (MSDs) in India. First, climate variables affecting spatio-temporal variation of precipitation at each MSD in India are identified. Following this, the data pertaining to the identified climate variables (predictors) at each MSD are classified using cluster analysis to form two groups, representing wet and dry seasons. For each MSD, SVM- based downscaling model (DM) is developed for season(s) with significant rainfall using principal components extracted from the predictors as input and the contemporaneous precipitation observed at the MSD as an output. The proposed DM is shown to be superior to conventional downscaling using multi-layer back-propagation artificial neural networks. Subsequently, the SVM-based DM is applied to future climate predictions from the second generation Coupled Global Climate Model (CGCM2) to obtain future projections of precipitation for the MSDs. The results are then analyzed to assess the impact of climate change on precipitation over India. It is shown that SVMs provide a promising alternative to conventional artificial neural networks for statistical downscaling, and are suitable for conducting climate impact studies.