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BSCALE: Downscaling of precipitation: development, calibration and validation of a probabilisitc Bayesian approach.

BSCALE：降水量缩小：概率贝叶斯方法的开发、校准和验证。

基本信息

批准号：
386938837
负责人：
Professor Paolo Reggiani, Ph.D.
金额：
--
依托单位：
Lehrstuhl Wasserwirtschaft
依托单位国家：
德国
项目类别：
Research Grants
财政年份：
2017
资助国家：
德国
起止时间：
2016-12-31 至 2021-12-31
项目状态：
已结题

来源：
https://gepris.dfg.de/gepris/projekt/386938837?language=en
关键词：
BSCALE Downscaling precipitation development calibration

项目摘要

Downscaling of atmospheric model output is necessary to map variables from low-resolution spatial scales of observation or model prediction down to local scales, at which variables are needed for a wide range of applications, including data gap filling, hydrological or glaciological predictions, climate prognosis, irrigation or energy forecasting. Statistical downscaling is performed by seeking stochastic relationships between large-scale observed indicators and/or model output, serving as predictors, and a local-scale predictand. The underlying transformations are usually linear regressions, or more general non-linear transformations, such as quantile matching. In both cases, stationary homoscedastic relationships between stochastic variables are assumed, which correctly map the conditional mean across the transformation, but not necessarily the tails of the distributions, which characterize extreme meteorological events. Here we propose a probabilistic downscaling approach for precipitation, implemented as a Bayesian conditional processor, which supports non-linear transformations between meso-scale observations and model predictions with local variables, whereby stochastic dependency relationships are modelled in the Gaussian space. The procedure allows using multiple predictors over a spatial window, and can be extended to include multiple source models. By using Multivariate Truncated Normal Distributions (MTND), heteroscedastic dependency structures between transformed variables can be modelled in the Gaussian space, then marginalized analytically with respect to predictors and back-transformed into the original space. The downscaling of the Bayesian conditional estimate of precipitation from the meso-scale to the local scale is performed with a non-Markovian non-stationary stochastic weather generator. The Bayesian processor and weather generator need to be calibrated and validated over a sufficiently long time window, for which continuous predictions and observations are available.

为了将观测或模型预测的低分辨率空间尺度的变量映射到地方尺度，必须缩小大气模型输出的尺度，在地方尺度上，各种应用都需要变量，包括填补数据空白、水文或冰川预测、气候预测、灌溉或能源预测。统计降尺度是通过寻找大规模观测指标和/或模型输出（作为预测因子）与局部尺度预测变量之间的随机关系来进行的。底层转换通常是线性回归，或者更一般的非线性转换，如分位数匹配。在这两种情况下，假设随机变量之间的平稳同方差关系，其正确地映射了变换中的条件均值，但不一定是分布的尾部，这是极端气象事件的特征。在这里，我们提出了一个概率降尺度降水的方法，实施贝叶斯条件处理器，它支持非线性转换中尺度观测和模型预测与局部变量，从而在高斯空间中建模随机依赖关系。该过程允许在空间窗口上使用多个预测因子，并且可以扩展到包括多个源模型。通过使用多元截断正态分布（MTND），变换后的变量之间的异方差依赖结构可以在高斯空间中建模，然后相对于预测因子进行分析边缘化，并将其转换回原始空间。用非马尔可夫非平稳随机天气发生器将贝叶斯条件降水估计从中尺度降到局地尺度。贝叶斯处理器和天气生成器需要在足够长的时间窗口内进行校准和验证，以便进行连续的预测和观测。