A sequential Monte Carlo approach for marine ecological prediction
A sequential Monte Carlo approach for marine ecological prediction
复制标题
DOI：
10.1002/env.780
复制
发表时间：
2006-08-01
期刊：
影响因子：
1.7
通讯作者：
Dowd, Michael
中科院分区：
文献类型：
--
作者：
Dowd, Michael
本研究考虑在线估计和预测的背景下，海洋生态预测的问题。面向过程的动态生态系统模型与海洋观测相结合。非线性。非高斯状态空间模型提供了统计框架。相关的过滤（nowcasting）和预测（预报）问题，通过顺序蒙特卡罗方法。在这种情况下，顺序重要性重采样器与Metropolis-Hastings MCMC相结合。具体的重点是一个典型的海洋生态系统模型组成的四个相互作用的人口（浮游植物。浮游动物营养物和碎屑：PZND），其共同进化由耦合的非线性微分方程系统描述。在状态演化方程中引入了随机环境变量，并引入了随机生长参数和动力学噪声。这个随机生态系统模型的动态行为是复杂的：它经常通过一个霍普夫分岔过渡，并表现出可变的幅度和持续时间的情节开花。该模型被应用到弱季节性的情况下。这是赤道东太平洋的海洋混合层。部分观测状态被认为是由一个五年的卫星（SeaWiFS）得出的海洋浮游植物浓度在12度N 95度W的时间序列。使用序贯蒙特卡罗方法获得了生态系统状态和动态参数的滤波估计。这些显示了在观察时间的预测-校正行为。包括在对测量无效的预测之后，中间水平的突然变化。还观察到相应的方差（也包括偏度和峰度）增长和随后的崩溃。预测实验表明，一些负偏差。并建议有预测能力预测10-15天。版权所有（c）2005年约翰威利父子。公司
This study considers the problem of marine ecological prediction in the context of online estimation and forecasting. Process oriented dynamic ecosystem models are combined with marine observations. The nonlinear. nonGaussian state space model provides the statistical framework. The associated filtering (nowcasting) and prediction (forecasting) problems are addressed via sequential Monte Carlo methods. in this instance a sequential importance resampler combined With Metropolis-Hastings MCMC. The specific focus is on a prototypical marine ecosystem model comprised Of four interacting populations (phytoplankton. zooplankton. nutrients and detritus: PZND) whose co-evolution is described by system of coupled nonlinear differential equations. Stochastic environmental variation is introduced through a stochastic growth parameter, as Well as through dynamical noise in the state evolution equations. The dynamic behaviour of this stochastic ecosystem model is complex: it regularly transitions through a Hopf bifurcation and exhibits episodic blooms of variable magnitude and duration. The model is applied to a case with Weak seasonality. that is the oceanic mixed layer in the eastern equatorial Pacific. A partially observed state is considered comprised of a five year satellite (SeaWiFS) derived time series of ocean phytoplankton concentration at 12 degrees N 95 degrees W. Filtering estimates for the ecosystem state and a dynamic parameter were obtained using the Sequential Monte Carlo approach. These showed predictor-corrector behaviour at observation times. including abrupt shifts in the median level after forecasts over measurement void. A corresponding variance (also skewness and kurtosis) growth and subsequent collapse Was also Seen. Forecasting experiments indicate some negative bias. and suggest there is predictive skill for forecasts out to 10-15 days. Copyright (c) 2005 John Wiley & Sons. Ltd.