Multi-scale Incremental Analysis Update Scheme and Its Application to Typhoon Mangkhut (2018) Prediction

Abstract

In the traditional incremental analysis update (IAU) process, all analysis increments are treated as constant forcing in a model’s prognostic equations over a certain time window. This approach effectively reduces high-frequency oscillations introduced by data assimilation. However, as different scales of increments have unique evolutionary speeds and life histories in a numerical model, the traditional IAU scheme cannot fully meet the requirements of short-term forecasting for the damping of high-frequency noise and may even cause systematic drifts. Therefore, a multi-scale IAU scheme is proposed in this paper. Analysis increments were divided into different scale parts using a spatial filtering technique. For each scale increment, the optimal relaxation time in the IAU scheme was determined by the skill of the forecasting results. Finally, different scales of analysis increments were added to the model integration during their optimal relaxation time. The multi-scale IAU scheme can effectively reduce the noise and further improve the balance between large-scale and small-scale increments in the model initialization stage. To evaluate its performance, several numerical experiments were conducted to simulate the path and intensity of Typhoon Mangkhut (2018) and showed that: (1) the multi-scale IAU scheme had an obvious effect on noise control at the initial stage of data assimilation; (2) the optimal relaxation time for large-scale and small-scale increments was estimated as 6 h and 3 h, respectively; (3) the forecast performance of the multi-scale IAU scheme in the prediction of Typhoon Mangkhut (2018) was better than that of the traditional IAU scheme. The results demonstrate the superiority of the multi-scale IAU scheme.

摘 要

在传统的分析增量更新 (IAU) 方案中, 所有的分析增量被当作强迫项, 在一段时间内加入模式预报方程中. 该方法能够有效地减少因资料同化而引入的高频振荡. 然而, 由于不同尺度的增量在数值模式中有各自不同的生命史和演变速度, 传统的 IAU 方法不能满足模式短临预报对于快速减少高频噪音的要求, 甚至有时候会进一步造成系统性的偏移. 因此, 本论文首次提出多尺度 IAU 方案. 通过空间滤波方法把分析增量分解成不同尺度的部分, 通过预报效果来确定不同尺度增量的最佳松弛时间. 最终, 不同尺度的分析增量在各自的最佳松弛时间中被加入到模式积分过程中. 多尺度 IAU 方案可以有效抑制模式积分过程中产生的噪音, 并进一步提高不同尺度增量在模式起始阶段的平衡. 为了评估这一方案的效果, 我们开展了几组数值试验对台风山竹的路径和强度进行模拟, 结果表明: (1) 多尺度 IAU 方案对资料同化初期所产生的噪音有明显的抑制效果; (2) 大尺度和小尺度增量的最佳松弛时间分别为 6 小时和 3 小时; (3) 多尺度 IAU 方案在台风山竹中的预报效果优于传统 IAU 方案. 这一系列模拟结果展现了多尺度 IAU 方案的优越性.