An Introduction to Ensemble-Based Data Assimilation Method in the Earth Sciences

In this chapter, the ensemble-based data assimilation methods are introduced, including their developments, applications and existing concerns. These methods include both traditional methods such as Kalman filter and its derivatives and some advanced algorithms such as sigma-point Kalman filters and particle filters. Emphasis is placed on the challenges of applying these methods onto high-dimensional systems in the earth sciences

Parallel Longest Increasing Subsequence and van Emde Boas Trees

This paper studies parallel algorithms for the longest increasing subsequence (LIS) problem. Let $n$ be the input size and $k$ be the LIS length of the input. Sequentially, LIS is a simple problem that can be solved using dynamic programming (DP) in $O(n\log n)$ work. However, parallelizing LIS is a long-standing challenge. We are unaware of any parallel LIS algorithm that has optimal $O(n\log n)$ work and non-trivial parallelism (i.e., $\tilde{O}(k)$ or $o(n)$ span). This paper proposes a parallel LIS algorithm that costs $O(n\log k)$ work, $\tilde{O}(k)$ span, and $O(n)$ space, and is much simpler than the previous parallel LIS algorithms. We also generalize the algorithm to a weighted version of LIS, which maximizes the weighted sum for all objects in an increasing subsequence. To achieve a better work bound for the weighted LIS algorithm, we designed parallel algorithms for the van Emde Boas (vEB) tree, which has the same structure as the sequential vEB tree, and supports work-efficient parallel batch insertion, deletion, and range queries. We also implemented our parallel LIS algorithms. Our implementation is light-weighted, efficient, and scalable. On input size $10^9$, our LIS algorithm outperforms a highly-optimized sequential algorithm (with $O(n\log k)$ cost) on inputs with $k\le 3\times 10^5$. Our algorithm is also much faster than the best existing parallel implementation by Shen et al. (2022) on all input instances.Comment: to be published in Proceedings of the 35th ACM Symposium on Parallelism in Algorithms and Architectures (SPAA '23

Comparison of Perturbation Strategies for the Initial Ensemble in Ocean Data Assimilation with a Fully Coupled Earth System Model

It is widely recognized that the initial ensemble describes the uncertainty of the variables and, thus, affects the performance of ensemble-based assimilation techniques, which is investigated in this paper with experiments using the Community Earth System Model (CESM) and the Data Assimilation Research Testbed (DART) assimilation software. Five perturbation strategies involving adding noises of different patterns and with/without extra integration are compared in the observation system simulation experiments framework, in which the SST is assimilated with the ensemble adjustment Kalman filter method. The comparison results show that for the observed variables (sea surface temperature), the differences in the initial ensemble lead to different rate of convergence in the assimilation, but all experiments reach convergence after three months. However, other variables (sea surface height and sea surface salinity) are more sensitive to the initial ensemble. The analysis of variance results reveal that the white-noise perturbation scheme has the largest RMSE. After excluding the effect of the white noise perturbation scheme, it can be found that the difference in the effect of different initial ensembles on the SSH with only assimilated SST is concentrated in the region of the Antarctic Circumpolar Current, which is related to the spread of the covariance between the SSH and the SST

The Influence of Wind-Induced Waves on ENSO Simulations

We evaluated the influence of wind-induced waves on El Niño-Southern Oscillation (ENSO) simulations based on the First Institute of Oceanography-Earth System Model version 2 (FIO-ESM 2.0), a global coupled general circulation model (GCM) with a wave component. Two sets of experiments, the GCM, with and without a wave model, respectively, were conducted in parallel. The simulated sea surface temperature (SST) was cooled by introducing the wave model via the enhancement of the vertical mixing in the ocean upper layer. The strength of ENSO was intensified and better simulated with the inclusion of wave-induced mixing, particularly the La Niña amplitude. Furthermore, the simulated amplitude and spatial pattern of El Niño events were slightly altered with the wave model. Heat budget analyses revealed the intensification of La Niña events to be generally attributed to wave-induced vertical advection, followed by the zonal and meridional advection terms

Comparison of Volatile Flavor Compounds from Seven Types of Spiced Beef by Headspace Solid-phase Microextraction Combined with Gas Chromatography-olfactometry-mass Spectrometry (HS-SPME-GC-O-MS)

Spiced beef is a representative traditional Chinese processed beef product. In order to establish quality evaluation of aroma profile, the widely accepted and characteristic volatile flavor of seven typical spiced beef was investigated using headspace solid-phase microextraction combined with gas chromatography-olfactometry-mass spectrometry. A total of 67 volatile flavor compounds were identified in the spiced beef, with 23 compounds in common and 30 aroma-active compounds. The common key aroma-active compounds of the seven types of spiced beef included p-allyl-anisole, 1-methoxy-4-(1-propenyl)-benzene, 3-methyl-1-butanol, linalool, chavicol, alpha-phellandrene, myristicin, (Z)-3-hexenol, 1-terpinen-4-ol, furfuryl alcohol, dimethyl pyrazine, and 3-mercaptothiophene. The seven types of spiced beef could be divided into three categories based on their characteristic odors. These three categories had rich odors of sulfur-containing compounds, aldehydes, and spice, respectively. The characteristic odor reflects the geographical differences in consumption habits in China.The results provided a theoretical reference for quality evaluation of packaged spiced beef aroma profile