LETKF-based Ocean Research Analysis (LORA) version 1.0

IF 3.3 3区地球科学 Q2 GEOSCIENCES, MULTIDISCIPLINARY

Geoscience Data Journal Pub Date : 2024-08-22 DOI:10.1002/gdj3.271

Shun Ohishi, Takemasa Miyoshi, Takafusa Ando, Tomohiko Higashiuwatoko, Eri Yoshizawa, Hiroshi Murakami, Misako Kachi

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引用次数: 0

Abstract

Local ensemble transform Kalman filter (LETKF)-based Ocean Research Analysis (LORA) version 1.0 datasets for western North Pacific (WNP) and Maritime Continent (MC) regions (LORA-WNP and -MC, respectively) are released through the JAXA-RIKEN Ocean Analysis website. The LORA datasets are created using an eddy-resolving LETKF-based ocean data assimilation system with satellite sea-surface temperature, salinity, and height data and with in-situ temperature and salinity data assimilated daily. The LORA datasets include 128-member ensemble analyses at the sea surface (2D), each term of mixed-layer temperature and salinity budget equations, and the related variables (2D) such as mixed-layer depth and heat and freshwater fluxes as well as system grid information and analysis ensemble mean and spread (3D), from August 2015 to January 2024 (as of June 2024). The LORA datasets are useful for geoscience research and practical applications, especially for particle tracking, boundary conditions of atmospheric models, and research on spatiotemporal variations in sea-surface temperature and salinity.

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基于 LETKF 的海洋研究分析（LORA）1.0 版

基于本地集合变换卡尔曼滤波器（LETKF）的北太平洋西部（WNP）和海洋大陆（MC）区域海洋研究分析（LORA）1.0版数据集（分别为LORA-WNP和-MC）通过JAXA-RIKEN海洋分析网站发布。LORA 数据集是利用基于 LETKF 的涡旋解析海洋数据同化系统和卫星海面温度、盐度和高度数据以及每日同化的原地温度和盐度数据创建的。LORA 数据集包括从 2015 年 8 月到 2024 年 1 月（截至 2024 年 6 月）的 128 个成员的海面集合分析（二维）、混合层温度和盐度预算方程的每个项、相关变量（二维），如混合层深度、热通量和淡水通量，以及系统网格信息和分析集合的平均值和分布（三维）。LORA 数据集可用于地球科学研究和实际应用，特别是粒子跟踪、大气模型的边界条件以及海面温度和盐度的时空变化研究。

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来源期刊

Geoscience Data Journal GEOSCIENCES, MULTIDISCIPLINARYMETEOROLOGY-METEOROLOGY & ATMOSPHERIC SCIENCES

CiteScore

5.90

自引率

9.40%

发文量

审稿时长

4 weeks

期刊介绍： Geoscience Data Journal provides an Open Access platform where scientific data can be formally published, in a way that includes scientific peer-review. Thus the dataset creator attains full credit for their efforts, while also improving the scientific record, providing version control for the community and allowing major datasets to be fully described, cited and discovered. An online-only journal, GDJ publishes short data papers cross-linked to – and citing – datasets that have been deposited in approved data centres and awarded DOIs. The journal will also accept articles on data services, and articles which support and inform data publishing best practices. Data is at the heart of science and scientific endeavour. The curation of data and the science associated with it is as important as ever in our understanding of the changing earth system and thereby enabling us to make future predictions. Geoscience Data Journal is working with recognised Data Centres across the globe to develop the future strategy for data publication, the recognition of the value of data and the communication and exploitation of data to the wider science and stakeholder communities.