数据同化
降水
气象学
卫星
环境科学
气候学
同化(音韵学)
集合卡尔曼滤波器
卡尔曼滤波器
计算机科学
地理
扩展卡尔曼滤波器
地质学
工程类
人工智能
航空航天工程
哲学
语言学
作者
Takemasa Miyoshi,Koji Terasaki,Shunji Kotsuki,Shigenori Otsuka,Yingwen Chen,Kaya Kanemaru,Kozo Okamoto,Keiichi Kondo,Guo-Yuan Lien,Hisashi Yashiro,Hirofumi Tomita,Masaki Satoh,Eugenia Kalnay
出处
期刊:Elsevier eBooks
[Elsevier]
日期:2022-01-01
卷期号:: 787-804
标识
DOI:10.1016/b978-0-12-822973-6.00020-2
摘要
This chapter describes the authors’ recent achievements on enhancing data assimilation of satellite precipitation observations using a global ensemble data assimilation system known as the Nonhydrostatic ICosahedral Atmospheric Model (NICAM)-Local Ensemble Transform Kalman Filter (LETKF). In precipitation science, satellite data have been providing precious, fundamental information, while numerical models have been playing an equally important role. Data assimilation integrates the numerical models and real-world data and brings synergy. We have been working on assimilating the Global Precipitation Measurement (GPM) data into the NICAM using the LETKF. We continue our effort on “Enhancing Precipitation Prediction Algorithm by Data Assimilation of GPM Observations” funded by JAXA, following successful completion of the 3-year project titled “Enhancing Data Assimilation of GPM Observations” from April 2016 to March 2019. The project first started in April 2013 on “Ensemble-based Data Assimilation of Tropical Rainfall Measuring Mission/GPM Precipitation Measurements,” where we developed a global data assimilation system NICAM-LETKF from scratch. This chapter highlights the recent achievements.
科研通智能强力驱动
Strongly Powered by AbleSci AI