Abstract
Abstract. Data assimilation (DA) provides initial states of model runs by combining observational information and models. Ensemble-based DA methods that depend on the ensemble run of a model have been widely used. In response to the development of seamless prediction based on coupled models or even Earth system models, coupled DA is now in the mainstream of DA development. In this paper, we focus on the technical challenges in developing a coupled ensemble DA system, especially how to conveniently achieve efficient interaction between the ensemble of the coupled model and the DA methods. We first propose a new DA framework, DAFCC1 (Data Assimilation Framework based on C-Coupler2.0, version 1), for weakly coupled ensemble DA, which enables users to conveniently integrate a DA method into a model as a procedure that can be directly called by the model ensemble. DAFCC1 automatically and efficiently handles data exchanges between the model ensemble members and the DA method without global communications and does not require users to develop extra code for implementing the data exchange functionality. Based on DAFCC1, we then develop an example weakly coupled ensemble DA system by combining an ensemble DA system and a regional atmosphere–ocean–wave coupled model. This example DA system and our evaluations demonstrate the correctness of DAFCC1 in developing a weakly coupled ensemble DA system and the effectiveness in accelerating an offline DA system that uses disk files as the interfaces for the data exchange functionality.
Highlights
Data assimilation (DA) methods, which provide initial states of model runs by combining observational information and models, have been widely used in weather forecasting and climate prediction
The experiences gained from Parallel Data Assimilation Framework (PDAF) and Employing Message Passing Interface for Researching Ensembles (EMPIRE) show that a framework with an online implementation that handles the data exchanges via MPI functionalities is essential for improving the interaction between the model and the DA software
We propose a new common, flexible and efficient framework for weakly coupled ensemble data assimilation based on C-Coupler2.0, DAFCC1
Summary
Data assimilation (DA) methods, which provide initial states of model runs by combining observational information and models, have been widely used in weather forecasting and climate prediction. Liu et al, 2018), employ disk files as the interfaces of data exchange between the model ensemble members and the DA methods, and iteratively switch between the run of the model ensemble and DA using software-based restart functionality that relies on disk files. Such an implementation (called offline implementation hereafter) can guarantee software independence between the models and the DA methods, so as to achieve flexibility and convenience in software integration; the extra I/O accesses of disk files, as well as the extra initialization of software modules introduced by the data exchange and the restarts, are time-consuming and can be a severe performance bottleneck under finer model resolution (Heinzeller et al, 2016; Craig et al, 2017).
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