DasPy 1.0 – the Open Source Multivariate Land Data Assimilation Framework in combination with the Community Land Model 4.5
- 1Forschungszentrum Jülich, Agrosphere (IBG 3), Jülich, Germany
- 2Key Laboratory of Remote Sensing of Gansu Province, Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou, China
- 3Centre for High-Performance Scientific Computing in Terrestrial Systems: HPSC TerrSys, Geoverbund ABC/J, Jülich, Germany
- 4CAS Center for Excellence in Tibetan Plateau Earth Sciences, Chinese Academy of Sciences, Beijing, China
- 5RIKEN Advanced Institute for Computational Science, Kobe, Japan
- 6Department of Civil Engineering, University of Bristol, Bristol, UK
- 7National-Local Joint Engineering Laboratory of Geo-Spatial information Technology, Hunan University of Science and Technology, Xiangtan, China
- 8Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
Abstract. Data assimilation has become a popular method to integrate observations from multiple sources with land surface models to improve predictions of the water and energy cycles of the soil-vegetation-atmosphere continuum. Multivariate data assimilation refers to the simultaneous assimilation of observation data from multiple model state variables into a simulation model. In recent years, several land data assimilation systems have been developed in different research agencies. Because of the software availability or adaptability, these systems are not easy to apply for the purpose of multivariate land data assimilation research. We developed an open source multivariate land data assimilation framework (DasPy) which is implemented using the Python script language mixed with the C++ and Fortran programming languages. LETKF (Local Ensemble Transform Kalman Filter) is implemented as the main data assimilation algorithm, and uncertainties in the data assimilation can be introduced by perturbed atmospheric forcing data, and represented by perturbed soil and vegetation parameters and model initial conditions. The Community Land Model (CLM) was integrated as the model operator. The implementation allows also parameter estimation (soil properties and/or leaf area index) on the basis of the joint state and parameter estimation approach. The Community Microwave Emission Modelling platform (CMEM), COsmic-ray Soil Moisture Interaction Code (COSMIC) and the Two-Source Formulation (TSF) were integrated as observation operators for the assimilation of L-band passive microwave, cosmic-ray soil moisture probe and land surface temperature measurements, respectively. DasPy has been evaluated in several assimilation studies of neutron count intensity (soil moisture), L-band brightness temperature and land surface temperature. DasPy is parallelized using the hybrid Message Passing Interface and Open Multi-Processing techniques. All the input and output data flows are organized efficiently using the commonly used NetCDF file format. Online 1-D and 2-D visualization of data assimilation results is also implemented to facilitate the post simulation analysis. In summary, DasPy is a ready to use open source parallel multivariate land data assimilation framework.
X. Han et al.
X. Han et al.
X. Han et al.
3 citations as recorded by crossref.
- Multivariate hydrological data assimilation of soil moisture and groundwater head D. Zhang et al. 10.5194/hess-20-4341-2016
- An Evaluation of Soil Moisture Anomalies from Global Model-Based Datasets over the People’s Republic of China D. Hagan et al. 10.3390/w12010117
- SMOS brightness temperature assimilation into the Community Land Model D. Rains et al. 10.5194/hess-21-5929-2017