This title appears in the Scientific Report :
2022
An ensemble-based parallel data assimilation and data analytics framework for the development of continental-scale high-resolution soil moisture reanalysis
An ensemble-based parallel data assimilation and data analytics framework for the development of continental-scale high-resolution soil moisture reanalysis
Soil moisture (SM) is a key variable which controls the exchange of water, energy and carbon fluxes between the land surface and atmosphere. Therefore, accurate characterization of spatial distribution and temporal variations of SM is critical for many regional–scale applications, including meteorol...
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Personal Name(s): | Naz, Bibi (Corresponding author) |
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Hendricks-Franssen, Harrie-Jan / Görgen, Klaus / Bourgart, Benjamin / Montzka, Carsten / Comito, Claudia / Coquelin, Daniel / Kollet, Stefan | |
Contributing Institute: |
Agrosphäre; IBG-3 |
Imprint: |
2022
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Conference: | ESA Living Planet Symposium 2022, Bonn (Germany), 2022-05-23 - 2022-05-27 |
Document Type: |
Poster |
Research Program: |
Agro-biogeosystems: controls, feedbacks and impact |
Publikationsportal JuSER |
Soil moisture (SM) is a key variable which controls the exchange of water, energy and carbon fluxes between the land surface and atmosphere. Therefore, accurate characterization of spatial distribution and temporal variations of SM is critical for many regional–scale applications, including meteorology, hydrology, flood forecasting, drought monitoring, agriculture and climate change impact studies. Many global estimates of surface SM are provided by satellite sensors, but at coarse spatial resolutions (lower than 25 km), which are not very suitable for regional hydrologic and agriculture applications. Here we use a parallel data assimilation framework (PDAF) to assimilate coarse-resolution satellite derived soil moisture data into the community land model (CLM3.5). Using this framework, we assimilate the surface SM data from the European Space Agency Climate Change Initiative (ESA-CCI) using an Ensemble Kalman Filter (EnKF) into CLM3.5, producing a 16 years (2000–2015) high-resolution spatially and temporally consistent surface soil moisture reanalysis dataset (3 km, daily) over Europe. Given the big data volumes produced from these ensemble-based simulations, the parallel Helmholtz Analytics Toolkit (HeAT) tool is used to accelerate data analysis and post-processing of the ensemble-based model outputs. We validate our results with daily time series of observed surface SM data from 112 in-situ stations across Europe. This comparison shows that the assimilated SM captures the daily, seasonal and annual variations in soil moisture fairly well, with RMSE ranges 0.04 to 0.06 m³m⁻³ and overall correlation above 0.50 for most stations. In this presentation, we describe the validation of this newly created surface SM reanalysis with in-situ observations, global satellite and reanalysis products, and present benchmark results showing the computational efficiency of the workflow using high performance computing infrastructure. The dataset presented here provides long-term daily surface SM at a high spatiotemporal resolution and will be beneficial for many hydrological applications from regional to continental scales. |