Continental satellite soil moisture data assimilation improves root-zone moisture analysis for water resources assessment

2014 ◽  
Vol 519 ◽  
pp. 2747-2762 ◽  
Author(s):  
L.J. Renzullo ◽  
A.I.J.M. van Dijk ◽  
J.-M. Perraud ◽  
D. Collins ◽  
B. Henderson ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Water Resources ◽  
Data Assimilation ◽  
Root Zone ◽  
Soil Moisture Data ◽  
Moisture Analysis ◽  
Water Resources Assessment ◽  
Resources Assessment

A Global Root-Zone Soil Moisture Analysis Using Simulated L-band Brightness Temperature in Preparation for the Hydros Satellite Mission

2006 ◽  
Vol 7 (5) ◽  
pp. 1126-1146 ◽  
Author(s):  
G. Balsamo ◽  
J-F. Mahfouf ◽  
S. Bélair ◽  
G. Deblonde
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Brightness Temperature ◽  
Land Surface ◽  
Root Zone ◽  
Satellite Observations ◽  
Reference State ◽  
Observation Error ◽  
Moisture Analysis ◽  
L Band

Abstract The aim of this study is to test a land data assimilation prototype for the production of a global daily root-zone soil moisture analysis. This system can assimilate microwave L-band satellite observations such as those from the future Hydros NASA mission. The experiments are considered in the framework of the Interaction Soil Biosphere Atmosphere (ISBA) land surface scheme used operationally at the Meteorological Service of Canada for regional and global weather forecasting. A land surface reference state is obtained after a 1-yr global land surface simulation, forced by near-surface atmospheric fields provided by the Global Soil Wetness Project, second initiative (GSWP-2). A radiative transfer model is applied to simulate the microwave L-band passive emission from the surface. The generated brightness temperature observations are distributed in space and time according to the satellite trajectory specified by the Hydros mission. The impact of uncertainties related to the satellite observations, the land surface, and microwave emission models is investigated. A global daily root-zone soil moisture analysis is produced with a simplified variational scheme. The applicability and performance of the system are evaluated in a data assimilation cycle in which the L-band simulated observations, generated from a land surface reference state, are assimilated to correct a prescribed initial root-zone soil moisture error. The analysis convergence is satisfactory in both summer and winter cases. In summer, when considering a 3-K observation error, 90% of land surface converges toward the reference state with a soil moisture accuracy better than 0.04 m3 m−3 after a 4-week assimilation cycle. A 5-K observation error introduces 1-week delay in the convergence. A study of the analysis error statistics is performed for understanding the properties of the system. Special features associated with the interactions between soil water and soil ice, and the presence of soil moisture vertical gradients, are examined.

The New Stand-Alone Surface Analysis at ECMWF: Implications for Land–Atmosphere DA Coupling

2019 ◽  
Vol 20 (10) ◽  
pp. 2023-2042 ◽  
Author(s):  
David Fairbairn ◽  
Patricia de Rosnay ◽  
Philip A. Browne
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Surface Analysis ◽  
Land Surface ◽  
Root Zone ◽  
Forecast Model ◽  
Near Surface ◽  
Weather Forecasts ◽  
Moisture Analysis ◽  
Screen Level

Abstract This article presents the “screen-level and surface analysis only” (SSA) system at the European Centre for Medium-Range Weather Forecasts (ECMWF). SSA is a simplification of the operational land–atmosphere weakly coupled data assimilation (WCDA). The goal of SSA is to provide 1) efficient research into land surface developments in NWP and 2) land reanalyses with land–atmosphere coupling. SSA maintains a coupled forecast model between assimilation cycles, but the atmospheric analysis is not performed; rather, it is forced from an archived analysis. Hence, SSA is much faster than WCDA, although it lacks feedback between the land and atmospheric analyses. A global sensitivity analysis was performed over one year to compare the WCDA and SSA systems. Prescribed proxy 2-m temperature/humidity screen-level observation errors were approximately doubled in the soil moisture data assimilation, thereby reducing the average size of the root-zone soil moisture analysis increments by about 60%. The systematic impact of these changes on the WCDA surface and near-surface atmospheric dynamics was effectively captured by SSA, although the short-term impact was underestimated. Importantly, the SSA forecast verification scores accurately reflected those of WCDA: atmospheric 1–10-day temperature/humidity forecasts were degraded in the tropics and lower midlatitudes up to about 700 hPa. The soil moisture analysis performance was not significantly impacted. These results endorse SSA as an NWP research tool and confirm the role of assimilating proxy screen-level observations in the soil moisture analysis to improve weather forecasts. Appropriate use and limitations of SSA are considered.

Global soil water estimates as landslide predictor: the effectiveness of observations, simulations and data assimilation results

2020 ◽  
Author(s):  
Anne Felsberg ◽  
Gabriëlle De Lannoy ◽  
Manuela Girotto ◽  
Jean Poesen ◽  
Rolf Reichle ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Water Content ◽  
Root Zone ◽  
Satellite Observations ◽  
Antecedent Rainfall ◽  
Landslide Prediction ◽  
Hydrological Conditions ◽  
Soil Moisture Data ◽  
Grace Data

<p>Hydrological triggering of landslides is strongly connected to the water content of the soil. Previous local studies showed that the inclusion of predisposing soil hydrological conditions, such as soil moisture, improved the landslide prediction abilities over using rainfall only as predictor variable. Existing global models that predict landslides however still mostly rely on antecedent rainfall indices as a proxy for soil moisture conditions, because global precipitation data has been more readily available than soil moisture data. Soil moisture data are now available from satellite observations or modeling, or combinations thereof (data assimilation). Our research seeks to quantify to which extent global landslide prediction can benefit from these data products.</p><p>To tackle this question, we examined soil hydrological conditions at the times and locations of known landslide occurrences (Global Landslide Catalog, Kirschbaum et al. 2015). More specifically, we investigated soil moisture estimates retrieved from the Soil Moisture Ocean Salinity (SMOS) mission, simulated by the Catchment Land Surface Model (CLSM), or resulting from assimilation of SMOS or Gravity Recovery And Climate Experiment (GRACE) data into CLSM.</p><p>A first coarse-scale, univariate global analysis for the years 2011 through mid-2016 indicates that soil moisture and total water storage estimates are adequate alternatives for antecedent rainfall indices to predict landslides. In particular, the assimilation of SMOS or GRACE data into CLSM improves root-zone soil moisture and preferentially increases root-zone soil moisture at landslide events. Whereas both assimilation schemes help to predict landslides based on an increased landslide probability with increased water content, the SMOS or GRACE satellite observations alone (that is, without data assimilation) are too sparse, noisy or coarse to clearly distinguish the different hydrological conditions between landslide and non-landslide events.</p>

Exploring trends for the H SAF ASCAT root-zone soil moisture data records

2021 ◽  
Author(s):  
David Fairbairn ◽  
Patricia de Rosnay ◽  
Peter Weston
Keyword(s):  
Soil Moisture ◽  
Root Zone ◽  
Weather Prediction ◽  
Climate Change Impacts ◽  
Soil Moisture Data ◽  
Negative Root ◽  
In Situ Observations ◽  
Prediction Systems ◽  
Water Scarce

<p>Environmental (e.g. floods, droughts) and weather prediction systems rely on an accurate representation of soil moisture (SM). The EUMETSAT H SAF aims to provide high quality satellite-based hydrological products, including SM.<br>ECMWF is producing ASCAT root zone SM for H SAF. The production relies on an Extended Kalman filter to retrieve root zone SM from surface SM satellite data. A 10 km sampling reanalysis product (1992-2020) forced by ERA5 atmospheric fields (H141/H142) is produced for H SAF, which assimilates ERS/SCAT (1992-2006) and ASCAT-A/B/C (2007-2020) derived surface SM. The root-zone SM performance is validated using sparse in situ observations globally and generally demonstrates a positive and consistent correlation over the period. A negative trend in root-zone SM is found during summer and autumn months over much of Europe during the period (1992-2020). This is consistent with expected climate change impacts and is particularly alarming over the water-scarce Mediterranean region. The recent hot and dry summer of 2019 and dry spring of 2020 are well captured by negative root-zone SM anomalies. Plans for the future H SAF data record products will be presented, including the assimilation of high-resolution EPS-SCA-derived soil moisture data.</p>

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