Review of the manuscript HESS-2018-24 - “Improving soil moisture and runoff simulations over Europe using a high-resolution data-assimilation modeling framework”

2018 ◽  
Author(s):  
Anonymous
Keyword(s):  
Soil Moisture ◽  
High Resolution ◽  
Data Assimilation ◽  
Modeling Framework ◽  
High Resolution Data ◽  
Resolution Data

scholarly journals Improving soil moisture and runoff simulations over Europe using a high-resolution data-assimilation modeling framework

2018 ◽  
Author(s):  
Bibi S. Naz ◽  
Wolfgang Kurtz ◽  
Carsten Montzka ◽  
Wendy Sharples ◽  
Klaus Goergen ◽  
...  
Keyword(s):  
Soil Moisture ◽  
High Resolution ◽  
Water Resources ◽  
Data Assimilation ◽  
Open Loop ◽  
Modeling Framework ◽  
High Resolution Data ◽  
Continental Scale ◽  
Community Land Model ◽  
Assimilation Experiment

Abstract. Accurate and reliable hydrologic simulations are important for many applications such as water resources management, future water availability projections and predictions of extreme events. However, the accuracy of water balance estimates is limited by the lack of observations at large scales and the uncertainties of model simulations due to errors in model structure and inputs (e.g. hydrologic parameters and atmospheric forcings). In this study, we assimilated ESA CCI soil moisture (SM) information to improve the estimation of continental-scale soil moisture and runoff. The assimilation experiment was conducted over a time period from 2000 to 2006 with the Community Land Model, version 3.5 (CLM3.5) integrated with the Parallel Data Assimilation Framework (PDAF) at spatial resolution of 0.0275° (~ 3 km) over Europe. The model was forced with the high-resolution reanalysis COSMO-REA6 from the Hans-Ertel Centre for Weather Research (HErZ). Our results show that estimates of soil moisture have improved, particularly in the summer and autumn seasons when cross-validated with independent CCI-SM observations. On average, the mean bias in soil moisture was reduced from 0.1 mm3/mm3 in open-loop simulations to 0.004 mm3/mm3 with SM assimilation. The assimilation experiment also shows overall improvements in runoff, particularly during peak runoff. The results demonstrate the potential of assimilating satellite soil moisture observations to improve high-resolution soil moisture and runoff simulations at the continental scale, which is useful for water resources assessment and monitoring.

scholarly journals Recent Progress of the NHM-4DVAR towards a Super-High Resolution Data Assimilation

SOLA ◽  
2014 ◽  
Vol 10 (0) ◽  
pp. 145-149 ◽  
Author(s):  
Takuya Kawabata ◽  
Kosuke Ito ◽  
Kazuo Saito
Keyword(s):  
High Resolution ◽  
Data Assimilation ◽  
Recent Progress ◽  
High Resolution Data ◽  
Resolution Data

scholarly journals Estimating cardiac contraction through high resolution data assimilation of a personalized mechanical model

2018 ◽  
Vol 24 ◽  
pp. 85-90 ◽  
Author(s):  
Henrik Finsberg ◽  
Gabriel Balaban ◽  
Stian Ross ◽  
Trine F. Håland ◽  
Hans Henrik Odland ◽  
...  
Keyword(s):  
High Resolution ◽  
Data Assimilation ◽  
Mechanical Model ◽  
Cardiac Contraction ◽  
High Resolution Data ◽  
Resolution Data

scholarly journals High-resolution data assimilation of cardiac mechanics applied to a dyssynchronous ventricle

2017 ◽  
Vol 33 (11) ◽  
pp. e2863 ◽  
Author(s):  
Gabriel Balaban ◽  
Henrik Finsberg ◽  
Hans Henrik  Odland ◽  
Marie E. Rognes ◽  
Stian Ross ◽  
...  
Keyword(s):  
High Resolution ◽  
Data Assimilation ◽  
Cardiac Mechanics ◽  
High Resolution Data ◽  
Resolution Data

scholarly journals Improving Short-Term Storm Predictions by Assimilating both Radar Radial-Wind and Reflectivity Observations

2008 ◽  
Vol 23 (3) ◽  
pp. 373-391 ◽  
Author(s):  
Qingyun Zhao ◽  
John Cook ◽  
Qin Xu ◽  
Paul R. Harasti
Keyword(s):  
High Resolution ◽  
Data Assimilation ◽  
Radar Data ◽  
Naval Research ◽  
Short Term ◽  
High Resolution Data ◽  
Reflectivity Data ◽  
The Impact ◽  
Data Assimilation System ◽  
Resolution Data

Abstract A high-resolution data assimilation system is under development at the Naval Research Laboratory (NRL). The objective of this development is to assimilate high-resolution data, especially those from Doppler radars, into the U.S. Navy’s Coupled Ocean–Atmosphere Mesoscale Prediction System to improve the model’s capability and accuracy in short-term (0–6 h) prediction of hazardous weather for nowcasting. A variational approach is used in this system to assimilate the radar observations into the model. The system is upgraded in this study with new capabilities to assimilate not only the radar radial-wind data but also reflectivity data. Two storm cases are selected to test the upgraded system and to study the impact of radar data assimilation on model forecasts. Results from the data assimilation experiments show significant improvements in storm prediction especially when both radar radial-wind and reflectivity observations are assimilated and the analysis incremental fields are adequately constrained by the model’s dynamics and properly adjusted to satisfy the model’s thermodynamical balance.

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