scholarly journals Effects of the Near-Surface Soil Moisture Profile on the Assimilation of L-band Microwave Brightness Temperature

2006 ◽  
Vol 7 (3) ◽  
pp. 433-442 ◽  
Author(s):  
Henning Wilker ◽  
Matthias Drusch ◽  
Gisela Seuffert ◽  
Clemens Simmer
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Brightness Temperature ◽  
Model Error ◽  
Moisture Distribution ◽  
Brightness Temperatures ◽  
Correction Scheme ◽  
L Band ◽  
Data Assimilation System ◽  
Assimilation System

Abstract The impact of model and observation errors in the European Land Data Assimilation System (ELDAS) data assimilation system on the analyzed surface variables has been studied using the Southern Great Plains Hydrology Experiment (SGP) 1997 and 1999 datasets. The model error for soil moisture was derived from an error propagation experiment based on perturbed rainfall forcing data. It was found that the errors for the top three model layers are 0.010, 0.010, and 0.0015 m3 m−3, respectively. Data assimilation experiments based on screen-level variables (2-m temperature and humidity) and L-band brightness temperature observations from SGP97 with this error distribution result in improved soil moisture forecasts when compared to model runs with a vertically constant model error of 0.005 m3 m−3. In the second part of this study, the effect of the vertical soil moisture distribution—which can hardly be resolved by large-scale hydrological models—in the assimilation system has been quantified using SGP99 data. The vertical profile has a significant impact on the modeled brightness temperatures. Based on the time elapsed between a rainfall event and the observation, a correction scheme has been developed that can be applied in observation space. The assimilation of brightness temperatures led to more accurate predictions of soil moisture and surface fluxes when the correction scheme was used.

scholarly journals Monitoring Soil Moisture Drought over Northern High Latitudes from Space

2019 ◽  
Author(s):  
Jostein Blyverket ◽  
Paul D. Hamer ◽  
Philipp Schneider ◽  
Clément Albergel ◽  
William A. Lahoz
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Brightness Temperature ◽  
Land Surface ◽  
Standardized Precipitation Index ◽  
Drought Indices ◽  
Additional Information ◽  
Data Assimilation System ◽  
Temporal Coverage ◽  
Assimilation System

Mapping drought from space using, e.g., surface soil moisture (SSM), has become viable in the last decade. However, state of the art SSM retrieval products suffer from very poor coverage over northern latitudes. In this study, we propose an innovative drought indicator with a wider spatial and temporal coverage than that obtained from satellite SSM retrievals. We evaluate passive microwave brightness temperature observations from the Soil Moisture and Ocean Salinity (SMOS) satellite as a surrogate drought metric, and introduce a Standardized Brightness Temperature Index (STBI). The STBI is validated against drought indices from a land surface data assimilation system (LDAS-Monde), two satellite dervied SSM indices and a standardized precipitation index. Finally, we evaluate the STBI against the before mentioned drought indices in a case study of the 2018 Nordic drought. The STBI is found to be superior to the drought index created from satellite derived SSM in both spatial and temporal coverage over the Nordic region. Our results indicate that when compared to drought indices from precipitation data and a land data assimilation system, the STBI is able to capture the 2018 drought onset, severity and extent. Thus, the STBI index could provide additional information for drought monitoring in regions where the SSM retrieval problem is difficult.

scholarly journals The Canadian Land Data Assimilation System (CaLDAS): Description and Synthetic Evaluation Study

2015 ◽  
Vol 16 (3) ◽  
pp. 1293-1314 ◽  
Author(s):  
Marco L. Carrera ◽  
Stéphane Bélair ◽  
Bernard Bilodeau
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Land Surface ◽  
Weather Prediction ◽  
Surface Model ◽  
Land Data Assimilation ◽  
L Band ◽  
Land Data Assimilation System ◽  
Data Assimilation System ◽  
Assimilation System

Abstract The Canadian Land Data Assimilation System (CaLDAS) has been developed at the Meteorological Research Division of Environment Canada (EC) to better represent the land surface initial states in environmental prediction and assimilation systems. CaLDAS is built around an external land surface modeling system and uses the ensemble Kalman filter (EnKF) methodology. A unique feature of CaLDAS is the use of improved precipitation forcing through the assimilation of precipitation observations. An ensemble of precipitation analyses is generated by combining numerical weather prediction (NWP) model precipitation forecasts with precipitation observations. Spatial phasing errors to the NWP first-guess precipitation forecasts are more effective than perturbations to the precipitation observations in decreasing (increasing) the exceedance ratio (uncertainty ratio) scores and generating flatter, more reliable ranked histograms. CaLDAS has been configured to assimilate L-band microwave brightness temperature TB by coupling the land surface model with a microwave radiative transfer model. A continental-scale synthetic experiment assimilating passive L-band TBs for an entire warm season is performed over North America. Ensemble metric scores are used to quantify the impact of different atmospheric forcing uncertainties on soil moisture and TB ensemble spread. The use of an ensemble of precipitation analyses, generated by assimilating precipitation observations, as forcing combined with the assimilation of L-band TBs gave rise to the largest improvements in superficial soil moisture scores and to a more rapid reduction of the root-zone soil moisture errors. Innovation diagnostics show that the EnKF is able to maintain a sufficient forecast error spread through time, while soil moisture estimation error improvements with increasing ensemble size were limited.

scholarly journals Parameter Estimation Using Ensemble-Based Data Assimilation in the Presence of Model Error

2015 ◽  
Vol 143 (5) ◽  
pp. 1568-1582 ◽  
Author(s):  
Juan Ruiz ◽  
Manuel Pulido
Keyword(s):  
Parameter Estimation ◽  
Data Assimilation ◽  
Model Error ◽  
Forecast Skill ◽  
Moist Convection ◽  
Treatment Techniques ◽  
Correction Technique ◽  
Error Treatment ◽  
Data Assimilation System ◽  
Assimilation System

Abstract This work explores the potential of online parameter estimation as a technique for model error treatment under an imperfect model scenario, in an ensemble-based data assimilation system, using a simple atmospheric general circulation model, and an observing system simulation experiment (OSSE) approach. Model error is introduced in the imperfect model scenario by changing the value of the parameters associated with different schemes. The parameters of the moist convection scheme are the only ones to be estimated in the data assimilation system. In this work, parameter estimation is compared and combined with techniques that account for the lack of ensemble spread and for the systematic model error. The OSSEs show that when parameter estimation is combined with model error treatment techniques, multiplicative and additive inflation or a bias correction technique, parameter estimation produces a further improvement of analysis quality and medium-range forecast skill with respect to the OSSEs with model error treatment techniques without parameter estimation. The improvement produced by parameter estimation is mainly a consequence of the optimization of the parameter values. The estimated parameters do not converge to the value used to generate the observations in the imperfect model scenario; however, the analysis error is reduced and the forecast skill is improved.

scholarly journals A Soil Moisture Data Assimilation System for Pakistan Using PODEn4DVar and CLM4.5

2019 ◽  
Vol 33 (6) ◽  
pp. 1182-1193
Author(s):  
Tariq Mahmood ◽  
Zhenghui Xie ◽  
Binghao Jia ◽  
Ammara Habib ◽  
Rashid Mahmood
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Soil Moisture Data ◽  
Data Assimilation System ◽  
Assimilation System

scholarly journals Simulation of Convective Initiation during IHOP_2002 Using the Flux-Adjusting Surface Data Assimilation System (FASDAS)

2006 ◽  
Vol 134 (1) ◽  
pp. 134-148 ◽  
Author(s):  
Peter P. Childs ◽  
Aneela L. Qureshi ◽  
Sethu Raman ◽  
Kiran Alapaty ◽  
Robb Ellis ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Soil Temperature ◽  
Experimental Simulation ◽  
Convective Initiation ◽  
Control Simulation ◽  
Surface Data ◽  
Data Assimilation System ◽  
Surface Observations ◽  
Assimilation System

Abstract The Flux-Adjusting Surface Data Assimilation System (FASDAS) uses the surface observational analysis to directly assimilate surface layer temperature and water vapor mixing ratio and to indirectly assimilate soil moisture and soil temperature in numerical model predictions. Both soil moisture and soil temperature are important variables in the development of deep convection. In this study, FASDAS coupled within the fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5) was used to study convective initiation over the International H2O Project (IHOP_2002) region, utilizing the analyzed surface observations collected during IHOP_2002. Two 72-h numerical simulations were performed. A control simulation was run that assimilated all available IHOP_2002 measurements into the standard MM5 four-dimensional data assimilation. An experimental simulation was also performed that assimilated all available IHOP_2002 measurements into the FASDAS version of the MM5, where surface observations were used for the FASDAS coupling. Results from this case study suggest that the use of FASDAS in the experimental simulation led to the generation of greater amounts of precipitation over a more widespread area as compared to the standard MM5 FDDA used in the control simulation. This improved performance is attributed to better simulation of surface heat fluxes and their gradients.

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.

scholarly journals Linkages between Rainfed Cereal Production and Agricultural Drought through Remote Sensing Indices and a Land Data Assimilation System: A Case Study in Morocco

2020 ◽  
Vol 12 (24) ◽  
pp. 4018
Author(s):  
El houssaine Bouras ◽  
Lionel Jarlan ◽  
Salah Er-Raki ◽  
Clément Albergel ◽  
Bastien Richard ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Data Assimilation ◽  
Time Scale ◽  
Condition Index ◽  
Cereal Production ◽  
Land Data Assimilation ◽  
Land Data Assimilation System ◽  
Data Assimilation System ◽  
Assimilation System

In Morocco, cereal production shows high interannual variability due to uncertain rainfall and recurrent drought periods. Considering the socioeconomic importance of cereal for the country, there is a serious need to characterize the impact of drought on cereal yields. In this study, drought is assessed through (1) indices derived from remote sensing data (the vegetation condition index (VCI), temperature condition index (TCI), vegetation health ind ex (VHI), soil moisture condition index (SMCI) and soil water index for different soil layers (SWI)) and (2) key land surface variables (Land Area Index (LAI), soil moisture (SM) at different depths, soil evaporation and plant transpiration) from a Land Data Assimilation System (LDAS) over 2000–2017. A lagged correlation analysis was conducted to assess the relationships between the drought indices and cereal yield at monthly time scales. The VCI and LAI around the heading stage (March-April) are highly linked to yield for all provinces (R = 0.94 for the Khemisset province), while a high link for TCI occurs during the development stage in January-February (R = 0.83 for the Beni Mellal province). Interestingly, indices related to soil moisture in the superficial soil layer are correlated with yield earlier in the season around the emergence stage (December). The results demonstrate the clear added value of using an LDAS compared with using a remote sensing product alone, particularly concerning the soil moisture in the root-zone, considered a key variable for yield production, that is not directly observable from space. The time scale of integration is also discussed. By integrating the indices on the main phenological stages of wheat using a dynamic threshold approach instead of the monthly time scale, the correlation between indices and yield increased by up to 14%. In addition, the contributions of VCI and TCI to VHI were optimized by using yield anomalies as proxies for drought. This study opens perspectives for the development of drought early warning systems in Morocco and over North Africa, as well as for seasonal crop yield forecasting.

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