Ensemble-based data assimilation and targeted observation of a chemical tracer in a sea breeze model

2007 ◽  
Vol 41 (14) ◽  
pp. 3082-3094 ◽  
Author(s):  
Amy L. Stuart ◽  
Altug Aksoy ◽  
Fuqing Zhang ◽  
John W. Nielsen-Gammon
Keyword(s):  
Data Assimilation ◽  
Sea Breeze ◽  
Chemical Tracer ◽  
Targeted Observation

scholarly journals EnKF and 4D-Var data assimilation with chemical transport model BASCOE (version 05.06)

2016 ◽  
Vol 9 (8) ◽  
pp. 2893-2908 ◽  
Author(s):  
Sergey Skachko ◽  
Richard Ménard ◽  
Quentin Errera ◽  
Yves Christophe ◽  
Simon Chabrillat
Keyword(s):  
Data Assimilation ◽  
Transport Model ◽  
Chemical Transport ◽  
Error Variance ◽  
Model Error ◽  
Chemical Processes ◽  
Observation Error ◽  
Chemical Transport Model ◽  
Error Covariance ◽  
Chemical Tracer

Abstract. We compare two optimized chemical data assimilation systems, one based on the ensemble Kalman filter (EnKF) and the other based on four-dimensional variational (4D-Var) data assimilation, using a comprehensive stratospheric chemistry transport model (CTM). This work is an extension of the Belgian Assimilation System for Chemical ObsErvations (BASCOE), initially designed to work with a 4D-Var data assimilation. A strict comparison of both methods in the case of chemical tracer transport was done in a previous study and indicated that both methods provide essentially similar results. In the present work, we assimilate observations of ozone, HCl, HNO3, H2O and N2O from EOS Aura-MLS data into the BASCOE CTM with a full description of stratospheric chemistry. Two new issues related to the use of the full chemistry model with EnKF are taken into account. One issue is a large number of error variance parameters that need to be optimized. We estimate an observation error variance parameter as a function of pressure level for each observed species using the Desroziers method. For comparison purposes, we apply the same estimate procedure in the 4D-Var data assimilation, where both scale factors of the background and observation error covariance matrices are estimated using the Desroziers method. However, in EnKF the background error covariance is modelled using the full chemistry model and a model error term which is tuned using an adjustable parameter. We found that it is adequate to have the same value of this parameter based on the chemical tracer formulation that is applied for all observed species. This is an indication that the main source of model error in chemical transport model is due to the transport. The second issue in EnKF with comprehensive atmospheric chemistry models is the noise in the cross-covariance between species that occurs when species are weakly chemically related at the same location. These errors need to be filtered out in addition to a localization based on distance. The performance of two data assimilation methods was assessed through an 8-month long assimilation of limb sounding observations from EOS Aura MLS. This paper discusses the differences in results and their relation to stratospheric chemical processes. Generally speaking, EnKF and 4D-Var provide results of comparable quality but differ substantially in the presence of model error or observation biases. If the erroneous chemical modelling is associated with moderately fast chemical processes, but whose lifetimes are longer than the model time step, then EnKF performs better, while 4D-Var develops spurious increments in the chemically related species. If, however, the observation biases are significant, then 4D-Var is more robust and is able to reject erroneous observations while EnKF does not.

scholarly journals EnKF and 4D-Var Data Assimilation with a Chemistry Transport Model

2016 ◽  
Author(s):  
Sergey Skachko ◽  
Richard Menard ◽  
Quentin Errera ◽  
Yves Christophe ◽  
Simon Chabrillat
Keyword(s):  
Data Assimilation ◽  
Transport Model ◽  
Model Error ◽  
The Other ◽  
Full Description ◽  
Observation Error ◽  
Tracer Transport ◽  
Chemistry Transport Model ◽  
Stratospheric Chemistry ◽  
Chemical Tracer

Abstract. We compare two optimized chemical data assimilation systems, one based on the ensemble Kalman filter (EnKF) and the other based on four-dimensional variational (4D-Var), using a comprehensive stratospheric chemistry transport model (CTM). The work is an extension of the Belgian Assimilation System for Chemical ObsErvations (BASCOE), initially designed to work with a 4D-Var data assimilation. A strict comparison of both methods in the case of chemical tracer transport was done in a previous study and indicated that both methods provide essentially similar results. In the present work, we assimilate observations of ozone, HCl, HNO3, H2O and N2O from EOS Aura-MLS data into the BASCOE CTM with a full description of stratospheric chemistry. Two new issues related to the use of full chemistry model with EnKF are taken into account. One issue concerns to a large number of error variance parameters that need to be optimized. We estimate an observation error parameter as function of pressure level for each observed species using the Desroziers' method. For comparison reasons, we apply the same estimate procedure in the 4D-Var data assimilation, where we keep both estimates: the background and observation error variances. However in EnKF, the background error covariance is modelled using the full chemistry model and a model error term. We found that it is adequate to have a single model error based on the chemical tracer formulation that is applied for all species. This is an indication that the main source of model error in chemical transport model is due to the transport. The second issue in EnKF with comprehensive atmospheric chemistry models is the sampling errors between species. When species are weakly chemically related, cross-species sampling noise errors occur at the same location. These errors need to be filtered out, in addition to a localization based on distance. The performance of two data assimilation methods was assessed through an eight-month long assimilation of limb sounding observations from EOS Aura-MLS. The paper discusses the differences in results and their relation to stratospheric chemical processes. Generally speaking, EnKF and 4D-Var provide results of comparable quality but differ substantially in presence of model error or observation biases. If the erroneous chemical modelling is associated with not too small chemical life-times, then EnKF performs better, while 4D-Var develops spurious increments in the chemically related species. If, on the other hand, the observation biases are significant, then 4D-Var is more robust and is able to reject erroneous observations, while EnKF does not.

A Flow-dependent Targeted Observation Method

2020 ◽  
Author(s):  
Youmin Tang ◽  
Yaling Wu
Keyword(s):  
Data Assimilation ◽  
Tropical Indian Ocean ◽  
Seasonal Prediction ◽  
Test Bed ◽  
Error Statistics ◽  
Background Error ◽  
Observation Method ◽  
Targeted Observation ◽  
Data Assimilation System ◽  
Assimilation System

<p>In this study, we developed a flow-dependent ensemble-based targeted observation method, by minimizing the analysis error variance under the framework of Ensemble Kalman filter (EnKF) data assimilation system. This method estimates the background error statistics as a flow dependent function. The covariance localization is also introduced for computing efficiency and alleviating the spurious observations.  As a test bed, an  optimal observation array of sea level anomalies (SLA) is designed for its seasonal prediction over the tropical Indian Ocean (TIO) region.  Furthermore, the observing system simulation experiments (OSSEs) is used to verify the resultant optimal observational array using our recently developed coupled data assimilation system. A comparison between this flow-dependent method and the traditional method is also given. ​</p>

scholarly journals Application of observability Gramian to targeted observation in WRF data assimilation

2019 ◽  
Vol 72 (1) ◽  
pp. 1-11
Author(s):  
Ryoichi Yoshimura ◽  
Aiko Yakeno ◽  
Takashi Misaka ◽  
Shigeru Obayashi
Keyword(s):  
Data Assimilation ◽  
Targeted Observation ◽  
Observability Gramian

scholarly journals Toward Improved Forecasts of Sea-Breeze Horizontal Convective Rolls at Super High Resolutions. Part I: Configuration and Verification of a Down-Scaling Simulation System (DS3)

2015 ◽  
Vol 143 (5) ◽  
pp. 1849-1872 ◽  
Author(s):  
Guixing Chen ◽  
Xinyue Zhu ◽  
Weiming Sha ◽  
Toshiki Iwasaki ◽  
Hiromu Seko ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Sea Breeze ◽  
Simulation System ◽  
Secondary Flows ◽  
Cfd Modeling ◽  
Cfd Model ◽  
Sea Breezes ◽  
Low Speed Streaks ◽  
Local Weather ◽  
Convective Rolls

Abstract Horizontal convective rolls (HCRs) that develop in sea breezes greatly influence local weather in coastal areas. In this study, the authors present a realistic simulation of sea-breeze HCRs over an urban-scale area at a resolution of a few meters. An advanced Down-Scaling Simulation System (DS3) is built to derive the analyzed data using a nonhydrostatic model and data assimilation scheme that drive a building-resolving computational fluid dynamics (CFD) model. The mesoscale-analyzed data well capture the inland penetration of the sea breeze in northeastern Japan. The CFD model reproduces the HCRs over Sendai Airport in terms of their coastal initiation, inland growth, streamwise orientation, specific locations, roll wavelength, secondary flows, and regional differences due to complex surfaces. The simulated HCRs agree fairly well with those observed by dual-Doppler lidar and heliborne sensors. Both the simulation and observation analyses suggest that roll updrafts typically originate in the narrow bands of low-speed streaks and warm air near the ground. The HCRs are primarily driven and sustained by a combination of wind shear and buoyancy forces within the slightly unstable sea-breeze layer. In contrast, the experiment without data assimilation exhibits a higher deficiency in the reproduction of roll characteristics. The findings highlight that CFD modeling, given reliable mesoscale weather and surface conditions, aids in high-precision forecasting of HCRs at unprecedented high resolutions, which may help determine the roll structure, dynamics, and impacts on local weather.

scholarly journals Toward Producing the Chukchi–Beaufort High-Resolution Atmospheric Reanalysis (CBHAR) via the WRFDA Data Assimilation System

2014 ◽  
Vol 142 (2) ◽  
pp. 788-805 ◽  
Author(s):  
Fuhong Liu ◽  
Jeremy R. Krieger ◽  
Jing Zhang
Keyword(s):  
High Resolution ◽  
Data Assimilation ◽  
Sea Breeze ◽  
Data Availability ◽  
Sensitivity Analyses ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Data Assimilation System ◽  
Assimilation System ◽  
Positive Impacts

Abstract The Weather Research and Forecasting Model (WRF) and its variational data assimilation system (WRFDA) are applied to the Chukchi–Beaufort Seas and adjacent Arctic Slope region for high-resolution regional atmospheric reanalysis study. To optimize WRFDA performance over the study area, a set of sensitivity experiments are carried out to analyze the model sensitivity to model background errors (BEs) and the assimilation of various observational datasets. Observational data are assimilated every 6 h and the results are verified against unassimilated observations. In the BE sensitivity analyses, the results of assimilating in situ surface observations with a customized, domain-dependent BE are compared to those using the WRF-provided global BE. It is found that the customized BE is necessary in order to achieve positive impacts from WRFDA assimilation for the study area. When seasonal variability is incorporated into the customized BE, the impacts are minor. Sensitivity analyses examining the assimilation of different datasets via WRFDA demonstrate that 1) positive impacts are always seen through the assimilation of in situ surface and radiosonde measurements, 2) assimilating Quick Scatterometer (QuikSCAT) winds improves the simulation of the 10-m wind field over ocean and coastal areas, and 3) selectively assimilating Moderate Resolution Imaging Spectroradiometer (MODIS) retrieved profiles under clear-sky and snow-free conditions is essential to avoid degradation of assimilation performance, while assimilation of Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC) retrievals has little impact, most likely due to limited data availability. Based on the sensitivity results, a 1-yr (2009) experimental reanalysis is conducted and consistent improvements are achieved, particularly in capturing mesoscale processes such as mountain barrier and sea-breeze effects.

scholarly journals Improving the thermal structure predictions in the Yellow Sea by conducting targeted observations in the CNOP-identified sensitive areas

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kun Liu ◽  
Wuhong Guo ◽  
Lianglong Da ◽  
Jingyi Liu ◽  
Huiqin Hu ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Yellow Sea ◽  
Thermal Structure ◽  
The Yellow Sea ◽  
Perturbation Approach ◽  
Optimal Perturbation ◽  
Sensitive Areas ◽  
Targeted Observations ◽  
Targeted Observation ◽  
The Impact

AbstractTargeted observation is an appealing procedure for improving model predictions. However, studies on oceanic targeted observations have been largely based on modeling efforts, and there is a need for field validating operations. Here, we report the results of a field targeted observation that is designed based on the sensitive areas identified by the Conditional Nonlinear Optimal Perturbation approach to improve the 7th day thermal structure prediction in the Yellow Sea. By introducing the technique of cycle data assimilation and the new concept of time-varying sensitive areas, an observing strategy is designed and validated by a set of Observing System Simulation Experiments. Then, the impact of targeted observations was investigated by a choreographed field campaign in the summer of 2019. The results of the in-field Observing System Experiments show that, compared to conventional local data assimilation, conducting targeted observations in the sensitive areas can yield more benefit at the verification time. Furthermore, dynamic analysis demonstrates that the refinement of vertical thermal structures is mainly caused by the changes in the upstream horizontal temperature advection driven by the Yellow Sea Cold Water Mass circulation. This study highlights the effectiveness of targeted observations on reducing the forecast uncertainty in the ocean.

Improving High-Resolution Numerical Weather Simulations by Assimilating Data from an Unmanned Aerial System

2012 ◽  
Vol 140 (11) ◽  
pp. 3734-3756 ◽  
Author(s):  
Marius O. Jonassen ◽  
Haraldur Ólafsson ◽  
Hálfdán Ágústsson ◽  
Ólafur Rögnvaldsson ◽  
Joachim Reuder
Keyword(s):  
High Resolution ◽  
Data Assimilation ◽  
Lower Troposphere ◽  
Sea Breeze ◽  
Unmanned Aerial System ◽  
Atmospheric Sciences ◽  
Numerical Weather ◽  
Vertical Extension ◽  
Wide Range ◽  
Thermally Driven

Abstract In this study, it is demonstrated how temperature, humidity, and wind profile data from the lower troposphere obtained with a lightweight unmanned aerial system (UAS) can be used to improve high-resolution numerical weather simulations by four-dimensional data assimilation (FDDA). The combined UAS and FDDA system is applied to two case studies of northeasterly flow situations in southwest Iceland from the international Moso field campaign on 19 and 20 July 2009. Both situations were characterized by high diurnal boundary layer temperature variation leading to thermally driven flow, predominantly in the form of sea-breeze circulation along the coast. The data assimilation leads to an improvement in the simulation of the horizontal and vertical extension of the sea breeze as well as of the local background flow. Erroneously simulated fog over the Reykjanes peninsula on 19 July, which leads to a local temperature underestimation of 8 K, is also corrected by the data assimilation. Sensitivity experiments show that both the assimilation of wind data and temperature and humidity data are important for the assimilation results. UAS represents a novel instrument platform with a large potential within the atmospheric sciences. The presented method of using UAS data for assimilation into high-resolution numerical weather simulations is likely to have a wide range of future applications such as wind energy and improvements of targeted weather forecasts for search and rescue missions.

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