scholarly journals The SPRINTARS version 3.80/4D-Var data assimilation system: development and inversion experiments based on the observing system simulation experiment framework

2013 ◽  
Vol 6 (6) ◽  
pp. 2005-2022 ◽  
Author(s):  
K. Yumimoto ◽  
T. Takemura
Keyword(s):  
Data Assimilation ◽  
Simulation Experiment ◽  
System Simulation ◽  
Inverse Modelling ◽  
Computational Time ◽  
Adjoint Model ◽  
Aerosol Emissions ◽  
Observing System Simulation Experiment ◽  
Data Assimilation System ◽  
Assimilation System

Abstract. We present an aerosol data assimilation system based on a global aerosol climate model (SPRINTARS – Spectral Radiation-Transport Model for Aerosol Species) and a four-dimensional variational data assimilation method (4D-Var). Its main purposes are to optimize emission estimates, improve composites, and obtain the best estimate of the radiative effects of aerosols in conjunction with observations. To reduce the huge computational cost caused by the iterative integrations in the models, we developed an offline model and a corresponding adjoint model, which are driven by pre-calculated meteorological, land, and soil data. The offline and adjoint model shortened the computational time of the inner loop by more than 30%. By comparing the results with a 1 yr simulation from the original online model, the consistency of the offline model was verified, with correlation coefficient R > 0.97 and absolute value of normalized mean bias NMB < 7% for the natural aerosol emissions and aerosol optical thickness (AOT) of individual aerosol species. Deviations between the offline and original online models are mainly associated with the time interpolation of the input meteorological variables in the offline model; the smaller variability and difference in the wind velocity near the surface and relative humidity cause negative and positive biases in the wind-blown aerosol emissions and AOTs of hygroscopic aerosols, respectively. The feasibility and capability of the developed system for aerosol inverse modelling was demonstrated in several inversion experiments based on the observing system simulation experiment framework. In the experiments, we used the simulated observation data sets of fine- and coarse-mode AOTs from sun-synchronous polar orbits to investigate the impact of the observational frequency (number of satellites) and coverage (land and ocean), and assigned aerosol emissions to control parameters. Observations over land have a notably positive impact on the performance of inverse modelling as compared with observations over ocean, implying that reliable observational information over land is important for inverse modelling of land-born aerosols. The experimental results also indicate that information that provides differentiations between aerosol species is crucial to inverse modelling over regions where various aerosol species coexist (e.g. industrialized regions and areas downwind of them).

scholarly journals The SPRINTARS version 3.80/4D-Var data assimilation system: development and inversion experiments based on the observing system simulation experiment framework

2013 ◽  
Vol 6 (2) ◽  
pp. 3427-3471
Author(s):  
K. Yumimoto ◽  
T. Takemura
Keyword(s):  
Data Assimilation ◽  
Simulation Experiment ◽  
Inverse Modelling ◽  
Adjoint Model ◽  
Line Model ◽  
On Line ◽  
Aerosol Emissions ◽  
Observing System Simulation Experiment ◽  
Data Assimilation System ◽  
Assimilation System

Abstract. We present an aerosol data assimilation system based on a global aerosol climate model (SPRINTARS) and a four-dimensional variational data assimilation method (4D-Var). Its main purposes are to optimize emission estimates, improve composites, and obtain the best estimate of the radiative effects of aerosols in conjunction with observations. To reduce the huge computational cost caused by the iterative integrations in the models, we developed an off-line model and a corresponding adjoint model, which are driven by pre-calculated meteorological, land, and soil data. The off-line and adjoint model shortened the computational time of the inner loop by more than 30%. By comparing the results with a 1yr simulation from the original on-line model, the consistency of the off-line model was verified, with correlation coefficient R^2 > 0.97 and absolute value of normalized mean bias NMB < 7% for the natural aerosol emissions and aerosol optical thickness (AOT) of individual aerosol species. Deviations between the off-line and original on-line models are mainly associated with the time interpolation of the input meteorological variables in the off-line model; the smaller variability and difference in the wind velocity near the surface and relative humidity cause negative and positive biases in the wind-blown aerosol emissions and AOTs of hygroscopic aerosols, respectively. The feasibility and capability of the developed system for aerosol inverse modelling was demonstrated in several inversion experiments based on the observing system simulation experiment framework. In the experiments, we generated the simulated observation data sets of fine- and coarse-mode AOTs from sun-synchronous polar orbits to investigate the impact of the observational frequency (number of satellites) and coverage (land and ocean). Observations over land have a notably positive impact on the performance of inverse modelling comparing with observations over ocean, implying that reliable observational information over land is important for inverse modelling of land-born aerosols. The experimental results also indicate that aerosol type classification is crucial to inverse modelling over regions where various aerosol species co-exist (e.g. industrialized regions and areas downwind of them).

scholarly journals Estimating surface carbon fluxes based on a local ensemble transform Kalman filter with a short assimilation window and a long observation window: an observing system simulation experiment test in GEOS-Chem 10.1

2019 ◽  
Vol 12 (7) ◽  
pp. 2899-2914
Author(s):  
Yun Liu ◽  
Eugenia Kalnay ◽  
Ning Zeng ◽  
Ghassem Asrar ◽  
Zhaohui Chen ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Simulation Experiment ◽  
System Simulation ◽  
Carbon Fluxes ◽  
Observation Window ◽  
Surface Carbon ◽  
Ensemble Transform Kalman Filter ◽  
Observing System Simulation Experiment ◽  
Ensemble Transform ◽  
Assimilation System

Abstract. We developed a carbon data assimilation system to estimate surface carbon fluxes using the local ensemble transform Kalman filter (LETKF) and atmospheric transport model GEOS-Chem driven by the MERRA-1 reanalysis of the meteorological field based on the Goddard Earth Observing System model, version 5 (GEOS-5). This assimilation system is inspired by the method of Kang et al. (2011, 2012), who estimated the surface carbon fluxes in an observing system simulation experiment (OSSE) as evolving parameters in the assimilation of the atmospheric CO2, using a short assimilation window of 6 h. They included the assimilation of the standard meteorological variables, so that the ensemble provided a measure of the uncertainty in the CO2 transport. After introducing new techniques such as “variable localization”, and increased observation weights near the surface, they obtained accurate surface carbon fluxes at grid-point resolution. We developed a new version of the local ensemble transform Kalman filter related to the “running-in-place” (RIP) method used to accelerate the spin-up of ensemble Kalman filter (EnKF) data assimilation (Kalnay and Yang, 2010; Wang et al., 2013; Yang et al., 2012). Like RIP, the new assimilation system uses the “no cost smoothing” algorithm for the LETKF (Kalnay et al., 2007b), which allows shifting the Kalman filter solution forward or backward within an assimilation window at no cost. In the new scheme a long “observation window” (e.g., 7 d or longer) is used to create a LETKF ensemble at 7 d. Then, the RIP smoother is used to obtain an accurate final analysis at 1 d. This new approach has the advantage of being based on a short assimilation window, which makes it more accurate, and of having been exposed to the future 7 d observations, which improves the analysis and accelerates the spin-up. The assimilation and observation windows are then shifted forward by 1 d, and the process is repeated. This reduces significantly the analysis error, suggesting that the newly developed assimilation method can be used with other Earth system models, especially in order to make greater use of observations in conjunction with models.

A Hybrid ETKF–3DVAR Data Assimilation Scheme for the WRF Model. Part I: Observing System Simulation Experiment

2008 ◽  
Vol 136 (12) ◽  
pp. 5116-5131 ◽  
Author(s):  
Xuguang Wang ◽  
Dale M. Barker ◽  
Chris Snyder ◽  
Thomas M. Hamill
Keyword(s):  
Data Assimilation ◽  
Simulation Experiment ◽  
Forecast Error ◽  
System Simulation ◽  
Wrf Model ◽  
Error Statistics ◽  
Model Assumption ◽  
Perfect Model ◽  
Variational Minimization ◽  
Observing System Simulation Experiment

Abstract A hybrid ensemble transform Kalman filter–three-dimensional variational data assimilation (ETKF–3DVAR) system for the Weather Research and Forecasting (WRF) Model is introduced. The system is based on the existing WRF 3DVAR. Unlike WRF 3DVAR, which utilizes a simple, static covariance model to estimate the forecast-error statistics, the hybrid system combines ensemble covariances with the static covariances to estimate the complex, flow-dependent forecast-error statistics. Ensemble covariances are incorporated by using the extended control variable method during the variational minimization. The ensemble perturbations are maintained by the computationally efficient ETKF. As an initial attempt to test and understand the newly developed system, both an observing system simulation experiment under the perfect model assumption (Part I) and the real observation experiment (Part II) were conducted. In these pilot studies, the WRF was run over the North America domain at a coarse grid spacing (200 km) to emphasize synoptic scales, owing to limited computational resources and the large number of experiments conducted. In Part I, simulated radiosonde wind and temperature observations were assimilated. The results demonstrated that the hybrid data assimilation method provided more accurate analyses than the 3DVAR. The horizontal distributions of the errors demonstrated the hybrid analyses had larger improvements over data-sparse regions than over data-dense regions. It was also found that the ETKF ensemble spread in general agreed with the root-mean-square background forecast error for both the first- and second-order measures. Given the coarse resolution, relatively sparse observation network, and perfect model assumption adopted in this part of the study, caution is warranted when extrapolating the results to operational applications.

Observing System Simulation Experiments (OSSEs) to Evaluate the Potential Impact of an Optical Autocovariance Wind Lidar (OAWL) on Numerical Weather Prediction

2015 ◽  
Vol 32 (9) ◽  
pp. 1593-1613 ◽  
Author(s):  
Robert Atlas ◽  
Ross N. Hoffman ◽  
Zaizhong Ma ◽  
G. David Emmitt ◽  
Sidney A. Wood ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Sampling Error ◽  
System Simulation ◽  
Weather Prediction ◽  
Simulation Experiments ◽  
Wind Lidar ◽  
Potential Impact ◽  
The Impact ◽  
Data Assimilation System ◽  
Assimilation System

AbstractThe potential impact of Doppler wind lidar (DWL) observations from a proposed optical autocovariance wind lidar (OAWL) instrument is quantified in observing system simulation experiments (OSSEs). The OAWL design would provide profiles of useful wind vectors along a ground track to the left of the International Space Station (ISS), which is in a 51.6° inclination low-Earth orbit (LEO). These observations are simulated realistically, accounting for cloud and aerosol distributions inferred from the OSSE nature runs (NRs), and measurement and sampling error sources. The impact of the simulated observations is determined in both global and regional OSSE frameworks. The global OSSE uses the ECMWF T511 NR and the NCEP operational Global Data Assimilation System at T382 resolution. The regional OSSE uses an embedded hurricane NR and the NCEP operational HWRF data assimilation system with outer and inner domains of 9- and 3-km resolution, respectively.The global OSSE results show improved analyses and forecasts of tropical winds and extratropical geopotential heights. The tropical wind RMSEs are significantly reduced in the analyses and in short-term forecasts. The tropical wind improvement decays as the forecasts lengthen. The regional OSSEs are limited but show some improvements in hurricane track and intensity forecasts.

scholarly journals Observing System Simulation Experiment to Reproduce Kelvin Wave in the Venus Atmosphere

Atmosphere ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 14
Author(s):  
Norihiko Sugimoto ◽  
Yukiko Fujisawa ◽  
Mimo Shirasaka ◽  
Asako Hosono ◽  
Mirai Abe ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Wind Velocity ◽  
General Circulation ◽  
Simulation Experiment ◽  
System Simulation ◽  
General Circulation Models ◽  
Horizontal Wind ◽  
Planetary Scale ◽  
Observing System Simulation Experiment ◽  
Venus Atmosphere

Planetary-scale 4-day Kelvin-type waves at the cloud top of the Venus atmosphere have been reported from the 1980s, and their significance for atmospheric dynamics has been pointed out. However, these waves have not been reproduced in Venus atmospheric general circulation models (VGCMs). Recently, horizontal winds associated with the planetary-scale waves at the cloud top have been obtained from cloud images taken by cameras onboard Venus orbiters, which could enable us to clarify the structure and roles of Kelvin-type waves. In order to examine this possibility, our team carried out an idealized observing system simulation experiment (OSSE) with a data assimilation system which we developed. The wind velocity data provided by a CCSR/NIES (Center for Climate System Research/National Institute for Environmental Studies) VGCM where equatorial Kelvin-type waves were assumed below the cloud bottom was used as idealized observations. Results show that 4-day planetary-scale Kelvin-type waves are successfully reproduced if the wind velocity between 15° S and 15° N latitudes is assimilated every 6 h at 70 km altitude. It is strongly suggested that the Kelvin-type waves could be reproduced and investigated by the data assimilation with the horizontal wind data derived from Akatsuki ultraviolet images. The present results also contribute to planning future missions for understanding planetary atmospheres.

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