scholarly journals Verification of the mesospheric winds within the Canadian Middle Atmosphere Model Data Assimilation System using radar measurements

2011 ◽  
Vol 116 (D16) ◽  
Author(s):  
Xiaoyong Xu ◽  
A. H. Manson ◽  
C. E. Meek ◽  
C. Jacobi ◽  
C. M. Hall ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Middle Atmosphere ◽  
Model Data ◽  
Radar Measurements ◽  
Atmosphere Model ◽  
Data Assimilation System ◽  
Assimilation System

scholarly journals Evaluating model performance of an ensemble-based chemical data assimilation system during INTEX-B field mission

2007 ◽  
Vol 7 (4) ◽  
pp. 9717-9767
Author(s):  
◽  
K. Raeder ◽  
J. L. Anderson ◽  
P. G. Hess ◽  
L. K. Emmons ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Source Region ◽  
Model Performance ◽  
Chemical Data ◽  
Horizontal Wind ◽  
Model Errors ◽  
Atmosphere Model ◽  
Ne Pacific ◽  
Data Assimilation System ◽  
Assimilation System

Abstract. We present a global chemical data assimilation system using a global atmosphere model, the Community Atmosphere Model (CAM3) with simplified chemistry and the Data Assimilation Research Testbed (DART) assimilation package. DART is a community software facility for assimilation studies using the ensemble Kalman filter approach. Here, we apply the assimilation system to constrain global tropospheric carbon monoxide (CO) by assimilating meteorological observations of temperature and horizontal wind velocity and satellite CO retrievals from the Measurement of Pollution in the Troposphere (MOPITT) satellite instrument. We verify the system performance using independent CO observations taken on board the NSF/NCAR C-130 and NASA DC-8 aircrafts during the April 2006 part of the Intercontinental Chemical Transport Experiment (INTEX-B). Our evaluations show that MOPITT data assimilation provides significant improvements in terms of capturing the observed CO variability relative to no MOPITT assimilation (i.e. the correlation improves from 0.62 to 0.71, significant at 99% confidence). The assimilation provides evidence of median CO loading of about 150 ppbv at 700 hPa over the NE Pacific during April 2006. This is marginally higher than the modeled CO with no MOPITT assimilation (~140 ppbv). Our ensemble-based estimates of model uncertainty also show model overprediction over the source region (i.e. China) and underprediction over the NE Pacific, suggesting model errors that cannot be readily explained by emissions alone. These results have important implications for improving regional chemical forecasts and for inverse modeling of CO sources and further demonstrates the utility of the assimilation system in comparing non-coincident measurements, e.g. comparing satellite retrievals of CO with in-situ aircraft measurements.

scholarly journals Evaluating model performance of an ensemble-based chemical data assimilation system during INTEX-B field mission

2007 ◽  
Vol 7 (21) ◽  
pp. 5695-5710 ◽  
Author(s):  
◽  
K. Raeder ◽  
J. L. Anderson ◽  
P. G. Hess ◽  
L. K. Emmons ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Source Region ◽  
Model Performance ◽  
Chemical Data ◽  
Horizontal Wind ◽  
Model Errors ◽  
Atmosphere Model ◽  
Ne Pacific ◽  
Data Assimilation System ◽  
Assimilation System

Abstract. We present a global chemical data assimilation system using a global atmosphere model, the Community Atmosphere Model (CAM3) with simplified chemistry and the Data Assimilation Research Testbed (DART) assimilation package. DART is a community software facility for assimilation studies using the ensemble Kalman filter approach. Here, we apply the assimilation system to constrain global tropospheric carbon monoxide (CO) by assimilating meteorological observations of temperature and horizontal wind velocity and satellite CO retrievals from the Measurement of Pollution in the Troposphere (MOPITT) satellite instrument. We verify the system performance using independent CO observations taken on board the NSF/NCAR C-130 and NASA DC-8 aircrafts during the April 2006 part of the Intercontinental Chemical Transport Experiment (INTEX-B). Our evaluations show that MOPITT data assimilation provides significant improvements in terms of capturing the observed CO variability relative to no MOPITT assimilation (i.e. the correlation improves from 0.62 to 0.71, significant at 99% confidence). The assimilation provides evidence of median CO loading of about 150 ppbv at 700 hPa over the NE Pacific during April 2006. This is marginally higher than the modeled CO with no MOPITT assimilation (~140 ppbv). Our ensemble-based estimates of model uncertainty also show model overprediction over the source region (i.e. China) and underprediction over the NE Pacific, suggesting model errors that cannot be readily explained by emissions alone. These results have important implications for improving regional chemical forecasts and for inverse modeling of CO sources and further demonstrate the utility of the assimilation system in comparing non-coincident measurements, e.g. comparing satellite retrievals of CO with in-situ aircraft measurements.

scholarly journals A novel model–data fusion approach to terrestrial carbon cycle reanalysis across the contiguous U.S using SIPNET and PEcAn state data assimilation system v. 1.7.2

2021 ◽  
Author(s):  
Hamze Dokoohaki ◽  
Bailey D. Morrison ◽  
Ann Raiho ◽  
Shawn P. Serbin ◽  
Michael Dietze
Keyword(s):  
Data Assimilation ◽  
Carbon Cycle ◽  
Soil Carbon ◽  
Model Data ◽  
Terrestrial Carbon ◽  
State Data ◽  
Flux Tower ◽  
Terrestrial Carbon Cycle ◽  
Data Assimilation System ◽  
Assimilation System

Abstract. The ability to monitor, understand, and predict the dynamics of the terrestrial carbon cycle requires the capacity to robustly and coherently synthesize multiple streams of information that each provide partial information about different pools and fluxes. In this study, we introduce a new terrestrial carbon cycle data assimilation system, built on the PEcAn model-data eco-informatics system, and its application for the development of a proof-of-concept carbon "reanalysis" product that harmonizes carbon pools (leaf, wood, soil) and fluxes (GPP, Ra, Rh, NEE) across the contiguous United States from 1986–2019. We first calibrated this system against plant trait and flux tower Net Ecosystem Exchange (NEE) using a novel emulated hierarchical Bayesian approach. Next, we extended the Tobit-Wishart Ensemble Filter (TWEnF) State Data Assimilation (SDA) framework, a generalization of the common Ensemble Kalman Filter which accounts for censored data and provides a fully Bayesian estimate of model process error, to a regional-scale system with a calibrated localization. Combined with additional workflows for propagating parameter, initial condition, and driver uncertainty, this represents the most complete and robust uncertainty accounting available for terrestrial carbon models. Our initial reanalysis was run on an irregular grid of ~500 points selected using a stratified sampling method to efficiently capture environmental heterogeneity. Remotely sensed observations of aboveground biomass (Landsat LandTrendr) and LAI (MODIS MOD15) were sequentially assimilated into the SIPNET model. Reanalysis soil carbon, which was indirectly constrained based on modeled covariances, showed general agreement with SoilGrids, an independent soil carbon data product. Reanalysis NEE, which was constrained based on posterior ensemble weights, also showed good agreement with eddy flux tower NEE and reduced RMSE compared to the calibrated forecast. Ultimately, PEcAn's carbon cycle reanalysis provides a scalable framework for harmonizing multiple data constraints and providing a uniform synthetic platform for carbon monitoring, reporting, and verification (MRV) and accelerating terrestrial carbon cycle research.

scholarly journals Use of the WRF-DA 3D-Var Data Assimilation System to Obtain Wind Speed Estimates in Regular Grids from Measurements at Wind Farms in Uruguay

Data ◽  
2019 ◽  
Vol 4 (4) ◽  
pp. 142 ◽  
Author(s):  
Gabriel Cazes Boezio ◽  
Sofía Ortelli
Keyword(s):  
Wind Speed ◽  
Data Assimilation ◽  
Wind Farms ◽  
Forecast Model ◽  
Model Data ◽  
Assimilation Process ◽  
The Mean ◽  
Data Assimilation System ◽  
Assimilation System

This work assessed the quality of wind speed estimates in Uruguay. These estimates were obtained using the Weather Research and Forecast Model Data Assimilation System (WRF-DA) to assimilate wind speed measurements from 100 m above the ground at two wind farms. The quality of the estimates was assessed with an anemometric station placed between the wind farms. The wind speed estimates showed low systematic errors at heights of 87 and 36 m above the ground. At both levels, the standard deviation of the total errors was approximately 25% of the mean observed speed. These results suggested that the estimates obtained could be of sufficient quality to be useful in various applications. The assimilation process proved to be effective, spreading the observational gain obtained at the wind farms to lower elevations than those at which the assimilated measurements were taken. The smooth topography of Uruguay might have contributed to the relatively good quality of the obtained wind estimates, although the data of only two stations were assimilated, and the resolution of the regional atmospheric simulations employed was relatively low.

scholarly journals Three decades of simulated global terrestrial carbon fluxes from a data assimilation system confronted to different periods of observations

2019 ◽  
Author(s):  
Karel Castro-Morales ◽  
Gregor Schürmann ◽  
Christoph Köstler ◽  
Christian Rödenbeck ◽  
Martin Heimann ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Gross Primary Production ◽  
Atmospheric Co2 ◽  
Model Data ◽  
Term Trend ◽  
Atmospheric Co2 Concentrations ◽  
Long Term Trend ◽  
Data Assimilation System ◽  
Assimilation System

Abstract. This paper presents global land carbon fluxes for the period 1982–2010 (gross primary production, GPP, and net ecosystem exchange, NEE) estimated with the Max Planck Institute – Carbon Cycle Data Assimilation System (MPI-CCDAS v1). The primary aim of this work is to analyze the performance of the MPI-CCDAS when it is confronted with three different time periods for data assimilation (DA), and thereby to assess its prognostic capability. To this extend we assimilated nearly three decades (1982–2010) of space borne measurements of the fraction of absorbed photosynthetic active radiation (FAPAR) and atmospheric CO2 concentrations from the global network of flask and in situ measurements. Both data sets were incorporated with different assimilation windows covering the periods 1982–1990, 1990–2000 and 1982–2010. The assimilation results show a considerable improvement in the long-term trend and seasonality of FAPAR in the Northern Hemisphere, as well as in the long term trend and seasonal amplitude of the atmospheric CO2 concentrations when compared to the observations in sites globally distributed. After the assimilation, the global net land-atmosphere CO2 exchange (NEE) was −1.2 PgC yr−1, in agreement with independent estimates, while gross primary production (GPP; 92.5 PgC yr−1) was somewhat below the magnitude of independent estimates. The NEE in boreal eastern regions (Northeast Asia) increased on average by −0.13 PgC yr−1, which translated into an intensification of the carbon uptake in those regions by nearly 30 % than the contribution to the global annual average in the model before the assimilation. Our results demonstrate that using information only over a decade already yielded a large fraction of the overall model improvement, in particular for the simulation of phenological seasonality, its interannual variability (IAV) and long-term trend. Adding longer than decadal data did only lead to very moderate improvements in the long-term trend of the FAPAR simulated by the model, which may be attributed to the small model-data mismatch at the long timescales compared to the significantly larger observational signal and model-data mismatch error at seasonal cycle time scale. Decadal data also significantly improved the seasonality, IAV and long-term simulated trend in atmospheric CO2. Importantly, when running the MPI-CCDAS v1 with 30 years of data, the results remained in line with observations throughout this period, suggesting that the model can represent land uptake to a sufficient degree to make it compatible with the atmospheric CO2 record. Using data from 1982 to 1990 in the assimilation yielded only a difference to the observations of 2 ± 1.3 ppm for the period 15 to 19 years after the end of the assimilation. This suggests that despite imperfections in the representation of IAV, model-data fusion can increase the prognostic capacity of land carbon cycle models at relevant time-scales.

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