Author
B. Imbiriba
Other affiliations: Federal University of Pará
Bio: B. Imbiriba is an academic researcher from University of Maryland, Baltimore County. The author has contributed to research in topics: Data assimilation & Northern Hemisphere. The author has an hindex of 1, co-authored 1 publications receiving 10 citations. Previous affiliations of B. Imbiriba include Federal University of Pará.
Papers
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TL;DR: In this paper, CO 2 data from the Atmospheric Infrared Sounder (AIRS) were assimilated into the GEOS-5 (Goddard Earth Observing System Model, Version 5) constituent assimilation system for the period 1 January 2005 to 31 December 2006.
Abstract: . Atmospheric CO 2 retrievals with peak sensitivity in the mid- to lower troposphere from the Atmospheric Infrared Sounder (AIRS) have been assimilated into the GEOS-5 (Goddard Earth Observing System Model, Version 5) constituent assimilation system for the period 1 January 2005 to 31 December 2006. A corresponding model simulation, using identical initial conditions, circulation, and CO 2 boundary fluxes was also completed. The analyzed and simulated CO 2 fields are compared with surface measurements globally and aircraft measurements over North America. Surface level monthly mean CO 2 values show a marked improvement due to the assimilation in the Southern Hemisphere, while less consistent improvements are seen in the Northern Hemisphere. Mean differences with aircraft observations are reduced at all levels, with the largest decrease occurring in the mid-troposphere. The difference standard deviations are reduced slightly at all levels over the ocean, and all levels except the surface layer over land. These initial experiments indicate that the used channels contain useful information on CO 2 in the middle to lower troposphere. However, the benefits of assimilating these data are reduced over the land surface, where concentrations are dominated by uncertain local fluxes and where the observation density is quite low. Away from these regions, the study demonstrates the power of the data assimilation technique for evaluating data that are not co-located, in that the improvements in mid-tropospheric CO 2 by the sparsely distributed partial-column retrievals are transported by the model to the fixed in situ surface observation locations in more remote areas.
10 citations
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01 Dec 2012
Abstract: We upscaled FLUXNET observations of carbon dioxide, water, and energy fluxes to the global scale using the machine learning technique, model tree ensembles (MTE). We trained MTE to predict site-level gross primary productivity (GPP), terrestrial ecosystem respiration (TER), net ecosystem exchange (NEE), latent energy (LE), and sensible heat (H) based on remote sensing indices, climate and meteorological data, and information on land use. We applied the trained MTEs to generate global flux fields at a 0.5 degrees x 0.5 degrees spatial resolution and a monthly temporal resolution from 1982 to 2008. Cross-validation analyses revealed good performance of MTE in predicting among-site flux variability with modeling efficiencies (MEf) between 0.64 and 0.84, except for NEE (MEf = 0.32). Performance was also good for predicting seasonal patterns (MEf between 0.84 and 0.89, except for NEE (0.64)). By comparison, predictions of monthly anomalies were not as strong (MEf between 0.29 and 0.52). Improved accounting of disturbance and lagged environmental effects, along with improved characterization of errors in the training data set, would contribute most to further reducing uncertainties. Our global estimates of LE (158 +/- 7 J x 10(18) yr(-1)), H (164 +/- 15 J x 10(18) yr(-1)), and GPP (119 +/- 6 Pg C yr(-1)) were similar to independent estimates. Our global TER estimate (96 +/- 6 Pg C yr(-1)) was likely underestimated by 5-10%. Hot spot regions of interannual variability in carbon fluxes occurred in semiarid to semihumid regions and were controlled by moisture supply. Overall, GPP was more important to interannual variability in NEE than TER. Our empirically derived fluxes may be used for calibration and evaluation of land surface process models and for exploratory and diagnostic assessments of the biosphere.
948 citations
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41 citations
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TL;DR: In this article, the authors describe techniques for bias-correcting surface fluxes derived from satellite observations of the Earth's surface to be consistent with constraints from inventories and in situ CO2 datasets.
Abstract: . The ability to monitor and understand natural and anthropogenic variability in
atmospheric carbon dioxide ( CO2 ) is a growing need of many stakeholders across the world. Systems that assimilate satellite observations, given their
short latency and dense spatial coverage, into high-resolution global models are valuable, if not essential, tools for addressing this need. A notable drawback of modern assimilation systems is the long latency of many vital input datasets; for example, inventories, in situ measurements, and reprocessed remote-sensing data can trail the current date by months to years. This paper describes techniques for bias-correcting surface fluxes derived from satellite
observations of the Earth's surface to be consistent with constraints from
inventories and in situ CO2 datasets. The techniques are applicable in both short-term forecasts and retrospective simulations, thus taking advantage of the coverage and short latency of satellite data while reproducing the major features of long-term inventory and in situ records. Our approach begins with
a standard collection of diagnostic fluxes which incorporate a variety of
remote-sensing driver data, viz. vegetation indices, fire radiative power, and nighttime lights. We then apply an empirical sink so that global budgets of the diagnostic fluxes match given atmospheric and oceanic growth rates for each year. This step removes coherent, systematic flux errors that produce biases in CO2 which mask the signals an assimilation system hopes to capture. Depending on the simulation mode, the empirical sink uses different choices of atmospheric growth rates: estimates based on observations in retrospective mode
and projections based on seasonal forecasts of sea surface temperature in
forecasting mode. The retrospective fluxes, when used in simulations with
NASA's Goddard Earth Observing System (GEOS), reproduce marine boundary layer
measurements with comparable skill to those using fluxes from a modern
inversion system. The forecasted fluxes show promising accuracy in their
application to the analysis of changes in the carbon cycle as they occur.
23 citations
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TL;DR: In this paper, a regional surface CO2 flux inversion system (Carbon Flux Inversion system and Community Multi-scale Air Quality, CFI-CMAQ) has been developed by applying the ensemble Kalman filter (EnKF) to constrain the CO2 concentrations and applying the EnKS to optimize the surface CO 2 fluxes.
Abstract: . In order to optimize surface CO2 fluxes at grid scales, a regional surface CO2 flux inversion system (Carbon Flux Inversion system and Community Multi-scale Air Quality, CFI-CMAQ) has been developed by applying the ensemble Kalman filter (EnKF) to constrain the CO2 concentrations and applying the ensemble Kalman smoother (EnKS) to optimize the surface CO2 fluxes. The smoothing operator is associated with the atmospheric transport model to constitute a persistence dynamical model to forecast the surface CO2 flux scaling factors. In this implementation, the "signal-to-noise" problem can be avoided; plus, any useful observed information achieved by the current assimilation cycle can be transferred into the next assimilation cycle. Thus, the surface CO2 fluxes can be optimized as a whole at the grid scale in CFI-CMAQ. The performance of CFI-CMAQ was quantitatively evaluated through a set of Observing System Simulation Experiments (OSSEs) by assimilating CO2 retrievals from GOSAT (Greenhouse Gases Observing Satellite). The results showed that the CO2 concentration assimilation using EnKF could constrain the CO2 concentration effectively, illustrating that the simultaneous assimilation of CO2 concentrations can provide convincing CO2 initial analysis fields for CO2 flux inversion. In addition, the CO2 flux optimization using EnKS demonstrated that CFI-CMAQ could, in general, reproduce true fluxes at grid scales with acceptable bias. Two further sets of numerical experiments were conducted to investigate the sensitivities of the inflation factor of scaling factors and the smoother window. The results showed that the ability of CFI-CMAQ to optimize CO2 fluxes greatly relied on the choice of the inflation factor. However, the smoother window had a slight influence on the optimized results. CFI-CMAQ performed very well even with a short lag-window (e.g. 3 days).
23 citations
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TL;DR: In this paper, a new geomagnetic data assimilation approach was developed which uses the minimum variance estimate for the analysis state, and which models both the forecast (or model output) and observation errors using an empirical approach and parameter tuning.
Abstract: S U M M A R Y We have developed a new geomagnetic data assimilation approach which uses the minimum variance’ estimate for the analysis state, and which models both the forecast (or model output) and observation errors using an empirical approach and parameter tuning. This system is used in a series of assimilation experiments using Gauss coefficients (hereafter referred to as observational data) from the GUFM1 and CM4 fieldmodels for the years 1590–1990.We show that this assimilation system could be used to improve our knowledge of model parameters, model errors and the dynamical consistency of observation errors, by comparing forecasts of the magnetic field with the observations every 20 yr. Statistics of differences between observation and forecast (O − F) are used to determine how forecast accuracy depends on the Rayleigh number, forecast error correlation length scale and an observation error scale factor. Experiments have been carried out which demonstrate that a Rayleigh number of 30 times the critical Rayleigh number produces better geomagnetic forecasts than lower values, with an Ekman number of E = 1.25 × 10−6, which produces a modified magnetic Reynolds number within the parameter domain with an ‘Earth like’ geodynamo. The optimal forecast error correlation length scale is found to be around 90 per cent of the thickness of the outer core, indicating a significant bias in the forecasts. Geomagnetic forecasts are also found to be highly sensitive to estimates of modelled observation errors: Errors that are too small do not lead to the gradual reduction in forecast error with time that is generally expected in a data assimilation system while observation errors that are too large lead to model divergence. Finally, we show that assimilation of L ≤ 3 (or large scale) gauss coefficients can help to improve forecasts of the L > 5 (smaller scale) coefficients, and that these improvements are the result of corrections to the velocity field in the geodynamo model.
14 citations