This paper addresses continental-scale snow estimates in North America using a recently developed snow radiance assimilation (RA) system. A series of RA experiments with the ensemble adjustment ...Kalman filter are conducted by assimilating the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) brightness temperature TB at 18.7- and 36.5-GHz vertical polarization channels. The overall RA performance in estimating snow depth for North America is improved by simultaneously updating the Community Land Model, version 4 (CLM4), snow/soil states and radiative transfer model (RTM) parameters involved in predicting TB
based on their correlations with the prior TB
(i.e., rule-based RA), although degradations are also observed. The RA system exhibits a more mixed performance for snow cover fraction estimates. Compared to the open-loop run (0.171m RMSE), the overall snow depth estimates are improved by 1.6% (0.168m RMSE) in the rule-based RA whereas the default RA (without a rule) results in a degradation of 3.6% (0.177m RMSE). Significant improvement of the snow depth estimates in the rule-based RA is observed for tundra snow class (11.5%, p < 0.05) and bare soil land-cover type (13.5%, p < 0.05). However, the overall improvement is not significant (p = 0.135) because snow estimates are degraded or marginally improved for other snow classes and land covers, especially the taiga snow class and forest land cover (7.1% and 7.3% degradations, respectively). The current RA system needs to be further refined to enhance snow estimates for various snow types and forested regions.
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Uncertain or inaccurate parameters in sea ice models influence seasonal predictions and climate change projections in terms of both mean and trend. We explore the feasibility and benefits of applying ...an ensemble Kalman filter (EnKF) to estimate parameters in the Los Alamos sea ice model (CICE). Parameter estimation (PE) is applied to the highly influential dry snow grain radius and combined with state estimation in a series of perfect model observing system simulation experiments (OSSEs). Allowing the parameter to vary in space improves performance along the sea ice edge but degrades in the central Arctic compared to requiring the parameter to be uniform everywhere, suggesting that spatially varying parameters will likely improve PE performance at local scales and should be considered with caution. We compare experiments with both PE and state estimation to experiments with only the latter and have found that the benefits of PE mostly occur after the data assimilation period, when no observations are available to assimilate (i.e., the forecast period), which suggests PE's relevance for improving seasonal predictions of Arctic sea ice.
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An observing system simulation experiment (OSSE) is presented in the Sea of Marmara. A high-resolution ocean circulation model (FESOM) and an ensemble data assimilation tool (DART) are coupled. The ...OSSE methodology is used to assess the possible impact of a FerryBox network in the eastern Sea of Marmara. A reference experiment without assimilation is performed. Then, synthetic temperature and salinity observations are assimilated along the track of the ferries in the second experiment. The results suggest that a FerryBox network in the Sea of Marmara has potential to improve the forecasts significantly. The salinity and temperature errors get smaller in the upper layer of the water column. The impact of the assimilation is negligible in the lower layer due to the strong stratification. The circulation in the Sea of Marmara, particularly the Bosphorus outflow, helps to propagate the error reduction towards the western basin where no assimilation is performed. Overall, the proposed FerryBox network can be a good start to designing an optimal sustained marine observing network in the Sea of Marmara for assimilation purposes.
Carbon, water and energy exchange between the land and atmosphere controls how ecosystems either accelerate or ameliorate the effect of climate change. However, evaluating improvements to processes ...controlling carbon cycling, water use and energy exchange in global land surface models (LSMs) remains challenging in part because of persistent model errors in estimating leaf area. Here we evaluate the changes in global carbon, water and energy exchange brought about when a LSM prognostic estimates of leaf area are made consistent with estimates from satellites. This approach achieves two aims; first to quantify the effect of ignoring errors in leaf area index (LAI) on land‐atmosphere fluxes and second, to evaluate how closely this LSM replicates fluxes with and without an LAI constraint. We implemented an ensemble Kalman filter with spatiotemporal adaptive inflation to more closely match community land model (CLM5.0) estimates of leaf area to those from the Global Inventory Modeling and Mapping Studies leaf area index (LAI3g) product. We then evaluate the model's estimates of gross primary productivity (GPP) and latent heat flux (LE) against well established global estimates of these fluxes. We find that the model is biased high by 27% relative to the LAI3g product. Moreover, the effect of bias in LAI is substantial for GPP (18%) and LE (6%) and likely to confound efforts to refine processes controlling these fluxes. This data assimilation approach serves as a method to evaluate the efficacy of refinements to flux processes until the processes controlling the dynamics of LAI are better resolved in LSMs.
Plain Language Summary
The climate system is influenced by the plants that grow on land. Leaves exchange carbon and water with the atmosphere and absorb and reflect energy. Over the whole globe it is difficult to predict when and how many leaves emerge and drop. Our global models disagree with each other and have errors and biases because of this. We forced a land surface model to agree with the leaf area estimated from satellite observations over an 11‐year period. When we did this, we found that carbon uptake and water loss went down on average. However the reductions did not occur everywhere equally; some regions saw much larger (nearly 50%) decreases in carbon and water exchange. Adjusting leaf area index (LAI) to match satellites did not lead to uniform improvements in forecasts of LAI on different vegetation types. It is likely that this model, and similar models, contain compensating errors in processes governing gross primary productivity (GPP) and leaf turnover. While we used only one estimate of the Earth's leaf area, there are several other estimates available. Using these and other datasets to bound the model estimates will likely improve estimates of the current carbon cycle and lead to better forecasts.
Key Points
Assimilating satellite derived observations of leaf area on average reduced Community Land Model estimates of Leaf Area Index
This reduced global estimates of gross primary production by 18% and latent heat flux by 6%, improving fit to independent data sets
The persistence in improvements of model forecasts was highly dependent on plant functional type, enabling discovery of model errors
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We examine the application of ensemble Kalman filter algorithms to the smoothing problem in high-dimensional geophysical prediction systems. The goal of smoothing is to make optimal estimates of the ...geophysical system state making best use of observations taken before, at, and after the analysis time. We begin by reviewing the underlying probabilistic theory, along with a discussion how to implement a smoother using an ensemble Kalman filter algorithm. The novel contribution of this paper is the investigation of various key issues regarding the application of ensemble Kalman filters to smoothing using a series of Observing System Simulation Experiments in both a Lorenz 1996 model and an Atmospheric General Circulation Model. The results demonstrate the impacts of non-linearities, ensemble size, observational network configuration and covariance localization. The Atmospheric General Circulation model results demonstrate that the ensemble Kalman smoother (EnKS) can be successfully applied to high-dimensional estimation problems and that covariance localization plays a critical role in its success. The results of this paper provide a foundation of understanding which will be useful in future applications of EnKS algorithms.
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This paper proposes a differential inflation scheme and applies this technique to driver estimation for the Global Ionosphere–Thermosphere Model (GITM) using the Ensemble Adjustment Kalman Filter ...(EAKF), which is a part of the Data Assimilation Research Testbed (DART). Driver estimation using EAKF is first demonstrated on a linear example and then applied to GITM. The Challenging Minisatellite Payload (CHAMP) neutral mass density measurements are assimilated into a biased version of GITM, and the solar flux index, F10.7, is estimated. Although the estimate of F10.7 obtained using DART does not converge to the measured values, the converged values are shown to drive the GITM output close to CHAMP measurements. In order to prevent the ensemble spread from converging to zero, the state and driver estimates are inflated. In particular, the F10.7 estimate is inflated to have a constant variance. It is shown that EAKF with differential inflation reduces the model bias from 73% down to 7% along the CHAMP satellite path when compared to the biased GITM output obtained without using data assimilation. The Gravity Recovery and Climate Experiment (GRACE) density measurements are used to validate the data assimilation performance at locations different from measurement locations. It is shown that the bias at GRACE locations is decreased from 76% down to 52% as compared to not using data assimilation, showing that model estimation of the thermosphere is improved globally.
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•CHAMP satellite data are assimilated into a space weather model (GITM).•Assimilation is performed by using the ensemble adjustment Kalman filter (EAKF).•Driver (F10.7) is estimated in addition to model states.•Driver estimate is inflated to have constant variance.•EAKF decreases the CHAMP-GITM residual from nominal 73% to 7%.
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The fundamental equations that model turbulent flow do not provide much insight into the size and shape of observed turbulent structures. We investigate the efficient and accurate representation of ...structures in two-dimensional turbulence by applying statistical models directly to the simulated vorticity field. Rather than extract the coherent portion of the image from the background variation, as in the classical signal-plus-noise model, we present a model for individual vortices using the non-decimated discrete wavelet transform. A template image, which is supplied by the user, provides the features to be extracted from the vorticity field. By transforming the vortex template into the wavelet domain, specific characteristics that are present in the template, such as size and symmetry, are broken down into components that are associated with spatial frequencies. Multivariate multiple linear regression is used to fit the vortex template to the vorticity field in the wavelet domain. Since all levels of the template decomposition may be used to model each level in the field decomposition, the resulting model need not be identical to the template. Application to a vortex census algorithm that records quantities of interest (such as size, peak amplitude and circulation) as the vorticity field evolves is given. The multiresolution census algorithm extracts coherent structures of all shapes and sizes in simulated vorticity fields and can reproduce known physical scaling laws when processing a set of vorticity fields that evolve over time.
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BFBNIB, FZAB, GIS, IJS, INZLJ, IZUM, KILJ, NLZOH, NMLJ, NUK, OILJ, PILJ, PNG, SAZU, SBCE, SBMB, UL, UM, UPUK, ZRSKP
Advances in computing power are allowing researchers to use Bayesian hierarchical models (BHM) on problems previously considered computationally infeasible. This article discusses the procedure of ...migrating a BHM from a workstation-class implementation to a massively parallel architecture, indicative of the current direction of advances in computing hardware. The parallel implementation is nearly 500 times larger than the workstation-class implementation from the data perspective. The BHM in question combines the information from a scatterometer on board a polar-orbiting satellite and the result of a numerical weather prediction model and produces an ensemble of high-resolution tropical surface wind fields with physically realistic variability at all spatial scales.
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20.
Quasi-stationary wave variability in NSCAT winds Milliff, Ralph F.; Hoar, Timothy J.; Loon, Harry ...
Journal of Geophysical Research, Washington, DC,
15 May 1999, Volume:
104, Issue:
C5
Journal Article
Peer reviewed
Open access
Planetary and interseasonal properties of the NASA scatterometer (NSCAT) winds are explored in the context of the quasi‐stationary waves (QSWs) of the southern hemisphere. The QSWs are examined by ...means of zonal asymmetries of 3‐month running averages of the meridional velocity derived from NSCAT. The study period spans the entire NSCAT record from September 15, 1996, through June 29, 1997. Meridional winds from the European Space Agency ERS 2 scatterometer are used to augment 3‐month averages centered on August 1996 through May 1997. The time period corresponds to the transition from year −1 to 0 of the 1997 warm event in the Southern Oscillation. Comparisons are made with QSW signals in geopotential height anomalies from the National Centers for Environmental Prediction/National Center for Atmospheric Research Climate Data Assimilation System. The zonal anomalies of meridional wind from NSCAT are shown to be in approximate geostrophic balance with zonal gradients in the zonal anomalies of geopotential height at 500 and 1000 hPa.
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