Showing papers by "Eugenia Kalnay published in 2013"

Effective assimilation of global precipitation: simulation experiments

Abstract: Past attempts to assimilate precipitation by nudging or variational methods have succeeded in forcing the model precipitation to be close to the observed values. However, the model forecasts tend to lose their additional skill after a few forecast hours. In this study, a local ensemble transform Kalman filter (LETKF) is used to effectively assimilate precipitation by allowing ensemble members with better precipitation to receive higher weights in the analysis. In addition, two other changes in the precipitation assimilation process are found to alleviate the problems related to the non-Gaussianity of the precipitation variable: (a) transform the precipitation variable into a Gaussian distribution based on its climatological distribution (an approach that could also be used in the assimilation of other non-Gaussian observations) and (b) only assimilate precipitation at the location where at least some ensemble members have precipitation. Unlike many current approaches, both positive and zero rain observations are assimilated effectively. Observing system simulation experiments (OSSEs) are conducted using the Simplified Parametrisations, primitivE-Equation DYnamics (SPEEDY) model, a simplified but realistic general circulation model. When uniformly and globally distributed observations of precipitation are assimilated in addition to rawinsonde observations, both the analyses and the medium-range forecasts of all model variables, including precipitation, are significantly improved as compared to only assimilating rawinsonde observations. The effect of precipitation assimilation on the analyses is retained on the medium-range forecasts and is larger in the Southern Hemisphere (SH) than that in the Northern Hemisphere (NH) because the NH analyses are already made more accurate by the denser rawinsonde stations. These improvements are much reduced when only the moisture field is modified by the precipitation observations. Both the Gaussian transformation and the new observation selection criterion are shown to be beneficial to the precipitation assimilation especially in the case of larger observation errors. Assigning smaller horizontal localisation length scales for precipitation observations further improves the LETKF analysis. Keywords: ensemble Kalman filter, data assimilation, precipitation, non-Gaussianity, anamorphosis (Published: 18 July 2013) Citation: Tellus A 2013, 65 , 19915, http://dx.doi.org/10.3402/tellusa.v65i0.19915

Estimating and including observation-error correlations in data assimilation

Abstract: Usually in data assimilation with geophysical systems, the observation-error covariance matrix R is assumed to be diagonal for simplicity and computational efficiency, although there are studies indicating that several types of satellite observations contain significantly correlated errors. This study brings to light the impact of the off-diagonal terms of R in data assimilation. The adaptive estimation method of Li et al., which allows online estimation of the observation-error variance using innovation statistics, is extended to include off-diagonal terms of R. The extended method performs well with the 40-variable Lorenz model in estimating non-diagonal observation-error covariances. Interestingly, the analysis accuracy is improved when the observation errors are correlated, but only if the observation-error correlations are explicitly considered in data assimilation. Further theoretical considerations relate the impact of observing systems (characterized by both R and an observation operator H) on ana...

A further assessment of vegetation feedback on decadal Sahel rainfall variability

Abstract: The effect of vegetation feedback on decadal-scale Sahel rainfall variability is analyzed using an ensemble of climate model simulations in which the atmospheric general circulation model ICTPAGCM (“SPEEDY”) is coupled to the dynamic vegetation model VEGAS to represent feedbacks from surface albedo change and evapotranspiration, forced externally by observed sea surface temperature (SST) changes. In the control experiment, where the full vegetation feedback is included, the ensemble is consistent with the observed decadal rainfall variability, with a forced component 60 % of the observed variability. In a sensitivity experiment where climatological vegetation cover and albedo are prescribed from the control experiment, the ensemble of simulations is not consistent with the observations because of strongly reduced amplitude of decadal rainfall variability, and the forced component drops to 35 % of the observed variability. The decadal rainfall variability is driven by SST forcing, but significantly enhanced by land-surface feedbacks. Both, local evaporation and moisture flux convergence changes are important for the total rainfall response. Also the internal decadal variability across the ensemble members (not SST-forced) is much stronger in the control experiment compared with the one where vegetation cover and albedo are prescribed. It is further shown that this positive vegetation feedback is physically related to the albedo feedback, supporting the Charney hypothesis.

Ensemble-based observation impact estimates using the NCEP GFS

Abstract: The impacts of the assimilated observations on the 24-hour forecasts are estimated with the ensemble-based method proposed by Kalnay et al. using an ensemble Kalman filter (EnKF). This method estimates the relative impact of observations in data assimilation similar to the adjoint-based method proposed by Langland and Baker but without using the adjoint model. It is implemented on the National Centers for Environmental Prediction Global Forecasting System EnKF that has been used as part of operational global data assimilation system at NCEP since May 2012. The result quantifies the overall positive impacts of the assimilated observations and the relative importance of the satellite radiance observations compared to other types of observations, especially for the moisture fields. A simple moving localisation based on the average wind, although not optimal, seems to work well. The method is also used to identify the cause of local forecast failure cases in the 24-hour forecasts. Data-denial experiments of the observations identified as producing a negative impact are performed, and forecast errors are reduced as estimated, thus validating the impact estimation. Keywords: data assimilation, observation impact, ensemble Kalman filter, skill dropout, ensemble sensitivity (Published: 5 September 2013) Citation: Tellus A 2013, 65 , 20038, http://dx.doi.org/10.3402/tellusa.v65i0.20038

Relative sea-level rise and the conterminous United States: Consequences of potential land inundation in terms of population at risk and GDP loss

Abstract: Global sea-level rise poses a significant threat not only for coastal communities as development continues but also for national economies. This paper presents estimates of how future changes in relative sea-level rise puts coastal populations at risk, as well as affect overall GDP in the conterminous United States. We use four different sea-level rise scenarios for 2010–2100: a low-end scenario (Extended Linear Trend) a second low-end scenario based on a strong mitigative global warming pathway (Global Warming Coupling 2.6), a high-end scenario based on rising radiative forcing (Global Warming Coupling 8.5) and a plausible very high-end scenario, including accelerated ice cap melting (Global Warming Coupling 8.5+). Relative sea-level rise trends for each US state are employed to obtain more reasonable rates for these areas, as long-term rates vary considerably between the US Atlantic, Gulf and Pacific coasts because of the Glacial Isostatic Adjustment, local subsidence and sediment compaction, and other vertical land movement. Using these trends for the four scenarios reveals that the relative sea levels predicted by century's end could range – averaged over all states – from 0.2 to 2.0 m above present levels. The estimates for the amount of land inundated vary from 26,000 to 76,000 km2. Upwards of 1.8 to 7.4 million people could be at risk, and GDP could potentially decline by USD 70–289 billion. Unfortunately, there are many uncertainties associated with the impact estimates due to the limitations of the input data, especially the input elevation data. Taking this into account, even the most conservative scenario shows a significant impact for the US, emphasizing the importance of adaptation and mitigation.

Lyapunov, singular and bred vectors in a multi-scale system: an empirical exploration of vectors related to instabilities

Abstract: We compute and compare the three types of vectors frequently used to explore the instability properties of dynamical models, namely Lyapunov vectors (LVs), singular vectors (SVs) and bred vectors (BVs) in two systems, using the Wolfe–Samelson (2007 Tellus A 59 355–66) algorithm to compute all of the Lyapunov vectors. The first system is the Lorenz (1963 J. Atmos. Sci. 20 130–41) three-variable model. Although the leading Lyapunov vector, LV1, grows fastest globally, the second Lyapunov vector, LV2, which has zero growth globally, often grows faster than LV1 locally. Whenever this happens, BVs grow closer to LV2, suggesting that in larger atmospheric or oceanic models where several instabilities can grow in different areas of the world, BVs will grow toward the fastest growing local unstable mode. A comparison of their growth rates at different times shows that all three types of dynamical vectors have the ability to predict regime changes and the duration of the new regime based on their growth rates in the last orbit of the old regime, as shown for BVs by Evans et al (2004 Bull. Am. Meteorol. Soc. 520–4). LV1 and BVs have similar predictive skill, LV2 has a tendency to produce false alarms, and even LV3 shows that maximum decay is also associated with regime change. Initial and final SVs grow much faster and are the most accurate predictors of regime change, although the characteristics of the initial SVs are strongly dependent on the length of the optimization window. The second system is the toy 'ocean-atmosphere' model developed by Pena and Kalnay (2004 Nonlinear Process. Geophys. 11 319–27) coupling three Lorenz (1963 J. Atmos. Sci. 20 130–41) systems with different time scales, in order to test the effects of fast and slow modes of growth on the dynamical vectors. A fast 'extratropical atmosphere' is weakly coupled to a fast 'tropical atmosphere' which is, in turn, strongly coupled to a slow 'ocean' system, the latter coupling imitating the tropical El Nino–Southern Oscillation. The bred vectors are able to separate the fast and slow modes of growth through appropriate selection of the breeding perturbation size and rescaling interval. The Lyapunov vectors are able to successfully separate the fast 'extratropical atmosphere', but are unable to completely decouple the 'tropical atmosphere' from the 'ocean'. This leads to 'coupled' Lyapunov vectors that are mainly useful in the (slow) 'ocean' system, but are still affected by changes in the (fast) 'tropical' system. The singular vectors are excellent in capturing the fast modes, but are unable to capture the slow modes of growth. The dissimilar behavior of the three types of vectors leads to a degradation in the similarities of the subspaces they inhabit and affects their relative ability of representing the coupled modes.This article is part of a special issue of Journal of Physics A: Mathematical and Theoretical devoted to 'Lyapunov analysis: from dynamical systems theory to applications'.

The local ensemble transform Kalman filter and the running-in-place algorithm applied to a global ocean general circulation model

Abstract: . The most widely used methods of data assimilation in large-scale oceanography, such as the Simple Ocean Data Assimilation (SODA) algorithm, specify the background error covariances and thus are unable to refine the weights in the assimilation as the circulation changes. In contrast, the more computationally expensive Ensemble Kalman Filters (EnKF) such as the Local Ensemble Transform Kalman Filter (LETKF) use an ensemble of model forecasts to predict changes in the background error covariances and thus should produce more accurate analyses. The EnKFs are based on the approximation that ensemble members reflect a Gaussian probability distribution that is transformed linearly during the forecast and analysis cycle. In the presence of nonlinearity, EnKFs can gain from replacing each analysis increment by a sequence of smaller increments obtained by recursively applying the forecast model and data assimilation procedure over a single analysis cycle. This has led to the development of the "running in place" (RIP) algorithm by Kalnay and Yang (2010) and Yang et al. (2012a,b) in which the weights computed at the end of each analysis cycle are used recursively to refine the ensemble at the beginning of the analysis cycle. To date, no studies have been carried out with RIP in a global domain with real observations. This paper provides a comparison of the aforementioned assimilation methods in a set of experiments spanning seven years (1997–2003) using identical forecast models, initial conditions, and observation data. While the emphasis is on understanding the similarities and differences between the assimilation methods, comparisons are also made to independent ocean station temperature, salinity, and velocity time series, as well as ocean transports, providing information about the absolute error of each. Comparisons to independent observations are similar for the assimilation methods but the observation-minus-background temperature differences are distinctly lower for LETKF and RIP. The results support the potential for LETKF to improve the quality of ocean analyses on the space and timescales of interest for seasonal prediction and for RIP to accelerate the spin up of the system.

Identifying Martian atmospheric instabilities and their physical origins using bred vectors

Abstract: The bred vector (BV) technique applied to the Geophysical Fluid Dynamics Laboratory (GFDL) Mars General Circulation Model (MGCM) identifies regions and seasons of instability of the Martian atmosphere, and a kinetic energy equation applied to the control and perturbed states elucidates their physical origins Instabilities prominent in the late autumn to early spring seasons of each hemisphere along the polar temperature front result from baroclinic conversions from BV potential to BV kinetic energy near the surface, whereas both baroclinic and barotropic conversions play a role for the westerly jets aloft The low-level tropics and the northern hemisphere summer are relatively stable, with negative bred vector growth rates Bred vector growth precedes initiation of travelling wave activity in the midlatitudes during the transition seasons, and their structure relates to the eddy field Topography plays a role in determining favoured locations for near-surface instabilities Bred vectors are also linked to forecast ensemble spread in data assimilation and help explain the growth of forecast errors We finally note that the ability to use breeding to identify instabilities as well as their physical origin depends on the fact that both the control and the perturbed solutions that give rise to bred vectors satisfy exactly the model's governing equations As a result, this approach can be used with any dynamical system represented by a model Copyright © 2012 Royal Meteorological Society

Improving the spin-up of regional EnKF for typhoon assimilation and forecasting with Typhoon Sinlaku (2008)

Abstract: The Running-In-Place (RIP) method is implemented in the framework of the Local Ensemble Transform Kalman Filter (LETKF) coupled with the Weather Research and Forecasting (WRF) model. RIP aims at accelerating the spin-up of the regional LETKF system when the WRF ensemble is initialised from a global analysis, which is obtained at a coarser resolution and lacks features related to the underlying mesoscale evolution. The RIP method is further proposed as an outer-loop scheme to improve the nonlinear evolution of the ensemble when the characteristics of the error statistics change rapidly owing to strong nonlinear dynamics. The impact of using RIP as an outer-loop for the WRF-LETKF system is evaluated for typhoon assimilation and prediction with Typhoon Sinlaku (2008) as a case study. For forecasts beyond one day, the typhoon track prediction is significantly improved after RIP is applied, especially during the spin-up period of the LETKF assimilation when Sinlaku is developing rapidly from a severe tropical storm to a typhoon. The impact of the dropsondes is significantly increased by RIP at early assimilation cycles. Results suggest that these improvements are because of the positive impact on the environmental condition of the typhoon. Results also suggest that using the RIP scheme adaptively allows RIP to be used as an outer-loop for the WRF-LETKF with further improvements. Keywords: regional data assimilation, ensemble Kalman Filter (EnKF), typhoon prediction, nonlinearity, observation impact (Published: 16 September 2013) Citation: Tellus A 2013, 65 , 20804, http://dx.doi.org/10.3402/tellusa.v65i0.20804

Ensemble Mars Atmosphere Reanalysis System (EMARS)

Abstract: Introduction: Data assimilation optimally combines spacecraft observations with short-term forecasts from an atmospheric model to produce a record of the atmospheric state and, with an ensemble, its uncertainties. When performed retroactively for a long period of time, this sequence of analyses is termed a reanalysis. The availability of Thermal Emission Spectrometer (TES) and Mars Climate Sounder (MCS) retrievals of temperature and aerosol opacities enables a comprehensive multiannual examination of Martian weather and climate, as data assimilation has proven to be an effective means of reconciling models with observations for Mars (Lewis et al., 2007; Hoffman et al., 2010; Lee et al., 2011; Navarro et al., 2013). We have used the Local Ensemble Transform Kalman Filter (LETKF), an advanced data assimilation system, to construct a sequence of synoptic maps (Figure 1) detailing the evolution of the temperature, wind, and surface pressure over the course of several Martian years (Mars Year 24 – 27, as well as during the MCS period).