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Recently, concerns have been growing about the intensification and increase in extreme weather events, including torrential rainfall and typhoons. For mitigating the damage caused by weather-induced disasters, recent studies have started developing weather control technologies to lead the weather to a desirable direction with feasible manipulations. This study proposes introducing the model predictive control (MPC), an advanced control method explored in control engineering, into the framework of the control simulation experiment (CSE). In contrast to previous CSE studies, the proposed method explicitly considers physical constraints, such as the maximum allowable manipulations, within the cost function of the MPC. As the first step toward applying the MPC to real weather control, this study performed a series of MPC experiments with the Lorenz 63 model. Our results showed that the Lorenz 63 system can be led to the positive regime with control inputs determined by the MPC. Furthermore, the MPC significantly reduced necessary forecast length compared to earlier CSE studies. It was beneficial to select a member that showed a larger regime shift for the initial state when dealing with uncertainty in initial states. [ABSTRACT FROM AUTHOR]

The predefined threshold is one of the main fundamental and methodological challenges underlying the uncertainty and delayed release of existing burned area (BA) products. To improve the accuracy and timeliness of BA mapping, a tuning-free moderate-scale BA detection algorithm (AT&RF algorithm) was proposed to carry out rapid BA mapping for the immediate post-fire assessment. A case study was carried out at the Chornobyl Exclusion Zone (ChEZ) to assess the performance of the algorithm. The evaluation results indicated that the algorithm successfully detected the BA in 2015 and 2020 in ChEZ, which outperforms the existing BA products by significantly decreasing the omission error by 18.6% and 15.1% and improving the Kappa coefficient and critical success index by 9.4% and 4.0% on average for MCD64A1 and FireCCI51 products, respectively. It is also possible to transfer the algorithm quickly to other regions without complicated adjustments and significantly improve the timeliness and accuracy compared to the existing global BA products for assessing environmental impact and scope. [ABSTRACT FROM AUTHOR]

Data assimilation is a crucial component in the Earth science field, enabling the integration of observation data with numerical models. In the context of numerical weather prediction (NWP), data assimilation is particularly vital for improving initial conditions and subsequent predictions. However, the computational demands imposed by conventional approaches, which employ iterative processes to minimize cost functions, pose notable challenges in computational time. The emergence of quantum computing provides promising opportunities to address these computation challenges by harnessing the inherent parallelism and optimization capabilities of quantum annealing machines. In this investigation, we propose a novel approach termed quantum data assimilation, which solves the data assimilation problem using quantum annealers. Our data assimilation experiments using the 40-variable Lorenz model were highly promising, showing that the quantum annealers produced an analysis with comparable accuracy to conventional data assimilation approaches. In particular, the D-Wave Systems physical quantum annealing machine achieved a significant reduction in execution time. [ABSTRACT FROM AUTHOR]

This study explores coupled land–atmosphere data assimilation (DA) for improving weather and hydrological forecasts by assimilating soil moisture (SM) data. This study integrates a land DA component into a global atmospheric DA system of the Nonhydrostatic ICosahedral Atmospheric Model and the local ensemble transform Kalman filter (NICAM-LETKF) and performs both strongly and weakly coupled land–atmosphere DA experiments. We explore various types of coupled DA experiments by assimilating atmospheric observations and SM data simultaneously. The results show that analyzing atmospheric variables by assimilating SM data improves the SM analysis and forecasts and mitigates a warm bias in the lower troposphere where a dry SM bias exists. On the other hand, updating SM by assimilating atmospheric observations has detrimental impacts due to spurious error correlations between the atmospheric observations and land model variables. We also find that assimilating SM by strongly coupled DA is beneficial in the Sahel and equatorial Africa from May to October. These regions are characterized by seasonal variations in the precipitation patterns and benefit from updates in the atmospheric variables through SM DA during periods of increased precipitation. Additionally, these regions coincide with those identified in the previous studies, where a global initialization of SM would enhance the prediction skill of seasonal precipitation. [ABSTRACT FROM AUTHOR]

Controlling weather is an outstanding and pioneering challenge for researchers around the world, due to the chaotic features of the complex atmosphere. A control simulation experiment (CSE) on the Lorenz-63 model, which consists of positive and negative regimes represented by the states of variable x , demonstrated that the variables can be controlled to stay in the target regime by adding perturbations with a constant magnitude to an independent model run. The current study tries to reduce the input manipulation of the CSE, including the total control times and magnitudes of perturbations, by investigating how controls affect the instability of systems. For that purpose, we first explored the instability properties of Lorenz-63 models without and under control. Experiments show that the maximum growth rate of the singular vector (SV) reduces when the variable x was controlled in the target regime. Subsequently, this research proposes to update the magnitude of perturbations adaptively based on the maximum growth rate of SV; consequently, the times to control will also change. The proposed method successfully reduces around 40 % of total control times and around 20 % of total magnitudes of perturbations compared to the case with a constant magnitude. Results of this research suggest that investigating the impacts of control on instability would be beneficial for designing methods to control the complex atmosphere with feasible manipulations. [ABSTRACT FROM AUTHOR]

Direct assimilation of Dual‐frequency Precipitation Radar (DPR) data of the Global Precipitation Measurement (GPM) core satellite is challenging mainly due to its long revisiting intervals relative to the time scale of precipitation, and precipitation location errors. This study explores a method for improving precipitation forecasts using GPM DPR through model parameter estimation. We developed a 28 km mesh global atmospheric data assimilation system that integrates the Nonhydrostatic ICosahedral Atmospheric Model (NICAM) and Local Ensemble Transform Kalman Filter (LETKF) coupled with a satellite radar simulator. Using the NICAM‐LETKF and GPM DPR observations, this study estimates a model cloud physics parameter corresponding to snowfall terminal velocity. To overcome the difficulties of long revisiting intervals and precipitation location errors, we propose a parameter estimation method based on a two‐dimensional histogram known as the contoured frequency by temperature diagram (CFTD). Parameter estimation effectively mitigated the gap between simulated and observed CFTD, resulting in improved 6 hr precipitation forecasts. Plain Language Summary: Direct assimilation of satellite‐borne radar data into weather forecasting models is challenging mainly due to its long revisiting intervals and precipitation location errors. This study explores a method for improving precipitation forecasts using the satellite radar data for optimizing an uncertain parameter in a weather forecasting model. We estimated a model cloud physics parameter corresponding to snowfall terminal velocity. Parameter estimation effectively mitigated the gap between simulated and observed radar reflectivity, resulting in improved 6 hr precipitation forecasts. Key Points: Direct assimilation of GPM DPR reflectivity is challenging due to its long revisiting intervals relative to the time scale of precipitationA new model parameter estimation approach based on a reflectivity‐temperature histogram is used to improve global precipitation forecastsParameter estimation of snow terminal velocity mitigated the gap between simulated and observed reflectivity, resulting in improved forecasts [ABSTRACT FROM AUTHOR]

A particle filter (PF) is an ensemble data assimilation method that does not assume Gaussian error distributions. Recent studies proposed local PFs (LPFs), which use localization, as in the ensemble Kalman filter, to apply the PF efficiently for high-dimensional dynamics. Among others, Penny and Miyoshi (2016) developed an LPF in the form of the ensemble transform matrix of the local ensemble transform Kalman filter (LETKF). The LETKF has been widely accepted for various geophysical systems, including numerical weather prediction (NWP) models. Therefore, implementing the LPF consistently with an existing LETKF code is useful. This study develops a software platform for the LPF and its Gaussian mixture extension (LPFGM) by making slight modifications to the LETKF code with a simplified global climate model known as Simplified Parameterizations, Primitive Equation Dynamics (SPEEDY). A series of idealized twin experiments were accomplished under the ideal-model assumption. With large inflation by the relaxation to prior spread, the LPF showed stable filter performance with dense observations but became unstable with sparse observations. The LPFGM showed a more accurate and stable performance than the LPF with both dense and sparse observations. In addition to the relaxation parameter, regulating the resampling frequency and the amplitude of Gaussian kernels was important for the LPFGM. With a spatially inhomogeneous observing network, the LPFGM was superior to the LETKF in sparsely observed regions, where the background ensemble spread and non-Gaussianity were larger. The SPEEDY-based LETKF, LPF, and LPFGM systems are available as open-source software on GitHub (https://github.com/skotsuki/speedy-lpf , last access: 16 November 2022) and can be adapted to various models relatively easily, as in the case of the LETKF. [ABSTRACT FROM AUTHOR]

Recent numerical weather prediction systems have significantly improved medium-range forecasts by implementing hybrid background error covariance, for which climatological (static) and ensemble-based (flow-dependent) error covariance are combined. While the hybrid approach has been investigated mainly in variational systems, this study aims at exploring methods for implementing the hybrid approach for the local ensemble transform Kalman filter (LETKF). Following Kretschmer et al., the present study constructed hybrid background error covariance by adding collections of climatological perturbations to the forecast ensemble. In addition, this study proposes a new localization method that attenuates the ensemble perturbation (Z-localization) instead of inflating observation error variance (R-localization). A series of experiments with a simplified global atmospheric model revealed that the hybrid LETKF resulted in smaller forecast errors than the LETKF, especially in sparsely observed regions. Due to the larger ensemble enabled by the hybrid approach, optimal localization length scales for the hybrid LETKF were larger than those for the LETKF. With the LETKF, the Z-localization resulted in similar forecast errors as the R-localization. However, Z-localization has an advantage in enabling us to apply different localization scales for flow-dependent perturbation and climatological static perturbations with the hybrid LETKF. The optimal localization for climatological perturbations was slightly larger than that for flow-dependent perturbations. This study also proposes optimal eigendecomposition (OED) ETKF formulation to reduce computational costs. The computational expense of the OED ETKF formulation became significantly smaller than that of standard ETKF formulations as the number of climatological perturbations was increased beyond a few hundred. [ABSTRACT FROM AUTHOR]

For the past two decades, precipitation radars (PR) onboard low‐orbiting satellites such as Tropical Rainfall Measuring Mission (TRMM) have provided invaluable insight into global precipitation variability and led to advancements in numerical weather prediction through data assimilation. Building upon this success, planning has begun on the next generation of satellite‐based PR instruments, with the consideration for a future geostationary‐based PR (GPR), bringing the advantage of higher observation frequency over previous and current PR satellites. Following the successful demonstration by a recent study to test the feasibility of a GPR to obtain three‐dimensional precipitation data, this study takes the first step to investigate the potential usefulness of GPR observations for numerical weather prediction by performing a perfect model observing system simulation experiment (OSSE) for a West Pacific tropical cyclone (TC). Data assimilation experiments are performed assimilating reflectivity observations obtained for a range of beam sampling spans, following a previous finding that oversampling improves observation quality. Results showed observations obtained with finer sampling spans of 5 km and 10 km were able to better capture key tropical cyclone features in analyses, including the eye, heavy rainfall associated with the eyewall, and outer convective rainbands. Results also showed that through increased moistening and upward velocity within the inner storm environment, assimilation of observations drove an intensification of the secondary circulation and deepening of the storm, leading to an improvement in TC intensity error. Intensity forecasts were found improved for assimilation of observations obtained with increasingly finer beam sampling span, suggesting an important benefit of oversampling. Plain Language Summary: In a recent study, the feasibility of a future precipitation radar based onboard a geostationary satellite (GPR) that could obtain three‐dimensional precipitation measurements was successfully tested. In this study, we take the first step to investigate whether reflectivity observations can be used to improve analyses and forecasts of global weather systems. We perform data assimilation experiments that use simulated GPR observations for a West Pacific tropical cyclone, with observations obtained with varying radar beam sampling spans to generate observation oversampling, following a previous finding that this improves observation quality. Results found that key convective features of the tropical cyclone (TC), including the eye, eyewall structure, and outer rainbands, were all better captured in simulations assimilating observations obtained with finer beam sampling spans, with 5 km sampling providing best results. Observations were also found to have a positive impact on TC intensity in both model analyses and forecasts, with forecast errors for minimum sea level pressure improved at all lead times up to 18 h. TC intensity forecasts were also improved with increasingly finer beam span, suggesting an important potential benefit of oversampling for TC prediction. Key Points: Reflectivity observations from a geostationary precipitation radar improved representation of convective features for a tropical cycloneOversampling with finer beam sampling span improved tropical cyclone intensity errors in analyses and forecastsOversampling improved precipitation and maximum surface wind intensity in forecasts [ABSTRACT FROM AUTHOR]

During the last decade, the replacement of static climatological forecast error covariance models with hybrid error covariance models that linearly combine localised ensemble covariances with static climatological error covariances has led to significant forecast improvements at several major forecasting centres. Here, a deeper understanding of why the hybrid's superficially ad hoc mix of ensemble‐based and climatological covariances yields such significant improvements is pursued. In practice, ensemble covariances are not equal to the true flow‐dependent forecast error covariance matrix. Here, the relationship between actual forecast error covariance and the corresponding ensemble covariance is empirically demonstrated. Using a simplified global circulation model and the local ensemble transform Kalman filter (LETKF), the covariance of the set of actual forecast errors corresponding to ensemble covariances close to a fixed target value is computed. By doing this for differing target values, an estimate of the actual forecast error covariance as a function of ensemble covariance is obtained. A demonstration that the hybrid is a much better approximation to this estimate than either the static climatological covariance or the localised ensemble covariance is given. The empirical estimate has two features that current hybrid error covariance models fail to represent: (i) The weight given to the static covariance matrix is an increasing function of the horizontal separation distance of the covarying model variables, and (ii) for small ensemble sizes and ensemble covariances near zero but negative, the actual forecast error covariance is a decreasing function of increasing ensemble covariance. While the first finding has been anticipated by other authors, the second finding has not been anticipated, as far as the authors are aware. Here, (ii) is hypothesised to be a consequence of spurious sample correlations and variances associated with reduced ensembles. Consistent with this hypothesis, the non‐monotonicity of this relationship is almost eliminated by quadrupling the ensemble size. [ABSTRACT FROM AUTHOR]

The Local Ensemble Transform Kalman Filter (LETKF) computes analysis by using a weighted average of the first‐guess ensemble with surrounding observations within a localization cut‐off radius. Since overlapped observations are assimilated at neighbouring grid points, the LETKF results in spatially smooth weights. This study explores the spatial structure of the weights with the intermediate atmospheric model SPEEDY (Simplified Parameterizations, Primitive Equation Dynamics). Based on the characteristics of the weight structure, we also aim to improve the weight interpolation (WI) method, which we use to compute the weights at coarser reference points and interpolate the weights into higher‐resolution model grid points. The results show that larger localization and sparser observations result in spatially smoother weights. WI is less detrimental when weight patterns are spatially smoother. An advanced WI method with observation‐density‐dependent reference points results in better forecasts than those with uniformly distributed reference points. This improvement may be due to the spatially inhomogeneous localization function realized by the WI method with observation‐density‐dependent reference points. The spatial distribution of the optimal localization scales shows that larger (smaller) localization is beneficial in sparsely (densely) observed regions. The WI method is computationally more efficient with larger ensembles since the additional computational cost for the WI is lower than that for the LETKF. [ABSTRACT FROM AUTHOR]

Evaluating impacts of observations on the skill of numerical weather prediction (NWP) is important. The Ensemble Forecast Sensitivity to Observation (EFSO) provides an efficient approach to diagnosing observation impacts, quantifying how much each observation improves or degrades a subsequent forecast with a given verification reference. This study investigates the sensitivity of EFSO impact estimates to the choice of the verification reference, using a global NWP system consisting of the Non‐hydrostatic Icosahedral Atmospheric Model (NICAM) and the Local Ensemble Transform Kalman Filter (LETKF). The EFSO evaluates observation impacts with the moist total energy norm and with recently proposed observation‐based verification metrics. The results show that each type of observation mainly contributes to the improvement of forecast departures of the observed variable maybe due to the limitation of localization in the EFSO. The EFSO overestimates the fraction of beneficial observations when verified with subsequent analyses, especially for shorter lead times such as 6 h. We may avoid this overestimation to some extent by verifying with observations, analyses from other data assimilation (DA) systems, or analyses of an independent run with the same DA system. In addition, this study demonstrates two important issues possibly leading to overestimating observation impacts. First, observation impacts would be overestimated if we apply relaxation‐to‐prior methods to the initial conditions of the ensemble forecasts in the EFSO; therefore, the ensemble forecasts in the EFSO should be independent of the ensemble forecasts in the DA cycle. Second, deterministic baseline forecasts of the EFSO, which represent the forecast without DA, should be initialized by the ensemble mean of the first guess at the analysis time, not by the previous analysis. [ABSTRACT FROM AUTHOR]

Spaceborne precipitation radars, such as the Tropical Rainfall Measuring Mission (TRMM) and the Global Precipitation Measurement (GPM) Core Observatory, have been important platforms to provide a direct measurement of three-dimensional precipitation structure globally. Building upon the success of TRMM and GPM Core Observatory, the Japan Aerospace Exploration Agency (JAXA) is currently surveying the feasibility of a potential satellite mission equipped with a precipitation radar on a geostationary orbit. The quasi-continuous observation realized by the geostationary satellite radar would offer a new insight into meteorology and would advance numerical weather prediction (NWP) through their effective use by data assimilation. Although the radar would be beneficial, the radar on the geostationary orbit measures precipitation obliquely at off-nadir points. In addition, the observing resolution will be several times larger than those on board TRMM and GPM Core Observatory due to the limited antenna size that we could deliver. The tilted sampling volume and the coarse resolution would result in more contamination from surface clutter. To investigate the impact of these limitations and to explore the potential usefulness of the geostationary satellite radar, this study simulates the observation data for a typhoon case using an NWP model and a radar simulator. The results demonstrate that it would be possible to obtain three-dimensional precipitation data. However, the quality of the observation depends on the beam width, the beam sampling span, and the position of precipitation systems. With a wide beam width and a coarse beam span, the radar cannot observe weak precipitation at low altitudes due to surface clutter. The limitation can be mitigated by oversampling (i.e., a wide beam width and a fine sampling span). With a narrow beam width and a fine beam sampling span, the surface clutter interference is confined to the surface level. When the precipitation system is located far from the nadir, the precipitation signal is obtained only for strong precipitation. [ABSTRACT FROM AUTHOR]

Over the past few decades, precipitation forecasts by numerical weather prediction (NWP) models have been remarkably improved. Yet, precipitation nowcasting based on spatiotemporal extrapolation tends to provide a better precipitation forecast at shorter lead times with much less computation. Therefore, merging the precipitation forecasts from the NWP and extrapolation systems would be a viable approach to quantitative precipitation forecast (QPF). Although the optimal weights between the NWP and extrapolation systems are usually defined as a global constant, the weights would vary in space, particularly for global QPF. This study proposes a method to find the optimal weights at each location using the local threat score (LTS), a spatially localized version of the threat score. We test the locally optimal weighting with a global NWP system composed of the local ensemble transform Kalman filter and the Nonhydrostatic Icosahedral Atmospheric Model (NICAM-LETKF). For the extrapolation system, the RIKEN's global precipitation nowcasting system called GSMaP_RNC is used. GSMaP_RNC extrapolates precipitation patterns from the Japan Aerospace Exploration Agency (JAXA)'s Global Satellite Mapping of Precipitation (GSMaP). The benefit of merging in global precipitation forecast lasts longer compared to regional precipitation forecast. The results show that the locally optimal weighting is beneficial. [ABSTRACT FROM AUTHOR]

Abstract: This study aims to improve precipitation forecasts by estimating model parameters of a numerical weather prediction model with an ensemble‐based data assimilation method. We implemented the parameter estimation algorithm into a global atmospheric data assimilation system NICAM‐LETKF, which incorporates Nonhydrostatic Icosahedral Atmospheric Model (NICAM) and the Local Ensemble Transform Kalman Filter (LETKF). This study estimated a globally uniform model parameter of a large‐scale condensation scheme known as the B1 parameter of Berry's parameterization. We conducted an online estimation of the B1 parameter using the Global Satellite Mapping of Precipitation (GSMaP) data and successfully reduced NICAM's precipitation forecast bias relative to the GSMaP data, especially for weak rains. The estimated B1 parameter evolved toward the optimal value obtained by manual tuning. The parameter estimation also mitigated a dry bias for the lower troposphere in the Tropics. However, the estimated B1 intensified biases for cloud water mixing ratio and outgoing long‐wave radiation in the regions where shallow clouds are dominant. This is because only precipitation data were used to estimate the optimal value of B1, and more constraints will be required to obtain a suitable value for climatological simulations. [ABSTRACT FROM AUTHOR]

Covariance inflation plays an important role in the ensemble Kalman filter because the ensemble-based error variance is usually underestimated due to various factors such as the limited ensemble size and model imperfections. Manual tuning of the inflation parameters by trial and error is computationally expensive; therefore, several studies have proposed approaches to adaptive estimation of the inflation parameters. Among others, this study focuses on the covariance relaxation method which realizes spatially dependent inflation with a spatially homogeneous relaxation parameter. This study performs a series of experiments with the non-hydrostatic icosahedral atmospheric model ( NICAM) and the local ensemble transform Kalman filter ( LETKF) assimilating the real-world conventional observations and satellite radiances. Two adaptive covariance relaxation methods are implemented: relaxation to prior spread based on Ying and Zhang (adaptive- RTPS), and relaxation to prior perturbation (adaptive- RTPP). Both adaptive- RTPS and adaptive- RTPP generally improve the analysis compared to a baseline control experiment with an adaptive multiplicative inflation method. However, the adaptive- RTPS and adaptive- RTPP methods lead to an over-dispersive (under-dispersive) ensemble in the sparsely (densely) observed regions compared with the adaptive multiplicative inflation method. We find that the adaptive- RTPS and adaptive- RTPP methods are robust to a sudden change in the observing networks and observation error settings. [ABSTRACT FROM AUTHOR]

Space-time extrapolation is a key technique in precipitation nowcasting. Motions of patterns are estimated using two or more consecutive images, and the patterns are extrapolated in space and time to obtain their future patterns. Applying space-time extrapolation to satellite-based global precipitation data will provide valuable information for regions where ground-based precipitation nowcasts are not available. However, this technique is sensitive to the accuracy of the motion vectors, and over the past few decades, previous studies have investigated methods for obtaining reliable motion vectors such as variational techniques. In this paper, an alternative approach applying data assimilation to precipitation nowcasting is proposed. A prototype extrapolation system is implemented with the local ensemble transform Kalman filter and is tested with the Japan Aerospace Exploration Agency's Global Satellite Mapping of Precipitation (GSMaP) product. Data assimilation successfully improved the global precipitation nowcasting with the real-case GSMaP data. [ABSTRACT FROM AUTHOR]

This study proposes using data assimilation (DA) for climate research as a tool for optimizing model parameters objectively. Mitigating radiation bias is very important for climate change assessments with general circulation models. With the Nonhydrostatic ICosahedral Atmospheric Model (NICAM), this study estimated an autoconversion parameter in a large‐scale condensation scheme. We investigated two approaches to reducing radiation bias: examining useful satellite observations for parameter estimation and exploring the advantages of estimating spatially varying parameters. The parameter estimation accelerated autoconversion speed when we used liquid water path, outgoing longwave radiation, or outgoing shortwave radiation (OSR). Accelerated autoconversion reduced clouds and mitigated overestimated OSR bias of the NICAM. An ensemble‐based DA with horizontal localization can estimate spatially varying parameters. When liquid water path was used, the local parameter estimation resulted in better cloud representations and improved OSR bias in regions where shallow clouds are dominant. Key Points: This study proposes using data assimilation for climate research as a tool for optimizing model parameters objectivelyWhen liquid water path or outgoing radiation was used, parameter estimation reduced clouds and mitigated radiation biases of a GCMEstimating spatially varying parameters was beneficial for improving cloud representations in regions where shallow clouds are dominant [ABSTRACT FROM AUTHOR]

In precipitation science, satellite data have been providing precious, fundamental information, while numerical models have been playing an equally important role. Data assimilation integrates the numerical models and real-world data and brings synergy. We have been working on assimilating the GPM data into the Nonhydrostatic ICosahedral Atmospheric Model (NICAM) using the Local Ensemble Transform Kalman Filter (LETKF). Our 3-year project titled "Enhancing Data Assimilation of GPM Observations", funded by JAXA, started in April 2016 and is ending its period in March 2019. The project follows the success of the previous 3-year effort on "Ensemble-based Data Assimilation of TRMM/GPM Precipitation Measurements", where we developed a global data assimilation system NICAM-LETKF from scratch. This presentation will provide a summary of the past 6-year effort with more emphasis on the recent achievements, including model parameter estimation with data assimilation of JAXA's multi-satellite precipitation analysis data known as the GSMaP (Global Satellite Mapping of Precipitation), data assimilation of GPM/DPR reflectivity, and comparison between GPM/ice-flag product and NICAM simulation and its implications to the NICAM's snow-density parameter. Based on the achievements, we will present our plans for the next 3-year project for improving precipitation forecast through data assimilation of GPM observations. [ABSTRACT FROM AUTHOR]