Variational data assimilation for parameter estimation: application to a simple morphodynamic model

Data assimilation is a sophisticated mathematical technique for combining observational data with model predictions to produce state and parameter estimates that most accurately approximate the current and future states of the true system. The technique is commonly used in atmospheric and oceanic modelling, combining empirical observations with model predictions to produce more accurate and well-calibrated forecasts. Here, we consider a novel application within a coastal environment and describe how the method can also be used to deliver improved estimates of uncertain morphodynamic model parameters. This is achieved using a technique known as state augmentation. Earlier applications of state augmentation have typically employed the 4D-Var, Kalman filter or ensemble Kalman filter assimilation schemes. Our new method is based on a computationally inexpensive 3D-Var scheme, where the specification of the error covariance matrices is crucial for success. A simple 1D model of bed-form propagation is used to demonstrate the method. The scheme is capable of recovering near-perfect parameter values and, therefore, improves the capability of our model to predict future bathymetry. Such positive results suggest the potential for application to more complex morphodynamic models.

Parameter sensitivity and predictive uncertainty in a new water quality model, Q(2)

A new dynamic model of water quality, Q(2), has recently been developed, capable of simulating large branched river systems. This paper describes the application of a generalized sensitivity analysis (GSA) to Q(2) for single reaches of the River Thames in southern England. Focusing on the simulation of dissolved oxygen (DO) (since this may be regarded as a proxy for the overall health of a river); the GSA is used to identify key parameters controlling model behavior and provide a probabilistic procedure for model calibration. It is shown that, in the River Thames at least, it is more important to obtain high quality forcing functions than to obtain improved parameter estimates once approximate values have been estimated. Furthermore, there is a need to ensure reasonable simulation of a range of water quality determinands, since a focus only on DO increases predictive uncertainty in the DO simulations. The Q(2) model has been applied here to the River Thames, but it has a broad utility for evaluating other systems in Europe and around the world.

Bias from farmer self-selection in genetically modified crop productivity estimates: Evidence from Indian data

In the continuing debate over the impact of genetically modified (GM) crops on farmers of developing countries, it is important to accurately measure magnitudes such as farm-level yield gains from GM crop adoption. Yet most farm-level studies in the literature do not control for farmer self-selection, a potentially important source of bias in such estimates. We use farm-level panel data from Indian cotton farmers to investigate the yield effect of GM insect-resistant cotton. We explicitly take into account the fact that the choice of crop variety is an endogenous variable which might lead to bias from self-selection. A production function is estimated using a fixed-effects model to control for selection bias. Our results show that efficient farmers adopt Bacillus thuringiensis (Bt) cotton at a higher rate than their less efficient peers. This suggests that cross-sectional estimates of the yield effect of Bt cotton, which do not control for self-selection effects, are likely to be biased upwards. However, after controlling for selection bias, we still find that there is a significant positive yield effect from adoption of Bt cotton that more than offsets the additional cost of Bt seed.

Gender stereotyping and decision processes: extending and reversing the gender bias in fame judgements

M. R. Banaji and A. G. Greenwald (1995) demonstrated a gender bias in fame judgments—that is, an increase in judged fame due to prior processing that was larger for male than for female names. They suggested that participants shift criteria between judging men and women, using the more liberal criterion for judging men. This "criterion-shift" account appeared problematic for a number of reasons. In this article, 3 experiments are reported that were designed to evaluate the criterion-shift account of the gender bias in the false-fame effect against a distribution-shift account. The results were consistent with the criterion-shift account, and they helped to define more precisely the situations in which people may be ready to shift their response criterion on an item-by-item basis. In addition, the results were incompatible with an interpretation of the criterion shift as an artifact of the experimental situation in the experiments reported by M. R. Banaji and A. G. Greenwald. (PsycINFO Database Record (c) 2010 APA, all rights reserved)

New strings for old : the role of similarity processing in an incidental learning task

This paper reports three experiments that examine the role of similarity processing in McGeorge and Burton's (1990) incidental learning task. In the experiments subjects performed a distractor task involving four-digit number strings, all of which conformed to a simple hidden rule. They were then given a forced-choice memory test in which they were presented with pairs of strings and were led to believe that one string of each pair had appeared in the prior learning phase. Although this was not the case, one string of each pair did conform to the hidden rule. Experiment 1 showed that, as in the McGeorge and Burton study, subjects were significantly more likely to select test strings that conformed to the hidden rule. However, additional analyses suggested that rather than having implicitly abstracted the rule, subjects may have been selecting strings that were in some way similar to those seen during the learning phase. Experiments 2 and 3 were designed to try to separate out effects due to similarity from those due to implicit rule abstraction. It was found that the results were more consistent with a similarity-based model than implicit rule abstraction per se.

Language experience shapes the development of the mutual exclusivity bias

Halberda (2003) demonstrated that 17-month-old infants, but not 14- or 16-month-olds, use a strategy known as mutual exclusivity (ME) to identify the meanings of new words. When 17-month-olds were presented with a novel word in an intermodal preferential looking task, they preferentially fixated a novel object over an object for which they already had a name. We explored whether the development of this word-learning strategy is driven by children's experience of hearing only one name for each referent in their environment by comparing the behavior of infants from monolingual and bilingual homes. Monolingual infants aged 17–22 months showed clear evidence of using an ME strategy, in that they preferentially fixated the novel object when they were asked to "look at the dax." Bilingual infants of the same age and vocabulary size failed to show a similar pattern of behavior. We suggest that children who are raised with more than one language fail to develop an ME strategy in parallel with monolingual infants because development of the bias is a consequence of the monolingual child's everyday experiences with words.

Stratospheric water vapour and high climate sensitivity in a version of the HadSM3 climate model

It has been shown previously that one member of the Met Office Hadley Centre single-parameter perturbed physics ensemble – the so-called "low entrainment parameter" member – has a much higher climate sensitivity than other individual parameter perturbations. Here we show that the concentration of stratospheric water vapour in this member is over three times higher than observations, and, more importantly for climate sensitivity, increases significantly when climate warms. The large surface temperature response of this ensemble member is more consistent with stratospheric humidity change, rather than upper tropospheric clouds as has been previously suggested. The direct relationship between the bias in the control state (elevated stratospheric humidity) and the cause of the high climate sensitivity (a further increase in stratospheric humidity) lends further doubt as to the realism of this particular integration. This, together with other evidence, lowers the likelihood that the climate system's physical sensitivity is significantly higher than the likely upper range quoted in the Intergovernmental Panel on Climate Change's Fourth Assessment Report.

When can we expect extremely high surface temperatures?

In the Essence project a 17-member ensemble simulation of climate change in response to the SRES A1b scenario has been carried out using the ECHAM5/MPI-OM climate model. The relatively large size of the ensemble makes it possible to accurately investigate changes in extreme values of climate variables. Here we focus on the annual-maximum 2m-temperature and fit a Generalized Extreme Value (GEV) distribution to the simulated values and investigate the development of the parameters of this distribution. Over most land areas both the location and the scale parameter increase. Consequently the 100-year return values increase faster than the average temperatures. A comparison of simulated 100-year return values for the present climate with observations (station data and reanalysis) shows that the ECHAM5/MPI-OM model, as well as other models, overestimates extreme temperature values. After correcting for this bias, it still shows values in excess of 50°C in Australia, India, the Middle East, North Africa, the Sahel and equatorial and subtropical South America at the end of the century.

Parameter estimation using a particle method: inferring mixing coefficients from sea level observations

This paper presents a first attempt to estimate mixing parameters from sea level observations using a particle method based on importance sampling. The method is applied to an ensemble of 128 members of model simulations with a global ocean general circulation model of high complexity. Idealized twin experiments demonstrate that the method is able to accurately reconstruct mixing parameters from an observed mean sea level field when mixing is assumed to be spatially homogeneous. An experiment with inhomogeneous eddy coefficients fails because of the limited ensemble size. This is overcome by the introduction of local weighting, which is able to capture spatial variations in mixing qualitatively. As the sensitivity of sea level for variations in mixing is higher for low values of mixing coefficients, the method works relatively well in regions of low eddy activity.