Averaging kernels for DOAS total-column satellite retrievals

International audienceThe Differential Optical Absorption Spectroscopy (DOAS) method is used extensively to retrieve total column amounts of trace gases based on UV-visible measurements of satellite spectrometers, such as ERS-2 GOME. In practice the sensitivity of the instrument to the tracer density is strongly height dependent, especially in the troposphere. The resulting tracer profile dependence may introduce large systematic errors in the retrieved columns that are difficult to quantify without proper additional information, as provided by the averaging kernel (AK). In this paper we discuss the DOAS retrieval method in the context of the general retrieval theory as developed by Rodgers. An expression is derived for the DOAS AK for optically thin absorbers. It is shown that the comparison with 3D chemistry-transport models and independent profile measurements, based on averaging kernels, is no longer influenced by errors resulting from a priori profile assumptions. The availability of averaging kernel information as part of the total column retrieval product is important for the interpretation of the observations, and for applications like chemical data assimilation and detailed satellite validation studies

A model of prenatal acquisition of vowels

Humans learn much about their language while still in the womb. Prenatal exposure has been repeatedly shown to affect newborn infants' processing of the prosodic characteristics of native language speech. Little is known about whether and how prenatal exposure affects infants' perception of speech sound segments. Here we simulated prenatal learning of vowels in two virtual fetuses whose mothers spoke (slightly) different languages. The learners were two-layer neural networks and were each exposed to vowel tokens sampled from an existent five-vowel language (Spanish and Czech, respectively). The input acoustic properties approximated the speech signal that could possibly be heard in the intrauterine environment, and the learners' auditory system was relatively immature. Without supervision, the virtual fetuses came to warp the continuous acoustic signal into “proto-categories” that were specific to their linguistic environment. Both learners came to create two categorization patterns and did so in language-specific ways, primarily on the basis of the vowels' first-formant characteristics. Such prenatally formed proto-categories were not adult-like in that they entirely collapsed some of the native-language contrasts. At the same time, the categories reflected features of the adult language in that they were language-specific. These results can inspire future work on speech and language acquisition in real young humans.</p

Phonological features emerge substance-freely from the phonetics and the morphology

Theories of phonology claim variously that phonological elements are either innate or emergent, and either substance-full or substance-free. A hitherto underdeveloped source of evidence for choosing between the four possible combinations of these claims lies in showing precisely how a child can acquire phonological elements. This article presents computer simulations that showcase a learning algorithm with which the learner creates phonological elements from a large number of sound–meaning pairs. In the course of language acquisition, phonological fea- tures gradually emerge both bottom-up and top-down, that is, both from the phonetic input (i.e., sound) and from the semantic or morphological input (i.e., structured meaning). In our computer simulations, the child’s phonological features end up with emerged links to sounds (phonetic sub- stance) as well as with emerged links to meanings (semantic substance), without containing either phonetic or semantic substance. These simulations therefore show that emergent substance-free phonological features are learnable. In the absence of learning algorithms for linking innate features to the language-specific variable phonetic reality, as well as the absence of learning algo- rithms for substance-full emergence, these results provide a new type of support for theories of phonology in which features are emergent and substance-free