A Note on Ontology and Ordinary Language

We argue for a compositional semantics grounded in a strongly typed ontology that reflects our commonsense view of the world and the way we talk about it. Assuming such a structure we show that the semantics of various natural language phenomena may become nearly trivial

Ontology and Formal Semantics - Integration Overdue

In this note we suggest that difficulties encountered in natural language semantics are, for the most part, due to the use of mere symbol manipulation systems that are devoid of any content. In such systems, where there is hardly any link with our common-sense view of the world, and it is quite difficult to envision how one can formally account for the considerable amount of content that is often implicit, but almost never explicitly stated in our everyday discourse. \ud The solution, in our opinion, is a compositional semantics grounded in an ontology that reflects our commonsense view of the world and the way we talk about it in ordinary language. In the compositional logic we envision there are ontological (or first-intension) concepts, and logical (or second-intension) concepts, and where the ontological concepts include not only Davidsonian events, but other abstract objects as well (e.g., states, processes, properties, activities, attributes, etc.) \ud It will be demonstrated here that in such a framework, a number of challenges in the semantics of natural language (e.g., metonymy, intensionality, metaphor, etc.) can be properly and uniformly addressed.\u

Reversibility of Arctic Sea Ice Retreat - A Multi-Scale Modeling Approach

Arctic summer sea ice has been retreating rapidly over past decade. Climate model projections show further retreat under typical forcing scenarios. The mode of the retreat is a matter of debate. Low-order models show reversible and irreversible retreat depending on the shape of the albedo parametrization. Climate models do not show irreversible sea ice losses, but generally underestimate the current trend of retreat

Multiangle observations of Arctic clouds from FIRE ACE: June 3, 1998, case study

In May and June 1998 the Airborne Multiangle Imaging Spectroradiometer (AirMISR) participated in the FIRE Arctic Cloud Experiment (ACE). AirMISR is an airborne instrument for obtaining multiangle imagery similar to that of the satellite-borne MISR instrument. This paper presents a detailed analysis of the data collected on June 3, 1998. In particular, AirMISR radiance measurements are compared with measurements made by two other instruments, the Cloud Absorption Radiometer (CAR) and the MODIS airborne simulator (MAS), as well as to plane-parallel radiative transfer simulations. It is found that the AirMISR radiance measurements and albedo estimates compare favorably both with the other instruments and with the radiative transfer simulations. In addition to radiance and albedo, the multiangle AirMISR data can be used to obtain estimates of cloud top height using stereoimaging techniques. Comparison of AirMISR retrieved cloud top height (using the complete MISR-based stereoimaging approach) shows excellent agreement with the measurements from the airborne Cloud Lidar System (CLS) and ground-based millimeterwave cloud radar

Logical Semantics and Commonsense Knowledge: Where Did we Go Wrong, and How to Go Forward, Again

We argue that logical semantics might have faltered due to its failure in distinguishing between two fundamentally very different types of concepts: ontological concepts, that should be types in a strongly-typed ontology, and logical concepts, that are predicates corresponding to properties of and relations between objects of various ontological types. We will then show that accounting for these differences amounts to the integration of lexical and compositional semantics in one coherent framework, and to an embedding in our logical semantics of a strongly-typed ontology that reflects our commonsense view of the world and the way we talk about it in ordinary language. We will show that in such a framework a number of challenges in natural language semantics can be adequately and systematically treated

A comparison of the ECMWF forecast model with observations over the annual cycle at SHEBA

A central objective of the Surface Heat Budget of the Arctic Ocean (SHEBA) experiment was to provide a comprehensive observational test for single-column models of the atmosphere-sea ice-ocean system over the Arctic Ocean. For single-column modeling, one must specify the time-varying tendencies due to horizontal and vertical advection of air through the column. Due to the difficulty of directly measuring these tendencies, it was decided for SHEBA to obtain them from short-range forecasts of the European Centre for Medium-Range Weather Forecasts (ECMWF) global forecast model, into which SHEBA rawinsonde and surface synoptic observations were routinely assimilated. The quality of these forecasts directly affects the reliability of the derived advective tendencies. In addition, the ECMWF-forecast thermodynamic and cloud fields, and radiative and turbulent fluxes present an illuminating comparison of the SHEBA observations with a state-of-the-art global numerical model. The authors compare SHEBA soundings, cloud and boundary layer observations with the ECMWF model output throughout the SHEBA year. They find that above the boundary layer, the model was faithful to the SHEBA rawinsonde observations and maintained a proper long-term balance between advective and nonadvective tendencies of heat and moisture. This lends credence to use of the ECMWF-predicted advective tendencies for single-column modeling studies. The model-derived cloud properties and precipitation (which were not assimilated from observations) are compared with cloud radar, lidar, microwave radiometer, surface turbulent and radiative measurements, and basic surface meteorology. The model s slab sea-ice model led to large surface temperature errors and insufficient synoptic variability of temperature. The overall height distribution of cloud was fairly well simulated (though somewhat overestimated) in all seasons, as was precipitation. However, the model clouds typically had a much higher ratio of cloud ice to cloud water than suggested by lidar depolarization measurements, and a smaller optical depth, leading to monthly biases of up to 50 W m^(-2) in the monthly surface downwelling longwave and shortwave radiation. Further biases in net radiation were due to the inaccurate model assumption of constant surface albedo. Observed turbulent sensible and latent heat fluxes tended to be small throughout SHEBA. During high-wind periods during the winter, the ECMWF model predicted sustained downward heat fluxes of up to 60 W m^(-2), much higher than observed. A detailed comparison suggests that this error was due to both inadequate resolution of the 31-level model and a deficient parameterization of sea-ice thermodynamics