Spatial process and data models: Toward integration of agent-based models and GIS

The use of object-orientation for both spatial data and spatial process models facilitates their integration, which can allow exploration and explanation of spatial-temporal phenomena. In order to better understand how tight coupling might proceed and to evaluate the possible functional and efficiency gains from such a tight coupling, we identify four key relationships affecting how geographic data (fields and objects) and agent-based process models can interact: identity, causal, temporal and topological. We discuss approaches to implementing tight integration, focusing on a middleware approach that links existing GIS and ABM development platforms, and illustrate the need and approaches with example agent-based models.     

Outlier detection algorithms and their performance in GOCE gravity field processing

The satellite missions CHAMP, GRACE, and GOCE mark the beginning of a new era in gravity field determination and modeling. They provide unique models of the global stationary gravity field and its variation in time. Due to inevitable measurement errors, sophisticated pre-processing steps have to be applied before further use of the satellite measurements. In the framework of the GOCE mission, this includes outlier detection, absolute calibration and validation of the SGG (satellite gravity gradiometry) measurements, and removal of temporal effects. In general, outliers are defined as observations that appear to be inconsistent with the remainder of the data set. One goal is to evaluate the effect of additive, innovative and bulk outliers on the estimates of the spherical harmonic coefficients. It can be shown that even a small number of undetected outliers (<0.2 of all data points) can have an adverse effect on the coefficient estimates. Consequently, concepts for the identification and removal of outliers have to be developed. Novel outlier detection algorithms are derived and statistical methods are presented that may be used for this purpose. The methods aim at high outlier identification rates as well as small failure rates. A combined algorithm, based on wavelets and a statistical method, shows best performance with an identification rate of about 99%. To further reduce the influence of undetected outliers, an outlier detection algorithm is implemented inside the gravity field solver (the Quick-Look Gravity Field Analysis tool was used). This results in spherical harmonic coefficient estimates that are of similar quality to those obtained without outliers in the input data.     

GGM02 – An improved Earth gravity field model from GRACE

A new generation of Earth gravity field models called GGM02 are derived using approximately 14 months of data spanning from April 2002 to December 2003 from the Gravity Recovery And Climate Experiment (GRACE). Relative to the preceding generation, GGM01, there have been improvements to the data products, the gravity estimation methods and the background models. Based on the calibrated covariances, GGM02 (both the GRACE-only model GGM02S and the combination model GGM02C) represents an improvement greater than a factor of two over the previous GGM01 models. Error estimates indicate a cumulative error less than 1 cm geoid height to spherical harmonic degree 70, which can be said to have met the GRACE minimum mission goals. Electronic Supplementary Material Supplementary material is available in the online version of this article at     

Investigating the propagation mechanism of unmodelled systematic errors on coordinate time series estimated using least squares

The propagation of unmodelled systematic errors into coordinate time series computed using least squares is investigated, to improve the understanding of unexplained signals and apparent noise in geodetic (especially GPS) coordinate time series. Such coordinate time series are invariably based on a functional model linearised using only zero and first-order terms of a (Taylor) series expansion about the approximate coordinates of the unknown point. The effect of such truncation errors is investigated through the derivation of a generalised systematic error model for the simple case of range observations from a single known reference point to a point which is assumed to be at rest by the least squares model but is in fact in motion. The systematic error function for a one pseudo-satellite two-dimensional case, designed to be as simple but as analogous to GPS positioning as possible, is quantified. It is shown that the combination of a moving reference point and unmodelled periodic displacement at the unknown point of interest, due to ocean tide loading, for example, results in an output coordinate time series containing many periodic terms when only zero and first-order expansion terms are used in the linearisation of the functional model. The amplitude, phase and period of these terms is dependent on the input amplitude, the locations of the unknown point and reference point, and the period of the reference point's motion. The dominant output signals that arise due to truncation errors match those found in coordinate time series obtained from both simulated data and real three-dimensional GPS data.     

Digital topography models for Martian surfaces

We propose to use an unsupervised automated classification of topographic features on Mars in order to speed up geomorphic and geologic mapping of the planet. We construct a digital topography model (DTM), a multilayer grid that stores various kinds of topographical information for every pixel in a site. The method uses a probabilistic clustering algorithm to assign topographically meaningful labels to all pixels in the DTM. The results are displayed as a thematic map of topography. Resultant topographical features are characterized and compared using statistics of their constituent pixels. We demonstrate the usage of our method by classifying and characterizing the topography of a landscape in the Tisia Valles region on Mars. We discuss extensions and further applications of our method.     

Analysis of geophysical networks derived from multiscale digital elevation models: a morphological approach

We provide a simple and elegant framework based on morphological transformations to generate multiscale digital elevation models (DEMs) and to extract topologically significant multiscale geophysical networks. These terrain features at multiple scales are collectively useful in deriving scaling laws, which exhibit several significant terrain characteristics. We present results derived from a part of Cameron Highlands DEM.     

Simulation of the east asian summer monsoon using a variable resolution atmospheric GCM

The East Asia summer monsoon (EASM) is simulated with a variable resolution global atmospheric general circulation model (GCM) developed at the Laboratoire de Météorologie Dynamique, France. The version used has a local zoom centered on China. This study validates the model's capability in reproducing the fundamental features of the EASM. The monsoon behaviors over East Asia revealed by the ECMWF reanalysis data are also addressed systematically, providing as observational evidence. The mean state of the EASM is generally portrayed well in the model, including the large-scale monsoon airflows, the monsoonal meridional circulation, the cross-equatorial low-level jets, the monsoon trough in the South China Sea, the surface cold high in Australia, and the upper-level northeasterly return flow. While the performance of simulating large-scale monsoonal climate is encouraging, the model's main deficiency lies in the rainfall. The marked rainbelt observed along the Yangtze River Valley is missed in the simulation. This is due to the weakly reproduced monsoonal components in essence and is directly related to the weak western Pacific subtropical high, which leads to a fragile subtropical southwest monsoon on its western flank and results in a weaker convergence of the southwest monsoon flow with the midlatitude westerlies. The excessively westward extension of the high, together with the distorted Indian low, also makes the contribution of the tropical southwest monsoon to the moisture convergence over the Yangtze River Valley too weak in the model. The insufficient plateau heating and the resulting weak land-sea thermal contrast are responsible for the weakly reproduced monsoon. It is the deficiency of the model in handling the low-level cloud cover over the plateau rather than the horizontal resolution and the associated depiction of plateau topography that results in the insufficient plateau heating. Comparison with the simulation employing regular coarser mesh model reveals that the local zoom technique improves, in a general manner, the EASM simulation.