Fine-resolution Data Research Articles

Fine-Resolution 4DVAR Data Assimilation for the Great Plains Tornado Outbreak of 3 May 1999

Abstract The National Centers for Environmental Prediction fine-resolution four-dimensional variational (4DVAR) data assimilation system is used to study the Great Plains tornado outbreak of 3 May 1999. It was found that the 4DVAR method was able to capture very well the important precursors for the tornadic activity, such as upper- and low-level jet streaks, wind shear, humidity field, surface CAPE, and so on. It was also demonstrated that, in this particular synoptic case, characterized by fast-changing mesoscale systems, the model error adjustment played a substantial role. The experimental results suggest that the common practice of neglecting the model error in data assimilation systems may not be justified in synoptic situations similar to this one.

Monitoring large areas for forest change using Landsat: Generalization across space, time and Landsat sensors

Landsat 7 ETM+ provides an opportunity to extend the area and frequency with which we are able to monitor the Earth's surface with fine spatial resolution data. To take advantage of this opportunity it is necessary to move beyond the traditional image-by-image approach to data analysis. A new approach to monitoring large areas is to extend the application of a trained image classifier to data beyond its original temporal, spatial, and sensor domains. A map of forest change in the Cascade Range of Oregon developed with methods based on such generalization shows accuracies comparable to a map produced with current state-of-the-art methods. A test of generalization across sensors to monitor forest change in the Rocky Mountains indicates that Landsat 7 ETM+ data can be combined with earlier Landsat 5 TM data without retraining the classifier. Methods based on generalization require less time and effort than conventional methods and as a result may allow monitoring of larger areas or more frequent monitoring at reduced cost. One key component to achieving this goal is the improved availability and affordability of Landsat 7 imagery. These results highlight the value of the existing Landsat archive and the importance for continuity in the Landsat Program.

Small area estimation of incidence of cancer around a known source of exposure with fine resolution data

OBJECTIVESTo describe the small area system developed in Finland. To illustrate the use of the system with analyses of incidence of lung cancer around an asbestos mine. To compare the...

National spatial crop yield simulation using GIS-based crop production model

Traditional decision support systems based on crop simulation models are normally site-specific. In policy formulation, however, spatial variability of crop production often need to be evaluated due to different soil conditions, weather conditions and agricultural practices within a target-region. To address the spatial variability, a spatial model ‘Spatial EPIC’ was developed based on a crop simulation model EPIC (Erosion Productivity Impact Calculator). Since site-specific crop simulation models require point-based or fine resolution data, it is necessary to feed the fine resolution data at each grid-cell in order to ‘spatialize’ crop simulation models. The authors proposed a method to generate fine resolution data from coarse resolution data, which are usually available at regional or national level. In addition, since the original EPIC crop management practices are static in nature, a dynamic adaptation loop is added to evaluate the impacts of agricultural practice changes over temporal scale. Validation of the spatial EPIC was conducted at different spatial scales, i.e. national scale (approx. 50 km cell-size) and regional scale (approx. 10 km cell-size) in India. Results showed that at both resolutions level crop yield varied significantly as a function of seasonal climatic variation, soil water holding characteristics and applied crop management strategies. Also, the study successfully demonstrated model applicability in evaluating an impact of climate changes over major cereal crops productivity at national level taking spatial variability into account.

The use of temporal metrics for land cover change detection at coarse spatial scales

Successful land cover change analysis requires selection of an appropriate set of variables for measuring and characterizing change. Coarse spatial resolution satellite sensors offer the advantage of frequent coverage of large areas and this facilitates the monitoring of surface processes. Fine spatial resolution satellite sensors provide reliable land cover information on a local basis. This work examines the ability of several temporal change metrics to detect land cover change in sub-Saharan Africa using remote sensing data collected at a coarse spatial resolution over 16 test sites for which fine spatial resolution data are available. We model change in the fine-resolution data as a function of the coarse spatial resolution metrics without regard to the type of change. Results indicate that coarse spatial resolution temporal metrics (i) relate in a statistically significant way to aggregate changes in land cover, (ii) relate more strongly to fine spatial resolution change metrics when including a measure of surface temperature instead of a vegetation index alone, and (iii) are most effective as land cover change indicators when various metrics are combined in multivariate models.

Evaluation of a long-range lagrangian dispersion model with ETEX

Performance of a Lagrangian dispersion model was examined in connection with its dependency on the boundary layer modelling and the input data resolution. The European Tracer Experiment (ETEX) data were used as reference. According to the sensitivity analysis of the model performance, the long-range dispersion model with the sparse input data was not noticeably different from that with the finer resolution data. The assumption of the prescribed constant mixing depth did not largely degrade the prediction results as compared with the simulation results with the temporally changing boundary layer. It is, therefore, concluded that the model is practical, considering the limited input data in the operational mode. However, it was also pointed out that the parameterization for the horizontal and vertical diffusion processes used in the present model enhanced the growth of plume. The improvement of input data resolution in time and space caused further dispersion of tracer deterministically. These resulted in the underestimation of the maximum concentration and the unfocussed concentration distribution map although the mean concentration was predicted fairly well.

Co-location of pixels in satellite remote sensing images with demonstrations using sea ice data

Quantitative integration of remote sensing data requires accurate co-location of pixels between data from different sensors. A technique has been developed, based on a simple spherical Earth model, to map ground resolution cells pertaining to pixel/grid points in a coarse-resolution sensor into an image acquired by a fine-resolution sensor. Pixels from the fine-resolution sensor, located within the coarse-resolution cells, can then be identified and their radiometric information can be compared to or combined with the coarse-resolution observations to improve the utility of both data sets. The technique is presented along with examples to demonstrate potential applications such as: (1) correlation between sensors radiometric parameters, (2) validation of products from a coarseresolution sensor using sub-pixel information from a coincident fine-resolution data set, and (3) contribution of ground target heterogeneity within a single pixel to observed radiometric parameters. Successful application of the technique, however, requires accurate geo-location of both data sets at the pixel level.

The scaling characteristics of remotely-sensed variables for sparsely-vegetated heterogeneous landscapes

With increasing interest in airborne and satellite-based sensors for mapping regional and global energy balance, there is a need to determine the uncertainty involved in aggregating remotely-sensed variables [surface temperature ( T k) and reflectance (π)] and surface energy fluxes [sensible ( H) and latent (λ E) heat flux] over large areas. This uncertainty is directly related to two factors: (1) the nonlinearity of the relation between the sensor signal and T k, π, H or λE; and (2) the heterogeneity of the site. In this study, we compiled several remotely-sensed data sets acquired at different locations within a semi-arid rangeland in Arizona, at a variety of spatial and temporal resolutions. These data sets provided the range of data heterogeneities necessary for an extensive analysis of data aggregation. The general technique to evaluate uncertainty was to compare remotely-sensed variables and energy balance components calculated in two ways: first, calculated at the pixel resolution and averaged to the coarser resolution; and second, calculated directly at the coarse resolution by aggregating the fine-resolution data to the coarse scale. Results showed that the error in the aggregation of T k and π was negligible for a wide range of conditions. However, the error in aggregation of H and λE was highly influenced by the heterogeneity of the site. Errors in H larger than 50% were possible under certain conditions. The conditions associated with the largest aggregation errors in H were: • sites which are composed of a mix of stable and unstable conditions; • sites which have considerable variations in aerodynamic roughness, especially for highly unstable conditions where the difference between surface and air temperature is large; and • sites which are characterized by patch vegetation, where the pixel resolution is less than or nearly-equal to the diameter of the vegetation ‘element’ (in most cases, the diameter of the dominant vegetation type or vegetation patch). Thus, knowledge of the surface heterogeneity is essential for minimizing error in aggregation of H and λE. Two schemes are presented for quantifying surface heterogeneity as a first step in data aggregation. These results emphasized the need for caution in aggregation of energy balance components over heterogeneous landscapes with sparse or mixed vegetation types.

Spatial Analysis of the Distribution of Tsetse Flies in the Lambwe Valley, Kenya, Using Landsat TM Satellite Imagery and GIS

Satellite imagery, geographic information systems (GIS) and spatial statistics provide tools for studies of population dynamics of disease vectors in association with habitat features on multiple spatial scales. Tsetse flies were collected during 1988-90 in biconical traps located along transects in Ruma National Park in the Lambwe Valley, western Kenya. Fine spatial resolution data collected by Landsat Thematic Mapper (TM) satellite and reference ground environmental data were integrated in a GIS to identify factors associated with local variations of fly density. Statistical methods of spatial autocorrelation and spatial filtering were applied to determine spatial components of these associations. Strong positive spatial associations among traps occurred within transects and within the two ends of the park. From satellite data, TM band 7, which is associated with moisture content of soil and vegetation, emerged as being consistently highly correlated with fly density. Using several spectral bands in a multiple regression, as much as 87% of the variance in fly catch values could be explained. When spatial filtering was applied, a large component of the association between fly density and spectral data was shown to be the result of other determinants underlying the spatial distributions of both fly density and spectral values. Further field studies are needed to identify these determinants. The incorporation of remotely sensed data imagery into a GIS with ground data on fly density and environnmental conditions can be used to predict favourable fly habitats in inaccessible sites, and to determine number and location of fly suppression traps in a local control programme.

Fractals in ecology: methods and interpretation

We agree that the method of computing fractals described by Mark (1984) is both more appropriate and more informative than the method we used (Bradbury and Reichelt, 1983). In this note, we argue that it is the departure from strict self-similarity in real structures that gives them their ecological interest. We support these arguments with the analysis of new finer-resolution data from the previously studied coral reef which suggests that reefs are extremely smooth structures at the scale of coral colonies, and that this smoothness is reduced at both finer a n d coarser scales. David Mark (1984) has voiced what we ourselves have also realized. The method of computing fractals that h e describes (Mandelbrot, 1977; Goodchild, 1980) is both more appropriate and more informative than the method we used (Bradbury and Reichelt, 1983). However, we feel that there is a risk that Mark's discussion may leave some readers with the impression that lack of self-similarity invalidates the whole fractal approach to ecosystem studies. In this note we wish to emphasize that this is not so; it is precisely the lack of self-similarity that is of greatest ecological relevance and interest. Problems with variograms. The variogram approach to fractal estimation assumes that one is dealing with a continuous function W(t) of some independent variable t. In this context, the only possible interpretation of t is 'straight-line' distance along a transect, while W must represent the corresponding distance measured along the reef surface. Fractal estimates are then based on the variance of increments where T = step length used (Berry and Lewis, 1980). The chief practical difficulty is that in the reef transects, our steps with dividers were made along the reef surface, whereas T in this context represents increments in the straight line distance. A further problem is that V(T) should be computed over all values of t (or at AIMS Publication No. 21 1 O Inter-Research/Printed in F. R. Germany least be estimated from some representative subsample), whereas with our sampling method we effectively used only those values of t that are multiples of T, thus introducing a systematic bias into the results. Ecological interpretation of fractal dimensions. True fractals are abstractions; no real-world phenomenon is self-similar at all scales. Individual biological (and other) processes always operate over a restricted range of scales. However, the geometric properties of the patterns they produce, i f extended over all possible scales, would define true fractal curves. Thus estimates of fractal dimension made over the restricted range of scales do reflect the geometric properties of the processes. Peaks and dips in the value D of this 'fractal index' (our term for the curve showing estimates of fractal dimension against scale) indicate shifts in the sources of biological pattern. The fractal index is thus a statistical tool for examining patterns and processes in coral communities. Estimates of D for real processes (whether obtained by the variogram or ratio method) may stray outside the range 1.0 to 2.0 in certain circumstances. Such transgressions should not be interpreted as evidence that the application of fractal theory is not valid. They might, on the contrary, provide useful information about the phenomenon concerned, for they may indicate limits to the range over which particular processes affect the system (growth of individual corals, for instance). Reanalysis of our data. We have now reanalysed the data presented in Bradbury and Reichelt (1983), together with a more comprehensive suite from the same site (Fig. 1) which w e have since collected. These new data range over 7 scales, with a finer degree of resolution than we were able to report before. Since the method recommended by Mark allows us to make estimates of D at each scale, w e are now able to examine the variation of D both within-scale and between-scale as well as with the finer resolution 296 Mar. Ecol. Prog. Ser. 14: 295-296, 1984 offered by the new data. We consider our new data set to be superior not only in terms of resolution but also in terms of accuracy (as will be explained below). Therefore we will focus on these new data in the remainder of this reply. Within-scale variation of D. We have split our samples in the following ways to obtain additional information on the within-scale variability of D: (1) In order to examine the stationarity (or stability) of D at each scale, the sequence of observations at each scale was split into 2 subsequences that join end-to-end. (2) In order to examine the local variability of D at each scale, 2 interlaced subsequences were formed by selecting alternate observations from the original sequence of data. 1.0 I 0 10 20 50 100 200 50

Reaction48Ca (p, n)48Sc fromE p =1.885 to 5.1 MeV

The total (p, n) reaction cross section for48Ca has been measured as a function of proton energy in the energy range 1.885 to 5.100 MeV with an overall resolution of ∼ 2 keV and in ∼ 5 keV energy steps. The fluctutions in fine resolution data have been analysed to determine the average coherence width 〈Γ〉. The excitation function averaged over large energy intervals has been analyzed in terms of the optical model. The isobaric analogue resonances atE p ∼ 1.95 and 4 MeV have been shape-analyzed to extract the proton partial width and the spectroscopic factorS n . A comparison of the gross structures observed in ∼ 55 keV averaged excitation function with the predictions of Izumo’s partial equilibrium model has also been made.

Magnetotail variations associated with the southward interplanetary magnetic field

Ten weeks of simultaneous fine time resolution data on the interplanetary magnetic field (IMF), the solar wind parameters observed by the Explorer 33 and Vela 3 satellites, the magnetic field, and the particle fluxes in the magnetotail from the Imp 3 satellite are examined together with auroral zone magnetograms monitoring substorm activity to study the magnetotail variations associated with the latitudinal changes of the IMF. It is found that the magnetotail magnetic field magnitude often increases when the north-south component of the IMF is southward; this increment is generally observed throughout the entire high-latitude tail (outside the plasma sheet). The increment is about 10% of the background field, and it increases to 20% or higher when the north-south component of the IMF increases from the usual 2 or 3 gamma to 7 or 8 gamma.