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Research Unit of the University of East Anglia ( Jones et al. 2001 ), the reader is referred to the Arctic Climate Impact Assessment ( ACIA 2005 ), for investigations of precipitation data from NCEP–NCAR and ERA reanalyses to Serreze and Hurst ( Serreze and Hurst 2000 ). The results of temporal analysis of these datasets ( Drobot et al. 2006 ), which utilizes values averaged over the entire spatial domain and station values, lead to the question of geographical distribution of agreement and
Research Unit of the University of East Anglia ( Jones et al. 2001 ), the reader is referred to the Arctic Climate Impact Assessment ( ACIA 2005 ), for investigations of precipitation data from NCEP–NCAR and ERA reanalyses to Serreze and Hurst ( Serreze and Hurst 2000 ). The results of temporal analysis of these datasets ( Drobot et al. 2006 ), which utilizes values averaged over the entire spatial domain and station values, lead to the question of geographical distribution of agreement and
of spatial variability andstructure in acid precipitation data. Variograms for H~, SO4, NO3, and NH4 are presented. They are shownto be clearly distance dependent in all cases, increasing with increasing distance between stations. The functionalform of the increase, however, was not consistent. Typical isopleths with their corresponding one sigmaconfidence limits are computed. The spatial extent of these confidence limits is considerable, illustrating thedifficulty of fine structure analysis of
of spatial variability andstructure in acid precipitation data. Variograms for H~, SO4, NO3, and NH4 are presented. They are shownto be clearly distance dependent in all cases, increasing with increasing distance between stations. The functionalform of the increase, however, was not consistent. Typical isopleths with their corresponding one sigmaconfidence limits are computed. The spatial extent of these confidence limits is considerable, illustrating thedifficulty of fine structure analysis of
1. Introduction Spatial analysis of observations, also called gridding, is a common task in oceanography and meteorology, and a series of methods and implementations exists and is widely used. Here N d data points of values d i , i = 1, …, N d at location ( x i , y i ) are generally distributed unevenly in space. Furthermore, the values of d i are affected by observational errors, including representativity errors. From this dataset an analysis on a regular grid is often desired. It
1. Introduction Spatial analysis of observations, also called gridding, is a common task in oceanography and meteorology, and a series of methods and implementations exists and is widely used. Here N d data points of values d i , i = 1, …, N d at location ( x i , y i ) are generally distributed unevenly in space. Furthermore, the values of d i are affected by observational errors, including representativity errors. From this dataset an analysis on a regular grid is often desired. It
VOL. 25, NO. 10 JOURNAL OF CLIMATE AND APPLIED METEOROLOGY OCTOBER 1986Analysis of Spatial Inhomogeneities in Cumulus Clouds Using High Spatial Resolution Landsat Data LINDSAY PARKER,* R. M. WELCH AND D. J. MUSILInstitute of Atmospheric Sciences, South Dakota School of Mines and Technology, Rapid City, SD 57701(Manuscript received 22 October 1985, in final form 10 February 1986)ABSTRACTAircraft observations and high
VOL. 25, NO. 10 JOURNAL OF CLIMATE AND APPLIED METEOROLOGY OCTOBER 1986Analysis of Spatial Inhomogeneities in Cumulus Clouds Using High Spatial Resolution Landsat Data LINDSAY PARKER,* R. M. WELCH AND D. J. MUSILInstitute of Atmospheric Sciences, South Dakota School of Mines and Technology, Rapid City, SD 57701(Manuscript received 22 October 1985, in final form 10 February 1986)ABSTRACTAircraft observations and high
FEBRUARY 1987 GENEVIEVE SEZE AND MICHEL DESBOIS 287Cloud Cover Analysis from Satellite Imagery Using Spatial and Temporal Characteristics of the Data GENEVIEVE SEZE AND MICHEL DESBOISLMD/CNRS, 91128 PALAISEAU CEDEX (France)(Manuscript received 6 June 1985, in final form 29 October 1986) ABSTRACT New developments of a cloud classification scheme based on histogram clustering by a
FEBRUARY 1987 GENEVIEVE SEZE AND MICHEL DESBOIS 287Cloud Cover Analysis from Satellite Imagery Using Spatial and Temporal Characteristics of the Data GENEVIEVE SEZE AND MICHEL DESBOISLMD/CNRS, 91128 PALAISEAU CEDEX (France)(Manuscript received 6 June 1985, in final form 29 October 1986) ABSTRACT New developments of a cloud classification scheme based on histogram clustering by a
sounder data gathering are presented. In section 3 , a joint procedure of noise filtering and cloud detection is discussed; results of spatial–spectral analysis of spectral measurements are reviewed. Then, in section 4 , results of temporal analysis are added, unphysical radiance changes from one time to the next are discussed, and the presence of large-scale coherent noise in the temporal spectral measurements is shown. In section 5 , statistical analysis of the temporal variability of the sounder
sounder data gathering are presented. In section 3 , a joint procedure of noise filtering and cloud detection is discussed; results of spatial–spectral analysis of spectral measurements are reviewed. Then, in section 4 , results of temporal analysis are added, unphysical radiance changes from one time to the next are discussed, and the presence of large-scale coherent noise in the temporal spectral measurements is shown. In section 5 , statistical analysis of the temporal variability of the sounder
and the κ statistic for climate model validation. By cluster analysis we find a representation of the spatial structure for observation and simulation data—that is, a spatial classification of climatologically similar grid cells or station locations. By using the κ statistic, we quantify how well the two spatial structures correspond to each other. We present the method in section 2 and demonstrate its application in section 3 using output from the resampling scheme known as the
and the κ statistic for climate model validation. By cluster analysis we find a representation of the spatial structure for observation and simulation data—that is, a spatial classification of climatologically similar grid cells or station locations. By using the κ statistic, we quantify how well the two spatial structures correspond to each other. We present the method in section 2 and demonstrate its application in section 3 using output from the resampling scheme known as the
numerical experiment demonstrates that if the errors in satellite-derived temperatures axe correlated spatially, the error of an optimum interpolation objective analysis using such temperature data isincreased. Moreover, increasing the density of such observations beyond a threshold value (a spacing ofabout 400 km in the experiment) does not yield any significant improvement in analysis accuracy, in contrastto the case of observations with spatially uncorrelated errors.1. Introduction An important
numerical experiment demonstrates that if the errors in satellite-derived temperatures axe correlated spatially, the error of an optimum interpolation objective analysis using such temperature data isincreased. Moreover, increasing the density of such observations beyond a threshold value (a spacing ofabout 400 km in the experiment) does not yield any significant improvement in analysis accuracy, in contrastto the case of observations with spatially uncorrelated errors.1. Introduction An important
(January 1891 through May 1980) for the Northern Hemisphere on a 5- x 10- latitude-longitude grid. This data set and the magnetic tape of the data are described.Other collections of surface temperature data are also described and compared on the bases of temporal andspatial coverage, and analysis methods.1. Introduction When Budyko (1969) published his energy-balanceclimate model in English, Western scientists becameaware of the availability of a Russian surface temperature data set. His Fig. 1
(January 1891 through May 1980) for the Northern Hemisphere on a 5- x 10- latitude-longitude grid. This data set and the magnetic tape of the data are described.Other collections of surface temperature data are also described and compared on the bases of temporal andspatial coverage, and analysis methods.1. Introduction When Budyko (1969) published his energy-balanceclimate model in English, Western scientists becameaware of the availability of a Russian surface temperature data set. His Fig. 1
)ABSTRACT Radar has been used to study insect flight for over 20 years. Radar, especially airborne radar, is unrivaled inits ability to observe the spatial organization of insect migration. This paper reports methods of data collectionand analysis used by current airborne entomological radar systems and, in particular, the method used to reviewthe data collected and visualize any large-scale structures detected. Examples of data from recent U.S. Department of Agriculture field experiments are
)ABSTRACT Radar has been used to study insect flight for over 20 years. Radar, especially airborne radar, is unrivaled inits ability to observe the spatial organization of insect migration. This paper reports methods of data collectionand analysis used by current airborne entomological radar systems and, in particular, the method used to reviewthe data collected and visualize any large-scale structures detected. Examples of data from recent U.S. Department of Agriculture field experiments are
