Validation of an annual cycle simulation with a T42-L20 GCM

An annual cycle of an atmospheric general circulation model (AGCM) is presented. The winter and summer zonal averages of the atmospheric fields are compared with an observed climatology. The main features of the observed seasonal means are well reproduced by the model. One of the main discrepancies is that the simulated atmosphere is too cold, particularly in its upper part. Some other discrepancies might be explained by the interannual variability. The AGCM surface fluxes are directly compared to climatological estimates. On the other hand, the calculation of meridional heat transport by the ocean, inferred from the simulated energy budget, can be compared to transport induced from climatologies. The main result of this double comparison is that AGCM fluxes generally are within the range of climatological estimates. The main deficiency of the model is poor partitioning between solar and non-solar heat fluxes in the tropical belt. The meridional heat transport also reveals a significant energy-loss by the Northern Hemisphere ocean north of 45° N. The possible implications of model surface flux deficiencies on coupling with an oceanic model are discussed.This paper was presented at the International Conference on Modelling of Global Climate Change and Variability, held in Hamburg 11–15 September 1989 under the auspices of the Meteorological Institute of the University of Hamburg and the Max Planck Institute for Meteorology. Guest Editor for these papers is Dr. L. Dümenil     

Geoeffective Disturbances in the Solar Wind near the Solar Activity Minimum according to the Data of a Two-year Series of Observations of Interplanetary Scintillations with the BSA LPI Radio Telescope

Astronomy Reports - A joint analysis of the monitoring data of interplanetary scintillations with solar and geophysical data showed that at the descending phase of the 24 solar activity cycle, the...     

Global temperature and monsoon activity

In this paper an attempt has been made to search a new parameter for the prediction of the Indian summer monsoon rainfall. For this purpose the relationship of the global surface-air temperature of four standard seasons viz., Winter (December-January-February), Spring (March-April-May), Summer (June-July-August), Autumn (September-October-November) with the Indian summer monsoon rainfall has been carried out. The same analysis is also carried out with surface-air temperature anomalies within the tropical belt (30°S to 30°N) and Indian summer monsoon rainfall. For the present study data for 30 years period from 1958 to 1988 have been used. The analysis reveals that there is a strong inverse relationship between the monsoon activity and the tropical belt temperature.     

Regional scale evapotranspiration estimation using satellite derived albedo and surface temperature

In the studies reteted to surface energy balance, satellite data provides important inputs for estimating regional surface albedo and evapotranspiration. The paper describes the use of satellite data in determining the surface emissivity over heterogeneous a’reas by taking Normalized Difference Vegetation Index (NDVI) as modulating parameter at pixel resolution. The estimated emissivity values have been used to find the surface temperature at the pixel scale. Landsat-TM-visible, NIR, TIR bands data and some ground meteorological data have been used in an energy balance model for estimating surface albedo and evapotranspiration. The ET values derived from the model are in good agreement with the values obtained with. ‘CENTURY MODEL’ and ground observations over the area, suggesting the possible use of this approach fot regional scale studies on evapotranspiration.     

Applying landscape-ecological concept and GIS modelling for assessing and mapping of ecological situation of mountainous landscapes (on the case of Syunik marz,Armenia)

Regional landscape-ecological studies have acquired a special topicality as they assure efficient environmental conservation and sustainable use of natural resources. A landscape-ecological analysis was performed based on four basic integral indices: (1) ecological potential, (2) ecological stability, (3) ecological load and (4) ecological tension. The values of basic integral indices of the landscape-ecological analysis were calculated as sums of respective factors expressed in scores and multiplied by their significance reflecting coefficients. In agreement with that rule, a formula was derived of calculation of basic indices of landscape-ecological analysis. Subsequently, those factors were integrated by ‘Model Builder’ in ArcGIS applying ‘Weighted Sum’ functions. Finally, through geographic information systems modelling, maps of basic indices of landscape-ecological analysis were produced. In conclusion – ecological potential, stability, load and tension are best indicators for the assessment of the landscape-ecological situation of the studied territory.     

Crop Residue Discrimination Using Ground-Based Hyperspectral Data

Crop residue has become an increasingly important factor in agriculture management. It assists in the reduction of soil erosion and is an important source of soil organic carbon (soil carbon sequestration). In recent past, remote sensing, especially narrowband, data have been explored for crop residue assessment. In this context, a study was carried out to identify different narrow-bands and evaluate the performance of SWIR region based spectral indices for crop residue discrimination. Ground based hyperspectral data collected for wheat crop residue was analyzed using Stepwise Discriminant Analysis (SDA) technique to select significant bands for discrimination. Out of the seven best bands selected to discriminate between matured crop, straw heap, combine-harvested field with stubbles and soil, four bands were from SWIR (1980, 2030, 2200, 2440 nm) region. Six spectral indices were computed, namely CAI, LCA, SINDRI, NDSVI, NDI5 and hSINDRI for crop residue discrimination. LCA and CAI showed to be best (F?>?115) in discriminating above classes, while LCA and SINDRI were best (F?>?100) among all indices in discriminating crop residue under different harvesting methods. Comparison of different spectral resolution (from 1 nm to 150 nm) showed that for crop residue discrimination a resolution of 100 nm at 2100–2300 m region would be sufficient to discriminate crop residue from other co-existing classes.     

Generation and validation of DEM using SAR interferometry and differential GPS supported by multispectral optical data

Digital elevation model (DEM) and the derived terrain parameters e.g. contour, slope, aspect, drainage pattern, etc are required for natural resources management, infrastructure planning and disaster management. The present paper aims at generating DEM from ERS tandem pair using interferometric technique supported by differential GPS measurements (DGPS) and multispectral optical data. Validation of DEM has been carried out by DGPS measurements. Ground Control Points (GCP) established by DGPS measurements have been used to georeference the IRS-1D optical data that has finally been co-registered with SAR amplitude image. Optical data, co-registered with ERS - I SAR data has helped in locating the GCP’s and check points, precisely, for refinement of DEM and its validation.     

Use of laser range and height texture cues for building identification

Airborne LiDAR has found application in an increasing number of mapping and Geo-data acquisition tasks. Apart from terrain information generation, applications such as automatic detection and modeling of objects like buildings or vegetation for the generation of 3-D city models have been explored. Besides the height itself, height texture defined by local variations of the height is a significant parameter for object recognition. The paper explores the potential of the analysis of height texture as a cue for the automatic detection of objects in LiDAR datasets. A number of texture measures were computed. Based on their definition and computation these measures were used as bands in a classification algorithm, and objects like buildings, single trees, and roads could be recognized.