Use of remote sensing techniques for agricultural land use survey,classification and planning

A critical appraisal of the need and use of remote sensing techniques for land use mapping with particular reference to Indian conditions have been made. Land use classification system based on physiography, utility, management and identification have been suggested using remote sensing techniques at different levels of mapping.     

Multi level land use surveys using remote sensing techniques

The use of Landsat imagery at 1:1 M and 1:250,000 and aerial photos on 1:65,000 and 1:20,000 scale have been used to study landuse. It has been possible to achieve identification using Landsat imagery up to utility level ( level 1 and II ) and with the aerial photographs further subdivisions of utility into management and identification (level III and IV) .     

Growth Of The Summer Daytime Convective Boundary Layer At Anand

The heights of the daytime convective boundary layer (CBL), computed by a one-dimensional model for a bare soil surface at a semi-arid station,Anand, during the dry and hot summer month of May 1997, are presented. As input, the model requires surface heat flux, friction velocity and air temperature as functions of time. Temperature data at the one-metre level from a tower and sonic anemometer data at 9.5 m collected during the period 13–17 May 1997 in the Land Surface Processes Experiment (LASPEX-97) are used to compute hourly values of surface heat flux, friction velocity and Obukhov length following the operational method suggested by Holtslag and Van Ulden [J. Climate Appl. Meteorol. 22,517–529 (1983)]. The model has been tested with different values for the potential temperature gradient ( ) above the inversion. The model-estimated CBL heights comparefavourably with observed heights obtained from radiosonde ascents.     

Hysteresis‐based flood‐wave analysis using the concept of strain

Hysteresis represents a loop in a rating curve and is a phenomenon which closely resembles that occurring in stress–strain curves used for studying the elastic properties of solid substances in engineering mechanics. Earlier hysteresis‐based studies used for defining floodwave propagation in open channels have qualitatively shown that hysteresis is an index of energy loss during floodwave propagation. Using the concept of elasticity, this paper introduces a new term called flow strain (defined as the ratio of change in discharge to the initial discharge) for investigating hysteresis. The usefulness of this new term is evaluated with use of four dam‐break studies. The study reveals that:

• 1 flow strain is a function of three wave speeds, Seddon speed, Lagrange speed, and elastic speed;
• 2 a single linear reservoir concept frequently used in flood routing is a specific variant of the Seddon speed formula;
• 3 the non‐linear storage–discharge relationship, widely used in overland flow modelling, is a variant of the kinematic wave representation;
• 4 the discharge ordinates on the recession part of a hydrograph follow a simple first‐order autoregressive form;
• 5 the hysteresis, phase difference and logarithmic decrement all define attenuation and are indices of energy loss during floodwave propagation.

Copyright © 2001 John Wiley & Sons, Ltd.     

Possible Evidence of Long-Term Oscillations in the Solar Atmosphere

We have obtained the temporal correlation function, Q(t), from time sequences of Caii K filtergrams and Dopplergrams from Antarctica, Taiwan Oscillation Network (TON) and Solar and Heliospheric Observatory (SOHO). Q(t) gives the time evolution of the pattern under examination, supergranulation in this case. It has been found that Q(t) shows oscillatory signals of both 5-min and long-term periods. The 5-min oscillations are suppressed by averaging the images over 10 min. An exponential decay curve which represents the lifetime trend of supergranules, is fitted to Q(t) and subtracted out. The Q(t) residuals thus obtained contain the oscillatory component and are then subjected to a periodogram analysis. Significant periodicities in the range of 1.4–10 hours have been noted. The causes of these oscillations are not fully known at present, but the instrumental and atmospheric factors can be ruled out, pointing to solar origin. Various possibilities are discussed. Some of the observed periodicities may be considered as probable candidates for long-term oscillations in the Sun, such as the elusive gravity modes.     

Sensitivity of Mesoscale Model Forecast During a Satellite Launch to Different Cumulus Parameterization Schemes in MM5

The identification of the model discrepancy and skill is crucial when a forecast is issued. The characterization of the model errors for different cumulus parameterization schemes (CPSs) provides more confidence on the model outputs and qualifies which CPSs are to be used for better forecasts. Cases of good/bad skill scores can be isolated and clustered into weather systems to identify the atmospheric structures that cause difficulties to the forecasts. The objective of this work is to study the sensitivity of weather forecast, produced using the PSU-NCAR Mesoscale Model version 5 (MM5) during the launch of an Indian satellite on 5th May, 2005, to the way in which convective processes are parameterized in the model. The real-time MM5 simulations were made for providing the weather conditions near the launch station Sriharikota (SHAR). A total of 10 simulations (each of 48 h) for the period 25th April to 04th May, 2005 over the Indian region and surrounding oceans were made using different CPSs. The 24 h and 48 h model predicted wind, temperature and moisture fields for different CPSs, namely the Kuo, Grell, Kain-Fritsch and Betts-Miller, are statistically evaluated by calculating parameters such as mean bias, root-mean-squares error (RMSE), and correlation coefficients by comparison with radiosonde observation. The performance of the different CPSs, in simulating the area of rainfall is evaluated by calculating bias scores (BSs) and equitable threat scores (ETSs). In order to compute BSs and ETSs the model predicted rainfall is compared with Tropical Rainfall Measuring Mission (TRMM) observed rainfall. It was observed that model simulated wind and temperature fields by all the CPSs are in reasonable agreement with that of radiosonde observation. The RMSE of wind speed, temperature and relative humidity do not show significant differences among the four CPSs. Temperature and relative humidity were overestimated by all the CPSs, while wind speed is underestimated, except in the upper levels. The model predicted moisture fields by all CPSs show substantial disagreement when compared with observation. Grell scheme outperforms the other CPSs in simulating wind speed, temperature and relative humidity, particularly in the upper levels, which implies that representing entrainment/detrainment in the cloud column may not necessarily be a beneficial assumption in tropical atmospheres. It is observed that MM5 overestimates the area of light precipitation, while the area of heavy precipitation is underestimated. The least predictive skill shown by Kuo for light and moderate precipitation asserts that this scheme is more suitable for larger grid scale (>30 km). In the predictive skill for the area of light precipitation the Betts-Miller scheme has a clear edge over the other CPSs. The evaluation of the MM5 model for different CPSs conducted during this study is only for a particular synoptic situation. More detailed studies however, are required to assess the forecast skill of the CPSs for different synoptic situations.     

Probabilities and distribution of monsoon rainfall in normal, flood and drought years over India

Summary The paper deals with the variability of summer-monsoon rainfall during normal, flood and drought years over India. During flood years the monsoon rainfall increases mostly all over parts of the country and large area less than 100 cm isohytel covers Orissa and adjoining Madhya Pradesh. During drought years the rainfall amount decreases over the entire country and isohytel of 100 cm shrinks to almost a point. The variability of monsoon rainfall from flood to normal to drought years depends upon the number of depression/low-pressure area which form over the North Bay and move inland. To understand the intraseasonal and interannual variability of the monsoon rainfall, daily and seasonal anomalies has been performed by using the Empirical Orthogonal Function analysis. Further Empirical Orthogonal Function (EOF) analysis is carried out on these data to find out the nature of rainfall distribution in different monsoon categories namely normal, flood and drought years. This technique thus serves to identify spatial and temporal patterns characteristics of possible physical significance. Received July 25, 2000/Revised September 26, 2000     

Multivariate ENSO index and Indian monsoon rainfall: relationships on monthly and subdivisional scales

Summary El Ni?o/Southern Oscillation (ENSO) is known to cause world-wide weather anomalies. It influences the Indian Monsoon Rainfall (IMR) also. But due to large spatial and temporal variability of monsoon rains, it becomes difficult to state any single uniform relationship between the ENSO and IMR that holds good over different subdivisions of India, though the general type of relationship between all India monsoon rainfall and ENSO is known since long. The selection of the most suitable ENSO index to correlate with the IMR is another problem. The purpose of the present study is twofold, namely, to examine the relationship between the ENSO and IMR for entire monsoon season by using an ENSO index which represents the ENSO phenomenon in a comprehensive way, namely, the Multivariate ENSO Index (MEI) and to establish the relationships between MEI and IMR for every meteorological subdivision of India for each monsoon month; i.e. June, July, August and September. A comparison of MEI/IMR correlations has been made with Southern Oscillation Index (SOI)/IMR correlations. The result may find applications in the long range forecasting of IMR on monthly and subdivisional scales, especially over the high monsoon rainfall variability regions of Northwestern and the Peninsular India. Received October 27, 2000