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A new method of interpreting magnetic anomalies of arbitrarily-magnetised horizontal circular cylinders, dipping dykes and vertical steps is presented. The method makes use of both horizontal and vertical gradients of the magnetic field of the model under consideration, rather than the observed magnetic anomaly. Vertical and horizontal gradients are calculated from the observed anomalies, and plotted one against the other to find out the locus of tip of the resultant gradient vector. This locus is a symmetrical curve for each of the three models mentioned above. The properties of these curves are used to deduce the various parameters of these models and the direction of magnetisation.

Operational forecasting of tropical cyclone (TC) track and intensity in the India Meteorological Department (IMD) relies more and more on the numerical weather prediction (NWP) model guidance from national and international agencies particularly, on the medium range (24–120 h). Any improvement in TC forecasts by the NWP models enhances the operational forecaster's confidence and capability. The real-time information from the National Centre for Medium Range Weather Forecasting (NCMRWF) global NWP model (NCUM-G) is routinely used by operational forecasters at IMD as model guidance. The present study documents the improved skill of NCUM-G in forecasting the North Indian Ocean (NIO) TCs during 2015–2019, based on a collection of 1810 forecasts involving 22 TC cases. The study highlights three significant changes in the modelling system during the recent five years, namely (i) increased grid resolution from 17 to 12 km, (ii) use of hybrid 4D-Var data assimilation (DA), and (iii) increased volume of assimilated data. The study results indicate a consistent improvement in the NCUM-G model forecasts during the pre-monsoon (April–May, AM) and post-monsoon (October–December, OND) TC seasons. In addition to a 44% reduction in the initial position error, the study also reports a statistically significant decrease in the direct position error (DPE) and error in the intensity forecast, resulting in a forecast gain of 24 hrs. Comparing NWP models with IMDs official track error shows that NCUM-G and ECMWF model forecasts feature lower DPE than IMD in 2019, particularly at higher (96, 108, and 120 h) lead times.

Continuous measurements of aerosol size distributions in the mid-point diameter range 20.5–500 nm were made from October 2005 to March 2006 at Pune (18°32′N, 73°51′E), India using Scanning Mobility Particle Sizer (SMPS). Volatilities of atmospheric aerosols were also measured at 40°, 125°, 175°, 300° and 350°C temperatures with Thermodenuder–SMPS coupled system to determine aerosol volatile fractions. Aerosols in nucleated, CCN and accumulated modes are characterized from the measured percentage of particles volatized at 40°, 125°, 175°, 300° and 350°C temperatures. Averaged monthly aerosol concentration is at its maximum in November and gradually decreases to its minimum at the end of March. The diurnal variations of aerosol concentrations gradually decrease in the night and in early morning hours (0400–0800 hr). However, concentration attains minimum in its variations in the noon (1400–1600 hr) due to higher ventilation factor (product of mixing height and wind speed). The half an hour averaged diurnal variation of aerosol number concentration shows about 5 to 10-fold increase despite the ventilation factor at higher side before 1200 hr. This sudden increase in aerosol concentrations is linked with prevailing conditions for nucleation bursts. The measurement of volatile fraction of ambient aerosols reveals that there are large number of highly volatile particles in the Aitken mode in the morning hours and these volatile fractions of aerosols at temperatures <150°C are of ammonium chloride and ammonium sulfate, acetic and formic acids.

An anomaly map of the Z component has been produced for the region of the Indian sub-continent for the first time by the Survey of India usingmagsat data. Data of thousands of kilometres of satellite tracks of varying altitude have been reduced to a common elevation of 400 km by removing the external field and linear trend. The entire data was plotted on a map of 1:6 M and mean values of 2°×2° blocks then accepted for contouring. A prominent magnetic low is reflected over the Himalayas and a prominent high over the Indian peninsula. The dividing line of positive and negative anomalies between the Himalayas and Deccan Traps falls along the Narmada lineament.

Over the years, a number of prediction methods have been proposed for the evaluation of probability of hydrological–meteorological variables or drought indices. In this study, the precipitation data recorded in four stations of northwestern Iran over the period 1960–2014 were used to develop the time-series and genetic programming (GP) models. Comparison of the observed and predicted data showed that although both models have acceptable accuracy in predicting precipitation, the time-series models had lower errors than the GP models. So, the autoregressive and periodic autoregressive moving average models were chosen as the superior models for annual and monthly series, respectively. Therefore, the Standard Precipitation Index (SPI) and Z-Score Index (ZSI) were used to assess the drought conditions. According to the results, the SPI recognised a higher percentage of historical and prediction periods as drought conditions than ZSI. The validation of indices showed that the ZSI was more capable for detecting the drought and wetness conditions. The trend analysis of SPI and ZSI showed significant decreasing trends in different stations at all-time scales, except yearly in Urmia and all-time scales in Zanjan, which statically had no significant trend. In conclusion, given the current precipitation trends, the droughts are increasing in both severity and numbers.

Results are presented of two instrumented rocket experiments performed from an equatorial station, one at night and the other shortly after sunrise. The ion neutral composition as well as electron density and the amplitude of plasma irregularities were monitored. During the latter flight, a sharp layer of ionisation with its lower boundary at 100 km was observed. The layer had a half width close to 1 km and a peak electron density of 5·6× 104cm−3. Large amplitude of plasma irregularities, noticed on the negative gradient portion of the layer indicates a downward direction of the polarisation electric field during the observations. The resulting downward drift of photoions as they are produced at sunrise followed by the local decrease of the drift is suggested to be the cause of the layer formation at that altitude. The long lasting nature of such layers once identified on ionograms indicates that they are constituted of metallic ions possibly of micrometeoritic origin deposited overnight in the lower thermosphere. The required photoionisation rate of production of the metallic ions at sunris eis about 2 cm−3 sec−1.

In this study, the global ocean analysis and forecast system is evaluated against the observations in the Indian Ocean for the 2017–2018 period. A comparison of temperature and salinity analysis with seven moored buoy observations showed an excellent mutual agreement with systematically underestimating the upper ocean variability. A similar comparison of ocean analysis with the ARGO dataset average over the different oceanic regions also shows an underestimation of temperature and salinity up to 300 m depth. Further, the temperature RMSE gradually increases from surface to thermocline depth for all regions. The sea surface temperature (SST) forecast is reasonably good at all buoy locations with RMSE less than 0.5°C and a correlation of more than 0.7 up to the day-7 forecasts for the 2017–2018 period. Similarly, the sea surface salinity (SSS) forecast is also good except northern Bay of Bengal (BoB) region where the high variability leads to poor correlation and large RMSE in this shallow and freshwater region. This study helps to understand how observations fit into the model through data assimilation and also quantifies the model forecast error. Further, the study is also vital for the development/research activity related to the model and data assimilation in the Tropical Indian Ocean (TIO) region.