Abstract. The Moderate Resolution Imaging Spectroradiometer (MODIS) onboard EOS Terra measures global aerosol optical depth and optical properties since 2000. MODIS aerosol products are freely available and are being used for numerous studies. In this paper, we present a comparison of aerosol optical depth (AOD) retrieved from MODIS with Aerosol Robotic Network (AERONET) data for the year 2004 over Kanpur, an industrial city lying in the Ganga Basin in the northern part of India. AOD retrieved from MODIS (τ aMODI S ) at 0.55µm wavelength has been compared with the AERONET derived AOD (τ aAERON ET ), within an optimum space-time window. Although the correlation between τ aMODI S and τ aAERONET during the post-monsoon and winter seasons (R 2 ∼ 0.71) is almost equal to that during the pre-monsoon and monsoon seasons (R 2 ∼0.72), MODIS is found to overestimate AOD during the pre-monsoon and monsoon period (characterized by severe dust loading) and underestimate during the post-monsoon and winter seasons. The absolute difference between τ aMODI S and τ aAERON ET is found to be low (0.12±0.11) during the non-dust loading season and much higher (0.4±0.2) during dust-loading seasons. The absolute error in τ aMODI S is found to be about ∼25% of the absolute values of τ aMODI S . Our comparison shows the importance of modifying the existing MODIS algorithm during the dustloading seasons, especially in the Ganga Basin in northern part of India.

Particle swarm optimization (PSO) is a global optimization strategy that simulates the social behavior observed in a flock (swarm) of birds searching for food. A simple search strategy in PSO guides the algorithm toward the best solution through constant updating of the cognitive knowledge and social behavior of the particles in the swarm. To evaluate the applicability of PSO to inversion of geophysical data, we inverted three noise-corrupted synthetic sounding data sets over a multilayered 1D earth model by using DC, induced polarization (IP), and magnetotelluric (MT) methods. The results show that acceptable solutions can be obtained with a swarm of about 300 particles and that convergence occurs in less than 100 iterations. The time required to execute a PSO algorithm is comparable to that of a genetic algorithm (GA). Similarly, the models estimated from PSO and GA are close to the true solutions. Whereas a ridge regression (RR) algorithm converges in four to eight iterations, it yields satisfactory results only when the initial model is very close to the true model. Models estimated from PSO explain observed, vertical electric sounding (VES) and MT data, from Bhiwani district, Haryana, India, and the Chottanagpur gneissic complex, Dhanbad, India. The results are consistent with RR and GA inversions.

S U M M A R YAmplitude of the 2-D analytic signal of the magnetic anomaly profile is independent of the directions of the Earth's magnetic field vector and remnant magnetization of the causative source. It exhibits peaks corresponding to the locations of the corners of a causative source, modelled by say a polygon. It also exhibits a peak corresponding to different idealized source geometries related to the structural indices. This amplitude is computed from the first-order horizontal and vertical derivatives of the observed magnetic anomaly and is relatively less noisy than second-order derivatives. The amplitude can also be computed directly from the measured derivatives. Particle swarm optimization (PSO)-a global optimization technique is applied to interpret this amplitude in terms of the horizontal location and depth, constant (related to magnetization) and various source geometries through structural indices. Applicability of the proposed technique is evaluated through the analyses of simulated magnetic anomalies (noise-free and corrupted with 20 per cent random noise) over different types of source geometries, namely, a thin dyke and a contact with high accuracy in parameter estimation. Studies on the choices of search parameter space reveal that a relatively wide search space can be assigned. Practical applicability of the proposed technique has been demonstrated through three magnetic anomaly profiles digitized from published literature. The results of PSO, Euler deconvolution, enhanced local wavenumber and drill hole are comparable. PSO results also seem to be more stable than other techniques.

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Understanding the factors that control the variability of oxygen isotopic ratios (δ18O) of Indian Summer Monsoon (ISM) rainfall (δ18Op) is of vital importance for the interpretation of δ18Op derived from climate proxies (e.g., speleothem and tree ring cellulose) of this region. Here we demonstrate the importance of moisture transport pathways on spatiotemporal variations of ISM δ18Op using a new set of daily observations from central and northern India and previously reported data aided by simulations from an isotope‐enabled General Circulation Model. 18O‐depleted rain events are characterized by a higher number of air parcel back trajectories through the Bay of Bengal branch of moisture transport, while those through the Arabian Sea branch are associated with 18O enriched rain events. This effect is observed on intraseasonal to interannual timescales in the long‐term observations at New Delhi as well. Thus, the shift in moisture transport regimes must be considered when interpreting δ 18Op from climate proxies of the ISM region.