Role of land state in a high resolution mesoscale model for simulating the Uttarakhand heavy rainfall event over India

In 2013, Indian summer monsoon witnessed a very heavy rainfall event (>30 cm/day) over Uttarakhand in north India, claiming more than 5000 lives and property damage worth approximately 40 billion USD. This event was associated with the interaction of two synoptic systems, i.e., intensified subtropical westerly trough over north India and north-westward moving monsoon depression formed over the Bay of Bengal. The event had occurred over highly variable terrain and land surface characteristics. Although global models predicted the large scale event, they failed to predict realistic location, timing, amount, intensity and distribution of rainfall over the region. The goal of this study is to assess the impact of land state conditions in simulating this severe event using a high resolution mesoscale model. The land conditions such as multi-layer soil moisture and soil temperature fields were generated from High Resolution Land Data Assimilation (HRLDAS) modelling system. Two experiments were conducted namely, (1) CNTL (Control, without land data assimilation) and (2) LDAS, with land data assimilation (i.e., with HRLDAS-based soil moisture and temperature fields) using Weather Research and Forecasting (WRF) modelling system. Initial soil moisture correlation and root mean square error for LDAS is 0.73 and 0.05, whereas for CNTL it is 0.63 and 0.053 respectively, with a stronger heat low in LDAS. The differences in wind and moisture transport in LDAS favoured increased moisture transport from Arabian Sea through a convectively unstable region embedded within two low pressure centers over Arabian Sea and Bay of Bengal. The improvement in rainfall is significantly correlated to the persistent generation of potential vorticity (PV) in LDAS. Further, PV tendency analysis confirmed that the increased generation of PV is due to the enhanced horizontal PV advection component rather than the diabatic heating terms due to modified flow fields. These results suggest that, two different synoptic systems merged by the strong interaction of moving PV columns resulted in the strengthening and further amplification of the system over the region in LDAS. This study highlights the importance of better representation of the land surface fields for improved prediction of localized anomalous weather event over India.     

Mylonitic volcanics near Puging,Upper Siang district,Arunachal Pradesh: Evidence of oblique-slip thrusting

The Abor volcanics of the continental flood basalt affinity are extensively exposed in different parts of the Siang valley. These are associated with Yinkiong Group of rocks of Paleocene–Eocene age and represent syn-sedimentary volcanism in a rift setting. Subsequent folding and thrusting of the Siyom and Rikor sequences above the Yinkiong Group of rocks represent changes from syn-to-post collisional brittle-ductile tectonic episodes. Mylonitic Abor volcanics in the thrust contacts are studied at several locations in the north and south of Puging in the Siang valley. Both the Abor volcanics and associated Rikor and Yinkiong Group of rocks preserve meso to micro-scale fabric asymmetries indicating that the thrust contacts are shear zones of brittle-ductile nature containing mylonitic textures of high shear strain. Two distinct hitherto unrecognised shear zones in the north and south of Puging are named as North Puging Shear Zone (NPSZ) and South Puging Shear Zone (SPSZ). The kinematic indicators along the thrust contact indicate oblique slip thrusting of the Rikor and Siyom thrust sheets above the Yinkiong Group of rocks. This paper provides field evidence proving that the compression due the Burmese plate made oblique slip thrusting and zones of mylonitised volcanics possible and associated metasediments were formed. The kinematic indicators in the NPSZ and SPSZ respectively indicate top-to-SSE and top-to-NNW sense of shears.     

Modelling discontinuous well log signal to identify lithological boundaries via wavelet analysis: An example from KTB borehole data

Identification of sharp and discontinuous lithological boundaries from well log signal stemming from heterogeneous subsurface structures assumes a special significance in geo-exploration studies. Well log data acquired from various geological settings generally display nonstationary/nonlinear characteristics with varying wavelengths and frequencies. Modelling of such complex well-log signals using the conventional signal processing techniques either fails to catch-up abrupt boundaries or at the best, do not provide precise information on insidious lithological discontinuities. In this paper, we have proposed a new wavelet transform-based algorithm to model the abrupt discontinuous changes from well log data by taking care of nonstationary characteristics of the signal. Prior to applying the algorithm on the geophysical well data, we analyzed the distribution of wavelet coefficients using synthetic signal generated by the first order nonstationary auto-regressive model and then applied the method on actual well log dataset obtained from the KTB bore hole, Germany. Besides identifying the formation of layered boundaries, the underlying method also maps some additional formation boundaries, which were hitherto undetected at the KTB site. The results match well with known geological lithostratigraphy and will be useful for constraining the future model of KTB bore hole data.     

A study on precursors leading to geomagnetic storms using artificial neural network

Space weather prediction involves advance forecasting of the magnitude and onset time of major geomagnetic storms on Earth. In this paper, we discuss the development of an artificial neural network-based model to study the precursor leading to intense and moderate geomagnetic storms, following halo coronal mass ejection (CME) and related interplanetary (IP) events. IP inputs were considered within a 5-day time window after the commencement of storm. The artificial neural network (ANN) model training, testing and validation datasets were constructed based on 110 halo CMEs (both full and partial halo and their properties) observed during the ascending phase of the 24th solar cycle between 2009 and 2014. The geomagnetic storm occurrence rate from halo CMEs is estimated at a probability of 79%, by this model.     

Trend analysis of rainfall time series for Sindh river basin in India

The main goal of this paper is to estimate a set of optimal seasonal, daily, and hourly values of atmospheric turbidity and surface radiative parameters Ångström’s turbidity coefficient (β), Ångström’s wavelength exponent (α), aerosol single scattering albedo (ω_o), forward scatterance (F_c) and average surface albedo (ρ_g), using the Brute Force multidimensional minimization method to minimize the difference between measured and simulated solar irradiance components, expressed as cost functions. In order to simulate the components of short-wave solar irradiance (direct, diffuse and global) for clear sky conditions, incidents on a horizontal surface in the Metropolitan Area of Rio de Janeiro (MARJ), Brazil (22° 51′ 27″ S, 43° 13′ 58″ W), we use two parameterized broadband solar irradiance models, called CPCR2 and Iqbal C, based on synoptic information. The meteorological variables such as precipitable water (u_w) and ozone concentration (u_o) required by the broadband solar models were obtained from moderate-resolution imaging spectroradiometer (MODIS) sensor on Terra and Aqua NASA platforms. For the implementation and validation processes, we use global and diffuse solar irradiance data measured by the radiometric platform of LabMiM, located in the north area of the MARJ. The data were measured between the years 2010 and 2012 at 1-min intervals. The performance of solar irradiance models using optimal parameters was evaluated with several quantitative statistical indicators and a subset of measured solar irradiance data. Some daily results for Ångström’s wavelength exponent α were compared with Ångström’s parameter (440–870 nm) values obtained by aerosol robotic network (AERONET) for 11 days, showing an acceptable level of agreement. Results for Ångström’s turbidity coefficient β, associated with the amount of aerosols in the atmosphere, show a seasonal pattern according with increased precipitation during summer months (December–February) in the MARJ.