Local onset and demise of the Indian summer monsoon

This paper introduces an objective definition of local onset and demise of the Indian summer monsoon (ISM) at the native grid of the Indian Meteorological Department’s rainfall analysis based on more than 100 years of rain gauge observations. The variability of the local onset/demise of the ISM is shown to be closely associated with the All India averaged rainfall onset/demise. This association is consistent with the corresponding evolution of the slow large-scale reversals of upper air and ocean variables that raise the hope of predictability of local onset and demise of the ISM. The local onset/demise of the ISM also show robust internannual variations associated with El Nino and the Southern Oscillation and Indian Ocean dipole mode. It is also shown that the early monsoon rains over northeast India has a predictive potential for the following seasonal anomalies of rainfall and seasonal length of the monsoon over rest of India.

     

Thirty-two-year ocean–atmosphere coupled downscaling of global reanalysis over the Intra-American Seas

This study examines the oceanic and atmospheric variability over the Intra-American Seas (IAS) from a 32-year integration of a 15-km coupled regional climate model consisting of the Regional Spectral Model (RSM) for the atmosphere and the Regional Ocean Modeling System (ROMS) for the ocean. It is forced at the lateral boundaries by National Centers for Environmental Prediction-Department of Energy (NCEP-DOE R-2) atmospheric global reanalysis and Simplified Ocean Data Assimilation global oceanic reanalysis. This coupled downscaling integration is a free run without any heat flux correction and is referred as the Regional Ocean–Atmosphere coupled downscaling of global Reanalysis over the Intra-American Seas (ROARS). The paper examines the fidelity of ROARS with respect to independent observations that are both satellite based and in situ. In order to provide a perspective on the fidelity of the ROARS simulation, we also compare it with the Climate Forecast System Reanalysis (CFSR), a modern global ocean–atmosphere reanalysis product. Our analysis reveals that ROARS exhibits reasonable climatology and interannual variability over the IAS region, with climatological SST errors less than 1 °C except along the coastlines. The anomaly correlation of the monthly SST and precipitation anomalies in ROARS are well over 0.5 over the Gulf of Mexico, Caribbean Sea, Western Atlantic and Eastern Pacific Oceans. A highlight of the ROARS simulation is its resolution of the loop current and the episodic eddy events off of it. This is rather poorly simulated in the CFSR. This is also reflected in the simulated, albeit, higher variance of the sea surface height in ROARS and the lack of any variability in the sea surface height of the CFSR over the IAS. However the anomaly correlations of the monthly heat content anomalies of ROARS are comparatively lower, especially over the Gulf of Mexico and the Caribbean Sea. This is a result of ROARS exhibiting a bias of underestimation (overestimation) of high (low) clouds. ROARS like CFSR is also able to capture the Caribbean Low Level Jet and its seasonal variability reasonably well.     

Hindcast skill and predictability for precipitation and two-meter air temperature anomalies in global circulation models over the Southeast United States

This paper presents an assessment of the seasonal prediction skill of current global circulation models, with a focus on the two-meter air temperature and precipitation over the Southeast United States. The model seasonal hindcasts are analyzed using measures of potential predictability, anomaly correlation, Brier skill score, and Gerrity skill score. The systematic differences in prediction skill of coupled ocean–atmosphere models versus models using prescribed (either observed or predicted) sea surface temperatures (SSTs) are documented. It is found that the predictability and the hindcast skill of the models vary seasonally and spatially. The largest potential predictability (signal-to-noise ratio) of precipitation anywhere in the United States is found in the Southeast in the spring and winter seasons. The maxima in the potential predictability of two-meter air temperature, however, reside outside the Southeast in all seasons. The largest deterministic hindcast skill over the Southeast is found in wintertime precipitation. At the same time, the boreal winter two-meter air temperature hindcasts have the smallest skill. The large wintertime precipitation skill, the lack of corresponding two-meter air temperature hindcast skill, and a lack of precipitation skill in any other season are features common to all three types of models (atmospheric models forced with observed SSTs, atmospheric models forced with predicted SSTs, and coupled ocean–atmosphere models). Atmospheric models with observed SST forcing demonstrate a moderate skill in hindcasting spring-and summertime two-meter air temperature anomalies, whereas coupled models and atmospheric models forced with predicted SSTs lack similar skill. Probabilistic and categorical hindcasts mirror the deterministic findings, i.e., there is very high skill for winter precipitation and none for summer precipitation. When skillful, the models are conservative, such that low-probability hindcasts tend to be overestimates, whereas high-probability hindcasts tend to be underestimates.     

A comparative study of the Indian summer monsoon hydroclimate and its variations in three reanalyses

This study examines the Indian summer monsoon hydroclimate in the National Centers for Environmental Prediction (NCEP)-Department of Energy (DOE) Reanalysis (R2), the Climate Forecast System Reanalysis (CFSR), and the Modern Era Retrospective-Analysis for Research and Applications (MERRA). The three reanalyses show significant differences in the climatology of evaporation, low-level winds, and precipitable water fields over India. For example, the continental evaporation is significantly less in CFSR compared to R2 and MERRA. Likewise the mean boreal summer 925?hPa westerly winds in the northern Indian Ocean are stronger in R2. Similarly the continental precipitable water in R2 is much less while it is higher and comparable in MERRA and CFSR. Despite these climatological differences between the reanalyses, the climatological evaporative sources for rain events over central India show some qualitative similarities. Major differences however appear when interannual variations of the Indian summer monsoon are analyzed. The anomalous oceanic sources of moisture from the adjacent Bay of Bengal and Arabian Sea play a significant role in determining the wet or dry year of the Indian monsoon in CFSR. However in R2 the local evaporative sources from the continental region play a more significant role. We also find that the interannual variability of the evaporative sources in the break spells of the intraseasonal variations of the Indian monsoon is stronger than in the wet spells. We therefore claim that instead of rainfall, evaporative sources may be a more appropriate metric to observe the relationship between the seasonal monsoon strength and intraseasonal activity. These findings are consistent across the reanalyses and provide a basis to improve the predictability of intraseasonal variability of the Indian monsoon. This study also has a bearing on improving weather prediction for tropical cyclones in that we suggest targeting enhanced observations in the Bay of Bengal (where it is drawing the most moisture from) for improved analysis during active spells of the intraseasonal variability of the Indian monsoon. The analysis suggests that the land–atmosphere interactions contribute significant uncertainty to the Indian monsoon in the reanalyses, which is consistent with the fact that most of the global reanalyses do not assimilate any land-surface data because the data are not available. Therefore, the land–atmosphere interaction in the reanalyses is highly dependent on the land-surface model and it’s coupling with the atmospheric model.     

Global seasonal climate predictability in a two tiered forecast system. Part II: boreal winter and spring seasons

We examine the Florida Climate Institute–Florida State University Seasonal Hindcast (FISH50) skill at a relatively high (50 km grid) resolution two tiered Atmospheric General Circulation Model (AGCM) for boreal winter and spring seasons at zero and one season lead respectively. The AGCM in FISH50 is forced with bias corrected forecast sea surface temperature averaged from two dynamical coupled ocean–atmosphere models. The comparison of the hindcast skills of precipitation and surface temperature from FISH50 with the coupled ocean–atmosphere models reveals that the probabilistic skill is nearly comparable in the two types of forecast systems (with some improvements in FISH50 outside of the global tropics). Furthermore the drop in skill in going from zero lead (boreal winter) to one season lead (boreal spring) is also similar in FISH50 and the coupled ocean–atmosphere models. Both the forecast systems also show that surface temperature hindcasts have more skill than the precipitation hindcasts and that land based precipitation hindcasts have slightly lower skill than the corresponding hindcasts over the ocean.     

An Objective Analysis of Support Vector Machine Based Classification for Remote Sensing

Accurate thematic classification is one of the most commonly desired outputs from remote sensing images. Recent research efforts to improve the reliability and accuracy of image classification have led to the introduction of the Support Vector Classification (SVC) scheme. SVC is a new generation of supervised learning method based on the principle of statistical learning theory, which is designed to decrease uncertainty in the model structure and the fitness of data. We have presented a comparative analysis of SVC with the Maximum Likelihood Classification (MLC) method, which is the most popular conventional supervised classification technique. SVC is an optimization technique in which the classification accuracy heavily relies on identifying the optimal parameters. Using a case study, we verify a method to obtain these optimal parameters such that SVC can be applied efficiently. We use multispectral and hyperspectral images to develop thematic classes of known lithologic units in order to compare the classification accuracy of both the methods. We have varied the training to testing data proportions to assess the relative robustness and the optimal training sample requirement of both the methods to achieve comparable levels of accuracy. The results of our study illustrated that SVC improved the classification accuracy, was robust and did not suffer from dimensionality issues such as the Hughes Effect.     

Optically stimulated luminescence chronology of terrace sediments of Siang River,Higher NE Himalaya: Comparison of Quartz and Feldspar chronometers

Four levels of terraces located along Siang River, north of Main Central Thrust at Tuting, NE Himalaya are dated using Optically Stimulated Luminescence (OSL). The dating technique is applied using (1) Blue LED stimulation on Quartz (2) Infrared Stimulated Luminescence (IRSL) stimulation on Feldspar at 50 °C and (3) Infrared Stimulated Luminescence stimulation on Feldspar at an elevated temperature of 225 °C. The results indicated that the later two protocols on feldspars yielded overestimated ages that suggested incomplete bleaching of luminescence signals in feldspar. The ages derived using quartz suggested a nearly continued valley aggradation from >21–8 ka with three phases of bedrock incision. The phase of aggradation coincides with a climatic transition from cold and dry Last Glacial phase to warm and wet Holocene Optimum. The bedrock incision phases centered at <21 ka, ∼11 ka and ∼8 ka indicate towards major episodes of tectonic uplift in the region around Tuting.     

A nonlinear Lagrangian particle model for grains assemblies including grain relative rotations

We formulate a discrete Lagrangian model for a set of interacting grains, which is purely elastic. The considered degrees of freedom for each grain include placement of barycenter and rotation. Further, we limit the study to the case of planar systems. A representative grain radius is introduced to express the deformation energy to be associated to relative displacements and rotations of interacting grains. We distinguish inter-grains elongation/compression energy from inter-grains shear and rotations energies, and we consider an exact finite kinematics in which grain rotations are independent of grain displacements. The equilibrium configurations of the grain assembly are calculated by minimization of deformation energy for selected imposed displacements and rotations at the boundaries. Behaviours of grain assemblies arranged in regular patterns, without and with defects, and similar mechanical properties are simulated. The values of shear, rotation, and compression elastic moduli are varied to investigate the shapes and thicknesses of the layers where deformation energy, relative displacement, and rotations are concentrated. It is found that these concentration bands are close to the boundaries and in correspondence of grain voids. The obtained results question the possibility of introducing a first gradient continuum models for granular media and justify the development of both numerical and theoretical methods for including frictional, plasticity, and damage phenomena in the proposed model.     

The Lower Permian coal seams from Singrauli coalfield (M.P.), India: petrochemical nature, rank, age and sedimentation

Biopetrographic and chemical investigations carried out on the Lower Permian (Barakar Formation) coal seams encountered in two bore-holes (NCSM-3 and CMSA-111) from the Singrauli coalfield, Son Valley, reveal that they are, in general, rich in inertinite and mineral matter towards the eastern part. Whereas, towards the western part they are rich in vitrinite with subordinate amounts of inertinite and mineral matter. The Turra and Purewa Top seams, in the eastern part, consist chiefly of mixed and fusic coal types and the Turra and Purewa Merged seams, in the western part of the basin, are characterized dominantly by vitric and mixed coal types. The basin of deposition was shallower in its major part but deepened towards west. Consequently frequent oxic fluctuations are most common in the coal seams of the eastern part.The rank of these coal seams ranges between subbituminous-A to high-volatile bituminous-C stages. They show normal increase in rank with depth. The coal seams in the western part of the area are higher in rank than their counterparts in the east. The Purewa Bottom seam (NCSM-3) is petro-palynologically correlatable with the Turra seam (CMSA-111) of the western part.It has been presumed that Glossopteridophyta (a complex group of gymnospermous plants), arthrophytes and ferns were the vegetal source for the formation of Barakar coal seams. These plants during the Early Permian grew as thick forests along river valleys and as upland and subaquatic vegetation, and experienced a warm, humid and windy temperate climate. The coal seams were presumably deposited in backwater and lacustrine swamps in fluvial environment from hypoauchthonous source material.An attempt has been made to explain reasons for the high incidence of mineral matter and its apparent relationship with inertinite content in the coal seams while also discussing the depositional history.