14 research outputs found
Multiscale interaction with topography and extreme rainfall events in the northeast Indian region
Get PDFFlash floods associated with extreme rain events are a major hydrological disaster in the northeast Indian (NEI) region because of the unique topographic features of the region as well as increased frequency of occurrence of such events. Knowledge of the spatiotemporal distribution of these events in the region and an understanding of the factors responsible for them, therefore, would be immensely useful for appropriate disaster preparedness. Using daily rainfall data from 15 stations over the region for 32 years (1975-2006), it is shown that the frequency of occurrence of these events is largest not during the premonsoon thunderstorm season but during the peak monsoon months (June-July-August). This fact together with the fact that most of these events occur during long rainy spells indicate that the extreme events in the NEI region largely occur in association with the monsoon synoptic events rather than isolated thunderstorms. We also find that the aggregate of extreme rain events over the region has a significant decreasing trend in contrast to a recent finding of an increasing trend of such events in central India (Goswami et al., 2006). This decreasing trend of extreme events is consistent with observed decreasing trend in convective available potential energy and increasing convective inhibition energy over the region for the mentioned period. Examination of the structure of convection associated with the extreme rain events in the region indicates that they occur through a multiscale interaction of circulation with the local topography. It is found that at all the stations, the events are associated with a mesoscale structure of convection that is embedded in a much larger scale convective organization. We identify that this large-scale organization is a manifestation of certain phases of the tropical convergence zone associated with the northward propagating active-break phases of the summer monsoon intraseasonal oscillation. Further, it is shown that the mesoscale circulation interacting with the local topography generates southward propagating gravity waves with diurnal period. The strong updrafts associated with the gravity waves within the mesoscale organization leads to very deep convective events and the extreme rainfall. The insights provided by our study would be useful when designing models to improve the prediction of extreme events
A pre-monsoon signal of false alarms of Indian monsoon droughts
Get PDFCurrent knowledge suggests a drought Indian monsoon (perhaps a severe one) when the El Nino Southern Oscillation and Pacific Decadal Oscillation each exhibit positive phases (a joint positive phase). For the monsoons, which are exceptions in this regard, we found northeast India often gets excess pre-monsoon rainfall. Further investigation reveals that this excess pre-monsoon rainfall is produced by the interaction of the large-scale circulation associated with the joint phase with the mountains in northeast India. We posit that a warmer troposphere, a consequence of excess rainfall over northeast India, drives a stronger monsoon circulation and enhances monsoon rainfall over central India. Hence, we argue that pre-monsoon rainfall over northeast India can be used for seasonal monsoon rainfall prediction over central India. Most importantly, its predictive value is at its peak when the Pacific Ocean exhibits a joint positive phase and the threat of extreme drought monsoon looms over India
Role of the eastern equatorial Indian Ocean warming in the Indian summer monsoon rainfall trend
No full textThe Indian summer monsoon rainfall (ISMR) has been declining since the 1950s. However, since 2002 it is reported to have revived. For these observed changes in the ISMR, several explanations have been reported. Among these explanations, however, the role of the eastern equatorial Indian Ocean (EEIO) is missing despite being one of the warmest regions in the Indian Ocean, and monotonously warming. A recent study reported that EEIO warming impacts the rainfall over northern India. Here we report that warming in the EEIO weakens the low-level Indian summer monsoon circulation and reduces ISMR. A warm EEIO drives easterly winds in the Indo–Pacific sector as a Gill response. The warm EEIO also enhances nocturnal convection offshore the western coast of Sumatra. The latent heating associated with the increased convection augments the Gill response and the resultant circulation opposes the monsoon low-level circulation and weakens the seasonal rainfall
An assessment of the ENSO-monsoon teleconnection in a warming climate
Get PDFAbstract The El Niño-Southern Oscillation (ENSO) and the Indian summer monsoon (ISM, or monsoon) are two giants of tropical climate. Here we assess the future evolution of the ENSO-monsoon teleconnection in climate simulations with idealized forcing of CO2 increment at a rate of 1% year-1 starting from a present-day condition (367 p.p.m.) until quadrupling. We find a monotonous weakening of the ENSO-monsoon teleconnection with the increase in CO2. Increased co-occurrences of El Niño and positive Indian Ocean Dipoles (pIODs) in a warmer climate weaken the teleconnection. Co-occurrences of El Niño and pIOD are attributable to mean sea surface temperature (SST) warming that resembles a pIOD-type warming pattern in the Indian Ocean and an El Niño-type warming in the Pacific. Since ENSO is a critical precursor of the strength of the Indian monsoon, a weakening of this relation may mean a less predictable Indian monsoon in a warmer climate
A Novel Deep Learning Method for Thermal to Annotated Thermal-Optical Fused Images
Get PDFThermal Images profile the passive radiation of objects and capture them in
grayscale images. Such images have a very different distribution of data
compared to optical colored images. We present here a work that produces a
grayscale thermo-optical fused mask given a thermal input. This is a deep
learning based pioneering work since to the best of our knowledge, there exists
no other work on thermal-optical grayscale fusion. Our method is also unique in
the sense that the deep learning method we are proposing here works on the
Discrete Wavelet Transform (DWT) domain instead of the gray level domain. As a
part of this work, we also present a new and unique database for obtaining the
region of interest in thermal images based on an existing thermal visual paired
database, containing the Region of Interest on 5 different classes of data.
Finally, we are proposing a simple low cost overhead statistical measure for
identifying the region of interest in the fused images, which we call as the
Region of Fusion (RoF). Experiments on the database show encouraging results in
identifying the region of interest in the fused images. We also show that they
can be processed better in the mixed form rather than with only thermal images
Examining Patterns of Bilateral Trade between Australia and Colombia by Using Cointegration Analysis and Error-Correction Models
Get PDFThe main objective of this paper is to understand whether there is a long-term relationship between Australia and Colombian imports by using macroeconomic fundamentals such as the real exchange rate, income, population and openness. We use multivariate cointegration techniques and error correction models along with time-series data (1960-2005). We focus on testing for cointegration in the presence of structural breaks. The findings suggest that the value of Australian imports from Colombia is cointegrated with three economic series: income of both participating countries and the Colombian population. The real value of Colombian imports from Australia is cointegrated with the real bilateral exchange rate and total Colombian world imports. The relationship between the value of bilateral imports and the cointegrated series can be seen as long-running bilateral import elasticities. High coefficients of the cointegrated variables indicate that opportunities exist to improve long-term trade relationships between the two countries.trade, gravity model, Latin America, Australia, cross-sectional data.
A Shallow‐Deep Unified Stochastic Mass Flux Cumulus Parameterization in the Single Column Community Climate Model
Get PDFAbstract Cumulus parameterization (CP) in state‐of‐the‐art global climate models is based on the quasi‐equilibrium assumption (QEA), which views convection as the action of an ensemble of cumulus clouds, in a state of equilibrium with respect to a slowly varying atmospheric state. This view is not compatible with the organization and dynamical interactions across multiple scales of cloud systems in the tropics and progress in this research area was slow over decades despite the widely recognized major shortcomings. Novel ideas on how to represent key physical processes of moist convection‐large‐scale interaction to overcome the QEA have surged recently. The stochastic multicloud model (SMCM) CP in particular mimics the dynamical interactions of multiple cloud types that characterize organized tropical convection. Here, the SMCM is used to modify the Zhang‐McFarlane (ZM) CP by changing the way in which the bulk mass flux and bulk entrainment and detrainment rates are calculated. This is done by introducing a stochastic ensemble of plumes characterized by randomly varying detrainment level distributions based on the cloud area fraction of the SMCM. The SMCM is here extended to include shallow cumulus clouds resulting in a unified shallow‐deep CP. The new stochastic multicloud plume CP is validated against the control ZM scheme in the context of the single column Community Climate Model of the National Center for Atmospheric Research using data from both tropical ocean and midlatitude land convection. Some key features of the SMCM CP such as it capability to represent the tri‐modal nature of organized convection are emphasized
An assessment of representing land‐ocean heterogeneity via CAPE relaxation timescale in the Community Atmospheric Model 6 (CAM6)
Get PDFThe time needed by deep convection to bring the atmosphere back to equilibrium is called convective adjustment timescale or simply adjustment timescale, typically denoted by . In the Community Atmospheric Model|Community Atmosphere Model (CAM), is the convective available potential energy (CAPE) relaxation timescale and is 1 hr, worldwide. Observational evidence suggests that is generally longer than 1 hr. Further, continental and oceanic convection are different in terms of the vigor of updrafts and can have different longevities. So using hour worldwide in CAM has two potential caveats. A longer improves the simulation of the mean climate. However, it does not address the land‐ocean heterogeneity of atmospheric deep convection. We investigate the prescription of two different CAPE relaxation timescales for land ( hr) and ocean ( to 4 hr). It is arguably an extremely crude parameterization of boundary layer control on atmospheric convection. We contrast a suite of 5‐year‐long simulations with two different for land and ocean to having one globally. The choice of longer over ocean is guided by previous studies and inspired by observational pieces of evidence. Nonetheless, to complement our variable experiments, we perform a simulation with hr and hrs. Most importantly, our key findings are immune to the exact values of prescribed and . The CAM model, with two values , improves convective‐stratiform rainfall partitioning and the Madden–Julian oscillation propagation characteristics
Intra - seasonal variability and forecasting: a review of recent research
No full textThe relative success of the Community Atmosphere Model with superparameterized convection (SP-CAM) in simulating the space-time characteristics of the Madden Julian Oscillation encourages us to examine its simulation of the Indian summer monsoon and monsoon intraseasonal oscillations (MISOs). While the model simulates the onset and withdrawal of the Indian monsoon realistically, it has a significant wet bias in boreal summer precipitation over the Asian monsoon region. The space-time characteristics of the MISOs simulated by the SP-CAM are examined in detail and compared with those of the observed MISO to gain insight into the model's bias in simulating the seasonal mean. During northern summer, the model simulates a 20 day mode and a 60 day mode in place of the observed 15 and 45 day modes, respectively. The simulated 20 day mode appears to have no observed analog with a baroclinic vertical structure and strong northward propagation over Indian longitudes. The simulated 60 day mode seems to be a lower-frequency version of the observed 45 day mode with relatively slower northward propagation. The model's underestimation of light rain events and overestimation of heavy rain events are shown to be responsible for the wet bias of the model. More frequent occurrence of heavy rain events in the model is, in turn, related to the vertical structure of the higher-frequency modes. Northward propagation of the simulated 20 day mode is associated with a strong cyclonic vorticity at low levels north of the heating maximum associated with a smaller meridional scale of the simulated mode. The simulated vertical structure of heating indicates a strong maximum in the upper troposphere between 200 and 300 hPa. Such a heating profile seems to generate a higher-order baroclinic mode response with smaller meridional structure, stronger low-level cyclonic vorticity, enhanced low-level moisture convergence, and higher precipitation. Therefore, the vertical structure of heating simulated by the cloud-resolving model within SP-CAM may hold the key for improving the precipitation bias in the model
Intra - seasonal variability and forecasting: a review of recent research
No full textThe relative success of the Community Atmosphere Model with superparameterized convection (SP-CAM) in simulating the space-time characteristics of the Madden Julian Oscillation encourages us to examine its simulation of the Indian summer monsoon and monsoon intraseasonal oscillations (MISOs). While the model simulates the onset and withdrawal of the Indian monsoon realistically, it has a significant wet bias in boreal summer precipitation over the Asian monsoon region. The space-time characteristics of the MISOs simulated by the SP-CAM are examined in detail and compared with those of the observed MISO to gain insight into the model's bias in simulating the seasonal mean. During northern summer, the model simulates a 20 day mode and a 60 day mode in place of the observed 15 and 45 day modes, respectively. The simulated 20 day mode appears to have no observed analog with a baroclinic vertical structure and strong northward propagation over Indian longitudes. The simulated 60 day mode seems to be a lower-frequency version of the observed 45 day mode with relatively slower northward propagation. The model's underestimation of light rain events and overestimation of heavy rain events are shown to be responsible for the wet bias of the model. More frequent occurrence of heavy rain events in the model is, in turn, related to the vertical structure of the higher-frequency modes. Northward propagation of the simulated 20 day mode is associated with a strong cyclonic vorticity at low levels north of the heating maximum associated with a smaller meridional scale of the simulated mode. The simulated vertical structure of heating indicates a strong maximum in the upper troposphere between 200 and 300 hPa. Such a heating profile seems to generate a higher-order baroclinic mode response with smaller meridional structure, stronger low-level cyclonic vorticity, enhanced low-level moisture convergence, and higher precipitation. Therefore, the vertical structure of heating simulated by the cloud-resolving model within SP-CAM may hold the key for improving the precipitation bias in the model
