R. Chattopadhyay

Objective identification of nonlinear convectively coupled phases of monsoon intraseasonal oscillation: implications for prediction

Abstract The nonlinear convectively coupled character of the summer monsoon intraseasonal oscillation (ISO) that manifests in its event-to-event variations is a major hurdle for skillful extended-range prediction of the active/break episodes. The convectively coupled character of the monsoon ISO implies that a particular nonlinear phase of the precipitation ISO is linked to a unique pattern of the large-scale variables. A methodology has been presented to capture different nonlinear phases of the precipitation ISO using a combination of a sufficiently large number of dynamical variables. This is achieved through a nonlinear pattern recognition technique known as self-organizing map (SOM) involving six daily large-scale circulation indices. It is demonstrated that the nonlinearly classified states of the large-scale circulation isolated at the SOM nodes without involving any information on rainfall are strongly linked to different phases of evolution of the rainfall ISO, including the active and break phas...

Indian summer monsoon rainfall simulation and prediction skill in the CFSv2 coupled model: Impact of atmospheric horizontal resolution

This study compares the simulation and prediction skill of the Indian summer monsoon at two different horizontal resolutions viz., T126 (~100 km) and T382 (~38 km) using 28 years of hindcast runs of the National Centers for Environmental Prediction Climate Forecast System version 2 (CFSv2) model. It is found that the simulation of the mean state of the South Asian summer monsoon, its variance, and prediction skill of the all India summer monsoon rainfall (AISMR) are better represented in the high-resolution configuration (T382) of the CFSv2 compared to the low-resolution (T126) configuration. In the high-resolution run, the systematic bias in the teleconnection between the AISMR and Indian Ocean Dipole (IOD) has considerably reduced and the teleconnections between the AISMR and El Nino–Southern Oscillation (ENSO) remained same. We hypothesize that the better simulation of mean climate and IOD-AISMR teleconnection in high-resolution configuration (T382) of CFSv2 are responsible for the improved prediction skill of AISMR in T382 configuration. Although the T382 configuration of CFSv2 has shown a significant improvement in the simulation and prediction of Indian summer monsoon as compared to the T126 configuration, several parallel efforts are still essential to understand the processes controlling some of the systematic biases of CFSv2 and those efforts are underway as part of the Monsoon Mission project.

A SST based large multi-model ensemble forecasting system for Indian summer monsoon rainfall

An ensemble mean and probabilistic approach is essential for reliable forecast of the All India Summer Monsoon Rainfall (AIR) due to the seminal role played by internal fast processes in interannual variability (IAV) of the monsoon. In this paper, we transform a previously used empirical model to construct a large ensemble of models to deliver useful probabilistic forecast of AIR. The empirical model picks up predictors only from global sea surface temperature (SST). Methodology of construction implicitly incorporates uncertainty arising from internal variability as well as from the decadal variability of the predictor-predictand relationship. The forecast system demonstrates the capability of predicting monsoon droughts with high degree of confidence. Results during independent verification period (1999-2008) suggest a roadmap for generating empirical probabilistic forecast of monsoon IAV for practical delivery to the user community.

Improved Spread–Error Relationship and Probabilistic Prediction from the CFS-Based Grand Ensemble Prediction System

AbstractThis study describes an attempt to overcome the underdispersive nature of single-model ensembles (SMEs). As an Indo–U.S. collaboration designed to improve the prediction capabilities of models over the Indian monsoon region, the Climate Forecast System (CFS) model framework, developed at the National Centers for Environmental Prediction (NCEP-CFSv2), is selected. This article describes a multimodel ensemble prediction system, using a suite of different variants of the CFSv2 model to increase the spread without relying on very different codes or potentially inferior models. The SMEs are generated not only by perturbing the initial condition, but also by using different resolutions, parameters, and coupling configurations of the same model (CFS and its atmosphere component, the Global Forecast System). Each of these configurations was created to address the role of different physical mechanisms known to influence error growth on the 10–20-day time scale. Last, the multimodel consensus forecast is de...

A Description of the Madden–Julian Oscillation Based on a Self-Organizing Map

AbstractThis study introduces a nonlinear clustering technique based on a self-organizing map (SOM) algorithm to identify horizontal and vertical structures of the Madden–Julian oscillation (MJO) through its life cycle. The SOM description of the MJO does not need intraseasonal bandpass filtering or selection of leading modes. MJO phases are defined by SOM based on state similarities in chosen variables. Spatial patterns of rainfall-related variables in a given MJO phase defined by SOM are distinct from those in other phases. The structural evolution of the MJO derived from SOM agrees with those from other methods in certain aspects and differs in others. SOM reveals that the dominant longitudinal structure in the diabatic heating and related fields of the MJO is a dipole or tripole pattern with a zonal scale close to that of zonal wavenumber 2, as opposed to zonal wavenumber 1 suggested by other methods. Results from SOM suggest that the MJO life cycle may be composed of quasi-stationary stages of strong...

Can El Niño–Southern Oscillation (ENSO) events modulate intraseasonal oscillations of Indian summer monsoon?

[1] Prediction of interannual variability (IAV) of Indian summer monsoon (ISM) rainfall is limited by “internal” dynamics, and the monsoon intraseasonal oscillations (MISOs) seems to be at the heart of producing internal IAV of the ISM. If one could find an identifiable way through which these MISOs are modulated by slowly varying “external” forcing, such as El Nino–Southern Oscillation (ENSO), the uncertainty in the prediction of IAV could be reduced, leading to improvement of seasonal prediction. Such efforts, so far, have been inconclusive. In this study, the modulation of MISOs by ENSO is assessed by using a nonlinear pattern recognition technique known as the Self‐Organizing Map (SOM). The SOM technique is efficient in handling the nonlinearity/event‐to‐event variability of the MISOs and capable of identifying various shades of MISO from large‐scale dynamical/thermodynamical indices, without providing information on rainfall. It is shown that particular MISO phases are preferred during ENSO years, that is, the canonical break phase is preferred more in the El Nino years and the typical active phase is preferred during La Nina years. Interestingly, if the SOM clustering is done by removing the ENSO effect on seasonal mean, the preference for the break node remains relatively unchanged; whereas, the preference reduces/vanishes for the active node. The results indicate that the El Nino–break relationship is almost independent of the ENSO‐monsoon relationship on seasonal scale whereas the La Nina–active association seems to be interwoven with the seasonal relationship.

Seminal role of stratiform clouds in large-scale aggregation of tropical rain in boreal summer monsoon intraseasonal oscillations

Modification of the vertical structure of non-adiabatic heating by significant abundance of the stratiform rain in the tropics has been known to influence the large-scale circulation. However, the role of the stratiform rain on the space–time evolution of the observed Boreal summer monsoon intraseasonal oscillations (MISO) has so far been ignored. In the present study, we unravel a feedback mechanism through which the stratiform component of the rain leads to aggregation (organization) of rain on the MISO scale, making it an indispensable component of the MISO evolution dynamics. Using TRMM 3A25 monthly mean data (between 1998 and 2013), the ratio between convective and stratiform rain (RCS) is shown to be strongly related to the total rainfall. Further, composites of rainfall and circulation anomalies corresponding to high (low) values of RCS over the Central India or over the Equatorial Indian Ocean show spatial structures remarkably similar to that associated with the MISOs. Analyzing lead–lag relationship between the convective rain, the stratiform rain and the large scale moisture convergence with respect to peak active (break) spells from daily modern era retrospective-analysis for research and applications data, we unravel that the initial isolated convective elements spawn the stratiform rain which in turn modifies the vertical distribution of heating and leads to stronger large scale moisture convergence thereby producing more convective elements and more stratiform rain ultimately leading to aggregation of rain on the MISO scale. Our finding indicates that large and persisting systematic biases in simulating the summer monsoon rainfall over the Asian monsoon region by climate models are likely to be related to the systematic biases in simulating the MISOs which in turn are related to the serious underestimation of stratiform rain in most climate models.

Development and Evaluation of an Objective Criterion for the Real-Time Prediction of Indian Summer Monsoon Onset in a Coupled Model Framework

AbstractThis study reports an objective criterion for the real-time extended-range prediction of monsoon onset over Kerala (MOK), using circulation as well as rainfall information from the 16 May initial conditions of the Grand Ensemble Prediction System based on the coupled model CFSv2. Three indices are defined, one from rainfall measured over Kerala and the others based on the strength and depth of the low-level westerly jet over the Arabian Sea. While formulating the criterion, the persistence of both rainfall and low-level wind after the MOK date has been considered to avoid the occurrence of “bogus onsets” that are unrelated to the large-scale monsoon system. It is found that the predicted MOK date matches well with the MOK date declared by the India Meteorological Department, the authorized principal weather forecasting agency under the government of India, for the period 2001–14. The proposed criterion successfully avoids predicting bogus onsets, which is a major challenge in the prediction of MOK...

Linkages of Subtropical Stratospheric Intraseasonal Intrusions with Indian Summer Monsoon Deficit Rainfall

AbstractThe authors investigate the life cycle of a strong subtropical stratospheric intrusion event and propose a hypothesis through which it might reduce the intensity of the Indian summer monsoon (ISM) rainfall (ISMR) after the monsoon onset during June 2014. The diagnostic analysis of ERA-Interim data revealed that stratospheric intrusion occurs in the region of the subtropical westerly jet (SWJ) as a result of Rossby wave breaking (RWB). The RWB event is associated with eddy shedding. These eddies transport extratropical stratospheric mass and energy fluxes downward and southward to north India (NI). As a result, the intrusion spreads dry, cold, and ozone-rich air deep into the troposphere (~500 hPa) over the NI. It enhanced the static stability and weakens the north–south upper-tropospheric temperature gradient. The intrusion of cold and dry air persisted for the entire June, which might have inhibited northward propagation of ISM convection and could be responsible for prolonged hiatus in northward...

A bias-correction and downscaling technique for operational extended range forecasts based on self organizing map

If a coarse resolution dynamical model can well capture the large-scale patterns even if it has bias in smaller scales, the spatial information in smaller domains may also be retrievable. Based on this hypothesis a method has been proposed to downscale the dynamical model forecasts of monsoon intraseasonal oscillations in the extended range, and thus reduce the forecast spatial biases in smaller spatial scales. A hybrid of clustering and analog technique, used in a self organizing map (SOM)-based algorithm, is applied to correct the bias in the model predicted rainfall. The novelty of this method is that the bias correction and downscaling could be done at any resolution in which observation/reanalysis data is available and is independent of the model resolution in which forecast is generated. A set of composite pattern of rainfall is identified by clustering the high resolution observed rainfall using SOM. These set of composite patterns for the clustered days in each cluster centers or nodes are saved and the model forecasts for any day are compared with these patterns. The closest historical pattern is identified by calculating the minimum Euclidean distance between the model rainfall forecast and the observed clustered pattern and is termed as the bias corrected SOM-based post-processed forecast. The bias-corrected and the SOM-based reconstructed forecasts are shown to improve the annual cycle and the skill of deterministic as well as probabilistic forecasts. Usage of the high resolution observational data improves the spatial pattern for smaller domain as seen from a case study for the Mahanadi basin flood during September 2011. Thus, downscaling and bias correction are both achieved by this technique.