S. Abhilash

Simulation of microphysical structure associated with tropical cloud clusters using mesoscale model and comparison with TRMM observations

An attempt has been made in the present study to examine the microphysical structure of a non‐squall Tropical Cloud Cluster (TCC). Three‐dimensional model simulations of cloud microphysical structure associated with a non‐squall TCC occurred on 26 October 2005 over the South Bay of Bengal have been carried out. The initial conditions for the model simulations were improved by incorporating upper air radiosonde observations and Indian Mesosphere Stratosphere Troposphere (MST) radar wind observations through analysis nudging. The horizontal and vertical distribution of the cloud hydrometeor fields observed from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) are compared to those simulated by a mesoscale model using a sophisticated microphysical scheme. Substantial differences are noticed in the amounts of cloud microphysical parameters, with simulated values of hydrometeors being higher than TMI retrievals. Spatial distribution of Cloud Liquid Water (CLW) and Rain Water (RNW) from TMI and model simulations correspond well with each other. The cloud microphysical structure during the initial and mature phases of the storm is also investigated. Comparisons of horizontal and vertical reflectivity structure from the TRMM‐Precipitation Radar (PR) and those simulated by the model show reflectivity cores of values greater than 30 dBZ. The TRMM‐PR echo tops are 3–4 km higher than the simulated echo tops. The 24 hr accumulated precipitation from model simulations are then verified with the combined rainfall product from the TRMM observations.

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...

The role of moist processes in the intrinsic predictability of Indian Ocean cyclones

The role of moist processes in short-range forecasts of Indian Ocean tropical cyclones (TCs) track and intensity and upscale error cascade from cloud-scale processes affecting the intrinsic predictability of TCs was investigated using the Weather Research and Forecasting model with parameterized and explicitly resolved convection. Comparing the results from simulations of four Indian Ocean TCs at 10 km resolution with parameterized convection and convection-permitting simulations at 1.1 km resolution, both reproduced the observed TC tracks and intensities significantly better than simulations at 30 km resolution with parameterized convection. “Identical twin” experiments were performed by introducing random perturbations to the simulations for each TC. Results show that moist convection plays a major role in intrinsic error growth that ultimately limits the intrinsic predictability of TCs, consistent with past studies of extratropical cyclones. More specifically, model intrinsic errors start to build up from the regions of convection and ultimately affect the larger scales. It is also found that the error at small scale grows faster compared to the larger scales. The gradual increase in error energy in the large scale is a manifestation of upscale cascade of error energy from convective to large scale. Rapid upscale error growth from convective scales limits the intrinsic predictability of the TCs up to 66 h. The intrinsic predictability limit estimated by the 10 km resolution runs is comparable to that estimated by the convection-permitting simulations, suggesting some usefulness of high-resolution (~10 km) models with parameterized convection for TC forecasting and predictability study.

Vertical structure and evolution of a supercell storm: observations using VHF radar

An attempt has been made in the present study to characterise the vertical kinematic structure of a supercell storm during its different phases of development. The present study utilises the high time and height resolution 53 MHz VHF radar observations at Gadanki (13.5° N, 79.2° E), India. A supercell storm passed over the radar site during 17–18 October, 2002, and has been sampled during its mature to dissipating stages of convective activity. The time height variation of the radar reflectivity in terms of signal to noise ratio (SNR) and spectral width along with vertical velocity have been used to separate two distinct phases of the convective activity associated with the supercell storm. The mature phase of the storm is characterised by enhancement of high value of SNR from the lower troposphere up to the height of the tropopause. During this period, the vertical velocity in the middle troposphere is of the order of 8 to 10 ms−1. The dissipating stage is characterised by diminished structure of SNR in the middle and upper troposphere. During this period, downwards motion is present in the troposphere. Oscillatory nature of the vertical velocity is found in the upper tropospheric/lower stratospheric (UT/LS) region and shows signatures of short period gravity waves. The power spectral and wavelet analysis of the vertical wind perturbations shows signatures of high frequency oscillations of periodicity between 8–30 min. These high frequency waves are possibly owing to the oscillating updrafts and downdrafts impinging on the tropopause owing to penetrative convection.

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.

Prediction and error growth in the daily forecast of precipitation from the NCEP CFSv2 over the subdivisions of Indian subcontinent

This study investigates the forecast skill and predictability of various indices of south Asian monsoon as well as the subdivisions of the Indian subcontinent during JJAS season for the time domain of 2001–2013 using NCEP CFSv2 output. It has been observed that the daily mean climatology of precipitation over the land points of India is underestimated in the model forecast as compared to observation. The monthly model bias of precipitation shows the dry bias over the land points of India and also over the Bay of Bengal, whereas the Himalayan and Arabian Sea regions show the wet bias. We have divided the Indian landmass into five subdivisions namely central India, southern India, Western Ghat, northeast and southern Bay of Bengal regions based on the spatial variation of observed mean precipitation in JJAS season. The underestimation over the land points of India during mature phase was originated from the central India, southern Bay of Bengal, southern India and Western Ghat regions. The error growth in June forecast is slower as compared to July forecast in all the regions. The predictability error also grows slowly in June forecast as compared to July forecast in most of the regions. The doubling time of predictability error was estimated to be in the range of 3–5 days for all the regions. Southern India and Western Ghats are more predictable in the July forecast as compared to June forecast, whereas IMR, northeast, central India and southern Bay of Bengal regions have the opposite nature.

Role of enhanced synoptic activity and its interaction with intra-seasonal oscillations on the lower extended range prediction skill during 2015 monsoon season

Indian summer monsoon of 2015 was deficient with prominence of short-lived (long-lived) active (break) spells. The real-time extended range forecasts disseminated by Indian Institute of Tropical Meteorology using an indigenous ensemble prediction system (EPS) based on National Center for Environmental Predictions’s climate forecast system could broadly predict these intraseasonal fluctuations at shorter time leads (i.e. up to 10 days), but failed to predict at longer leads (15–20 days). Considering the multi-scale nature of Indian Summer Monsoon system, this particular study aims to examine the inability of the EPS in predicting the active/break episodes at longer leads from the perspective of non-linear scale interaction between the synoptic, intraseasonal and seasonal scale. It is found that the 2015 monsoon season was dominated by synoptic scale disturbances that can hinder the prediction on extended range. Further, the interaction between synoptic scale disturbances and low frequency mode was prominent during the season, which might have contributed to the reduced prediction skill at longer leads.