Mahen Konwar

Estimates of the potential predictability limit (PPL) for seasonal climate, typically based on a perfect model framework, sometimes encounter challenges of being paradoxical, as actual skill surpasses the PPL. The signal-to-noise paradox (SNP) gets its name from the use of model-based signal-to-noise ratios to estimate the PPL. Here, we study seasonal climate predictability in the tropical and subtropical regions during the boreal summer (June to September), with a focus on the SNP. We estimate PPL within the perfect model framework, only considering error growth from initial conditions. Signal and noise components display temporal non-orthogonality and a weak association between estimates of PPL and actual prediction skill, contradicting its intended purpose. A significant correlation between signal and noise violates the perfect-model framework. Moreover, paradoxical regions show no clear correspondence with signal–noise correlation, indicating that while accurate signal–noise separation is necessary, it is not sufficient to eliminate paradoxes. We have also demonstrated that sub-seasonal components, which are building blocks of seasonal mean, substantially contribute to seasonal anomalies in association with major global predictors. The co-variability between sub-seasonal components and seasonal anomalies is wide-ranging and often skewed compared to observations, thereby influencing seasonal prediction skills and PPL. Therefore, a robust PPL estimation should consider errors from initial conditions and model-related factors such as physics, dynamics, and numerical methods. In this context, we present a simple diagnostic approach to estimate the maximum achievable seasonal prediction skill, which may be interpreted as an empirical upper bound of skill or the PPL.

Summer monsoon rainfall over the Western Ghats (WG) of India exhibits pronounced variability across multiple timescales, including the diurnal scale, which plays a key role in shaping regional precipitation. Although recent observational studies have improved understanding of the WG diurnal cycle, the physical mechanisms governing variability in storm structures and convective processes remain unclear. Using high-resolution satellite and reanalysis data, this study shows that diurnal rainfall variability over the WG is primarily governed by two physical regimes: a dynamical regime (DR) and a thermodynamical regime (TR). We demonstrate that contrasts in low-level jet strength and land–sea breeze intensity regulate the diurnal amplitude, phase, and storm depth. The DR (TR) is associated with a strong (weak) Somali jet and weak (strong) land–sea breeze within the boundary layer, with land–sea breeze intensity nearly four times stronger during TR. Consequently, diurnal rainfall is more vigorous and spatially extensive during TR. Shallow storms dominate during DR, indicating warm-rain processes, whereas TR features deeper convection involving both warm and cold microphysics, with storm-top heights reaching 5 km in DR and 8 km in TR. Favourable vertical profiles of equivalent potential temperature, geopotential height, and vertical velocity during TR, together with stronger afternoon CAPE, lower midday CINE, and increasing total column water vapour, support deep convection. In contrast, DR exhibits localized intense rainfall mainly over the northern WG with a latitudinal shift in diurnal peak timing. Although daily mean rainfall is higher during DR, the diurnal component contributes about 10% and 50% of the daily mean rainfall during DR and TR, respectively. These results provide a framework for regime-dependent diurnal rainfall variability over the WG and have implications for improving weather and climate models.

Estimates of the potential predictability limit (PPL) for seasonal climate, typically based on a perfect model framework, sometimes encounter challenges of being paradoxical, as actual skill surpasses the PPL. The signal-to-noise paradox (SNP) gets its name from the use of model-based signal-to-noise ratios to estimate the PPL. Here, we study seasonal climate predictability in the tropical and subtropical regions during the boreal summer (June to September), with a focus on the SNP. We estimate PPL within the perfect model framework, only considering error growth from initial conditions. Signal and noise components display temporal non-orthogonality and a weak association between estimates of PPL and actual prediction skill, contradicting its intended purpose. Moreover, paradoxical regions do not align with significant correlations between signal and noise, indicating that the accurate separation of seasonal forecasts into signal and noise components alone is not sufficient to avoid paradoxes. We have also demonstrated that sub-seasonal components, which are building blocks of seasonal mean, substantially contribute to seasonal anomalies in association with major global predictors. The co-variability between sub-seasonal components and seasonal anomalies is wide-ranging and often skewed compared to observations, thereby influencing seasonal prediction skills and PPL. Therefore, a robust PPL estimation should consider errors from initial conditions and model-related factors such as physics, dynamics, and numerical methods. In this context, we propose a novel method to estimate the PPL of seasonal climate, which can be free from paradoxical situations.

Estimates of the potential predictability limit (PPL) for seasonal climate, typically based on a perfect model framework, sometimes encounter challenges of being paradoxical, as actual skill surpasses the PPL. The signal-to-noise paradox (SNP) gets its name from the use of model-based signal-to-noise ratios to estimate the PPL. Here, we study seasonal climate predictability in the tropical and subtropical regions during the boreal summer (June to September), with a focus on the SNP. We estimate PPL within the perfect model framework, only considering error growth from initial conditions. Signal and noise components display temporal non-orthogonality and a weak association between estimates of PPL and actual prediction skill, contradicting its intended purpose. Moreover, paradoxical regions do not align with significant correlations between signal and noise, indicating that the accurate separation of seasonal forecasts into signal and noise components alone is not sufficient to avoid paradoxes. We have also demonstrated that sub-seasonal components, which are building blocks of seasonal mean, substantially contribute to seasonal anomalies in association with major global predictors. The co-variability between sub-seasonal components and seasonal anomalies is wide-ranging and often skewed compared to observations, thereby influencing seasonal prediction skills and PPL. Therefore, a robust PPL estimation should consider errors from initial conditions and model-related factors such as physics, dynamics, and numerical methods. In this context, we propose a novel method to estimate the PPL of seasonal climate, which can be free from paradoxical situations.

Estimates of the potential predictability limit (PPL) for seasonal climate, typically based on a perfect model framework, sometimes encounter challenges of being paradoxical, as actual skill surpasses the PPL. The signal-to-noise paradox (SNP) gets its name from the use of model-based signal-to-noise ratios to estimate the PPL. Here, we study seasonal climate predictability in the tropical and subtropical regions during the boreal summer (June to September), with a focus on the SNP. We estimate PPL within the perfect model framework, only considering error growth from initial conditions. Signal and noise components display temporal non-orthogonality and a weak association between estimates of PPL and actual prediction skill, contradicting its intended purpose. Moreover, paradoxical regions do not align with significant correlations between signal and noise, indicating that the accurate separation of seasonal forecasts into signal and noise components alone is not sufficient to avoid paradoxes. We have also demonstrated that sub-seasonal components, which are building blocks of seasonal mean, substantially contribute to seasonal anomalies in association with major global predictors. The co-variability between sub-seasonal components and seasonal anomalies is wide-ranging and often skewed compared to observations, thereby influencing seasonal prediction skills and PPL. Therefore, a robust PPL estimation should consider errors from initial conditions and model-related factors such as physics, dynamics, and numerical methods. In this context, we propose a novel method to estimate the PPL of seasonal climate, which can be free from paradoxical situations.

Estimates of the potential predictability limit (PPL) for seasonal climate, typically based on a perfect model framework, sometimes encounter challenges of being paradoxical, as actual skill surpasses the PPL. The signal-to-noise paradox (SNP) gets its name from the use of model-based signal-to-noise ratios to estimate the PPL. Here, we study seasonal climate predictability in the tropical and subtropical regions during the boreal summer (June to September), with a focus on the SNP. We estimate PPL within the perfect model framework, only considering error growth from initial conditions. Signal and noise components display temporal non-orthogonality and a weak association between estimates of PPL and actual prediction skill, contradicting its intended purpose. Moreover, paradoxical regions do not align with significant correlations between signal and noise, indicating that the accurate separation of seasonal forecasts into signal and noise components alone is not sufficient to avoid paradoxes. We have also demonstrated that sub-seasonal components, which are building blocks of seasonal mean, substantially contribute to seasonal anomalies in association with major global predictors. The co-variability between sub-seasonal components and seasonal anomalies is wide-ranging and often skewed compared to observations, thereby influencing seasonal prediction skills and PPL. Therefore, a robust PPL estimation should consider errors from initial conditions and model-related factors such as physics, dynamics, and numerical methods. In this context, we propose a novel method to estimate the PPL of seasonal climate, which can be free from paradoxical situations.

Estimates of the potential predictability limit (PPL) for seasonal climate, typically based on a perfect model framework, sometimes encounter challenges of being paradoxical, as actual skill surpasses the PPL. The signal-to-noise paradox (SNP) gets its name from the use of model-based signal-to-noise ratios to estimate the PPL. Here, we study seasonal climate predictability in the tropical and subtropical regions during the boreal summer (June to September), with a focus on the SNP. We estimate PPL within the perfect model framework, only considering error growth from initial conditions. Signal and noise components display temporal non-orthogonality and a weak association between estimates of PPL and actual prediction skill, contradicting its intended purpose. Moreover, paradoxical regions do not align with significant correlations between signal and noise, indicating that the accurate separation of seasonal forecasts into signal and noise components alone is not sufficient to avoid paradoxes. We have also demonstrated that sub-seasonal components, which are building blocks of seasonal mean, substantially contribute to seasonal anomalies in association with major global predictors. The co-variability between sub-seasonal components and seasonal anomalies is wide-ranging and often skewed compared to observations, thereby influencing seasonal prediction skills and PPL. Therefore, a robust PPL estimation should consider errors from initial conditions and model-related factors such as physics, dynamics, and numerical methods. In this context, we propose a novel method to estimate the PPL of seasonal climate, which can be free from paradoxical situations.

Estimates of the potential predictability limit (PPL) for seasonal climate, typically based on a perfect model framework, sometimes encounter challenges of being paradoxical, as actual skill surpasses the PPL. The signal-to-noise paradox (SNP) gets its name from the use of model-based signal-to-noise ratios to estimate the PPL. Here, we study seasonal climate predictability in the tropical and subtropical regions during the boreal summer (June to September), with a focus on the SNP. We estimate PPL within the perfect model framework, only considering error growth from initial conditions. Signal and noise components display temporal non-orthogonality and a weak association between estimates of PPL and actual prediction skill, contradicting its intended purpose. Moreover, paradoxical regions do not align with significant correlations between signal and noise, indicating that the accurate separation of seasonal forecasts into signal and noise components alone is not sufficient to avoid paradoxes. We have also demonstrated that sub-seasonal components, which are building blocks of seasonal mean, substantially contribute to seasonal anomalies in association with major global predictors. The co-variability between sub-seasonal components and seasonal anomalies is wide-ranging and often skewed compared to observations, thereby influencing seasonal prediction skills and PPL. Therefore, a robust PPL estimation should consider errors from initial conditions and model-related factors such as physics, dynamics, and numerical methods. In this context, we propose a novel method to estimate the PPL of seasonal climate, which can be free from paradoxical situations.

Estimates of the potential predictability limit (PPL) for seasonal climate, typically based on a perfect model framework, sometimes encounter challenges of being paradoxical, as actual skill surpasses the PPL. The signal-to-noise paradox (SNP) gets its name from the use of model-based signal-to-noise ratios to estimate the PPL. Here, we study seasonal climate predictability in the tropical and subtropical regions during the boreal summer (June to September), with a focus on the SNP. We estimate PPL within the perfect model framework, only considering error growth from initial conditions. Signal and noise components display temporal non-orthogonality and a weak association between estimates of PPL and actual prediction skill, contradicting its intended purpose. Moreover, paradoxical regions do not align with significant correlations between signal and noise, indicating that the accurate separation of seasonal forecasts into signal and noise components alone is not sufficient to avoid paradoxes. We have also demonstrated that sub-seasonal components, which are building blocks of seasonal mean, substantially contribute to seasonal anomalies in association with major global predictors. The co-variability between sub-seasonal components and seasonal anomalies is wide-ranging and often skewed compared to observations, thereby influencing seasonal prediction skills and PPL. Therefore, a robust PPL estimation should consider errors from initial conditions and model-related factors such as physics, dynamics, and numerical methods. In this context, we propose a novel method to estimate the PPL of seasonal climate, which can be free from paradoxical situations.

Accurate precipitation forecasting hinges on the representation of microphysical processes within numerical models. A key approach to understanding these processes is through the analysis of hydrometeor drop size distribution (DSD). The characteristics of DSD bulk parameters – Mass Weighted Mean Diameter (D_m) and the Normalized Intercept Parameter (N_w), are estimated from the double moment cloud microphysical scheme (CASIM: Cloud-Aerosol Interacting Microphysics) employed in the operational convection permitted model of National Centre for Medium-Range Weather Forecasting (NCUM-R). The observations from the Joss-Valdvogel Disdrometer (JWD) and the Global Precipitation Mission – Dual Frequency Precipitation Radar (GPM-DPR) are analyzed for providing essential validation. An algorithm for separating the monsoon precipitation into convective and stratiform types in NCUM-R and a new parameter estimation module to obtain DSD parameters from the CASIM are established in the study. The model exhibits agreement with the characteristics of the DSD of raindrops with D_m ranging from 0.5 to 2.5 mm marking the majority of the monsoon precipitation events. However, the underestimation when it comes to the larger drops (with D_m > 3.25 mm and Rainrate ≥ 8 mm h⁻¹) demands a reassessment in microphysical parameterizations. The advanced autoconversion parameterization scheme applied in CASIM favored the growth of large drops compared to the existing scheme. The enhanced growth of larger drops is reflected in the increased accuracy in the prediction of extreme precipitation associated with a convective event. The current study underscores the importance of refining microphysical parameterizations to improve the accuracy of precipitation forecasts offering a pathway for enhanced model performance in future operational forecasting systems.