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The present study focuses on addressing the issue of too frequent triggers of deep convection in climate models, which are primarily based on physics-based classical trigger functions such as convective available potential energy (CAPE) or cloud work function (CWF). To overcome this problem, the study proposes using machine learning (ML) based deep convective triggers as an alternative. The deep convective trigger is formulated as a binary classification problem, where the goal is to predict whether deep convection will occur or not. Two elementary classification algorithms, namely support vector machines and neural networks, are adopted in this study. Additionally, a novel method is proposed to rank the importance of input variables for the classification problem, which may aid in understanding the underlying mechanisms and factors influencing deep convection. The accuracy of the ML-based methods is compared with the widely used convective available potential energy (CAPE)-based and dynamic generation of CAPE (dCAPE) trigger function found in many convective parameterization schemes. Results demonstrate that the elementary machine learning-based algorithms can outperform the classical CAPE-based triggers, indicating the potential effectiveness of ML-based approaches in dealing with this issue. Furthermore, a method based on the Mahalanobis distance is presented for binary classification, which is easy to interpret and implement. The Mahalanobis distance-based approach shows accuracy comparable to other ML-based methods, suggesting its viability as an alternative method for deep convective triggers. By correcting for deep convective triggers using ML-based approaches, the study proposes a possible solution to improve the probability density of rain in the climate model. This improvement may help overcome the issue of excessive drizzle often observed in many climate models.

In spite of the summer monsoon’s importance in determining the life and economy of an agriculture-dependent country like India, committed efforts toward improving its prediction and simulation have been limited. Hence, a focused mission mode program Monsoon Mission (MM) was founded in 2012 to spur progress in this direction. This article explains the efforts made by the Earth System Science Organization (ESSO), Ministry of Earth Sciences (MoES), Government of India, in implementing MM to develop a dynamical prediction framework to improve monsoon prediction. Climate Forecast System, version 2 (CFSv2), and the Met Office Unified Model (UM) were chosen as the base models. The efforts in this program have resulted in 1) unparalleled skill of 0.63 for seasonal prediction of the Indian monsoon (for the period 1981–2010) in a high-resolution (~38 km) seasonal prediction system, relative to present-generation seasonal prediction models; 2) extended-range predictions by a CFS-based grand multimodel ensemble (MME) prediction system; and 3) a gain of 2-day lead time from very high-resolution (12.5 km) Global Forecast System (GFS)-based short-range predictions up to 10 days. These prediction skills are on par with other global leading weather and climate centers, and are better in some areas. Several developmental activities like coupled data assimilation, changes in convective parameterization, cloud microphysics schemes, and parameterization of land surface processes (including snow and sea ice) led to the improvements such as reducing the strong model biases in the Indian summer monsoon simulation and elsewhere in the tropics.

Thunderstorms are one of the disastrous weather events that affect various parts of the Indian region during the pre-monsoon summer months of March–April–May (MAM). Keeping this societal impact in account, this paper has documented the vertical distribution of cloud microphysical properties and dynamical fields associated with number of thunderstorms over different thunderstorm-prone regions of India. The main objective of this study was to bring out the spatial heterogeneity in the structure of thunderstorms during MAM. In spite of being such an important weather system, the present-day skill of forecasting such systems is particularly poor. This as such prompts us to ponder whether any existing microphysical scheme is able to capture the inherent heterogeneous structure of thunderstorms over different parts of India. To find an answer to this question, the regions are divided based on pre-monsoon lightning distribution obtained from the tropical rainfall measuring mission lightning imaging sensor data. Keeping this observation in background, cloud-resolving simulations are performed using the Weather Research and Forecasting-Advanced Research Weather Model (version 3.2) along with three explicit microphysical schemes for a number of thunderstorm cases those occurred over five different regions of India. The composite structure of all thunderstorms simulated over a region is compared with observation to identify the systematic model bias. It is clearly brought out that the Thompson scheme with the present form is not able to capture different phases of thunderstorms over different parts of the country. However, WDM6 and Morrison are able to capture some of the features reasonably well, along with some degree of uncertainty. The inner structure of thunderstorms is very well brought out by contour frequency altitude diagram. This study therefore provides a framework and a basis of further modifications of WDM6 and Morrison for improving the model forecast for thunderstorms over the Indian region.

An extensive analysis of radiative flux biases in the Climate Forecast System Model Version 2 (CFSv2) is done. Annual mean and seasonal variations of biases at the surface and top of the atmosphere (TOA) are reported in the global domain. Large regional biases in shortwave (SW) and longwave (LW) radiation are observed over convectively active zones in the tropics. The relative contribution of various processes responsible for the reported biases is quantified. The poor simulation of clouds and inadequate representation of surface properties seem to be major contributors. Over certain regions, errors due to different processes add up, whereas, over other regions, errors tend to nullify each other. Surface and atmospheric variables taken as input parameters in the radiative transfer modules are compared with satellite-based observations. The maximum biases in SW and LW radiation are observed over the regions of persistent low clouds. The magnitude of the SW and LW biases at the TOA is in phase with the biases in cloud fraction by and large. However, the error in the radiative fluxes due to errors in surface radiative properties is of equal importance. The cold bias in near-surface air temperature reported in other studies may partly be attributed to an underestimation in the net SW radiation at the surface. In the present study, a plausible prescription is also provided to correct the source of the biases.

In an attempt to develop a better simulation of the climatology of monsoon precipitation in climate models, this paper investigates the impacts of different convective closures on systematic biases of an Indian monsoon precipitation climatology in a high-resolution regional climate model. For this purpose, the Weather Research Forecast (WRF) model is run at 45- and 15-km (two-way nested) resolution with three convective parameterization schemes, namely the Grell–Devenyi (GD), the Betts–Miller–Janjić (BMJ), and the Kain–Fritsch (KF), for the period 1 May–31 October 2001–07. The model is forced with the NCEP–NCAR reanalysis data as the initial and boundary conditions. The simulated June–September (JJAS) mean monsoon rainfall with the three convective schemes is compared with the observations. KF is found to have a high moist bias over the central and western coastal Indian region while GD shows the opposite. Among the three, BMJ is able to produce a reasonable mean monsoon pattern. In an attempt to get further insight into the seasonal bias and its evolution, the probability distribution function (PDF) of different rain-rate categories and their percentage contribution to the seasonal total are computed. BMJ and KF underestimate the observations for lighter rain rates and overestimate for rain-rate categories of more than 10 mm day−1. GD shows an overestimation for lighter rain and an underestimation of PDF for moderate categories. The seasonal patterns of evolution of PDF plots of three rain-rate categories are analyzed to determine whether the convective schemes show any systematic bias throughout the season or if they have problems during certain phases of the monsoon. This shows that the GD systematically overestimates the lighter rain rate and underestimates the moderate rain rate throughout the season, whereas BMJ and KF have problems in the initial stages. The heavy rain category is systematically overestimated by the KF compared to the other two. To further evaluate the proportionate contribution of each rain-rate bin to the total rain, the percentage contribution of each rain rate to the seasonal total is computed. Analyzing all the rain-rate simulations produced by the three schemes, it is found that KF has a moist bias and GD has a dry bias in the spatiotemporal distribution of the monsoon precipitation. Further, this paper investigates the causes behind the mean monsoon precipitation bias. It is shown that GD produces a model climate where the vertical velocity is less than that of the observations up to 500 hPa and the vertically integrated moist instability is also weaker. KF, on the other hand, shows a higher than the observed vertical velocity and a stronger moist instability. Along with this, the vertical profile of heating suggests a warmer middle level in the KF case and significantly reduced midlevel heating for GD. Thus, KF (GD) has produced a model atmosphere that has a stronger (weaker) convective instability to produce the observed bias in the model precipitation. BMJ is found to simulate a reasonable heating profile, along with the realistic moist instability and seasonal cycle of evaporation and condensation. Insight derived from the analysis is expected to help improve the convective parameterizations.

An analysis of a 5-yr (from 1 January 2009 to 31 December 2013) free run of the superparameterized (SP) Climate Forecast System (CFS) version 2 (CFSv2) (SP-CFS), implemented for the first time at a spectral triangular truncation at wavenumber 62 (T62) atmospheric horizontal resolution, is presented. The SP-CFS simulations are evaluated against observations and traditional convection parameterized CFSv2 simulations at T62 resolution as well as at some higher resolutions. The metrics for evaluating the model performance are chosen in order to mainly address the improvement in systematic biases observed in the CFSv2 documented in earlier studies. While the primary focus of this work is on evaluating the improvement of the simulation of the Indian summer monsoon (ISM) by the SP-CFS model, some results are also presented within the context of the global climate. The SP-CFS significantly reduces the dry bias of precipitation over the Indian subcontinent and better captures the monsoon intraseasonal oscillation (MISO) modes. SP-CFS also improves the northward and eastward propagation of high- and low-frequency modes of ISM. Compared to CFSv2, the SPCFS model simulates improved convectively coupled equatorial waves; better temperature structures both spatially and vertically, leading to a significantly improved relative distribution of variance for the synoptic disturbances and low-frequency tropical intraseasonal oscillations (ISOs). This analysis of the development of SP-CFS is particularly important as it shows promise for improving the cloud process representation through an SP framework and is able to improve the mean as well as intraseasonal characteristics of CFSv2 within the context of the ISM.

We have evaluated the simulation of Indian summer monsoon and its intraseasonal oscillations in the National Centers for Environmental Prediction climate forecast system model version 2 (CFSv2). The dry bias over the Indian landmass in the mean monsoon rainfall is one of the major concerns. In spite of this dry bias, CFSv2 shows a reasonable northward propagation of convection at intraseasonal (30–60 day) time scale. In order to document and understand this dry bias over the Indian landmass in CFSv2 simulations, a two pronged investigation is carried out on the two major facets of Indian summer monsoon: one, the air–sea interactions and two, the large scale vertical heating structure in the model. Our analysis shows a possible bias in the co-evolution of convection and sea surface temperature in CFSv2 over the equatorial Indian Ocean. It is also found that the simulated large scale vertical heat source (Q1) and moisture sink (Q2) over the Indian region are biased relative to observational estimates. Finally, this study provides a possible explanation for the dry precipitation bias over the Indian landmass in the simulated mean monsoon on the basis of the biases associated with the simulated ocean–atmospheric processes and the vertical heating structure. This study also throws some light on the puzzle of CFSv2 exhibiting a reasonable northward propagation at the intraseasonal time scale (30–60 day) despite a drier monsoon over the Indian land mass.

Flash 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.