Cold wave (CW) events over India are usually observed during the boreal winter months, November to February. This study proposes an objective criterion using the actual, departure from normal and the percentile values of the daily gridded minimum temperature (Tmin) data for the monitoring of the CW events over the Indian region and also checks its usefulness in a multi-model ensemble extended range prediction system. The large-scale features associated with these CW events are also discussed.The CW-prone region has been identified by utilizing this proposed criterion and considering the number of average CW days/year for the entire study period and recent decades. By calculating the standardized area-averaged (over the CW-prone region) Tmin anomalies time series, the CW events are identified from 1951 to 2022. Analyzing the temporal variability of these events, it is seen that there is no compromise in the occurrences of the CW events, even under the general warming scenarios. It is found that the long CW events (>7 days) are favoured by the La-Nina condition, and short CW events (≤7 days) are favoured by the neutral condition in the Pacific. Also, the blocking high to the northwest of Indian longitude with the very slow movement of the westerly trough to the east is found to be associated with the long CW events. In contrast, in the case of short events, the blocking high is not so significant. The multi-model ensemble prediction system is found to be reasonably skilful in predicting the CW events over the CW-prone region up to 2-3 weeks in advance with decreasing confidence in longer leads. Based on the forecast verifications, it is noticed that this forecasting system has a remarkable strength to provide an overall indication about the forthcoming CW events with sufficient lead time despite its uncertainties in space and time.

Global warming has significantly increased the risk of heat waves (HWs) globally, with India being particularly vulnerable during the summer months (March-June; MAMJ). This study investigated the critical relationship between Indian summer monsoon rainfall (ISMR) and the occurrence of premonsoon HWs in subsequent years across the Indian subcontinent. It has been hypothesized that droughts during the ISMR could lead to more frequent HWs in the following MAMJ period. Using the Indian Meteorological Department's (IMD) gridded observed surface air daily maximum temperature (Tmax) dataset for the period 1951–2023, we analyzed the climatic patterns, interannual variability (IAV), and coefficient of variation (CV) of Tmax across India. The analysis compares two distinct periods: 1951–1999 (P1) and 2000–2023 (P2), with focus on Tmax trends and HW duration, distinguishing between short-duration HWs (SHWs, 2 days) and long-duration HWs (LHWs, 5 days or more). A key purpose of this study is to examine the relationship between the preceding all India summer monsoon rainfall (AISMR) and the occurance of various types of HW in the subsequent premonsoon season. In particular extreme AISMR events, such as droughts or excess rainfall, influence HW occurrence. The findings reveal a significant rise in Tmax across many regions of India during the MAMJ period, with the highest temperatures (> 37 °C) observed in northwestern, central, and eastern coastal areas. Northern India, particularly the Himalayan region, exhibits a greater interannual variability in Tmax, with June showing the most pronounced fluctuations. The study also highlights an increase in the frequency and intensity of HWs, especially in central and southern India, with the Chandigarh-Haryana-Delhi region recording the highest occurrences. A critical finding is the strong inverse relationship between the AISMR and conditions in the subsequent premonsoon season. Specifically, drought in the antecedent AISMR results in reduced soil moisture, which is strongly associated with higher premonsoon Tmax and an increased frequency of extreme heat events across India, particularly in regions prone to severe heat during this season. Drought conditions during AISMR are closely linked to higher HW frequencies in the following summer, especially in the central, northeast-central, and east-coastal regions. The frequencies of HW days, SHWs, and LHWs are significantly greater in years following AISMR droughts than in those following excess rainfall, indicating that drought years are more likely to lead to widespread HW activity. Despite the overall warming trends, some regions, such as the Indo-Gangetic Plain and parts of the Himalayan region, show cooling trends, although these trends are less widespread. The onset of the monsoon in June tends to reduce the intensity and spatial extent of warming, particularly in the central and eastern coastal regions, although significant HW trends persist in northwestern India and along the east coast. This study underscores the crucial role of AISMR in influencing HW events across India and highlights the need for adaptive strategies that account for the interactions between monsoon rainfall and HW risk, providing valuable insights for mitigating the impacts of HWs in the context of global warming.

Global warming increases the risk of heatwaves (HWs) globally. In India, HWs during the summer (March-June; MAMJ) are characterized by prolonged high temperatures, exacerbated by low soil moisture. Speculation suggests that droughts during the Indian summer monsoon (ISM), which provides 80% of India's annual rainfall, may lead to more HWs in the following MAMJ period. In this research, an examination is carried out on the climatic patterns, inter-annual variability (IAV), and coefficient of variation (CV) of maximum temperatures (Tmax) throughout MAMJ across the Indian subcontinent using India Meteorological Department's (IMD) gridded observed Tmax dataset covering 1951 to 2023. The dataset is divided into two periods: an earlier period (1951–1999, P1) and a recent warming period (2000–2023, P2). This study compares Tmax between these periods and evaluates HW duration using IMD criteria, distinguishing between short-duration HWs (SHWs, lasting 2 days) and long-duration HWs (LHWs, lasting 5 days or more). Additionally, it explores the relationship between preceding All India Summer Monsoon Rainfall (AISMR) and various HW types, while analyzing the impact of extreme AISMR events (such as drought or excess rainfall) on heatwave occurrences. This study thoroughly examines how Tmax and HWs are distributed across India, shedding light on notable variations in Tmax patterns and HW occurrences. It finds a clear rise in Tmax across various regions, accompanied by an increase in the frequency of HW days, particularly evident during the MAMJ. The study emphasizes the crucial role of AISMR in shaping HW events, highlighting that drought conditions during AISMR are closely linked to a higher chance of experiencing above-normal HW frequencies. This study is very useful in determining the effects on various sectors in planning of adaptation techniques through appropriate strategies for a sustainable future over India in the present global warming era.

This chapter explores the role of reduction in sea ice concentration due to Arctic amplification in the modulation of summer monsoon circulation over the Indian region. The study is based on model simulations in which the latitudinal extent of Arctic sea ice is reduced in one experiment and increased in another. The reduction or increase in the sea-ice extent is based on observation of sea-ice concentration data from 1981 to 2015. The month with the minimum (maximum) sea-ice extent forces the GFS model to represent the warming (cooling) scenario due to Arctic amplification. The twin experiment is conducted to see if monsoon response is the opposite in warming and cooling scenarios. Surprisingly, the monsoon weakens in both scenarios, indicating that monsoon circulation is related to the equator to pole heat gradients rather than being an equatorial phenomenon. The analysis also suggests a substantial change in the mid-latitude circulation, affecting the tropical–extratropical interaction, heat and momentum fluxes transport, and teleconnections. The most significant result is that the analysis represents an increase in rainfall over the Himalayan region and the foothills of Himalaya and the north-eastern parts of India. The recent rise in extreme events over these regions could indicate such changes in the sea-ice concentration. Keywords: Arctic amplification, Sea-ice concentration, Transient eddy transport, Indian monsoon variability.

Abstract 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.

Documentation of the skill of a prediction system and its comparison with those of leading modelling centres are crucial in model development. This facilitates understanding the limitations of the existing prediction system and aids in its improvement. The current study compares the extended range prediction skill of the Indian Institute of Tropical Meteorology (IITM) generated real-time forecast with that of the UK Met Office (UKMO) forecast during the boreal summer monsoon season. It is found that both models suffer from biases in the climatological mean state of the monsoon. IITM forecast possesses a skill comparable to UKMO coupled seasonal forecast as compared to the observation in the first two weeks leads over most of the meteorological subdivisions during the monsoon months of June to September. However, at longer leads, the UKMO model outperforms the IITM model, which could be credited to its enhanced skill in predicting the monsoon intraseasonal oscillations and the better representation of monsoon variability at the intraseasonal time scale.

Cold wave (CW) events over India are usually observed during the boreal winter months, November–February. This study proposes an objective criterion using the actual, departure from normal and the percentile values of the daily gridded minimum temperature (Tmin) data for the monitoring of the CW events over the Indian region and also checks its usefulness in a multi-model ensemble extended range prediction system. The large scale features associated with these CW events are also discussed. Utilizing this proposed criterion and considering the number of average CW days/year for the entire study period and recent decades, the CW prone region has been identified. By calculating the standardized area-averaged (over the CW prone region) Tmin anomalies time series, the CW events are identified over the period 1951–2022. Analyzing the temporal variability of these events, it is seen that there is no compromise in the occurrences of the CW events even under the general warming scenarios. It is found that the long CW events (> 7 days) are favoured by the La-Nina condition and short CW events ( ≤ 7 days) are favoured by the neutral condition in the Pacific. Also, the blocking high to the north-west of Indian longitude with very slow movement of westerly trough to the east is found to be associated with the long CW events, whereas in case of short events the blocking high is not so significant. The multi-model ensemble prediction system is found to be reasonably skilful in predicting the CW events over the CW prone region up to 2–3 weeks in advance with decreasing confidence in longer leads. Based on the forecast verifications it is noticed that this forecasting system has a remarkable strength to provide an overall indication about the forthcoming CW events with sufficient lead time in spite of its uncertainties in space and time.

Approximated and simplified real-atmospheric process impact in physical parameterization is a primary correspondent of biases in the model, particularly for extreme events. The present study discusses how event genesis in the small and large-scale quintessential environment is incongruously simulated within a set of multiple convection parameterizations. Despite a few inherent errors, most of the selected convective parameterization schemes could indicate 10-15 days in advance the Uttarakhand heavy rains resulted from large-scale background interaction. The runs without any convection scheme, followed by new-Tiedtke and BMJ schemes, outperform in this case. Further, almost all schemes except new-Tiedtke flunked for the case of Mount-Abu flood originated from relatively local-scale interaction even from 5-day advance initialization. Results are further extended for a few other cases using best performers of both extreme events and new-Tiedtke found to be more efficient. The better representation of convection (especially the shallow) and low clouds in this scheme makes it superior to other schemes for simulating extreme precipitation events.

An assessment of a multiphysics multimodel ensemble (MPMME) strategy is provided to simulate the critical aspects of the Indian summer monsoon and its intraseasonal variability. Using various physics combinations of Climate Forecast System (CFS) and its atmospheric component Global Forecast System (GFS), a 15‐year hindcast for May–October is generated. Three different convective parametrizations, simplified Arakawa–Shubert (sas), revised deep‐convection sas (nsas), and revised sas with modified shallow‐convection (nsas_sc) are coupled with two microphysics schemes Zhao and Carr (zc) and Ferrier. Spatiotemporal characteristics of predicted Indian summer monsoon climatology and 20–70‐day periodic intraseasonal oscillations (ISOs) are evaluated using observations. MPMME members reproduce the overall characteristics of the seasonal mean, but they have significant biases over different regions. Pattern correlations reveal that CFS_nsaszc performs best among MPMME in simulating the observed characteristics of rainfall ISOs and providing significant ISO forecast up to pentad 3 lead. A diagnostic based on the vorticity budget equation during strong convective events (SCEs) associated with ISOs is used to understand better the mechanism of northward‐propagating ISOs and the responsible factors that develop a vorticity tendency to the north of convection maxima. The tilting term in the vorticity equation shows northward propagation and leads precipitation maxima by about a week over the Bay of Bengal. Vertical shear of mean zonal winds and meridional gradients of vertical winds are found to be essential in developing vorticity tendency. SCEs are better represented in CFS than in GFS. Notably, along with CFS_nsaszc, two CFS_sas members capture the occurrence of SCEs reasonably well. However, errors in vertical shear of mean zonal winds are remarkably high after pentad 2 lead in CFS_sas and GFS, explaining their relative weakness in simulating ISOs during June–September. This study demonstrates that the MPMME strategy could utilize individual physical schemes' strengths to provide better subseasonal forecasts.

In this study, to explore the wave–mean interaction during the monsoon season, we investigate (a) the potential role of transient eddy forcing and the wave–mean interaction on the monsoon weather during the June 2013 heavy rainfall event over the Himalayan regions (especially the Uttarakhand State of India and the nearby regions) and (b) how they are captured in a set of operational models. Some studies have pointed out how prolonged breaks can occur due to extratropical trough incursions. However, there is a lack of clarity on how transient eddy forcing associated with such interactions can lead to modulation of monsoonal circulation or whether such interaction can lead to heavy rainfall events. E‐vector fields are analysed to quantify the eddy forcing from extratropical transient eddies and the feedback mechanism between transient eddies and the mean flow during June 2013. Analysis reveals that along with local factors (orography, moisture convergence), the large‐scale heavy rainfall event over the Uttarakhand region during June 16–17, 2013 was influenced by eddy forcing due to the intrusion of extratropical Rossby waves over the Indian region. The location of eddy affects the location of regional occurrence of the eddy‐mean interaction. Model hindcast analysis results suggest that operational models cannot forecast the upper‐level eddy forced circulation patterns, and the improper representation of the E‐vector divergence field leads to the underestimation of intensity and the spatial pattern of rainfall.

The seasonal prediction skill of climate forecast system model with two different resolutions, namely T126 and T382, is studied using hindcast data. Using novel diagnostic tools such as total variation distance and two‐state Markov Chain analysis, it is shown that increasing the horizontal resolution of the model has minimal impact on the quality of seasonal prediction. The underlying rain distribution and associated transition probabilities are very similar in both model versions. The Markov chain analysis also provides critical clues about the issues associated with convective processes in the model. Both the models produce longer (shorter) wet (dry) spells compared to the observations. Models are trying to bring the atmosphere closer to convective quasi‐equilibrium, leading to a substantial departure from observed features. Although the conventional error metrics are helpful to assess the prediction skills, the new metrics used in the study provide further insights on possible pathways to improve model moist physics.

The intraseasonal fluctuations of Indian summer monsoon rainfall (ISMR) are mainly controlled by northward propagating monsoon intraseasonal oscillation (MISO) and eastward propagating Madden Julian oscillation (MJO). In the current study, we examine the relationship between the intraseasonal fluctuations (active and break spells) of ISMR with the phase propagation and amplitude of MISO and MJO. We notice that active spells generally occur during MISO phases 2–5 (MJO phases 3–6), and break spells mainly occur during MISO phases 6–8 (MJO phases 6–8 and 1). The association of active/break spells with MISO phases is more prominent than with MJO phases. We show the phase composite of unfiltered and regression-based reconstructed rainfall for eight MISO and MJO phases, which is consistent with the earlier findings. We notice that the reconstructed field shows a systematic and well-organised northward propagation compared to the unfiltered field. Phase composite also indicates a lead-lag relationship between MISO and MJO phases. MISO phase composite shows more robust northward propagation than the MJO phase composite. MISO reconstructed rainfall explained more percentage variance than MJO reconstructed rainfall with reference to 20–90-day filtered rainfall. It is found that long active (> 7 days) predominantly occurs when either MISO or MJO, or both of them are active, and the associated signal is somewhere in between phases 2 and 5. A long break occurs when both (MISO and MJO) or at least one (MJO/MISO) is feeble, or even though associated signals are strong, they are primarily located in phases 1, 6, 7 and 8.

The seamless forecast approach of subseasonal to seasonal scale variability has been succeeding in the forecast of multiple meteorological scales in a uniform framework. In this paradigm, it is hypothesized that reduction in initial error in dynamical forecast would help to reduce forecast error in extended lead-time up to 2-3 weeks. This is tested in a version of operational extended range forecasts based on Climate Forecast System version 2 (CFSv2) developed at Indian Institute of Tropical Meteorology (IITM), Pune. Forecast skills are assessed to understand the role of initial errors on the prediction skill for MJO. A set of lowest and highest initial day error (LIDE & HIDE) cases are defined and the error-growth for these categories are analysed for the strong MJO events during May to September (MJJAS). The MJO forecast initial errors are categorized and defined using the well-known multivariate MJO index introduced by Wheeler &Hendon (2004). The probability distribution of bivariate RMSE and error growth evolution (first order difference of index error for each successive lead days) with respect to extended range lead-time are used as metrics in this analysis. The result showed that initial error is not showing any influence in the skill of model after a lead time of 7-10 days and the error growth remains the same for both set of errors. A rapid error growth evolution of same order is seen for both the classified cases. Further the physical attribution of these errors is studied and found that the errors originate from the events with initial phase in Western Pacific and Indian Ocean. The spatial distribution of OLR and the zonal winds also confirms the same. The study emphasises the importance of better representation of MJO phases especially over Indian ocean in the model to improve the MJO prediction rather than focusing primarily on the initial condition

The performance of the operational extended-range forecast (ERF) issued by IMD is evaluated for the southwest monsoon 2020. The normal onset of monsoon over Kerala (the southern tip of India) with subsequent rapid progress northward in covering the entire country is very well captured in the ERF with 2 to 3 weeks lead time. The ERF also captured very well the transitions from normal to weaker phase of monsoon in July and the active phase of monsoon during entire August with a lead time of about 3 weeks. The active monsoon condition in the second half of September associated with delayed withdrawal from northwest India was also reasonably well captured in the ERF. Quantitatively, the ERF shows significant skill up to 3 weeks on all India levels. On smaller spatial domains for 36 meteorological subdivisions (met-subdivisions) over India, the performance of category forecasts is evaluated in terms of above normal, normal and below normal. The spatial distribution of the met-subdivision level forecast skill of predicting above normal, normal and below normal categories for the 36 subdivisions during the entire monsoon season of 2020 in terms of correct (forecast and observed category matching) to partially correct (forecast and observed category out by one category) combined categories is found to be 89%, 83%, 80% and 78% for week 1 to week 4 forecasts respectively. The wrong forecasts (forecast and observed category out by two categories) are found to be between 11% in week 1 and 22% in week 4 forecast. Thus, the met-subdivision level forecast shows useful skill and is being used operationally for agrometeorological advisory services of IMD.

The performance of operational extended range forecast (ERF) issued by IMD is evaluated for the southwest monsoon 2019, which is one of the excess monsoon years. The delayed onset of monsoon over southern tip of India, sluggish progress northward and delayed withdrawal of monsoon from Northwest India are very well captured in the ERF with two to three weeks lead time. The revival of monsoon towards the end of June & first week of July, weak phase during 2nd/3rd weeks of July, transition into active phase of monsoon during last week of July & first half of August and very active monsoon conditions in September are also very well captured in the ERF. Quantitatively, over the country as a whole and over the four homogeneous regions of India the ERF provided useful guidance with the country as a whole, the central India and the monsoon zone of India indicating more promising results with the correlation coefficient (CC) between observed and forecast rainfall is significant up to three weeks. Over the Northwest India and South Peninsula, it shows useful skill up to two weeks. Northeast India shows significant skill up to one week only followed by weaker CC in week 2 and again increases in week 3. During the 2019 monsoon season the transition of monsoon from above normal to below normal in July month is well captured in the ERF in smaller spatial scales of meteorological subdivision level forecasts, which is being used widely for agrometeorological advisory purposes up to two weeks.

In an endeavor to design better forecasting tools for real-time prediction, the present work highlights the strength of the multi-model multi-physics ensemble over its operational predecessor version. The exiting operational extended range prediction system (ERPv1) combines the coupled, and its bias-corrected sea-surface temperature forced atmospheric model running at two resolutions with perturbed initial condition ensemble. This system had accomplished important goals on the sub-seasonal scale skillful forecast; however, the skill of the system is limited only up to 2 weeks. The next version of this ERP system is seamless in resolution and based on a multi-physics multi-model ensemble (MPMME). Similar to the earlier version, this system includes coupled climate forecast system version 2 (CFSv2) and atmospheric global forecast system forced with real-time bias-corrected sea-surface temperature from CFSv2. In the newer version, model integrations are performed six times in a month for real-time prediction, selecting the combination of convective and microphysics parameterization schemes. Additionally, more than 15 years hindcast are also generated for these initial conditions. The preliminary results from this system demonstrate appreciable improvements over its predecessor in predicting the large-scale low variability signal and weekly mean rainfall up to 3 weeks lead. The subdivision-wise skill analysis shows that MPMME performs better, especially in the northwest and central parts of India.

In the seamless forecast paradigm, it is hypothesized that the reduction in initial error in the dynamical model forecast would help to reduce forecast error in the extended range lead time up to 2–3 weeks. This hypothesis is tested in a version of an operational extended range forecast model based on National Centre for Environmental Prediction (US) Climate Forecast System version 2. Forecast skills are assessed to understand the role of initial errors on the prediction skill for Madden-Julian Oscillation (MJO). Retrospective forecasts are categorized in two groups. One group defines the lowest initial day’s error and the other with the highest initial day’s errors. Then, the error growth for these two categories is analyzed for the strong MJO events during May to September. The initial errors of MJO forecast are categorized and defined using the multivariate MJO index introduced by Wheeler and Hendon (Mon Wea Rev 132(8):1917–1932, 2004). The probability distribution of bivariate root mean square error (BVRMSE) and error growth evolution is used as metrics. The results showed that the initial error does not show any significant difference in the amplitude after a lead time of 7–10 days, and the error growth remains the same for both sets of runs. It is also found that the errors originate from the events with the initial phase in the western Pacific and the Indian Ocean. The study advocates the importance of better representation of MJO phases over the ocean in the model to improve the MJO prediction rather than simply focusing on improving the initial conditions.

The heat and momentum flux transfer by transient eddies from the extratropical to the tropical (E2T) region causes a significant variation of weather and climate in the tropical region. Such transports also cause a regional departure from zonally symmetric large scale circulation patterns caused by the Hadley type transports. During boreal summer monsoon season, the zonally symmetric Hadley type circulation is typically considered in theoretical as well as modelling studies. The current study introduces a method based on the calculation of the E‐vector components and co‐spectra analysis to diagnose the zonally asymmetric climatological patterns of E2T transport and its subseasonal variability. The spectral analysis identifies the climatological as well as low and high frequency patterns of the transient eddy heat and momentum flux transfer over the Indian region and compares the result with traditional Eliassen‐Palm (EP) Flux based stationary eddy patterns. The study identifies the zonal, seasonal, and vertical asymmetries in the transport of transient eddy heat and momentum flux. The study also clearly categorizes the seasonality and frequency dependence of northeast to the southwest tilt of transient eddies transporting momentum and heat flux. Based on the climatological patterns, the wavelet transform approach is used to study the subseasonal variability of the eddy transport indices. We define a set of indices, which identify the subseasonal temporal variation of the E2T transfer mechanism. These indices could be used to operationally track the E2T eddy flux transfer both in observation and in forecast models.

The seasonal genesis parameters for tropical cyclogenesis developed by W. M. Gray, is widely used for the climatological and seasonal monitoring of cyclogenesis over the tropical oceans. Over the North Indian Ocean (NIO), cyclogenesis and evolution is monitored and predicted in the short, medium and extended ranges by India Meteorological Department with the implementation of different deterministic and probabilistic forecasting techniques. This paper provides a review of an in-house developed tropical cyclone prediction system involving an improved storm evolution index and an objective tracking algorithm for detecting cyclogenesis, evolution and storm tracks from post-processed Multi-model ensemble (MME) outputs from the Climate Forecast System-based Grand Ensemble Prediction System (CGEPS) implemented for operational extended range prediction. In the first part, the reliability of cyclogenesis prediction when more than one storm systems develop simultaneously is discussed using a case study. Prominent cyclogenesis indices and constituent parameters are used to analyse the atmospheric and oceanic features which affected the evolution two consecutive storms over NIO by using ERA-Interim daily averaged datasets. The performance of indices from MME outputs is also analysed. Further the reliability of objective track prediction system is discussed by using ERA-5 and ERA-Interim datasets. Finally, the performance of the CGEPS-MME in predicting the recent tropical cyclones, Amphan and Nisarga are discussed in detail. Real-time implementation of this prediction system has proven to be critical in providing early guidance on the formation of storms, enabling the cyclone warning community to be on alert thereby providing enough lead time for better planning and mitigation strategies.