Extended-range Prediction Research Articles

Unraveling Subseasonal Drought Dynamics in India: Insights from NCMRWF Extended Range Prediction System

Abstract Drought, a prolonged natural event, profoundly impacts water resources and societies, particularly in agriculturally dependent nations like India. This study focuses on subseasonal droughts during the Indian summer monsoon season using standardized precipitation index (SPI). Analyzing hindcasts from the National Centre for Medium Range Weather Forecasting (NCMRWF) Extended Range Prediction (NERP) system spanning 1993–2015, we assess NERP’s strengths and limitations. NERP replicates climatic patterns well but overestimates rainfall in the Himalayan foothills and the Indo-Gangetic Plain while underestimating it in the core monsoon zone and western coastline. Nonetheless, the NERP system demonstrates its ability to predict subseasonal drought conditions across India. Our research explores the model’s dynamics, emphasizing tropical and extratropical influences. We evaluate the impact of monsoon intraseasonal oscillation (MSIO) and Madden–Julian oscillation (MJO) on drought onset and persistence, noting model performance and discrepancies. While the model consistently identifies MSIO locations, variations in phase propagation affect drought severity in India. Remarkably, NERP excels in predicting MJO phases during droughts. The study underscores the robust response in the near-equatorial Indian Ocean, a crucial factor in subseasonal drought development. Furthermore, we explored upper-level dynamic interactions, demonstrating NERP’s ability to capture subseasonal drought dynamics. For example, unusual westerly winds weaken the tropical easterly jet, and a cyclonic anomaly transports cold air at midlevels and upper levels. These interactions reduce thermal contrast, weakening monsoon flow and favoring drought conditions. Hence, the NERP system demonstrates its skill in assessing prevailing drought conditions and associated teleconnection patterns, enhancing our understanding of subseasonal droughts and their complex triggers.

Exploring the Origin of the Two-Week Predictability Limit: A Revisit of Lorenz’s Predictability Studies in the 1960s

The 1960s was an exciting era for atmospheric predictability research: a finite predictability of the atmosphere was uncovered using Lorenz’s models and the well-acknowledged predictability limit of two weeks was estimated using a general circulation model (GCM). Here, we delve into details regarding how a correlation between the two-week predictability limit and a doubling time of five days was established, recognize Lorenz’s pioneering work, and suggest non-impossibility for predictability beyond two weeks. We reevaluate the outcomes of three different approaches—dynamical, empirical, and dynamical-empirical—presented in Lorenz’s and Charney et al.’s papers from the 1960s. Using the intrinsic characteristics of the irregular solutions found in Lorenz’s studies and the dynamical approach, a doubling time of five days was estimated using the Mintz–Arakawa model and extrapolated to propose a predictability limit of approximately two weeks. This limit is now termed “Predictability Limit Hypothesis”, drawing a parallel to Moore’s Law, to recognize the combined direct and indirect influences of Lorenz, Mintz, and Arakawa under Charney’s leadership. The concept serves as a bridge between the hypothetical predictability limit and practical model capabilities, suggesting that long-range simulations are not entirely constrained by the two-week predictability hypothesis. These clarifications provide further support to the exploration of extended-range predictions using both partial differential equation (PDE)-physics-based and Artificial Intelligence (AI)—powered approaches.

Understanding the association between global forest fire products and hydrometeorological variables

Climate change and anthropogenic activities have influenced the frequency and magnitude of forest fires both globally and regionally. While skilful short- to extended-range prediction of forest fires is essential for effective mitigation in local communities, it is also important to identify the implications of forest fires on different sectors, including water resources and sustainable development. Limited studies have investigated the association between forest fires and hydrometeorological variables at the regional scale in developing countries due to the lack of necessary datasets, which can now be leveraged using the newly hosted global reanalysis of fire danger indices (referred to as fire indices). The current study presents a comprehensive analysis of the spatio-temporal variations of eight fire indices across India, as well as their association with hydro-meteorological variables, such as precipitation, temperature, and the streamflow of a major river basin (Mahanadi) in India. The accuracy of these indices in capturing real fire events and the potential benefit of incorporating fire indices into long-term hydrologic simulations are also explored. The results show that fire indices can accurately yield fire seasons (i.e., post-monsoon and summer) in India. Furthermore, forest fires are found to be strongly associated with hydro-meteorological variables, typically resulting in low streamflow regimes. Fire indices can also capture actual fire events, maintaining high scalar accuracy. Finally, an improvement in uncalibrated hydrologic model simulations is observed when simulated streamflow is post-processed using the fire indices as predictors. Overall, the current study has valuable implications for fire indices forecasting and hydrologic simulations in ungauged basins.

The Impact of Analysis Correction-Based Additive Inflation on Subseasonal Tropical Prediction in the Navy Earth System Prediction Capability

Abstract Accurately simulating the Madden–Julian oscillation (MJO), which dominates intraseasonal (30–90 day) variability in the tropics, is critical to predicting tropical cyclones (TCs) and other phenomena at extended-range (2–3 week) time scales. MJO biases in intensity and propagation speed are a common problem in global coupled models. For example, the MJO in the Navy Earth System Prediction Capability (ESPC), a global coupled model, has been shown to be too strong and too fast, which has implications for the MJO–TC relationship in that model. The biases and extended-range prediction skill in the operational version of the Navy ESPC are compared to experiments applying different versions of analysis correction-based additive inflation (ACAI) to reduce model biases. ACAI is a method in which time-mean and stochastic perturbations based on analysis increments are added to the model tendencies with the goals of reducing systematic error and accounting for model uncertainty. Over the extended boreal summer (May–November), ACAI reduces the root-mean-squared error (RMSE) and improves the spread–skill relationship of the total tropical and MJO-filtered OLR and low-level zonal winds. While ACAI improves skill in the environmental fields of low-level absolute vorticity, potential intensity, and vertical wind shear, it degrades the skill in the relative humidity, which increases the positive bias in the genesis potential index (GPI) in the operational Navy ESPC. Northern Hemisphere integrated TC genesis biases are reduced (increased number of TCs) in the ACAI experiments, which is consistent with the positive GPI bias in the ACAI simulations.

A Modified Kain–Fritsch Convection Scheme for Extended-Range Prediction

Abstract This paper describes the convection parameterization in the Navy Earth System Prediction Capability (ESPC) system developed at the Naval Research Laboratory, with a focus on the scheme configuration in the v2.0 system. The parameterization is an update of a modification of the Kain–Fritsch convection scheme by Ridout et al. based on an assumed quasi-balance of updraft parcel buoyancy at the cloud-base level. Scheme updates include the treatment of updraft/environment mixing and additional updraft model features, including a parameterized reduction in net detrainment in cases of significant near-cloud upward motion, and a modified cloud-top condition. The scheme includes two convection modes: a turbulence-triggered and a dynamically triggered mode. Hindcast sensitivity with Navy ESPC to features of the scheme is investigated with 45-day integrations from 1 November 2011 for a portion of the Dynamics of the Madden–Julian Oscillation (DYNAMO) research program observational period that overlaps with the occurrence of two episodes of the MJO. The modified updraft mixing is critical in the hindcasts for consistent MJO eastward propagation, whereas the additional updraft updates significantly improve the representation of small-scale rainfall variability, while helping to inhibit development of excessive low-level easterly flow. The added turbulence-triggered convection mode helps to improve the representation of the separation of periods of enhanced MJO convection. The relative occurrence frequency of convective cloud-top height and column water vapor in the equatorial Indo-Pacific is investigated in the hindcasts, showing significant similarities with satellite retrieval results. Significance Statement This study describes the scheme used to represent the effects of convective clouds such as cumulus and cumulonimbus (thunderstorm clouds) in computerized 45-day global forecasts of the Earth system in a forecast model developed at the Naval Research Laboratory, focusing on the version currently undergoing testing for use by the U.S. Navy. Some of the development history and physical basis for the scheme are presented, and results from test simulations are included. The test results investigate potential forecast sensitivity to various features of the scheme and illustrate that the scheme can successfully represent certain effects of convective clouds on large-scale storm systems in the tropics that have global-scale impacts on extended-range (several weeks) prediction of the Earth’s atmosphere/ocean system.

An integrated method for extended-range prediction of heavy Precipitation process in the flood season over Hunan Province based on S2S models

Abstract Floods in the middle reaches of Yangtze River threaten millions of people and cause casualties and economic losses. Yet, the prediction of floods especially on the sub-seasonal scale in this region is still challenging. To better predict the floods during the flood season (from April to August) in Hunan Province, the models from the China Meteorological Administration (CMA), the European Centre for Medium-Range Weather Forecasts (ECMWF) and the National Centers for Environmental Prediction (NCEP) that participated in the sub-seasonal to seasonal (S2S) prediction project were chosen to evaluate their extended-range (the next 11–30 days) prediction skills for heavy precipitation. The original prediction score of single model (original score), the score of single model using optimal threshold of heavy precipitation (adjusted score) and the score of multi-model integration (integrated score) were calculated by the scoring rules for heavy precipitation process. The results show that the integrated score in the extended-range is 75.1, which is 10.3 and 6.9 higher than the average scores of original models and adjusted method, respectively. The false alarm (missing) rate of the integrated method is 5.4% (33.9%), which is 8.6% (4.7%) and 2.9% (3.3%) smaller than the average rates of original models and adjusted method, respectively.

Evaluation and Process-Oriented Diagnosis of the GEFSv12 Reforecasts

Abstract Three levels of process-oriented model diagnostics are applied to evaluate the Global Ensemble Forecast System version 12 (GEFSv12) reforecasts. The level-1 diagnostics are focused on model systematic errors, which reveals that precipitation onset over tropical oceans occurs too early in terms of column water vapor accumulation. Since precipitation acts to deplete water vapor, this results in prevailing negative biases of precipitable water in the tropics. It is also associated with overtransport of moisture into the mid- and upper troposphere, leading to a dry bias in the lower troposphere and a wet bias in the mid–upper troposphere. The level-2 diagnostics evaluate some major predictability sources on the extended-range time scale: the Madden–Julian oscillation (MJO) and North American weather regimes. It is found that the GEFSv12 can skillfully forecast the MJO up to 16 days ahead in terms of the Real-time Multivariate MJO indices (bivariate correlation ≥ 0.6) and can reasonably represent the MJO propagation across the Maritime Continent. The weakened and less coherent MJO signals with increasing forecast lead times may be attributed to humidity biases over the Indo-Pacific warm pool region. It is also found that the weather regimes can be skillfully predicted up to 12 days ahead with persistence comparable to the observation. In the level-3 diagnostics, we examined some high-impact weather systems. The GEFSv12 shows reduced mean biases in tropical cyclone genesis distribution and improved performance in capturing tropical cyclone interannual variability, and midlatitude blocking climatology in the GEFSv12 also shows a better agreement with the observations than in the GEFSv10. Significance Statement The latest U.S. operational weather prediction model—Global Ensemble Forecast System version 12—is evaluated using a suite of physics-based diagnostic metrics from a climatic perspective. The foci of our study consist of three levels: 1) systematic biases in physical processes, 2) tropical and extratropical extended-range predictability sources, and 3) high-impact weather systems like hurricanes and blockings. Such process-oriented diagnostics help us link the model performance to the deficiencies of physics parameterization and thus provide useful information on future model improvement.

The Philippine springtime (February–April) sub-seasonal rainfall extremes and extended-range forecast skill assessment using the S2S database

During the first half month of April 2022, the Philippines experienced severe disasters associated with the weak but deadly tropical storm Megi that caused 214 deaths and two sunken ships. This prompted us to investigate the extended-range prediction skill of the springtime Philippine sub-seasonal scale rainfall extremes in the subseasonal-to-seasonal (S2S) prediction database. The results suggest that the S2S models can well predict the extremity of the 2022 springtime sub-seasonal peak rainfall event (SPRE) ten days ahead. In addition to the La Niña sea surface temperature anomalies, this prolonged rainfall event, from March 26 - April 14, 2022, was associated with an anomalous cyclonic circulation straddled over the South China Sea (SCS) and the Philippine Sea and persisted for two weeks. The strong relationship between the El Niño and Southern Oscillation (ENSO) and the springtime (February–April) rainfall variability in the Philippines is clearly revealed in the analysis of 25 years of observational and hindcast data. The extremely wet SPREs tend to occur during the La Niña springs, while the extremely dry SPREs tend to occur during the El Niño springs. The Madden-Julian oscillation (MJO) and equatorial Rossby (ER) waves that are capable of modulating the sub-seasonal rainfall extremes were weak when the deadly SPRE occurred in April 2022. Thus, the extended-range forecast skill of this example can be interpreted as the baseline skill of the current S2S prediction revealed in the multi-model database. The findings suggest that the SPRE is a useful item to be included in the operational forecast as potential opportunities to harness the benefits of S2S prediction and applications.

Remote sensing and climate services improve irrigation water management at farm scale in Western-Central India

The enormous progress in weather and extended range predictions for the Indian monsoon over the last decade has not been translated to operationalized irrigation water management tools despite many agricultural advisories from operational agencies. The limited implementation is mainly due to the resolution mismatches of forecasts and decision-needs and a lack of soil moisture monitoring networks. Sustained soil moisture monitoring suffers from the high cost to farmers in installing distributed sensors. Here we develop an irrigation water management tool for the farmers at farm scale, which starts with utilizing and merging a few available soil moisture sensors and L-band satellite observations of surface soil moisture using machine learning. Such derived soil moisture field is used as the initial condition with the multi-ensemble future rainfall for the following few weeks given the weather/extended range forecasts in a farm-scale ecohydrological model. This ecohydrological model is integrated with Monte-Carlo simulations within a stochastic optimization framework to minimize water use while not allowing the soil moisture to drop below a threshold level with a certain probability. The optimization results in water arrangement decisions 2 weeks in advance and water application decisions 1–7 days in advance. We also estimate the water storage capacity needed at farm scale for effective water utilization. We find that 20–45 % and 17–35 % water savings were achievable for Kharif and Rabi seasons, respectively, without losing any yield when applied to grape farms of Nashik, Maharashtra, India. The proposed framework is co-developed with the farmers and can be used in any region for any crops, since it is generalized and easy to transfer. This is an extension of our earlier work to an end-to-end system using satellite data for soil moisture.

Stochastic schemes for the perturbation of the atmospheric boundary conditions in ocean general circulation models

Advancing the representation of uncertainties in ocean general circulation numerical models is required for several applications, ranging from data assimilation to climate monitoring and extended-range prediction systems. The atmospheric forcing represents one of the main uncertainty sources in numerical ocean models. Here, we formulate and revise different approaches to perturb the air-sea fluxes used within the atmospheric boundary conditions. In particular, perturbation of the fluxes is performed either through i) stochastic modulation of the air-sea transfer coefficients; ii) stochastic modulation of the air-sea flux tendencies; iii) coarse-graining of stochastic sub-grid computation of the fluxes; or iv) multiple bulk formulas. The schemes are implemented and tested in the NEMO4 ocean model, implemented at an eddy-permitting resolution on a domain covering the North Atlantic and Arctic oceans and the Mediterranean Sea. A series of 22-year 4-member ensemble experiments with different stochastic schemes are performed and analyzed for the period 2000-2021, and results are compared in terms of the ensemble mean and, when applicable, ensemble spread of the principal oceanic diagnostics. Results indicate that the schemes, in general, can significantly improve some verification skill scores (e.g. against drifter current speed, SST analyses, and hydrographic profiles) and, in some cases, enhance the mesoscale activity and weaken the large-scale circulation. The response, however, is different depending on the specific scheme, whose choice thus depends on the target application, as detailed in the paper. These findings foster the adoption of these schemes in both extended-range operational ocean forecasts and coupled long-range climate prediction systems, where the boundary conditions perturbations may contribute to performance increases.

Monthly-scale extended predictions using the atmospheric model coupled with a slab ocean

Abstract. Given the good persistence of sea surface temperature (SST) due to the slow-varying nature of the ocean, an atmospheric model coupled with a slab ocean model (SOM) instead of a 3-D dynamical ocean model is designed as an efficient approach for extended-range predictions. The prediction experiments from July to December 2020 are performed based on the Weather Research and Forecasting (WRF) model coupled to the SOM (WRF-SOM) with the initial and boundary conditions same as the WRF coupled to the Regional Ocean Model System (WRF-ROMS). The WRF-SOM is verified to have better performance of SSTs in the extended-range predictions than WRF-ROMS since it avoids the complicated model biases from the ocean dynamics and seabed topography when extended-range predictions are made using a 3-D dynamical ocean model. The improvement of SSTs can lead to the remarkable impact on the response of the atmosphere from the surface to the upper layer. Taking typhoon as an example of extreme events, the WRF-SOM can obtain comparable intensity predictions and slightly improved track predictions due to the improved SSTs in the initialized WRF-SOM system. Overall, the WRF-SOM can ensure the stability of extended-range prediction and reduce the demand for computing resources by roughly 50 %.

Monthly extended ocean predictions based on a convolutional neural network via the transfer learning method

Sea surface temperature anomalies (SSTAs) and sea surface height anomalies (SSHAs) are indispensable parts of scientific research, such as mesoscale eddy, current, ocean-atmosphere interaction and so on. Nowadays, extended-range predictions of ocean dynamics, especially in SSTA and SSHA, can provide daily prediction services in the range of 30 days, which bridges the gap between synoptic-scale weather forecasts and monthly average scale climate predictions. However, the forecast efficiency of extended range remains problematic. With the development of ocean reanalysis and satellite remote sensing products, large amounts datasets provide an unprecedented opportunity to use big data for the extended range prediction of ocean dynamics. In this study, a hybrid model, combing convolutional neural network (CNN) model with transfer learning (TL), was established to predict SSTA and SSHA at monthly scales, which makes full use of these data resources that arise from delayed gridding reanalysis products and real-time satellite remote sensing observations. The proposed model, where both ocean and atmosphere reanalysis datasets serve as the pretraining dataset and the satellite remote sensing observations are employed for fine-tuning based on the transfer learning (TL) method, can effectively capture the evolving spatial characteristics of SSTAs and SSHAs with low prediction errors over the 30 days range. When the forecast lead time is 30 days, the root means square errors for the SSTAs and SSHAs model results are 0.32°C and 0.027 m in the South China Sea, respectively, indicating that this model has not only satisfactory prediction performance but also offers great potential for practical operational applications in improving the skill of extended-range predictions.

Diagnostics and real-time extended range prediction of cold waves over India

Diagnostics and real-time extended range prediction of cold waves over India

Water Savings with Irrigation Water Management at Multi-week Lead Time Using Extended Range Predictions

The Extended Range Predictions (ERP) provide Sub-seasonal to Seasonal (S2S) prediction of meteorological variables at a lead time of multiple weeks. Despite the considerable improvements in the ERP system, their applications in real-time irrigation water management are limited. Such limited use of ERP is due to uncertainty in predictions and a spatial mismatch in scales between the model grids and farms. Here, we develop a chance-constrained optimization framework for farm-scale irrigation water management planning for multiple weeks in advance. The degrading model performance with time and the related uncertainty are addressed by generating large ensembles using weather generators. Working with grape farmers in India has revealed that such an advance planning could save water by 3–25 % at a farm-scale without losing yield, compared with the existing approach requiring near real-time decisions. Moreover, the co-development of such irrigation tools with farmers increases the use and usability of S2S predictions.

Extended-Range Forecasting of PM2.5 Based on the S2S: A Case Study in Shanghai, China

Air pollution has become one of the most challenging problems in China, especially in economically developed and densely populated regions such as Shanghai. In this study, the long short-term memory (LSTM) model is introduced for the application in extended-range forecasting of PM2.5 in Shanghai by incorporating three members of the Subseasonal-to-Seasonal Prediction project (S2S) forecasting, moderate-resolution imaging spectroradiometer (MODIS) aerosol optical depth (AOD) data, and large-scale circulation factors derived from ERA-5 reanalysis. Therefore, an accurate ∼40-day PM2.5 prediction model over Shanghai was developed, providing new insights for air pollution extended-range forecasting. The new model not only exhibited much better accuracy but also captured the pollution process more closely than traditional methods, such as multiple regression (MLR). The prediction root-mean-square errors (RMSEs) based on the China Meteorological Administration (CMA), the U.K. model, and the European Centre for Medium-Range Weather Forecasts (ECMWF) were 24.84, 24.35, and 22.27 μg m−3, respectively, and their Heidke Skill Scores (HSSs) were between 0.1 and 0.5. As a result, the S2S-LSTM model for extension period pollution prediction with higher accuracy developed in this study could further burst the hot spots of pollution extended-range prediction research. However, limitations of the prediction model are still in existence, especially in dealing with only a single site instead of a two-dimensional prediction, which requires further investigation in future studies.

Changes in MJO Characteristics and Impacts in the Past Century

Abstract The changes of the MJO behavior and its impact on global precipitation, precipitation extremes, and high-frequency variability during the past century (1901–2010) were investigated using the NOAA-20CR dataset. It was found that the MJO amplitude was significantly strengthened, while its eastward phase speed hardly changed. The impacts of the MJO on precipitation in South China (SC), northern Australia (AU), and California (CA) were investigated. The anomalous rainfall in the regions was strengthened from the early to late twentieth century, with the percentage increase ranging from 67% in AU to 14% in CA. A moisture budget analysis indicated that the enhanced precipitation was primarily attributed to the effect of anomalous wind while the effect of the mean moisture change was small. The impact of the local meridional wind anomaly was critical in SC, while in AU and CA the zonal wind component dominated. The precipitation extremes had a significant increase from the early to late twentieth century. The cumulative extreme precipitation amount increased by 140%–150% in SC and AU and by 100% in CA, and the number of the extreme days increased by 110% in SC and AU and 70% in CA. Such increases are consistent with the strengthening of the high-frequency (HF; with a period less than 10 days) variability across the globe. The pattern of percentage increase of the HF variability resembles the pattern of percentage increase of MJO-scale precipitation. This implies that the MJO teleconnection pattern could exert a large-scale control on the HF variability and weather extremes. Significance Statement The Madden–Julian oscillation (MJO), a dominant mode of tropical intraseasonal oscillations, plays an important role in affecting global weather and climate. To understand to what extent the MJO and associated teleconnections change with global warming, this study adopts an alternative approach by analyzing a century-long reanalysis dataset rather than the outputs of climate models that consist of large uncertainties due to poor MJO simulations. We found that MJO-induced tropical and midlatitude precipitation and associated extremes and high-frequency variabilities were strengthened during the past century while the global mean surface temperature increases approximately by 1°C. The results derived from the present study provide a basis for assessing the future MJO behavior and associated climate impacts and for improving the extended-range prediction of severe weather and climate extremes.

Skill of the extended range prediction (NERP) for Indian summer monsoon rainfall with NCMRWF global coupled modelling system

AbstractAn extended range prediction system (NERP) based on the Unified global coupled modelling system has been implemented at National Centre for Medium Range Weather Forecasting (NCMRWF). Predictions of weekly anomalies for rainfall, winds and surface temperature are issued once a week for the subsequent 4 weeks. This study is an assessment of the skill of the NERP system over India during the summer monsoon period of June to September (JJAS). The week‐1 and week‐2 rainfall anomaly forecasts exhibit good prediction skill over the Indian region. The evaluations made on meteorologically homogeneous regions of India show moderate to good skill even in week‐3 and week‐4 forecasts for some regions. The variability of the monsoon seasonal cycle in the model is well represented along with the magnitude of summer monsoon rainfall. The model brings out the mean monsoon rainfall features – the peaks during JJAS occurring over the Western Ghats, the Arakan coast and the Bay of Bengal. The onset of the monsoon and the interannual variability of the northward propagation of the rainfall anomalies over the Indian longitudes match reasonably well with observations. The model has some wet bias over the equatorial Indian Ocean and mild dry bias over the Indian land region that develop during the 4‐week period. The anomaly correlations of rainfall are significant over most of the subdivisions of India till week‐3 and week‐4. Probabilistic scores of reliability Receiver Operating Characteristic (ROC) and fair continuous ranked probability score (FCRPS) show that the model exhibits good skill over most regions till week‐2 and moderate skill in weeks 3–4 over other regions. The estimates of forecast reliability are low, a likely consequence of the limited ensemble size. A case‐study of year 2018 showed that the JJAS rainfall regime shifts from wet to dry and vice versa are captured well.

Understanding the intraseasonal variability over Indian region and development of an operational extended range prediction system

Understanding the intraseasonal variability over Indian region and development of an operational extended range prediction system

A review of the development and implementation of a tropical cyclone prediction system for North Indian Ocean in a multi-model ensemble framework

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.

Role of initial error growth in the extended range prediction skill of Madden-Julian Oscillation (MJO)

Role of initial error growth in the extended range prediction skill of Madden-Julian Oscillation (MJO)