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A high resolution regional reanalysis of the Indian Monsoon Data Assimilation and Analysis (IMDAA) project is made available to researchers for deeper understanding of the Indian monsoon and its variability. This 12 km resolution reanalysis covering the satellite-era from 1979 to 2018 using 4D-Var data assimilation method and the UK Met Unified Model is presently the highest resolution atmospheric reanalysis carried out for the Indian monsoon region. Conventional and satellite observations from different sources are used, including Indian surface and upper air observations, of which some were not used in any previous reanalyses. Various aspects of this reanalysis, like quality control and bias correction of observations, data assimilation system, land surface analysis, and verification of reanalysis products, are presented in this paper. Representation of important weather phenomena of each season over India in the IMDAA reanalysis verifies reasonably well against India Meteorological Department (IMD) observations and compares closely with ERA5. Salient features of the Indian summer monsoon are found to be well represented in the IMDAA reanalysis. Characteristics of major semi-permanent summer monsoon features (e.g., Low-level Jet and Tropical Easterly Jet) in IMDAA reanalysis are consistent with ERA5. The IMDAA reanalysis has captured the mean, inter-annual, and intra-seasonal variability of summer monsoon rainfall fairly well. IMDAA produces a slightly cooler winter and a hotter summer than the observations; the reverse for ERA5. IMDAA captured the fine-scale features associated with a notable heavy rainfall episode over complex terrain. In this study, the fine grid spacing nature of IMDAA is compromised due to the lack of comparable resolution observations for verification.

Lightning has emerged as one of the major weather hazards in India. Lightning forecasts are introduced into the operational National Centre for Medium Range Weather Forecasting regional unified model (NCUM-R) to predict the events in advance. A new blended electric scheme following McCaul et al. (2009) is employed to predict the lightning flash count as a useful tool for day-to-day prediction of thunderstorm activity and intensity. A total of four numerical experiments, namely CNTL, EXP1, EXP2, and EXP3, were conducted by using the NCUM-R based on the graupel water path (GWP) amount and the process allowing the snow-rain collisions to form a graupel. The numerical simulation forecasts are compared with the Indian Air Force and Indian Institute of Tropical Meteorology earth network lightning sensor data. A case study of a convective system associated with a severe lightning event that occurred on 7 February 2019 over the northern region of India is diagnosed. The observations indicate stronger lightning cells present over the Haryana–Punjab region, with a leaf-like extension through south-eastwards and continuing up to the Himalaya foothills. Such south-eastward progression of the lightning system is well captured in all the experiments. However, when the GWP threshold is set to 200 g m−3, and allowing for the snow-rain collision process, the counts are improved by approximately 50% compared to the control run, and is closely agree with the observation count. Temporal evolution characteristics of the vertical distribution of the hydrometeors and vertical velocity support the formulation of the revised lightning parameterization scheme. Statistical metrics were computed for the pre-monsoon month indicating the robustness of the model with the revised scheme. Hence, the revised scheme is chosen for the operational implementation of the lightning flash prediction system of the NCUM-R. Further modifications of the electric scheme are warranted based on the cloud microphysics response over different weather regimes.

Dust-induced modifications to the radiative fluxes/heating during a light-blocking haze episode over Delhi, India, 7–9th Nov 2017—triggered by long range transport from a severe Saudi Arabian dust storm—have been investigated with a regional NWP model, NCUM-R with prognostic dust-radiation feedbacks. The study employs ‘Double Radiation Calls’, wherein parallel runs of the radiation scheme ‘with’ (prognostic) and ‘without’ (diagnostic) dust radiative effects—while prognostic fields drive the forecast—isolate the dust-induced perturbations. The forecasted dust optical depth agreed spatially with the AOD from MODIS with Angstrom Exponent > 0.5, indicating that the dust was well mixed with the fine mode anthropogenic aerosols upwind. The ‘downward shortwave (SW) flux’ was diminished (upto − 12.9 Wm−2) in layers sampling (i) near-surface (L1Avg), (ii) well-mixed layer within the planetary boundary layer (PBL, L2Avg) and (iii) free-troposphere (FT, L3Avg). Dust-induced ‘Solar heating’ dominated in FT (upto 9.5 × 10–7 Ks−1) and the patches below (in L2Avg) exhibited a cooling, leading to thermal dipoles. The ‘upward longwave (LW) flux’ in FT was reduced and ‘LW heating’ prevailed in all levels—peak (2.5 × 10–6 Ks−1) in L2Avg—along with well-defined cooling zones in L1Avg. The dust–radiation interaction in turn influenced the boundary layer meteorology, manifested as (i) shallow PBLs that spatially correlate with dust-induced cooling of the boundary layer column, (ii) enhanced surface humidity and (iii) reduced visibility. The study is an instance of prognostic dust-radiation feedbacks improving the skill of NWP models in dust-laden regions.

A good understanding of the general circulation features of the oceans, particularly of the coastal waters, and ability to predict the key oceanographic parameters with good accuracy and sufficient lead time are necessary for the safe conduct of maritime activities such as fishing, shipping, and offshore industries. Considering these requirements and buoyed by the advancements in the field of ocean modeling, data assimilation, and ocean observation networks along with the availability of the high-performance computational facility in India, Indian National Centre for Ocean Information Services has set up a “High-Resolution Operational Ocean Forecast and Reanalysis System” (HOOFS) with an aim to provide accurate ocean analysis and forecasts for the public, researchers, and other types of users like navigators and the Indian Coast Guard. Major components of HOOFS are (i) a suite of numerical ocean models configured for the Indian Ocean and the coastal waters using the Regional Ocean Modeling System (ROMS) for forecasting physical and biogeochemical state of the ocean and (ii) the data assimilation based on local ensemble transform Kalman filter that assimilates in situ and satellite observations in ROMS. Apart from the routine forecasts of key oceanographic parameters, a few important applications such as (i) Potential Fishing Zone forecasting system and (ii) Search and Rescue Aid Tool are also developed as part of the HOOFS project. The architecture of HOOFS, an account of the quality of ocean analysis and forecasts produced by it and important applications developed based on HOOFS are briefly discussed in this article.

South Asian monsoon exhibits multiscale spatiotemporal variability. Analyzing the nature and behavior of numerical weather forecast error associated with these space-time heterogeneities will eventually help in improving the models. We investigate the spatiotemporal error characteristics of the National Centre for Medium-Range Weather Forecasting (NCMRWF) Global Forecast System (NGFS) model over South Asian land and ocean separately. Although error grows with lead-time, it saturates within 3–5 days of forecast initiation. The saturated error is only about 15–25% higher than that of day-1, indicating that most of the error accumulates within first 24-h of forecast. Increase in error over oceanic regions is due to an increase in the area with high error at all precipitation ranges with large day-to-day variability. However, over land error growth is primarily confined at locations of high mean precipitation. Decomposition of error arising due to intensity and phase variations reveals that about 90% of it arises from the model’s inability to capture phase of precipitation at various timescales. We show that NGFS cannot capture synoptic scale variations ($$

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.

This study utilizes cluster analysis to produce sets of weather patterns for the Indian subcontinent. These patterns have been developed with future applications in mind; specifically relating to the occurrence of high-impact weather and meteorologically induced hazards such as landslides. The weather patterns are also suited for use within probabilistic medium- to long-range weather pattern forecasting tools driven by ensemble prediction systems. A total of 192 sets of weather patterns have been generated by varying the parameter which is clustered, the spatial domain and the number of weather patterns. Non-hierarchical k-means clustering was applied to daily 1200 UTC ERA-Interim reanalysis data between 1979 and 2016 using pressure at mean sea level (PMSL) and u- and v-component winds at 10-m, 925-hPa and 850-hPa. The resultant weather pattern sets (clusters) were analysed for their ability to represent the main climatic precipitation patterns over India using the explained variation score. Weather patterns generated using 850-hPa winds are among the most representative, with 30 patterns being enough to represent variability within different phases of the Indian climate. For example, several weather pattern variants are evident within the active monsoon, break monsoon and retreating monsoon. There are also several variants of weather patterns susceptible to western disturbances. These weather pattern variants are useful when it comes to identifying periods most susceptible to high-impact weather within a large-scale regime, such as identifying the most flood prone periods within the active monsoon. They hence have potentially many forecasting applications.

Tibetan Plateau (TP), a high elevation region in the Asian subcontinent, play an influential role in the Indian summer monsoon. In this numerical model study, sensitivity to the local changes in the microphysics over Tibet on the model forecast of circulation and precipitation over Indian monsoon regions is assessed. The local modification of the cloud microphysical parameters, riming, over TP is attempted. The simulation experiments have been carried out for different synoptic situations during the summer monsoon season. The riming gave differing responses in the two synoptic cases with the ice to rain conversion displaying a uniform distribution throughout the atmospheric column for the active monsoon case, whereas it is restricted up to an altitude of 8000 m in pre-monsoon case. The experiment over TP gives a 1.97% increase (0.54% reduction) in the all India rainfall for the pre-monsoon (active monsoon) case, which are mainly driven by the changes in the monsoon core zone. The maximum impact is found in Western Ghats rainfall with a 3.74% reduction (10.49% increases) for the pre-monsoon (active monsoon) case. Modulations in Tropical Easterly Jet and surface circulations in the experiments have substantial effect over the head Bay and the Western Ghats.

During August 2018 and 2019 the southern state of India, Kerala received unprecedented heavy rainfall which led to widespread flooding. We aim to characterize the convective nature of these events and the large-scale atmospheric forcing, while exploring their predictability by three state of the art global prediction systems, the National Centre for Environmental Prediction (NCEP) based India Meteorological Department (IMD) operational Global Forecast System (GFS), the European Centre for Medium Range Weather Forecast (ECMWF) integrated forecast system (IFS) and the Unified Model based NCUM being run at the National Centre for Medium Range Weather Forecasting (NCMRWF).Satellite, radar and lightning observations suggest that these rain events were dominated by cumulus congestus and shallow convection with strong zonal flow leading to orographically enhanced rainfall over the Ghats mountain range, sporadic deep convection was also present during the 2019 event. A moisture budget analyses using the ERA5 (ECMWF Reanalyses version 5) reanalyses and forecast output revealed significantly increased moisture convergence below 800 hPa during the main rain events compared to August climatology. The total column integrated precipitable water tendency, however is found to be small throughout the month of August, indicating a balance between moisture convergence and drying by precipitation. By applying a Rossby wave filter to the rainfall anomalies it is shown that the large-scale moisture convergence is associated with westward propagating barotropic Rossby waves over Kerala, leading to increased predictability of these events, especially for 2019.Evaluation of the deterministic and ensemble rainfall predictions revealed systematic rainfall differences over the Ghats mountains and the coastline. The ensemble predictions were more skilful than the deterministic forecasts, as they were able to predict rainfall anomalies (>3 standard deviations from climatology) beyond day 5 for August 2019 and up to day 3 for 2018.

Prediction of heavy/extreme rains is still a challenge, even for the most advanced state-of-the-art high-resolution Numerical Weather Prediction (NWP) modelling systems. Hydrological models use the rainfall forecasts from the NWP models as input. This study evaluates the performance of the UK Met Office Unified Model (UM) in predicting the rainfall exceeding 80th and 90th percentiles. Such high rainfall amounts occur over the Western Ghats (WGs) and North East (NE) India mainly due to the forced ascent of air parcels. Apart from the significant upgrades in the UM's dynamical core, the model features an increased horizontal grid (40–10 km) and vertical resolution (50–70 levels). The prediction skill of heavy rainfall events improves with an increased horizontal resolution of the model. The probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI) are the verification metrics used. As per these metrics, model rainfall forecasts have improved during 2007–2018 (increase in CSI from 0.29 to 0.38, POD from 0.45 to 0.55, and decrease in FAR from 0.55 to 0.45). Additionally, to verify extreme and rare events, the symmetric extremal dependence index (SEDI) is also used. SEDI also shows an increase from 0.47 to 0.62 and 0.16 to 0.41 over WGs and NE India during the study period, suggesting an improved skill of predicting heavy rains over the mountains. The improved forecast performance is consistent and relatively higher over WGs than over NE states.

This study uses INCOMPASS aircraft, radiosonde and satellite observations for verifying hydrometeors and associated state variables predicted by the regional model of the NCMRWF (NCUM-R) for contrasting phases of the 2016 monsoon. INCOMPASS flights B957 and B975 took place between Lucknow in northern India and Bhubaneswar near the east coast, and represent a contrast between dry pre-monsoon and active monsoon conditions, respectively. A moist profile above 4 km in Bhubaneswar measured on B957 showed a dry-air intrusion being eroded by mid-level clouds, whereas the Lucknow profile showed a drier, pre-monsoon profile. Aerosol extinction coefficient and cloud-top height measured using lidar showed an influx of continental aerosol, and intermittent multiple clouds below the aircraft in the mid-troposphere and boundary layer. Measurements from B975 match well with cyclonic wind patterns estimated from satellite observations and the convective tendency represented in radiosonde profiles. Extensive clouds were detected below 5 km during the active monsoon. Two-model formulations for cloud representation (prognostic cloud and prognostic condensate, PC2, and diagnostic schemes, Diag) are compared with observations during the campaign. Vertical structures of state variables from both schemes are generally in agreement along the flight tracks. Surface energy budget and cloud diagnoses indicate higher cloud cover in Diag consistent with lower surface temperatures through reduced surface downwelling shortwave flux than in PC2, while the latent heat flux is found to be insensitive to cloud scheme chosen. In-cloud water content is larger in PC2 for lower cloud fraction, and the autoconversion process is faster with respect to Diag. Higher total condensed-water content in the model with respect to aircraft measurements and an enhanced light precipitation bias with respect to satellite data is common to both cloud schemes. Further work to improve the representation of clouds and precipitation for the tropical implementation of the model is clearly warranted.

There are increasing efforts towards the prediction of high-impact weather systems and understanding of related dynamical and physical processes. High-resolution numerical model simulations can be used directly to model the impact at fine-scale details. Improvement in forecast accuracy can help in disaster management planning and execution. National Centre for Medium Range Weather Forecasting (NCMRWF) has implemented high-resolution regional unified modeling system with explicit convection embedded within coarser resolution global model with parameterized convection. The models configurations are based on UK Met Office unified seamless modeling system. Recent land use/land cover data (2012–2013) obtained from Indian Space Research Organisation (ISRO) are also used in model simulations. Results based on short-range forecast of both the global and regional models over India for a month indicate that convection-permitting simulations by the high-resolution regional model is able to reduce the dry bias over southern parts of West Coast and monsoon trough zone with more intense rainfall mainly towards northern parts of monsoon trough zone. Regional model with explicit convection has significantly improved the phase of the diurnal cycle of rainfall as compared to the global model. Results from two monsoon depression cases during study period show substantial improvement in details of rainfall pattern. Many categories in rainfall defined for operational forecast purposes by Indian forecasters are also well represented in case of convection-permitting high-resolution simulations. For the statistics of number of days within a range of rain categories between ‘No-Rain’ and ‘Heavy Rain’, the regional model is outperforming the global model in all the ranges. In the very heavy and extremely heavy categories, the regional simulations show overestimation of rainfall days. Global model with parameterized convection have tendency to overestimate the light rainfall days and underestimate the heavy rain days compared to the observation data.

Forecasting of heavy rainfall events is still a challenge even for the most advanced state-of-art high resolution NWP modelling systems. Very often the models fail to accurately predict the track and movement of the low pressure systems leading to large spatial errors in the predicted rain. Quantification of errors in forecast rainfall location and amounts is important for forecasters (to choose a forecast and interpret) and modelers for monitoring the impact of changes and improvements in model physics and dynamics configurations. This study aims to quantify and summarize errors in rainfall forecast for heavy rains associated with a Bay of Bengal (BOB) low pressure systems. The verification analysis is based on three heavy rain events during June to September (JJAS) 2015. The performance of the three deterministic models, NCMRWF’s Global Forecast Systems (NGFS), NCMRWF’s Unified Model (NCUM) and Australian Community Climate and Earth-System Simulator – Global (ACCESS-G) in predicting these heavy rainfall events has been analysed. In addition to standard verification metrics like RMSE, ETS, POD and HK Score, this paper also uses new family of scores like EDS (Extreme Dependency Score), EDI (Extremal Dependence Index) and Symmetric EDI with special emphasis on verification of extreme rainfall to bring out the relative performance of the models for these three rainfall events. The results indicate that Unified modeling framework in NCUM and ACCESS-G by and large performs better than NGFS in rainfall forecasts over India specially at higher lead times. Relatively improved skill in NCUM forecasts can be attributed to (i) improved resolution (~17 km) and (ii) END Game dynamics of NCUM.

The quantitative precipitation forecast (QPF) performance for heavy rains is still a challenge, even for the most advanced state-of-art high-resolution Numerical Weather Prediction (NWP) modeling systems. This study aims to evaluate the performance of UK Met Office Unified Model (UKMO) over India for prediction of high rainfall amounts (>2 and >5 cm/day) during the monsoon period (JJAS) from 2007 to 2015 in short range forecast up to Day 3. Among the various modeling upgrades and improvements in the parameterizations during this period, the model horizontal resolution has seen an improvement from 40 km in 2007 to 17 km in 2015. Skill of short range rainfall forecast has improved in UKMO model in recent years mainly due to increased horizontal and vertical resolution along with improved physics schemes. Categorical verification carried out using the four verification metrics, namely, probability of detection (POD), false alarm ratio (FAR), frequency bias (Bias) and Critical Success Index, indicates that QPF has improved by >29 and >24% in case of POD and FAR. Additionally, verification scores like EDS (Extreme Dependency Score), EDI (Extremal Dependence Index) and SEDI (Symmetric EDI) are used with special emphasis on verification of extreme and rare rainfall events. These scores also show an improvement by 60% (EDS) and >34% (EDI and SEDI) during the period of study, suggesting an improved skill of predicting heavy rains.

The Southern Ocean (SO) is one of the important regions where significant processes and feedbacks of the Earth's climate take place. Expeditions to the SO provide useful data for improving global weather/climate simulations and understanding many processes. Some of the uncertainties in these weather/climate models arise during the first few days of simulation/forecast and do not grow much further. NCMRWF issued real-time five day weather forecasts of mean sea level pressure, surface winds, winds at 500 hPa & 850 hPa and rainfall, daily to NCAOR to provide guidance for their expedition to Indian sector of SO during the austral summer of 2014–2015. Evaluation of the skill of these forecasts indicates possible error growth in the atmospheric model at shorter time scales. The error growth is assessed using the model analysis/reanalysis, satellite data and observations made during the expedition. The observed variability of sub-seasonal rainfall associated with mid-latitude systems is seen to exhibit eastward propagations and are well reproduced in the model forecasts. All cyclonic disturbances including the sub-polar lows and tropical cyclones that occurred during this period were well captured in the model forecasts. Overall, this model performs reasonably well over the Indian sector of the SO in medium range time scale.

The medium-range forecast of the dust aerosols over Indian region produced by the NCMRWF numerical weather prediction model with mineral dust scheme from May 2013 to May 2014 is examined in this study. Coarse mode aerosol observations are only used for comparison with dust forecast with the assumption that coarse mode aerosol over Indian region largely represents dust aerosol, especially over the areas of high dust load. Accuracy and trends of the day-to-day dust forecast are studied at three AERONET locations in Indo-Gangetic Plains (IGP) using surface and MODIS satellite retrievals of coarse mode aerosol optical depth for entire one year (May 2013–May 2014). Seasonal mean geographical distribution of the medium-range forecast of dust by the model over Indian region is validated with different satellite retrievals for all four seasons. Availability of suitable observations is one of the limiting factors and big challenges for the validation of the dust forecast. The main focus of this study is to assess dust forecast by the model over Indian region for all seasons, to know the biases and errors of the model forecast for its optimal use. The study finds that model dust forecast is comparable to AERONET observations over three locations for all seasons except monsoon season.

National Centre for Medium Range Weather Forecasting (NCMRWF) high resolution regional convective scale Unified Model (NCUM-R) with latest tropical science settings is used to evaluate vertical structure of cloud and precipitation over two prominent monsoon regions: Western Ghats (WG) and Monsoon Core Zone (MCZ). Model radar reflectivity generated using Cloud Feedback Model Inter-comparison Project (CFMIP) observation simulator package (COSP) along with CloudSat profiling radar reflectivity is sampled for an active synoptic situation based on a new method using Budyko's index of turbulence (BT). Regime classification based on BT-precipitation relationship is more predominant during the active monsoon period when convective scale model's resolution increases from 4 km to 1.5 km. Model predicted precipitation and vertical distribution of hydrometeors are found to be generally in agreement with Global Precipitation Measurement (GPM) products and BT based CloudSat observation respectively. Frequency of occurrence of radar reflectivity from model implies that the low level clouds below freezing level is underestimated compared to the observations over both regions. In addition, high level clouds in the model predictions are much lesser over WG than MCZ.

This paper describes the direct assimilation of water vapour (WV) clear sky brightness temperatures (CSBTs) from the INSAT-3D imager in the National Centre for Medium Range Weather Forecasting (NCMRWF) Unified Model (NCUM) assimilation and forecast system. INSAT-3D imager WV CSBTs show a systematic bias of 2–3 K compared to the data simulated from the model first guess fields in the pre-assimilation study. The bias in the INSAT-3D imager WV CSBTs is removed using a statistical bias correction prior to assimilation. The impact of INSAT-3D imager WV channel CSBTs is investigated through different approaches: (i) single observation experiments and (ii) global assimilation experiments using the hybrid-four-dimensional variational technique. Single observation experiments of channels of the same frequency from different instruments like the INSAT-3D imager and sounder, and the Meteosat visible and infrared imager (MVIRI) onboard Meteosat-7, show the INSAT-3D imager and MVIRI WV channels have a similar impact on the analysis increment. Global assimilation clearly shows the positive impact of the INSAT-3D imager WV CSBTs on the humidity and upper tropospheric wind fields, whereas the impact on the temperature field, particularly over the tropics, is neutral. Validation of model forecasted parameters with the in situ radio sonde observations also showed the positive impact of assimilation on the humidity and wind fields. INSAT-3D imager WV CSBTs have been assimilated operationally in NCUM since August 2018.

Prediction of heavy rains associated with orography is still a challenge, even for the most advanced state-of-art high-resolution Numerical Weather Prediction (NWP) modeling systems. The aim of this study is to evaluate the performance of UK Met Office Unified Model (UM) in predicting heavy and very heavy rainfall exceeding 80th and 90th percentiles which occurs mainly due to the forced ascent of air parcels over the mountainous regions of the Western Ghats (WGs) and North East (NE) – states of India during the monsoon seasons of 2007 to 2018. Apart from the major upgrades in the dynamical core of UM from New Dynamics (ND) to Even Newer Dynamics for General Atmospheric Modeling of the environment (ENDGame), the horizontal resolution of the model has been increased from 40 km and 50 vertical levels in 2007 to 10 km and 70 vertical levels in 2018. In general, it is expected that the prediction of heavy rainfall events improves with increased horizontal resolution of the model. The evaluation based on verification metrics, including Probability of Detection (POD), False Alarm Ratio (FAR), Frequency Bias (Bias) and Critical Success Index (CSI), indicate that model rainfall forecasts from 2007 to 2018 have improved from 0.29 to 0.38 (CSI), 0.45 to 0.55 (POD) and 0.55 to 0.45 in the case of FAR over WGs for rainfall exceeding the 80th percentile (CAT-1) in the Day-1 forecast. Additionally, the Symmetric Extremal Dependence Index (SEDI) is also used with special emphasis on verification of extreme and rare events. SEDI also shows an improvement from 0.47 to 0.62 and 0.16 to 0.41 over WGs and NE-states during the period of study, suggesting an improved skill of predicting heavy rains over the mountains. It has also been found that the improvement is consistent and comparatively higher over WGs than NE-states.

The current study reports for the first time an application of orography from the Cartosat-1 satellite digital elevation model (DEM) generated at a source resolution of 30 m in a convection-permitting numerical weather prediction model. The effects of improvements in the representation of orography have been examined in the high-resolution regional National Centre for Medium Range Weather Forecasting (NCMRWF) Unified Model predictions for a heavy rainfall event over the city of Chennai. A time-lagged ensemble method is employed to account for the uncertainties associated with the initial conditions, which can better forecast extreme weather events than single forecasts. The simulations reveal that the predictions based on Cartosat-1 DEM capture the local details of the rainfall distribution better than the National Aeronautics and Space Administration shuttle radar topography mission DEM-based predictions, and better represent the orographic and thermal uplifting. The spatio-temporal patterns of the simulated rainfall over Chennai are superior in Cartosat-1 DEM-based simulations mainly due to the enhanced wind convergence and moisture transport. The present study reveals the role of mountains in the enhancement of heavy rainfall events over coastal cities and highlights the potential use of high-resolution orography in the improvement of the operational weather forecasting skill of the NCMRWF Unified Model.

Ensemble prediction systems help in quantifying the inherent uncertainties in numerical weather prediction models. Verification of forecasts is essential, in order to monitor and improve forecast quality. Also, verification can be used to compare the capabilities of different numerical weather prediction models in predicting the weather. This study deals with a comparison of probabilistic rainfall forecasts obtained from the National Centre for Medium Range Weather Forecasting (NCMRWF) Global Ensemble Forecast System (NGEFS) and the UK Met Office Global and Regional Ensemble Prediction System (MOGREPS) for four monsoon seasons, June–September 2012–2015. Verification is done based on the Brier score, the Brier skill score, the reliability diagram, the relative operating characteristic (ROC) curve and the area under the ROC curve (AROC). The NMSG (India Meteorological Department NCMRWF merged satellite and gauge) observation dataset is used for verification. The Brier score values for verification of the MOGREPS are lower by approximately 6–14% across all rainfall thresholds and lead times, indicating that these forecasts match better with observations than the NGEFS. This is further reiterated by Brier skill score values of MOGREPS forecasts which are approximately 13–47% higher than the NGEFS. Furthermore, the reliability diagram shows that forecast probabilities are closer to observed frequencies for the MOGREPS than the NGEFS. The AROC for the MOGREPS is also higher than for the NGEFS, hence indicating a better skill of the MOGREPS. From this study, it can be concluded that the MOGREPS showed a better capability in predicting rainfall during the southwest monsoons of 2012–2015 over the Indian region compared to the NGEFS.

This study delineates the relative performance of the 12-km resolution NCMRWF regional Unified Model (NCUM-R) over the operational global NCUM (NCUM-G) model. Forecasts of four Bay of Bengal (BoB) landfalling tropical cyclones (TCs) using several different initial conditions (ICs) are used to compare the performance of two models. The position and intensity errors of the TCs are estimated with respect to the India Meteorological Department (IMD) and Joint Typhoon Warning Center (JTWC) best-track datasets and an inter-comparison study is also carried out between IMD and JTWC. The overall results suggest that the NCUM-R simulates the position and intensity of TCs more accurately compared to the NCUM-G. A majority of the TC tracks in the NCUM-G diverge more from the IMD track when compared to NCUM-R simulated tracks. It is also clearly noticed that both the models are more skillful in track prediction when initialized at intensity stages greater than “cyclone” category. However, the mean position errors at different forecast hours and landfall errors of TCs are reduced by approximately 31 and 47% in the NCUM-R simulations compared to NCUM-G simulations, respectively. The mean gain in skill of the NCUM-R in cross track (CT) and along track (AT) error is around 29 and 24% over NCUM-G, respectively. The intensity errors are less in the NCUM-R simulations. The mean rainfall skill scores are considerably improved in the NCUM-R simulations in day-1 and day-2 as compared to the NCUM-G simulations. It is noticed that the mean position errors of the TCs are approximately 8% lower when compared against the JTWC tracks than the IMD tracks. However, the intensity errors are higher against the JTWC than that of IMD most likely due to the averaging period of the wind speed.

Boreal summer sub-seasonal variability in the Asian monsoon, otherwise known as the monsoon intra-seasonal oscillation (MISO), is one of the dominant modes of intraseasonal variability in the tropics, with large impacts on total monsoon rainfall and India’s agricultural production. However, our understanding of the mechanisms involved in MISO is incomplete and its simulation in various numerical models is often flawed. In this study, we focus on the objective evaluation of the fidelity of MISO simulation in the Met Office Global Seasonal forecast system version 5 (GloSea5), an initialized coupled model. We analyze a series of nine-member hindcasts from GloSea5 over 1996-2009 during the peak monsoon period (July-August) over the South-Asian monsoon domain focusing on aspects of the time-mean background state and air-sea interaction processes pertinent to MISO. Dominant modes during this period are evident in power spectrum analysis, but propagation and evolution characteristics of the MISO are not realistic. We find that simulated air-sea interactions in the central Indian Ocean are not supportive of MISO initiation in that region, likely a result of the low surface wind variance there. As a consequence, the expected near-quadrature phase relationship between SST and convection is not represented properly over the central equatorial Indian Ocean, and northward propagation from the equator is poorly simulated. This may reinforce the equatorial rainfall mean state bias in GloSea5.

Surface level soil moisture from two gridded datasets over India are evaluated in this study. The first one is the UK Met Office (UKMO) soil moisture analysis produced by a land data assimilation system based on Extended Kalman Filter method (EKF), which make use of satellite observation of Advanced Scatterometer (ASCAT) soil wetness index as well as the screen level meteorological observations. Second dataset is a satellite soil moisture product, produced by National Remote Sensing Centre (NRSC) using passive microwave Advanced Microwave Scanning Radiometer 2 measurements. In-situ observations of soil moisture from India Meteorological Department (IMD) are used for the validation of the gridded soil moisture products. The difference between these datasets over India is minimum in the non-monsoon months and over agricultural regions. It is seen that the NRSC data is slightly drier (0.05%) and UKMO soil moisture analysis is relatively wet during southwest monsoon season. Standard AMSR-2 satellite soil moisture product is used to compare the NRSC and UKMO products. The standard AMSR-2 and UKMO values are closer in monsoon season and AMSR-2 soil moisture is higher than UKMO in all seasons. NRSC and AMSR-2 showed a correlation of 0.83 (significant at 0.01 level). The probability distribution of IMD soil moisture observation peaks at 0.25 m3/m3, NRSC at 0.15 m3/m3, AMSR-2 at 0.25 m3/m3 and UKMO at 0.35 m3/m3 during June–September period. Validation results show UKMO analysis has better correlation with in-situ observations compared to the NRSC and AMSR-2 datasets. The seasonal variation in soil moisture is better represented in UKMO analysis. Underestimation of soil moisture during monsoon season over India in NRSC data suggests the necessity of incorporating the actual vegetation for a better soil moisture retrieval using passive microwave sensors. Both products have good agreement over bare soil, shrubs and grassland compared to needle leaf tree, broad leaf tree and urban land cover types.

Simulations of a mesoscale convective system (MCS), which propagated across Northern India on 2nd May 2018 - leading to many fatalities when the gust front knocked down homes and tore apart building roofs - have been performed using the National Centre for Medium Range Weather Forecasting (NCMRWF) Unified Model – Regional (4 km horizontal grid spacing), to evaluate the model’s convective treatments. Though the model captures many of the qualitative and quantitative features, it slightly lags behind the observed MCS organisation and movement, produces lesser precipitation, and lacks the spatial separation between two adjacent organised convective systems in the satellite observations – leading to a faintly offset MCS track. Sensitivity simulations are then performed, for this non-equilibrium MCS case, with different partitioning between parametrized and explicit convection to assess the reliance of the convective treatments on the large-scale environment, as well as to test the notion of a breakdown of convective parametrization at the mesoscale model resolution. Fully parametrized (FP) convection produces even lesser rainfall and are dominated by orographic precipitations along the foot hills of Himalayas with no any trace of the MCS. Fully explicit (FE) convection realistically simulates most of the prominent convective cells and enhance precipitation along the MCS track that agree better with the observations, though the ‘two lobes’ of intense precipitation are not resolved; instead it produces a squall line of precipitation. The FE configuration generates the most vigorous convective updraft, along with a vertical shear that is tilted westward. The simulation with partially parametrized and partially explicit convection resembles the fashion in the FP and FE scenarios, with a transition over the duration of the run from parametrized to explicit precipitation. The results are in line with the notion from previous studies; that the majority of successful explicit simulations of mesoscale organisation are those associated with strong large-scale forcing for convection, wherein resolved vertical motions are sufficient to minimise delays in onset.

Ensemble Prediction System (EPS) estimates the probability distribution of atmospheric states through multiple integrations of the Numerical Weather Prediction (NWP) model/s with number of perturbed initial conditions. The choice of an ensemble size can affect the skill of the ensemble forecast. Since more ensemble members require more computational requirements, it is important to determine whether the larger ensemble size significantly improves the forecast uncertainty estimates. The EPS at National Centre for Medium Range Weather Forecasting (NEPS) is assessed for the prediction of track and intensity of Tropical Cyclones (TC) over Bay of Bengal (BoB). Medium range forecast of four tropical cyclones over BoB with three different ensemble sizes namely E11 (11 perturbed members), E22 (22 perturbed members) and E44 (44 perturbed members) are presented here. The results of the study indicate that doubling of the ensemble population reduced the mean initial position errors by 25% and increase of ensemble size by four times (E11 to E44) reduced the errors by about 38%. The ensemble mean forecast track has lower errors compared to the deterministic forecast (CNTL) at shorter lead times for all ensemble sizes. The impact of ensemble size is significant on the improvement of mean track prediction skill at longer lead times. Median of central pressure error is very close to zero for all the sizes and the forecasts are slightly underestimated compared to the observed values. The initial error in Maximum Sustained surface Wind speed (MSW) is smaller for E44 as compared to E22 and E11. Interestingly, in all lead time forecasts and all the ensemble sizes, ensemble mean MSW errors are nearly within ±5 ms−1. There is only slight improvement in the ensemble mean precipitation forecasts with the increase of ensemble size. NEPS fails to resolve the heavier rainfall distribution and has the tendency to over-predict light precipitation. Ensembles with larger size predict the TC strike area of intense storms with higher probabilities due to better consensus among member tracks fueled by improved steering flow. But few cases have shown E11 or E22 perform equally well in forecasting TC propagation and in some cases even better compared to E44. Threshold oriented verification was carried out for winds at 850 hPa considering all the cases collectively. Brier Score (BS) and its components are very close to one another for higher thresholds of winds. In general, E44 has lower BS for all different thresholds with small lead time forecasts. Smaller ensembles have slightly better reliability while large ensembles improved the resolution for lower thresholds of wind speed. Continuous ranked probability score indicate that predicted probability distribution agrees well with the analyzed cumulative distribution. Area under relative operating characteristic curve exhibits better discriminating ability of probabilistic events with larger ensemble sizes. This study clearly showed that an increase in the ensemble size has significant beneficial impact on the reduction of the percentage of outliers thereby improving the potential predictive skill of the unexpected events.

In depth examination of the extreme precipitation over Kerala during 15–17th August 2018 – triggering major flood episode in the peninsular Indian state - have been made via the National Centre for Medium Range Weather Forecasting (NCMRWF) Unified Model. A rare alignment of an intense low pressure area (LPA) over head Bay of Bengal (BoB) and a couple of typhoons over South China Sea and West Pacific, driven by anomalous sea surface conditions along the south-east Asian coastal belts, kept the low level jet meander from central Arabian Sea to West North Pacific (WNP) and remain relentless throughout the episode. Positive velocity potentials (VP, at 850 hPa) were seen prevailing for the region and some of these VP fringes unevenly crossed over to Kerala - suggestive of an uneven wind convergence – and associated rainfall necessarily not dictated by orography alone. A single system of negative stream functions (SF) suggested the prevalence of a conveyor belt like flow – termed, the ‘Remotely Aligned Intense Tropical Circulations’ (RAITC) – helping bring ‘an additional supply of moisture’ from WNP to Kerala; the cyclones over the WNP provided large amounts of moisture to the upper air and while some of these cyclones were weakening, a part of this moisture got injected to the south-westward flow and was then propelled further by the BoB LPA. While the NCUM global forecasts could capture the general circulation pattern for the intense precipitation over Kerala in the medium range, the spatial distribution of the heavy spells couldn’t be captured well in day-7 forecast; the strength and movement of the BoB LPA as well as the alignment and intensity modulation of the multiple tropical circulations showed some deviations. The convection permitting regional version of the model, NCUM-R (4 km grid length), exhibited lesser frequency for the light rainfall events, while over-predicting the frequency for the extreme precipitations.

Imranali, M.M.; Mitra, A.K.; Waters, J.; Martin, M.J., and Rajagopal, E., 2020. Impact of Altika Sea Level Anomaly data on variational assimilation system. In: Sheela Nair, L.; Prakash, T.N.; Padmalal, D., and Kumar Seelam, J. (eds.), Oceanic and Coastal Processes of the Indian Seas. Journal of Coastal Research, Special Issue No. 89, pp. 46-51. Coconut Creek (Florida), ISSN 0749-0208.Altimeter data provide measurements of dynamic topography over the global ocean and is useful for the constraining numerical ocean modeling systems through data assimilation. In this study, sensitivity experiments are carried out to understand the relative impact of Sea Level Anomaly (SLA) extracted from satellite data on a variational assimilation system for the month of August 2013. A first experiment, without assimilation of SLA observations is called CNTR while a second experiment assimilating Ka band Altika SLA into the variational assimilation system is called ALTK. Both experiments assimilated all other observations such as surface and in situ observations of temperature and salinity as well as sea ice concentration. To understand the impact of altimeter observations, we computed the root mean square difference (RMSD) of analyzed temperature from both experiments and showed that the maximum impact is over the Indian Ocean, North Atlantic Ocean and tropical Pacific Ocean regions due to the large displacement change applied to the thermocline depth in the ALTK experiment. We also note that the impact of altimeter on analyzed temperature is over different depths. Finally, the analyzed SSH has less root mean square error (RMSE) with respect to the altimeter observations for ALTK experiment than CNTR experiment over the global ocean and shallow water over the Indian Region (60-105°E, 5-40°N).

Ensemble Prediction Systems (EPSs) are used to estimate the uncertainty in a weather forecast as well as the most likely outcome. To improve weather forecasts and services, the global EPS at the NCMRWF has been upgraded to 12 km horizontal resolution (with 22 perturbed +1 control members). Although the upgraded version of EPS had become operational from 1st June 2018 in the newly acquired high-performance computing system (HPCS), the previous EPS at 33 km horizontal resolution (with 44 perturbed +1 control members) was also running in the old HPCS till 16th July 2018. As compared to the old EPS in NCMRWF, the new EPS includes perturbations in initial state of sea surface temperature, soil moisture content and deep soil temperature. The analysis of the new EPS also includes some more sources of observations. The perturbed 22 members long forecast of the new EPS provided at 00 UTC is the combination of 11 members from 00 UTC cycle and lagged 11 members from 12 UTC cycle of previous day. In the present study, the performances of the new and old EPS are compared for the period from 1st June to 16th July 2018. Performances of both the systems are assessed considering different attributes of probabilistic forecasting. The verification of precipitation, geopotential height at 500 hPa and zonal wind at 850 hPa has been carried out over the northern hemisphere, southern hemisphere and a smaller tropical region which includes India. The metrics used for verification are ensemble-spread, ensemble root-mean-square error, brier score, brier skill score, relative operating characteristics, reliability, sharpness diagrams, rank histogram, ranked probability score, continuous ranked probability score and continuous ranked probability skill score. Two heavy rainfall events have also been considered for subjective comparison of probabilistic prediction capabilities of both the systems. The results show that the new EPS is more skilful in probabilistic forecasts especially over the Indian region and it significantly outperforms the old EPS in probabilistic forecasts of the selected extreme weather events.

State-of-the-art Numerical Weather Prediction (NWP) models can provide useful weather information in the medium-range timescales (3 to 10 days ahead) which can be applied for decision-making in different sectors like agriculture, power distribution, disaster management and water resource management. Forecasting of monsoon weather system and associated rainfall is one of the most difficult areas in NWP due to complexities in land–ocean–atmosphere interactions and due to interactions between convective systems of cloud scale, mesoscale and synoptic and planetary scales. However, significant improvements can be noticed, in recent years. Some of the chief contributors to the improvement are improved data assimilation methods, enhanced satellite coverage, high-performance computers (HPCs) and high-resolution NWP models. This chapter documents current activities at NCMRWF involving the Unified Model and its Data Assimilation used for summer monsoon forecast in the medium-range timescales. Additionally, the research efforts made for testing and implementation of ensemble models and coupled models are also discussed.

The INCOMPASS field campaign combines airborne and ground measurements of the 2016 Indian monsoon, towards the ultimate goal of better predicting monsoon rainfall. The monsoon supplies the majority of water in South Asia, but forecasting from days to the season ahead is limited by large, rapidly developing errors in model parametrizations. The lack of detailed observations prevents thorough understanding of the monsoon circulation and its interaction with the land surface: a process governed by boundary- layer and convective-cloud dynamics. INCOMPASS used the UK Facility for Airborne Atmospheric Measurements (FAAM) BAe-146 aircraft for the first project of this scale in India, to accrue almost 100 h of observations in June and July 2016. Flights from Lucknow in the northern plains sampled the dramatic contrast in surface and boundary-layer structures between dry desert air in the west and the humid environment over the northern Bay of Bengal. These flights were repeated in pre-monsoon and monsoon conditions. Flights from a second base at Bengaluru in southern India measured atmospheric contrasts from the Arabian Sea, over the Western Ghats mountains, to the rain shadow of southeast India and the south Bay of Bengal. Flight planning was aided by forecasts from bespoke 4 km convection-permitting limited-area models at the Met Office and India's NCMRWF. On the ground, INCOMPASS installed eddy- covariance flux towers on a range of surface types, to provide detailed measurements of surface fluxes and their modulation by diurnal and seasonal cycles. These data will be used to better quantify the impacts of the atmosphere on the land surface, and vice versa. INCOMPASS also installed ground instrumentation supersites at Kanpur and Bhubaneswar. Here we motivate and describe the INCOMPASS field campaign. We use examples from two flights to illustrate contrasts in atmospheric structure, in particular the retreating mid-level dry intrusion during the monsoon onset.

Current state of the art weather/climate models are representation of the fully coupled aspects of the components of the earth system. Sea-ice is one of the most important components of these models. Simulation of sea-ice in these models is a challenging problem. In this study, evaluation of the hind-cast data of 14 boreal summer seasons with global coupled model HadGEM3 in its seasonal set-up has been performed over the Arctic region from $$9\mathrm{th}$$ May start dates. Along with the biases of the sea-ice variables, related atmosphere and oceanic variables have also been examined. The model evaluation is focused on seasonal mean of sea-ice concentration, sea-ice thickness, ocean surface current, SST, ice-drift velocity and sea-ice extent. To diagnose the sea-ice biases, atmospheric variables like, 10 m wind, 2 m air temperature, sea-level pressure and ocean sub-surface temperatures were also examined. The sea-ice variables were compared with GIOMAS dataset. The atmospheric and the oceanic variables were compared with the ERA Interim and the ECMWF Ocean re-analysis (ORAP5) datasets, respectively. The model could simulate the sea-ice concentration and thickness patterns reasonably well in the Arctic Circle. However, both sea-ice concentration and thickness in the model are underestimated compared to observations. A positive (warm) bias is seen both in 2 m air temperature and SST, which are consistent with the negative sea-ice bias. Biases in ocean current and related ice drift are not related to biases in the atmospheric winds. The magnitude of the oceanic subsurface warm biases is seen to be gradually decreasing with depth, but consistent with sea-ice biases. These analyses indicate a possibility of deeper warm subsurface water in the western Arctic Ocean sector (Pacific and Atlantic exchanges) affecting the negative biases in the sea-ice at the surface. The model is able to simulate reasonably well the summer sea-ice melting process and its inter-annual variability, and has useable skill for application purpose.

The operational medium range rainfall forecasts of the Met Office Unified Model (UM) are evaluated over India using the Contiguous Rainfall Area (CRA) verification technique. In the CRA method, forecast and observed weather systems (defined by a user-specified rain threshold) are objectively matched to estimate location, volume, and pattern errors. In this study, UM rainfall forecasts from nine (2007–2015) Indian monsoon seasons are evaluated against $$0.5^{\circ }\times 0.5^{\circ }$$ IMD–NCMRWF gridded observed rainfall over India $$(6.5^{\circ }{-}38.5^{\circ }\hbox {N}, 66.5^{\circ }{-}100.5^{\circ }\hbox {E})$$ . The model forecasts show a wet bias due to excessive number of rainy days particularly of low amounts $$({

This study compares the changes of land use/land cover (Lu/Lc) or the surface type in last decades over India. Recent surface-type fractions show few major regional changes over India. There is a decrease in vegetation fraction, increase in urban and bare soil fractions over India. The Unified Model coupled with Joint UK Land Environment Simulator land surface model was used to investigate the recent Lu/Lc impact on weather prediction. Preliminary results show improvement in weather prediction by the incorporation of the recent Lu/Lc data. This highlights the need to incorporate more realistic Lu/Lc in the dynamical models for better weather prediction.

Forecasting rainfall in the tropics is a challenging task further hampered by the uncertainty in the numerical weather prediction models. Ensemble prediction systems (EPSs) provide an efficient way of handling the inherent uncertainty of these models. Verification of forecasts obtained from an EPS is a necessity, to build confidence in using these forecasts. This study deals with the verification of the probabilistic rainfall forecast obtained from the National Centre for Medium Range Weather Forecasting (NCMRWF) Global Ensemble Forecast system (NGEFS) for three monsoon seasons, i.e., JJAS 2012, 2013 and 2014. Verification is done based on the Brier Score (BS) and its components (reliability, resolution and uncertainty), Brier Skill Score (BSS), reliability diagram, relative operating characteristic (ROC) curve and area under the ROC (AROC) curve. Three observation data sets are used (namely, NMSG, CPC-RFE2.0 and TRMM) for verification of forecasts and the statistics are compared. BS values for verification of NGEFS forecasts using NMSG data are the lowest, indicating that the forecasts have a better match with these observations as compared to both TRMM and CPC-RFE2.0. This is further strengthened by lower reliability, higher resolution and BSS values for verification against this data set. The ROC curve shows that lower rainfall amounts have a higher hit rate, which implies that the model has better skill in predicting these rainfall amounts. The reliability plots show that the events with lower probabilities were under forecasted and those with higher probabilities were over forecasted. From the current study it can be concluded that even though NGEFS is a coarse resolution EPS, the probabilistic forecast has good skill. This in turn leads to an increased confidence in issuing operational probabilistic forecasts based on NGEFS.

Reliable information of rainfall and its variability at finer spatio-temporal scales over the Indian monsoon region is crucial for various applications in hydrology, meteorology, water resource management, and evaluation of high-resolution numerical model outputs. In this study, the Tropical Rainfall Measuring Mission (TRMM)-based high-resolution multisatellite rainfall products namely, TRMM Multisatellite Precipitation Analysis (TMPA)-3B42 and Global Satellite Mapping of Precipitation (GSMaP) are evaluated against recently released gauge-based India Meteorological Department (IMD) gridded data set. Both the research (delayed mode) and near real-time rainfall products of TMPA-3B42 (V7 and RT) and GSMaP (MVK and NRT) are evaluated over India at a daily scale for the peak southwest monsoon months of July–August. Two study periods (2000–2010 and 2009–2013) are used to accommodate the analyses based on data availability. Comparison of TMPA-3B42 (V7 and RT) and GSMaP-MVK with IMD gauge-based data for 2000–2010 shows that these data sets are able to represent large-scale monsoon rainfall spatial features, but are having region-specific biases. The interannual variability of monsoon rainfall averaged over India is also reasonably captured. However in terms of biases, TMPA-3B42RT overestimates rainfall by about 21% and GSMaP-MVK underestimates by about 22% at all-India scale. In terms of continuous skill metrics, TMPA-3B42V7 is closer to the IMD gauge-based observations possibly due to gauge adjustments. Categorical verification against gauge-based observations shows that the satellite-based data sets have difficulties in rainfall detection primarily over the northern Jammu & Kashmir regions, northeast India, and southern peninsular India. At subregional scale within India, satellite rainfall estimates show rather lower skill score in rainfall identification and larger uncertainty in rainfall estimation over the northeast India. Comparison of near real-time products of TMPA and GSMaP for July–August months of 2009–2013 shows that TMPA-3B42RT overestimates and GSMaP-NRT underestimates rainfall over most parts of India. Even though TMPA-3B42 (V7 and RT) and GSMaP-MVK rainfall data sets show similar diurnal variations over the Indian monsoon region, TMPA-3B42RT (GSMaP-MVK) shows larger (smaller) magnitude in diurnal cycle as compared to TMPA-3B42V7. Results from different skill metrics suggest that both research and near real-time versions of TMPA products can be used with relatively higher confidence as compared to GSMaP over the Indian region during peak monsoon period.