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GPS PWV (Precipitable Water Vapour) is estimated using three years ECMWF, JRA55, MERRA2, and NCEP datasets for two IGS stations HYDE and IISC in India to assess their accuracies over the Indian region. The NWP derived temperature, pressure, zenith hydrostatic delay (ZHD), zenith wet delay (ZWD), and PWV values are validated using AWS observations and derived products. The obtained PWV values are also compared with the total column water product from ECMWF. The results show absolute mean temperature differences of 1-2 °K and pressure differences of 1-4 hPa for HYDE and IISC. NWP models' pressure and temperature values strongly correlate with AWS values with R² close to 0.9 in most of the cases. The estimated zenith tropospheric delay (ZTD) shows an average difference between 4mm and 9mm with respect to IGS ZTD. The mean differences between NWP and reference PWV values are observed from 1mm to 3 mm. All the derived products obtained using NWP datasets are close to the reference AWS products. However, PWV derived using ECMWF, JRA55 and NCEP is very much close to AWS PWV and can be used for precise PWV computation in this region. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Skillful quantitative precipitation forecast using the numerical weather prediction model relies on an accurate estimate of the atmospheric state as an initial condition. Variational assimilation methods (VAR) have the potential to provide improved initial state estimation to the numerical weather prediction model using observations, prior data (background), and their respective error covariance. The quality of variational assimilation hinges on the background error statistics (BES) as it weights the error in prior state and determines the spread of assimilated observations in model space. Traditional approaches used to model stationary BES in a three‐dimensional variational assimilation system often fail to represent the model error in BES. In this study, we have proposed an ensemble method using Stochastically Perturbed Parameterization Tendency to represent the model error in BES. The characteristics of the proposed BES are compared with the traditional approaches using the National Meteorological Centre method for different control variables choices. We have further tested the performance of the proposed method in improving the skill of precipitation forecast for an extreme rainfall event, which caused devastating flood over Chennai city, India, on December 2015. Results demonstrate that the use of the proposed method results in better forecast skill of convective precipitation in terms of both position and intensity than traditional National Meteorological Centre‐based BES. Best results are obtained when zonal and meridional momentum control variables are used for modeling ensemble‐based BES. Key Points: New method is proposed to represent model error of background error statisticsPerformance of proposed method improves forecast skill for an extreme eventBest results are obtained when using zonal and meridional momentum control variables [ABSTRACT FROM AUTHOR]

The Indian summer monsoon is one of the most important phenomenon that bring vital rain to India, so the Indian summer monsoon forecast and its verification is always of great interest due to monsoons' importance for India's agriculture. In the present study the categorical (Yes / No) and quantitative verification of rainfall forecast of the Global Forecasting System model running at India Meteorological Department, IMD GFS T574 (25 km resolution) and National Centre for Environmental Prediction, NCEP GFS T1534 is done over Indian domain against 0.25° gridded rainfall observations during summer monsoon season 2015. A detailed verification study for rainfall forecast at 0.25° × 0.25° grid for Indian Window (0 - 40° N and 60 - 100° E) is conducted using two models output; both the / models are indicating that skill of the rainfall forecast is good for all parts of the country except high terrain regions. Regional verification also carried over 5 homogeneous regions of India, i.e., North India, West Coast India, North East India, Central India and Peninsular India using both model outputs. Results show that, in general, both the GFS T1534 and GFS T574 forecasts are skillful to capture climatologically heavy rainfall regions. However, the accuracy in prediction of location and magnitude of rainfall fluctuates considerably. The results documented are expected to be useful to the operational forecasters in day-to-day weather forecasting over Indian monsoon regions. [ABSTRACT FROM AUTHOR]

• Meteorological analysis of an MCS over northern India on 2nd May 2018. • MCS evolution in the NCMRWF Unified Model – Regional (4 km). • Sensitivity simulations to assess the reliance of convective treatments on large-scale environment. • Successful explicit simulations of MCS are those associated with strong large-scale forcing for convection. 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. [ABSTRACT FROM AUTHOR]