A global forecast system model at a horizontal resolution of T1534 (∼12.5 km) has been evaluated for the monsoon seasons of 2016 and 2017 over the Indian region. It is for the first time that such a high-resolution global model is being run operationally for monsoon weather forecast. A detailed validation of the model therefore is essential. The validation of mean monsoon rainfall for the season and individual months indicates a tendency for wet bias over the land region in all the forecast lead time. The probability distribution of forecast rainfall shows an overestimation (underestimation) of rainfall for the lighter (heavy) categories. However, the probability distribution functions of moderate rainfall categories are found to be reasonable. The model shows fidelity in capturing the extremely heavy rainfall categories with shorter lead times. The model reasonably predicts the large-scale parameters associated with the Indian summer monsoon, particularly, the vertical profile of the moisture. The diurnal rainfall variability forecasts in all lead times show certain biases over different land and oceanic regions and, particularly, over the northwest Indian region. Although the model has a reasonable fidelity in capturing the spatiotemporal variability of the monsoon rain, further development is needed to enhance the skill of forecast of a higher rain rate with a longer lead time.

[1] Extreme low cold point tropopause (CPT) temperatures (T ≤ 191 K) are often observed during the monsoon season over the Bay of Bengal (BOB) and adjoining areas. This paper reports frequent occurrences of extreme low CPT temperature over the Arabian Sea (AS) and adjoining areas using radiosonde observations during the Arabian Sea Monsoon Experiment (ARMEX) from 24 June to 15 August 2002. Day-to-day variations in temperature at CPT and at the 100 hPa level observed during the ARMEX campaign show modulation by the wave activity with a period of ∼15 days, and it is observed to be closely associated with the Tropical Easterly Jet (TEJ). Characteristics of wave modulating the temperature at the CPT and at the 100 hPa level are brought out and discussed. Spatial and temporal distribution of low CPT temperature over a wide scale is examined using CHAMP and COSMIC satellite temperature data. These observations show occurrences of low CPT temperatures during the early period of the monsoon season over BOB, AS, and adjoining areas, which often extend to Africa's Horn region. An enhanced low CPT temperature occurrence during the early part of the monsoon appears to be due to the modulation of outgoing long wave-radiation (OLR), CPT temperature, and height by intraseasonal oscillation. Modulation of CPT by intraseasonal oscillation suggests that this oscillation could contribute to dehydration of the lower stratosphere. In addition, a close association is noted between the seasonal variations of the latitude of low CPT temperature and low OLR, which is similar to the anticipated seasonal movement of the Intertropical Convergence Zone (ITCZ).Citation: Jain, A. R., V. Panwar, C. J. Johny, T. K. Mandal, V. R. Rao, R. Gautam, and S. K. Dhaka (2011), Occurrence of extremely low cold point tropopause temperature during summer monsoon season: ARMEX campaign and CHAMP and COSMIC satellite observations,

The hybrid two-way coupled 3DEnsVar assimilation system was tested with the NCMRWF global data assimilation forecasting system. At present, this system consists of T574L64 deterministic model and the grid-point statistical interpolation analysis scheme. In this experiment, the analysis system is modified with a two-way coupling with an 80 member Ensemble Kalman Filter of T254L64 resolution and runs are carried out in parallel to the operational system for the Indian summer monsoon season (June-September) for the year 2015 to study its impact. Both the assimilation systems are based on NCEP GFS system. It is found that hybrid assimilation marginally improved the quality of the forecasts of all variables over the deterministic 3D Var system, in terms of statistical skill scores and also in terms of circulation features. The impact of the hybrid system in prediction of extreme rainfall and cyclone track is discussed.

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.