This study quantifies the volume transport and water mass composition of the Southwest Monsoon Current (SMC) in the southern Bay of Bengal (BOB) using a combination of in-situ observations from a field campaign along 85.5 degrees E in June 2022 and outputs from an assimilated numerical model. The model demonstrates strong skill in reproducing observed thermohaline and velocity structures, validated by high correlation coefficients for potential density and velocity components. A fuzzy C-means clustering algorithm applied to isopycnal-surface temperature and salinity data identifies four water masses associated with the SMC-BOB Water (BOBW), Upper Mixed Water (UMW), Arabian Sea Water (ASW) and Indian Equatorial Water (IEW)-with model distributions closely matching observations. During the 2022 SMC period, the average volume transport across 6 degrees N reached approximately 11.0 +/- 2.5 Sv, a 67% increase above the climatological mean of 6.6 +/- 1.4 Sv. Of this inflow, UMW contributed similar to 36.9%, ASW similar to 31.5%, and IEW similar to 28.8%, while BOBW only accounted for similar to 2.7%. The elevated transport is overwhelmingly dominated by anomalous geostrophic transports rather than wind-driven Ekman transports. Remote anomalies triggered by a negative Indian Ocean Dipole and local anomalies associated with the Sri Lanka Dome jointly strengthened the SMC, with the former enhancing cross-flow pressure gradients and the latter transporting additional coastal water that merged into the SMC.
Marine heatwaves (MHWs) are extreme ocean warming events with significant ecological and climatic impacts. While extensively studied in various oceanic regions, the characteristics and underlying mechanisms of MHWs in the Indonesian Maritime Continent (IMC) remain poorly understood. This study investigates the statistics and drivers of MHWs in two key regions of the IMC, South Java and the Karimata Strait, during the period 1982-2021, in conjunction with climatic modes. In both regions low-intensity MHWs occurred regularly as a baseline independent of any climate events, whereas intense and prolonged events were strongly associated with active climate modes. In South Java prolonged and intense MHWs occurred most frequently during the negative phase of the Indian Ocean Dipole mainly by reduced latent heat loss due to weakened wind speeds. The second most frequent but low-intensity MHWs occurred independently of any climate events. In the Karimata Strait, intense and frequent MHWs were closely related with El Nino events, driven by increased shortwave radiation and, secondarily, by reduced latent heat loss. Our findings provide critical insights into the physical mechanisms driving MHWs onset in the IMC, particularly the role of air-sea coupling and ocean dynamics, which are useful for understanding future warming risks.
The current study evaluates the impact of precipitation assimilation using the 4D-Var approach on the forecast of convective afternoon rainfall events relative to hydrometeor complexity. Three afternoon convective rainfall cases during the pre-monsoon season over a southern state of the Indian subcontinent are chosen for investigation. To quantify the impact of precipitation assimilation on forecasting performance, data assimilation experiments are conducted using the Weather Research and Forecasting (WRF) model 4D-Var, and the resulting forecasts are verified using standard spatial verification methods. Data from Integrated Multi-satellite Retrievals for the Global Precipitation Measurement Mission (IMERG) are assimilated, and forecasts are evaluated using observations from a dense network of over 6,000 automated rain gauges. Based on hydrometeor complexities, WSM3, WSM5, and WSM6 schemes are used as the non-linear microphysics in the 4D-Var assimilation system. Results indicate that assimilating precipitation observations improves the accuracy of quantitative precipitation forecasts, as observed in the improved skill scores. The impact of assimilating precipitation observations is case, scheme and assimilation configuration dependent, with the WSM5 scheme showing the improvement in skill scores. Additionally, precipitation assimilation is found to produce a drier and warmer atmosphere with reduced low-level moisture during early forecast hours in certain cases, leading to delayed cloud growth or inhibited cloud development. Meanwhile, in other cases, precipitation assimilation has led to a cooler and moister atmosphere. For the WSM3 and WSM6 experiments, precipitation assimilation results in reduced hydrometeor activity in the mid-level atmosphere, whereas the WSM5 experiment shows increased hydrometeor activity in both the lower and mid-level atmosphere. Analyzing hydrometeor tendencies of graupel, snow, and rainwater of WSM6 experiment, it is observed that the depletion of graupel in the upper atmosphere is accompanied by only a small increase in low-level rainwater, while snow increases at higher levels. This desynchronized behavior may be associated with certain microphysical processes and co-occurs with a weaker surface-precipitation response in the WSM6 experiments. Meanwhile, for the WSM5 scheme, the transition toward low-level rainwater is comparatively more temporally coherent, which may have resulted in higher surface rainfall intensity. The findings highlight the importance of incorporating precipitation observations with suitable microphysics schemes and assimilation configuration to improve quantitative precipitation forecasts as observed for the selected convective rainfall events.
The southwest monsoon (June-September; JJAS) is the primary source of water for agriculture, reservoirs, and socioeconomic activities across the Godavari River Basin (GRB), one of the largest river basins in India. This study investigates spatiotemporal variability, long-term changes, and climatic controls of monsoon rainfall over the GRB during 1965-2024 using the India Meteorological Department (IMD) gridded rainfall observations, ERA5 reanalysis products, NOAA Outgoing Longwave Radiation (OLR), NCEP-NCAR reanalysis winds, and the Nino3.4 sea surface temperature (SST) index. An integrated assessment of rainfall characteristics, extremes, atmospheric moisture conditions, circulation variability, and El Nino-Southern Oscillation (ENSO) teleconnections was performed for twelve GRB sub-basins. To evaluate multidecadal changes, a Pre-Global-Warming period (PGW; 1965-1994) was compared with a Recent-Global-Warming period (RGW; 1995-2024). Results reveal substantial spatial heterogeneity, with the highest monsoon rainfall occurring in the central and northeastern sub-basins. Significant positive rainfall trends were identified in sub-basins G2, G4, G6, G7, G10, and G12 (p < 0.05). Basin-scale rainfall variability was strongly controlled by rainy-day frequency (r = 0.92, R-2 = 0.84, p < 0.001). Mean JJAS rainfall increased modestly from 922.3 mm during PGW to 966.9 mm during RGW (+4.8%), while rainy-day frequency changed negligibly (+0.15%). In contrast, moderate (>= 15.6 mm day-1), heavy (>= 64.5 mm day-1), and very heavy (>= 115.6 mm day-1) rainfall events increased by 3.6%, 23.4%, and 49.9%, respectively. The frequency of R99p extreme-rainfall events also increased significantly by 37.1% (p = 0.039), providing evidence of increasing very extreme precipitation during the recent period. Rainfall exhibited significant positive relationships with relative humidity(RH), specific humidity(SH), soil moisture, and vertically integrated moisture convergence(VIMD), and a significant inverse relationship with OLR, highlighting the importance of atmospheric moisture availability and deep convection in regulating monsoon variability. Latitude-pressure analyses revealed a weakening of the Tropical Easterly Jet and lowertropospheric monsoon circulation during RGW. Standardized rainfall anomalies were significantly negatively correlated with Nino3.4 SST anomalies (r = -0.48, p < 0.001), confirming a strong ENSO influence on monsoon rainfall over the basin. Overall, the results indicate that recent decades have experienced relatively small changes in seasonal rainfall totals but a statistically significant increase in very extreme rainfall events, suggesting a redistribution of monsoon rainfall toward higher-intensity precipitation with important implications for flood risk, waterresource management, and climate adaptation across the GRB.
Wind farms are frequently exposed to lightning hazards. Therefore, meteorological parameter prediction for lightning activity and physically interpretable process verification are needed. In this study, very-short-term forecasting of lightning-related meteorological variables, lightning-day identification, and WRF-ELEC simulation were integrated to construct a lightning-warning framework for wind farms. Specifically, the CNN-BiLG-ECA model was proposed to predict hub-height wind speed and relative humidity in the Dezhou region, the Logistic-NN model was used for lightning-day identification, and WRF-ELEC was applied to simulate lightning activity and provide physical interpretation for the warning framework. The CNN-BiLG-ECA model achieved R & sup2; values of 0.9791 and 0.9845 for very-short-term forecasting of wind speed and relative humidity, respectively. The Logistic-NN model obtained Precision = 0.522, Recall = 0.889, F1 = 0.658, and AUC = 0.946 at a threshold of 0.55. When forecast wind speed and relative humidity were used in the test stage, F1 and AUC decreased to 0.639 and 0.933, respectively. The high-value regions of IUI generally appeared near the lightning-active areas simulated by WRF-ELEC, with a clearer correspondence during the mature convective stage. Neighborhood-based comparison with LMI observations showed that the WRF-ELEC lightning-detection metrics were sensitive to the ERA5 forcing interval. However, FAR increased to 0.4167 and 0.6400 at the 3-h and 6-h forcing intervals, respectively. This framework provides a technical pathway that combines risk screening and physical verification for lightning warning in wind farms, but further multi-process and probabilistic validation is still needed.
