To improve the understanding and prediction of convective storms over the Kingdom of Saudi Arabia (KSA), the National Center for Meteorology (NCM) established a network of Doppler weather radars (DWRs) across the country in 2009. Despite this advancement, assimilation of DWR observations into numerical weather prediction models has not yet been explored in KSA. In this study, we investigate, for the first time, the impact of assimilating DWR observations on the simulation of the heavy rainfall event that affected Jeddah on 25 November 2009 using a high-resolution (1-km) configuration of the Weather Research and Forecasting (WRF) model. Three radar reflectivity assimilation strategies are evaluated: (i) direct assimilation of radar reflectivity, (ii) indirect assimilation through relative humidity (RH) retrieved from radar reflectivity, and (iii) indirect assimilation through hydrometeor mixing ratios derived from radar reflectivity. The results demonstrate a clear positive impact of DWR data assimilation on the representation of the initiation, intensity, and duration of the heavy rainfall event. These improvements are primarily associated with corrections to the low-level wind field and moisture distribution, which enhance moisture convergence and lead to more realistic rainfall patterns. While all assimilation experiments capture the location and large-scale characteristics of the storm, the indirect assimilation of hydrometeor mixing ratios derived from DWR reflectivity provides the most realistic representation of the storm’s thermodynamic structure, small-scale convective cells, and quasi-stationary behavior over Jeddah, highlighting its potential to improve the prediction of heavy rainfall events.
Drought has a strong dependence on climatic factors, including rainfall and temperature, and has a serious effect on agricultural production. In this study, we analyzed the effects of climatic factors on agricultural drought, the spatial-temporal distribution and variability of agricultural drought during April to June, the period of the most severe drought in the Democratic People’s Republic of Korea (DPRK), using the Vegetation Health Index (VHI). Correlation analysis between three-month Standardized Precipitation evapotranspiration index (SPEI3) and VHI confirmed the effectiveness of VHI in agricultural drought assessment. To analyze the effect of climatic factors on agricultural drought, a simple linear regression model was applied. The correlation coefficients between VHI and SPEI3 are 0.57–0.71, highest in the west coast zone and lowest in the northern inland zone. VHI showed a significant decreasing trend in all climatic zones except the east coast zone. The frequency of agricultural drought in the study area is 4–8 times and the recurrence interval is once every 2.5-5 years. The frequency, intensity and variability are highest in the west coast zone. VHI is strongly positively correlated with soil moisture, precipitation and negatively correlated with air temperature. The impact of climate factors on VHI was highest in the west coast zone. The climatic factors with the highest correlation with VHI are rainfall and soil moisture, and the relatively lower climate factor is air temperature. These results can help stakeholders monitor agricultural drought occurrence, improve early warning systems, and take drought mitigation measures.
Accurate characterization of precipitation variability is fundamental for climate diagnostics and water resource management. This study investigates the statistical relationships between near-surface meteorological conditions and seasonal-decadal precipitation variability over mainland Vietnam using ERA5 reanalysis data from 1971 to 2020. Ten key atmospheric variables were analyzed via factor analysis (FA) to extract latent modes of covariability, which were subsequently used as factor-based meteorological indices in a Gamma regression framework to quantify their statistical associations with total precipitation. Two-way analysis of variance (ANOVA) was employed to examine seasonal, decadal, and interactive effects on both precipitation and the derived indices. The analysis identified three robust latent factors, representing a thermodynamic-moisture factor (F1), a near-surface circulation factor (F2), and a cloud- cover factor (F3), together explaining 85.52
Accurate rainfall forecasting remains a major challenge, particularly in regions characterized by complex topography and high climatic variability. Small errors arising from observations, data assimilation, and model configuration can grow nonlinearly leading to rapid loss of forecast skill with time. Traditionally, rainfall forecasts have been generated at grid-based spatial resolutions; however, weather varies markedly within a grid box and therefore grid-based ensemble forecasts often struggle to capture sub-grid variability and localized extremes, limiting their usefulness for operational forecasting at specific points of interest. The ecPoint post-processing approach, developed at the European Centre for Medium-Range Weather Forecasts (ECMWF), addresses this limitation by tailoring ensemble forecasts to point-specific locations. This study adapts the approach to the China Meteorological Administration Global Ensemble Prediction System (CMA-GEPS) through regional calibration over the Yangtze River Basin in eastern China. The performance of the post processed GEPS was evaluated using probabilistic verification metrics compared with both the raw GEPS and the original ECMWF ecPoint system, the latter used as an independent benchmark reference. A case study of the 30 July 2022 Xuzhou flooding event was also studied to assess spatial rainfall representation. Five months of verification demonstrated that between the raw and post-processed forecasts, ecPoint is more reliable and skillful markedly improving forecast performance across all rainfall thresholds and lead times, with reductions in CRPS and higher AUC values relative to the raw ensemble. The Xuzhou case study further illustrated ecPoint’s ability to capture localized heavy rainfall with greater spatial accuracy compared to the raw GEPS. The ECMWF_ecPoint exhibited higher skill and a stronger diurnal cycle nonetheless, the GEPS_ecPoint demonstrated substantial improvement and value-added skill over the raw GEPS forecasts. Overall, the results demonstrate that ecPoint provides a transferable framework for improving ensemble rainfall forecast reliability, with strong potential of improving operational flood risk and early-warning systems.
The northwestern Himalayan region frequently experiences extreme rainfall in the form of localized cloudbursts, yet the multiscale dynamical forcing behind these events remains poorly understood. This study examines the mechanisms governing five catastrophic cloudburst events on 13–14 August 2023 over Uttarakhand, India. A synoptic-scale, quasi-stationary moisture transport corridor associated with a monsoon-embedded Category 1 Atmospheric River (AR) exhibited integrated water vapor flux exceeding 460 kg m⁻¹s⁻¹. Its interaction with orography (in a very low Froude-number ( 0.01–0.03) blocked-flow regime) enhanced upslope convergence, producing a pronounced stau effect and strong forced ascent (vertical velocities up to -4.5 Pa s⁻¹). A mesoscale potential vorticity (PV) dipole with a negative core -6 PVU confined convective activity over steep terrain gradients. A strong boundary-layer instability with total energy of 3.2–3.4 × 10⁵ J kg⁻¹ was present. Intermittent mid-level dry-air intrusion further modulated convection, promoting rapid intensification and collapse of convective towers. Rainfall patterns followed terrain-controlled corridors, indicating dynamic confinement and shifting moisture convergence zones. Based on these findings, a four-stage framework is proposed: (i) AR-driven moisture transport, (ii) orographic blocking, (iii) PV-induced trapping, and (iv) thermodynamic modulation. This integrated mechanism explains the short-lived, episodic, and localized intense nature of Himalayan cloudbursts.
