Atmosphere

This study develops a practical framework for forecasting long-term drought conditions in Karaman Province, a semi-arid region of Turkey, where accurate climate information is vital for water planning and agriculture. Since the area has limited rainfall records and strong year-to-year fluctuations, traditional modeling approaches often fall short. To better capture local conditions, drought intensity was defined using a simple monthly wetness anomaly measure based directly on precipitation; here, positive values indicate wetter months and negative values indicate drier ones. This makes the method suitable for regions where detailed hydrological data are scarce. Rainfall observations from 1965 to 2011 were expanded using a combination of kernel density estimation and Cholesky-based correlation reconstruction. These steps preserved the main statistical and temporal patterns of the original data while increasing sample diversity. The enriched dataset was then used to train artificial neural networks to predict both precipitation and drought intensity. The models reached R² values of 0.76 and 0.72, with mean absolute errors of 12.8 mm and 28.4%, which represents an improvement of roughly 10–15% over traditional statistical methods. They were also able to capture the seasonal and year-to-year variability that strongly affects drought conditions in the region. To understand what drives the predictions, the model was examined with LIME, which consistently highlighted lagged rainfall and seasonal indicators as the most influential inputs. A walk-forward validation approach was also used to mimic real forecasting conditions and demonstrated that the model remains stable when projecting into the future. Overall, the proposed framework offers a reliable and practical basis for early-warning efforts and drought-management strategies in semi-arid regions like Karaman.

Much of the information for studying the processes of lightning discharge initiation and development is provided by studying thundercloud radio emissions in various frequency bands. The High-Frequency (HF) band better corresponds to the characteristic scales of lightning development but has been undeservedly forgotten and is used quite rarely. Based on observations carried out in the Upper Volga region it is shown that the intensity of HF radio emissions from lightning is high enough to be reliably recorded in nearby thunderstorms. It is found that at all stages of lightning development the intensity of its radio emission in the HF band up to 10 MHz exceeds the average background intensity significantly. The amplitudes of lightning pulses exceed the background level more significantly, up to 60 dB and more. The feasibility of using the HF band for lightning observations including tracing the direction of arrival of a radio emission is clearly demonstrated.

Acoustic precipitation enhancement (APE) is an emerging non-chemical weather-modification technique, yet quantitative three-dimensional evidence of its impact on rainy clouds remains scarce. This study investigates a stratiform precipitation event over the Bayinbuluke Basin in the central Tianshan Mountains of northwestern China, 29–30 August 2024, using an X-band phased-array weather radar (X-PAR) coordinated with an upward-directed acoustic source. Rapid volumetric scans and sector-aligned range-height indicators were combined to reconstruct the three-dimensional cloud structure before, during, and after acoustic operation. During acoustic operation, the results were stronger and more persistent than during the non-operation period, with localized values exceeding 40 dBZ. Within the 3 km influence zone, low-level reflectivity increased across all azimuthal sectors with clear directional dependence. Dual-ratio analysis showed statistically significant enhancement in the windward sector (247°, $DR$ = 1.91, $p$ = 0.0004) and the leeward sector (137°, $DR$ = 1.51, $p$ = 0.008), indicating that acoustic-induced responses extended beyond the primary radiation sector and propagated downstream with cloud advection. These results, based on a single stratiform precipitation case, demonstrate that volumetric X-PAR observations can detect localized cloud-structure responses during acoustic operation.