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Faiz Rohman Fajary

Publications and source records attributed to Faiz Rohman Fajary.

2 recordsLinked to original sources

Preliminary sonification of ENSO using traditional Javanese gamelan scales

Sonification -- the mapping of data to non-speech audio -- offers an underexplored channel for representing complex dynamical systems. We treat El Niño-Southern Oscillation (ENSO), a canonical example of low-dimensional climate chaos, as a test case for culturally-situated sonification evaluated through complex systems diagnostics. Using parameter-mapping sonification of the Niño 3.4 sea surface temperature anomaly index (1870--2024), we encode ENSO variability into two traditional Javanese gamelan pentatonic systems (pelog and slendro) across four composition strategies, then analyze the resulting audio as trajectories in a two-dimensional acoustic phase space. Recurrence-based diagnostics, convex hull geometry, and coupling analysis reveal that the sonification pipeline preserves key dynamical signatures: alternating modes produce the highest trajectory recurrence rates, echoing ENSO's quasi-periodicity; layered polyphonic modes explore the broadest phase space regions; and the two scale families induce qualitatively distinct coupling regimes between spectral brightness and energy -- predominantly anti-phase in pelog but near-independent in slendro. Phase space trajectory analysis provides a rigorous geometric framework for comparing sonification designs within a complex systems context. Perceptual validation remains necessary; we contribute the dynamical systems methodology for evaluating such mappings.

physics.soc-ph↗

Three-Dimensional Variational Data Assimilation with Rapid Update Cycling for Short-Range Precipitation Forecasting: A Case Study of Heavy Rainfall in Bali, Indonesia

This study evaluates the effectiveness of three-dimensional variational (3D-Var) data assimilation coupled with a Rapid Update Cycle (RUC) framework for improving short-range precipitation forecasts over the Indonesian Maritime Continent (IMC). We employ the Weather Research and Forecasting (WRF) model and its data assimilation component (WRFDA) to assimilate surface observations from Automatic Weather Stations (AWS) at cycling intervals of 1, 3, 6, and 12 hours. Our test case is a heavy rainfall event on 7 July 2023 in Bali Province, during which accumulated precipitation exceeded 193 mm.day$^{-1}$. The 1-hour cycling interval yields the lowest root-mean-square error (RMSE) for both 2-meter temperature (0.0-0.3$\,^\circ$C) and hourly precipitation (1.295 mm.h$^{-1}$), corresponding to reductions of roughly 75% and 57%, respectively, relative to non-assimilated forecasts. Frequent cycling constrains initial-condition errors and captures mesoscale convective evolution, as confirmed by improved spatial agreement with radar reflectivity observations. These results demonstrate that high-frequency assimilation cycling offers clear advantages for nowcasting in tropical maritime environments.

physics.ao-ph↗