Using the spectral scaling exponent for validation of quantitative precipitation forecasts

• Main Authors: Purnawirman, P., Bhatt, B. C., Cheung, K. K. W., Teo, C. K., Lee, Y. H., Roth, Matthias, Koh, Tieh-Yong
• Other Authors: School of Electrical and Electronic Engineering
• Format: Article
• Language: English
• Published: 2014
• Subjects: DRNTU::Science::Physics::Meteorology and climatology
• Online Access: https://hdl.handle.net/10356/79609; http://hdl.handle.net/10220/19301
• Institution: Nanyang Technological University

This study evaluates the spectral scaling of a heavy rainfall event and assesses the performance of the Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS) model in terms of the multiscale variability of rainfall in the temporal spectral domain. The event occurred over southern Malay Peninsula on 18 December 2006 and was simulated at high resolutions. 10, 5 and 1 min aggregate rainfall data from rain gauge stations in Singapore and simulated rainfall sampled at different evaluation points on 0.9, 0.3 and 0.1 km grids were utilized. The simulated and observed rain rates were compared via Fourier and wavelet analyses. A scaling regime was noted in the observed rainfall spectra in the timescales between 60 min and 2 min. The scaling exponent obtained from the observed spectra has a value of about 2, which may be indicative of the physics of turbulence and raindrop coalescence and might suggest the predominance of a characteristic raindrop size. At 0.9 km resolution, the model rainfall spectra showed similar scaling to the observed down to about 10 min, below which a fall-off in variance was noted as compared to observations. Higher spatial resolution of up to 0.1 km was crucial to improve the ability of the model to resolve the shorter timescale variability. We suggest that the evaluation of dynamical models in the spectral domain is a crucial step in the validation of quantitative precipitation forecasts and assessing the minimal grid resolution necessary to capture rainfall variability for certain short timescales may be important for hydrological predictions.