Stochastic precipitation downscaling with the RainFARM method.
rainfarmr is a R package implementing the RainFARM (Rainfall Filtered Autoregressive Model) stochastic precipitation downscaling method. Adapted for climate downscaling according to (D'Onofrio et al. 2018) and with fine-scale orographic weights (Terzago et al. 2018).
RainFARM (Rebora et al. 2006) is a metagaussian stochastic downscaling procedure based on the extrapolation of the coarse-scale Fourier power spectrum of a spatio-temporal precipitation field to small scales.
# Make some sample synthetic rainfall data # 10 fields of 8 by 8 pixel resolution r <- exp(rnorm(8 * 8 * 10)) # The corresponding latitudes and longitudes lon <- seq(5, 8.5, 0.5) lat <- seq(43.5, 47, 0.5) dim(r) <- c(8, 8, 10) nf <- 8 # This is the factor by which we will increase resolution # Downscale with spectral slope=1.7 to size 64x64 rd <- rainfarm(r, 1.7, nf, fsmooth = TRUE) # Get the corresponding fine-scale longitudes and latititudes grid <- lon_lat_fine(lon, lat, nf) grid$lon[1:4] #  4.78125 4.84375 4.90625 4.96875
Terzago, S., Palazzi, E., and von Hardenberg, J. (2018). Stochastic downscaling of precipitation in complex orography: a simple method to reproduce a realistic fine-scale climatology, Nat. Hazards Earth Syst. Sci., 18, 2825-2840, doi: https://doi.org/10.5194/nhess-18-2825-2018
D’Onofrio, D., Palazzi, E., von Hardenberg, J., Provenzale, a., & Calmanti, S. (2014). Stochastic Rainfall Downscaling of Climate Models. Journal of Hydrometeorology, 15(2), 830–843. doi: https://doi.org/10.1175/JHM-D-13-096.1
Rebora, N., Ferraris, L., von Hardenberg, J., & Provenzale, A. (2006). RainFARM: Rainfall Downscaling by a Filtered Autoregressive Model. Journal of Hydrometeorology, 7(4), 724–738. doi: https://doi.org/10.1175/JHM517.1
A julia version by the same author is available.
R version - J. von Hardenberg, ISAC-CNR (2019)
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