TY - JOUR
AU - Ahmad Osama Musleh,
AU - Fayez Ahmad Abdulla,
PY - 2022/06/26
Y2 - 2024/06/15
TI - Generation of Synthetic Daily Rainfall Data in Jordan
JF - American Scientific Research Journal for Engineering, Technology, and Sciences
JA - ASRJETS-Journal
VL - 88
IS - 1
SE - Articles
DO -
UR - https://asrjetsjournal.org/index.php/American_Scientific_Journal/article/view/7615
SP - 213-232
AB - <p>This study aims to generate synthetic daily rainfall data for 39 meteorological stations in Jordan by estimating the distributional parameters of daily rainfall occurrence and amounts. Daily rainfall occurrence was modeled by the use of rainfall interarrival times, which were fitted to the one-parameter exponential distribution, except zero values which were represented using the ratio of the number of zero interarrival times to the total number of times, which was called zero ratio. Daily rainfall amounts were fitted to the two-parameter gamma distribution. Goodness-of-fit for one of the stations was tested using chi-square test. This test was performed using Microsoft Office Excel. Distributional parameters were calculated for both occurrence and amounts models, and 100 sequences of synthetic daily rainfall data were then generated, of which every sequence included 1000 non-zero daily rainfall data points (1000 wet days) and 1000 interarrival times (1000 dry spells of which some have a length of zero). These sequences were generated using the embedded random number generators in Python, for one-parameter exponential distribution, two-parameter gamma distribution, and uniform distribution. Percent errors were then calculated and found all to be less than 10%, which was considered acceptable.</p>
ER -