Ensemble-based Model for Rainfall Nowcasting using Automatic Weather Station Data

Shah, Nita H.; Shukla, Bipasha Paul; Priamvada, Anupam

doi:10.25103/jestr.154.16

Cited by 1 publication

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“…In the absence of such tools, KE is often approximated using empirical equations that derive KE from observed rainfall intensity (Martínez et al, 2016). The examination of disdrometer data for the mountainous region of Shillong for the year 2005 was also completed by us earlier and found that light rainfall days are signi cantly more common than heavy rainfall days there (Nita et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Yearly variation of rainfall rate and drop size distribution of RD-80 Disdrometer for Ahmedabad and Thiruvananthapuram

Shah,

Shukla,

Chahal

2023

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The study of the microphysics of precipitation and the development of better rainfall estimation methods heavily rely on studies of the raindrop size distribution (RDSD). This study uses data from the RD-80 Disdrometer collected over three years for Ahmedabad and Thiruvananthapuram (2005–2007). By visualizing the histograms of rainfall bins and calculating the correlation coefficient between rainfall rate and each drop size class of the disdrometer for the monsoon months of June, July, and August, we were able to observe the variability of rainfall year-over-year and month-over-month. The year 2007 appears to have a good link with the greater raindrops for both places, even though the two areas have different characteristics and hence produce different outcomes. Also, except for the year 2005, there is no negative link between rainfall rate and the tiniest droplets in Thiruvananthapuram, which is not the case in Ahmedabad.

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Section: Introductionmentioning

confidence: 99%