An evaluation strategy of skill of high‐resolution rainfall forecast for specific agricultural applications. (July 2016)

• Record Type: Journal Article
• Authors: Rakesh, V.; Goswami, Prashant
• Is Part Of: Meteorological applications. Volume 23:Number 3(2016)
• Page Start: 529
• Page End: 540
• Publication Date: 2016-07
• Subjects: agro‐advisories -- rainfall forecasts -- forecast evaluation -- forecast skill -- risk‐free forecasts -- best practices; Meteorology -- Periodicals; Meteorological services -- Periodicals; 551.5
• DOI: 10.1002/met.1576
• Languages: English
• ISSNs: 1350-4827

Abstract

ABSTRACT: A strategy for validation of rainfall forecasts for specific agricultural applications is presented. The focus is mainly on the design of specific forecast advisories that are risk‐free and useful in spite of their inherent errors. The strategy works for these specific applications because the forecast advisories are based on when NOT to irrigate or apply fertilizer/pesticide because rain is predicted (risk‐free because wrong forecast only delays irrigation/application of fertilizer/pesticide within tolerance). Thus, unlike in conventional forecast evaluation, a forecast is considered as valid if the forecasted rain (or no rain) is correct for the day of the forecast (D0C) or the next day or the day after (designated D1C and D2C, respectively), as the farmer can afford to postpone the field application for a couple of days beyond the scheduled date. The methodology has been evaluated for rainfall forecasts over Karnataka (a state in southwest India with nearly 56% of the workforce engaged in agriculture). Here, forecast validation against rain gauge observations is presented at comparable resolutions for the southwest (June to September) and the northeast (October to December) monsoon seasons during 2011–2014. Analyses demonstrate that forecasts over several areas which may appear to be less reliable based on conventional evaluation (D0C) are found to have useful skill for the specific agro‐applications as evident from evaluation based on D1C and D2C criteria. Our analysis shows that the evaluation strategy presented is effective during the non‐rainy (January–May) season also. It is pointed out that such an approach can help to meet the challenges in designing and implementing best practices in agriculture by combining immediate gains for the end users with long‐term sustainability.