Verification and bias correction of ECMWF forecasts for Irish weather stations to evaluate their potential usefulness in grass growth modelling

Abstract

Typical weather in Ireland provides conditions favourable for sustaining grass growth throughout most of the year. This affords grass based farming a significant economic advantage due to the low input costs associated with grass production. To optimize the productivity of grass based systems, farmers must manage the resource over short time scales. While research has been conducted into developing predictive grass growth models for Ireland to support on-farm decision making, short term weather forecasts have not yet been incorporated into these models. To assess their potential for use in predictive grass growth models, deterministic forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) were verified for lead times up to 10 days using observations from 25 Irish weather stations. Forecasts of air temperature variables were generally precise at all lead times, particularly up to 7 days. Verification of ECMWF soil temperature forecasts is limited, but here they were shown to be accurate at all depths and most precise at greater depths such as 50 cm. Rainfall forecasts performed well up to approximately 5 days. Seven bias correction techniques were assessed to minimize systematic biases in the forecasts. Based on the root mean squared error values, no large improvement was identified for rainfall forecasts on equivalent ECMWF forecasts, but the optimum bias corrections improved air and soil temperature forecasts greatly. Overall, the results demonstrated that forecasts predict observations accurately up to approximately a week in advance and therefore could prove valuable in grass growth prediction at farm level in Ireland.

Validation of ECMWF climatic data, 1979–2017, and implications for modelling water balance for tropical climates

“…Studies from around the world have reported this problem: McDonnell et al . (2018) and Ghosh et al . (2018) reported inconsistent P ECMWF results for Ireland and southern Asia, respectively.…”

“…McDonnell et al . (2018) found ECMWF errors <10% between P data from meteorological ground stations and ECMWF data in Ireland and ensured that ECMWF data could be used to estimate grass biomass.…”

“…The ECMWF data tended to have higher P in the rainy season compared to the data from the surface stations. McDonnell et al (2018) found ECMWF errors <10% between P data from meteorological ground stations and ECMWF data in Ireland and ensured that ECMWF data could be used to estimate grass biomass.…”

Weather forecasts to enhance an Irish grass growth model

“…Further details on these bias correction techniques are available in McDonnell et al (2018) and Joliffe and Stephenson (2011).…”

“…The inclusion of forecasts will introduce an extra level of uncertainty to the model. McDonnell et al (2018) assessed the accuracy of European Centre for Medium-Range Weather Forecasts (ECMWF) forecasts at 25 Irish weather stations, and applied bias correction techniques to improve forecast accuracy. Air temperatures were forecast accurately up to ten days in advance, with improvements after bias correction, and rainfall forecasts generally performed well up to five days in advance.…”