Assessing spatial precipitation uncertainties in a convective‐scale ensemble

Abstract

New techniques have recently been developed to quantify the location‐dependent spatial agreement between ensemble members, and the spatial spread–skill relationship. In this article a summer of convection‐permitting ensemble forecasts are analysed to better understand the factors influencing location‐dependent spatial agreement of precipitation fields and the spatial spread–skill relationship over the UK. The aim is to further investigate the agreement scale method, and to highlight the information that could be extracted for a more long‐term routine model evaluation. Overall, for summer 2013, the UK 2.2 km grid spacing ensemble system was found to be reasonably well spread spatially, although there was a tendency for the ensemble to be overconfident in the location of precipitation. For the forecast lead times considered (up to 36 h), a diurnal cycle was seen in the spatial agreement and in the spatial spread–skill relationship: the forecast spread and error did not increase noticeably with forecast lead time. Both the spatial agreement and the spatial spread–skill were dependent on the fractional coverage and average intensity of precipitation. A poor spread–skill relationship was associated with a low fractional coverage of rain and low average rain rates. The times with a smaller fractional coverage, or lower intensity, of precipitation were found to have lower spatial agreement. The spatial agreement was found to be location dependant, with higher confidence in the location of precipitation to the northwest of the UK.

The Met Office convective‐scale ensemble, MOGREPS‐UK

“…There are also plans to introduce the use of neighbourhood post‐processing that adapts to the spatial ensemble spread following the work of Dey et al . (, ).…”

“…Methods to adapt the neighbourhood size to the spatial ensemble following the approach in Dey et al . (, ) have been tested and show promise.…”

Spatial spread‐skill relationship in terms of agreement scales for precipitation forecasts in a convection‐allowing ensemble

“…Recently, Dey et al (, ) have developed a new neighbourhood‐based method to identify agreement scales and used it to evaluate the scale‐dependent spatial spread‐skill relationship for ensemble precipitation forecasts in a convection‐allowing EPS developed over the United Kingdom. The spatial spread‐skill relationship refers to the comparison of the average agreement scale for ensemble members, denoted as , and the average agreement scale for ensemble members and observations, denoted as .…”

“…The spatial spread‐skill relationship refers to the comparison of the average agreement scale for ensemble members, denoted as , and the average agreement scale for ensemble members and observations, denoted as . Through a long‐term routine model evaluation, they got useful information about ensemble performance and characteristics in summer 2013 over the United Kingdom (Dey et al , ). In order to test the feasibility of this latest method under different climate conditions in different regions, this study applies this neighbourhood‐based method to verify a convection‐allowing EPS over the Yangtze‐Huaihe river basin of China.…”

“…Surcel et al () found out that the precipitation fractional coverage is the best indicator associated with precipitation predictability among three indicators ( τ c , large‐scale forcing for ascent and precipitation fractional coverage). Although Dey et al () did not verify the spatial spread‐skill relationship for different weather regimes, their study also suggested that this relationship is highly associated with precipitation fractional coverage. Therefore, in this study, the precipitation fractional coverage is selected as the classification criterion of weather regime, and the cases during the Meiyu season of 2013 over the Yangtze‐Huaihe river basin are categorized to large fractional coverage (LC) and small fractional coverage (SC) groups, respectively.…”