A Spatial View of Ensemble Spread in Convection Permitting Ensembles

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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.

“…selecting heavier precipitation) showing larger

S_{ij}^{normalA (\overset{mm}{false})}

. This is expected as higher precipitation thresholds select a lower fractional coverage of precipitation, and agrees with the work of Dey et al ().…”

Section: Discussionmentioning

confidence: 99%

“…Roberts and Lean, ; Ebert, ; Gilleland et al , ; Johnson and Wang, ). More recently, new methods have been explored for characterising both the skill and dispersion of convective‐scale ensemble forecasts (Clark et al ; Johnson et al ; Surcel et al ; Dey et al ).…”

Section: Introductionmentioning

confidence: 99%

Assessing spatial precipitation uncertainties in a convective‐scale ensemble

Dey

Plant

Roberts³

et al. 2016

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“…Dey et al . () discuss the spatial structure of ensemble spread from MOGREPS‐UK. They characterized the spatial scale of ensemble spread by considering how the Fractions Skill Score metric (Roberts and Lean, ) varied with neighbourhood size.…”

Section: Description Of Mogreps‐ukmentioning

confidence: 99%

The Met Office convective‐scale ensemble, MOGREPS‐UK

Hagelin

Son

Swinbank

et al. 2017

116

145

Since 2012 the Met Office has been running a short‐range convective‐scale ensemble prediction system over the United Kingdom, known as MOGREPS‐UK. In this article we consider MOGREPS‐UK in its past, present and future configurations. We describe the evolution of the system during its first few years as an operational model and explain the rationale behind its development. The operational configuration of MOGREPS‐UK is evaluated using neighbourhood verification techniques which allow the comparison of ensemble and deterministic forecasts in a probabilistic sense. We compare the performance of MOGREPS‐UK to that of the higher‐resolution UK deterministic convective‐scale model, the UKV, and show that over a 3‐month long trial MOGREPS‐UK performs better for all variables considered. Plans of future upgrade options of MOGREPS‐UK that take advantage of the increased computing capacity at the Met Office are discussed. Three different options are compared: increasing the domain size (now implemented), decreasing the horizontal grid‐spacing, and increasing the number of ensemble members. Objective verification results from month‐long winter and summer trials show that all options have their benefits, with the most improvement seen with the increase in ensemble size, particularly for precipitation.

“…Dey et al () used the FSS to estimate the domain‐averaged spatial ensemble spread and skill by comparing all independent pairs of ensemble members and all ensemble member–radar pairs. Here, a similar approach is applied to the agreement scales

S_{ij}^{A (f_{1} f_{2})}

to calculate how the spatial agreement between ensemble members and the spatial agreement between ensemble members and radar observations varies with location across the domain.…”

Section: Calculation Of Location‐dependent Agreement Scalesmentioning

confidence: 99%

A new method for the characterization and verification of local spatial predictability for convective‐scale ensembles

Dey

Roberts

Plant

et al. 2016

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The use of kilometre‐scale ensembles in operational weather forecasting provides new challenges for forecast interpretation and evaluation to account for uncertainty on the convective scale. A new neighbourhood‐based method is presented for evaluating and characterizing the local predictability variations from convective‐scale ensembles. Spatial scales over which ensemble forecasts agree (agreement scales, SA) are calculated at each grid point ij, providing a map of the spatial agreement between forecasts. By comparing the average agreement scale obtained from ensemble member pairs (SijA(falsemm¯)) with that between members and radar observations (SijA(falsemo¯)), this approach allows the location‐dependent spatial spread–skill relationship of the ensemble to be assessed. The properties of the agreement scales are demonstrated using an idealized experiment. To demonstrate the methods in an operational context, SijA(falsemm¯) and SijA(falsemo¯) are calculated for six convective cases run with the Met Office UK Ensemble Prediction System (MOGREPS‐UK). SijA(falsemm¯) highlights predictability differences between cases, which can be linked to physical processes. Maps of SijA(falsemm¯) are found to summarize the spatial predictability in a compact and physically meaningful manner that is useful for forecasting and model interpretation. Comparison of SijA(falsemm¯) and SijA(falsemo¯) demonstrates the case‐by‐case and temporal variability of the spatial spread–skill, which can again be linked to physical processes.