Recent upgrades to the Met Office convective‐scale ensemble: An hourly time‐lagged 5‐day ensemble

Abstract

In this article, we introduce a new configuration of the Met Office convective‐scale ensemble for numerical weather prediction, for the Met Office Global and Regional Ensemble Prediction System over the United Kingdom (MOGREPS‐UK). The new version, which became operational in March 2019, uses an hourly time‐lagged configuration to take advantage of the hourly 4D‐Var data assimilation run in the deterministic UK model with variable horizontal resolution, the UKV. An 18‐member ensemble is created by running three members every hour and time‐lagging these over a 6 hr window. This configuration is compared against the previous operational configuration, a 6‐hourly convective‐scale ensemble running 12 members. The main benefits of the time‐lagged ensemble are to increase the ensemble size, to add small‐scale uncertainties in the initial conditions and to generate more timely forecasts. The time‐lagged configuration is shown to objectively improve the forecast at all lead times, with larger improvements in the first few hours. The improvement is seen in the ranked probability scores and is mainly associated with the improvements in the spread of the ensemble with an increase of about 5 to 10% in both summer and winter seasons. A larger ensemble size is necessary in the time‐lagged configuration for it to outperform or maintain as good a performance against the previous 6‐hourly configuration for all lead times. Alongside the update to an hourly configuration, the forecast length is more than doubled to 120 hr. Objective verification shows that the time‐lagged configuration performs better than the high‐resolution deterministic, UKV, and the global ensemble, MOGREPS‐G, up to T + 120 hr. Increasing the size of the time‐lagged ensemble through lagging over additional cycles leads to small but significant improvements, larger in most cases than those that can be obtained through neighbourhood processing.

Do Convection-Permitting Ensembles Lead to More Skillful Short-Range Probabilistic Rainfall Forecasts over Tropical East Africa?

“…Arguably, the most appealing way to improve CP ensemble spread is way to improve the CP ensemble spread is to improve the spread of the initial conditions of the parent driving ensemble. Porson et al (2019), for instance, showed that that perturbing the sea surface temperatures (SSTs) in the initial conditions of the parent model generates a higher spread also in the driven CP ensemble than just having fixed SSTs. Another way to enhance the ensemble spread is through the representation of model error in the physics scheme (Bouttier et al 2012).…”

“…The simulations supporting the SWIFT forecasting testbed were run from 19 April to 12 May 2019, giving a total number of 24 days. The Met Office Unified Model (MetUM) Tropical East Africa CP ensemble model (hereafter CP-ENS) was run as a downscaler of the of the global ensemble, similar to the set-up used by the Met Office CP model (MOGREPS-UK) up to March 2016 (Hagelin et al 2017) and for the CP model over Singapore (Porson et al 2019). Here, the initial and boundary conditions for each CP-ENS member are taken from the MetUM global ensemble (MOGREPS-G, Bowler et al (2009), running with a horizontal grid spacing of 0.288 with 18 members.…”

“…1). It consisted of 18 members, initialized four times a day (at 0000, 0600, 1200, and 1800 UTC) and ran out to 72 h. The science configuration of the dynamics and physics schemes of the atmosphere and land used for the tropical regions, denoted with ''RAL1-T,'' are documented in Bush et al (2019) and is the same used in Porson et al (2019). In particular the tropical configuration differs from the midlatitude configuration used for MOGREPS-UK for these reasons: a different set of vertical levels (more levels in the upper troposphere to allow for a higher tropopause), the presence of boundary layer stochastic perturbations in the midlatitude configuration (useful to initiate convection earlier) and not in the tropical configuration, as well as the use of a prognostic cloud scheme (PC2) in the tropical configuration.…”

A regional coupled approach to water cycle prediction during winter 2013/14 in the United Kingdom

“…There is also a strong requirement to move to the assessment of land surface and river flow simulations in probabilistic terms. Work is in progress to run the UK coupled system in ensemble mode, with the atmosphere component driven by the MOGREPS-UK operational NWP ensemble (Porson et al, 2020). Driving regional river flow predictions with an ensemble of precipitation input, and introducing stochastic and parameter perturbations in the land surface and river routing components offers many opportunities to better understand the propagation of uncertainty through the system, as well as consider appropriate design of regional coupled ensemble systems when coupling a range of potential flow solutions with ensemble ocean model components.…”

Incorporating the North Atlantic Oscillation into the post‐processing of MOGREPS‐G wind speed forecasts

“…This study utilises 10‐m wind speed forecasts extracted from the Met Office's global ensemble prediction system, MOGREPS‐G (Walters et al ., 2017; Porson et al ., 2020), issued in the 2‐year period between January 1, 2018 and December 31, 2019. No major model upgrades have been performed since this period, and hence the model configuration is very similar to that currently in operation.…”