Quantitative View on the Processes Governing the Upscale Error Growth up to the Planetary Scale Using a Stochastic Convection Scheme

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This study provides a process‐based perspective on the amplification of forecast uncertainty and forecast errors in ensemble forecasts. A case from the North Atlantic Waveguide and Downstream Impact Experiment that exhibits large forecast uncertainty is analysed. Two aspects of the ensemble behaviour are considered: (a) the mean divergence of the ensemble members, indicating the general amplification of forecast uncertainty, and (b) the divergence of the best and worst members, indicating extremes in possible error‐growth scenarios. To analyse the amplification of forecast uncertainty, a tendency equation for the ensemble variance of potential vorticity (PV) is derived and partitioned into the contributions from individual processes. The amplification of PV variance is, on average for the midlatitudes of the Northern Hemisphere, dominated by near‐tropopause dynamics. Locally, however, other processes can dominate the variance amplification, for example, in the region where tropical storm Karl interacts with the Rossby‐wave pattern during extratropical transition. In this region, the variance amplification is dominated by upper‐tropospheric divergence and tropospheric–deep interaction and is thereby mostly related to (moist baroclinic) cyclone development. The differences between the error growth in the best and worst ensemble members can, to a large part, be attributed to differences in the representation of cut‐off evolution around 3 days, which subsequently amplifies substantially in the highly nonlinear region of the Rossby‐wave pattern until 5 days. In terms of the processes, the differences in error growth are dominated by differences in the error growth by near‐tropopause dynamics. The approach presented provides flow‐dependent insight into the dynamics of forecast uncertainty and forecast errors and helps to understand better the different contributions of specific weather systems to the medium‐range amplification of ensemble spread.

P = - g \frac{\partial θ}{\partial p} ((), ζ_{θ} + f),

where g is the gravitational acceleration, θ the potential temperature, p the pressure, ζ θ the vertical component of the isentropic relative vorticity, and f the Coriolis parameter.…”

Section: Data and Methods To Quantify The Amplification Of Forecast Umentioning

confidence: 88%

Section: Quantitative View Of the Variance Amplificationmentioning

confidence: 99%

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Processes governing the amplification of ensemble spread in a medium‐range forecast with large forecast uncertainty

Baumgart

Riemer

2019

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“…After five days, the PV250 differences due to the MCS perturbations lay along the tropopause, consistent with findings from previous studies of error growth related to the representation of moist processes using PV‐based analysis (Schäfler and Harnisch, ; Joos and Forbes, ; Baumgart et al . ), but the position of these differences varied between the ensemble members, corresponding to the different Rossby‐wave structures generated by the IC perturbations. Despite this variability, the MCS perturbations have been shown systematically to shift the location of a surface low‐pressure system over northern Russia, downstream of the location where the MCS perturbations were inserted, to the west of northwest; this shift is consistent with other studies that have found slowed eastwards Rossby‐wave propagation due to intense convection (Riemer et al .…”

Section: Discussionmentioning

confidence: 98%

“…The impact of differences in the representation of convection on upscale error growth has been investigated by Baumgart et al . () by applying diagnostics based on PV and the envelope of Rossby waves to convection‐parametrizing ensemble forecasts that use the Plant–Craig stochastic convection scheme (Plant and Craig, ). Similar to the findings in our study, they found that the strong PV gradient associated with the tropopause is displaced by error growth associated with moist processes occurring below (with associated latent heat release) and upper‐level divergence projects these errors effectively into the tropopause region.…”

Section: Downstream Influence Of Mcs Perturbations: Overviewmentioning

confidence: 99%

Downstream influence of mesoscale convective systems. Part 2: Influence on ensemble forecast skill and spread

Clarke

Gray

Roberts

2019

Ensemble forecasts are run operationally to determine the forecast uncertainty arising from initial condition, model physics and boundary condition uncertainty. However, global configuration ensembles, which use a convection parametrization scheme, may miss uncertainty because of the misrepresentation of intense convection by such schemes. Here, the impacts of the misrepresentation of mesoscale convective systems (MCSs) on downstream ensemble forecast skill and evolution are determined for a case study. MCS perturbations (calculated‐15 from the difference between outputs from convection‐parametrizing and convection‐permitting Met Office model configurations) are added to six members of a global configuration ensemble created by downscaling forecasts from the global version of the Met Office Global and Regional Ensemble Prediction System. For the first 36 hours, differences grow on the convective scale related to the MCSs, leading to systematic deepening of a developing UK cyclone, although there is damping of the perturbations found in root mean square difference calculations between the forecasts with and without the perturbations (particularly in mean sea level pressure). Subsequently, differences grow rapidly to the synoptic scale, and by five days impact the entire Northern Hemisphere. The MCS perturbations can have systematic effects on ensemble forecasts (e.g., a systematic displacement of a downstream cyclone is found), but, for this case, there is no discernible change in forecast skill as measured by the root mean square error of the ensemble means and the effects of the MCS perturbations are smaller than those generated by the initial condition perturbations. The spread of the combined ensemble (the two ensembles with and without the MCS perturbations) is larger than that of the individual ensembles. Thus, perturbing convection‐parametrizing models to include potential vorticity anomalies associated with MCSs represented in convection‐permitting forecasts, or idealized representations of them, produces alternative realizations from those generated by initial condition perturbations and has the potential to be useful operationally.

Examining model error in potential temperature and potential vorticity weather forecasts at different lead times

Martínez‐Alvarado

Sánchez

2020

The examination of model error is fundamental to improve weather forecasts at any time-scale. Here, model errors for two forecast lead times (12, 24 hr) at the grid-point level are analysed using (a) the total Eulerian changes in variables, such as potential temperature and potential vorticity (PV), both conserved under adiabatic, frictionless conditions, and (b) Lagrangian diabatic tracers. The latter refines the Eulerian analysis by decomposing the total Eulerian changes into materially conserved and diabatically generated components. For both analyses, the behaviour of a theoretical unbiased model, for which the only assumption is that forecast error is zero when averaged over a large number of cases, is used as a reference. Deviations from this theoretical behaviour are used to highlight conditions leading to large errors. The analyses are performed on a set of forecasts produced with the UK Met Office Unified Model for a 25-day period during the NAWDEX (North Atlantic Waveguide and Downstream Impact Experiment) field campaign (16 September-22 October 2016). The Eulerian approach indicates that changes in potential temperature and PV are underestimated with respect to the theoretical behaviour of an unbiased model. The grid points with the largest changes in 12 hr forecasts have the largest underestimation in the 24 hr forecast, highlighting the importance of the underestimation for the most dynamically and thermodynamically active grid points. The Lagrangian-tracer investigation reveals very large deviations from the theoretical behaviour of an unbiased model regardless of the level of Eulerian change, in particular for PV, and an unrealistic similarity in magnitude between parametrized diabatic changes of PV in the 24 hr and 12 hr forecasts. This is at odds with what would be otherwise required to obtain unbiased behaviour. Addressing the deviations from the behaviour of a theoretical unbiased model found in this work could be a step forward towards an operational unbiased model. K E Y W O R D S diabatic processes, diabatic tracers, Eulerian flow description, Lagrangian flow description, model error, potential vorticity, potential temperature Q J R Meteorol Soc. 2020;146:1264-1280.wileyonlinelibrary.com/journal/qj