Estimating the Intrinsic Limit of Predictability Using a Stochastic Convection Scheme

Selz, Tobias

doi:10.1175/jas-d-17-0373.1

Cited by 28 publications

(32 citation statements)

References 23 publications

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“…Forecasts from almost identical initial states gradually diverge such that the root‐mean‐square difference between them approaches a factor of

\sqrt{2}

the difference between either state and climatology (Leith, ). For synoptic‐scale midlatitude weather systems, such error saturation and loss of predictability occurs after roughly 2 weeks (Selz, ; Zhang et al, ). Our DWLP models were trained to minimize the error in a 6‐hr time step; do they continue to predict realistic atmospheric states on 2‐week time scales?…”

Section: Resultsmentioning

confidence: 99%

Can Machines Learn to Predict Weather? Using Deep Learning to Predict Gridded 500‐hPa Geopotential Height From Historical Weather Data

Weyn

Durran

Caruana

2019

J Adv Model Earth Syst

239

166

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We develop elementary weather prediction models using deep convolutional neural networks (CNNs) trained on past weather data to forecast one or two fundamental meteorological fields on a Northern Hemisphere grid with no explicit knowledge about physical processes. At forecast lead times up to 3 days, CNNs trained to predict only 500‐hPa geopotential height easily outperform persistence, climatology, and the dynamics‐based barotropic vorticity model, but do not beat an operational full‐physics weather prediction model. These CNNs are capable of forecasting significant changes in the intensity of weather systems, which is notable because this is beyond the capability of the fundamental dynamical equation that relies solely on 500‐hPa data, the barotropic vorticity equation. Modest improvements to the CNN forecasts can be made by adding 700‐ to 300‐hPa thickness to the input data. Our best performing CNN does a good job of capturing the climatology and annual variability of 500‐hPa heights and is capable of forecasting realistic atmospheric states at lead times of 14 days. Although our simple models do not perform better than an operational weather model, machine learning warrants further exploration as a weather forecasting tool; in particular, the potential efficiency of CNNs might make them attractive for ensemble forecasting.

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“…Forecasts from almost identical initial states gradually diverge such that the root‐mean‐square difference between them approaches a factor of

\sqrt{2}

Section: Resultsmentioning

confidence: 99%

Can Machines Learn to Predict Weather? Using Deep Learning to Predict Gridded 500‐hPa Geopotential Height From Historical Weather Data

Weyn

Durran

Caruana

2019

J Adv Model Earth Syst

239

166

View full text Add to dashboard Cite

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“…During the first 1–2 days, the near‐tropopause tendency dominates, on average, the amplification of PV variance. This result is in contrast to results from upscale‐error‐growth simulations, in which the ensemble members differ only in terms of the stochastic seed of the convection scheme (Selz, ). In these simulations, error growth in the early phase of the simulations is dominated by moist processes (Baumgart et al .…”

Section: Summary and Discussionmentioning

confidence: 99%

“…The technicalities of the flow partitioning are also the same as in Teubler and Riemer (2016) and Baumgart et al (2018;2019): We use a Helmholtz partitioning to separate the divergent flow from the nondivergent flow, following Lynch (1989). The nondivergent flow is further partitioned into those parts associated with upper-and lower-level PV anomalies, respectively, using piecewise PV inversion (PPVI) under nonlinear balance (Charney, 1955), following Davis and Emanuel (1991) and Davis (1992).…”

Section: Quantitative Pv Framework For the Amplification Of Forecast mentioning

confidence: 99%

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

Baumgart

Riemer

2019

Quart J Royal Meteoro Soc

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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.

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“…The error growth is different here because we have excluded initial condition uncertainty to focus on model uncertainty. In ensemble experiments with differences generated only by a stochastic convection parametrization, there is upscale error growth (Selz, ; Baumgart et al ., ). Baumgart et al .…”

Section: Discussionmentioning

confidence: 99%

Reduced‐precision parametrization: lessons from an intermediate‐complexity atmospheric model

Saffin

Hatfield

Dueben

et al. 2020

Quart J Royal Meteoro Soc

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Reducing numerical precision can save computational costs which can then be reinvested for more useful purposes. This study considers the effects of reducing precision in the parametrizations of an intermediate complexity atmospheric model (SPEEDY). We find that the difference between double‐precision and reduced‐precision parametrization tendencies is proportional to the expected machine rounding error if individual timesteps are considered. However, if reduced precision is used in simulations that are compared to double‐precision simulations, a range of precision is found where differences are approximately the same for all simulations. Here, rounding errors are small enough to not directly perturb the model dynamics, but can perturb conditional statements in the parametrizations (such as convection active/inactive) leading to a similar error growth for all runs. For lower precision, simulations are perturbed significantly. Precision cannot be constrained without some quantification of the uncertainty. The inherent uncertainty in numerical weather and climate models is often explicitly considered in simulations by stochastic schemes that will randomly perturb the parametrizations. A commonly used scheme is stochastic perturbation of parametrization tendencies (SPPT). A strong test on whether a precision is acceptable is whether a low‐precision ensemble produces the same probability distribution as a double‐precision ensemble where the only difference between ensemble members is the model uncertainty (i.e., the random seed in SPPT). Tests with SPEEDY suggest a precision as low as 3.5 decimal places (equivalent to half precision) could be acceptable, which is surprisingly close to the lowest precision that produces similar error growth in the experiments without SPPT mentioned above. Minor changes to model code to express variables as anomalies rather than absolute values reduce rounding errors and low‐precision biases, allowing even lower precision to be used. These results provide a pathway for implementing reduced‐precision parametrizations in more complex weather and climate models.

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Estimating the Intrinsic Limit of Predictability Using a Stochastic Convection Scheme

Cited by 28 publications

References 23 publications

Can Machines Learn to Predict Weather? Using Deep Learning to Predict Gridded 500‐hPa Geopotential Height From Historical Weather Data

Can Machines Learn to Predict Weather? Using Deep Learning to Predict Gridded 500‐hPa Geopotential Height From Historical Weather Data

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

Reduced‐precision parametrization: lessons from an intermediate‐complexity atmospheric model

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