Generating Probabilistic Forecasts from Convection-Allowing Ensembles Using Neighborhood Approaches: A Review and Recommendations

Schwartz, Craig S.; Sobash, Ryan A.

doi:10.1175/mwr-d-16-0400.1

Cited by 126 publications

(84 citation statements)

References 79 publications

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“…Since most of the PERs showed similar cost functions in Equation , especially the last few ones, the principle used to select PERs in OPTP must be relaxed to include enough ensemble members. NEP significantly outperformed PRO in accuracy, reliability and discrimination for rainfall with smaller threshold, which was consistent with the conclusion of Schwartz and Sobash () that NEP typically does not possess good reliability or resolution for rare events because of sharpness loss.…”

Section: Results Of a Batch Experimentssupporting

confidence: 89%

Application of a convection‐permitting ensemble prediction system to quantitative precipitation forecasts over southern China: Preliminary results during SCMREX

Zhang¹

2018

Quart J Royal Meteoro Soc

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As a preliminary attempt to cope with the low predictability of heavy rainfall over southern China in the pre‐summer rainy season, an experimental convection‐permitting ensemble prediction system (GM‐CPEPS) based on the Global/Regional Assimilation and Prediction System (GRAPES) is developed. GM‐CPEPS produces 12 h forecasts at 0.03° horizontal resolution based on 16 perturbed members. Perturbations from downscaling, ensemble of data assimilation, time‐lagged scheme and topography are combined to generate the initial perturbations. Sea‐surface temperature is perturbed and a combination of downscaling and balanced random perturbations is used to perturb the lateral boundary conditions. Stochastically perturbed parametrization tendencies, multi‐physics, and perturbed parameters are all implemented. In this study, GM‐CPEPS was verified over a 15‐day period during the Southern China Monsoon Rainfall Experiment (SCMREX) in May 2014. It was indicated that GM‐CPEPS provided estimates of forecast uncertainty that are comparable to some international peers. Compared with the control forecasts (DET), some deterministic guidance, including the forecast distribution with 90th percentile, probability‐matched mean, and linear combination of both (NPM), showed advantages in forecasting moderate and heavy rainfall; and the optimal‐member technique was superior in reducing bias. Probabilistic guidance demonstrated the value over DET in detecting potential threats of severe weather, with both the optimal‐probability and neighbourhood‐probability technique leading to improvements in predicting lighter rainfall. Two cases were used to display the deterministic and probabilistic guidance intuitively and to illustrate corresponding advantages and drawbacks.

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Section: Results Of a Batch Experimentssupporting

confidence: 89%

Application of a convection‐permitting ensemble prediction system to quantitative precipitation forecasts over southern China: Preliminary results during SCMREX

Zhang¹

2018

Quart J Royal Meteoro Soc

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“…The UH between 0‐ and 1‐km AGL is defined as

{UH}_{0 - 1 km} = \int_{0 km}^{1 km} italicζWdz,

where ζ , W , z are vertical vorticity, vertical wind, and height (AGL), respectively. Figure shows the neighborhood maximum ensemble probabilities (Schwartz & Sobash, ) of high‐UH within 10 km of each point calculated with 51‐member ensemble forecasts in 1200–1300 JST. It was larger near the paths of the tornadoes in Z‐PTB than those in NZ.…”

Section: Resultsmentioning

confidence: 99%

Improving Short‐Term Rainfall Forecasts by Assimilating Weather Radar Reflectivity Using Additive Ensemble Perturbations

et al. 2018

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To improve short‐term rainfall forecasts through direct assimilation of radar reflectivity, atmospheric variables associated with rainfall should be modified based on their correlation with reflectivity. However, it is difficult to estimate such correlations. The ensemble Kalman filter can estimate the correlation by means of ensemble forecasts, although the estimation is limited to when rainfall is forecast by at least one member at analysis points. To assimilate reflectivity effectively even at points at which no rainfall is forecast, we suggest adding ensemble reflectivity perturbations, which are correlated with atmospheric variables, before ensemble Kalman filter assimilation. In the present study, this correlation is calculated in the whole computational domain including the rainfall regions. We apply this procedure to assimilation experiments with 1‐km horizontal grid interval for two tornadic supercells that occurred on 6 May 2012 and on 2 September 2013, and we succeed in improving short‐term rainfall forecasts by modifying wind, temperature, and water vapor.

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“…In order to address this issue, spatial verification methods have been proposed, that aim at relaxing the constraint of an exact match between forecast and observed values at the grid scale (Gilleland et al ). An example is the neighbourhood approach (Schwartz and Sobash, ), which introduces spatial and/or temporal tolerances in the scores computation. Another method, intuitive to many users, consists of extracting from the forecast information at a larger, and thus more predictable, scale under the form of “precipitating objects”, which define areas where the distribution of precipitation is homogeneous (in terms of intensity and/or spatial distribution).…”

Section: Introductionmentioning

confidence: 83%

Object‐based verification metrics applied to the evaluation and weighting of convective‐scale precipitation forecasts

Raynaud

Pechin

Arbogast

et al. 2019

Quart J Royal Meteoro Soc

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Traditional pointwise verification scores are not always appropriate for the evaluation of high‐resolution precipitation forecasts because of double‐penalty problems. An alternative approach, based on the identification of homogeneous rainfall areas called “precipitating objects”, allows forecast evaluation at a larger and thus more predictable scale, and specific information about the nature of errors (e.g. location, size, intensity) can be obtained. A novel object detection method is first introduced and the object‐based verification of precipitation forecasts from the convective‐scale deterministic and ensemble models Arome and Arome‐EPS is then discussed, using several scores and diagnostics. Three types of precipitating objects characterizing total, moderate and heavy rainfall are considered. In the second part, object‐based metrics are used to compute objective weights for time‐lagged ensemble forecasts, based on their performance at early forecast ranges. The weights obtained clearly depend on the meteorological situation and on the precipitation type, reflecting for instance the lower predictability of moderate precipitation compared to total precipitation. There is also a dependence on the production time with, on average, slightly larger and more homogeneous weights associated with the most recent run. However, in some situations of moderate and heavy rainfall, a relevant signal can be extracted from older runs. It is finally shown that object‐based weights are better suited than classical quadratic weights to improve nowcasting performance.

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Generating Probabilistic Forecasts from Convection-Allowing Ensembles Using Neighborhood Approaches: A Review and Recommendations

Cited by 126 publications

References 79 publications

Application of a convection‐permitting ensemble prediction system to quantitative precipitation forecasts over southern China: Preliminary results during SCMREX

Application of a convection‐permitting ensemble prediction system to quantitative precipitation forecasts over southern China: Preliminary results during SCMREX

Improving Short‐Term Rainfall Forecasts by Assimilating Weather Radar Reflectivity Using Additive Ensemble Perturbations

Object‐based verification metrics applied to the evaluation and weighting of convective‐scale precipitation forecasts

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