Impact of radar data assimilation and orography on predictability of deep convection

Bachmann, Kevin; Keil, Christian; Weißmann, Martin

doi:10.1002/qj.3412

Cited by 39 publications

(32 citation statements)

References 51 publications

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“…; Bachmann et al . ) are used to evaluate the spatial variability and to estimate displacement scales of deep convection caused by the various perturbations.…”

Section: Spatial Precipitation Variabilitymentioning

confidence: 99%

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Relative contribution of soil moisture, boundary‐layer and microphysical perturbations on convective predictability in different weather regimes

Keil

Baur

Bachmann³

et al. 2019

Quart J Royal Meteoro Soc

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The relative contributions of soil moisture heterogeneities, a stochastic boundary‐layer perturbation scheme and varied aerosol concentrations representing microphysical uncertainties on the diurnal cycle of convective precipitation and its spatial variability are examined conditional on the prevailing weather regime. To achieve this, separate perturbed‐parameter ensemble simulations are performed with the Consortium for Small‐scale Modeling (COSMO) model at convection‐permitting horizontal grid spacing for 10 days during a high‐impact weather episode in 2016 in Central Europe. We consider hourly precipitation amounts and their spatial distribution, focus on ensemble mean and spread aggregated over strong and weak forcing conditions, and employ spatial evaluation techniques. The convective adjustment time‐scale diagnostic is used to distinguish the different precipitation regimes. While the total amount of daily precipitation is hardly changed by the different perturbation approaches (less than 5%), the spatial variability of precipitation exhibits clear differences. Soil moisture heterogeneity primarily introduces variability during convection initiation causing a steeper increase in normalized rainfall spread prior to the onset of afternoon precipitation. The stochastic boundary‐layer perturbations lead to the largest spatial variability impacting precipitation from initial time onwards with an amplitude comparable to the operational ensemble spread. Similarly, perturbed aerosol concentrations impact spatial precipitation variability from the model start onwards, but to a smaller degree. Soil moisture heterogeneity shows the strongest weather regime dependence, with the greatest impact on convection during weak synoptic forcing. All types of perturbation increase dispersion of precipitation while maintaining the domain‐averaged precipitation rates.

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“…; Bachmann et al . ) are used to evaluate the spatial variability and to estimate displacement scales of deep convection caused by the various perturbations.…”

Section: Spatial Precipitation Variabilitymentioning

confidence: 99%

“…The Fraction Skill Score (FSS; Roberts and Lean, 2008) and the decorrelation scale (Surcel et al, 2017;Bachmann et al, 2019) are used to evaluate the spatial variability and to estimate displacement scales of deep convection caused by the various perturbations.…”

Section: Spatial Precipitation Variabilitymentioning

confidence: 99%

Relative contribution of soil moisture, boundary‐layer and microphysical perturbations on convective predictability in different weather regimes

Keil

Baur

Bachmann³

et al. 2019

Quart J Royal Meteoro Soc

Self Cite

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“…Given the complexity of the Alpine environment in the area covered by the TAASRAD19 dataset and the direct known relationships between convective precipitation and the underlying orographical characteristics [9,[41][42][43][44], we add to the stack of the input images three layers of information, derived from the orography of the area: the elevation, the degree of orientation (aspect), and the slope percentage. The three features are computed by resampling the digital terrain model [45] of the area at the spatial resolution of the radar grid (500 m), and computing the relevant features in a GIS suite [46].…”

Section: Orographic Featuresmentioning

confidence: 99%

Precipitation Nowcasting with Orographic Enhanced Stacked Generalization: Improving Deep Learning Predictions on Extreme Events

Franch

Nerini

Pendesini³

et al. 2020

Atmosphere

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One of the most crucial applications of radar-based precipitation nowcasting systems is the short-term forecast of extreme rainfall events such as flash floods and severe thunderstorms. While deep learning nowcasting models have recently shown to provide better overall skill than traditional echo extrapolation models, they suffer from conditional bias, sometimes reporting lower skill on extreme rain rates compared to Lagrangian persistence, due to excessive prediction smoothing. This work presents a novel method to improve deep learning prediction skills in particular for extreme rainfall regimes. The solution is based on model stacking, where a convolutional neural network is trained to combine an ensemble of deep learning models with orographic features, doubling the prediction skills with respect to the ensemble members and their average on extreme rain rates, and outperforming them on all rain regimes. The proposed architecture was applied on the recently released TAASRAD19 radar dataset: the initial ensemble was built by training four models with the same TrajGRU architecture over different rainfall thresholds on the first six years of the dataset, while the following three years of data were used for the stacked model. The stacked model can reach the same skill of Lagrangian persistence on extreme rain rates while retaining superior performance on lower rain regimes.Plain Positions Indicators (PPI) or Constant Altitude Plain Position Indicator (CAPPI), or the Maximum vertical reflectivity (CMAX or MAX(Z)). Sequences of reflectivity maps are used as input for prediction models. More formally, given a reflectivity field at time T 0 , radar-based nowcasting methods aim to extrapolate m future time steps T 1 , T 2 , ..., T m in the sequence, using as input the current and n previous observations T −n , ..., T −1 , T 0 .Traditional nowcasting models are manly based on Lagrangian echo extrapolation [7,8], with recent modification that try to infer precipitation growth and decay [9,10] or integrate with Numerical Weather Predictions to extend the time horizon of the prediction [11,12]. In the last few years, Deep Learning (DL) models based on combination of Recurrent Neural Networks (RNN) and Convolutional Neural Networks (CNN) have shown substantial improvement over nowcasting methods based on Lagrangian extrapolations for quantitative precipitation forecasting (QPF) [13]. Shi et al. [14] introduced the application of the Convolutional Long Short-Term Memory (Conv-LSTM) network architecture with the specific goal of improving precipitation nowcasting over extrapolation models, where LSTM is modified using a convolution operator in the state-to-state and input-to-state transitions. Subsequent work introduced dynamic recurrent connections [15] (TrajGRU) that allowed the improvement of prediction skills, spatial resolution, and temporal length of the forecast, with comparable number of parameters and memory requirements. Subsequent works introduced more complex memory blocks and architectures [16] and increased num...

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“…This indicates To provide a complete picture of forecast performance, the 0-3 h accumulated precipitation forecast at each cycle is also evaluated (Figure 8). To get rid of the influence of lateral boundary on precipitation simulation, we only quantitatively and qualitatively evaluate the accumulated precipitation within subdomains (27)(28)(29)(30)(31)(32)(33)(34)(35) • N, 107-117 • E). The precipitation observations are produced by a method called CMORPH (NOAA CPC Morphing technique) [62].…”

Section: Qualitative Forecast Evaluationmentioning

confidence: 99%

“…Another problem is that too much water vapor and latent heating are added to the cloud analysis, resulting in an increased false alarm rate and over-prediction, especially when many data assimilation cycles are involved [21][22][23]. To solve these problems, more data assimilation approaches have been used, such as latent heat nudging [24], variational techniques [1,[7][8][9][10][11]25,26], the ensemble Kalman filter (EnKF) [5,6,12,[27][28][29], and hybrid variational and ensemble approaches [13,14,30]. These studies have demonstrated that assimilation of reflectivity can improve short-term forecasts.…”

mentioning

confidence: 99%

Assimilation of Radar Data, Pseudo Water Vapor, and Potential Temperature in a 3DVAR Framework for Improving Precipitation Forecast of Severe Weather Events

et al. 2020

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An improved approach to derive pseudo water vapor mass mixing ratio and in-cloud potential temperature was developed in this paper to better initialize numerical weather prediction (NWP) and build convective-scale predictions of severe weather events. The process included several steps. The first was to identify areas of deep moist convection, utilizing Vertically Integrated Liquid water (VIL) derived from a mosaicked 3D radar reflectivity field. Then, pseudo-water vapor and pseudo-in-cloud potential temperature observations were derived based on the VIL. For potential temperature, the latent heat initialization for stratiform cloud and moist adiabatic initialization for deep moist convection were used based on a cloud analysis method. The third step was to assimilate the derived pseudo-water vapor and potential temperature observations, together with radar radial velocity and reflectivity into a convective-scale NWP model during data assimilation cycles spanning several hours. Finally, 3-h forecasts were launched each hour during the data assimilation period. The effects of radar data and pseudo-observation assimilation on the prediction of rainfall associated with convective systems surrounding the Meiyu front in 2018 were explored using two real cases. Two sets of experiments, each including several experiments in each real case, were designed to compare the effects of assimilation radar and pseudo-observations on the ensuing forecasts. Relative to the control experiment without data assimilation and radar experiment, the analyses and forecasts of convections were found to be improved for the two Meiyu front cases after pseudo-water vapor and potential temperature information was assimilated.Atmosphere 2020, 11, 182 2 of 24 variables is nonlinear. Yet, the effective assimilation of reflectivity into convective-scale models is essential for properly initializing convective-scale NWP models. One simple way to assimilate reflectivity data for initializing the convective-scale NWP is the cloud analysis method [15][16][17][18][19]. For example, the Advanced Regional Prediction System (ARPS [20]) cloud analysis [18] can specify hydrometeor variables and adjust in-cloud temperatures for the initialization. Although this method has been proven useful, problems still remain. One problem is that the cloud analysis method relies on empirical algorithms to relate the hydrometeor variables and the reflectivity, and requires tuning of many uncertain parameters [7]. Another problem is that too much water vapor and latent heating are added to the cloud analysis, resulting in an increased false alarm rate and over-prediction, especially when many data assimilation cycles are involved [21][22][23]. To solve these problems, more data assimilation approaches have been used, such as latent heat nudging [24], variational techniques [1,[7][8][9][10][11]25,26], the ensemble Kalman filter (EnKF) [5,6,12,[27][28][29], and hybrid variational and ensemble approaches [13,14,30]. These studies have demonstrated that assimilation of reflect...

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Impact of radar data assimilation and orography on predictability of deep convection

Cited by 39 publications

References 51 publications

Relative contribution of soil moisture, boundary‐layer and microphysical perturbations on convective predictability in different weather regimes

Relative contribution of soil moisture, boundary‐layer and microphysical perturbations on convective predictability in different weather regimes

Precipitation Nowcasting with Orographic Enhanced Stacked Generalization: Improving Deep Learning Predictions on Extreme Events

Assimilation of Radar Data, Pseudo Water Vapor, and Potential Temperature in a 3DVAR Framework for Improving Precipitation Forecast of Severe Weather Events

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