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

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

Lagrangian matches between observations from aircraft, lidar and radar in a warm conveyor belt crossing orography

Warm conveyor belts (WCBs) are important airstreams in extratropical cyclones, often leading to the formation of intense precipitation and the amplification of upper-level ridges. This study presents a case study that involves aircraft, lidar and radar observations in a WCB ascending from western Europe towards the Baltic Sea during the Hydrological Cycle in the Mediterranean Experiment (HyMeX) and T-NAWDEX-Falcon in October 2012, a preparatory campaign for the THORPEX North Atlantic Waveguide and Downstream Impact Experiment (T-NAWDEX). Trajectories were used to link different observations along the WCB, that is, to establish so-called Lagrangian matches between observations. To this aim, an ensemble of wind fields from the global analyses produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) Ensemble of Data Assimilations (EDA) system were used, which allowed for a probabilistic quantification of the WCB occurrence and the Lagrangian matches. Despite severe air traffic limitations for performing research flights over Europe, the German Aerospace Center (DLR) Falcon successfully sampled WCB air masses during different phases of the WCB ascent. The WCB trajectories revealed measurements in two distinct WCB branches: one branch ascended from the eastern North Atlantic over southwestern France, while the other had its inflow in the western Mediterranean. Both branches passed across the Alps, and for both branches Lagrangian matches coincidentally occurred between lidar water vapour measurements in the inflow of the WCB south of the Alps, radar measurements during the ascent at the Alps and in situ aircraft measurements by Falcon in the WCB outflow north of the Alps. An airborne release experiment with an inert tracer could confirm the long pathway of the WCB from the inflow in the Mediterranean boundary layer to the outflow in the upper troposphere near the Baltic Sea several hours later. The comparison of observations and ensemble analyses reveals a moist bias in the analyses in parts of the WCB inflow but a good agreement of cloud water species in the WCB during ascent. In between these two observations, a precipitation radar measured strongly precipitating WCB air located directly above the melting layer while ascending at the southern slopes of the Alps. The trajectories illustrate the complexity of a continental and orographically influenced WCB, which leads to (i) WCB moisture sources from both the Atlantic and Mediterranean, (ii) different pathways of WCB ascent affected by orography, and (iii) locally steep WCB ascent with high radar reflectivity values that might result in enhanced precipitation where the WCB flows over the Alps. The linkage of observational data by ensemble-based WCB trajectory calculations, the confirmation of the WCB transport by an inert tracer and the model evaluation using the multi-platform observations are the central elements of this study and reveal important aspects of orographically modified WCBs.</p

Lagrangian matches between observations from aircraft, lidar and radar in an orographic warm conveyor belt

Warm conveyor belts (WCBs) are important airstreams in extratropical cyclones, often leading to the formation of intense precipitation and the amplification of upper-level ridges. This study presents a case study that involves aircraft, lidar and radar observations in a WCB ascending from western Europe towards the Baltic Sea during the field experiments HyMeX and T-NAWDEX-Falcon in October 2012. Trajectories were used to link different observations along the WCB, that is to establish so-called Lagrangian matches between observations. To this aim, wind fields of the ECMWF ensemble data assimilation system were used, which allowed for a probabilistic quantification of the WCB occurrence and the Lagrangian matches. Despite severe air traffic limitations for performing research flights over Europe, the DLR Falcon successfully sampled WCB air masses during different phases of the WCB ascent. The WCB trajectories revealed measurements in two distinct WCB branches: one branch ascended from the eastern North Atlantic over southwestern France, while the other had its inflow in the western Mediterranean. Both branches passed across the Alps, and for both branches, Lagrangian matches coincidentally occurred between lidar water vapour measurements in the inflow of the WCB south of the Alps, radar measurements during the ascent at the Alps, and in situ aircraft measurements by Falcon in the WCB outflow north of the Alps. An airborne release experiment with an inert tracer could confirm the long pathway of the WCB from the inflow in the Mediterranean boundary layer to the outflow in the upper troposphere near the Baltic Sea several hours later. The comparison of observations and ensemble analyses reveals a moist bias in the analyses in parts of the WCB inflow but a good agreement of cloud water species in the WCB during ascent. In between these two observations, a precipitation radar measured strongly precipitating WCB air located directly above the melting layer while ascending at the southern slopes of the Alps. The trajectories illustrate the complexity of a continental and orographically influenced WCB, which leads to (i) WCB moisture sources from both the Atlantic and Mediterranean, (ii) different pathways of WCB ascent affected by orography, and (iii) locally steep WCB ascent with high radar reflectivity values that might result in enhanced precipitation where the WCB flows over the Alps. The linkage of observational data by ensemble-based WCB trajectory calculations and confirmed by an inert tracer, and the model evaluation using the multi-platform observations are the central elements of this study and reveal important aspects of orographically modified WCBs

Stochastic Super-Resolution for Downscaling Time-Evolving Atmospheric Fields with a Generative Adversarial Network

Generative adversarial networks (GANs) have been recently adopted for super-resolution, an application closely related to what is referred to as "downscaling" in the atmospheric sciences: improving the spatial resolution of low-resolution images. The ability of conditional GANs to generate an ensemble of solutions for a given input lends itself naturally to stochastic downscaling, but the stochastic nature of GANs is not usually considered in super-resolution applications. Here, we introduce a recurrent, stochastic super-resolution GAN that can generate ensembles of time-evolving high-resolution atmospheric fields for an input consisting of a low-resolution sequence of images of the same field. We test the GAN using two datasets, one consisting of radar-measured precipitation from Switzerland, the other of cloud optical thickness derived from the Geostationary Earth Observing Satellite 16 (GOES-16). We find that the GAN can generate realistic, temporally consistent super-resolution sequences for both datasets. The statistical properties of the generated ensemble are analyzed using rank statistics, a method adapted from ensemble weather forecasting; these analyses indicate that the GAN produces close to the correct amount of variability in its outputs. As the GAN generator is fully convolutional, it can be applied after training to input images larger than the images used to train it. It is also able to generate time series much longer than the training sequences, as demonstrated by applying the generator to a three-month dataset of the precipitation radar data. The source code to our GAN is available at https://github.com/jleinonen/downscaling-rnn-gan.Comment: Accepted for publication in IEEE Transactions in Geoscience and Remote Sensin

Ensemble precipitation nowcasting: limits to prediction, localization and seamless blending

Nowcasting precipitation, that is, accurately predicting its location and intensity minutes to a few hours ahead, is a difficult task. Extreme rainfall events can be a threat to the population and challenge the limits of weather monitoring and prediction systems. For practical use, the forecasting techniques at our disposal range from physically-based numerical models to heuristic extrapolation procedures based on weather radars. Radar-based nowcasting models rely on the high spatial and temporal resolution of radar measurements and thus benefit from the best possible initial conditions, but the assumption of persistence leads to a rapid decay of predictive skill with increasing lead time and decreasing spatial scale. Numerical models provide physically consistent precipitation forecasts, but their practical relevance for nowcasting can be undermined by uncertain initial conditions, spinup issues, model approximations, or simply by their computational limits. Analyses presented in this study show that, on average, radar-based nowcasting outperforms numerical simulations during the first three hours and that it is particularly useful for forecasting precipitation patterns with a horizontal dimension below 60 kilometers, as state-of-the-art numerical models cannot provide useful skill at those small spatial scales. An interesting finding is that the predictive uncertainty of numerical predictions relatively to radar nowcasting improves during warm convective days, which is explained by the combined effect of shorter precipitation lifetimes and more effective model assimilation of locally triggered air mass convection. After a lead time of 4.5 hours, we observed that precipitation on all scales below 150 kilometers is poorly predictable by all forecasting means. Such serious limits to predictability determine the need to represent forecast uncertainty as accurately as possible. In radar nowcasting, ensemble methods use stochastic simulations to perturb a deterministic extrapolation and thus quantify forecast errors. We found that a more precise representation of the statistical properties of the precipitation field through localization can have a positive impact on the realism of the simulations as well as in terms of probabilistic forecast skill. We showed that localized nowcasts perform better in terms of ensemble reliability and resolution, as well as conditional bias. However, we also found that a too strong a localization can lead to lower skill as it implicitly relies on a stricter assumption of persistence. The quantitative estimation of forecast uncertainty provided by ensembles was finally used to design a seamless blending procedure that integrates all available sources of predictive skill. Implemented using a recursive formulation of the Bayesian update equations, the blending scheme involves a prediction step through a stochastic radar extrapolation, while a subsequent correction step updates the extrapolation using information from the most recent numerical model run. It is found that such an approach is able to capture the flow dependence of both the numerical forecast and the radar nowcast ensemble spreads resulting in an adaptive blending scheme that depends on the relative uncertainty of the individual forecasts. Despite the non-Gaussian nature of rainfall data, we were able to produce blended precipitation forecasts that are at least as skillful as the radar-only or the numerical model-only forecasts at any lead time

dnerini/ssft-generator: First release of SSFT stochastic generator

This first release includes a basic implementation of the short-space Fourier transform (SSFT) stochastic generator and four radar rainfall fields used as examples