Conventionally, radar-based nowcasting methods assume that the short-term evolution of precipitation can be obtained by simply shifting the last observed precipitation along a stationary motion field. A major limitation of this assumption arises from neglecting lifecycles of precipitation cells, for example, the decay of mature cells and the initiation of new ones. In this perspective, P2 aims to improve the prediction skill of advection-based nowcasting by considering statistical properties and life cycles of precipitation.
In a first step, trends of intensity, size, and shape of precipitation cells observed during previous time steps will be extrapolated in time. In a second step, polarimetric radar signatures indicative for potential changes in precipitation generation will be exploited for refinements of the approach. In both steps, P2 will derive an ensemble of nowcasted precipitation fields based upon the precipitation fields provided by P1 while using the 3D multi-sensor composite (radar-satellite) provided by C1. The Quantitative Precipitation Nowcasting (QPN) ensembles will serve as input for seamless prediction (P3) and flood prediction (P4).

Although the SPROG model statistically outperforms pure advection-based nowcasting, it tends to smooth excessively small-scale convective precipitation. Therefore, the SPROG approach has been refined into its localized version: the SPROG-LOC approach (Reinoso-Rondinel et al., 2022). The SPROG-LOC approach uses a 2-dimensional autoregressive process to model the temporal evolution of each cascade level considering the inherent heterogeneity of observed precipitation fields. Figure P2-1 and figure P2-2 illustrate how the SPROG approach rapidly smooths small convective cells with increasing lead-time whereas the SPROG-LOC model is more reluctant to smooth such areas of convective precipitation. The SPROG-LOC model shows its advantage over the SPROG model on the convective band with small-scale structures of high precipitation (red, yellow and orange areas).



Figure P2-1: Comparison between the QPE fields (left), the SPROG model (centre) and the SPROG-LOC model (right) based on a three hours nowcast. The two nowcasts have been performed on the 2019-07-20 from 14:00 to 17:00 UTC.



Figure P2-2: Comparison between the QPE fields (left), the SPROG model (centre) and the SPROG-LOC model (right) based on the last frame of a three hours nowcast performed on the 2019-07-20 from 14:00 to 17:00 UTC

Lastly, the STEPS approach is currently being refined into its localized version, namely the STEPS-LOC approach, to better represent the stochastic perturbations and therefore the spatio-temporal representation of the ensemble-members. As a next step, the skill of the SPROG-LOC approach and the ensemble spread of the STEPS-LOC approach will undergo an in-depth evaluation within a joint effort of the entire RealPEP research group. RealPEP-P1 will apply the most recent hybrid precipitation algorithms developed (Chen et al., 2021) to a 4 months national polarimetric C-band radar data set, which serve as the data base for STEPS-LOC. These new QPE products use an optimized combination of reflectivity 𝑍𝐻, specific attenuation 𝐴𝐻, and specific differential phase 𝐾𝐷𝑃 and also take into account when the radar beam monitors within or above the melting layer. The resulting benchmark data set will also be used for a robust estimation of QPE uncertainties using the national gauge network and thus to further optimize the ensemble generation in STEPS-LOC.

Finally, vertically extensive enhancements of differential reflectivity, so-called 𝑍𝑑𝑟-columns, are investigated to further improve observation-based nowcasting. They are polarimetric signatures indicative of the location of updrafts and thus precipitation development. In order to exploit this information content for the refinement of our nowcast, algorithms for the detection and tracking of 𝑍𝑑𝑟-columns have been developed. Preliminary results (Reinoso-Rondinel et al., 2021 and Evaristo et al., 2021) suggest columns lifetimes of 20 to 30 min and a lag time up to 30 min for the precipitation increments at the surface. Statistical relationships between the features of the 𝑍𝑑𝑟-columns and its given intensification of precipitation will be used during the Phase II of P2 to increase the skill of our nowcast approach.

References

• Evaristo, R., Reinoso-Rondinel, R, Trömel, S., Simmer, C., 2021: Validation of Wind Fields Retrieved by Dual-Doppler Techniques Using a Vertically Pointing Radar. 2021 21st International Radar Symposium (IRS), Berlin, Germany, 2021, 1-7, https://doi.org/10.23919/IRS51887.2021.9466173

• Reinoso-Rondinel, R., Rempel, M., Schultze, M., Trömel, S., 2022: Nationwide Radar-Based Precipitation Nowcasting – A localization filtering approach and its Application for Germany. IEE J. Select. Topics Appl. Earth observation. and Remote Sensing, (15), 1670-1691, https://doi.org/10.1109/JSTARS.2022.3144342

• Reinoso-Rondinel, R., Evaristo, R., Schmidt, M., Crijnen, F., Trömel, S., 2021: Storm Cell Observation And Prediction Using Polarimetric Weather Radars. 2021 21st International Radar Symposium (IRS), Berlin, Germany, 2021, 1-7, https://doi.org/10.23919/IRS51887.2021.9466168

• Chen, J., Trömel, S., Ryzhkov, A., Simmer, C., 2021: Assessing the Benefits of Specific Attenuation for Quantitative precipitation Estimation with a C-band Radar Network. J. of hydrometeor., (22)(10), 2617-2631, https://doi.org/10.1175/JHM-D-20-0299.1

Contribution of Free University of Berlin


P2 will derive an ensemble of nowcasted precipitation fields based upon the precipitation fields provided by P1 while using the 3D multi-sensor composite (radar-satellite) provided by C1. The predictive potential of satellite-based information on convective cell initiation and its integration into the observation-based nowcasting method is investigated using machine learning methods. The QPN ensembles will serve as input for seamless prediction (P3) and flood prediction (P4).

The amount of water vapor in the atmosphere plays a key role in convective initiation processes. Here, we specifically investigate the potential of satellite-based total column water vapor (TCWV) for improving observation-based nowcasting of convective initiation (CI). The main idea is that these clear-sky satellite observations of water vapor fields enable the monitoring and characterization of (pre-)convective environments before convective cloud development and the onset of precipitation.
To this end, we first developed and evaluated two new satellite-based TCWV products. High-resolution spatial information of water vapor fields is obtained from the OLCI TCWV product (Preusker et al., 2021), while information on temporal evolution of water vapor fields comes from the SEVIRI TCWV product (El Kassar et al., 2021). Then, a multi-annual set of OLCI and SEVIRI TCWV fields is merged with NWC/SAF products related to in-cloud CI and thunderstorm development (www.nwcsaf.org), based on SEVIRI observations. Statistical assessments are performed to identify suitable TCWV metrics for the characterization of pre-convective environments in Germany. In next steps, we will apply machine learning methods (specifically predictive recurrent neural network) to merge satellite-based CI information with radar-based QPN, with the aim to increase lead times.Moreover, to benefit from the newest observational capabilities of Meteosat Third Generation (MTG) in the very near future, we can build on the TCWV retrieval framework set up for the synergy measurements from OLCI and SLSTR. The new MTG Flexible Combined Imager (FCI) based TCWV product is expected to enable improved spatio-temporal monitoring and characterization of (pre-)convective environments.



Figure P2-3: An example of a high resolution (~300 m) OLCI TCWV field in the morning time.



Animation P2-1: An example of a time series of SEVIRI TCWV fields and collocated NWC/SAF-SEVIRI observations of convective cloud development in the morning time and early afternoon.

References

• Preusker, R., C. Carbajal Henken , J. Fischer, 2021: Retrieval of daytime total column water vapour from OLCI measurements over land surfaces. Remote Sens., 13(5), 932, https://doi.org/10.3390/rs13050932.

• El Kassar, J., C. Carbajal Henken, R. Preusker, J. Fischer, 2021: Optimal Estimation MSG-SEVIRI Clear-Sky Total Column Water Vapour Retrieval Using the Split Window Difference. Atmosphere, 12(10), 1256, https://doi.org/10.3390/atmos12101256.

The conventional advection-based nowcasting is being improved by applying the filtering approach known as the Spectral Prognosis (S-PROG) method in order to model the spatio-temporal properties of precipitation. It is based on the assumption that large spatial structures of precipitation have longer lifetimes than smaller ones. The rain field is decomposed in the spectral domain into a multifractal geometry with K cascade levels. The temporal evolution of each cascade level is then managed by a 1-dimensional autoregressive model of order p. This way, the AR coefficients control the evolution of each cascade level consistent with their expected lifetime.
To consider the uncertainties, the probabilistic STEPS approach (Bowler et al., 2006) has been considered as well. It is based upon the SPROG approach but perturbs each cascade level with a Gaussian white noise that is spatially correlated with the precipitation such that the statistical properties of nowcast errors (spatial and temporal correlations) are simulated. Thanks to this approach, it has been possible to generate ensembles, for instance, 20 ensemble-members every 5 min with a lead time of 2 hr at 5 min time steps. The STEPS approach has been applied, among others, to three rain events that produced floods in the Mehlemer Bach region and resulting nowcast ensembles have been used as input for flash flood prediction in P4.
Parallel to the application of STEPS, object-oriented nowcasting of convective cells is being developed based on Kalman filtering. As a first task, a 2D storm cell identification and tracking algorithm have been implemented and tested on the Radolan Reflectivity product. As an example, Figure P2-1 indicates the tracks of 42 identified storm cells during a 2 hr period on July 25 2017.

Figure P2-1: Different colors and symbols indicate tracks of 42 storm cells identified in Radolan RW during a 2 hr period on July 25 2017.





References

• Bowler, N., C. E. Pierce, and A. W. Seed: 2006: STEPS: A probabilistic rainfall forecasting scheme which merges an extrapolation nowcast with downscales NWP, Q. J. R. Meteorol. Soc., 132, 2127–2155.