C. Marsigli

Calibration of Limited-Area Ensemble Precipitation Forecasts for Hydrological Predictions

AbstractThe main objective of this study is to investigate the impact of calibration for limited-area ensemble precipitation forecasts, to be used for driving discharge predictions up to 5 days in advance. A reforecast dataset, which spans 30 years, based on the Consortium for Small Scale Modeling Limited-Area Ensemble Prediction System (COSMO-LEPS) was used for testing the calibration strategy. Three calibration techniques were applied: quantile-to-quantile mapping, linear regression, and analogs. The performance of these methodologies was evaluated in terms of statistical scores for the precipitation forecasts operationally provided by COSMO-LEPS in the years 2003–07 over Germany, Switzerland, and the Emilia-Romagna region (northern Italy). The calibration provided a beneficial impact for the ensemble forecast over Switzerland and Germany; whereas, it resulted as less effective for Emilia-Romagna. The analog-based method seemed to be preferred because of its capability of correct position errors and spr...

Data assimilation of radar reflectivity volumes in a LETKF scheme

Quantitative precipitation forecast (QPF) is still a challenge for numerical weather prediction (NWP), despite the continuous improvement of models and data assimilation systems. In this regard, the assimilation of radar reflectivity volumes should be beneficial, since the accuracy of analysis is the element that most affects short-term QPFs. Up to now, few attempts have been made to assimilate these observations in an operational set-up, due to the large amount of computational resources needed and due to several open issues, like the rise of imbalances in the analyses and the estimation of the observational error. In this work, we evaluate the impact of the assimilation of radar reflectivity volumes employing a local ensemble transform Kalman filter (LETKF), implemented for the convection-permitting model of the COnsortium for Small-scale MOdelling (COSMO). A 4-day test case on February 2017 is considered and the verification of QPFs is performed using the fractions skill score (FSS) and the SAL technique, an object-based method which allows one to decompose the error in precipitation fields in terms of structure (S), amplitude (A) and location (L). Results obtained assimilating both conventional data and radar reflectivity volumes are compared to those of the operational system of the Hydro-Meteo-Climate Service of the EmiliaRomagna Region (Arpae-SIMC), in which only conventional observations are employed and latent heat nudging (LHN) is applied using surface rainfall intensity (SRI) estimated from the Italian radar network data. The impact of assimilating reflectivity volumes using LETKF in combination or not with LHN is assessed. Furthermore, some sensitivity tests are performed to evaluate the effects of the length of the assimilation window and of the reflectivity observational error (roe). Moreover, balance issues are assessed in terms of kinetic energy spectra and providing some examples of how these affect prognostic fields. Results show that the assimilation of reflectivity volumes has a positive impact on QPF accuracy in the first few hours of forecast, both when it is combined with LHN or not. The improvement is further slightly enhanced when only observations collected close to the analysis time are assimilated, while the shortening of cycle length worsens QPF accuracy. Finally, the employment of too small a value of roe introduces imbalances into the analyses, resulting in a severe degradation of forecast accuracy, especially when very short assimilation cycles are used.

Development and Implementation of a Soil Moisture Perturbation Method for EPS Initial Conditions

The interaction between the surface and the lower troposphere determines the development of fluxes close to the ground. Soil moisture is of primary importance in determining the partition of energy between surface heat fluxes, thus affecting near-surface forecasts such as air temperature and precipitation forecasts. Ensemble forecasts usually suffer from a lack of variability among their members, which is mainly prominent near the surface rather than higher in the troposphere. In the framework of the Priority Project CONSENS of COSMO consortium, a method has been developed and adapted for perturbing soil moisture initial conditions. This method is based on the identification of the variability behavior in long data sets forming the model’s initial conditions—for each grid point of the domain—and determining the different members of the Ensemble Prediction Systems (EPS). The implementation of this method aims at ameliorating the variability of the solutions of COSMO EPS forecasts near the ground by exploring its impacts on the variability of the members for different forecast parameters, namely, 2 m air temperature, heat fluxes and accumulated precipitation.