On 20 July 2021, parts of China’s Henan Province received the highest precipitation levels ever recorded in the region. Floods caused by heavy rainfall resulted in hundreds of casualties and tens of billions of dollars’ worth of property loss. Due to the highly dynamic nature of flood disasters, rapid and timely spatial monitoring is conducive for early disaster prevention, mid-term disaster relief, and post-disaster reconstruction. However, existing remote sensing satellites cannot provide high-resolution flood monitoring results. Seeing as spaceborne global navigation satellite system-reflectometry (GNSS-R) can observe the Earth’s surface with high temporal and spatial resolutions, it is expected to provide a new solution to the problem of flood hazards. Here, using the Cyclone Global Navigation Satellite System (CYGNSS) L1 data, we first counted various signal-to-noise ratios and the corresponding reflectivity to surface features in Henan Province. Subsequently, we analyzed changes in the delay-Doppler map of CYGNSS when the observed area was submerged and not submerged. Finally, we determined the submerged area affected by extreme precipitation using the threshold detection method. The results demonstrated that the flood range retrieved by CYGNSS agreed with that retrieved by the Soil Moisture Active Passive (SMAP) mission and the precipitation data retrieved and measured by the Global Precipitation Measurement mission and meteorological stations. Compared with the SMAP results, those obtained by CYGNSS have a higher spatial resolution and can monitor changes in the areas affected by the floods over a shorter period.

In the present work, we investigate the impacts on the observation error (OE) statistics due to different types of errors in the forward operator (FE) for both radar reflectivity and radial wind data, in the context of convective-scale data assimilation in the summertime. A series of sensitivity experiments were conducted with the Efficient Modular VOlume RADar Operator (EMVORADO), using the operational data assimilation system of the Deutscher Wetterdienst (DWD, German Weather Service). The investigated FEs are versatile, including errors caused by neglecting the terminal fall speed of hydrometeor, the reflectivity weighting, and the beam broadening and attenuation effects, as well as errors caused by different scattering schemes and formulations for melting particles. For reflectivity, it is found that accounting for the beam broadening effect evidently reduces the standard deviations, especially at higher altitudes. However, it does not shorten the horizontal or along-beam correlation length scales. In comparison between the Rayleigh and the Mie schemes (with specific configurations), the former one results in much smaller standard deviations for heights up to 4 km, and aloft, slightly larger standard deviations. Imposing the attenuation to the Mie scheme slightly reduces the standard deviations at lower altitudes; however, it largely increases the standard deviations at higher altitudes and it also leads to longer correlation length scales. For radial wind, positive impacts of considering the beam broadening effect on standard deviations and neutral impacts on correlations are observed. For both reflectivity and radial wind, taking the terminal fall speed of hydrometeor and the reflectivity weighting into account does not make remarkable differences in the estimated OE statistics.

Abstract. Assimilation of weather radar measurements including radar reflectivity and radial wind data has been operational at the Deutscher Wetterdienst, with a diagonal observation error (OE) covariance matrix. For an implementation of a full OE covariance matrix, the statistics of the OE have to be a priori estimated, for which the Desroziers method has been often used. However, the resulted statistics consists of contributions from different error sources and are difficult to interpret. In this work, we use an approach that is based on samples for truncation error in radar observation space to approximate the representation error due to unresolved scales and processes (RE) and compare its statistics with the OE statistics estimated by the Desroziers method. It is found that the statistics of the RE help the understanding of several important features in the variances and correlation length scales of the OE for both reflectivity and radial wind data and the other error sources from the microphysical scheme, radar observation operator and the superobbing technique may also contribute, for instance, to differences among different elevations and observation types. The statistics presented here can serve as a guideline for selecting which observations are assimilated and for assignment of the OE covariance matrix that can be diagonal or full and correlated.

In order to further investigate the influence of ensemble generation methods on the storm-scale ensemble forecast (SSEF) system, a new ensemble sensitivity analysis-based ensemble transform with 3D rescaling (ET_3DR_ESA) method was developed. The Weather Research and Forecasting (WRF) Model was used to numerically simulate a squall line that occurred in the Jianghuai region in China on 12 July 2014. In this study, initial perturbations were generated via ET_3DR_ESA, and the ensemble forecast performance was compared to that of the dynamical downscaling (Down) method and the ensemble transform with 3D rescaling (ET_3DR) method. Results from a set of experiments indicate that ET_3DR_ESA linked to multi-scale environmental fields generates initial perturbations that can not only capture analysis uncertainties, but also match the actual synoptic conditions. Such perturbations produce faster ensemble spread growth, lower root-mean-square error, and a lower percentage of outliers, especially during the peak period of the squall line. In addition, ET_3DR_ESA can effectively reduce the energy dissipation on different scales through the analysis of the power spectrum. Moreover, the intensity and distribution forecasts of heavy rainfall from the ET_3DR_ESA ensemble forecast system were demonstrated to better match the observation. Furthermore, according to results of the relative operating characteristic (ROC) test, Brier score (BS), and equitable threat score (ETS), ET_3DR_ESA significantly improved the forecast skills for heavy rain (15–30 mm/12 h) and extreme rain (>30 mm/12 h), which are critical to the realization of accurate storm-scale system precipitation forecasts. In general, these results suggest that ET_3DR_ESA can be effectively applied to SSEF systems.