Investigating 3D and 4D variational rapid-update-cycling assimilation of weather radar reflectivity for a heavy rain event in central Italy

Forecasting precipitation over the Mediterranean basin is still a challenge because of the complex orographic region that amplifies the need for local observation to correctly initialize the forecast. In this context, data assimilation techniques play a key role in improving the initial conditions and consequently the timing and position of the precipitation forecast. For the first time, the ability of a cycling 4D-Var to reproduce a heavy rain event in central Italy, as well as to provide a comparison with the largely used cycling 3D-Var, is evaluated in this study. The radar reflectivity measured by the Italian ground radar network is assimilated in the Weather Research and Forecasting (WRF) model to simulate an event that occurred on 3 May 2018 in central Italy. In order to evaluate the impact of data assimilation, several simulations are objectively compared by means of a fraction skill score (FSS), which is calculated for several threshold values, and a receiver operating characteristic (ROC) curve. The results suggest that both assimilation methods in the cycling mode improve the 1-, 3- and 6-hourly quantitative precipitation estimation. More specifically, the cycling 4D-Var with a warm start initialization shows the highest FSS values in the first hours of the simulation both with light and heavy precipitation. Finally, the ROC curve confirms the benefit of 4D-Var: the area under the curve is 0.91 compared to 0.88 for the control experiment without data assimilation.

Geological aspects of shallow landslides induced by the Heavy Rain Event of July 2018 within Late Cretaceous rhyolite, southern Hiroshima Prefecture, Japan

<p>Rain-induced landslides often occur in clusters on hillslopes that have unique geological characteristics, such as lithology, weathering patterns, and hydrothermal alteration. However, the effects of geological factors on landslides involving rhyolites are not fully understood. A heavy rain event during July 2018 caused numerous debris avalanches and debris flows within areas underlain by the Late Cretaceous Takada Rhyolites, southern Hiroshima Prefecture, Japan. To understand the geological factors that influence landslides in areas underlain by rhyolites, we performed GIS analyses and field investigations of outcrops and landslide scars. The study area is rectangular, 9 km long, and 3 km wide, and the long sides, oriented NE–SW in Kure City. The Norosan Welded Tuff, which forms the rhyolite unit in the study area, has near-vertical joints spaced 0.1–5.0 m, and a large number of high-angle veinlets that record hydrothermal alteration. The average joint spacing is 1.8 m in the SW of the study area (0–3.5 km), decreases from 1.8 to 0.4 m in the center (3.5–5.0 km), and 0.4 m in the NE of the study area (5.0–9.0 km). Tors are developed on the ground surface on hillslopes in the SW of the study area, but the NE of the study area is underlain by clay-rich altered soil without corestones. The 45 h and 4 h cumulative rainfall distributions prior to the landslide event were similar in the SW and NE parts of the study area. Furthermore, the NE and SW parts of the study area have a comparable proportion of surface area with similar topographic parameters (slope, planar curvature, and catchment area) to those of landslide scars. In spite of these similarities, the landslide density is about ten times higher in the NE of the study area (10–55 /km<sup>2</sup>), than in the SW. This difference is attributed to differences in joint density, and the intense weathering and alteration on joints within the rhyolite.</p>

Ensemble flash flood predictions using a high-resolution nationwide distributed rainfall-runoff model: case study of the heavy rain event of July 2018 and Typhoon Hagibis in 2019

The heavy rain event of July 2018 and Typhoon Hagibis in October 2019 caused severe flash flood disasters in numerous parts of western and eastern Japan. Flash floods need to be predicted over a wide range with long forecasting lead time for effective evacuation. The predictability of flash floods caused by the two extreme events is investigated by using a high-resolution (~ 150 m) nationwide distributed rainfall-runoff model forced by ensemble precipitation forecasts with 39 h lead time. Results of the deterministic simulation at nowcasting mode with radar and gauge composite rainfall could reasonably simulate the storm runoff hydrographs at many dam reservoirs over western Japan for the case of heavy rainfall in 2018 (F18) with the default parameter setting. For the case of Typhoon Hagibis in 2019 (T19), a similar performance was obtained by incorporating unsaturated flow effect in the model applied to Kanto Region. The performance of the ensemble forecast was evaluated based on the bias ratios and the relative operating characteristic curves, which suggested the higher predictability in peak runoff for T19. For the F18, the uncertainty arises due to the difficulty in accurately forecasting the storm positions by the frontal zone; as a result, the actual distribution of the peak runoff could not be well forecasted. Overall, this study showed that the predictability of flash floods was different between the two extreme events. The ensemble spreads contain quantitative information of predictive uncertainty, which can be utilized for the decision making of emergency responses against flash floods.