Accurate observational data and reliable prediction models are both essential to improve the quality of precipitation forecasts. The spiraling trajectories of air parcels within a tropical cyclone (TC) coupled with the large sizes of these systems brings special challenges in making accurate short-term forecasts, or nowcasts. Doppler weather radars are ideal instruments to observe TCs when they move over land, and traditional nowcasts incorporate radar data. However, data from dozens of radars must be mosaicked together to observe the entire system. Traditional single-radar-based reflectivity tracking methods commonly employed in nowcasting are not suitable for TCs as they are not able to capture the circular motion of these systems. Thus, this paper focuses on improving short-term predictability of TC precipitation with Doppler weather radar observations based on: a multi-scale motion vector retrieval algorithm, an optimization technique and a semi-Lagrangian advection scheme. Motion fields of precipitation regions are obtained by a multi-level motion vector retrieval algorithm, then corrected and smoothed by the optimization technique using mass and smooth constraints. Predicted precipitation regions are then extrapolated using the semi-Lagrangian advection scheme. A case study of Hurricane Isabel (2003) shows that the combination of these methods may increase reliable rainfall prediction to about 5 h as the TC moves over land.

Hurricane Harvey (2017) caused widespread flash flooding by extremely heavy rainfall and resulted in tremendous damage, including 82 fatalities and huge economic loss in the Houston, Texas area. To reduce hazards, loss, and to improve urban resilience, it is important to understand the factors that influence the occurrence of flooding events. People rely on natural resources and different land uses to reduce the severity of flood impacts and mitigate the risk. In this study, we focused the impacts of land use on Hurricane-Harvey-induced flooding inside and outside the Houston city center. With the recent trend that more citizen scientists serve in delivering information about natural disaster response, local residents in Houston areas participated in delineating the flooded areas in Hurricane Harvey. The flooding information used here generated a published map with citizen-contributed flooding data. A regional model framework with spatial autocovariates was employed to understand those interactions. Different land use patterns and types affected the potential of flooding events differently inside and outside Houston’s city center. Explicitly, we found agricultural and open space were associated with high risk of flooding outside the city center, industrial lands increased the high risk of flooding in city center, and residential areas reduced the potential of flooding both inside and outside the city center. The results can assist with future land use strategy in Houston and other areas, and mitigate potential flash flooding. This study also highlighted the contribution of citizen science to responses to natural hazards.

Although tropical cyclone (TC) rain fields assume varying spatial configurations, many studies only use areal coverage to compare TCs. To provide additional spatial information, this study calculates metrics of closure, or the tangential completeness of reflectivity regions surrounding the circulation center, and dispersion, or the spread of reflectivity outwards from the storm center. Two hurricanes that encountered different conditions after landfall are compared. Humberto (2007) experienced rapid intensification (RI), stronger vertical wind shear, and more moisture than Jeanne (2004), which was more intense, weakened gradually, and became extratropical. A GIS framework was used to convert radar reflectivity regions into polygons and measure their area, closure, and dispersion. Closure corresponded most closely to storm intensity, as the eye became exposed when both TCs weakened to tropical storm intensity. Dispersion increased by 10 km·hr⁻¹ as both TCs developed precipitation along frontal boundaries. As closure tended to change earlier than dispersion and area, closure may be most sensitive to subtle changes in environmental conditions, particularly as the storm’s core experiences the entrainment of dry air and erodes. Displacement provided a combined radial and tangential component to the location of the rainfall regions to confirm placement along the frontal boundaries. Examining area alone cannot reveal these patterns. The spatial metrics reveal changes in TC structure, such as the lag between onset of RI and maximum closure, which should be generalizable to TCs experiencing similar conditions. Future work will calculate these metrics for additional TCs to quantify structural changes in response to their surrounding environment.

The creation of a 3D-mosaic is often the first step when using the high spatial and temporal resolution data produced by ground-based radars. Efficient yet accurate methods are needed to mosaic data from dozens of radar to better understand precipitation processes in synoptic-scale systems such as tropical cyclones. Research-grade radar mosaic methods of analyzing historical weather events should utilize data from both sides of a moving temporal window and process them in a flexible data architecture which is not available in most standalone software tools or real-time systems. Thus, these historical analyses require a different strategy optimizing flexibility and scalability by removing time constraints from the design. This paper presents a map-reduce-based playback framework using Apache Spark’s computational engine to interpolate large volumes of radar reflectivity and velocity data onto 3D grids. Designed as being friendly to use on a high performance computing cluster, these methods may also...