Identification Of Rainfall Events Research Articles

Identification of Rainfall Events and Heavy Rainfall Events From Radar Measurements in Southeastern Australia

AbstractRadar data can be of significant utility in investigating characteristics of rainfall events that cannot be studied with rain gauges alone. The recent establishment of a long‐term, quality‐controlled data set covering most of the radars on the Australian continent enables a deeper characterization of rainfall, including heavy rainfall events. This study develops a methodology to identify and characterize rainfall events from radar data and tests its utility by applying it to the regions surrounding the major cities of Brisbane, Sydney, and Melbourne. The event characteristics studied include rainfall accumulation and intensity, event duration and spatial extent, and the contribution convective areas make to the overall event rainfall. Rainfall events in Brisbane and Sydney are found to be more intense, more convective, and smaller in extent whilst producing larger rainfall accumulations than Melbourne rainfall events. Rainfall event duration and total accumulated rainfall are strongly positively correlated, as are the overall event intensity and the intensity of convective rainfall. The events that produce the largest rainfall accumulations exhibit significant differences from the events that produce the highest rainfall intensities. Overall, the study demonstrates that long‐term radar data sets in Australia provide an invaluable and rich source to study rainfall characteristics in a variety of regions at a high spatial and temporal resolution.

Identification of Rainfall events on Climate Phenomena in Medan based on Machine Learning

Indonesia has diverse topographical conditions that result in Indonesia having a unique climate. One of the unique climate elements to be studied is rainfall, because rainfall has a different pattern in each region, this different rainfall pattern is caused by several climate phenomena factors that affect the rainfall pattern, including El-Nino Southern Oscillation (ENSO), Indian Ocean Dipole (IOD) and Madden Julian Oscillation (MJO). Medan City is the capital of North Sumatra province which is one of the areas in the flood-prone category in North Sumatra, where the factor of flooding is due to rainfall events in a long period of time, so the author wants to know which climatic phenomena factors can affect rainfall events in Medan city by using Machine Learning technology through the Matlab application, where in this study has a method by forming four combination models, namely the combination of the influence of IOD, SOI and MJO; second combination of IOD and SOI; third combination of SOI and MJO; and fourth combination of MJO and IOD, these four combinations will be the rainfall value of the four models. Furthermore, the rainfall value of the model is compared with the observed rainfall value and verification test using Mean Absolute Error (MAE) and correlation. Then the calculation of the comparison between the four rainfall models with the observed rainfall obtained the lowest MAE value during the SOI and MJO phenomenon of 15.0 mm and the highest correlation value during the IOD and SOI and SOI and MJO phenomena. So it is concluded that the combination of SOI and MJO has the best verification value. This shows that based on Machine Learning modeling, the model shown as the best predictor in Medan city is when the model combination consists of SOI and MJO.

Impact-based classification of extreme rainfall events using a simplified overland flow model

ABSTRACT In this study, we present a new rainfall classification method based on the potential of individual rainstorm events to generate and sustain overland flow on a simplified urban model surface. Conventional rainfall classification approaches using peak intensities or rainfall sums can lead to misjudgements of the impact of rainfall events, as they do not explicitly consider the potential of rain to generate and sustain substantial amounts of overland flow on typical sealed urban surfaces. The new method of impact-based rainfall classification proposed here comprises three steps: Identification of rainfall events from high-resolution time series, computation of overland flow depths for each event, and impact-based classification of rainfall events as a function of the magnitude of overland flow. The accuracy of this approach was evaluated by its ability to describe seasonal and annual occurrences of and variations in rainstorm impacts for high-resolution series at three different urban stations in Germany.