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The climate is warming and this is changing some aspects of storms, but we have relatively little knowledge of storm characteristics beyond intensity, which limits our understanding of storms overall. In this study, we apply a cell-tracking algorithm to 20 years of radar data at a mid-latitude coastal-site (Sydney, Australia), to establish a regional precipitation system climatology. The results show that extreme storms in terms of translation-speed, size and rainfall intensity usually occur in the warm season, and are slower and more intense over land between ~ 10 am and ~ 8 pm (AEST), peaking in the afternoon. Precipitation systems are more frequent in the cold season and often initiate over the ocean and move northward, leading to precipitation mostly over the ocean. Using clustering algorithms, we have found five precipitation system types with distinct properties, occurring throughout the year but peaking in different seasons. While overall rainfall statistics don't show any link to climate modes, links do appear for some system types using a multivariate approach. This climatology for a variety of precipitation system characteristics will allow future study of any changes in these characteristics due to climate change. [ABSTRACT FROM AUTHOR]

Short-duration rainfall extremes can cause flash flooding with associated impacts. Previous studies of climate impacts on extreme precipitation have focused mainly on daily rain totals. Subdaily extremes are often generated in small areas that can be missed by gauge networks or satellites and are not resolved by climate models. Here, we show a robust positive trend of at least 20% per decade in subhourly extreme rainfall near Sydney, Australia, over 20 years, despite no evidence of trends at hourly or daily scales. This trend is seen consistently in storms tracked using multiple independent ground radars, is consistent with rain-gauge data, and does not appear to be associated with known natural variations. This finding suggests that subhourly rainfall extremes may be increasing substantially faster than those on more widely reported time scales. [ABSTRACT FROM AUTHOR]

High-resolution datasets offer the potential to improve our understanding of spatial and temporal precipitation patterns and storm structures. The goal of this study is to evaluate the similarities and differences of object-based storm characteristics as observed using space- or land-based sensors. The Method of Object-based Diagnostic Evaluation (MODE) Time Domain (MTD) is used to identify and track storm objects in two high-resolution merged datasets: the Integrated Multisatellite Retrievals for Global Precipitation Measurement (IMERG) final product V06B and gauge-corrected ground-radar-based Multi-Radar Multi-Sensor (MRMS) quantitative precipitation estimations. Characteristics associated with landfalling hurricanes were also examined as a separate category of storm. The results reveal that IMERG and MRMS agree reasonably well across many object-based storm characteristics. However, there are some discrepancies that are statistically significant. MRMS storms are more concentrated, with smaller areas and higher peak intensities, which implies higher flash flood risks associated with the storms. On the other hand, IMERG storms can travel longer distances with a higher volume of precipitation, which implies higher risk of riverine flooding. Agreement between the datasets is higher for faster-moving hurricanes in terms of the averaged intensity. Finally, MRMS indicates a higher average precipitation intensity during the hurricane's lifetime. However, in non-hurricanes, the opposite result was observed. This is likely related to MRMS having higher resolution; monitoring the hurricanes from many viewing angles, leading to different signal saturation properties compared to IMERG; and/or the dominance of droplet aggregation effects over evaporation effects at lower altitudes. [ABSTRACT FROM AUTHOR]

This research aims to develop a novel method named region probability matching method (RPMM) for calibrating the Amir-Abad weather radar located in the north of Iran. This approach also can overcome the limitations of probability matching method (PMM), window probability matching method (WPMM), and window correlation matching method (WCMM). The employing of these methods for calibrating the radars in light precipitation is associated with many errors. Additionally, in developing countries like Iran where ground stations have low temporal resolution, these methods cannot be benefited from. In these circumstances, RPMM by utilizing 18 synoptic stations with a temporal resolution of 6 h and radar data with a temporal resolution of 15 min has indicated an accurate estimation of cumulative precipitation over the entire study area in a specific period. Through a comparison of the two methods (RPMM and traditional matching method (TMM)) on March 22, 2014, the obtained correlation coefficients for TMM and RPMM were 0.13 and 0.95, respectively. It is noted that the cumulative precipitation of the whole rain gauges and the calibrated radar precipitation at the same pixels were 38.5 and 36.9 mm, respectively. Therefore, the obtained results prove the inefficiency of TMM and the capability of RPMM in the calibration process of the Amir-Abad weather radar. Besides, in determining the uncertainty associated with the calculated values of A and B in the Ze-R relation, a sensitivity analysis method was employed during the estimation of cumulative light precipitation for the period from 2014 to 2015. The results expressed that in the worst conditions, 69% of radar data are converted to R values by a maximum error less than 30%. [ABSTRACT FROM AUTHOR]

Ground observation absence in many parts of the world highlights the importance of merged satellite precipitation products. In this study, we aim to evaluate the effect of different sources of data in the uncertainties of a merged satellite product, by comparing the Integrated Multi-satellitE Retrievals for GPM (IMERG) Final product (V06B) with a ground-based radar product, Multi-Radar Multi-Sensor (MRMS), using both pixel-based and object-based approaches. This study is focused on the eastern United States (land-only) during the hurricane days that occurred in 2016–2018. The results showed that IMERG had better agreement in terms of the average precipitation intensity and area with a bias reduction of 75% and 65%, respectively, when the passive microwave (PMW) sensor overpass is matched instantaneously with MRMS compared to temporally averaged MRMS data (MRMS-Averaged). PMW observations tend to show storms with smaller areas in the IMERG Final product in comparison with MRMS, possibly due to the effect of light precipitation not detected properly by PMW sensors. However, by removing the light precipitation (less than 1 mm/h) in the object-based approach, hurricane objects in the IMERG Final product tend to be larger during the PMW observations, which might be related to different viewing angles of sensors contributing to MRMS and IMERG products. Precipitation estimates have smaller areas with higher average intensity during the PMW observations in the IMERG Final product compared to data estimated by Morphed or IR (morph/IR) observations, which is probably related to the effect of morphing technique, leading to homogenization of the varying rainstorm characteristics. In addition, with the longer absence of PMW observations, the quality of morph/IR estimates in IMERG Final product deteriorates with a decreasing correlation coefficient, a growth in precipitation area and a downward trend in average precipitation intensity. Finally, the inter-comparison of PMW sensors showed the priority of imagers over sounders with GMI as the best among imagers and MHS as the best among sounders in terms of correlation and average intensity compared to MRMS; however, SSMIS was the best in capturing the precipitation area. • PMW estimates in IMERG have better agreement with MRMS when matched instantaneously. • PMW sensors often represent the storms with smaller areas in IMERG compared to MRMS. • PMW sensors show the storms smaller with higher intensity compared to morphed/IR • Morphed/IR data quality in IMERG decreases with the longer absence of PMW sensors. • PMW sensors in IMERG should be prioritized based on the precipitation properties. [ABSTRACT FROM AUTHOR]

Accuracy of rainfall quantification is one of the most important concerns in meteorological and hydrological modelling. Satellites and weather radars can provide meteorological information with higher temporal and spatial resolution than ground stations. Rain gauges measure rain rate directly; however, weather radars estimate rain rate by converting radar reflectivity aloft to rain rate at ground level. This conversion with a power law relation between radar reflectivity and rain rate could be altered from place to place or in various precipitation types. This variety may be the source of errors and uncertainty of radar rainfall estimates. One way to assess the uncertainty of radar rainfall is simulation of rainfall fields. In this article, after calibrating two radars located in the south-western and northern parts of Iran, uncertainty of rainfall estimates of these radars has been analysed using the Gaussian Copula model. Reliability of this model was examined for 10% of the rainfall events that were not included in the simulation process. Obtained results of the current research indicate that recalibration of radars can considerably reduce bias and root mean error. In addition, the Copula-based model can generate rainfall fields with similarly spatial structures to those of observed rainfall data. [ABSTRACT FROM PUBLISHER]