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IMERG is a global satellite-based precipitation dataset, produced by NASA. It has provided valuable rainfall information to facilitate the design or the operation of the disaster and risk management worldwide. In operation, NASA offers three types of IMERG Level 3 (L3) products, with different levels of trade-offs in terms of time latency and accuracy. These are Early run (4-hour latency), Late run (14-hour latency) and Final run(3.5-month latency). The final-run product integrates multi-sensor retrievals and provides the highest-quality precipitation estimates among three IMERG products. It however suffers from a long processing latency, which hinders its applicability to near real-time applications. In the past 10 years, deep learning techniques have made significant breakthroughs in various scientific fields, including short-term rainfall forecasting. Deep learning models have shown to have the potential to learn the complex variations in weather systems and to outperform the Numerical Weather Prediction (NWP) in terms of short lead-time predictability and the required computational resources for operation. In this research, we would like to explore the potential of deep learning (DL) in generating high-quality satellite-based precipitation product with low latency. More specifically, we investigate if DL models can learn the difference between Final- and Early-run products, and thus predict a Final-run-like product using Early-run product as input. Low-latency yet high-quality IMERG precipitation product can be therefore obtained. Various DL techniques are being tested in this work, including Auto-Encoder(AE), ConvLSTM and Deep Generative model. IMERG data between 2018 and 2020 over a rectangular area centred in the UK is used for model training and testing, and ground rain gauge records will be used to evaluate the performance of the original and predicted products. This pilot includes both ocean and land regions, which enables the comparison of the model performance between two different surface conditions. Preliminary analysis suggests that given patterns do exist in the differences between Early- and Final-run products, and the capacity of the selected DL models to learn the differences will be further investigated. The proposed work is of great potential to improve the applicability of IMERG products in an operational context.

Universal Multifractal (UM) has been a useful tool to model rainfall processes across a wide range of spatialtemporal scales. Double Trace Moment (DTM) is a technique that helps estimate parameters for the UM model. Based upon the estimated UM parameters, a discrete random cascade process can be used to generate samples with realistic rainfall properties. UM parameters are of physical meanings, representing the levels of mean intermittence (C1) and the changing rate of the mean intermittency deviating from the average field (α, know as the multifractality index), respectively. Therefore, these parameters are also widely used to characterise rainfall features across scales. UM has been tested in many countries under various weather conditions. However, its applications to extreme storm events, such as typhoons, are limited. In light of this, this study intends to analyse UM’s capacity of capturing and modelling extreme storm events recorded by a rainfall monitoring network in the South of Taipei City. On the roof of the Civil Engineering Research Building at National Taiwan University, an innovative extreme rainfall monitoring campaign has been set up and collecting high-quality rainfall measurements at fine timescales over the past two years. Rainfall data from several extreme rainfall events, including four typhoons and 10+ thunderstorms, has been collected. In this work, high-resolution rainfall time series from the laser disdrometer for typhoon Nalgae is used for analysis. Rainfall measurements are first aggregated from the native 10-second resolution to 80-second and coarser resolution and then downscaled back to 10-second to verify the downscaling results. The UM analysis is conducted in three different ways. The first way is to apply UM analysis to the entire time series. The resulting parameters are α = 1.32 and C1 = 0.108. Then, the time series is equally divided into 16 sections such that the temporal variations in rainfall features can be observed. Similarly to the first way, the second way applies the ’standard’ UM analysis but to each section. This leads to α ranging from 1.1 to 1.9 and C1 from 0.05 to 0.18. Finally, the third way applies ’ensemble’ UM analysis that concatenates divided sections into a single matrix. This results in α = 1.55 and C1 = 0.125. The derived parameters are then used to sample 10-second rainfall estimates with a discrete cascade process. The performance is quantified based upon the capacity of preserving observed extreme features. We first analyse the ranges of α and C1 resulting from the samples downscaled from the first and the third ways. We can see that the resulting α ranging from 1.2 to 1.8 and C1 from 0.06 to 0.16, which fails resembling the aforementioned variability of the UM parameters (i.e. 1.1−1.9 and 0.05−0.18). In fact, only the second way leads to satisfactory result. This preliminary study suggests that typhoon rainfall experiences drastic behaviour changes within a short period, which requires a more ’dynamic’ way to model these changes well. Similar analyses will be conducted over other collected typhoons and thunderstorm events to see if the findings can be generalised.

Stochastic rainfall modeling has been a useful tool to generate long rainfall time series for hydrological applications. One of the widely-used stochastic rainfall generators in the UK water industry to support drainage system design is the Bartlett-Lewis Rectangular Pulse model (BLRP). In practice, there are two main challenges that need to be addressed in the development of BLRP models: 1) capacity of preserving standard and extreme rainfall properties across a wide range of timescales, e.g. from sub-hourly to monthly; 2) ability to reflect the variations in the underlying climate/weather.For the first challenge, some breakthroughs have been achieved over the past few years. Onof and Wang (2020) reformulated the original BLRP model to overcome its deficiency in underestimating rainfall extremes at sub-hourly timescales. Kim and Onof (2020) further extended Onof and Wang’s work by introducing an additional parameter to enable reproducing rainfall properties across a wide range of timescales –from sub-hourly to monthly or longer. The second challenge is however yet to be addressed. The concept of weather analogs is often adopted in the literature to incorporate the impact of climate dynamics. A set of atmospheric variables, which are assumed to be able to well represent the underlying weather/climate condition, are selected and associated with the co-located local rainfall properties. Cross (2020), e.g., proposed a regression method to associate the monthly temperature with the parameters of the BLRP model. However, the concept of ‘calendar month’ –a man-made period of time– was still used in this method, which hindered the capacity of resembling the natural variations in seasons between years. To better resemble nature, Dai (2021) proposed a moving-window approach Dynamic Time Warping (DTW) method. Dai’s method sliced the original rainfall time series with a 30-day width and 10-day step moving window to reduce the impact of artificial separation of seasons. In addition, the DTW was employed to provide a more robust metric than the eulerian distance for quantifying the similarity between any two climate conditions. Dai’s work suggests that an unconventional metric may be required to better identify weather/climate analogs. Hoffmann and Lessig (2022) proposed a deep-learning method, called AtmoDist, that transforms the original atmospheric variables into a number of high-dimensional features and computes the distance from the extracted features. The result showed that the AtmoDist outperforms the traditional distance in identifying weather analogs. In this research, we extend the moving-window DTW based analog method proposed in Dai (2021) by replacing the DTW with the AtmoDist. Similarly to Dai (2021), selected atmospheric variables from the ERA5 hourly data on pressure levels are used for model training and validation. The local rainfall properties derived from the periods of the identified weather analogs resulting from the AtmoDist and the DTW methods will be first compared to evaluate their ability to identify weather analogs. Then, the derived local rainfall properties will be used as input to the BLRP model. This will enable the quantification of the impact of large-scale atmospheric variations to the local rainfall properties.

Self-expandable metallic (SEM) airway stents are an important approach to treating malignant central airway obstruction (CAO). Standard over-the-while (OTW) stent needs the guidance of a guide-wire. It should be implanted under flouroscopy or the guidance of bronchoscope visualization. In this study, we evaluated the operation time and safety between OTW stent and a novel through-the-scope (TTS) SEM airway stent.In this multi-center, randomized, parallel-group superiority study, malignant CAO patients were enrolled randomly assigned (2:1) to the TTS stent implantation group (TTS group) or the standard OTW stent group (OTW group) in six sites across China. The entire process of all surgical procedures was recorded by video. Primary endpoint was the operation time of the airway stent implantation and secondary endpoint was the success rate of the stent implantation as well as its efficacy and safety.From May 15, 2017, to December 30, 2018, 148 patients were enrolled from the six sites. We analyzed 134 patients (including 91 patients from the TTS group and 43 patients from the OTW group) according to the per-protocol set. There were no significant differences in the ages, genders, underlying diseases, and stenosis sites between the two groups. The operation time in the TTS group was significantly shorter than that in the OTW group (104±68The TTS stent implantation procedure time was significantly shorter than that of the OTW airway stent with similar efficacy and complications, which might reduce the risk and flexibility of stent implantation.Chinese Clinical Trial Registry ChiCTR-IOR-17011431.

When alien species enter new environments, they face new challenges. They encounter new predators, parasites, or pathogenic bacteria, which leads to the rapid evolution of invasive species to adapt to new habitats, where immune-related genes play a significant role. Lithobates catesbeianus is one of the worst invasive species in the world; it rapidly spread worldwide in the 19th century and invaded China in the 1950s. We predicted the possible transmission routes of bullfrogs in China through microsatellite loci and clustered the populations at different points. We studied the adaptive selection and drift reshaping of the genetic structure of an immune genetic major histocompatibility complex (MHC) in invasive bullfrogs in 23 Chinese invasive populations (13 island and 10 mainland sites) and two American populations (Kansas and California). The Chinese bullfrog populations were divided into 6 clusters by microsatellite structure, and the MHC diversity in each cluster was different. The genetic diversity of both microsatellite and MHC genes decreased due to the bottleneck effect and the rate of diversity loss at functional and neutral loci, and there was no significant difference. We found that two MHC alleles were endemic in Chinese populations, which corresponded to distinct functional supertypes. We also analyzed the impact of environmental factors and island effects on the diversity of MHC genes. These results indicate that rapid evolution plays an important role in maintaining functionally important MHC variation during the bullfrog invasion process and provide evidence that low genetic diversity can result in successful invasion.

Raindrop size distribution (DSD) is the key factor to derive reliable rainfall estimates. It is highly related to a number of integral rainfall parameters, including rain intensity (R), rain water content (W) and radar echo (Z). Disdrometers are the sensors commonly used to measure DSD based upon microwave or laser technologies; for example, JWD (Joss-Waldvogel Disdrometer), Parsivel and 2DVD (Two-Dimensional Video Disdrometer). These sensors may have their own strengths and weakness, but they are all relatively expensive. This hinders the possibility to have a high-density network for observing DSD at large scales. In this work, the ultimate goal is to develop a lightweight and low-cost disdrometer with descent accuracy.We started with establishing a model that can well simulate the signal response of a single drop falling on a cantilever piezo film. A series of experiments were conducted to test the reaction of drops at different sizes (i.e. diameters ranging from 2 - 4 mm) and as drops fall onto various locations of the film. We then modelled the collision by assuming the piezo film to be a damped cantilever beam and drop force to be a step force. The drop force can be derived based upon the measurement of the deflection of beam end, which can be further used to calibrate the damp ratio. Preliminary results suggest that the signal response of a single drop hits can be well simulated based upon the proposed model under current experimental setting. We then developed an algorithm to optimize the simulation of signal responses with four four variables; these include drop’s weight, film thickness, film damping ratio and drop force. The result shows that the simulated drop force constitutes a strong linear relationship with the real drop’s weight.We are now experimenting on the capacity of the developed model to work with a more complex yet realistic setting. For this purpose, we have created a more realistic rainfall condition by employing a micro pump. This pump can help control the size and timing of drops, so we can generate continuous single drops of consistent quality. In addition, we utilise a simple 1-D laser device to simultaneously measure the size of drops by analyzing the fluctuation in the laser signal. This would enable better understanding the actual size distribution of drops. We expect that the outcome of the experiments will provide useful insights on developing low-cost disdrometers with a cantilever piezo film.

The Computational Hydrometeorology Lab in National Taiwan University (NTU CompHydroMet Lab) recently launched a rainfall monitoring network, with a special focus on observing extreme storm events, such as typhoons and thunderstorms, over the south area of Taipei. Due to the topographic effect and the constant humidity brought by the sea breeze, together with the high temperature, south Taipei is a hotspot for the occurrence of thunderstorms in summer. The monitoring network constitutes a collocated pair of an OTT Pluvio S and an OTT Pluvio L weighing rain gauges, as well as two ‘unconventional’ rain sensors – an OTT Parsivel2 disdrometer and a Lufft WS100 radar precipitation sensor. These rain sensors are co-located within a 10 x 10 m2 area, providing rainfall estimates at high temporal resolutions, ranging from 10 seconds to 1 minute.Since the launch of the monitoring network in March 2021, the monitoring network has collected rainfall data for two typhoons and a number of thunderstorms, with the highest peak intensity at 245.6 mm/h. The measurements are generally consistent between four sensors; in particular, those from two weighing gauges are of the highest consistency. In addition, a preliminary comparison shows that the high-intensity rainfall measured by weighing gauges and disdrometer are in high agreement. This suggests that weighing gauges –which were widely used as a verification gauge for the tipping bucket gauges in the operational context– can provide reliable rainfall measurements with high accuracy, including capturing extreme rainfall. As compared to other sensors, WS100 tends to underestimate rainfall at high intensities. However, it is more sensitive to low-intensity rainfall than others; and, similarly to the disdrometer, it provides reflectivity data and requires less maintenance. The cause of underestimation is currently under investigation, which could potentially be improved through the calibration of the current algorithm with weighing gauges’ measurements. At the next stage of the work, these ground measurements will be compared with the coincidental three-dimensional radar data product from the Central Weather Bureau (CWB), Taiwan. The radar data product from CWB is available at approximately 1.2 km spatial resolution and 10-min intervals. The comparison result will be presented, and the potential of using the monitoring network to support the correction of radar data will be discussed.

The use of Poisson cluster processes to model rainfall time series at a range of scales now has a history of more than 30 years. Among them, the randomised (also called modified) Bartlett–Lewis model (RBL1) is particularly popular, while a refinement of this model was proposed recently (RBL2; Kaczmarska et al., 2014). Fitting such models essentially relies upon minimising the difference between theoretical statistics of the rainfall signal and their observed estimates. The first statistics are obtained using closed form analytical expressions for statistics of the orders 1 to 3 of the rainfall depths, as well as useful approximations of the wet–dry structure properties. The second are standard estimates of these statistics for each month of the data. This paper discusses two issues that are important for the optimal model fitting of RBL1 and RBL2. The first issue is that, when revisiting the derivation of the analytical expressions for the rainfall depth moments, it appears that the space of possible parameters is wider than has been assumed in past papers. The second issue is that care must be exerted in the way monthly statistics are estimated from the data. The impact of these two issues upon both models, in particular upon the estimation of extreme rainfall depths at hourly and sub-hourly timescales, is examined using 69 years of 5 min and 105 years of 10 min rainfall data from Bochum (Germany) and Uccle (Belgium), respectively.

Stochastic rainfall models are commonly used in practice for long-term flood risk management. One of the most widely used model types is based on point processes. Despite the widespread use of such models, whether their known simplifications in describing the space–time structure of rainfall will affect the accuracy of flood estimation has not been quantified. In this study, we quantify the biases introduced by the rainfall model limitations to flood estimates in two medium-sized river catchments (717 k m 2 and 844 k m 2 ) in the South East of the UK. To achieve this, we used nine years of hourly radar rainfall data, a dense network of hourly rain gauges, a spatial–temporal rainfall stochastic model based on point processes, and a fully distributed hydrological model. We modelled the corresponding catchment water dynamics using observed and simulated hourly rainfall and then assessed whether the errors introduced by the stochastic model will propagate in the river flow dynamics. Our results show that the stochastic rainfall model properly captures the point-scale rainfall statistics, including point extremes and the cross-site spatial correlations. However, the model results in a bias on extremes of areal statistics, including an overestimation of the areal reduction factor, extreme areal mean precipitation, and the areal fraction of rain (wet area ratio). Using this as input for continuous hydrological simulations, we find that the flow duration curves are well preserved, particularly in the high flow seasons (relative bias is less than 7%). The model also reproduces well the flood frequency curves at a daily scale with an averaged relative bias of 0.36–16.9% at 10-year return levels, confirming its ability to infer the long-term flood risk for medium-sized catchments. However, the summer-season hourly peak discharge is highly overestimated with a relative bias of over 163.5% at the same return level. The overestimation in summer hourly peak discharge is explained by the dominating convective systems in summer and the misrepresented spatial structure in the model.

In this work a two-stage (rainfall nowcasting + flood prediction) analogue model for real-time urban flood forecasting is presented. The proposed approach accounts for the complexities of urban rainfall nowcasting while avoiding the expensive computational requirements of real-time urban flood forecasting.The model has two consecutive stages:(1) Rainfall nowcasting: 0-6h lead time ensemble rainfall nowcasting is achieved by means of an analogue method, based on the assumption that similar climate condition will define similar patterns of temporal evolution of the rainfall. The framework uses the NORA analogue-based forecasting tool (Panziera et al., 2011), consisting of two layers. In the first layer, the 120 historical atmospheric (forcing) conditions most similar to the current atmospheric conditions are extracted, with the historical database consisting of ERA5 reanalysis data from the ECMWF and the current conditions derived from the US Global Forecasting System (GFS). In the second layer, twelve historical radar images most similar to the current one are extracted from amongst the historical radar images linked to the aforementioned 120 forcing analogues. Lastly, for each of the twelve analogues, the rainfall fields (at resolution of 1km/5min) observed after the present time are taken as one ensemble member. Note that principal component analysis (PCA) and uncorrelated multilinear PCA methods were tested for image feature extraction prior to applying the nearest neighbour technique for analogue selection. (2) Flood prediction: we predict flood extent using the high-resolution rainfall forecast from Stage 1, along with a database of pre-run flood maps at 1x1 km2 solution from 157 catalogued historical flood events. A deterministic flood prediction is obtained by using the averaged response from the twelve flood maps associated to the twelve ensemble rainfall nowcasts, where for each gridded area the median value is adopted (assuming flood maps are equiprobabilistic). A probabilistic flood prediction is obtained by generating a quantile-based flood map. Note that the flood maps were generated through rolling ball-based mapping of the flood volumes predicted at each node of the InfoWorks ICM sewer model of the pilot area. The Minworth catchment in the UK (~400 km2) was used to demonstrate the proposed model. Cross‑assessment was undertaken for each of 157 flooding events by leaving one event out from training in each iteration and using it for evaluation. With a focus on the spatial replication of flood/non-flood patterns, the predicted flood maps were converted to binary (flood/non-flood) maps. Quantitative assessment was undertaken by means of a contingency table. An average accuracy rate (i.e. proportion of correct predictions, out of all test events) of 71.4% was achieved, with individual accuracy rates ranging from 57.1% to 78.6%). Further testing is needed to confirm initial findings and flood mapping refinement will be pursued.The proposed model is fast, easy and relatively inexpensive to operate, making it suitable for direct use by local authorities who often lack the expertise on and/or capabilities for flood modelling and forecasting.References: Panziera et al. 2011. NORA–Nowcasting of Orographic Rainfall by means of Analogues. Quarterly Journal of the Royal Meteorological Society. 137, 2106-2123.

Raindrop size distribution (DSD) is the key factor to derive reliable rainfall estimates. It is strongly related to a number of integral rainfall parameters, including rain intensity (R), rain water content (W) and radar echo (Z). Disdrometers are the senors commonly used to measure DSD based upon microwave or laser technologies; such as JWD (Joss-Waldvogel Disdrometer), Parsivel and 2DVD (Two-Dimensional Video Disdrometer). These sensors have different strengths and weakness, and they are relatively expensive. This hinders the possibility to have a large-scale and high-density observation of DSD. In this work, our goal is to explore the possibility to develop a lightweight and low-cost disdrometer with high accuracy.We start with establish a model that can well simulate the signal reaction of a single drop falling on a cantilever piezo film. A series of experiments were conducted to test the reaction of drops with different sizes (diameters ranging from 2 - 4 mm) and as drops falling onto different locations of the film. We then modelled the collision by assuming the piezo film to be a damped cantilever beam and drop force to be a step force; and the drop force is derived based upon the measurement of the deflection of beam end, which is further used to fit the damp ratio. Preliminary results suggest that the signal reaction of a single drop hits can be well simulated based upon the proposed model under current experimental setting. More experiments and simulations are currently undergoing to explore the capacity of the proposed model with different drop falling velocity, size and position, as well as its reaction of multiple drops.

Stochastic rainfall modelling is an increasingly popular technique used by the water and weather risk industries. It can be used to synthesise sufficiently long rainfall time series to support hydrological applications (such as sewer system design) or weather-related risk analysis (such as excess rainfall insurance product design). The Bartlett-Lewis (BL) rectangular pulse model is a type of stochastic model that represents rainfall using a Poisson cluster point process. It is calibrated with standard statistical properties of rainfall data (e.g. mean, coefficient of variation, skewness and auto-correlation and so on), but it can well preserve extreme statistics of rainfall at multiple timescales simultaneously. In addition, it is found to be less sensitive to observational data length than the existing rainfall frequency analysis methods based upon, for example, annual maxima time series, so it provides an alternative to rainfall extremes analysis when long rainfall datasets are not available. In this work, we would like to introduce an open source Python package for a BL model: pyBL, implemented based upon the state-of-the-art BL model developed in Onof and Wang (2020). In the pyBL package, the BL model is separated into three main modules. These are statistical properties calculation, BL model calibration and model sampling (i.e. simulation) modules. The statistical properties calculation module processes the input rainfall data and calculates its standard statistical properties at given timescales. The BL model calibration module conducts the model fitting based upon the re-derived BL equations given in Onof and Wang (2020). A numerical solver, based upon Dual Annealing optimization and Nelder-Mead local minimization techniques, is implemented to ensure the efficiency as well as to prevent from being drawn to local optima during the solving process. Finally, one can use the sampling module to generate stochastically rainfall time series at a given timescale and for any required data length, based upon a calibrated BL model.The design of the pyBL is highly modularized, and the standard CSV data format is used for file exchange between modules. Users could easily incorporate given modules into their existing applications. In addition, a team, consisting of researchers from National Taiwan University and Imperial College London, will consistently implement the new breakthroughs in BL model to the package, so users will have access to the latest developments. The package is now undergoing the final quality check and will be available on Github (https://github.com/NTU-CompHydroMet-Lab/pyBL) in due course.

Stochastic modelling is an increasingly popular method to generate long rainfall time series as input for the subsequent hydrological applications, such as the design of urban drainage system. It aims to resemble the physical process of rainfall using parameters with physical meanings, instead of its statistical features. There are, however, two main challenges yet to be overcome in stochastic rainfall modelling. These are 1) reproduction of rainfall extremes at sub-hourly timescales, and 2) incorporation of the impact of climate change.Some recent breakthroughs have been made to address the first challenge. Onof and Wang (2020) reformulated the equations of the randomised Bartlett-Lewis rectangular pulse (BLRP) models and showed that the improved models can well preserving rainfall extremes at sub-hourly (5- and 10-min) and hourly timescales. The second challenge is however yet to be explored. Cross et al. (2020) recently presented a multivariate regression method that associates BLRP parameters to temperature estimates on a monthly basis, attempting to capture the dynamics of the underlying climate. However, the concept of ‘calendar month’ - an artificial period of time - was still employed to represent natural seasonality. This may fail capturing the natural shift and length difference of seasons between years. To address the above drawback, it is critical to ‘relax’ the concept of calendar month, so that the most similar climate conditions between different years can be better identified. An innovative approach is proposed in this work to circumvent the above drawback, where two main improvements are implemented. First, instead of following calendar month, we slice the original rainfall time series using an overlapping moving window with 30-day window width and 10-day step size. This enables a stronger continuity in representing climate variations. Second, the dynamic time warping (DTW) algorithm is employed to quantify the similarity of climate conditions between different years. DTW is a widely-used algorithm in measuring the similarity between two time series, and is known to be less sensitive to the distortion in time axis as compared to the Euclidean distance metrics. Then, based upon DTW measures, we can identify the historical periods with the most similar climate conditions to the target ones. The statistical properties of the local gauge data for these specific periods are used to build the BLRP model in a dynamic fashion. Selected atmospheric variables (including geopotential, temperature, U-component of wind, and V-component of wind ) from the ERA5 re-analysis datasets and five-minute rainfall data from 6 long recording rain gauges in Germany (one with 69 years of data; others with 49 years) are used to test the impact of the proposed approach. Preliminary results show that the statistical behaviours of newly identified periods of data are more analogous to the target period as compared to those identified from the traditional method relying on calendar month. This demonstrates the potential to use the proposed new approach to better incorporating the impact of climate change into stochastic rainfall time series modelling.

Accurate information on extreme rainfall frequency at sub-hourly timescales is useful for many hydrological applications, such as urban drainage design and stormwater management. However, the availability of sub-hourly rainfall records with sufficient length and quality is generally limited in most countries. With these short datasets, the conventional rainfall frequency analysis methods (e.g. annual maxima (AM) series) are prone to systematic biases and large uncertainties. In this work, we take advantage of long sub-hourly rainfall archives to explore the potential of alternative methods that exploit a larger fraction of the available data (or features), thus promising accurate estimates from relatively short data records.The first method is based upon the Metastatistical Extreme Value (MEV) framework, which relaxes the asymptotic assumption of traditional AM methods. MEV considers, year by year, the full distribution of the underlying ordinary events and their number of occurrences. The second method, the Simplified MEV (SMEV, a variant of MEV), in which inter-annual variability is neglected in favour of simpler parametrisation and more robust parameter estimation, is also tested. So far, these two methods were shown to outperform traditional methods for daily amounts, but were never used on sub-hourly data.The third method is based upon point process theory, which represents the temporal rainfall process in a realistic yet simple way, such that the hierarchical structure of rainfall is explicitly incorporated, and several parameters have a physical interpretation. Models based upon point process theory were known to be incapable of preserving extreme rainfall statistics at hourly and sub‑hourly timescales. Nonetheless, a recent breakthrough has overcome this deficiency (Onof and Wang, 2019). In this work, a revised randomised Bartlett-Lewis rectangular pulse model (RBL) is employed.Five-minute rainfall data from 5 long recording rain gauges in Germany – Bochum (69 years), Aplerbeck, Kruckel, Marten and Nettebach (49 years) – are used. The comparison is conducted by resembling the scenarios where sub-hourly rainfall time series data are available with various short lengths (i.e. 5/10/15/20 years). SMEV and RBL generally outperform the MEV and AM in preserving sub-hourly rainfall extremes and are both much less sensitive to the use of short data records. SMEV outperforms RBL in preserving rainfall extremes at short return periods (< 10-year return periods), while they perform similarly at long return periods. RBL however has the advantage of preserving rainfall extremes across multiple timescales (i.e. from sub-hourly, hourly to 1-day) at the same time. The unsatisfactory performance of MEV is related to the influence of the low-intensity tail of yearly distributions.

This paper proposes a new object-based storm tracking algorithm, based upon TITAN (Thunderstorm Identification, Tracking, Analysis and Nowcasting). TITAN is a widely-used convective storm tracking algorithm but has limitations in handling small-scale yet high-intensity storm entities due to its single-threshold identification approach. It also has difficulties to effectively track fast-moving storms because of the employed matching approach that largely relies on the overlapping areas between successive storm entities. To address these deficiencies, a number of modifications are proposed and tested in this paper. These include a two-stage multi-threshold storm identification, a new formulation for characterizing storm's physical features, and an enhanced matching technique in synergy with an optical-flow storm field tracker, as well as, according to these modifications, a more complex merging and splitting scheme. High-resolution (5-min and 529-m) radar reflectivity data for 18 storm events over Belgium are used to calibrate and evaluate the algorithm. The performance of the proposed algorithm is compared with that of the original TITAN. The results suggest that the proposed algorithm can better isolate and match convective rainfall entities, as well as to provide more reliable and detailed motion estimates. Furthermore, the improvement is found to be more significant for higher rainfall intensities. The new algorithm has the potential to serve as a basis for further applications, such as storm nowcasting and long-term stochastic spatial and temporal rainfall generation.

The description of precise intrabronchial positions for the sampling of mediastinal-hilar lymph nodes is critical to successfully perform conventional transbronchial needle aspiration. Previously published maps of mediastinal-hilar lymph nodes were primarily drawn based on experts' experience. We generated a virtual map of the most frequently sampled intrathoracic lymph nodes from an intrabronchial perspective using a virtual bronchoscopic navigation system, to assist with training in conventional transbronchial needle aspiration.

Background: In evaluating patients with suspected lung cancer, it is important to not only obtain a tissue diagnosis, but also to obtain enough tissue for both histologic and molecular analysis in order to appropriately stage the patient with a safe and efficient strategy. The diagnostic approach may often be dependent on local resources and practice patterns rather than current guidelines. We Describe lung cancer staging at two large academic medical centers to identify the impact different procedural approaches have on patient outcomes. Methods: We conducted a retrospective cohort study of all patients undergoing a lung cancer diagnostic evaluation at two multidisciplinary centers during a 1-year period. Identifying complication rates and the need for multiple biopsies as our primary outcomes, we developed a multivariate regression model to determine features associated with complications and need for multiple biopsies. Results: Of 830 patients, 285 patients were diagnosed with lung cancers during the study period. Those staged at the institution without an endobronchial ultrasound (EBUS) program were more likely to require multiple biopsies (OR 3.62, 95% CI: 1.71–7.67, P=0.001) and suffer complications associated with the diagnostic procedure (OR 10.2, 95% CI: 3.08–33.58, P Conclusions: Lung cancer evaluation at centers with a dedicated EBUS program results in fewer biopsies and complications than at multidisciplinary counterparts without an EBUS program.

Radar‐rain gauge merging techniques have been widely used to improve the applicability of radar and rain gauge rainfall estimates by combining their advantages, while partially overcoming their individual weaknesses. Despite significant research in this area, guidance on the suitability of and factors affecting merging techniques at the fine spatial‐temporal resolutions required for urban hydrological applications is still insufficient. In this paper, an in‐depth review of radar‐rain gauge merging techniques is conducted, with a focus on their potential for urban hydrological applications. An overview is first given of existing merging techniques and an application‐oriented categorization is proposed: (1) radar bias adjustment methods, (2) rain gauge interpolation methods using radar spatial association as additional information, and (3) radar‐rain gauge integration methods. A detailed review is given of studies focusing on the evaluation and intercomparison of merging methods, based upon which the most widely used and best performing techniques from each category are identified. These are mean field bias adjustment, kriging with external drift, and Bayesian merging. Climatological, operational, and methodological factors affecting merging performance are then reviewed and their relevance for urban applications discussed. Based on this review, conclusions on merging potential for urban applications are drawn and research gaps are identified, which should be addressed to provide further guidance on the use of merging techniques for urban hydrological applications.

FloodCitiSense aims at developing an urban pluvial flood early warning service for, but also by citizens and city authorities, building upon the state-of-the-art knowledge, methodologies and smart technologies provided by research units and private companies. FloodCitiSense targets the co-creation of this innovative public service in an urban living lab context with all local actors. This service will reduce the vulnerability of urban areas and citizens to pluvial floods, which occur when heavy rainfall exceeds the capacity of the urban drainage system. Due to their fast onset and localized nature, they cause significant damage to the urban environment and are challenging to manage. Monitoring and management of peak events in cities is typically in the hands of local governmental agencies. Citizens most often just play a passive role as people negatively affected by the flooding, despite the fact that they are often the ‘first responders’ and should therefore be actively involved. The FloodCitiSense project aims at integrating crowdsourced hydrological data, collaboratively monitored by local stakeholders, including citizens, making use of low-cost sensors and web-based technologies, into a flood early warning system. This will enable ‘citizens and cities’ to be better prepared for and better respond to urban pluvial floods. Three European pilot cities are targeted: Brussels – Belgium, Rotterdam – The Netherlands and Birmingham – UK.

Background The optimal procedure for maximizing the diagnostic yield and minimizing the procedural complexity of endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) is controversial. We conducted a prospective randomized controlled trial to determine the optimal procedure of EBUS-TBNA for mediastinal and hilar lymphadenopathy, with a particular focus on the roles of the inner-stylet and suction. Methods Consecutive patients with enlarged mediastinal and hilar lymph nodes (LNs), detected by computed tomography (CT) or positron emission tomography-CT (PET-CT), who underwent EBUS-TBNA were included. Each LN was sampled with three needle passes using suction–stylet, suction–no stylet, and stylet–no suction procedures. The samples were smeared onto glass slides for cytological evaluation. A single, blinded cytopathologist evaluated each set of slides. The primary outcomes were cytological specimen adequacy rate and diagnostic yield of malignant LNs. The secondary outcomes were tissue-core acquisition rate, procedural time, and the amount of bleeding. Results This study evaluated 97 patients with a total of 255 LNs. The final LN diagnosis was benign in 144, malignant in 104, and inadequate in 7 cases. There were no significant differences among the suction–stylet, suction–no stylet, and stylet–no suction groups in specimen adequacy rate (87.1, 88.2, 85.9%, respectively) or diagnostic yield of malignancy (32.2, 31.8, 31.0%, respectively). However, the use of suction was associated with an increase in tissue-core acquisition rate (P

The Cottonseed oil methyl ester (COME) was prepared using an alkali catalyzed trans esterification reaction, and its chemical composition and cold flow properties (CFP) were studied. Two approaches, viz. blending with petrodiesel and treating with cold flow improver (CFI) were used to improve the CFP of COME. The results showed that COME was mainly composed of fatty acid methyl esters (FAMEs): C14:0–C22:0, C16:1–C20:1, C18:2 and C18:3–C20:3. The mass fractions of saturated and unsaturated FAMEs were 27.69% and 71.65%, respectively. The cold filter plugging point (CFPP) and kinematic viscosity (KV) at 40 °C of COME are -1 °C and 4.63 mm2/s respectively. Blending with -10 petrodiesel (-10PD) and 0 petrodiesel (0PD) can decrease the CFPP of COME/-10PD (B30-B40) and COME/0PD (B40-B50) -12 and -8 °C respectively. With the increase in petrodiesel content, the KV at 40 °C of blending oil decreased, and viscosity-temperature characteristics of blending oil were improved. When used Flow Fit (≤3vol.%), the CFPPs were reduce from COME/-10PD B5, B7, B10, B20, B50 and COME -8, -8, -9, -11, -11 and -1 °C to -28, -27, -26, -25, -16 and -5 °C respectively; COME/0PD -3, -3, -4, -5, -8 and -1 °C to -26, -25, -24, -24, -16 and -5 °C respectively.

This note points out that the cost functions within Zanoni and Jaber [Zanoni, S. and M. Y. Jaber. 2015. “A Two-level Supply Chain with Consignment Stock Agreement and Stock-dependent Demand.” International Journal of Production Research 53: 3561–3572] might be incorrect due to misuse of demand representation. By incorporating Braglia and Zavanella’s consignment stock model and relaxing the zero-inventory reordering constraint, Zanoni and Jaber made extensions of the integrated vendor–buyer supply chain with stock-dependent demand in Sajadieh, Thorstenson, and Akbari Jokar’s proposed model. However, Sajadieh, Thorstenson, and Akbari Jokar’s model has different scenarios than Braglia and Zavanella’s model, and these inconsistencies cause false formulation in Zanoni and Jaber’s paper. In this note, we first formulate the justified cost functions and then present characteristics of the corrected model.

Background. Lung cancer is the leading cause of malignancy related mortality in the United States. Accurate staging of NSCLC influences therapeutic decisions. Transbronchial needle aspiration (TBNA) and endobronchial ultrasound-guided TBNA (EBUS-TBNA) has been accepted as a procedure for the diagnosis and staging of lung cancer. The aim of this study is to evaluate the efficacy and adequacy of TBNA and EBUS-TBNA for sampling of mediastinal adenopathy using the Wang’s eleven lymph node map stations. Methods. We retrospectively reviewed 99 consecutive cases diagnosed with malignancy by EBUS-TBNA and a series 74 patients evaluated for mediastinal adenopathy or a pulmonary lesion using conventional transbronchial needle aspiration. The IASLC lymph node map was correlated with Wang’s map. Results. A total of 182 lymph node stations were sampled using EBUS-TBNA. 96 were positive for nodal metastasis. A total of four cases of samples taken from station 2R showed malignant cells. From the 74 cases series using cTBNA 167 nodes were sampled in 222 passes. Lymphoid or malignant tissue was obtained in 67 (91.8%) cases; 55.1% of the nodes were 1 cm or less. Conclusions. The use of the eleven stations described in Wang’s map to guide TBNA of the mediastinal nodes allows sampling of radiologically considered nonpathological nodes. These data suggest that Wang’s map covers the most frequent IASLC nodal stations compromised with metastasis.

Urban catchments are typically characterised by high spatial variability and fast runoff processes resulting in short response times. Hydrological analysis of such catchments requires high resolution precipitation and catchment information to properly represent catchment response. This study investigated the impact of rainfall input resolution on the outputs of detailed hydrodynamic models of seven urban catchments in North-West Europe. The aim was to identify critical rainfall resolutions for urban catchments to properly characterise catchment response. Nine storm events measured by a dual-polarimetric X-band weather radar, located in the Cabauw Experimental Site for Atmospheric Research (CESAR) of the Netherlands, were selected for analysis. Based on the original radar estimates, at 100 m and 1 min resolutions, 15 different combinations of coarser spatial and temporal resolutions, up to 3000 m and 10 min, were generated. These estimates were then applied to the operational semi-distributed hydrodynamic models of the urban catchments, all of which have similar size (between 3 and 8 km2), but different morphological, hydrological and hydraulic characteristics. When doing so, methodologies for standardising model outputs and making results comparable were implemented. Results were analysed in the light of storm and catchment characteristics. Three main features were observed in the results: (1) the impact of rainfall input resolution decreases rapidly as catchment drainage area increases; (2) in general, variations in temporal resolution of rainfall inputs affect hydrodynamic modelling results more strongly than variations in spatial resolution; (3) there is a strong interaction between the spatial and temporal resolution of rainfall input estimates. Based upon these results, methods to quantify the impact of rainfall input resolution as a function of catchment size and spatial–temporal characteristics of storms are proposed and discussed. publisher: Elsevier articletitle: Impact of spatial and temporal resolution of rainfall inputs on urban hydrodynamic modelling outputs: A multi-catchment investigation journaltitle: Journal of Hydrology articlelink: http://dx.doi.org/10.1016/j.jhydrol.2015.05.035 content_type: article copyright: Copyright © 2015 The Authors. Published by Elsevier B.V. ispartof: Journal of Hydrology vol:531 issue:2 pages:389-407 status: published

Following extensive surface water flooding (SWF) in England in summer 2007, progress has been made in improving the management and prediction of this type of flooding. A rainfall threshold-based extreme rainfall alert (ERA) service was launched in 2009 and superseded in 2011 by the surface water flood risk assessment (SWFRA). Through survey responses from local authorities (LAs) and the outcome of workshops with a range of flood professionals, this paper examines the understanding, benefits, limitations and ways to improve the current SWF warning service. The current SWFRA alerts are perceived as useful by district and county LAs, although their understanding of them is limited. The majority of LAs take action upon receipt of SWFRA alerts, and their reactiveness to alerts appears to have increased over the years and as SWFRA superseded ERA. This is a positive development towards increased resilience to SWF. The main drawback of the current service is its broad spatial resolution. Alternatives for providing localised SWF forecast and warnings were analysed, and a two-tier national-local approach, with pre-simulated scenario-based local SWF forecasting and warning systems, was deemed most appropriate by flood professionals given current monetary, human and technological resources.

Background The role of transbronchial needle aspiration (TBNA) in the diagnosis and staging of lung cancer has been well established. Recently, the efficacy of conventional TBNA in the staging of lung cancer has been enhanced by the use of endobronchial ultrasound (EBUS)-TBNA. Our study sought to evaluate the adequacy of TBNA of International Association for the Study of Lung Cancer (IASLC) stations 4R, 4L and 7 using endobronchial landmarks provided by the Wang nodal mapping system in the staging of lung cancer. Methods We retrospectively analyzed all bronchoscopic cases with conventional TBNA punctures positive for malignancy at our institution from 1 January to 31 October 2014. The endobronchial puncture site was guided by the Wang nodal mapping system. The Wang stations were correlated with the IASLC lymph node map. No endobronchial ultrasound or rapid on-site evaluation was used. Pathological analysis included cytological and histological examination. Results Diagnosis by histological analysis was obtained in 115 (55.3%) out of 208 puncture sites. The metastatic lymph nodes were distributed at IASLC stations 4R (W1, 3, 5) 46.6 %, 7 (W2, 8, 10) 19.7%, 4L (W4, 6) 11.5%, 11R (W7, W9) 11.1% 11L (W11) 9.6%, 2R (high station W3) 0.5%, and the proximal portion of station 8 (station W10 beyond the middle lobe orifice) 1%. No complications were observed. Conclusion IASLC station 4R (W1, 3, 5), 7 (W2, 8, 10) and 4L (W4, 6) are adequate for the staging of lung cancer.