Light Rainfall Research Articles

Spatial Characteristics of Rain Fields Associated with Tropical Cyclones Landfalling over the Western Gulf of Mexico and Caribbean Sea

AbstractThe western Gulf Coast and Caribbean coast are regions that are highly vulnerable to precipitation associated with tropical cyclones (TCs). Defining the spatial dimensions of TC rain fields helps determine the timing and duration of rainfall for a given location. Therefore, this study measured the area, dispersion, and displacement of light and moderate rain fields associated with 35 TCs making landfalls in this region and explored conditions contributing to their spatial variability. The spatial patterns of satellite-estimated rain rates are determined through hot spot analysis. Rainfall coverage is largest as TCs approach the western Caribbean coast, and smaller as TCs move over the Gulf of Mexico (GM) after making landfall over the Yucatan Peninsula. The rain fields are displaced eastward and northward over the western and central Caribbean Sea and the central GM. Rainfall fields have more displacement toward the west and south, which is over land, when TCs move over the southern GM, possibly as a result of the influence of Central American gyres. The area and dispersion of rainfall are significantly correlated with storm intensity and total precipitable water. The displacement of rainfall is significantly correlated with vertical wind shear. Over the Bay of Campeche, TC precipitation extends westward, which may be related to the convergence of moisture above the boundary layer from the Pacific Ocean and near-surface convergence enhanced by land. Additionally, half of the storms produce rainfall over land about 48 h before landfall. TCs may produce light rainfall over land for more than 72 h in this region.

Shift of daily rainfall peaks over the Beijing–Tianjin–Hebei region: An indication of pollutant effects?

Aerosol impacts on diurnal variations of rainfall over the Beijing–Tianjin–Hebei region are investigated by comparing the observations between polluted and clean days in early summer (June 1 to July 20) before the rainy season begins. The 2002–2012 hourly rainfall measurements in conjunction with environmental data reveal that during high‐pollution days the peak of heavy rainfall shifted ~4 hr earlier while the peak of light rainfall delayed by 2–3 hr. Detailed analyses of meteorological conditions associated with the shifts suggest that the former is mainly caused by enhanced lower‐level aerosol heating during the daytime which increases the lower‐troposphere instability and facilitates the convective rainfall occurrence, and the latter is related to decreases of conversion of cloud droplets into raindrops associated with aerosol increases. Nevertheless, conducting model sensitivity simulations to better understand aerosol–cloud microphysics–precipitation interactions is warranted.

Role of underground fissure flow in near-surface rainfall-runoff process on a rock mantled slope in the karst rocky desertification area

Because of the complex near-surface double-layer geologic structure (i.e., surface and underground fissures) of slopes in karst regions, it is difficult to collect data on all flows, especially the flows generated in underground fissures. This lack of information leads to a poor understanding of the rainfall-runoff process associated with soil loss in karst landscapes. The goal of this study was to determine the output characteristics of surface, subsurface and underground fissure flows and their conversion relationships, as well as the role of underground fissure flow in the near-surface rainfall-runoff process, using rainfall experiments. The results revealed the following findings. (і) Light rainfall intensities (15 and 30 mm/h) generated subsurface lateral flow and underground fissure flow, whereas greater rainfall intensities generated surface runoff in addition to subsurface and underground flows. (ii) The runoff/drainage depths of all the above flows increased with increasing rainfall intensity, while the runoff/drainage coefficient exhibited no obvious regular change, except for the surface runoff. Moreover, the distribution ratio of surface runoff to total flow also increased with increasing rainfall intensity but decreased with increasing underground fissure degree, while the distribution ratio of underground fissure flow exhibited the opposite trend. (iii) Underground fissure flow, with distribution ratios of 27.8–78.0%, dominated the total flow yield. This study has significance for controlling soil erosion and water shortage in the karst rocky desertification region and also improves the understanding of the near-surface rainfall-runoff process in this region

Quantifying the effect of non-spatial and spatial forest stand structure on rainfall partitioning in mountain forests, Southern China

Forest stand structure plays an important role in rainfall interception and is a focal point in forest hydrology. Previous studies mainly looked at the effect of non-spatial attributes of stands while a few studies addressed the influence of spatial features. The aim of this study was to quantify the effect of stand structure on rainfall partitioning using diameter, height, leaf area index (LAI), neighbourhood comparison, mingling index and uniform angle index. The results revealed that the average accumulative throughfall, stemflow and interception loss accounted for 72.8%–83.2%, 0.5%–11.3% and 13.3%–26.2% of total precipitation, respectively, and significant differences existed in rainfall partitioning. The accumulative interception loss was negatively related to uniform angle index (a measure of tree spatial distribution patterns) as stand structure attribute was not available at each rainfall event. The effects of stand structure on throughfall, stemflow and interception loss varies considerably under different rainfall conditions. The LAI was significantly associated with interception loss for heavy rainfalls. The mingling index was negatively related to stemflow; however, significant relationships existed between mingling index, throughfall, and interception loss for light rainfall (drizzle). Significant positive relationships existed between uniform angle index and stemflow, while significant relationships existed for interception loss for light and heavy rainfalls. The results highlight that stand structure in combination with rainfall patterns influence rainfall partitioning.

How Does the Evaluation of the GPM IMERG Rainfall Product Depend on Gauge Density and Rainfall Intensity?

Abstract This study investigates the dependency of the evaluation of the Integrated Multisatellite Retrievals for Global Precipitation Measurement (IMERG) rainfall product on the gauge density of a ground-based rain gauge network as well as rainfall intensity over five subregions in mainland China. High-density rain gauges (1.5 gauges per 100 km2) provide exceptional resources for ground validation of satellite rainfall estimates over this region. Eight different gauge networks were derived with contrasting gauge densities ranging from 0.04 to 4 gauges per 100 km2. The evaluation focuses on two warm seasons (April–October) during 2014 and 2015. The results show a strong dependency of the evaluation metrics for the IMERG rainfall product on gauge density and rainfall intensity. A dense rain gauge network tends to provide better evaluation metrics, which implies that previous evaluations of the IMERG rainfall product based on a relatively low-density gauge network might have underestimated its performance. The decreasing trends of probability of detection with gauge density indicate a limited ability to capture light rainfall events in the IMERG rainfall product. However, IMERG tends to overestimate (underestimate) light (heavy) rainfall events, which is a consistent feature that does not show strong dependency on gauge densities. The results provide valuable insights for the improvement of a rainfall retrieval algorithm adopted in the IMERG rainfall product.

Extent of Entry Capacity Loss at Roundabouts Caused by Rainy Conditions

Entry capacity loss at multilane roundabouts caused by rainfall was investigated. Traffic and rainfall data were collected continuously for eight weeks at selected sites in South Africa and Malaysia. Three classes of rainfall (light, moderate, and heavy) were used. Both linear and exponential techniques were used to model the relationship between entry and circulating traffic flows. Correction factor for entry angle and radius was introduced in the linear model equations to allow for variations in geometric data. Correction factor is not required for the exponential model technique. Notwithstanding, both techniques predicted that entry capacity loss would result from rainfalls irrespective of their intensity. Results show that light rainfall may cause about 5% entry and circulating capacity loss, moderate rainfall may cause about 12% capacity loss, and heavy rainfall may cause about 17% capacity loss. This paper proposed further studies into the effect of two circulating lanes rather than one on entry capacity at roundabouts.

AGRAPES‐based mesoscale ensemble prediction system for tropical cyclone forecasting: configuration and performance

To improve short‐range tropical cyclone (TC) forecasting in China from both deterministic and probabilistic standpoints, a mesoscale ensemble prediction system (TREPS) based on the Global/Regional Assimilation and Prediction System (GRAPES) is constructed as a planned operational system.TREPScomprises one unperturbed and 30 perturbed forecasts ranging up to 60 h at 0.09° horizontal resolution. Downscaling perturbations from the European Centre for Medium‐range Forecasts' (ECMWF) ensemble prediction system (ENS) are used to generate initial perturbations by blending them with balanced random perturbations implemented around theTCand to generate perturbations for lateral boundary conditions. The surface temperature around theTCis also perturbed, with a combination of multi‐physics and stochastically perturbed parametrization tendencies considered to represent uncertainties in the model physics. These perturbation methods contribute a full‐area, multi‐scale and multi‐variable mutual complement to increasing forecast perturbations.TREPSis evaluated for 19TCsmaking landfall in China during 2014–2016. For deterministic guidance,TREPSsignificantly improvesTCforecasting for intensity, rainfall and wind compared toECMWFdeterministic and ensemble forecasts.TREPSdemonstrates significant advantages over its deterministic forecasts in predicting track, intensity, (extremely) heavy rainfall and wind. Furthermore,TREPSeffectively reduces the degree of underdispersion in intensity forecasting compared toENS. For probabilistic forecasting,TREPSoutperformsENSin accurately predicting extremely heavy rainfall and (extremely) strong wind owing to its better discriminating ability, but performs worse in predicting modest wind, light and heavy rainfall. Two post‐processing techniques – optimal member and optimal probability – to improve, respectively, deterministic and probabilistic guidance ofTREPSin rainfall and wind forecasting are proposed. The former is shown to be effective only for light rainfall, while the latter demonstrates consistent (partial) improvements in rainfall (wind). The superiorities and inferiorities ofTREPS, as well as possible reasons for these, are intuitively illustrated in a case‐study forRammasun.

Evaluation of seven mulch treatments for erosion control and vegetation establishment on steep slopes

Soil erosion and sediment pollution in and around construction sites can result from land disturbing activities that leave areas of unprotected soil during construction. This study evaluated different types of hydromulch for erosion control and grass establishment in comparison to straw alone or straw plus an application of polyacrylamide (PAM) on steep slopes at five construction sites in North Carolina. One site was located in the Coastal Plain (CP) region, and another in the Mountain (M) region, with the remaining three in the Piedmont (P1 through P3). At each site, 20 plots (3 m [10 ft] wide by 6 to 9 m [20 to 30 ft] long) were established on a contiguous area. After applying grass seed, the erosion control treatments were as follows: wheat straw (<i>Triticum aestivum</i>) and wheat straw + 22.4 kg ha⁻¹ (20 lb ac⁻¹) of granular, linear, anionic PAM on all five sites; and hydromulches including flexible growth medium (FGM), stabilized mulch matrix (SMM), bonded fiber matrix (BFM), wood fiber/cellulosic blend (WCB), and wood fiber mulch (WFM) applied at three sites each. Runoff volumes, turbidity, eroded sediment, and nutrient concentration data were collected after natural rain events; grass growth and cover were also evaluated. At both the CP and M sites, there were no differences between treatments, most likely due to the sandy soil texture at CP and a combination of sandy soil texture and relatively light rainfall events at M. At P1, there was a trend of straw cover producing higher runoff volume, turbidity, total suspended solids (TSS), and in general higher concentrations and amount of nutrient loss compared to all hydromulch treatments. In contrast, at P2 and P3, hydromulches tended to have greater runoff volumes, turbidity, and TSS compared to straw. Also, on these sites, straw + PAM treatment had lower concentrations of total phosphorus (P) in runoff compared to WCB. The addition of PAM did not have an effect on runoff volumes at any site, but it reduced average turbidity on P2. Overall, there was no clear pattern of hydromulch advantages over straw as a mulch for reducing erosion or establishing grass on steep slopes.

Short-Range Prediction of Monsoon Precipitation by NCMRWF Regional Unified Model with Explicit Convection

There are increasing efforts towards the prediction of high-impact weather systems and understanding of related dynamical and physical processes. High-resolution numerical model simulations can be used directly to model the impact at fine-scale details. Improvement in forecast accuracy can help in disaster management planning and execution. National Centre for Medium Range Weather Forecasting (NCMRWF) has implemented high-resolution regional unified modeling system with explicit convection embedded within coarser resolution global model with parameterized convection. The models configurations are based on UK Met Office unified seamless modeling system. Recent land use/land cover data (2012–2013) obtained from Indian Space Research Organisation (ISRO) are also used in model simulations. Results based on short-range forecast of both the global and regional models over India for a month indicate that convection-permitting simulations by the high-resolution regional model is able to reduce the dry bias over southern parts of West Coast and monsoon trough zone with more intense rainfall mainly towards northern parts of monsoon trough zone. Regional model with explicit convection has significantly improved the phase of the diurnal cycle of rainfall as compared to the global model. Results from two monsoon depression cases during study period show substantial improvement in details of rainfall pattern. Many categories in rainfall defined for operational forecast purposes by Indian forecasters are also well represented in case of convection-permitting high-resolution simulations. For the statistics of number of days within a range of rain categories between ‘No-Rain’ and ‘Heavy Rain’, the regional model is outperforming the global model in all the ranges. In the very heavy and extremely heavy categories, the regional simulations show overestimation of rainfall days. Global model with parameterized convection have tendency to overestimate the light rainfall days and underestimate the heavy rain days compared to the observation data.

Analysing regional climate forcing on historical precipitation variability in Northeast Puerto Rico

ABSTRACTThe tropical forests of northeast Puerto Rico (NE PR) and the Luquillo Mountains (LM) are a large repository for biodiversity and have an important role in regional biogeochemical processes. Precipitation is a key driver of the productivity of these sensitive ecosystems. This study analyses historical precipitation variability from 1985 to 2014 at El Verde Field Station (EVFS) at 380 m on the north facing slope of the LM. The primary objective of this study is to identify atmospheric states that lead to extreme wet/dry conditions at EVFS. This study also investigates how those wet/dry atmospheric states change over the study period through an epoch approach on annual and seasonal timescales.Self‐organizing maps (SOMs) are used to produce atmospheric states from ERA‐Interim low‐tropospheric moisture and circulation variables. These atmospheric states are downscaled to precipitation at the EVFS rain gauge. A probability density function of observed precipitation is calculated for each atmospheric state. Changes in node frequency, which is the number of days mapping to a particular node compared to the total number of days in the temporal period, are used to evaluate changes in wet/dry atmospheric states at EVFS.Results indicate that low‐precipitation days at EVFS are associated with atmospheric states with high 1000–700 hPa bulk wind shear and decreased 700 hPa moisture. Wet days in the study region are associated with moist low‐tropospheric environments with low wind shear. Our results indicate an increased frequency of dry season atmospheric states with lower 700 hPa moisture. Over the study period, the dry season has a decrease in median and extreme precipitation during rainy days (days &gt;0 mm). A decrease in early rainfall season median precipitation on rainy days is observed despite an increase in days with measurable precipitation, likely driven by an increase in light rainfall days (&lt;5 mm).

Systematical estimation of GPM-based global satellite mapping of precipitation products over China

As the Global Precipitation Measurement (GPM) Core Observatory satellite continues its mission, new version 6 products for Global Satellite Mapping of Precipitation (GSMaP) have been released. However, few studies have systematically evaluated the GSMaP products over mainland China. This study quantitatively evaluated three GPM-based GSMaP version 6 precipitation products for China and eight subregions referring to the Chinese daily Precipitation Analysis Product (CPAP). The GSMaP products included near-real-time (GSMaP_NRT), microwave-infrared reanalyzed (GSMaP_MVK), and gauge-adjusted (GSMaP_Gau) data. Additionally, the gauge-adjusted Integrated Multi-Satellite Retrievals for Global Precipitation Measurement Mission (IMERG_Gau) was also assessed and compared with GSMaP_Gau. The analyses of the selected daily products were carried out at spatiotemporal resolutions of 1/4° for the period of March 2014 to December 2015 in consideration of the resolution of CPAP and the consistency of the coverage periods of the satellite products. The results indicated that GSMaP_MVK and GSMaP_NRT performed comparably and underdetected light rainfall events (<5mm/day) in the northwest and northeast of China. All the statistical metrics of GSMaP_MVK were slightly improved compared with GSMaP_NRT in spring, autumn, and winter, whereas GSMaP_NRT demonstrated superior Pearson linear correlation coefficient (CC), fractional standard error (FSE), and root-mean-square error (RMSE) metrics during the summer. Compared with GSMaP_NRT and GSMaP_MVK, GSMaP_Gau possessed significantly improved metrics over mainland China and the eight subregions and performed better in terms of CC, RMSE, and FSE but underestimated precipitation to a greater degree than IMERG_Gau. As a quantitative assessment of the GPM-era GSMaP products, these validation results will supply helpful references for both end users and algorithm developers. However, the study findings need to be confirmed over a longer future study period when the longer-period IMERG retrospectively-processed data are available.

Correcting Biased Evaporation in CloudSat Warm Rain

The CloudSat mission’s Cloud Profiling Radar has provided the first global-scale estimates of light rainfall from warm marine clouds. Because surface radar reflection prevents hydrometeor detection below ~720 m, the CloudSat 2C-RAIN-PROFILE retrieval uses an evaporation–sedimentation model to extend its profile estimates to the surface. We use in situ radar measurements from the ship-based Marine ARM GPCI Investigation of Clouds (MAGIC) campaign in the Northeastern subtropical Pacific, which sampled a diverse set of shallow marine cloud regimes, to evaluate the evaporation–sedimentation model near the surface. We suggest an empirical bias correction, which can be applied to the 2C-RAIN-PROFILE retrieval. Before this correction, the evaporation–sedimentation model produced a mean surface rain rate bias along the MAGIC transect of 57%; after correction, this bias was effectively eliminated. We also present the evidence of the validity of this bias correction across regimes in the MAGIC region, and also in the southeast subtropical Pacific, where the magnitude of the mean bias for surface rain due to the evaporation–sedimentation model is reduced by a factor of 4.

A convection-allowing ensemble forecast based on the breeding growth mode and associated optimization of precipitation forecast

A convection-allowing ensemble forecast experiment on a squall line was conducted based on the breeding growth mode (BGM). Meanwhile, the probability matched mean (PMM) and neighborhood ensemble probability (NEP) methods were used to optimize the associated precipitation forecast. The ensemble forecast predicted the precipitation tendency accurately, which was closer to the observation than in the control forecast. For heavy rainfall, the precipitation center produced by the ensemble forecast was also better. The Fractions Skill Score (FSS) results indicated that the ensemble mean was skillful in light rainfall, while the PMM produced better probability distribution of precipitation for heavy rainfall. Preliminary results demonstrated that convection-allowing ensemble forecast could improve precipitation forecast skill through providing valuable probability forecasts. It is necessary to employ new methods, such as the PMM and NEP, to generate precipitation probability forecasts. Nonetheless, the lack of spread and the overprediction of precipitation by the ensemble members are still problems that need to be solved.

Association between cloudiness and rainfall over Fars province in Iran

Relationship between precipitation sum and cloud properties over Fars province in Iran was analyzed for the cases of light (4 mm), moderate (17 mm), and heavy (62 mm) precipitation. The cloud properties (temperature and pressure at the top, cloud optical thickness and cloud water path) were obtained from satellite data of spectoradiometer MODIS (MODO6). The spatial distribution of rainfall was obtained from the 3-hourly data of TRMM (3B42). The multivariate regression model was developed to predict the spatial distribution of rainfall. A strong significant positive association between the spatial distribution of cloud characteristics and heavy precipitation was found, while no clear correlation was revealed between light precipitation and cloud properties. The developed regression model comprised 64, 47, and 24% of spatial variance of heavy, moderate, and light rainfall, respectively. The influence of cloud water path on the spatial distribution of rainfall dominates.

An Integrated Examination of AMSR2 Products Over Ocean

Integrated processing and analysis are presented for oceanic retrievals of precipitation, sea ice concentration, columnar water vapor and cloud water, sea surface temperature, and near-surface wind from the advanced microwave scanning radiometer-2 (AMSR2) sensor. By developing a common algorithmic framework and permitting iterative interaction between historically separate science algorithms, ambiguous and contradictory retrieval results are minimized. The integration also serves to improve each algorithm individually, decreasing reliance on ancillary datasets. Case studies are presented that exemplify both ongoing challenges for these retrievals and potential research uses of such an integrated satellite product. Two cases are presented each for precipitation at high latitudes and marginal sea ice detection, with analysis supplemented by satellite data from CloudSat profiles and Himawari-8 imagery. Detection and retrieval of snowfall remains a challenge, while light rainfall detection can be aided by a variational algorithm. Sea ice concentrations below 20% cause disagreements, but the algorithms otherwise agree well on the sea ice edge. Potential mitigation strategies for ambiguous areas of light rainfall and marginal sea ice are discussed. The analysis demonstrates potential avenues for future algorithm development, but also some physical limitations of remote sensing with the AMSR2 frequencies.

Shallow Precipitation Detection and Classification Using Multifrequency Radar Observations and Model Simulations

AbstractDetection of shallow warm rainfall remains a critical source of uncertainty in remote sensing of precipitation, especially in regions of complex topographic and radiometric transitions, such as mountains and coastlines. To address this problem, a new algorithm to detect and classify shallow rainfall based on space–time dual-frequency correlation (DFC) of concurrent W- and Ka-band radar reflectivity profiles is demonstrated using ground-based observations from the Integrated Precipitation and Hydrology Experiment (IPHEx) in the Appalachian Mountains (MV), United States, and the Biogenic Aerosols–Effects on Clouds and Climate (BAECC) in Hyytiala (TMP), Finland. Detection is successful with false alarm errors of 2.64% and 4.45% for MV and TMP, respectively, corresponding to one order of magnitude improvement over the skill of operational satellite-based radar algorithms in similar conditions. Shallow rainfall is misclassified 12.5% of the time at MV, but all instances of low-level reverse orographic enhancement are detected and classified correctly. The classification errors are 8% and 17% for deep and shallow rainfall, respectively, in TMP; the latter is linked to reflectivity profiles with dark band but insufficient radar sensitivity to light rainfall ( mm h−1) remains the major source of error. The potential utility of the algorithm for satellite-based observations in mountainous regions is explored using an observing system simulation (OSS) of concurrent CloudSat Cloud Profiling Radar (CPR) and GPM Dual-Frequency Precipitation Radar (DPR) during IPHEx, and concurrent satellite observations over Borneo. The results suggest that integration of the methodology in existing regime-based classification algorithms is straightforward, and can lead to significant improvements in the detection and identification of shallow precipitation.

High temporal resolution rainfall rate estimation from rain gauge measurements

Abstract Rainfall rates derived from tipping bucket rain gauges generally ignore the detailed variation at a finer temporal scale that particularly occurs in light rainfall events. This study extends the exploration of using artificial neural networks (ANNs), in comparison with the conventional linear interpolation method (LIM) and the cubic spline algorithm (CSA) for rainfall rate estimation at fine temporal resolution using rain gauge data based on a case study at Chilbolton and Sparsholt observatories, UK. A supervised feed-forward neural network integrated with the backpropagation algorithm is used to identify the complex nonlinear relationships between input and target variables. The results indicate that the ANN considerably outperforms the CSA and LIM with higher Nash–Sutcliffe efficiency, lower root mean square error and lower rainfall amount differences when compared to the disdrometer observations when the model is trained within a broad span of input values. Consistent stability in accurately estimating rainfall rate in different sites shows the intrinsic advantage of ANNs in learning and self-adaptive abilities in modelling complex nonlinear relationships between the inputs and target variables.

Combined Space and Ground Radars for Improving Quantitative Precipitation Estimations in the Eastern Downstream Region of the Tibetan Plateau. Part I: Variability in the Vertical Structure of Precipitation in ChuanYu Analyzed from Long-Term Spaceborne Observations by TRMM PR

AbstractThis study presents a statistical analysis of the variability of the vertical structure of precipitation in the eastern downstream region of the Tibetan Plateau as measured by the Precipitation Radar (PR) on the National Aeronautics and Space Administration Tropical Rainfall Measuring Mission (TRMM) satellite. Data were analyzed over an 11-yr time span (January 2004–December 2014). The results show the seasonal and spatial variability of the storm height, freezing level, and bright band for different types of precipitation as well as the characteristics of intensity-related and type-related vertical profiles of reflectivity (VPR). Major findings were as follows: About 90% of the brightband peak reflectivity of stratiform precipitation was less than 32 dBZ, and 40% of the maximum reflectivity of convective precipitation exceeded 35 dBZ. The intensity of surface rainfall rates also depended on the shapes of VPRs. For stratiform precipitation, ice–snow aggregation was faster during moderate and heavy rainfall than it was in light rainfall. Since both the moisture and temperature are lower in winter, the transformation efficiency of hydrometeors becomes slower. Typical Ku-band representative climatological VPRs (CPRs) for stratiform precipitation have been created on the basis of the integration of normalized VPR shape for the given area and the rainfall intensity. All of the findings indicate that the developed CPRs can be used to improve surface precipitation estimates in regions with complex terrain where the ground-based radar net has limited visibility at low levels.

Impact of Rainfall on Multilane Roundabout Flowrate Contraction

In this study, roundabouts at two sites in the Malaysia were investigated under rainy and dry weather conditions. Two automatic traffic counters per roundabout arm as well as two rain gauge stations were used to collect data at each surveyed site. Nearly one million vehicles were investigated at four sites. Vehicle volume, speeds and headways for entry and circulating flows were collected continuously at each roundabout about arm for six weeks between November 2013 and January 2014. Empirical regression technique and gap-acceptance models were modified and used to analyze roundabout capacity. Good fits to the data were obtained; the results also fit models developed in other countries. It was assumed that entry capacity depends on the geometric characteristics of the roundabout, particularly the diameter of the outside circle of the intersection. It was also postulated that geometric characteristics determine the speed of vehicles around the central island and, therefore, have an impact on the gap-acceptance process and consequently the capacity. Only off-peak traffic data per light, moderate or heavy rainfall were analysed. Peak traffic data were not used because of the presence of peak traffic flow. Passenger car equivalent values being an instrument of conversion from traffic volume to flow were modified. Results show that, average entry capacity loss is about 22.6% under light rainfall, about 18.1% under moderate rainfall and about 5.6% under heavy rainfall. Significant entry capacity loss would result from rainfall irrespective of their intensity. It can be postulated that entry capacity loss under heavy rainfall is lowest because the advantage enjoyed by circulating flow would be greatly reduced with increased rainfall intensity. The paper concluded that rainfall has significant impact of flowrate contraction at roundabouts.

Urban extreme rainfall events: categorical skill of WRF model simulations for localized and non‐localized events

An objective method is used for determining the rainfall threshold for identifying extreme rainfall events (EREs) over the urban city, Bangalore, using observed rainfall data for a period of 35 years (1971–2005). Using this threshold, 52 EREs were identified during the period 2010–2014 using high‐resolution rain‐gauge observations. From these EREs, 15 localized and non‐localized events were chosen based on spatial distribution to examine the forecast skill of the Weather Research and Forecasting (WRF) model. Apart from the conventional verification methods, a number of skill scores and indices were defined for a comprehensive evaluation of rainfall model skill. In general, the forecast underpredicted the magnitude of localized and non‐localized EREs for the majority of cases; however, the model overpredicted light rainfall (≤10 mm day−1). The model showed a success rate of 59% in simulating light rainfall for localized EREs while 12% of events were missed and 29% were wrongly predicted. The success rate was significantly reduced at higher rainfall categories for localized and non‐localized EREs, where the forecast missed the majority of rainfall events. The Reliability Index (RI) computed clearly showed that model skill is relatively higher for non‐localized EREs compared to localized EREs. The average forecast reliability for non‐localized and localized EREs were 74 and 51%, respectively. For localized EREs, model skill is relatively higher in rainfall location prediction (61%) compared to area (44%) and intensity (46%) prediction; while in the case of non‐localized EREs, model skill is similar for location, intensity and area prediction. It is found that coupling an urban canopy model with WRF reduces the model errors particularly for lower rainfall thresholds.