scholarly journals Estimation of Rain Intensity Spectra over the Continental United States Using Ground Radar–Gauge Measurements

2012 ◽  
Vol 25 (6) ◽  
pp. 1901-1915 ◽  
Author(s):  
Xin Lin ◽  
Arthur Y. Hou
Keyword(s):  
United States ◽  
Heavy Rain ◽  
Rain Intensity ◽  
Light Rain ◽  
Geographical Variations ◽  
Satellite Sensors ◽  
Rain Events ◽  
Rain Rates ◽  
The Impact ◽  
Microwave Sensors

Abstract A high-resolution surface rainfall product is used to estimate rain characteristics over the continental United States as a function of rain intensity. By defining data at 4-km horizontal resolutions and 1-h temporal resolutions as an individual precipitating or nonprecipitating sample, statistics of rain occurrence and rain volume including their geographical and seasonal variations are documented. Quantitative estimations are also conducted to evaluate the impact of missing light rain events due to satellite sensors’ detection capabilities. It is found that statistics of rain characteristics have large seasonal and geographical variations across the continental United States. Although heavy rain events (>10 mm h−1) only occupy 2.6% of total rain occurrence, they may contribute to 27% of total rain volume. Light rain events (<1.0 mm h−1), occurring much more frequently (65%) than heavy rain events, can also make important contributions (15%) to the total rain volume. For minimum detectable rain rates setting at 0.5 and 0.2 mm h−1, which are close to sensitivities of the current and future spaceborne precipitation radars, there are about 43% and 11% of total rain occurrence below these thresholds, and they respectively represent 7% and 0.8% of total rain volume. For passive microwave sensors with their rain pixel sizes ranging from 14 to 16 km and the minimum detectable rain rates around 1 mm h−1, the missed light rain events may account for 70% of rain occurrence and 16% of rain volume. Statistics of rain characteristics are also examined on domains with different temporal and spatial resolutions. Current issues in estimates of rain characteristics from satellite measurements and model outputs are discussed.

Investigating the Impact of Rainfall on Travel Speed

2014 ◽  
Vol 71 (3) ◽  
Author(s):  
Nordiana Mashros ◽  
Johnnie Ben-Edigbe ◽  
Hashim Mohammed Alhassan ◽  
Sitti Asmah Hassan
Keyword(s):  
Empirical Studies ◽  
Heavy Rain ◽  
Weather Conditions ◽  
Travel Speed ◽  
Operating Conditions ◽  
Speed Reduction ◽  
Light Rain ◽  
Rainfall Condition ◽  
Adverse Weather ◽  
The Impact

The road network is particularly susceptible to adverse weather with a range of impacts when different weather conditions are experienced. Adverse weather often leads to decreases in traffic speed and subsequently affects the service levels. The paper is aimed at investigating the impact of rainfall on travel speed and quantifying the extent to which travel speed reduction occurs. Empirical studies were conducted on principle road in Terengganu and Johor, respectively for three months. Traffic data were collected by way of automatic traffic counter and rainfall data from the nearest raingauge station were supplied by the Department of Irrigation and Drainage supplemented by local survey data. These data were filtered to obtain traffic flow information for both dry and wet operating conditions and then were analyzed to see the effect of rainfall on percentile speeds. The results indicated that travel speed at 15th, 50th and 85th percentiles decrease with increasing rainfall intensities. It was observed that allpercentile speeds decreased from a minimum of 1% during light rain to a maximum of 14% during heavy rain. Based on the hypothesis that travel speed differ significantly between dry and rainfall condition; the study found substantial changes in percentile speeds and concluded that rainfalls irrespective of their intensities have significant impact on the travel speed.

scholarly journals An Investigation into the Impact of Rainfall on Freeway Traffic Flow

2020 ◽  
Author(s):  
Brian L. Smith ◽  
Kristi G. Byrne ◽  
Rachel B. Copperman ◽  
Susan M. Hennessy ◽  
Noah Goodall
Keyword(s):  
Research Effort ◽  
Heavy Rain ◽  
Weather Data ◽  
Freeway Traffic ◽  
Light Rain ◽  
Highway Capacity Manual ◽  
Hampton Roads ◽  
Average Decrease ◽  
Freeway Capacity ◽  
The Impact

The purpose of this research effort was to investigate the impact of rainfall, at varying levels of intensity, on freeway capacity and operating speeds. Findings were derived from traffic and weather data collected in the Hampton Roads region of Virginia. Light rain (0.01 to 0.25 inches per hour) decreases freeway capacity by 4 to 10 percent. Heavy rain (0.25 inches per hour or greater) decreases freeway capacity by 25 to 30 percent. The presence of rain, regardless of intensity, results in approximately a 3 to 5 percent average decrease in operating speed. The findings indicated that the impact of rain is more significant than currently reported in the Highway Capacity Manual.

Spatial controls of microbial pesticide degradation in soils – A model-based scenario analysis

2021 ◽  
Author(s):  
Erik Schwarz ◽  
Swamini Khurana ◽  
Luciana Chavez Rodriguez ◽  
Johannes Wirsching ◽  
Christian Poll ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Scenario Analysis ◽  
Soil Column ◽  
Heavy Rain ◽  
Spatial Aggregation ◽  
Pesticide Degradation ◽  
Natural Soils ◽  
Light Rain ◽  
Rain Events ◽  
Precipitation Events

<p>Despite all legislative efforts, pesticides persist in soils at low concentrations and are leached to groundwater. This environmental issue has previously been associated with control factors relevant in natural soils but elusive in lab experiments and standard modeling approaches. One such factor is the small-scale spatial distribution of pesticide-degrading microorganisms in soil. Microbes are distributed heterogeneously in natural soils. They are aggregated in biogeochemical “hotspots” at the centimeter scale. The aim of our study is to investigate the relevance of such aggregation for pesticide degradation. For this, we upscaled the effect of the heterogeneity-induced accessibility limitations to degradation to the soil-column scale and analyzed kinetic constraints and amplifying factors under contrasting unsaturated flow regimes.</p><p>We performed a 2D spatially explicit, site-specific model-based scenario analysis for bioreactive transport of the model pesticide 4-chloro-2-methylphenoxyacetic acid (MCPA) in an arable soil (Luvisol). Stochastic centimeter-scale spatial distributions of microbial degraders were simulated with a spatial statistical model (log Gaussian Cox process), parametrized to meet experimentally observed spatial distribution metrics. Three heterogeneity levels were considered, representing homogenized soil conditions, and the lower and upper limit of expected microbial spatial aggregation in natural soils. Additionally, two contrasting precipitation scenarios (continuous light rain vs. heavy rain events directly following MCPA application) were assessed. A reactive transport model was set up to simulate a 0.3 m x 0.9 m soil column based on hydraulic and bioreactive measurements from a soil monitoring station (Germany, SM#3/ DFG CRC 1253 CAMPOS).</p><p>Our simulations revealed that heavy precipitation events were the main driver of pesticide leaching. Leached amounts from the topsoil increased by two to five orders of magnitude compared to the light rain scenario and at max. ca. 20 ng was leached from 90 cm after one year. With the increasing spatial aggregation of microbial degraders, upscaled pesticide degradation rates decreased, and considerable differences emerged between homogeneous and highly aggregated scenarios. In the latter, leaching from the plow layer into the subsoil was more pronounced and MCPA was detectable (LOD = 4 µg/kg) 5-6 times longer. In heterogeneous scenarios, degradation in microbial hotspots was mainly diffusion-limited during “hot moments” (times of high substrate availability), with a fraction of MCPA simultaneously “locked in” in coldspots with low microbial abundance. During intense precipitation events MCPA was remobilised from these coldspots by advective-dispersive transport, thereby increasing pesticide accessibility.</p><p>Our results indicate that predicted environmental concentrations and detectability of pesticides might be underestimated if spatial heterogeneity of microbial degraders is neglected, and they highlight the importance of heavy rain events as drivers of leaching and substrate accessibility.</p>

scholarly journals Comparisons of Instantaneous TRMM Ground Validation and Satellite Rain-Rate Estimates at Different Spatial Scales

2008 ◽  
Vol 47 (8) ◽  
pp. 2215-2237 ◽  
Author(s):  
David B. Wolff ◽  
Brad L. Fisher
Keyword(s):  
Rain Rate ◽  
Spatial Scales ◽  
Tropical Rainfall Measuring Mission ◽  
Heavy Rain ◽  
Global Ocean ◽  
Ground Validation ◽  
Microwave Imager ◽  
Rain Events ◽  
Trmm Microwave Imager ◽  
Rain Rates

Abstract This study provides a comprehensive intercomparison of instantaneous rain rates observed by the two rain sensors aboard the Tropical Rainfall Measuring Mission (TRMM) satellite with ground data from two regional sites established for long-term ground validation: Kwajalein Atoll and Melbourne, Florida. The satellite rain algorithms utilize remote observations of precipitation collected by the TRMM Microwave Imager (TMI) and the Precipitation Radar (PR) aboard the TRMM satellite. Three standard level II rain products are generated from operational applications of the TMI, PR, and combined (COM) rain algorithms using rain information collected from the TMI and the PR along the orbital track of the TRMM satellite. In the first part of the study, 0.5° × 0.5° instantaneous rain rates obtained from the TRMM 3G68 product were analyzed and compared to instantaneous Ground Validation (GV) program rain rates gridded at a scale of 0.5° × 0.5°. In the second part of the study, TMI, PR, COM, and GV rain rates were spatiotemporally matched and averaged at the scale of the TMI footprint (∼150 km2). This study covered a 6-yr period (1999–2004) and consisted of over 50 000 footprints for each GV site. In the first analysis, the results showed that all of the respective rain-rate estimates agree well, with some exceptions. The more salient differences were associated with heavy rain events in which one or more of the algorithms failed to properly retrieve these extreme events. Also, it appears that there is a preferred mode of precipitation for TMI rain rates at or near 2 mm h−1 over the ocean. This mode was noted over ocean areas of Kwajalein and Melbourne and has been observed in TRMM tropical–global ocean areas as well.

Investigation of PR and TMI Version 6 and Version 7 Rainfall Algorithms in Landfalling Tropical Cyclones Relative to the NEXRAD Stage-IV Multisensor Precipitation Estimate Dataset

2013 ◽  
Vol 52 (12) ◽  
pp. 2809-2827 ◽  
Author(s):  
Joseph P. Zagrodnik ◽  
Haiyan Jiang
Keyword(s):  
Tropical Cyclones ◽  
Stage Iv ◽  
Tropical Rainfall Measuring Mission ◽  
Heavy Rain ◽  
Light Rain ◽  
Hourly Rainfall ◽  
Wide Range ◽  
Landfalling Tropical Cyclones ◽  
Rain Rates ◽  
Rainfall Estimates

AbstractRainfall estimates from versions 6 (V6) and 7 (V7) of the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) 2A25 and Microwave Imager (TMI) 2A12 algorithms are compared relative to the Next Generation Weather Radar (NEXRAD) Multisensor Precipitation Estimate stage-IV hourly rainfall product. The dataset consists of 252 TRMM overpasses of tropical cyclones from 2002 to 2010 within a 230-km range of southeastern U.S. Weather Surveillance Radar-1988 Doppler (WSR-88D) sites. All rainfall estimates are averaged to a uniform 1/7° square grid. The grid boxes are also divided by their TMI surface designation (land, ocean, or coast). A detailed statistical analysis is undertaken to determine how changes to the TRMM rainfall algorithms in the latest version (V7) are influencing the rainfall retrievals relative to ground reference data. Version 7 of the PR 2A25 is the best-performing algorithm over all three surface types. Over ocean, TMI 2A12 V7 is improved relative to V6 at high rain rates. At low rain rates, the new ocean TMI V7 probability-of-rain parameter creates ambiguity in differentiating light rain (≤0.5 mm h−1) and nonraining areas. Over land, TMI V7 underestimates stage IV more than V6 does at a wide range of rain rates, resulting in an increased negative bias. Both versions of the TMI coastal algorithm are also negatively biased at both moderate and heavy rain rates. Some of the TMI biases can be explained by uncertain relationships between rain rate and 85-GHz ice scattering.

scholarly journals The role of different factors related to social impact of heavy rain events: considerations about the intensity thresholds in densely populated areas

2014 ◽  
Vol 14 (7) ◽  
pp. 1843-1852 ◽  
Author(s):  
L. Barbería ◽  
J. Amaro ◽  
M. Aran ◽  
M. C. Llasat
Keyword(s):  
Social Impact ◽  
Rainfall Intensity ◽  
Heavy Rain ◽  
Weather Event ◽  
Impact Index ◽  
The Social ◽  
Intensity Duration Frequency ◽  
Rain Events ◽  
The Impact

Abstract. In the assessment of social impact caused by meteorological events, factors of different natures need to be considered. Not only does hazard itself determine the impact that a severe weather event has on society, but also other features related to vulnerability and exposure. The requests of data related to insurance claims received in meteorological services proved to be a good indicator of the social impact that a weather event causes, according to studies carried out by the Social Impact Research Group, created within the framework of the MEDEX project. Taking these requests as proxy data, diverse aspects connected to the impact of heavy rain events have been studied. The rainfall intensity, in conjunction with the population density, has established itself as one of the key factors in social impact studies. One of the conclusions we obtained is that various thresholds of rainfall should be applied for areas of varying populations. In this study, the role of rainfall intensity has been analysed for a highly populated urban area like Barcelona. A period without significant population changes has been selected for the study to minimise the effects linked to vulnerability and exposure modifications. First, correlations between rainfall recorded in different time intervals and requests were carried out. Afterwards, a method to include the intensity factor in the social impact index was suggested based on return periods given by intensity–duration–frequency (IDF) curves.

scholarly journals Thailand Daily Rainfall and Comparison with TRMM Products

2008 ◽  
Vol 9 (2) ◽  
pp. 256-266 ◽  
Author(s):  
Roongroj Chokngamwong ◽  
Long S. Chiu
Keyword(s):  
Daily Rainfall ◽  
Tropical Rainfall Measuring Mission ◽  
Heavy Rain ◽  
Absolute Difference ◽  
Rain Event ◽  
Critical Success ◽  
Global Precipitation Climatology Centre ◽  
Temporal And Spatial ◽  
Rain Events ◽  
Rain Rates

Abstract Daily rainfall data collected from more than 100 gauges over Thailand for the period 1993–2002 are used to study the climatology and spatial and temporal characteristics of Thailand rainfall variations. Comparison of the Thailand gauge (TG) data binned at 1° × 1° with the Global Precipitation Climatology Centre (GPCC) monitoring product shows a small bias (1.11%), and the differences can be reconciled in terms of the increased number of stations in the TG dataset. Comparison of daily TG with Tropical Rainfall Measuring Mission (TRMM) version 6 (V6) 3B42 rain estimates shows improvements over version 5 (V5) in terms of bias and mean absolute difference (MAD). The V5 is computed from the adjusted Geostationary Operational Environmental Satellite (GOES) precipitation index (AGPI) and V6 is computed using the TRMM Multisatellite Precipitation Analysis (TMPA) algorithm. The V6 histogram is much closer to that of TG than V5 in terms of rain fraction and conditional rain rates. Scatterplots show that both versions of the satellite products are deficient in capturing heavy rain events. In terms of detecting rain events, a critical success index (CSI) shows no difference between V6 and V5 3B42. The CSI for V6 is higher for the rainy season than the dry season. These results are generally insensitive to rain-rate threshold and averaging periods. The temporal and spatial autocorrelation of daily rain rates for TG, V6, and V5 3B42 are computed. Autocorrelation function analyses show improved temporal and spatial autocorrelations for V6 compared to TG over V5 with e-folding times of 1, 1, and 2 days, and isotropic spatial decorrelation distances of 1.14°, 1.87°, and 3.61° for TG, V6, and V5, respectively. Rain event statistics show that the V6 3B42 overestimates the rain event durations and underestimates the rain event separations and the event conditional rain rates when compared to TG. This study points to the need to further improve the 3B42 algorithm to lower the false detection rate and improve the estimation of heavy rainfall events.

scholarly journals Evaluation of a near-real time NEXRAD DSP product in evolution of heavy rain events on the Upper Guadalupe River Basin, Texas

2012 ◽  
Vol 15 (2) ◽  
pp. 464-485 ◽  
Author(s):  
Xianwei Wang ◽  
Hongjie Xie ◽  
Newfel Mazari ◽  
Jon Zeitler ◽  
Hatim Sharif ◽  
...  
Keyword(s):  
Real Time ◽  
River Basin ◽  
Flash Flood ◽  
Heavy Rain ◽  
Accurate Information ◽  
Total Rainfall ◽  
Light Rain ◽  
Guadalupe River ◽  
Rain Events ◽  
Resource Budget

This study evaluates the Next Generation Weather Radar (NEXRAD) Digital Storm-Total Precipitation product (DSP) by analyzing 30 rain events on the Upper Guadalupe River Basin, Texas, from September 2006 to May 2007. The DSP product provides relatively accurate information on the evolution of rain events at high spatial and temporal resolutions in near-real time. This is particularly important for rainfall estimation of heavy rain events and flash flood forecasting. The DSP's accuracy is comparable to the other NEXRAD product MPE (multisensor precipitation estimator, at hourly resolution and 4 km grid spacing) at both hourly and event total scales for some heavy rain events, although the DSP is inferior to the MPE product for total rainfall of all 30 rain events analyzed, especially for light rain events. The DSP product shows the best agreement with gauges at ranges of 50–150 km from the radar (with mean absolute estimation bias (MAEB) of +15–22% for total rainfall of 30 rain events), while underestimating precipitation at both close ranges (<30 km) and far ranges (>180 km). The DSP product also tends to underestimate (overestimate) precipitation during event growth (dissipation). However, the total rainfall estimate for all rain events over a long period from DSP shows range dependence and is not recommended for calculation of water resource budget.

scholarly journals Effects of Rain on Freeway Traffic in Southern California

2017 ◽  
Vol 2616 (1) ◽  
pp. 69-80 ◽  
Author(s):  
Sawanpreet Singh Dhaliwal ◽  
Xinkai Wu ◽  
John Thai ◽  
Xudong Jia
Keyword(s):  
Los Angeles ◽  
Traffic Management ◽  
Southern California ◽  
Management Strategies ◽  
Heavy Rain ◽  
Flow Speed ◽  
Free Flow ◽  
Light Rain ◽  
Free Flow Speed ◽  
The Impact

A number of studies in the past quantified the effect of rain on traffic parameters but were limited to wet areas. This research expands the literature by studying the effect of rain in a dry area such as Southern California and considering regional differences in the impact. Traffic data (loop detectors) and precipitation data (rain gauges) from the Los Angeles, California, metropolitan area were analyzed to access the effect of rain on traffic stream parameters such as free-flow speed, speed at capacity, and capacity. Rainfall events were categorized as light, medium, and heavy as discussed in the 2010 Highway Capacity Manual. Density plots and fundamental diagrams for rain types proved that free-flow speed, speed at capacity, and capacity were reduced by 5.7%, 6.91%, and 8.65%, respectively, for light rain; 11.71%, 12.34%, and 17.4%, respectively, for medium rain; and 10.22%, 11.85%, and 15.34%, respectively, for heavy rain. The reductions for free-flow speed were lower, whereas for speed at capacity and for capacity, they were higher than those reported in the 2010 manual. Moreover, headway increased during rain; this finding shows cautious driving behavior. Multiplicative weather adjustment factors were computed to compensate for the loss of speed and capacity. Also demonstrated was the spatial and temporal effect of rain on traffic. Downstream traffic was not much affected by a rainfall event, whereas the upstream traffic was negatively affected. This study is expected to support weather-responsive traffic management strategies for dry areas.

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