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The National Oceanic and Atmospheric Administration (NOAA) Geostationary Operational Environmental Satellite series R (GOES-R) will greatly expand the ability to observe the earth from geostationary orbit compared to the current-generation GOES, with more than 3 times as many spectral bands and a 75% reduction in footprint size. These enhanced capabilities are beneficial to rainfall rate estimation since they provide sensitivity to cloud-top properties such as phase and particle size that cannot be achieved using the limited channel selection of current GOES. The GOES-R rainfall rate algorithm, which is an infrared-based algorithm calibrated in real time against passive microwave rain rates, has been previously described in an algorithm theoretical basis document (ATBD); this paper describes modifications since the release of the ATBD, including a correction for evaporation of precipitation in dry regions and improved calibration updates. These improvements have been evaluated using a simplified version applicable to current-generation GOES to take advantage of the high-resolution ground validation data routinely available over the conterminous United States. Correcting for subcloud evaporation using relative humidity from a numerical model reduced false alarm rainfall by half and reduced the overall error by 35% for hourly accumulations validated against the National Centers for Environmental Prediction stage IV radar-gauge field; however, the number of missed events did increase slightly. Reducing the size of the calibration regions and increasing the training data requirements improved the consistency of the retrieved rates in time and space and reduced the overall error by an additional 4%. [ABSTRACT FROM AUTHOR]

The partitioning of rainfall into surface runoff and infiltration influences many other aspects of the hydrologic cycle including evapotranspiration, deep drainage and soil moisture. This partitioning is an instantaneous non‐linear process that is strongly dependent on rainfall rate, soil moisture and soil hydraulic properties. Though all rainfall datasets involve some degree of spatial or temporal averaging, it is not understood how this averaging affects simulated partitioning and the land surface water balance across a wide range of soil and climate types. We used a one‐dimensional physics‐based model of the near‐surface unsaturated zone to compare the effects of different rainfall discretization (5‐min point‐scale; hourly point‐scale; hourly 0.125° gridded) on the simulated partitioning of rainfall for many locations across the United States. Coarser temporal resolution rainfall data underpredicted seasonal surface runoff for all soil types except those with very high infiltration capacities (i.e., sand, loamy sand). Soils with intermediate infiltration capacities (i.e., loam, sandy loam) were the most affected, with less than half of the expected surface runoff produced in most soil types when the gridded rainfall dataset was used as input. The impact of averaging on the water balance was less extreme but non‐negligible, with the hourly point‐scale predictions exhibiting median evapotranspiration, drainage and soil moisture values within 10% of those predicted using the higher resolution 5‐min rainfall. Water balance impacts were greater using the gridded hourly dataset, with average underpredictions of ET up to 27% in fine‐grained soils. The results suggest that "hyperresolution" modelling at continental to global scales may produce inaccurate predictions if there is not parallel effort to produce higher resolution precipitation inputs or sub‐grid precipitation parameterizations. [ABSTRACT FROM AUTHOR]

Despite the importance of soil moisture (SM) in various applications and the need to validate satellite SM products, the current in situ SM network is still inadequate, even for developed country such as the United States. Recently, SM2RAIN (Soil Moisture to Rain) algorithm has prominently emerged as a bottom-up approach to derive rainfall data from SM. In this study, we evaluated whether SM2RAIN algorithm and rain gauges, which are more abundant and readily available than in situ SM, can be used to validate satellite-based SMAP SM estimates. Since errors in SMAP SM propagate to SMAP-derived rainfall, the skills of SM2RAIN might be able to provide insights on the accuracy of SMAP SM observations. While the correlation between SM2RAIN skills and SMAP SM skills was found to be statistically significant, the strength of the correlation varied among different climate zones and annual rainfall classes. Specifically, weaker correlations were observed in arid and lower rainfall regions (median R value of 0.12), while stronger correlations were found in temperate and higher rainfall regions (median R value of 0.54). In term of over/under-estimation tendencies, 56% of the stations had the same tendencies (SM2RAIN skills and satellite SM skills both have positive or negative PBIAS value). [ABSTRACT FROM AUTHOR]

Characterization of the error associated with satellite rainfall estimates is a necessary component of deterministic and probabilistic frameworks involving spaceborne passive and active microwave measurements for applications ranging from water budget studies to forecasting natural hazards related to extreme rainfall events. The authors focus here on the error structure of NASA's Tropical Rainfall Measurement Mission (TRMM) Precipitation Radar (PR) quantitative precipitation estimation (QPE) at ground. The problem is addressed by comparison of PR QPEs with reference values derived from ground-based measurements using NOAA/NSSL ground radar-based National Mosaic and QPE system (NMQ/Q2). A preliminary investigation of this subject has been carried out at the PR estimation scale (instantaneous and 5 km) using a 3-month data sample in the southern part of the United States. The primary contribution of this study is the presentation of the detailed steps required to derive a trustworthy reference rainfall dataset from Q2 at the PR pixel resolution. It relies on a bias correction and a radar quality index, both of which provide a basis to filter out the less trustworthy Q2 values. Several aspects of PR errors are revealed and quantified including sensitivity to the processing steps with the reference rainfall, comparisons of rainfall detectability and rainfall-rate distributions, spatial representativeness of error, and separation of systematic biases and random errors. The methodology and framework developed herein applies more generally to rainfall-rate estimates from other sensors on board low-earth-orbiting satellites such as microwave imagers and dual-wavelength radars such as with the Global Precipitation Measurement (GPM) mission. [ABSTRACT FROM AUTHOR]

A method to retrieve total vertical amounts of cloud liquid and ice in stratiform precipitating systems is described. The retrievals use measurements from the vertically pointing Ka- and W-band cloud radars operated by the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) Program and auxiliary measurements from a scanning National Weather Service radar and a ground-based disdrometer. Separation between the cloud liquid and rain is based on estimations of the total attenuation of millimeter-wavelength radar signals in the liquid hydrometeor layer. Disdrometer measurements are used for the retrieval constraints. Because the liquid phase hydrometeor retrievals use only differential measurements, they are immune to the absolute radar calibration uncertainties. Estimates of the ice cloud phase are performed using empirical relations between absolute radar reflectivity and ice water content. Data from the nearby scanning weather-service radar, which operates at a lower frequency, are used to correct cloud radar measurements observed above the freezing level for attenuation caused by the layers of liquid and melting hydrometeors and also by wet radomes of cloud radars. Polarimetric and vertical Doppler measurements from ARM cloud radars provide a distinct separation between regions of liquid and ice phases, and therefore the corresponding retrievals are performed in each region separately. The applicability of the suggested method is illustrated for a stratiform precipitation event observed at the ARM Southern Great Plains facility. Expected uncertainties for retrievals of cloud liquid water path are estimated at about 200–250 g m-2 for typical rainfall rates observed in stratiform systems (∼3–4 mm h-1). These uncertainties increase as rainfall rate increases. The ice water path retrieval uncertainties can be as high as a factor of 2. [ABSTRACT FROM AUTHOR]

A description is given of rainfall characteristics associated with the remnants of Tropical Storm Helene, which made landfall on the panhandle of Florida and moved through parts of Georgia, South Carolina, and North Carolina during 22–23 September 2000. Also included in the description are rainfall events in advance of the approach of Helene to South Carolina. Emphasis is placed on the implications of these characteristics regarding the ability of the Weather Surveillance Radar-1988 Doppler (WSR-88D) to measure rainfall accurately for this system when employing reflectivity factor–rainfall rate (Z–R) algorithms as defined by the National Weather Service. The results are based on analysis of raindrop size spectra collected with a disdrometer in upstate South Carolina at the Clemson University Atmospheric Research Laboratory. It was found that there were pronounced variations in the Z–R relationship from day to day and within a day that would limit the accuracy of radar measurement of rainfall. A discussion is presented of the effects of these variations on the decisions made by meteorologists with regard to the choice of Z–R used during operation of the radar. [ABSTRACT FROM AUTHOR]

The structure and evolution of flash flood-producing storms over a small urban watershed in the mid-Atlantic United States with a prototypical flash flood response is examined. Lagrangian storm properties are investigated through analyses of the 32 storms that produced the largest peak discharges in Moores Run between January 2000 and May 2014. The Thunderstorm Identification, Tracking, Analysis, and Nowcasting (TITAN) algorithm is used to track storm characteristics over their life cycle with a focus on storm size, movement, intensity, and location. First, the 13 June 2003 and 1 June 2006 storms, which produced the two largest peak discharges for the study period, are analyzed. Heavy rainfall for the 13 June 2003 and 1 June 2006 storms were caused by a collapsing thunderstorm cell and a slow-moving, low-echo centroid storm. Analyses of the 32 storms show that collapsing storm cells play an important role in peak rainfall rate production and flash flooding. Storm motion is predominantly southwest-to-northeast, and approximately half of the storms exhibited some linear organization. Mean storm total rainfall for the 32 storms displayed an asymmetric distribution around Moores Run, with sharply decreasing gradients southwest of the watershed (upwind and into the city) and increased rainfall to the northeast (downwind and away from the city). Results indicate urban modification of rainfall in flash flood-producing storms. There was no evidence that the storms split around Baltimore. Flood-producing rainfall was highly concentrated in time; on average, approximately 21% of the storm total rainfall fell within 15 min. [ABSTRACT FROM AUTHOR]

A procedure for the estimation of rainfall rate, capitalizing on a radar-based raindrop size distribution (RSD) parameter retrieval and neural network (NN) inversion techniques, is validated using an extensive and quality-controlled archive. The RSD retrieval algorithm utilizes polarimetric variables measured by the polarimetric prototype of the Weather Surveillance Radar-1988 Doppler (WSR-88D) in Norman, Oklahoma (KOUN), through an ad hoc regularized neural network method. Evaluation of rainfall estimation from the NN-based method is accomplished using a large radar data and surface gauge observation dataset collected in central Oklahoma during the multiyear Joint Polarization Experiment (JPOLE) field campaign. Point estimates of hourly rainfall accumulations and instantaneous rainfall rates from NN-based and parametric polarimetric rainfall relations are compared with dense surface gauge observations. Rainfall accumulations from RSD retrieval-based methods are shown to be sensitive to the choice of a raindrop fall speed model. To minimize the impact of this choice, a new “direct” neural network approach is tested. Proposed NN-based approaches exhibit bias and root-mean-square error characteristics comparable with those obtained from parametric relations, specifically optimized for the JPOLE dataset, indicating an appealing generalization capability with respect to the climatological context. All tested polarimetric relations are shown to be sensitive to hail contamination as inferred from the results of automatic polarimetric echo classification and available storm reports. [ABSTRACT FROM AUTHOR]

Filtering of nonpredictable scales of precipitation can be used to improve forecast precision (rms). Previous papers have studied the scale dependence of predictability of patterns of instantaneous rainfall rate and of probabilistic forecasts. In this paper, motivated by the often localized, intermittent nature of rainfall, the wavelet transform is used to develop measures of predictability at each scale. These measures are then used to design optimal forecast filters. This method is applied to radar composites of rainfall reflectivity over much of the continental United States and is developed to be appropriate for operational forecasts of rainfall rates and raining areas. For the four precipitation events studied, the average correlation at 4-h lead time was increased from 0.50 for the original nowcasts to 0.62 with forecast filtering. This forecast filtering is incorporated into the McGill Algorithm for Precipitation Nowcasting by Lagrangian Extrapolation (MAPLE), which now includes variational echo tracking, a semi-Lagrangian advection scheme, scale-based filtering, and appropriate rescaling of the filtered nowcast fields. [ABSTRACT FROM AUTHOR]

Impacts of diurnal radiative forcing on flow and rainfall patterns during summer flood and drought conditions (1993 and 1988, respectively) in the central United States were investigated using a regional climate model. The modeling approach, which included evaluation of sensitivity to modification in the solar hour, enabled evaluation of the impact on an event-by-event basis. The effect of the solar hour forward shift of 12 h on boundary layer wind speed over north-central Texas, which is often related to rainfall in the central United States through northward moisture advection, followed almost exactly the shift in solar hour. Domain-averaged daily rainfall in the central United States simulated with 12-h solar shift frequently showed in the flood year a backward or forward time shift of ∼12 h in the timing of its peak, an increase or decrease of rainfall rate, and on a few occasions noticeable formation of short-lived rainfall events. This pattern suggests relatively high sensitivity to the timing of the diurnal radiative forcing with respect to the large-scale perturbations. In contrast, in the drought year 12-h solar shifted simulations these modifications were weaker. The climatological domain-average diurnal cycle of rainfall showed for the flood year a well-defined 12-h shift when comparing the control and perturbed simulations. In contrast, in the drought year such a shift was not well defined. [ABSTRACT FROM AUTHOR]

Supercell thunderstorms, the storm systems responsible for most tornadoes, have often been dismissed as flood hazards. The role of supercell thunderstorms as flood agents is examined through analyses of storm systems that occurred in Texas (5–6 May 1995), Florida (26 March 1992), Nebraska (20–21 June 1996), and Pennsylvania (18–19 July 1996). Particular attention is given to the “Dallas Supercell,” which resulted in 16 deaths from flash flooding and more than $1 billion in property damage during the evening of 5 May 1995. Rainfall analyses using Weather Surveillance Radar-1988 Doppler (WSR-88D) reflectivity observations and special mesonet rain gauge observations from Dallas, Texas, show that catastrophic flash flooding resulted from exceptional rainfall rates at 5–60-min timescales. The spatial structure of extreme rainfall was linked to supercell structure and motion. The “Orlando Supercell” produced extreme rainfall rates (greater than 300 mm h[sup -1] ) at 1–5-min timescales over a dense rain gauge network. The Nebraska and Pennsylvania storm systems produced record flooding over larger spatial scales than the Texas and Florida storms, by virtue of organization and motion of multiple storms over the same region. For both the Nebraska and Pennsylvania storms, extreme rainfall and tornadoes occurred in tandem. Severe rainfall measurement problems arise for supercell thunderstorms, both from conventional gauge networks and weather radar. It is hypothesized that supercell storms play a significant role in the “climatology” of extreme rainfall rates (100-yr return interval and greater) at short time intervals (1–60 min) in much of the central and eastern United States. [ABSTRACT FROM AUTHOR]

Numerous studies have evaluated the reliability and hydrologic utility of various rainfall data sets through hydrological modeling. However, the calibration of hydrological models compensates for errors in rainfall inputs. The drivers, conditions, and factors affecting the calibration of hydrological models given the accuracy of rainfall inputs are not well understood. Here, we explore hydrological model adaptability to rainfall inputs of varied quality and its potential mechanisms. Twenty‐eight rainfall products from multiple sources are collected for a headwater catchment in the Southern United States. These rainfall data sets include measurements from rain gauges, weather radars, satellites, reanalysis products, and Weather Research and Forecasting model simulations. Such rainfall data sets with varied errors are used to independently calibrate a widely used conceptual Xin'anjiang (XAJ) hydrological model. Results suggest that the hydrological model can often adapt well to two scenarios of inaccurate rainfall inputs producing high‐performance streamflow simulations. This adaptive ability is controlled by an adaptable threshold of the overall bias of the rainfall inputs. Moreover, hydrological model adaptability to rainfall inputs is further influenced by how event‐based rainfall bias shapes the overall rainfall bias, especially from those of heavy rainstorms. The hydrological model can adapt to those rainfall inputs that contain important information content for model calibration. Notably, the adaptability to rainfall inputs of the XAJ model is mainly controlled by a bias reduction through adjustment of evapotranspiration and soil moisture storage, yielding satisfactory effective rainfall. The study quantitatively sheds new light on hydrological model adaptability to rainfall input quality. Plain Language Summary: Accurate rainfall is an ideal input for the hydrological model to simulate streamflow reliably. However, some inaccurate rainfall data sets can be compensated by hydrological model calibration to generate good streamflow. It is still unclear what kind of rainfall inputs the model calibration can and cannot adapt to. Here, we explore a large number of rainfall data sets from different sources and their corresponding streamflow simulation performance after independent model calibrations. It is found that the hydrological model can adapt well to two scenarios of inaccurate rainfall data. The adaptive ability of the hydrological model calibration to rainfall inputs is not only affected by the accuracy over the entire period but also by the accuracy of individual rainfall events, especially those of the most severe rainstorms. Therefore, a good hydrological simulation does not always mean its rainfall input data are reliable. This study enhances a quantitative understanding of how the model calibration adapts to the errors in rainfall input data. The findings are expected to provide quantitative guidance for bias correction and data fusion of rainfall products. Key Points: Two scenarios of inaccurate rainfall inputs and a threshold of overall rainfall bias are identified for hydrological model adaptabilityHydrological model adaptability is further influenced by how event‐based bias of individual rainstorms shapes the overall rainfall biasHydrological model adaptability is mainly controlled by a bias reduction through adjustment of evapotranspiration and soil moisture storage [ABSTRACT FROM AUTHOR]

A 2D video disdrometer (2DVD) probe was used to gather detailed drop measurements over a 770-min rain event. Accumulated totals of the rainfall and of the number of drops for each square centimeter showed persistent, significant correlated structures across the approximately 11 cm × 11 cm grid of the 2DVD. This is surprising because larger-scale studies suggest that the values in each square centimeter should be highly correlated with very little variation. Nevertheless, this correlation remains strikingly similar to what is observed at a coarser resolution, suggesting that it somehow scales with spatial resolution. However, because the correlation functions are not power laws, the origin of this scaling must be due to a factor other than fractal geometry. Analysis reveals that this occurs because of a filtering effect such that as the domain size (or resolution of a remote sensor) becomes finer, it is only the smaller wavelengths that contribute most to the variance so that the correlation function also scales. Consequently, correlated finescale structures can apparently occur even over 10 cm. This fine structure was also found for the kinetic energy and impact power of the rain, important for understanding the initiation of soil erosion. The patterns in the integrated parameters appeared to arise almost exclusively from patterns in the total number of drops with patterns in the drop sizes playing an insignificant role. Therefore, in future studies of rain it is recommended that the total number of drops be retained as a crucial variable. [ABSTRACT FROM AUTHOR]

In this study, we investigate both the changes of tropical cyclone (TC) rainfall hazard in the United States under climate change and the relative importance of the factors that cause the changes. We find that under the SSP5 8.5 scenario, the 100‐year TC rainfall level can increase by up to 320% along the U.S. coastline by the end of this century. The influence of TC rainfall‐producing ability increase is more significant than the influence of TC frequency increase on the increase of the 100‐year TC rainfall level (up to 180% vs. 60% increase). Among the different physical drivers for the increase in storm rainfall‐producing ability, the increase of TC intensity is the leading factor, followed by changes in TC duration and atmospheric temperature. The projected increase of TC rainfall hazard is robust against the uncertainty in the TC frequency projection. Plain Language Summary: Rainfall associated with landfalling tropical cyclones (TC) can cause extreme flooding and landslides, which are hazardous for coastal and inland areas. We project that by the end of the 21st century, TC rainfall hazard in the United States will increase by as much as 320% compared to its current value. The increase in TC rainfall hazard can be caused by two factors: more TCs making impact and TCs becoming more capable of producing rainfall in the future. The increased ability for TCs to produce rainfall is linked to increased TC intensity and duration as well as a warmer and more humid atmosphere in the future. Though uncertainties exist in the projected trend of future TC landfall frequency, TC rainfall hazard is very likely to increase due to the significant increase in TC rainfall‐producing ability. Key Points: U.S. landfalling tropical cyclone (TC) rainfall hazard will increase in the future due to increased TC landfalling frequency and TC rainfall‐producing abilityStorm intensity change is the leading factor for TC rainfall‐producing ability to increaseThe projected increase of TC rainfall hazard is robust against the uncertainty in TC frequency projection [ABSTRACT FROM AUTHOR]

Abstract: Turfgrass irrigation policies have been implemented in various regions of the U.S. to reduce domestic water consumption. Mandatory restrictions are often enforced by issuing citations to violators with the intent to promote compliance and deter violations. This study provides a detailed investigation of past water restrictions and compliance, and attempts to determine which factors are related to high rates of water usage within communities of Tampa, FL. The adjusted rainfall rate had the most significant relationship with water usage in the communities under study. Water usage increased in each examined community after it transitioned to more stringent water usage restrictions, with cited restriction violators increasing usage to a greater extent than their uncited counterparts. This increase may primarily be attributed to conflicts between the local water consumption policy and binding homeowner association rules. When the once-a-week usage restriction was in place, the area faced drought conditions. Therefore, homeowners irrigated more to meet the water needs of their lawns despite the restrictions imposed on them by their local government. [Copyright &y& Elsevier]

The rain gauge network associated with the Walnut Gulch Experimental Watershed (WGEW) in southeastern Arizona provides a unique opportunity for direct comparisons of in situ measurements and satellite-based instantaneous rain rate estimates like those from the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR). The WGEW network is the densest rain gauge network in the PR coverage area for watersheds greater than 10 km2. It consists of 88 weighing rain gauges within a 149-km2 area. On average, approximately 10 gauges can be found in each PR field of view (~5-km diameter). All gauges are very well synchronized with 1-min reporting intervals. This allows generating very-high-temporal-resolution rain rate fields and obtaining accurate estimates of the area-average rain rate for the entire watershed and for a single PR field of view. In this study, instantaneous rain rate fields from the PR and the spatially interpolated gauge measurements (on a 100 m × 100 m grid, updated every 1 min) are compared for all TRMM overpasses in which the PR recorded rain within the WGEW boundaries (25 overpasses during 1999-2010). The results indicate very good agreement between the fields with low bias values (<10%) and high correlation coefficients, especially for the near-nadir cases (>0.9). The correlation is high at overpass time but the peak occurs several minutes after the overpass, which can be explained by the fact that it takes several minutes for the raindrops to reach the gauge from the time they are observed by the PR. The correlation improves with the new version of the TRMM algorithm (V7). The study includes assessment of the accuracy of the reference products. [ABSTRACT FROM AUTHOR]

Flooding in the eastern United States reflects a mixture of flood-generating mechanisms, with landfalling tropical cyclones and extratropical systems playing central roles. The authors examine the climatology of heavy rainfall and flood magnitudes for the eastern United States through analyses of long-duration records of flood peaks and maximum daily rainfall series. Spatial heterogeneities in flood peak distributions due to orographic precipitation mechanisms in mountainous terrain, coastal circulations near land--ocean boundaries, and urbanization impacts on regional climate are central elements of flood peak distributions. Lagrangian analyses of rainfall distribution and storm evolution are presented for flood events in the eastern United States and used to motivate new directions for stochastic modeling of rainfall. Tropical cyclones are an important element of the upper tail of flood peak distributions throughout the eastern United States, but their relative importance varies widely, and abruptly, in space over the region. Nonstationarities and long-term persistence of flood peak and rainfall distributions are examined from the perspective of the impacts of human-induced climate change on flood-generating mechanisms. Analyses of flood frequency for the eastern United States, which are based on observations from a dense network of U.S. Geological Survey (USGS) stream gauging stations, provide insights into emerging problems in flood science. [ABSTRACT FROM AUTHOR]

Heavy precipitation in the northeastern United States is examined through observational and numerical modeling analyses for a weather system that produced extreme rainfall rates and urban flash flooding over the New York–New Jersey region on 4–5 October 2006. Hydrometeorological analyses combine observations from Weather Surveillance Radar-1988 Doppler (WSR-88D) weather radars, the National Lightning Detection Network, surface observing stations in the northeastern United States, a vertically pointing lidar system, and a Joss–Waldvogel disdrometer with simulations from the Weather Research and Forecasting Model (WRF). Rainfall analyses from the Hydro-Next Generation Weather Radar (NEXRAD) system, based on observations from WSR-88D radars in State College, Pennsylvania, and Fort Dix, New Jersey, and WRF model simulations show that heavy rainfall was organized into long-lived lines of convective precipitation, with associated regions of stratiform precipitation, that develop along a frontal zone. Structure and evolution of convective storm elements that produced extreme rainfall rates over the New York–New Jersey urban corridor were influenced by the complex terrain of the central Appalachians, the diurnal cycle of convection, and the history of convective evolution in the frontal zone. Extreme rainfall rates and flash flooding were produced by a “leading line–trailing stratiform” system that was rapidly dissipating as it passed over the New York–New Jersey region. Radar, disdrometer, and lidar observations are used in combination with model analyses to examine the dynamical and cloud microphysical processes that control the spatial and temporal structure of heavy rainfall. The study illustrates key elements of the spatial and temporal distribution of rainfall that can be used to characterize flash flood hazards in the urban corridor of the northeastern United States. [ABSTRACT FROM AUTHOR]

The hydrometeorological processes that control flash flooding are examined through analyses of space–time rainfall variability and flood response in the Milwaukee metropolitan region. The analyses focus on four flood events in the Menomonee River basin that occurred 21 June 1997, 2 July 1997, 6 August 1998, and 21 July 1999. The June 1997 and August 1998 flood events produced record flood peaks in the Menomonee River and its tributaries. Rainfall analyses, which are based on WSR-88D radar reflectivity observations and rainfall measurements from a dense network of rain gauges maintained by the city of Milwaukee, provide rainfall fields for each event at 1-km spatial resolution and 5-min timescale. The June 1997 and August 1998 storms exhibited striking contrasts in storm structure, evolution, and motion. Analyses of the structure and evolution of these storms are presented in conjunction with scaling analyses of the rainfall fields. The contrasting storm-scale properties of the June 1997 and August 1998 events resulted in sharp contrasts in extreme flood response between the two events. The regional flood response of the Menomonee River basin is examined in terms of space–time rainfall variability and heterogeneous land surface properties. Analyses are based on radar rainfall fields and 15-min discharge observations from stream gauging stations, with drainage area ranging from 47 to 319 km[sup 2] for the four flood events. Extreme flood response is examined in terms of flood peak magnitudes, peak response times, and event water balance. A distributed hydrologic model, which includes a Hortonian infiltration model and a network-based representation of hillslope and channel response, plays a central role in examining the regional flood response. [ABSTRACT FROM AUTHOR]

A bias-adjusted radar rainfall product is created and used for evaluation of two satellite rainfall estimation algorithms. Three years of collocated rainfall estimates from radar, rain gauges, a microwave satellite algorithm, and a multispectral (visible through near-infrared) algorithm were collected over the continental United States from July 1998 through July 2001. The radar and gauge data are compared to determine the locations and times at which the rainfall occurrences estimated by these two sensors are in sufficient agreement for the data to be used for validation. This procedure serves as quality control for both sensors and determines the locations at which the radar has difficulty detecting rainfall and should not be used in a validation dataset. For the data remaining after quality control, the gauge data are used for multiplicative adjustment of the radar estimates to remove the radar bias with respect to the gauges. These bias-adjusted estimates are compared with the satellite rainfall estimates to observe the evolution of the satellite biases over the 3-yr period. The multispectral algorithm was under development throughout the 3-yr period, and improvement is evident. The microwave algorithm overestimates rainfall in the summer months, underestimates in the winter months, and has an east-to-west bias gradient, all of which are consistent with physical explanations and previous findings. The multispectral algorithm bias depends highly on diurnal sampling; there is much greater overestimation for the daytime overpasses. These results are applicable primarily to the eastern half of the United States, because few data in the western half remain after quality control. [ABSTRACT FROM AUTHOR]

Examines radar reflectivity, rain rate, and kinetic energy flux relationships based on raindrop spectra in Mississippi. Effectiveness of raindrop spectrometer in obtaining rainfall momentum flux; Description of raindrop size distribution of intense rainfall; Effect of sample size on rainfall parameters derived from drop spectra samples.

This note documents the effects of a radar calibration error that affected 5% of the data used in an earlier study by Carbone et al. While the original intent of this correction was merely to set the record straight, a significant finding emerged from the revised time series. The main impact is to reveal a more rapid spatial decorrelation between island Froude number (Fr) and oceanic rainfall, a measure of the dynamical blocking effects upstream of Hawaii. There no longer exists a residual correlation between Fr and cumulative rainfall over the remote ocean, defined in this instance as distances >50 km from shore. This result both simplifies and strengthens the conclusions of the original work, which was based on a very short time series. The strong dependence of rainfall on Fr over the coastal lowlands and near shore remains unchanged from the results presented by Carbone et al. To the extent that remote oceanic rainfall is expected to be uncorrelated with Fr, there is increased confidence with respect to the causes and effects of island blocking on rainfall production over and near the island. [ABSTRACT FROM AUTHOR]

The Global Precipitation Measurement (GPM) constellation of spaceborne sensors provides a variety of direct and indirect measurements of precipitation processes. Such observations can be employed to derive spatially and temporally consistent gridded precipitation estimates either via data-driven retrieval algorithms or by assimilation into physically based numerical weather models. We compare the data-driven Integrated Multisatellite Retrievals for GPM (IMERG) and the assimilation-enabled NASA-Unified Weather Research and Forecasting (NU-WRF) model against Stage IV reference precipitation for four major extreme rainfall events in the southeastern United States using an object-based analysis framework that decomposes gridded precipitation fields into storm objects. As an alternative to conventional "grid-by-grid analysis," the object-based approach provides a promising way to diagnose spatial properties of storms, trace them through space and time, and connect their accuracy to storm types and input data sources. The evolution of two tropical cyclones are generally captured by IMERG and NU-WRF, while the less organized spatial patterns of two mesoscale convective systems pose challenges for both. NU-WRF rain rates are generally more accurate, while IMERG better captures storm location and shape. Both show higher skill in detecting large, intense storms compared to smaller, weaker storms. IMERG's accuracy depends on the input microwave and infrared data sources; NU-WRF does not appear to exhibit this dependence. Findings highlight that an object-oriented view can provide deeper insights into satellite precipitation performance and that the satellite precipitation community should further explore the potential for "hybrid" data-driven and physics-driven estimates in order to make optimal usage of satellite observations. [ABSTRACT FROM AUTHOR]

This research applies an automated mesoscale convective system (MCS) segmentation, classification, and tracking approach to composite radar reflectivity mosaic images that cover the contiguous United States (CONUS) and span a relatively long study period of 22 years (1996–2017). These data afford a novel assessment of the seasonal and interannual variability of MCSs. Additionally, hourly precipitation data from 16 of those years (2002–17) are used to systematically examine rainfall associated with radar-derived MCS events. The attributes and occurrence of MCSs that pass over portions of the CONUS east of the Continental Divide (ECONUS), as well as five author-defined subregions—North Plains, High Plains, Corn Belt, Northeast, and Mid-South—are also examined. The results illustrate two preferred regions for MCS activity in the ECONUS: 1) the Mid-South and Gulf Coast and 2) the Central Plains and Midwest. MCS occurrence and MCS rainfall display a marked seasonal cycle, with most of the regions experiencing these events primarily during the warm season (May–August). Additionally, MCS rainfall was responsible for over 50% of annual and seasonal rainfall for many locations in the ECONUS. Of particular importance, the majority of warm-season rainfall for regions with high agricultural land use (Corn Belt) and important aquifer recharge properties (High Plains) is attributable to MCSs. These results reaffirm that MCSs are a significant aspect of the ECONUS hydroclimate. [ABSTRACT FROM AUTHOR]

The Global Precipitation Measurement (GPM) mission provides a new generation of precipitation measurements from space. In particular, Dual-frequency Precipitation Radar (DPR), on board the GPM core observatory, has the advantage in precipitation retrievals compared to its single-frequency counterparts on Tropical Rainfall Measurement Mission (TRMM). However, it is still challenging for DPR to obtain instantaneous rainfall rate with higher accuracy, especially for widely varying rainfall types (e.g., hurricane, storms, etc). In this study, we propose a hierarchal Bayesian approach for improving the DPR rainfall retrievals and perform an initial experiment in Southeastern United States (US), where the US Weather Service Dual-Polarization Radar network is used to obtain locally accurate rainfall estimates for each event. The analysis presented here demonstrates the feasibility of this method for enhancing the DPR instantaneous rainfall product. In summary, this approach demonstrates the potential for enhancing the GPM rainfall product. [ABSTRACT FROM AUTHOR]

Precipitation forecasts from convection-allowing ensembles with 3- and 1-km horizontal grid spacing were evaluated between 15 May and 15 June 2013 over central and eastern portions of the United States. Probabilistic forecasts produced from 10- and 30-member, 3-km ensembles were consistently better than forecasts from individual 1-km ensemble members. However, 10-member, 1-km probabilistic forecasts usually were best, especially over the first 12 h and at rainfall rates ≥ 5.0 mm h−1 at later times. Further object-based investigation revealed that better 1-km forecasts at heavier rainfall rates were associated with more accurate placement of mesoscale convective systems compared to 3-km forecasts. The collective results indicate promise for 1-km ensembles once computational resources can support their operational implementation. [ABSTRACT FROM AUTHOR]

This study examines the effects of five cumulus closure assumptions on simulations of summer precipitation in the continental U.S. by utilizing an ensemble cumulus parameterization (ECP) that incorporates multiple alternate closure schemes into a single cloud model formulation. Results demonstrate that closure algorithms significantly affect the summer mean, daily frequency and intensity, and diurnal variation of precipitation, with strong regional dependence. Overall, the vertical velocity (W) closure produces the smallest summer mean biases, while the moisture convergence (MC) closure most realistically reproduces daily variability. Both closures have advantages over others in simulating U.S. daily rainfall frequency distribution, though both slightly overestimate intense rain events. The MC closure is superior at capturing summer rainfall amount, daily variability, and heavy rainfall frequency over the Central U.S., but systematically produces wet biases over the North American Monsoon (NAM) region and Southeast U.S., which can be reduced by using the W closure. The instability tendency (TD) and the total instability adjustment (KF) closures are better at capturing observed diurnal signals over the Central U.S. and the NAM, respectively. The results reasonably explain the systematic behaviors of several major cumulus parameterizations. A preliminary experiment combining two optimal closures (averaged moisture convergence and vertical velocity) in the ECP scheme significantly reduced the wet (dry) biases over the Southeast U.S. in the summer of 1993 (2003), and greatly improved daily rainfall correlations over the NAM. Further improved model simulation skills may be achieved in the future if optimal closures and their appropriate weights can be derived at different time scales based on specific climate regimes. [ABSTRACT FROM AUTHOR]

This paper investigates the possibility that some aggregate gradations could be considered functionally ‘pervious’, based on comparison of an aggregate's permeability with either natural soil permeabilities or local rainfall intensities. Permeability was estimated by calculation for 27 American standard gradations, based on their porosity, coefficient of uniformity and effective particle size. In addition aggregate's cost relative to other urban surfaces was estimated. The results show that gradations of ASTM D448 No. 9, 89 and larger have high permeabilities relative to both the most permeable soil and the most intense rainfall in the central and eastern United States and so could be considered pervious surfaces in that large region. Denser gradations have lower permeabilities and less claim to be considered pervious. Properly selected aggregate's combination of high permeability and low cost make it an attractive option for reducing impervious cover, under the detailed traffic types for which it is suited. [ABSTRACT FROM AUTHOR]

The number of tropical cyclone (TC) genesis over the South China Sea and the Northwest Pacific Ocean in 2009 is significantly less than the average (27.4). However, the number of landfall TC over mainland China and its associated rainfall is more than the average. This paper focuses on the performance of numerical weather prediction (NWP) of landfall TC precipitation over China in 2009. The China Meteorological Administration (CMA) and Japan Meteorological Agency (JMA) models are compared. Although the schemes of physical processes, the data assimilation system and the dynamic frame are entirely different for the two models, the results of forecast verification are similar to each other for TC rainfall and track except for TC Goni. In this paper, a day with daily rainfall amount greater than 50 mm was selected as a storm rain day when there was a TC affecting the mainland. There are 32 storm rain days related to the landing of typhoons and tropical depressions. The rainfall forecast verification methods of National Meteorological Centre (NMC) of CMA are selected to verify the models' rainfall forecast. Observational precipitation analyses related to TCs in 2009 indicate a U-shape spatial distribution in China. It is found that the rain belt forecasted by the two models within 60 hours shows good agreement with observations, both in the location and the maximum rainfall center. Beyond 3 days, the forecasted rainfall belt shifts northward on average, and the rainfall amount of the model forecasts becomes under-predicted. The rainfall intensity of CMA model forecast is more reasonable than that of JMA model. For heavy rain, the JMA model made more missing forecasts. The TC rainfall is verified in Guangdong, Guangxi, Fujian and Hainan where rainfall amount related to TCs is relatively larger than in other regions. The results indicate that the model forecast for Guangdong and Guangxi is more skillful than that for Hainan. The rainfall forecast for Hainan remains difficult for the models because of insufficient observation data and special tropical ocean climate. [ABSTRACT FROM AUTHOR]

Heavy precipitation and flash flooding have been extensively studied in the central United States, but less so in the Northeast. This study examines 187 warm-season flash flood events identified in Storm Data to better understand the structure of the precipitation systems that cause flash flooding in the Northeast. Based on the organization and movement of these systems on radar, the events are classified into one of four categories-back-building, linear, multiple, and other/size-and then further classified into subtypes for each category. Eight of these subtypes were not previously recognized in the literature. The back-building events were the most common, followed by the multiple, other/size, and linear types. The linear event types appear to produce flash flooding less commonly in the Northeast than in other regions. In general, the subtypes producing the highest precipitation estimates are those whose structures are most conducive to a long duration of sustained moderate to heavy rainfall. The event types were found to differ from those in the central United States in that the events were more often found to be more disorganized in the Northeast. One event type in particular, back-building with merging features, while not more disorganized than the previously recognized event types, offers promise for improved forecasting because its radar signature makes the duration of sustained heavy precipitation potentially easier to predict. [ABSTRACT FROM AUTHOR]

From the suite of future Global Precipitation Mission (GPM) satellites we have selected 11 of the possible contributors to the NASA's International precipitation measurement program. The Observing System Simulation Experiments (OSSE) presented here explores the predictive usefulness of this suite of satellites. In order to carry out such experiments a Nature Run based on results from a state of the model is required. For that purpose we have selected recent past runs from the European Center for Medium Range Forecasts (ECMWF). These were designated as special data sets for OSSEs in partnership between NASA, NCEP/EMC, and NOAA. In order to test the usefulness of these future GPM-based precipitation measurements we first identify the typical orbits of eleven satellites. Along these orbital tracks we generate proxy precipitation data sets from the ECMWF Nature Run. This method of extraction of precipitation data set from a Nature Run is described in this paper. This methodology also requires a fraternal twin model (different from the Nature Run) in which the usefulness of the proposed GPM proxy data sets from the Nature Run are systematically evaluated in a forecast mode. The procedure for incorporation of the rainfall data sets is called the rain rate initialization. Data from one or more satellites are sequentially introduced into the fraternal twin model (which is the Florida State University Global Spectral Model) during the initialization phase for a number of experiments. After the initialization of such precipitation data sets, forecast experiments are carried out with the fraternal twin. The question asked is, as we introduce more and more GPM satellites how close do the forecasts from the fraternal twin approach the Nature Run? The results from this experimentation show that very promising improvements for short-range precipitation forecast skills are attainable from the proposed suite of GPM satellites. [ABSTRACT FROM AUTHOR]

Using 13 yr of data from the Tropical Rainfall Measuring Mission (TRMM) satellite, a regional climatology of monsoonal precipitation is created for portions of the southwest United States. The climatology created using precipitation features defined from the TRMM precipitation radar (PR) shows that the population of features includes a large number of small, weak features that do not produce much rain and are very shallow. A lesser percentage of large, stronger features contributes most of the region's rainfall. Dividing the features into categories based on the median values of volumetric rainfall and maximum height of the 30-dB Z echo is a useful way to visualize the population of features, and the categories selected reflect the life cycle of monsoonal convection. An examination of the top rain-producing features at different elevations reveals that extreme features tend to occur at lower elevations later in the day. A comparison with the region studied in the North American Monsoon Experiment (NAME) shows that similar diurnal patterns occur in the Sierra Madre Occidental region of Mexico. The population of precipitation features in both regions is similar, with the NAME region producing slightly larger precipitation systems on average than the southwest United States. Both regions on occasion demonstrate the pattern of convection initiating at high elevations and moving downslope while growing upscale through the afternoon and evening; however, there are also days on which convection remains over the high terrain. [ABSTRACT FROM AUTHOR]

A portable truck-mounted C-band Doppler weather radar was deployed to observe rainfall over the Station Fire burn area near Los Angeles, California, during the winter of 2009/10 to assist with debris-flow warning decisions. The deployments were a component of a joint NOAA-U.S. Geological Survey (USGS) research effort to improve definition of the rainfall conditions that trigger debris flows from steep topography within recent wildfire burn areas. A procedure was implemented to blend various dual-polarized estimators of precipitation (for radar observations taken below the freezing level) using threshold values for differential reflectivity and specific differential phase shift that improves the accuracy of the rainfall estimates over a specific burn area sited with terrestrial tipping-bucket rain gauges. The portable radar outperformed local Weather Surveillance Radar-1988 Doppler (WSR-88D) National Weather Service network radars in detecting rainfall capable of initiating post-fire runoff-generated debris flows. The network radars underestimated hourly precipitation totals by about 50%%. Consistent with intensity-duration threshold curves determined from past debris-flow events in burned areas in Southern California, the portable radar-derived rainfall rates exceeded the empirical thresholds over a wider range of storm durations with a higher spatial resolution than local National Weather Service operational radars. Moreover, the truck-mounted C-band radar dual-polarimetric-derived estimates of rainfall intensity provided a better guide to the expected severity of debris-flow events, based on criteria derived from previous events using rain gauge data, than traditional radar-derived rainfall approaches using reflectivity-rainfall relationships for either the portable or operational network WSR-88D radars. Part of the reason for the improvement was due to siting the radar closer to the burn zone than the WSR-88Ds, but use of the dual-polarimetric variables improved the rainfall estimation by ~12%% over the use of traditional Z- R relationships. [ABSTRACT FROM AUTHOR]

This study assesses the impact of satellite rainfall error structure on soil moisture simulations with the NASA Catchment land surface model. Specifically, the study contrasts a complex satellite rainfall error model (SREM2D) with the standard rainfall error model used to generate ensembles of rainfall fields as part of the Land Data Assimilation System (LDAS) developed at the NASA Global Modeling and Assimilation Office. The study is conducted in the Oklahoma region, which offers good coverage by weather radars and in situ meteorological and soil moisture measurement stations. The authors used high-resolution (25 km, 3-hourly) satellite rainfall fields derived from the NOAA/Climate Prediction Center morphing (CMORPH) global satellite product and rain gauge--calibrated radar rainfall fields (considered as the reference rainfall). The LDAS simulations are evaluated in terms of rainfall and soil moisture error. Comparisons of rainfall ensembles generated by SREM2D and LDAS against reference rainfall show that both rainfall error models preserve the satellite rainfall error characteristics across a range of spatial scales. The error structure in SREM2D is shown to generate rainfall replicates with higher variability that better envelop the reference rainfall than those generated by the LDAS error model. Likewise, the SREM2D-generated soil moisture ensemble shows slightly higher spread than the LDAS-generated ensemble and thus better encapsulates the reference soil moisture. Soil moisture errors, however, are less sensitive than precipitation errors to the complexity of the precipitation error modeling approach because soil moisture dynamics are dissipative and nonlinear. [ABSTRACT FROM AUTHOR]

Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) and National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis data are used to indicate mechanisms responsible for extreme summer convection over South America. The three-dimensional reflectivity field is analyzed to define three types of extreme echo, deep convective cores, wide convective cores, and broad stratiform regions. The location and timing of these echoes are sensitive to midlatitude synoptic disturbances crossing the Andes. At the leading edges of these disturbances the nocturnal South American low-level jet (SALLJ) transports moisture along the eastern edge of the Andes from the tropical to the subtropical part of the continent. Where the SALLJ rises over lower but steep mountains on the east side of the southern central Andes, deep and wide convective cores are triggered in the evening. When the SALLJ withdraws to the north as the disturbance passes, nocturnal triggering occurs in the northeastern foothills of the central Andes. Extreme convection over the Amazon basin takes the form of broad stratiform regions that evolve from systems with wide convective cores moving into the center of the region from both the southwest and northeast. The systems from the northeast form at the northeast coast and are likely squall lines. Along the coast of the Brazilian Highlands, diurnal/topographic forcing leads to daytime maxima of deep convective cores followed a few hours later by wide convective cores. Wide convective cores and broad stratiform regions form in the South Atlantic convergence zone (SACZ) with a diurnal cycle related to continental heating. [ABSTRACT FROM AUTHOR]

A comparative study of daily and monthly rainfall between research and operational gauges was conducted at the mid-Atlantic region. Fifty research tipping-bucket gauges were deployed to 20 sites where each site had dual or triple gauges. The gauges were in place to validate the National Aeronautics and Space Administration’s newly developed polarimetric radar rainfall estimate. For logistic purposes, these research gauges were collocated with operational gauges and were operated over a year at each site. Therefore, this is an experimental study, which involves a mixture of one to five sites of seven operational gauge networks. A very good to excellent agreement between the two collocated research gauges at daily time scale raised the authors’ confidence to consider them as a reference before comparing with the operational gauges. Among operational networks, the National Weather Service’s (NWS) Automated Surface Observing Systems (ASOS) weighing bucket and the Climate Reference Network and Forest Services tipping-bucket gauges demonstrated high performance for both daily and monthly rainfall, while the Federal Aviation Administration’s Automated Weather Observing Systems (AWOS) tipping-bucket gauges performed poorly. Among the other networks, the ASOS tipping-bucket and Cooperative observer program’s stick gauges seemed to be reliable for monthly rainfall, but not always for daily rainfall. The Virginia Agricultural Experimental Station (VAES) tipping-bucket gauges, on the other hand, had a mixture of high and low performance for daily and monthly rainfall. Unlike other gauge networks, VAES gauges were in place for long-term research applications. [ABSTRACT FROM AUTHOR]

Assimilation experiments have been performed with the Weather Research and Forecasting (WRF) model’s three-dimensional variational data assimilation (3DVAR) scheme to assess the impacts of NASA’s Quick Scatterometer (QuikSCAT) near-surface winds, and Special Sensor Microwave Imager (SSM/I) wind speed and total precipitable water (TPW) on the analysis and on short-range forecasts over the Indian region. The control (without satellite data) as well as WRF 3DVAR sensitivity runs (which assimilated satellite data) were made for 48 h starting daily at 0000 UTC during July 2006. The impacts of assimilating the different satellite dataset were measured in comparison to the control run, which does not assimilate any satellite data. The spatial distribution of the forecast impacts (FIs) for wind, temperature, and humidity from 1-month assimilation experiments for July 2006 demonstrated that on an average, for 24- and 48-h forecasts, the satellite data provided useful information. Among the experiments, WRF wind speed prediction was improved by QuikSCAT surface wind and SSM/I TPW assimilation, while temperature and humidity prediction was improved due to the assimilation of SSM/I TPW. The rainfall prediction has also been improved significantly due to the assimilation of SSM/I TPW, with the largest improvement seen over the west coast of India. Through an improvement of the surface wind field, the QuikSCAT data also yielded a positive impact on the precipitation, particularly for day 1 forecasts. In contrast, the assimilation of SSM/I wind speed degraded the humidity and rainfall predictions. [ABSTRACT FROM AUTHOR]

The study uses storm tracking information to evaluate error statistics of satellite rain estimation at different maturity stages of storm life cycles. Two satellite rain retrieval products are used for this purpose: (i) NASA's Multisatellite Precipitation Analysis–Real Time product available at 25-km/hourly resolution (3B41-RT) and (ii) the University of California (Irvine) Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) product available at 4-km–hourly resolution. Both algorithms use geostationary satellite infrared (IR) observations calibrated to an array of passive microwave (PM) earth-orbiting satellite sensor rain retrievals. The techniques differ in terms of algorithmic structure and in the way they use the PM rainfall to calibrate the IR rain algorithms. The satellite retrievals are evaluated against rain gauge–calibrated radar rainfall estimates over the continental United States. Error statistics of hourly rain volumes are determined separately for thunderstorm and shower-type convective systems and for different storm life durations and stages of maturity. The authors show distinct differences between the two satellite retrieval error characteristics. The most notable difference is the strong storm life cycle dependence of 3B41-RT relative to the nearly independent PERSIANN behavior. Another is in the algorithm performance between thunderstorms and showers; 3B41-RT exhibits significant bias increase at longer storm life durations. PERSIANN exhibits consistently improved correlations relative to the 3B41-RT for all storm life durations and maturity stages. The findings of this study support the hypothesis that incorporating cloud type information into the retrieval (done by the PERSIANN algorithm) can help improve the satellite retrieval accuracy. [ABSTRACT FROM AUTHOR]

A regional-scale weather model is used to determine the potential for flood forecasting based on model-predicted rainfall. Extreme precipitation and flooding events are a significant concern in central Texas, due to both the high occurrence and severity of flooding in the area. However, many current regional prediction models do not provide sufficient accuracy at the watershed scale necessary for flood mitigation efforts. The Weather Research and Forecasting (WRF) model, created with the purpose of improving upon the current fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5), is specifically designed for regional grid spacings of 1–10 km. Previous research by the authors resulted in the development of a regional-scale prediction system over the San Antonio River basin, using a geographic information system (GIS) database, a hydrologic model, and a hydraulic model. Observed precipitation drives the prediction system; the authors hypothesize that the WRF model has the potential to predict flooding, at a lead time of several days, with a level of accuracy near that of observed precipitation. Causes of model error are also investigated, to determine the relative errors caused by model physics, initialization interval, buffer zone and domain size, and small-amplitude random errors. Results show that the Betts–Miller–Janjić cumulus and Lin et al. microphysics schemes, 48-h initialization interval, and two-domain configuration covering minimal ocean and having a parent-to-nest area ratio of greater than 10 best simulates a recent (July 2002) large storm event over the San Antonio River basin. This particular storm was selected because it produced extreme rainfall volumes and intensities, and also because its meteorological characteristics are typical of central Texas storm events. Location errors in rainfall are most significant because of their typically nonlinear patterns (increasing location error does not linearly modify streamflow output). Errors in intensity and timing show a more predictable (linear) watershed response that may be useful in the estimation of streamflow ranges for flood forecasting. [ABSTRACT FROM AUTHOR]

A space-filling algorithm (SFA) based on 2D spectral estimation techniques was developed to extrapolate the spatial domain of the narrow-swath near-instantaneous rain-rate estimates from Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) and TRMM Microwave Imager (TMI) using thermal infrared imagery (Meteosat-5) without making use of calibration or statistical fitting. A comparison against rain gauge observations and the original PR 2A25 and TMI 2A12 estimates in the central Himalayas during the monsoon season (June–September) over a 3-yr period of 1999–2001 was conducted to assess the algorithm’s performance. Evaluation over the continental United States was conducted against the NCEP stage IV combined radar and gauge analysis for selected events. Overall, the extrapolated PR and TMI rainfall fields derived using SFA exhibit skill comparable to the original TRMM estimates. The results indicate that probability of detection and threat scores of the reconstructed products are significantly better than the original PR data at high-elevation stations (>2000 m) on mountain ridges, and specifically for rainfall rates exceeding 2–5 mm h-1 and for afternoon convection. For low-elevation stations located in steep narrow valleys, the performance varies from year to year and deteriorates strongly for light rainfall (false alarm rates significantly increase). A preliminary comparison with other satellite products (e.g., 3B42, a TRMM-adjusted merged infrared-based rainfall product) suggests that integrating this algorithm in currently existing operational multisensor algorithms has the potential to improve significantly spatial resolution, texture, and detection of rainfall, especially in mountainous regions, which present some of the greatest challenges in precipitation retrieval from satellites over land, and for hydrological operations during extreme events. [ABSTRACT FROM AUTHOR]

A radar-based automated technique for the identification of tropical precipitation was developed to improve quantitative precipitation estimation during extreme rainfall events. The technique uses vertical profiles of reflectivity to identify the potential presence of warm rain (i.e., tropical rainfall) microphysics and delineates the tropical rainfall region to which the tropical Z–R relationship is applied. The performance of the algorithm is examined based on case studies of five storms that produced extreme precipitation in the United States. Results demonstrate relative improvements in radar-based quantitative precipitation estimation through the automated identification of tropical rainfall and the subsequent adaptation of the tropical Z–R relation to account for the potential warm rain processes. [ABSTRACT FROM AUTHOR]

Urban streams play a significant role in the transport of dissolved inorganic nitrogen (DIN) from uplands to sensitive coastal receiving waters. In this study, we investigate the timing of DIN export through monitoring conducted during several storm events of different magnitude and with different antecedent conditions in an urban catchment. Our monitoring was conducted at a first-order stream site in a highly urban catchment in the northeastern United States during four rainstorms. Nitrate made up the dominant portion of the DIN load during all four events and the response of nitrate and ammonia were very different. Discharge, rather than concentration, was the most important factor in determining nitrate export with hot moments of nitrate export coinciding with peaks in flow. However, the highest nitrogen concentrations were observed after the streamflow peak during all of the events. During extreme rainstorms, this delayed response may constitute an important secondary hot-moment of nitrate export. These results may be significant for the development of nitrogen management plans for urban watersheds, especially since many water quality improvement BMPs (best management practices) are being designed to treat the first-flush of stormwater and would miss much of the DIN load. [ABSTRACT FROM AUTHOR]

Recent studies using vertically pointing S-band profiling radars showed that coastal winter storms in California and Oregon frequently do not display a melting-layer radar bright band and inferred that these nonbrightband (NBB) periods are characterized by raindrop size spectra that differ markedly from those of brightband (BB) periods. Two coastal sites in northern California were revisited in the winter of 2003/04 in this study, which extends the earlier work by augmenting the profiling radar observations with collocated raindrop disdrometers to measure drop size distributions (DSD) at the surface. The disdrometer observations are analyzed for more than 320 h of nonconvective rainfall. The new measurements confirm the earlier inferences that NBB rainfall periods are characterized by greater concentrations of small drops and smaller concentrations of large drops than BB periods. Compared with their BB counterparts, NBB periods had mean values that were 40% smaller for mean-volume diameter, 32% smaller for rain intensity, 87% larger for total drop concentration, and 81% larger (steeper) for slope of the exponential DSDs. The differences are statistically significant. Liquid water contents differ very little, however, for the two rain types. Disdrometer-based relations between radar reflectivity (Z) and rainfall intensity (R) at the site in the Coast Range Mountains were Z = 168R1.58 for BB periods and Z = 44R1.91 for NBB. The much lower coefficient, which is characteristic of NBB rainfall, is poorly represented by the Z–R equations most commonly applied to data from the operational network of Weather Surveillance Radar-1988 Doppler (WSR-88D) units, which underestimate rain accumulations by a factor of 2 or more when applied to nonconvective NBB situations. Based on the observed DSDs, it is also concluded that polarimetric scanning radars may have some limited ability to distinguish between regions of BB and NBB rainfall using differential reflectivity. However, differential-phase estimations of rain intensity are not useful for NBB rain, because the drops are too small and nearly spherical. On average, the profiler-measured echo tops were 3.2 km lower in NBB periods than during BB periods, and they extended only about 1 km above the 0°C altitude. The findings are consistent with the concept that precipitation processes during BB periods are dominated by ice processes in deep cloud layers associated with synoptic-scale forcing, whereas the more restrained growth of hydrometeors in NBB periods is primarily the result of orographically forced condensation and coalescence processes in much shallower clouds. [ABSTRACT FROM AUTHOR]

This paper addresses the following open question: What set of error metrics for satellite rainfall data can advance the hydrologic application of new-generation, high-resolution rainfall products over land? The authors’ primary aim is to initiate a framework for building metrics that are mutually interpretable by hydrologists (users) and algorithm developers (data producers) and to provide more insightful information on the quality of the satellite estimates. In addition, hydrologists can use the framework to develop a space–time error model for simulating stochastic realizations of satellite estimates for quantification of the implication on hydrologic simulation uncertainty. First, the authors conceptualize the error metrics in three general dimensions: 1) spatial (how does the error vary in space?); 2) retrieval (how “off” is each rainfall estimate from the true value over rainy areas?); and 3) temporal (how does the error vary in time?). They suggest formulations for error metrics specific to each dimension, in addition to ones that are already widely used by the community. They then investigate the behavior of these metrics as a function of spatial scale ranging from 0.04° to 1.0° for the Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN) geostationary infrared-based algorithm. It is observed that moving to finer space–time scales for satellite rainfall estimation requires explicitly probabilistic measures that are mathematically amenable to space–time stochastic simulation of satellite rainfall data. The probability of detection of rain as a function of ground validation rainfall magnitude is found to be most sensitive to scale followed by the correlation length for detection of rain. Conventional metrics such as the correlation coefficient, frequency bias, false alarm ratio, and equitable threat score are found to be modestly sensitive to scales smaller than 0.24° latitude/longitude. Error metrics that account for an algorithm’s ability to capture rainfall intermittency as a function of space appear useful in identifying the useful spatial scales of application for the hydrologist. It is shown that metrics evolving from the proposed conceptual framework can identify seasonal and regional differences in reliability of four global satellite rainfall products over the United States more clearly than conventional metrics. The proposed framework for building such error metrics can lay a foundation for better interaction between the data-producing community and hydrologists in shaping the new generation of satellite-based, high-resolution rainfall products, including those being developed for the planned Global Precipitation Measurement (GPM) mission. [ABSTRACT FROM AUTHOR]

Intense rainfall over coastal areas characterized by significant urban development, predominantly clay soils, and flat slopes can cause significant flooding problems. Extreme floods in coastal areas occur when storm-water systems lose capacity due to high levels in receiving streams. The Texas Medical Center is located near downtown in Houston, Texas, and extensive flooding during Tropical Storm Allison in June of 2001 crippled many of the facilities with approximately $1.5 billion in damages. In order to better understand dynamic responses and to design abatements to the existing infrastructure, the performance of the storm-water pipe systems under extreme receiving water conditions in a highly urbanized area was quantified by using an integrated model. A hydrologic/hydraulic model was created based on the storm water management model with NEXRAD radar rainfall and hydrographs generated from Hydrologic Engineering Center’s hydrologic modeling system. The model was accurately calibrated against historic storm events. The simulated double-peaked hydrographs explained that the significant backwater effects in Harris Gully worsened the flooding situation within streets by limiting storm-water outflow and overland flow. The analysis evaluated the existing storm-water system to identify potential pathways for overland flow and pipe flow to the receiving bayou, and proposed mitigation alternatives by incorporating additional major sewers to bypass the main culverts in the Harris Gully area in order to provide significant flood relief. [ABSTRACT FROM AUTHOR]

The Saffir-Simpson scale is useful for evaluating maximum sustained hurricane winds and storm surge over open water in the prelandfall window, but it fails to accurately account for the observed impacts over land. A new postland-fall hurricane classification system (HCS) is proposed that redistributes the categorization of hurricanes into types according to six variables: open water storm surge, rainfall, duration of hurricane force winds, maximum sustained winds, gust score, and minimum central pressure. Hurricanes are assigned values for each variable and summed for a numerical 0–100 grade. Principal component analysis and hierarchical cluster analysis are also performed on the six variables to categorize U.S. land-falling hurricanes into storm types. A total of 41 land-falling hurricanes in the United States since 1960 have been analyzed. The summation scores show many hurricanes that are of strength similar to their Saffir-Simpson classifications, with several notable exceptions. The cluster analysis identifies five different hurricane types. These types can be arranged to identify hurricane strength and structure more effectively than the Saffir-Simpson scale. In focusing on the observed storm intensity over land and the resulting human experience, the HCS allows a way to compare hurricane impacts across different periods. [ABSTRACT FROM AUTHOR]

Directionally averaged time series of precipitation rates for eight warm seasons (1996-2003) over the continental United States derived from Next Generation Weather Radar (NEXRAD) measurements are analyzed using spectral decomposition methods. For the latitudinally averaged data, in addition to previously identified diurnal and semidiurnal cycles, the temporal spectra show cross-scale self-similarity and periodicity. This property is revealed by a power-law scaling with an exponent of -4/3 for the frequency band higher than semidiurnal and -3/4 for the 1-3-day band. For the longitudinally averaged series the scaling exponent for the frequency band higher than semidiurnal changes from -4/3 to -5/3 revealing anisotropic properties. The dominant periods and propagation speeds display temporal variability on about 1/2, 1, 4, 11, and 25 days. Composite patterns describing periods of <5 days display the eastward propagation characteristic of classical mesoscale convective organization. The lower-frequency (>5 days) patterns propagate westward suggesting the influence of large-scale waves, and both dominant periods and propagation speeds show marked interannual variability. The implied dependence between propagation and mean-flow for <5 days is consistent with the macrophysics of warm-season convective organization, and extends known dynamical mechanisms to a statistical framework. [ABSTRACT FROM AUTHOR]

A weeklong workshop in Brazil in August 2004 provided the opportunity for 28 scientists from southern South America to examine daily rainfall observations to determine changes in both total and extreme rainfall. Twelve annual indices of daily rainfall were calculated over the period 1960 to 2000, examining changes to both the entire distribution as well as the extremes. Maps of trends in the 12 rainfall indices showed large regions of coherent change, with many stations showing statistically significant changes in some of the indices. The pattern of trends for the extremes was generally the same as that for total annual rainfall, with a change to wetter conditions in Ecuador and northern Peru and the region of southern Brazil, Paraguay, Uruguay, and northern and central Argentina. A decrease was observed in southern Peru and southern Chile, with the latter showing significant decreases in many indices. A canonical correlation analysis between each of the indices and sea surface temperatures (SSTs) revealed two large-scale patterns that have contributed to the observed trends in the rainfall indices. A coupled pattern with ENSO-like SST loadings and rainfall loadings showing similarities with the pattern of the observed trend reveals that the change to a generally more negative Southern Oscillation index (SOI) has had an important effect on regional rainfall trends. A significant decrease in many of the rainfall indices at several stations in southern Chile and Argentina can be explained by a canonical pattern reflecting a weakening of the continental trough leading to a southward shift in storm tracks. This latter signal is a change that has been seen at similar latitudes in other parts of the Southern Hemisphere. A similar analysis was carried out for eastern Brazil using gridded indices calculated from 354 stations from the Global Historical Climatology Network (GHCN) database. The observed trend toward wetter conditions in the southwest and drier conditions in the northeast could again be explained by changes in ENSO. [ABSTRACT FROM AUTHOR]

This study examines the characteristics of a large number of extreme rain events over the eastern two-thirds of the United States. Over a 5-yr period, 184 events are identified where the 24-h precipitation total at one or more stations exceeds the 50-yr recurrence amount for that location. Over the entire region of study, these events are most common in July. In the northern United States, extreme rain events are confined almost exclusively to the warm season; in the southern part of the country, these events are distributed more evenly throughout the year. National composite radar reflectivity data are used to classify each event as a mesoscale convective system (MCS), a synoptic system, or a tropical system, and then to classify the MCS and synoptic events into subclassifications based on their organizational structures. This analysis shows that 66% of all the events and 74% of the warm-season events are associated with MCSs; nearly all of the cool-season events are caused by storms with strong synoptic forcing. Similarly, nearly all of the extreme rain events in the northern part of the country are caused by MCSs; synoptic and tropical systems play a larger role in the South and East. MCS-related events are found to most commonly begin at around 1800 local standard time (LST), produce their peak rainfall between 2100 and 2300 LST, and dissipate or move out of the affected area by 0300 LST. [ABSTRACT FROM AUTHOR]

Technology for the 14- and 35-GHz Second-Generation Precipitation Radar (PR-2) is currently being developed by the National Aeronautics and Space Administration Jet Propulsion Laboratory to support the development of future spaceborne radar missions. PR-2 will rely on high-performance onboard processing techniques in order to improve the observation capabilities (swath width, spatial resolution, and precision) of a low-earth orbiting rainfall radar. Using field-programmable gate arrays (FPGAs), we have developed a prototype spaceborne processor and controller module that will support advanced capabilities in the PR-2 such as autotargeting of rain and compression of rainfall science data. In this paper, we describe the new technology components designed for the onboard processor, including an FPGA-based 40 × 109 op/s pulse-compression receiver/filter with a range sidelobe performance of -72 dB, and an adaptive scanning controller which yields a six-fold increase in the number of radar looks over areas of precipitation. [ABSTRACT FROM AUTHOR]