Search

Extreme floods in the Delaware River basin are examined through analyses of a sequence of record and near-record floods during September 2004, April 2005, and June 2006. The three flood episodes reflect three principal flood-generating mechanisms in the eastern United States: tropical cyclones (September 2004); late winter–early spring extratropical systems (April 2005); and warm-season convective systems (June 2006). Extreme flooding in the Delaware River basin is the product of heavy rainfall and runoff from high-gradient portions of the watershed. Orographic precipitation mechanisms play a central role in the extreme flood climatology of the Delaware River basin and, more generally, for the eastern United States. Extreme flooding for the 2004–06 events was produced in large measure from forested portions of the watershed. Analyses of flood frequency based on annual flood peak observations from U.S. Geological Survey (USGS) stream gauging stations with “long” records illustrate the striking heterogeneity of flood response over the region, the important role of landfalling tropical cyclones for the upper tail of flood peak distributions, and the prevalence of nonstationarities in flood peak records. Analyses show that changepoints are a more common source of nonstationarity than linear time trends. Regulation by dams and reservoirs plays an important role in determining changepoints, but the downstream effects of reservoirs on flood distributions are limited. [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]

During the 2007 NOAA Hazardous Weather Testbed Spring Experiment, the Center for Analysis and Prediction of Storms (CAPS) at the University of Oklahoma produced a daily 10-member 4-km horizontal resolution ensemble forecast covering approximately three-fourths of the continental United States. Each member used the Advanced Research version of the Weather Research and Forecasting (WRF-ARW) model core, which was initialized at 2100 UTC, ran for 33 h, and resolved convection explicitly. Different initial condition (IC), lateral boundary condition (LBC), and physics perturbations were introduced in 4 of the 10 ensemble members, while the remaining 6 members used identical ICs and LBCs, differing only in terms of microphysics (MP) and planetary boundary layer (PBL) parameterizations. This study focuses on precipitation forecasts from the ensemble. The ensemble forecasts reveal WRF-ARW sensitivity to MP and PBL schemes. For example, over the 7-week experiment, the Mellor–Yamada–Janjić PBL and Ferrier MP parameterizations were associated with relatively high precipitation totals, while members configured with the Thompson MP or Yonsei University PBL scheme produced comparatively less precipitation. Additionally, different approaches for generating probabilistic ensemble guidance are explored. Specifically, a “neighborhood” approach is described and shown to considerably enhance the skill of probabilistic forecasts for precipitation when combined with a traditional technique of producing ensemble probability fields. [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]

Reliable precipitation measurement is a crucial component in hydrologic studies. Although satellite-based observation is able to provide spatial and temporal distribution of precipitation, the measurements tend to show systematic bias. This paper introduces a grid-based precipitation merging procedure in which satellite estimates from the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Cloud Classification System (PERSIANN–CCS) are adjusted based on the Climate Prediction Center (CPC) daily rain gauge analysis. To remove the bias, the hourly CCS estimates were spatially and temporally accumulated to the daily 1° × 1° scale, the resolution of CPC rain gauge analysis. The daily CCS bias was then downscaled to the hourly temporal scale to correct hourly CCS estimates. The bias corrected CCS estimates are called the adjusted CCS (CCSA) product. With the adjustment from the gauge measurement, CCSA data have been generated to provide more reliable high temporal/spatial-resolution precipitation estimates. In the case study, the CCSA precipitation estimates from the proposed approach are compared against ground-based measurements in high-density gauge networks located in the southwestern United States. [ABSTRACT FROM AUTHOR]

Inland flooding from tropical cyclones is a significant factor in storm-related deaths in the United States and other countries, with the majority of tropical cyclone fatalities recorded in the United States resulting from freshwater flooding. Information collected during National Aeronautics and Space Administration (NASA) tropical cyclone field experiments suggests that surface water and flooding can be detected and therefore monitored at a greater spatial resolution by using passive microwave airborne radiometers than by using satellite sensors. The 10.7-GHz frequency of the NASA Advanced Microwave Precipitation Radiometer (AMPR) has demonstrated high-resolution detection of anomalous surface water and flooding in numerous situations. In this study, an analysis of three cases is conducted utilizing satellite and airborne radiometer data. Data from the 1998 Third Convection and Moisture Experiment (CAMEX-3) are utilized to detect surface water during the landfalling Hurricane Georges in both the Dominican Republic and Louisiana. Another case studied was the landfalling Tropical Storm Gert in eastern Mexico during the Tropical Cloud Systems and Processes (TCSP) experiment in 2005. AMPR data are compared to topographic data and vegetation indices to evaluate the significance of the surface water signature visible in the 10.7-GHz information. The results illustrate the AMPR’s utility in monitoring surface water that current satellite-based passive microwave radiometers are unable to monitor because of their coarser resolutions. This suggests the benefit of a radiometer with observing frequencies less than 11 GHz deployed on a manned aircraft or unmanned aircraft system to provide early detection in real time of expanding surface water or flooding conditions. [ABSTRACT FROM AUTHOR]

The article provides information on the climate condition of a number of nations and all continents for 2008. It presents an overview of the temperature changes, precipitation rate drought and wildfires and notable events related to climate in various counties including in Canada, U.S., Northern America, and Mexico. Moreover, it presents an analysis on the factors that causes the climatic changes across the continents and regional perspective of the global climate.

The article presents a study which examined the development of a field scale vegetative filter strip (VFS) submodel for the Soil and Water Assessment Tool (SWAT) in the U.S. VFS is used as a conservation measure to remove pollutants from agricultural runoff. Under the research, the Vegetative Filter Strip MODel (VFSMOD) was adapted to operate at the field scale by determining the effect of flow concentration from plot scale experiments. Researchers noted that the said model enhances the ability of SWAT to address the effectiveness of VFS at the watershed scale.

For the first time, the full national feed of Weather Surveillance Radar's (WSR-88D) highest resolution Level II data is available in real-time from the National Weather Service via a collaborative distribution partnership with universities. This data provides observations of precipitation and wind fields with extraordinarily fine temporal and spatial resolution, which are critical for understanding, monitoring, and predicting severe weather and flooding events. The Level II radar data stream also presents an exciting opportunity for universities and the broader community—including commercial enterprises—for use in severe storm research and prediction, hydrological cycles research, precipitation estimation and measurement, transportation logistics, combination and co-location with complementary in situ and remote sensing networks, 3-D visualization, model-data assimilation, emergency response, homeland security assessments, and education enterprises at all levels. By enabling free, unrestricted, and real-time access to Level II data, the stage is set for a major evolution in our ability to probe and understand atmospheric and hydrologic processes and phenomena. [ABSTRACT FROM AUTHOR]

Real-time flood forecasting has benefited greatly by the application of physically based hydrological models and real-time rainfall data obtained from weather radar. An application of a distributed hydrological model (Vflo™) to an urban catchment using distributed rainfall data from Nexrad radar images illustrates some important issues. The benefits of using Nexrad data with pixels approximately 1 km² as opposed to using a single rain-gauge located at the edge of the catchment are demonstrated through model simulation. It was also possible to improve the model results by developing a new conceptual model of the 10.25km² Harris Gully catchment (in Houston, Texas) and replacing an earlier model calibrated on the much larger Brays Bayou catchment. In the case of the Harris Gully catchment, flooding has been caused by several factors including heavy rainfall on the catchment exceeding the design capacity of the existing storm sewer system, and backwater effects caused by the larger Brays Bayou (which Harris Gully flows into) running at full capacity. Improving the existing flood forecasting system (FAS, the flood alert system) for Harris Gully is of high priority since the catchment contains the Texas Medical Center and the main campus of Rice University. It is planned to do this through the installation of a state-of-the-art NetRAD weather radar in the near future. [ABSTRACT FROM AUTHOR]

An overview of the National Oceanic and Atmospheric Administration's (NOAA) current operational Smoke Forecasting System (SFS) is presented. This system is intended as guidance to air quality forecasters and the public for fine particulate matter (≤2.5 μm) emitted from large wildfires and agricultural burning, which can elevate particulate concentrations to unhealthful levels. The SFS uses National Environmental Satellite, Data, and Information Service (NESDIS) Hazard Mapping System (HMS), which is based on satellite imagery, to establish the locations and extents of the fires. The particulate matter emission rate is computed using the emission processing portion of the U.S. Forest Service's BlueSky Framework, which includes a fuel-type database, as well as consumption and emissions models. The Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model is used to calculate the transport, dispersion, and deposition of the emitted particulate matter. The model evaluation is carried out by comparing predicted smoke levels with actual smoke detected from satellites by the HMS and the Geostationary Operational Environmental Satellite (GOES) Aerosol/Smoke Product. This overlap is expressed as the figure of merit in space (FMS), the intersection over the union of the observed and calculated smoke plumes. Results are presented for the 2007 fire season (September 2006–November 2007). While the highest FMS scores for individual events approach 60%, average values for the 1 and 5 μg m-3 contours for the analysis period were 8.3% and 11.6%, respectively. FMS scores for the forecast period were lower by about 25% due, in part, to the inability to forecast new fires. The HMS plumes tend to be smaller than the corresponding predictions during the winter months, suggesting that excessive emissions predicted for the smaller fires resulted in an overprediction in the smoke area. [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]

The article offers information on the multiscale modeling framework (MMF) for parameterizing convective clouds and cloud systems by the U.S. National Aeronotics and Space Administration (NASA), Goddard Space Flight Center (GSFC). The Goddard finite-volume MMF is based on the NASA Goddard finite-volume Generic Circulation Model (GCM) and the Goddard Cumulus Ensemble model (GCE). The Goddard MMF has been evaluated against observations at interannual, intraseasonal, and diurnal time scales using a two year long simulation representing different climate scenarios. MMFs can bridge the gap between traditional Cloud Resolving Models (CRM) simulations, regional weather forecast models, and current and future non hydrostatic global cloud-resolution models.

A novel method has been proposed for validating satellite radar snowfall retrievals using surface station observations over the western United States mountainous region, where the mean snowfall rate at a station depends on its elevation. First, all station data within a 1° × 1° grid are used to develop a snowfall rate versus elevation relation. This relation is then used to compute snowfall rate in other locations within the 1° × 1° grid, as if surface observations were available everywhere in the grid. Grid mean snowfall rates are then derived, which should be more representative to the mean snowfall rate of the grid than using data at any one station or from a simple mean of all stations in the grid. Comparison of the so-derived grid mean snowfall rates with CloudSat retrievals shows that the CloudSat product underestimates snowfall by about 65% when averaged over all the 768 grids in the western United States mountainous regions. The bias does not seem to have clear dependency on elevation but strongly depends on snowfall rate. As an application of the method, we further estimated the snowfall to precipitation ratio using both ground and satellite measured data. It is found that the rates of increase with elevation of the snowfall to precipitation ratio are quite similar when calculating from ground and satellite data, being about 25% per kilometer elevation up or approximately 4% per every degree Cuisses of temperature drop. [ABSTRACT FROM AUTHOR]

Over land, remotely sensed surface soil moisture and rainfall accumulation retrievals contain complementary information that can be exploited for the mutual benefit of both product types. Here, a Kalman filtering–based tool is developed that utilizes a time series of spaceborne surface soil moisture retrievals to enhance short-term (2- to 10-day) satellite-based rainfall accumulation products. Using ground rain gauge data as a validation source, and a soil moisture product derived from the Advanced Microwave Scanning Radiometer aboard the NASA Aqua satellite, the approach is evaluated over the contiguous United States. Results demonstrate that, for areas of low to moderate vegetation cover density, the procedure is capable of improving short-term rainfall accumulation estimates extracted from a variety of satellite-based rainfall products. The approach is especially effective for correcting rainfall accumulation estimates derived without the aid of ground-based rain gauge observations. Special emphasis is placed on demonstrating that the approach can be applied in continental areas lacking ground-based observations and/or long-term satellite data records. [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]

This study compares instantaneous rainfall estimates provided by the current generation of retrieval algorithms for passive microwave sensors using retrievals from the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) and merged surface radar and gauge measurements over the continental United States as references. The goal is to quantitatively assess surface rain retrievals from cross-track scanning microwave humidity sounders relative to those from conically scanning microwave imagers. The passive microwave sensors included in the study are three operational sounders—the Advanced Microwave Sounding Unit-B (AMSU-B) instruments on the NOAA-15, -16, and -17 satellites—and five imagers: the TRMM Microwave Imager (TMI), the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) instrument on the Aqua satellite, and the Special Sensor Microwave Imager (SSM/I) instruments on the Defense Meteorological Satellite Program (DMSP) F-13, -14, and -15 satellites. The comparisons with PR data are based on “coincident” observations, defined as instantaneous retrievals (spatially averaged to 0.25° latitude and 0.25° longitude) within a 10-min interval collected over a 20-month period from January 2005 to August 2006. Statistics of departures of these coincident retrievals from reference measurements as given by the TRMM PR or ground radar and gauges are computed as a function of rain intensity over land and oceans. Results show that over land AMSU-B sounder rain retrievals are comparable in quality to those from conically scanning radiometers for instantaneous rain rates between 1.0 and 10.0 mm h-1. This result holds true for comparisons using either TRMM PR estimates over tropical land areas or merged ground radar/gauge measurements over the continental United States as the reference. Over tropical oceans, the standard deviation errors are comparable between imager and sounder retrievals for rain intensities above 5 mm h-1, below which the imagers are noticeably better than the sounders; systematic biases are small for both imagers and sounders. The results of this study suggest that in planning future satellite missions for global precipitation measurement, cross-track scanning microwave humidity sounders on operational satellites may be used to augment conically scanning microwave radiometers to provide improved temporal sampling over land without degradation in the quality of precipitation estimates. [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]

The article presents a study to improve the capability of hurricane intensity and structure forecasts by radar data assimilation using the landfall data of Hurricane Isabel in the U.S. on September 18, 2003. It says that tropical cyclone (TC) track improves well over the past years, but TC intensity forecasts have been recorded for its errors. This study will prove that the use of land-based Doppler radar observations in a mesoscale numerical weather prediction (NWP) model can improve TC intensity forecasts. An overview of the landfall data of hurricane Isabel is offered, along with the conducted radar observations. Meanwhile, the study finds out that the use of radar data assimilation has a potential for improving hurricane intensity and structure forecasts.

The diurnal occurrence of warm-season rainfall over the U.S. mainland is examined, particularly in light of forcings at multiple scales. The analysis is based on a radar dataset of 12-seasons duration covering the U.S. mainland from the Continental Divide eastward. The dataset resolves 2-km features at 15-min intervals, thus providing a detailed view of both large- and regional-scale diurnal patterns, as well as the statistics of events underlying these patterns. The results confirm recent findings with respect to the role of propagating rainfall systems and the high frequency at which these are excited by sensible heating over elevated terrain. Between the Rockies and the Appalachians, ∼60% of midsummer rainfall occurs in this manner. Most rainfall in the central United States is nocturnal and may be attributed to the following three main forcings: 1) the passage of eastward-propagating rainfall systems with origins near the Continental Divide at 105°W; 2) a nocturnal reversal of the mountain–plains solenoid, which is associated with widespread ascent over the plains; and 3) the transport of energetic air and moisture convergence by the Great Plains low-level jet. Other features of interest include effects of the Appalachians, semidiurnal signals of regional significance, and the impact of breezes along the Gulf of Mexico. A modest effort was put forth to discern signals associated with El Niño and the Southern Oscillation. While tendencies in precipitation patterns are observed, the record is too short to draw conclusions of general significance. [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]

Over the past century, a significant proportion of the native grasslands that once occupied much of the middle third of the United States have been converted to cultivated agriculture, causing short- and long-term variations in soil physical and hydraulic properties that influence runoff-infiltration partitioning and runoff water quality. Eastern Arkansas is highly agriculturally productive when irrigated. However, the long history of irrigated agriculture and more recent shifts in rainfall patterns during the growing season have resulted in regional groundwater aquifer depletion with minimal regional recharge. The objective of this study was to evaluate the effects of land use on soil physical properties, runoff-infiltration partitioning, and runoff water quality on a typical agricultural soil in the Mississippi River Delta region of eastern Arkansas. Small-plot rainfall simulations were conducted on an Immanuel silt loam, (tine-silty, mixed, active, thermic, Oxyaquic Glossudalf) in plots (1.5 × 2 m) under native tallgrass prairie (PR) and two cultivated agroecosystems, ridge-tillage (RT), and conventional-tillage (CN). Residue cover was greatest in the PR and lowest in the CN system and differed significantly (p < 0.05) among all three land uses. However, runoff from the two agroecosystems did not differ, averaging 87% of the total amount of water applied, but both were significantly greater (p < 0.05) than that from the PR system, which averaged 62% of the total amount of water applied. Runoff turbidity and suspended solid concentration and load were greater (p < 0.05) from the CN than from the RT or PR systems. The CN system also had the greatest (p < 0.05) total P concentration and load, but the RT system produced runoff with the greatest (p < 0.05) soluble reactive P concentration and load of the three systems. Results indicate that land use and the degree of soil disturbance significantly affects runoff-infiltration partitioning and runoff water quality on this typical eastern Arkansas agricultural soil. Should results prove consistent on other regional soils, further depletion of local aquifers and degradation of surface water quality will likely continue if more sustainable agricultural practices are not increased. [ABSTRACT FROM AUTHOR]

A disproportionate increase in precipitation coming from intense rain events, in the situation of general warming (thus, an extension of the vegetation period with intensive transpiration), and an insignificant change in total precipitation could lead to an increase in the frequency of a potentially serious type of extreme events: prolonged periods without precipitation (even when the mean seasonal rainfall totals increase). This paper investigates whether this development is already occurring during the past several decades over the conterminous United States, for the same period when changes in frequency of intense precipitation events are being observed. Lengthy strings of “dry” days without sizeable (>1.0 mm) precipitation were assessed only during the warm season (defined as a period when mean daily temperature is above the 5°C threshold) when water is intensively used for transpiration and prolonged periods without sizable rainfall represent a hazard for terrestrial ecosystem’s health and agriculture. During the past four decades, the mean duration of prolonged dry episodes (1 month or longer in the eastern United States and 2 months or longer in the southwestern United States) has significantly increased. As a consequence the return period of 1-month-long dry episodes over the eastern United States has reduced more than twofold from 15 to 6–7 yr. The longer average duration of dry episodes has occurred during a relatively wet period across the country but is not observed over the northwestern United States. [ABSTRACT FROM AUTHOR]

This study describes an experimental system for making continuous river discharge measurements using real-time, image-based, noncontact technologies. The system uses particle image velocimetry (PIV) to estimate displacements of river surface flow tracers on recorded images. The velocity-area method is then applied to estimate discharge at a cross section of known bathymetry. The system has been operational since August 2004, making continuous discharge estimates at an experimental site on the Iowa River. Based on preliminary results from its first 23 months of operation, the authors identify illumination of the river surface by natural lighting, wind, and rain effects, PIV parametrization for variable flow conditions, and stage measurement, as the most pressing challenges related to image-based discharge measurement. The experimental data archive generated from the continuous long-term operation at the site will facilitate the research needed to address these problems and improve the reliability of image-based discharge measurement technologies. [ABSTRACT FROM AUTHOR]

This study aims at deriving intensity-duration-frequency (IDF) curves from bivariate rainfall frequency analysis using the copula method, which does not assume the rainfall variables to be independent or normal or have the same type of marginal distributions. The bivariate distributions are then employed to determine conditional return periods and IDF curves needed for urban drainage design. The IDF curves are tested using rainfall data from catchments in Louisiana and are also compared with those of Technical Paper 40 (TP-40) of the U.S. National Weather Service as well as those published in the literature. [ABSTRACT FROM AUTHOR]

Incorporating the time variant function of the otherwise used as steady infiltration rate, this paper presents revised versions of the popular and frequently used Kostiakov and modified Kostiakov infiltration models. The proposed, along with the existing, versions are tested on 40 datasets of infiltration observed on five different soils in India and USA The former indicated an improved performance over their respective existing versions. Further generalization of the improved modified Kostiakov model incorporating time to ponding showed an enhanced performance on all the soils. [ABSTRACT FROM AUTHOR]

Storm event exports of dissolved $$ {\text{NH}}^{ + }_{4} $$ were explored for multiple events in the Point Peter Brook watershed (PPBW), a glaciated, forested watershed located in Western New York, USA. Investigations were performed across four catchments (1.6–696 ha) with varying topography and the extent of surface-saturated areas. While wetland and riparian waters were important sources of $$ {\text{NH}}^{ + }_{4} $$ during non-storm periods, throughfall and litter leachate were the dominant contributors of $$ {\text{NH}}^{ + }_{4} $$ during storm events. Ammonium concentrations in catchment discharge displayed a sinusoidal seasonal pattern with a maximum during early spring (March) and a minimum in late summer (August–September). Storm event concentrations of $$ {\text{NH}}^{ + }_{4} $$ in streamflow were much greater than baseflow values and showed a consistent temporal pattern with an increase in concentrations on the hydrograph rising limb, a peak at or before the discharge peak, followed by a decline in concentrations. Storm event patterns of DON were similar to $$ {\text{NH}}^{ + }_{4} $$ while the patterns of $$ {\text{NO}}^{ - }_{3} $$ differed from $$ {\text{NH}}^{ + }_{4} $$ for the summer and fall events. The storm event expression of $$ {\text{NH}}^{ + }_{4} $$ was attributed to throughfall and throughfall-mediated leaching of the litter layer. The reactive behavior of $$ {\text{NH}}^{ + }_{4} $$ precluded its use in an end member mixing model (EMMA) for predicting streamflow concentrations. While concentrations of $$ {\text{NH}}^{ + }_{4} $$ in precipitation and streamflow were high for the spring events, exports of $$ {\text{NH}}^{ + }_{4} $$ in streamflow were highest for the large and intense storm events. Baseflow $$ {\text{NH}}^{ + }_{4} $$ concentrations increased with the percent wetland/saturated area in the catchment but the same trend did not hold for storm-event concentrations. [ABSTRACT FROM AUTHOR]

The impact of clouds and precipitation on the climate is a strong function of their spatial distribution and microphysical properties, characteristics that depend, in turn, on the environments in which they form. Simulating feedbacks between clouds, precipitation, and their surroundings therefore places an enormous burden on the parameterized physics used in current climate models. This paper uses multisensor observations from the Tropical Rainfall Measuring Mission (TRMM) to assess the representation of the response of regional energy and water cycles in the tropical Pacific to the strong 1998 El Niño event in (Atmospheric Model Intercomparison Project) AMIP-style simulations from the climate models that participated in the Intergovernmental Panel on Climate Change’s (IPCC’s) most recent assessment report. The relationship between model errors and uncertainties in their representation of the impacts of clouds and precipitation on local energy budgets is also explored. With the exception of cloud radiative impacts that are often overestimated in both regions, the responses of atmospheric composition and heating to El Niño are generally captured in the east Pacific where the SST forcing is locally direct. Many models fail, however, to correctly predict the magnitude of induced trends in the west Pacific where the response depends more critically on accurate representation of the zonal atmospheric circulation. As a result, a majority of the models examined do not reproduce the apparent westward transport of energy in the equatorial Pacific during the 1998 El Niño event. Furthermore, the intermodel variability in the responses of precipitation, total heating, and vertical motion is often larger than the intrinsic ENSO signal itself, implying an inherent lack of predictive capability in the ensemble with regard to the response of the mean zonal atmospheric circulation in the tropical Pacific to ENSO. While ENSO does not necessarily provide a proxy for anthropogenic climate change, the results suggest that deficiencies remain in the representation of relationships between radiation, clouds, and precipitation in current climate models that cannot be ignored when interpreting their predictions of future climate. [ABSTRACT FROM AUTHOR]

Freezing precipitation is a persistent winter weather problem that costs the United States millions of dollars annually. Costs and infrastructure disruption may be greatly reduced by ice-storm warnings issued by the National Weather Service (NWS), and by the development of climatologies that allow improved design of infrastructure elements. However, neither the NWS nor developers of climatologies have had direct measurements of ice-storm accumulations as a basis for issuing warnings and developing storm design standards. This paper describes the development of an aviation routine/special weather report (METAR/SPECI) remark that will report quantitative ice thickness at over 650 locations during ice storms using new algorithms developed for the Automated Surface Observing System (ASOS). Characteristics of the ASOS icing sensor, a field program to develop the algorithms, tests of accuracy, application of the algorithms, and sources of error are described, as is the implementation of an ice-thickness METAR/SPECI remark. The algorithms will potentially allow freezing precipitation events to be tracked with regard to ice accumulation in near–real time as they progress across the United States. [ABSTRACT FROM AUTHOR]

A scheme for validating quantitative precipitation forecasts (QPFs) for landfalling tropical cyclones is developed and presented here. This scheme takes advantage of the unique characteristics of tropical cyclone rainfall by evaluating the skill of rainfall forecasts in three attributes: the ability to match observed rainfall patterns, the ability to match the mean value and volume of observed rainfall, and the ability to produce the extreme amounts often observed in tropical cyclones. For some of these characteristics, track-relative analyses are employed that help to reduce the impact of model track forecast error on QPF skill. These characteristics are evaluated for storm-total rainfall forecasts of all U.S. landfalling tropical cyclones from 1998 to 2004 by the NCEP operational models, that is, the Global Forecast System (GFS), the Geophysical Fluid Dynamics Laboratory (GFDL) hurricane model, and the North American Mesoscale (NAM) model, as well as the benchmark Rainfall Climatology and Persistence (R-CLIPER) model. Compared to R-CLIPER, all of the numerical models showed comparable or greater skill for all of the attributes. The GFS performed the best of all of the models for each of the categories. The GFDL had a bias of predicting too much heavy rain, especially in the core of the tropical cyclones, while the NAM predicted too little of the heavy rain. The R-CLIPER performed well near the track of the core, but it predicted much too little rain at large distances from the track. Whereas a primary determinant of tropical cyclone QPF errors is track forecast error, possible physical causes of track-relative differences lie with the physical parameterizations and initialization schemes for each of the models. This validation scheme can be used to identify model limitations and biases and guide future efforts toward model development and improvement. [ABSTRACT FROM AUTHOR]

The article describes the Atmospheric Radiation Measurement Mobile Facility (AMF) instrumentation and its charter as a U.S. Department of Energy Facility. The first international field deployment of the AMF in Niamey, Niger is associated with the Geostationary Earth Radiation Budget (GERB) experiment and the African Monsoon Multidisciplinary Analysis (AMMA) project. Remote detectors in AMF collect and analyze data that can be utilized toward the improvement of radiation and clouds in global climate models (GCMs). INSET: USE OF THE FULL SPECTRUM OF….

The article offers information on the impact of sea-surface temperature (SST) specification during summer on the interannual variability of the simulated precipitation and air temperature over Southeastern South America (SESA). Using the Regional Climate Model version 3 (RegCM3), analysis were performed on several climatological forces including monthly observed SST (OSST), and composed by the November SST anomalies on the climatological observed cycle (SST-PSST). The study reveals that the PSST impact is low on the seasonal precipitation and air temperature climatologies over the SESA.

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]

In January 2001, the U.S. Supreme Court ruled that the U.S. Army Corps of Engineers exceeded its statutory authority by asserting Clean Water Act (CWA) jurisdiction over non-navigable, isolated, intrastate waters based solely on their use by migratory birds. The Supreme Court's majority opinion addressed broader issues of CWA jurisdiction by implying that the CWA intended some "connection" to navigability and that isolated waters need a "significant nexus" to navigable waters to be jurisdictional. Subsequent to this decision (SWANCC), there have been many lawsuits challenging CWA jurisdiction, many of which are focused on head- water, intermittent, and ephemeral streams. To inform the legal and policy debate surrounding this issue, we present information on the geographic distribution of headwater streams and intermittent and ephemeral streams throughout the U.S., summarize major findings from the scientific literature in considering hydrological connectivity between headwater streams and downstream waters, and relate the scientific information presented to policy issues surrounding the scope of waters protected under the CWA. Headwater streams comprise approximately 53% (2,900,000 km) of the total stream length in the U.S., excluding Alaska, and intermittent and ephemeral streams comprise approximately 59% (3,200,000 km) of the total stream length and approximately 50% (1,460,000 km) of the headwater stream length in the U.S., excluding Alaska. Hillslopes, headwater streams, and downstream waters are best described as individual elements of integrated hydrological systems. Hydrological connectivity allows for the exchange of mass, momentum, energy, and organisms longitudinally, laterally, vertically, and temporally between headwater streams and downstream waters. Via hydrological connectivity, headwater, intermittent and ephemeral streams cumulatively contribute to the functional integrity of downstream waters; hydrologically and ecologically, they are a part of the tributary system. As this debate continues, scientific input from multiple fields will be important for policymaking at the federal, state, and local levels and to inform water resource management regardless of the level at which those decisions are being made. Strengthening the interface between science, policy, and public participation is critical if we are going to achieve effective water resource management. [ABSTRACT FROM AUTHOR]

Dual-polarization radar observations of precipitation depend on size–shape relations of raindrops. There are several studies presented in literature dedicated to the investigation of this relation. In this work a new approach of investigating raindrop size–shape relation on short time and spatial scales from radar observations is presented. The presented method is based on the use of dual-polarization Doppler power spectral analysis. By measuring complete Doppler spectra at a sufficiently high elevation angle at two polarization settings, namely, horizontal and vertical, it is possible to retrieve drop size distribution (DSD) parameters, ambient air velocity, spectral broadening, and the slope of the assumed linear dependence of raindrop size–shape relation. This paper is mainly focused on the development of the retrieval algorithm and analysis of its performance. As a part of the proposed method an efficient algorithm for DSD parameter retrieval was developed. It is shown that the DSD parameter retrieval method, which usually requires the solution of five-parameter nonlinear optimization problems, can be simplified to a three-parameter nonlinear least squares problem. Furthermore, the performance of the proposed retrieval technique is illustrated on the dual-polarization measurements collected by the S-band Transportable Atmospheric Radar (TARA) at Cabauw, Netherlands, and by the Colorado State University–University of Chicago–Illinois State Water Survey (CSU–CHILL) radar from Greeley, Colorado. [ABSTRACT FROM AUTHOR]

A large-domain explicit convection simulation is used to investigate the life cycle of nocturnal convection for a one-week period of successive zonally propagating heavy precipitation episodes occurring over the central United States. Similar to climatological studies of phase-coherent warm-season convection, the longest-lived precipitation episodes initiate during the late afternoon over the western Great Plains (105°–100°W), reach their greatest intensity at night over the central Great Plains (100°–95°W), and typically weaken around or slightly after sunrise over the Midwest (95°–85°W). The longest-lived episodes exhibit average zonal phase speeds of ∼20 m s-1, consistent with radar observations during the period. Composite analysis of the life cycle of five long-lived nocturnal precipitation episodes indicates that convection both develops and then propagates eastward along an east–west-oriented lower-tropospheric frontal zone. An elevated ∼2-km-deep layer of high-θe air helps sustain convection during its period of greatest organization overnight. Trajectory analysis for individual episodes reveals that the high-θe air originates both from within the frontal zone and to its south where, in this latter case, it is transported northward by the nocturnal low-level jet (LLJ). The mature (nocturnal) stage composite evinces a thermally direct cross-frontal circulation, within which the trajectories ascend 0.5–2 km to produce the elevated conditionally unstable layer. This transverse vertical circulation is forced by deformation frontogenesis, which itself is supported by the intensification of the nocturnal LLJ. The frontal zone also provides an environment of strong vertical shear, which helps organize the zonally propagating component of convection. Overnight the convection exhibits squall-line characteristics, where its phase speed is typically consistent with that which arises from deep convectively induced buoyancy perturbations combined with the opposing environmental surface flow. In a large majority of cases convection weakens as it reaches the Midwest around sunrise, where environmental thermodynamic stability is greater, and environmental vertical shear, frontogenesis, and vertical motions are weaker than those located farther west overnight. [ABSTRACT FROM AUTHOR]

The Second Global Soil Wetness Project (GSWP-2) is an initiative to compare and evaluate 10-year simulations by a broad range of land surface models under controlled conditions. A major product of GSWP-2 is the first global gridded multimodel analysis of land surface state variables and fluxes for use by meteorologists, hydrologists, engineers, biogeochemists, agronomists, botanists, ecologists, geographers, climatologists, and educators. Simulations by 13 land models from five nations have gone into production of the analysis. The models are driven by forcing data derived from a combination of gridded atmospheric reanalyses and observations. The resulting analysis consists of multimodel means and standard deviations on the monthly time scale, including profiles of soil moisture and temperature at six levels, as well as daily and climatological (mean annual cycle) fields for over 50 land surface variables. The monthly standard deviations provide a measure of model agreement that may be used as a quality metric. An overview of key characteristics of the analysis is presented here, along with information on obtaining the data. [ABSTRACT FROM AUTHOR]

The article describes the derivation of the meteorological forcing data and presents information on the process of hybridization of observational and model data to point out issues on Second Global Soil Wetness Project (GSWP-2) analysis in the U.S. All GSWP-2 models are on the land grid points of the International Satellite Land Surface Climatology Project (ISLSCP) Initiative II. Meteorological data from the National Centers for Environmental Prediction/Department of Energy reanalysis was used for ISLSCP. Reanalysis of data include those of precipitation, temperature, surface pressure, and specific humidity.

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]

Small watersheds in urbanizing and suburbanizing regions are an increasingly important element of the flood hazard problem in the United States and abroad. This paper presents a methodology to estimate flood response in urban watersheds, with the goal of assessing the performance of systems of small flood control reservoirs for extreme floods. The setting of this study is the Brandywine Creek watershed in Chester County, Pa. A number of flood control structures were built within the basin, and the availability of data from U.S. Geological Survey (USGS) stream gauging stations and rainfall estimates from National Weather Service weather radars provides the opportunity to study the performance of a small watershed flood control system. In this paper, the flood response to rainfall from Hurricane Floyd, which produced record flooding in Brandywine Creek on September 16, 1999, is examined. A distributed hydrologic model was developed and used to estimate flood discharges both with and without the existing flood control structures. The model was calibrated using observed discharge data from the USGS gauging stations, and model simulations were then performed with the flood control structures removed to estimate their impact on the watershed response to the storm. Results from the model showed that the flood control structures mitigated flood hazards to a greater degree than had been expected by the studies completed prior to construction. This paper shows how the USGS streamflow measurement program can be augmented with hydrologic modeling and WSR-88D weather radars to provide useful hydrologic information for analysis of extreme floods. [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 recently developed method of defining rain areas for the purpose of verifying precipitation produced by numerical weather prediction models is described. Precipitation objects are defined in both forecasts and observations based on a convolution (smoothing) and thresholding procedure. In an application of the new verification approach, the forecasts produced by the Weather Research and Forecasting (WRF) model are evaluated on a 22-km grid covering the continental United States during July–August 2001. Observed rainfall is derived from the stage-IV product from NCEP on a 4-km grid (averaged to a 22-km grid). It is found that the WRF produces too many large rain areas, and the spatial and temporal distribution of the rain areas reveals regional underestimates of the diurnal cycle in rain-area occurrence frequency. Objects in the two datasets are then matched according to the separation distance of their centroids. Overall, WRF rain errors exhibit no large biases in location, but do suffer from a positive size bias that maximizes during the later afternoon. This coincides with an excessive narrowing of the rainfall intensity range, consistent with the dominance of parameterized convection. Finally, matching ability has a strong dependence on object size and is interpreted as the influence of relatively predictable synoptic-scale systems on the larger areas. [ABSTRACT FROM AUTHOR]

A procedure is developed to incorporate equality constraints in Kalman filters, including the ensemble Kalman filter (EnKF), and is referred to as the constrained ensemble Kalman filter (CEnKF). The constraint is carried out as a two-step filtering approach, with the first step being the standard (ensemble) Kalman filter. The second step is the constraint step carried out by another Kalman filter that optimally redistributes any imbalance from the first step. The CEnKF is implemented over a 75 000 km² domain in the southern Great Plains region of the United States, using the terrestrial water balance as the constraint. The observations, consisting of gridded fields of the upper two soil moisture layers from the Oklahoma Mesonet system, Atmospheric Radiation Measurement Program Cloud and Radiation Testbed (ARM-CART) energy balance Bowen ratio (EBBR) latent heat estimates, and U.S. Geological Survey (USGS) streamflow from unregulated basins, are assimilated into the Variable Infiltration Capacity (VIC) land surface model. The water balance was applied at the domain scale, and estimates of the water balance components for the domain are updated from the data assimilation step so as to assure closure. [ABSTRACT FROM AUTHOR]

The State of the Climate 2005 report summarizes global and regional climate conditions and places them, where possible, into the context of historical records. Descriptions and analyses of notable climatic anomalies, both global and regional, also are presented. According to the Smith and Reynolds global land and ocean surface temperature dataset in use at the NOAA National Climatic Data Center (NCDC), the globally averaged annual mean surface temperature in 2005 was the warmest since the inception of consistent temperature observations in 1880. Unlike the previous record positive anomaly of 1998 (+0.50°C), the 2005 global anomaly of 0.53°C above the 1961–90 mean occurred in the absence of a strong El Niño signal. The record ranking of 2005 was corroborated by a dataset maintained at NASA, while United Kingdom archives placed 2005 second behind 1998. However, statistically, the 2005 global temperature anomaly could not be differentiated from either 1998 or any of the past four years. The majority of the top 10 warmest years on record have occurred in the past decade, and 2005 continues a marked upward trend in globally averaged temperature since the mid-1970s. Lower-tropospheric temperature was the second warmest on record, with northern polar regions the warmest at 1.3°C above the 1979–98 mean. Unlike air temperatures, globally averaged precipitation was near normal relative to the 1961–90 period mean value. The global 2005 anomaly was just -0.87 mm. Over the past 25 years, only 7 years have had above-normal precipitation. Additionally, in 2005, only September–November experienced a positive anomaly. Northern Hemisphere snow cover extent was 0.9 million km2 below the 36-year average (fifth lowest) and Arctic sea ice extent was record lowest in all months of 2005 except May, resulting in a record lowest annual average Arctic sea ice extent for the year and continuing a roughly 8% yr-1 decline in ice extent. Carbon dioxide (CO2) concentrations rose to a global average of 378.9 parts per million (ppm); about 2 ppm over the value from 2004. This record CO2 concentration in 2005 continues a trend toward increased atmospheric CO2 since the preindustrial era values of around 280 ppm. The globally averaged methane (CH4) concentration in 2005 was 1774.8 parts per billion (ppb), or 2.8 ppb less than in 2004. Stratospheric ozone over Antarctica reached a minimum of 110 Dobson units (DU) on 29 September. This represented the 10th lowest minimum level in the 20 years of measurement of stratospheric ozone. In the global ocean, sea level was above the 1993–2001 base period mean and rose at a rate of 2.9 ±0.4 mm yr-1. The largest positive anomalies were in the Tropics and Southern Hemisphere. Globally averaged sea surface temperature (SST) also was above normal in 2005 (relative to the 1971–2002 mean), reflecting the general warming trend in SST observed since 1971. In the Tropics, only a weak warm phase of El Niño materialized, but dissipated by March. A relatively active Madden–Julian oscillation (MJO) resulted in the disruption of normal convective patterns in the tropical Pacific and generated several Kelvin waves in the oceanic mixed layer. In the Atlantic Ocean basin, there was record tropical storm activity, with 27* named storms (15 hurricanes). Three became category 5 storms on the Saffir–Simpson scale, and Hurricane Wilma set a new record for the lowest pressure (882 hPa) recorded in the basin. Both Hurricanes Stan and Katrina had exceptional death tolls, and Katrina became the costliest storm on record. Below-normal tropical storm activity in several other basins resulted in near-normal conditions globally in 2005. Regionally, annual and monthly averaged temperatures were above normal across most of the world. Australia experienced its warmest year on record, as well is its hottest April. For both Russia and Mexico 2005 was the second warmest year on record. Intermittent and delayed monsoons in Africa and East Asia resulted in below-normal precipitation in many areas. Drought continued in much of the Greater Horn of Africa and developed in the central United States. Record severe drought occurred over both the Iberian Peninsula and western Amazonia in 2005. In the Amazon, river levels dropped by as much as 11 m between May and September. Conversely, heavy snows early in 2005 combined with a warm boreal spring to generate widespread flooding in areas of southwest Asia. Canada experienced its wettest year on record in 2005, with flooding in Alberta, Manitoba, and Ontario. In July, the South Asian monsoon delivered a record 944.2 mm of precipitation over 24 h to areas around Mumbai, India. [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]

Transmitting an arbitrary state of polarization while receiving horizontal–vertical polarization states is termed the hybrid polarization mode of operation. A theoretical model is developed for hybrid mode dual-polarization measurements in terms of the covariance matrix under linear horizontal–vertical polarization basis. The cross polarization encountered introduces biases in the copolar parameters estimated in the hybrid mode. Such biases are investigated for different precipitation types and propagation effects resulting from hydrometeor orientation and antenna properties. Polarimetric data measured by the Colorado State University–University of Chicago–Illinois State Water Survey (CSU–CHILL) radar transmitting horizontal–vertical polarization states is alternately used to demonstrate the measurement accuracy that would be expected in different storm scenarios observed in the hybrid mode. [ABSTRACT FROM AUTHOR]