Search

• Precipitation annual maxima estimated from NEXRAD are evaluated over the CONUS. • Accuracy of radar-based estimates varies with magnitude and duration. • Uncertainty in radar-based IDF curves exhibits climate dependence. The lack of knowledge on precipitation frequency over ungauged areas introduces a significant source of uncertainty in relevant engineering designs and risk estimation procedures. Radar-based observations offer precipitation information over ungauged areas and thus have gained increasing attention as a potential solution to this problem. However, due to their relative short data records and inherent uncertainty sources, their ability to provide accurate estimates on the frequency of precipitation extremes requires evaluation. This study involves the evaluation of at-site precipitation frequency estimates from NEXRAD Stage IV radar precipitation dataset. We derive precipitation annual maxima series from the 16yrs record (2002–2017) of NEXRAD and we compare against 539 long-term (50yrs) hourly gauge records. In addition, Intensity-Duration-Frequency (IDF) curves are estimated from both radar and gauge dataset and compared. IDF estimation is based on fitting the Generalize Extreme Value distribution to annual precipitation maxima. Evaluation is carried out over the contiguous United States and results are grouped and presented for five dominant climate classes and for a range of return period and precipitation durations. NEXRAD was shown to overestimate intensities at shorter durations (1- and 3-h) and low quantiles, while it tends to underestimate higher quantiles at longer durations (24 h). In addition, evaluation of the IDF curves estimated from NEXRAD revealed a distinct geographic dependence with certain regions exhibiting a tendency to overestimation (e.g. east of the Rocky Mountains) or underestimation (Midwest). Overall, this analysis suggests that, while significant discrepancies may exist, there are several cases where NEXRAD provide estimates within the uncertainty bounds of the reference rain gauge dataset. The climate/geographic region and the temporal duration are important aspects to consider. Findings provided in this work on these aspects will hopefully serve as a general guideline for those interested in using NEXRAD estimates for further research or applications on precipitation extremes. [ABSTRACT FROM AUTHOR]

Abstract: Precipitation is one important input variable for land surface hydrologic and ecological models. Next Generation Radar (NEXRAD) can provide precipitation products that cover most of the conterminous United States at high resolution (approximately 4 km × 4 km). There are two major issues concerning the application of NEXRAD data: 1) the lack of a NEXRAD geo-processing and geo-referring program and 2) bias correction of NEXRAD estimates. However, in public domain, there is no Geographic Information System (GIS) software that can use geostatistical approaches to calibrate NEXRAD data using raingauge data, and automatically process NEXRAD data for hydrologic and ecological models. In this study, we developed new GIS software for NEXRAD validation and calibration (NEXRAD-VC) using raingauge data. NEXRAD-VC can automatically read in NEXRAD data in NetCDF or XMRG format, transform projection of NEXRAD data to match with raingauge data, apply different geostatistical approaches to calibrate NEXRAD data using raingauge data, evaluate performance of different calibration methods using leave-one-out cross-validation scheme, output spatial precipitation maps in ArcGIS grid format, calculate spatial average precipitation for each spatial modeling unit used by hydrologic and ecological models. The major functions of NEXRAD-VC are illustrated in the Little River Experimental Watershed (LREW) in Georgia using daily precipitation records of fifteen raingauges and NEXRAD products of five years. The validation results show that NEXRAD has a high success rate for detecting rain and no-rain events: 82.8% and 95.6%, respectively. NEXRAD estimates have high correlation with raingauge observations (correlation coefficient of 0.91), but relatively larger relative mean absolute error value of 36%. It is also worth noting that the performance of NEXRAD increases with the decreasing of rainfall variability. Three methods (Bias Adjustment method (BA), Regressing Kriging (RK), and Simple Kriging with varying local means (SKlm)) were employed to calibrate NEXRAD using raingauge data. Overall, SKlm performed the best among these methods. Compared with NEXRAD, SKlm improved the correlation coefficient to 0.96 and the relative mean absolute error to 22.8%, respectively. SKlm also increased the success rate of detection of rain and no-rain events to 87.47% and 96.05%, respectively. Further analysis of the performance of the three calibration methods and NEXRAD for daily spatial precipitation estimation shows that no one method can consistently provide better results than the other methods for each evaluation coefficient for each day. It is suggested that multiple methods be implemented to predict spatial precipitation. The NEXRAD-VC developed in this study can serve as an effective and efficient tool to batch process large amounts of NEXRAD data for hydrologic and ecological modeling. [Copyright &y& Elsevier]

Abstract: A major freezing rain storm causing catastrophic losses occurred in early January 1998 over eastern Canada and the northeastern United States. The various types of precipitation and associated flow and precipitation features of this storm are discussed with a particular emphasis on the effects due to precipitation phase changes in the Montréal area. Hourly surface observations, Doppler radar data and model reanalysis information are used to describe the hydrometeor phase and production. There were two periods of significant ice accumulation at the ground. They were characterized by more organized precipitation compared to other times but there were also distinct differences between the two periods in terms of wind fields. This hydrometeor development was linked in part to the flow fields in the St. Lawrence River Valley and its topographic features. The results from a time dependent microphysical model suggest that there was considerable horizontal temperature advection that balanced the warming/cooling of the atmosphere due to precipitation phase changes aloft and at the surface. Overall, local-scale factors significantly affected the nature of this major event. [Copyright &y& Elsevier]

We analyzed 4-years of Advanced Scatterometer (ASCAT) incidence angle normalized C-band backscatter, Next-Generation Radar (NEXRAD) precipitation and United States Department of Agriculture (USDA) crop yield data over the contiguous United States (U.S.). Large negative anomalies in backscatter are evident over the U.S. during the 2011 and 2012 growing seasons, coincident with two instances of severe drought. The backscatter anomalies were correlated with diminished growing season precipitation and with reduced crop yields reflecting where drought-induced impacts on above-ground biomass production occurred. During periods of acute drought, differences in diurnal backscatter were reduced and in some cases reversed reflecting diminished nocturnal leave rehydration. The results indicate that the C-band, VV-polarized ASCAT backscatter data is sensitive to interannual variability in agricultural productivity rather than soil moisture, and can offer an important geophysical monitoring tool capable of identifying and assessing drought-related vegetation responses independent of and complementary to satellite optical/near-infrared remote sensing data. This technique is flexible and adaptable, providing a capability to address region-specific needs. The twice-daily, weather independent and near-real time global monitoring capabilities of ASCAT and similar microwave instruments (e.g. RapidSCAT, Sentinel-1) can help improve drought preparedness and mitigation planning allowing improved water resource management for agriculture on the local as well as the state level. [ABSTRACT FROM AUTHOR]

Abstract: The hazards attributed to supercell thunderstorms are primarily considered to be tornadoes, large hail, and damaging winds, but flooding from heavy and extreme rainfall is not as often considered with these storms. As a result there has been little research on the role that supercells play in the production of heavy and extreme rainfall events. In order to assess the contribution of supercells to the climatology of short-duration precipitation extremes the present study uses the Warning Decision Support System — Integrated Information to objectively identify supercell thunderstorms from mosaicked radar data during the year 2009 and associate them with high-resolution, accurate multisensor precipitation estimates at time scales of one hour. Supercells are found to be more likely than non-supercells to produce extreme and heavy precipitation, and comparisons are also made between storm types according to month. [Copyright &y& Elsevier]

Abstract: Short-term high-resolution precipitation forecasting has important implications for navigation, flood forecasting, and other hydrological and meteorological concerns. This article introduces a pixel-based algorithm for Short-term Quantitative Precipitation Forecasting (SQPF) using radar-based rainfall data. The proposed algorithm called Pixel- Based Nowcasting (PBN) tracks severe storms with a hierarchical mesh-tracking algorithm to capture storm advection in space and time at high resolution from radar imagers. The extracted advection field is then extended to nowcast the rainfall field in the next 3hr based on a pixel-based Lagrangian dynamic model. The proposed algorithm is compared with two other nowcasting algorithms (WCN: Watershed-Clustering Nowcasting and PER: PERsistency) for ten thunderstorm events over the conterminous United States. Object-based verification metric and traditional statistics have been used to evaluate the performance of the proposed algorithm. It is shown that the proposed algorithm is superior over comparison algorithms and is effective in tracking and predicting severe storm events for the next few hours. [Copyright &y& Elsevier]

Abstract: Remotely sensed estimates of snow thaw offer the potential of more complete spatial coverage across remote, undersampled areas such as the terrestrial Arctic drainage basin. We compared the timing of spring thaw determined from approximately 25km resolution daily radar backscatter data with observed daily river discharge time series and model simulated snow water content data for 52 basins (5000–10,000km2) across Canada and Alaska for the spring of 2000. Algorithms for identifying critical thaw transitions were applied to daily backscatter time series from the SeaWinds scatterometer aboard NASA QuikSCAT, the observed discharge data, and model snow water from the pan-Arctic Water Balance Model (PWBM). Radar-derived thaw shows general agreement with discharge increases (Mean Absolute Difference, MAD=21 days, r=0.45), with better agreement (16 days) in basins with moderate-high runoff due to snow thaw. Even better agreement is noted when comparing the scatterometer-derived primary thaw timing with model simulated snow water increase (MAD=14 days, r=0.75). Good correspondence is found across higher latitude basins in western Canada and Alaska, while the largest discrepancies appear at the driest watersheds with lower snow and daily discharge amounts. Extending this analysis to the entire pan-Arctic drainage basin, we compared scatterometer-derived date of the primary (maximum) thaw with the timing of simulated snow water increases from the PWBM. Good agreement is found across much of the pan-Arctic; discrepancies for over half of the analyzed grid cells are less than one week. MADs are 11.7 days for the Arctic basin in Eurasian and 15.1 days across North America. Mean biases are low; 2.1 and −3.1 days for Eurasia and North America, respectively. Stronger backscatter response (high signal-low noise) is noted with higher seasonal snow accumulation, low to moderate tree cover and low topographic complexity. Although our results show inconsistent performance along coastal regions and warmer southerly parts of the study domain, active radar instruments such as SeaWinds offer the potential for monitoring high-latitude snow thaw at spatial scales appropriate for pan-Arctic applications in near real time. Applications include hydrological model verification, analysis of lags between snow thaw and river response, and determination of large-scale snow extent. [Copyright &y& Elsevier]

The objective of this paper is to investigate long-term inter-annual, seasonal, and diurnal rainfall characteristics in the Southeastern United States. In order to capture precipitation features at high resolution, we use precipitation estimates from the Tropical Rainfall Measuring Mission (TRMM); the TRMM Precipitation Radar (TPR 2A25: 0.05°×0.05°/daily) and the TRMM Multi-satellite Precipitation Analysis (TMPA 3B42: 0.25°×0.25°/3-h) datasets to create a 13-year rainfall climatology. The higher resolution climatology (2A25) displays a greater ability to capture more localized landform precipitation features when compared with 3B42. On an annual basis, the Southeastern US is characterized by a succession of cold and warm precipitation regimes. The cold season is characterized by higher rain intensity West of 82°W (roughly Atlanta, GA) and the warm season is characterized by higher rain intensity over the coastal areas. The cold/warm rainfall regime duality is modulated by local topographic characteristics that prevail along a W–E direction. During the cold season, the diurnal cycle of precipitation is characterized by a quasi-constant repartition of rain events throughout the day and an absence of land/ocean contrast. On the contrary for summertime there is a strong land/ocean signature with a predominance of late morning/early afternoon (12:00–15:00LST) rainfall over ocean and afternoon/early evening (15:00–18:00LST) precipitation events over land that account for more than 25% of the daily events along the coasts. Differences are observed for the Florida peninsula, where the diurnal cycle displays an afternoon maximum of variable intensity due to sea breeze effects regardless of the season. [ABSTRACT FROM AUTHOR]

Abstract: A principal method for studying past hydroclimatic change is the reconstruction of paleo-lake levels. Here, we provide high-resolution lake-level records from New Long Pond and Rocky Pond in southeastern Massachusetts, which each contain evidence for multiple, sub-centennial-to-millennial scale low stands during the transition between the Late Pleistocene (15.0ka) and Middle Holocene (ca 7.0ka). Data from New Long Pond also demonstrate sedimentary evidence for a drop in water levels in the early to mid AD 20th century, when long-term trends in instrumental data show lower-than-average precipitation in the northeastern United States. Local data show the most precipitous declines in precipitation and groundwater levels are concurrent with the most severe drought in the AD 1960s, which occurred during a period of low sea-surface temperatures in the western North Atlantic. Ground penetrating radar and sediment core data indicate five intervals with numerous paleo-shoreline deposits between ca 15.0 and 7.0ka, similar to the layer deposited in the AD 1960s. Many of the intervals of low lake levels coincide with proposed meltwater release events or abrupt climate oscillations in the circum North Atlantic. For example, we document at least three low stands during the Younger Dryas (12.9–11.6ka) and in association with the “9.2” and “8.2”ka events. The combined evidence of (1) concurrent paleo-droughts in southeastern New England with documented North Atlantic abrupt cooling events and (2) recent drought with the modern association of low sea-surface temperatures indicates that freshening and cooling of the western North Atlantic is a viable mechanism for decreasing moisture within the region. Large-scale changes in seasonality and ice sheet extent also may have increased the susceptibility of the northeast to dry conditions triggered by changes in the North Atlantic. [Copyright &y& Elsevier]

Abstract: Convective systems are tracked for two summer months over the continental USA and their properties at different stages of life are investigated in terms of Cloud to Ground (CG) lightning flashes, cloud kinematics, and precipitation. The study employs lightning data from the National Lightning Detection Network (NLDN) along with 1/2-hourly satellite Infrared observations, and hourly rainfall accumulation fields from the nationwide WSR-88D radar network. Results show strong dependence of the amount of CG lightning flashes with the stage of the storm''s life cycle. The highest number of CG lightning flashes and highest area expansion rates are also seen to be during the growth stage of the systems. The stronger the lightning density, or cloud top area expansion rate, of the storms at their initiation stage is the longer is shown to be their life duration. The average rainfall yield per flash in the growing, mature and dissipation stages of storms with duration greater than 4 h is 4.7, 12.5 and 8.5 107 kg, respectively. The temporal evolution of the storms'' area and CG flash rate are shown to correlate well with the evolution of rainfall parameters, such as storm''s rainfall yield (m3), conditional mean rainfall rate, and the fraction of rain area. The consideration of the kinematics and CG lightning flashes of convective systems and the knowledge of their life cycle stage demonstrate a potential for drawing information useful in rain rate retrievals and their variability. [Copyright &y& Elsevier]

Floods have gained increasing global significance in the recent past due to their devastating nature and potential for causing significant economic and human losses. Until now, flood characterization studies in the United States have been limited due to the lack of a comprehensive database matching flood characteristics such as peak discharges and flood duration with geospatial and geomorphologic information. The availability of a representative and long archive of flooding events spanning 78 years over a variety of hydroclimatic regions results in a spatially and temporally comprehensive flood characterization over the continental U.S. This study, for the first time, employs a large-event database that is based on actual National Weather Service (NWS) definitions of floods instead of the frequently-adopted case study or frequentist approach, allowing us to base our findings on real definitions of floods. It examines flooding characteristics to identify how space and time scales of floods vary with climatic regimes and geomorphology. Flood events were characterized by linking flood response variables in gauged basins to spatially distributed variables describing climatology, geomorphology, and topography. The primary findings of this study are that the magnitude of flooding is highest is regions such as West Coast and southeastern U.S. which experience the most extraordinary precipitation. The seasonality of flooding varies greatly from maxima during the cool season on the West Coast, warm season in the desert Southwest, and early spring in the Southeast. The fastest responding events tend to be in steep basins of the arid Southwest caused by intense monsoon thunderstorms and steep terrain. The envelope curves of unit peak discharge are consistent with those reported for Europe and worldwide. But significant seasonal variability was observed in floods of the U.S. compared to Europe that is attributed to the diversity of causative rainfall ranging from synoptic scales with orographic enhancements in the West Coast, monsoon thunderstorms in the desert Southwest, to land-falling tropical storms and localized, intense thunderstorms in the Southeast. [ABSTRACT FROM AUTHOR]

Summary Flash flooding is one of the most costly and deadly natural hazards in the United States and across the globe. This study advances the use of high-resolution quantitative precipitation forecasts (QPFs) for flash flood forecasting. The QPFs are derived from a stormscale ensemble prediction system, and used within a distributed hydrological model framework to yield basin-specific, probabilistic flash flood forecasts (PFFFs). Before creating the PFFFs, it is important to characterize QPF uncertainty, particularly in terms of location which is the most problematic for hydrological use of QPFs. The SAL methodology (Wernli et al., 2008), which stands for structure, amplitude, and location, is used for this error quantification, with a focus on location. Finally, the PFFF methodology is proposed that produces probabilistic hydrological forecasts. The main advantages of this method are: (1) identifying specific basin scales that are forecast to be impacted by flash flooding; (2) yielding probabilistic information about the forecast hydrologic response that accounts for the locational uncertainties of the QPFs; (3) improving lead time by using stormscale NWP ensemble forecasts; and (4) not requiring multiple simulations, which are computationally demanding. [ABSTRACT FROM AUTHOR]