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Gridded data products, for example interpolated daily measurements of precipitation from weather stations, are commonly used as a convenient substitute for direct observations because these products provide a spatially and temporally continuous and complete source of data. However, when the goal is to characterize climatological features of extreme precipitation over a spatial domain (e.g., a map of return values) at the native spatial scales of these phenomena, then gridded products may lead to incorrect conclusions because daily precipitation is a fractal field and hence any smoothing technique will dampen local extremes. To address this issue, we create a new "probabilistic" gridded product specifically designed to characterize the climatological properties of extreme precipitation by applying spatial statistical analysis to daily measurements of precipitation from the Global Historical Climatology Network over the contiguous United States. The essence of our method is to first estimate the climatology of extreme precipitation based on station data and then use a data-driven statistical approach to interpolate these estimates to a fine grid. We argue that our method yields an improved characterization of the climatology within a grid cell because the probabilistic behavior of extreme precipitation is much better behaved (i.e., smoother) than daily weather. Furthermore, the spatial smoothing innate to our approach significantly increases the signal-to-noise ratio in the estimated extreme statistics relative to an analysis without smoothing. Finally, by deriving a data-driven approach for translating extreme statistics to a spatially complete grid, the methodology outlined in this paper resolves the issue of how to properly compare station data with output from earth system models. We conclude the paper by comparing our probabilistic gridded product with a standard extreme value analysis of the Livneh gridded daily precipitation product. Our new data product is freely available on the Harvard Dataverse (https://bit.ly/2CXdnuj). [ABSTRACT FROM AUTHOR]

This paper presents an evaluation of next generation weather radar (NEXRAD) Stage III and MPE precipitation estimates for the Missouri River Basin River Forecast Center (MBRFC) using daily gauge data from the National Weather Service cooperative network. This gauge network is independent of data used to develop the NEXRAD operational products, and, thus, provides an independent assessment of NEXRAD error characteristics. NEXRAD bias, correlation, and percent detection are mapped across the MBRFC to demonstrate the spatial distribution of NEXRAD errors. In general terms, the NEXRAD estimates perform much better in the southeastern portion of the basin than they do in the central and mountainous areas. Although the overall NEXRAD-gauge statistics appear to be worse than those reported in other published studies, the results of this study show marked improvement in NEXRAD estimates of warm season precipitation over the eight-year study period. [ABSTRACT FROM AUTHOR]

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

Noting a strong imperative to understand precipitation extremes, and that considerable uncertainty affects observational data sets, this paper compares the representation of extremes in a number of widely used daily gridded products, derived from rain gauge data, satellite retrieval and reanalysis for the conterminous United States. Analysis is based upon the concept of "tail dependence" arising in multivariate extreme value theory, and we infer the level of temporal dependence in the joint tail of the precipitation probability distribution for pairwise comparisons of products. In this way, we consider the range of products more like an ensemble and examine the relationships between members, and do not attempt to define, or compare products to, some ground truth. Linear correlation between products is also computed. Considerable discrepancy between groups of products, both annually and seasonally, is linked to source data and complex terrain. In particular, products based on rain gauge data showed remarkable similarity, but differed considerably, showing almost total loss of extremal dependence during DJF in mountainous regions, when compared with satellite products. Additionally, simulated re-forecasts revealed reasonable temporal agreement with large scale generated extremes. The diversity and extent of discrepancies identified across all products raises important questions about their use, and we urge caution, particularly for products derived from satellite data. [ABSTRACT FROM AUTHOR]

In a changing climate, there is an interest in predicting how extreme rainfall events may change. Using historical records, several recent papers have evaluated whether high-intensity precipitation scales with temperature in accordance with the Clausius--Clapeyron (C--C) relationship. For varying locations in Europe, these papers have identified both super C--C relationships as well as a breakdown of the C--C relationship under dry conditions. In this paper, a similar analysis is carried out for the United States using data from 14 weather stations clustered in four different hydroclimatic regions: the coastal northeast, interior New York, the central plains, and the western plains. In all regions except interior New York state, 99th percentile 1-h precipitation generally followed the C--C relation. In interior New York, there was evidence that intensity scaled with a super C--C relationship. For the 99.9th percentile precipitation, interior New York displayed some moderate evidence of a super C--C relationship, the western plains showed little relation between precipitation and temperature, and the remainder of sites generally scaled with the C--C relationship. Also, if only July, August, and September precipitation is considered, all stations except those in interior New York have little relation between temperature and precipitation, suggesting that precipitation intensity during summer months may not be well constrained by the C--C relationship. Overall, the C--C relationship (or a variation thereof) does not appear to constrain extreme precipitation in all regions and in all seasons, and its ability to aid in constraining future predictions of extreme precipitation may only be relevant to certain locales and time periods. [ABSTRACT FROM AUTHOR]

In this paper, the results of an examination of precipitation forecasts for 1--30-day leads from global models run at the European Centre for Medium-Range Weather Forecasts (ECMWF) and the National Centers for Environmental Prediction (NCEP) during November 2007--February 2008 are presented. The performance of the model precipitation forecasts are examined in global and regional contexts, and results of a case study of precipitation variations that are associated with a moderate to strong Madden--Julian oscillation (MJO) event are presented. The precipitation forecasts from the ECMWF and NCEP operational prediction models have nearly identical temporal correlation with observed precipitation at forecast leads from 2 to 9 days over the Northern Hemisphere during the cool season, despite the higher resolution of the ECMWF operational model, while the ECMWF operational model forecasts are slightly better in the tropics and the Southern Hemisphere during the warm season. The ECMWF Re-Analysis Interim (ERA-Interim) precipitation forecasts perform only slightly worse than the NCEP operational model, while NCEP''s Climate Forecast System low-resolution coupled model forecasts perform the worst among the four models. In terms of bias, the ECMWF operational model performs the best among the four model forecasts that were examined, particularly with respect to the ITCZ regions in both the Atlantic and Pacific. Local temporal correlations that were computed on daily precipitation totals for day-2 forecasts against observations indicate that the operational models at ECMWF and NCEP perform the best during the 4-month study period, and that all of the models have low to insignificant correlations over land and over much of the tropics. They perform the best in subtropical and extratropical oceanic regions. Also presented are results that show that striking improvements have been made over the past two decades in the ability of the models to represent precipitation variations that are associated with MJO. The model precipitation forecasts exhibit the ability to characterize the evolution of precipitation variations during a moderate--strong period of MJO conditions for forecast leads as long as 10 days. [ABSTRACT FROM AUTHOR]

This study explores how the Atlanta, Georgia (United States), urban region influences warm-season (May through September) cloud-to-ground lightning flashes and precipitation. Eight years (1995–2003) of flashes from the National Lightning Detection Network and mean accumulated precipitation from the North American Regional Reanalysis model were mapped under seven different wind speed and direction combinations derived from cluster analysis. Overlays of these data affirmed a consistent coupling of lightning and precipitation enhancement around Atlanta. Maxima in precipitation and lightning shifted in response to changes in wind direction. Differences in the patterns of flash metrics (flash counts versus thunderstorm counts), the absence of any strong urban signal in the flashes of individual thunderstorms, and the scales over which flashes and precipitation enhancement developed are discussed in light of their support for land-cover- and aerosol-based mechanisms of urban weather modification. This study verifies Atlanta’s propensity to conjointly enhance cloud-to-ground lightning and precipitation production in the absence of strong synoptic forcing. However, because of variability in aerosol characteristics and the dynamics of land use change, it may be a simplification to assume that this observed enhancement will be persistent across all scales of analysis. [ABSTRACT FROM AUTHOR]

The paper 'Climate and agricultural land use change impacts on streamflow in the upper midwestern United States' by Satish C. Gupta, Andrew C. Kessler, Melinda K. Brown, and Francis Zvomuya (hereafter referred to as Gupta et al.) purports to evaluate 'the relative importance of changes in precipitation and LULC (land use, land cover) on streamflow in 29 Hydrologic Unit Code 008 watersheds in the Upper Midwestern United States.' However, as we report here, the approach used by Gupta et al. is wholly inadequate for making such an evaluation. Gupta et al. use strong language to criticize other studies and imply a level of certainty that goes well beyond, and in some cases is entirely unsupported by, the results they have presented. We take this opportunity to point out several critical flaws in their study. [ABSTRACT FROM AUTHOR]

The 1981-2010 'U.S. Climate Normals' released by the National Oceanic and Atmospheric Administration's (NOAA) National Climatic Data Center include a suite of monthly, seasonal, and annual statistics that are based on precipitation, snowfall, and snow-depth measurements. This paper describes the procedures used to calculate the average totals, frequencies of occurrence, and percentiles that constitute these normals. All parameters were calculated from a single, state-of-the-art dataset of daily observations, taking care to produce normals that were as representative as possible of the full 1981-2010 period, even when the underlying data records were incomplete. In the resulting product, average precipitation totals are available at approximately 9300 stations across the United States and parts of the Caribbean Sea and Pacific Ocean islands. Snowfall and snow-depth statistics are provided for approximately 5300 of those stations, as compared with several hundred stations in the 1971-2000 normals. The 1981-2010 statistics exhibit the familiar climatological patterns across the contiguous United States. When compared with the same calculations for 1971-2000, the later period is characterized by a smaller number of days with snow on the ground and less total annual snowfall across much of the contiguous United States; wetter conditions over much of the Great Plains, Midwest, and northern California; and drier conditions over much of the Southeast and Pacific Northwest. These differences are a reflection of the removal of the 1970s and the addition of the 2000s to the 30-yr-normals period as part of this latest revision of the normals. [ABSTRACT FROM AUTHOR]

Precipitation over the contiguous United States exhibits large multi-decadal oscillations since the early twentieth century, and they often lead to dry (e.g., 1946-1976 and 1999-present) and wet (e.g., 1977-1998) periods and apparent precipitation trends (e.g., from the 1950s to 1990s) over most of the western and central US. The exact cause of these inter-decadal variations is not fully understood. Using observational and reanalysis data and model simulations, this paper examines the influence of the Inter-decadal Pacific Oscillation (IPO) on US precipitation. The IPO is a leading mode of sea surface temperatures (SSTs) seen mostly in the Pacific Ocean. It is found that decadal precipitation variations over much of the West and Central US, especially the Southwest, closely follow the evolution of the IPO ( r = 0.85 during 1923-2010 for the Southwest US), and the dry and wet periods are associated, respectively, with the cold and warm phases of the IPO. In particular, the apparent upward trend from the 1950s-1990s and the dry decade thereafter in precipitation over much of the West and Central US are largely caused by the IPO cycles, which switched to a warm phase around 1977 and back to a cold phase around 1999. An atmospheric model forced with observed SSTs reproduces much of this association of US precipitation with the IPO ( r = 0.95 between smoothed observed and simulated Southwest US precipitation during 1950-2009 and r = 0.88 between the simulated Southwest US precipitation and the IPO). Atmospheric reanalysis and model data both show a strong high (low) pressure center and anti-cyclonic (cyclonic) anomaly circulation over the North Pacific in the lower troposphere during cold (warm) phases of the IPO, which lead to dry and cold northwesterly and northerly winds and below-normal precipitation over much of the West US during IPO cold periods. The IPO induced changes are most pronounced during the boreal cold season. The results reinforce the notion that tropical Pacific SSTs (and the accompanying SST anomalies in the North Pacific) have large impacts on US precipitation and highlight the need to understand and simulate the IPO for decadal prediction of US precipitation. [ABSTRACT FROM AUTHOR]

The NOAA National Climatic Data Center maintains tables for temperature and precipitation extremes in each of the U.S. states. Many of these tables were several years out of date, however, and therefore did not include a number of recent record-setting meteorological observations. Furthermore, there was no formal process for ensuring the currency of the tables or evaluating observations that might tie or break a statewide climate record. This paper describes the evaluation and revision of the statewide climate-extremes tables for all-time maximum and minimum temperature, greatest 24-h precipitation and snowfall, and greatest snow depth (the five basic climate elements observed on a daily basis by the NOAA Cooperative Weather Network). The process resulted in the revision of 40% of the values listed in those tables and underscored both the necessity of manual quality-assurance methods and the importance of continued climate-monitoring and data-rescue activities to ensure that potential record values are not overlooked. [ABSTRACT FROM AUTHOR]

A methodology for maximum precipitation (MP) estimation that uses a physically based numerical atmospheric model is proposed in this paper. As a case study, the model-based 72-h MP was estimated for the American River watershed (ARW) in California for the December 1996-January 1997 flood event. First, a regional atmospheric model, MM5, was calibrated and validated for the December 1996-January 1997 historical major storm event for the ARW, on the basis of the U.S. National Center for Atmospheric Research (NCAR) reanalysis data to demonstrate the model capability during the historical period. Then, the model-simulated historical storm event was maximized by modifying its boundary conditions. The model-simulated precipitation field in the ARW was successfully validated at nine individual rain gauge stations in the watershed. The computed basin-averaged precipitation was somewhat higher than observations obtained by the spatial interpolation of the rain gauge observations. This result suggests a limitation of the spatial interpolation of ground rain gauge observations because they are mostly located in valleys, and the distribution of precipitation is highly heterogeneous over the mountainous terrain of the ARW. Next, to maximize precipitation over the watershed, the initial and boundary conditions in the outer nesting domain of the atmospheric model were modified. In this demonstrative study, the boundary conditions were modified by three methods: (1) maximizing the atmospheric moisture by setting the relative humidity at 100%; (2) maintaining the atmospheric boundary conditions corresponding to the state of the heaviest precipitation (maintaining equilibrium conditions); and (3) spatially shifting the atmospheric conditions to render the atmospheric moisture flux to hit the watershed. Because these modifications significantly increased the precipitation over the ARW, they clearly show the importance of wind and moisture conditions at the boundary of the atmospheric modeling domain. These different maximization methods produced similar 72-h precipitation depths, which were 549 mm by the combination of 100% relative humidity and equilibrium high precipitation conditions at the outer boundary of the model domain, and 541 mm from shifting the historical atmospheric conditions to the south by 5.0°. Accordingly, the 72-h maximum precipitation over the ARW was estimated to be approximately 550 mm. Although this study presents only a demonstrative maximization work, it shows that the presented modeling approach can be a potential alternative to standard probable maximum precipitation (PMP) estimation without depending on the linear relationships required in the standard PMP method. Also, because the proposed modeling approach is based on the initial and boundary atmospheric conditions from a synoptic scale that may be obtained from the NCAR/National Centers for Environmental Prediction reanalysis data for the historical period and from the general circulation model (GCM) climate projections, it can account for any nonstationarity that may be present in the hydro-climate system. [ABSTRACT FROM AUTHOR]

Heavy precipitation and the resulting flooding are the most serious weather-related hazards over the west coast of North America. This paper analyzes the trends in heavy precipitation for the period 1950--2009 by examining the decadal distributions of the top 60, 40, and 20 two-day precipitation events for a collection of stations along the coastal zone of the United States and British Columbia, as well as the decadal distribution of maximum daily discharge for unregulated rivers from northern California to Washington State. During the past 60 years there has been a modest increase in heavy precipitation events over southern and central coastal California, a decline in heavy events from northern California through the central Oregon coast, a substantial increase in major events over Washington, and a modest increase over coastal British Columbia. Most of these trends are not significantly different from zero at the 95%% level. The trends in maximum daily discharge of unregulated rivers are consistent with the above pattern, with increasing discharges over the past three decades over Washington and northern Oregon and declines over the remainder of Oregon and northern California. Finally, the above trends in heavy rainfall and daily discharge are compared to the future patterns indicated by general circulation models under various global warming scenarios. [ABSTRACT FROM AUTHOR]

This paper analyzes the ability of the North American Regional Climate Change Assessment Program (NARCCAP) ensemble of regional climate models to simulate extreme monthly precipitation and its supporting circulation for regions of North America, comparing 18 years of simulations driven by the National Centers for Environmental Prediction (NCEP)--Department of Energy (DOE) reanalysis with observations. The analysis focuses on the wettest 10%% of months during the cold half of the year (October--March), when it is assumed that resolved synoptic circulation governs precipitation. For a coastal California region where the precipitation is largely topographic, the models individually and collectively replicate well the monthly frequency of extremes, the amount of extreme precipitation, and the 500-hPa circulation anomaly associated with the extremes. The models also replicate very well the statistics of the interannual variability of occurrences of extremes. For an interior region containing the upper Mississippi River basin, where precipitation is more dependent on internally generated storms, the models agree with observations in both monthly frequency and magnitude, although not as closely as for coastal California. In addition, simulated circulation anomalies for extreme months are similar to those in observations. Each region has important seasonally varying precipitation processes that govern the occurrence of extremes in the observations, and the models appear to replicate well those variations. [ABSTRACT FROM AUTHOR]

Significant concern has been expressed regarding the ability of satellite-based precipitation products such as the National Aeronautics and Space Administration (NASA) Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B42 products (version 6) and the U.S. National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center’s (CPC) morphing technique (CMORPH) to accurately capture rainfall values over land. Problems exist in terms of bias, false-alarm rate (FAR), and probability of detection (POD), which vary greatly worldwide and over the conterminous United States (CONUS). This paper directly addresses these concerns by developing a methodology that adjusts existing TMPA products utilizing ground-based precipitation data. The approach is not a simple bias adjustment but a three-step process that transforms a satellite precipitation product. Ground-based precipitation is used to develop a filter eliminating FAR in the authors’ adjusted product. The probability distribution function (PDF) of the satellite-based product is adjusted to the PDF of the ground-based product, minimizing bias. Failure of precipitation detection (POD) is addressed by utilizing a ground-based product during these periods in their adjusted product. This methodology has been successfully applied in the hydrological modeling of the San Pedro basin in Arizona for a 3-yr time series, yielding excellent streamflow simulations at a daily time scale. The approach can be applied to any satellite precipitation product (i.e., TRMM 3B42 version 7) and will provide a useful approach to quantifying precipitation in regions with limited ground-based precipitation monitoring. [ABSTRACT FROM AUTHOR]

In this paper, the authors evaluate the significance of multidecadal trends in seasonal-mean summertime precipitation and precipitation characteristics over the southwestern United States using stochastic, chain-dependent daily rainfall models. Unlike annual-mean precipitation, trends during the summertime monsoon, covering the period 1931–2000, indicate an overall increase in seasonal precipitation, the number of rainfall events, and the coverage of rainfall events in peripheral regions north of the “core” monsoon area of Arizona and western New Mexico. In addition, there is an increasing trend in intense storm activity and a decreasing trend in extreme dry-spell lengths. Over other regions of the domain, there are no discernible trends found in any of the observed characteristics. These trends are robust to the choice of start dates and, in the case of seasonal-mean precipitation, appear to persist into the current century. [ABSTRACT FROM AUTHOR]

This paper advances the physical understanding of the flow duration curve (FDC) regional variation. It provides a process-based analysis of the interaction between climate and landscape properties to explain disparities in FDC shapes. We used (i) long term measured flow and precipitation data over 73 catchments from the eastern US. (ii) We calibrated the Sacramento model (SAC-SMA) to simulate soil moisture and flow components FDCs. The catchments classification based on storm characteristics pointed to the effect of catchments landscape properties on the precipitation variability and consequently on the FDC shapes. The landscape properties effect was pronounce such that low value of the slope of FDC (SFDC)—hinting at limited flow variability—were present in regions of high precipitation variability. Whereas, in regions with low precipitation variability the SFDCs were of larger values. The topographic index distribution, at the catchment scale, indicated that saturation excess overland flow mitigated the flow variability under conditions of low elevations with large soil moisture storage capacity and high infiltration rates. The SFDCs increased due to the predominant subsurface stormflow in catchments at high elevations with limited soil moisture storage capacity and low infiltration rates. Our analyses also highlighted the major role of soil infiltration rates on the FDC despite the impact of the predominant runoff generation mechanism and catchment elevation. In conditions of slow infiltration rates in soils of large moisture storage capacity (at low elevations) and predominant saturation excess, the SFDCs were of larger values. On the other hand, the SFDCs decreased in catchments of prevalent subsurface stormflow and poorly drained soils of small soil moisture storage capacity. The analysis of the flow components FDCs demonstrated that the interflow contribution to the response was the higher in catchments with large value of slope of the FDC. The surface flow FDC was the most affected by the precipitation as it tracked the precipitation duration curve (PDC). In catchments with low SFDCs, this became less applicable as surface flow FDC diverged from PDC at the upper tail (> 40% of the flow percentile). The interflow and baseflow FDCs illustrated most the filtering effect on the precipitation. The process understanding we achieved in this study is key for flow simulation and assessment in addition to future works focusing on process-based FDC predictions. [ABSTRACT FROM AUTHOR]

Identifying regions that mediate regional propagation of atmospheric perturbations is important to assessing the susceptibility and resilience of complex hydroclimate systems. Detecting the regional gateways through causal inference, can help unravel the interplay of physical processes and inform projections of future changes. In this study, we characterize the causal interactions among nine climate regions in the contiguous United States using long‐term (1901–2018) precipitation data. The constructed causal networks reveal the cross‐regional propagation of precipitation perturbations. Results show that the Ohio Valley region acts as an atmospheric gateway for precipitation and moisture transport in the U.S., which is largely regulated by the regional convective uplift. The findings have implications for improving predicative capacity of hydroclimate modeling of regional precipitation. Plain Language Summary: Successful detection of causality in complex systems is important to unraveling the underlying mechanisms of system dynamics. The dynamic interactions in Earth's climate system are often nonlinear, weakly or moderately coupled, and essentially non‐separable, which renders conventional approaches of causal inference, such as statistical correlation or Granger causality, infeasible or ineffective. Here we applied the convergent cross mapping method to detect causal influence among different climate regions in the contiguous U.S. in response to precipitation perturbations. The results of our study show that the Ohio Valley region, as an atmospheric convergence zone, acts as a regional gateway and mediator for the long‐term precipitation perturbations in the U.S. The temporal evolution of causal effect and susceptibility exhibits superposition of climate variability at various time scales, highlighting the impact of prominent climate variabilities such as El Niño–Southern Oscillation on the dynamics of causality. Key Points: We use the convergent cross mapping algorithm (CCM), based on embedding theory, for causality inference in this studyCCM is used to detect causal influence in precipitation perturbations among different climate regions of USThe Ohio Valley region emerges as a causal gateway of moisture transport and propagation of regional precipitation anomalies in the US [ABSTRACT FROM AUTHOR]

This study examines U.S. Northeast daily precipitation and extreme precipitation characteristics for the 1979-2008 period, focusing on daily station data. Seasonal and spatial distribution, time scale, and relation to large-scale factors are examined. Both parametric and nonparametric extreme definitions are considered, and the top 1% of wet days is chosen as a balance between sample size and emphasis on tail distribution. The seasonal cycle of daily precipitation exhibits two distinct subregions: inland stations characterized by frequent precipitation that peaks in summer and coastal stations characterized by less frequent but more intense precipitation that peaks in late spring as well as early fall. For both subregions, the frequency of extreme precipitation is greatest in the warm season, while the intensity of extreme precipitation shows no distinct seasonal cycle. The majority of Northeast precipitation occurs as isolated 1-day events, while most extreme precipitation occurs on a single day embedded in 2-5-day precipitation events. On these extreme days, examination of hourly data shows that 3 h or less account for approximately 50% of daily accumulation. Northeast station precipitation extremes are not particularly spatially cohesive: over 50% of extreme events occur at single stations only, and 90% occur at only 1-3 stations concurrently. The majority of extreme days (75%-100%) are related to extratropical storms, except during September, when more than 50% of extremes are related to tropical storms. Storm tracks on extreme days are farther southwest and more clustered than for all storm-related precipitation days. [ABSTRACT FROM AUTHOR]

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: Severe and more persistent droughts in the arid regions such as the western US have increased the interest in cloud seeding programs or weather modification (WM) operations to increase precipitation. An anticipated increase in precipitation could augment annual and seasonal streamflow and reduce the impacts during dry periods. This paper evaluates hydrological impacts of WM operations in the North Platte River watershed, by utilizing a hydrologic model. The Variable Infiltration Capacity (VIC) land surface hydrological model is calibrated and validated for the periods of 1950–80 and 1981–2000 respectively, using daily meteorological forcing and monthly streamflow data. Two sets of WM scenarios are developed and forced into the VIC model to quantify the impacts of increased precipitation on streamflow. The first scenario is based on existing WM operations in the State of Wyoming. The second scenario hypothetically apply WM throughout the watershed to identify suitable regions for cloud seeding operations. For the first scenario, an increase of 0.3–1.5% in annual streamflow is observed from model simulations for a 1–5% increase in precipitation. Follow-on scenarios have identified the central-west and south-west regions of the watershed, which consist of a higher coverage of Evergreen Needleleaf Forest, to generate higher streamflow during WM operations. The north-east and north-west regions, which consist of a higher coverage of open shrublands and grasslands, are found to generate lower increases in streamflow during these operations. The observed annual precipitation is higher for central and southern regions when compared to northern regions of the watershed. It can be considered that the simulated changes in streamflow from different regions could also be attributed to variation in annual precipitation distribution within the watershed rather than solely based on cloud seeding operations. For the proposed WM programs or programs that are claimed effective based on precipitation augmentation, the hydrological impacts can be evaluated based on this analysis. [Copyright &y& Elsevier]

Purpose: This paper investigates the relationship between precipitation and traffic crashes in the US during the period 1975–2000. Traffic crashes represent the leading cause of death and injury for young adults in the US, and the ninth leading cause of death for the overall population. Prior studies have found that precipitation raises the risk of traffic crashes significantly.Methods: A negative binomial regression approach is employed. Two different units of analysis are examined: state–months and state–days. The sample includes all 48 contiguous states.Results: A surprising negative and significant relationship between monthly precipitation and monthly fatal crashes is found. However, in the daily level analysis, a strong positive relationship is estimated, as in prior studies. The source of the contrasting results appears to be a substantial negative lagged effect of precipitation across days within a state–month. In other words, if it rained a lot yesterday, then on average, today there are fewer crashes. Additional analysis shows that the risk imposed by precipitation increases dramatically as the time since last precipitation increases. For example, 1 cm of precipitation increases the fatal crash rate for a state–day by about 3% if exactly 2 days have passed since the last precipitation and by about 9% if more than 20 days have passed. This basic pattern holds for non-fatal crashes as well.Conclusions: The lagged effects of precipitation across days may be explained by the clearing of oil that accumulates on roads during dry periods or by the conditioning of people to drive more safely in wet conditions. Either way, policy interventions that prepare drivers more adequately for the risks of precipitation following dry periods are likely to be beneficial. [Copyright &y& Elsevier]

Soil moisture influences precipitation mainly through its impact on land–atmosphere interactions. Understanding and correctly modeling soil moisture–precipitation (SM–P) coupling is crucial for improving weather forecasting and subseasonal to seasonal climate predictions, especially when predicting the persistence and magnitude of drought. However, the sign and spatial structure of SM–P feedback are still being debated in the climate research community, mainly due to the difficulty in establishing causal relationships and the high degree of nonlinearity in land–atmosphere processes. To this end, we developed a causal inference model based on the Granger causality analysis and a nonlinear machine learning model. This model includes three steps: nonlinear anomaly decomposition, nonlinear Granger causality analysis, and evaluation of the quality of SM–P feedback, which eliminates the nonlinear response of interannual and seasonal variability and the memory effects of climatic factors and isolates the causal relationship of local SM–P feedback. We applied this model by using National Climate Assessment–Land Data Assimilation System (NCA-LDAS) datasets over the United States. The results highlight the importance of nonlinear atmosphere responses in land–atmosphere interactions. In addition, the strong feedback over the southwestern United States and the Great Plains both highlight the impacts of topographic factors rather than only the sensitivity of evapotranspiration to soil moisture. Furthermore, the SM–P index defined by our framework is used to benchmark Earth system models (ESMs), which provides a new metric for efficiently identifying potential model biases in modeling local land–atmosphere interactions and may help the development of ESMs in improving simulations of water cycle variability and extremes. [ABSTRACT FROM AUTHOR]

Time-dependent changes in extreme precipitation occurrence across the northeastern United States are evaluated in terms of areal extent. Using gridded precipitation data for the period from 1950 to 2018, polygons are defined that are based on isohyets corresponding to extreme daily precipitation accumulations. Across the region, areal precipitation is characterized on the basis of the annual and seasonal number of extreme precipitation polygons and the area of the polygons. Using the subset of grid points that correspond to station locations in the northeastern United States, gridded precipitation replicates the observed trends in extreme precipitation based on station observations. Although the number of extreme precipitation polygons does not change significantly through time, there is a marked increase in the area covered by the polygons. The median annual polygon area nearly doubles from 1950 to 2013. Consistent results occur for percentiles other than the median and a range of extreme precipitation amount thresholds, with the most pronounced changes observed in spring and summer. Like trends in station data, outside the northeastern United States trends in extreme precipitation polygon area are negative, particularly in the western United States, or they are not statistically significant. Collectively, the results suggest that the increases in heavy precipitation frequency and amount observed at stations in the northeastern United States are a manifestation of an expansion of the spatial area over which extreme precipitation occurs rather than a change in the number of unique extreme precipitation polygons. [ABSTRACT FROM AUTHOR]

Tropical cyclones (TCs) are an important source of precipitation for much of the eastern United States. However, our understanding of the spatiotemporal variability of tropical cyclone precipitation (TCP) and the connections to large-scale atmospheric circulation is limited by irregularly distributed rain gauges and short records of satellite measurements. To address this, we developed a new gridded (0.25° × 0.25°) publicly available dataset of TCP (1948–2015; Tropical Cyclone Precipitation Dataset, or TCPDat) using TC tracks to identify TCP within an existing gridded precipitation dataset. TCPDat was used to characterize total June–November TCP and percentage contribution to total June–November precipitation. TCP totals and contributions had maxima on the Louisiana, North Carolina, and Texas coasts, substantially decreasing farther inland at rates of approximately 6.2–6.7 mm km−1. Few statistically significant trends were discovered in either TCP totals or percentage contribution. TCP is positively related to an index of the position and strength of the western flank of the North Atlantic subtropical high (NASH), with the strongest correlations concentrated in the southeastern United States. Weaker inverse correlations between TCP and El Niño–Southern Oscillation are seen throughout the study site. Ultimately, spatial variations of TCP are more closely linked to variations in the NASH flank position or strength than to the ENSO index. The TCP dataset developed in this study is an important step in understanding hurricane–climate interactions and the impacts of TCs on communities, water resources, and ecosystems in the eastern United States. [ABSTRACT FROM AUTHOR]

Upright convection can impact extratropical cyclone (ETC) precipitation and dynamics differently than isentropic ascent, but how often it occurs and how much it contributes to total precipitation within an ETC have not been systematically documented in previous literature. Herein, convection in ETCs is analyzed using ground‐based radar observations over the Eastern United States. Convection occupies 1%–5% of the precipitating region and constitutes 1%–15% of total cyclone precipitation. Furthermore, the location of convective activity occurs preferentially in the warm sector early in a cyclone's life cycle, and then as the storm evolves, more convection occurs along and behind the cold front. The precipitation rates in regions with convection are more likely to be stronger than those in other precipitating regions of the cyclone. However, the cyclones with the largest area‐average precipitation rates include only a small contribution from convection. Plain Language Summary: This research is motivated by the potential influence of convection on extratropical cyclone circulation. We utilize a newly derived dataset of upright convection and a cyclone‐tracking framework to quantify convective activity in extratropical cyclones over the United States, east of the Rocky Mountains. We find that upright convection only occupies a small portion of the precipitating region, and the relative contribution to total precipitation has relatively small variability as the cyclone evolves. We find that the location of convective activity occurs preferentially in the warm sector early in a cyclone's life cycle, and as the storm evolves, more convection occurs along and behind the cold front. We find that precipitation rates in regions with convection are more likely to be stronger than those in other precipitating regions of the cyclone. However, the cyclones with the largest area‐average precipitation rates include only a small contribution from convection. Key Points: Convection occurs in 1%–5% of precipitating regions of extratropical cyclones over eastern United States and contributes 1%–15% of total precipitationThe majority of upright convection occurs ahead of the cold front and disperses into other regions as the cyclone agesWhile convection produces larger rain rates, cyclones with greater total precipitation do not have a large convective contribution [ABSTRACT FROM AUTHOR]

In this paper we analyze the relationship between the sunspot numbers and the average monthly precipitation measured at meteorological stations in the US. The results indicate that there is a significant correlation between the solar activity and the monthly average precipitation data for selected months and time delays. In order to confirm the relationship, for the precipitation data we check the forecasting abilities of a popular time series model with and without additional information on the sun activity. Namely, first we fit the autoregressive moving average (ARMA) time series model to the US precipitation data and calculate one-step ahead forecasts. Next, we repeat the same steps with the ARMA model with exogenous input (ARMAX) which is the sunspot number data. We show that the forecast errors are essentially lower for the ARMAX model for months and time delays found in the correlation analysis. • Influence of solar activity on precipitation in the US is addressed. • The impact is studied by means of ARMA and ARMAX models. • Information on the number of sunspots helps to improve precipitation forecasts. • The presented scheme can be also applied to other meteorological parameters. [ABSTRACT FROM AUTHOR]

Extratropical regional-scale extreme precipitation events (EPEs) are usually associated with certain synoptic perturbations superimposed on slow-varying background circulations. These perturbations induce a dynamically forced ascent that destabilizes the atmospheric stratification and stimulates deep convection, which further drives the perturbation by releasing latent heat. This study identifies the characteristics of large-scale perturbations associated with summer EPEs in two representative regions, East China (ECN) and the southeastern United States (SUS), and analyzes the roles of dynamic forcings and diabatic heating using the quasigeostrophic omega equation. Composites of 39 events in each region show that the upper-level absolute vorticity advection and tropospheric warm advection promote dynamically forced ascent in EPEs, and the moisture advection premoistens the local environment. The background circulation and synoptic perturbations in ECN and the SUS have significant differences. The background vorticity, temperature, and moisture advection form the quasi-steady mei-yu front in ECN, which provides favorable conditions for heavy rainfall. In the SUS, weaker background ascents are forced mainly through vorticity advection. In the synoptic scale, the EPEs in ECN are triggered by short-wavelength wave trains, and in the SUS the EPEs are triggered by longer wavelength potential vorticity intrusions. Although the amplitudes of the dynamically forced ascent in the two regions are similar, diabatic heating contributes much more to the vertical motion in ECN than the SUS, which indicates that there is stronger diabatic heating feedback there. The stronger diabatic heating feedback in ECN appears to be due to stronger moisture advection, convective environments with more humidity, and stronger coupling between convection and large-scale dynamics. [ABSTRACT FROM AUTHOR]

The sequencing, or persistence, of daily precipitation influences variability in streamflow, soil moisture, and vegetation states. As these factors influence water availability and ecosystem health, it is important to identify spatial and temporal trends in precipitation persistence and predictability. We take an information theoretic perspective to address regional and temporal trends in daily patterns, based on the Climate Prediction Center (CPC) gridded gauge-based dataset of daily precipitation over the continental United States from 1948 to 2018. We apply information measures to binary sequences of precipitation occurrence to quantify uncertainty, predictability in the form of lagged mutual information between the current state and two time-lagged histories, and associated dominant time scales. We find that this information-based predictability is highest in the western United States, but the relative influence of longer lagged histories in comparison to a 1-day history is highest in the east. Information characteristics and time scales vary seasonally and regionally and constitute an information climatology that can be compared with traditional indices of precipitation and climate. Trend analyses over the 70-yr time period also show varying regional characteristics that differ between seasons. In addition to increasing precipitation frequency over most of the country, we detect increasing and decreasing predictability in western and eastern regions, respectively, with average trend magnitudes corresponding to shifts in predictability ranging from −50% to 110%. This new perspective on precipitation persistence has broad potential to link shifts in climate and weather to patterns and predictability of related environmental factors. [ABSTRACT FROM AUTHOR]

Hydrologic intensity is often quantified using precipitation without directly incorporating atmospheric water demand. We develop a hydrologic intensity index called the surplus deficit intensity (SDI) index that accounts for variation in supply and demand. SDI is the standardized sum of standardized surplus intensity (mean of daily surplus when supply > demand) and deficit time (mean of consecutive days when demand > supply). Using an observational ensemble of global daily precipitation and atmospheric water demand during 1979–2017, we document widespread hydrologic intensification (SDI; +0.11 z‐score per decade) driven primarily by increased surplus intensity. Using a climate model ensemble of the United States, hydrologic intensification is projected for the mid‐21st century (+0.86 in z‐score compared to 1971–2000), producing greater apparent intensification when compared to an index that does not explicitly incorporate demand. While incorporating demand had a minor effect on observed hydrologic intensification, it doubles hydrological intensification for the mid‐21st century. Plain Language Summary: Increasing air temperatures have resulted in an intensification, or acceleration, of the terrestrial hydrologic cycle, which is defined as an increase of the water fluxes (precipitation and evapotranspiration) between the surface and the atmosphere. Efforts to quantify hydrologic intensification have traditionally only considered "supply" variables such as precipitation rates without considering "demand" that varies over space and time. For this work we develop a method to quantify hydrologic intensification using supply and demand. This approach shows widespread hydrologic intensification from 1979–2017 across much of the global land surface, which is expected to continue into the future. We additionally compare this new method to a previously developed method that uses only supply without explicitly incorporating demand. Incorporating demand results in a notable hydrologic intensification compared to methods that only use supply or a simplified representation of supply and demand. This work suggests that demand needs to be considered when quantifying hydrologic intensification. Key Points: We develop a hydrologic intensity metric that incorporates daily supply and demand using precipitation and reference evapotranspirationWidespread hydrologic intensification occurred over the past four decades and will continue into the futureAdding demand couples intensity metrics to ecohydrologic conditions and produces greater increases than metrics with static demand [ABSTRACT FROM AUTHOR]

This study investigated the characteristics of extreme precipitation events associated with mesoscale convective systems (MCSs) in East China (the area east of 96°E) during 2016–17. Over the entire region, 204 events were first identified and classified into synoptic, tropical, MCS, small-scale-storm (SSS), and unclassified types. For 73 MCS-type events, further division and analysis were conducted according to the organizational modes. Results show that MCS-related events occurred most frequently near southern Fujian Province and from April to October with a peak in July. The area of occurrence shifted from the south in spring to the north in summer before going back to the south in autumn. The events occurred most commonly from afternoon to early evening, matured around late afternoon, and ended before dark. Among MCS subcategories, the longest average duration was seen in the multiple-MCS cases. Of the 15 selected multiple-MCS events, 11 were defined as early-maturing type with peak rainfall occurrence before the midpoint of duration while the others were late maturing. Although multiple-MCS events were accompanied by a southwest low-level jet, strong warm-air advection, and convective instability, early-maturing cases had stronger synoptic-scale ascent, moister environments, and smaller surface-based convective available potential energy (SBCAPE) and convection inhibition (SBCIN) at the most extreme rainfall-occurrence point. Compared to the MCS type within all extreme precipitation events over the United States, the percentage was lower in China. However, the events in China exhibit more pronounced seasonal cycle. [ABSTRACT FROM AUTHOR]

Despite the widespread application of statistical downscaling tools, uncertainty remains regarding the role of model formulation in determining model skill for daily maximum and minimum temperature (Tmax and Tmin), and precipitation occurrence and intensity. Impacts of several key aspects of statistical transfer function form on model skill are evaluated using a framework resistant to model overspecification. We focus on: (a) model structure: simple (generalized linear models, GLMs) versus complex (artificial neural networks, ANNs) models. (b) Predictor selection: Fixed number of predictors chosen a priori versus stepwise selection of predictors and inclusion of grid point values versus predictors derived from application of principal components analysis (PCA) to spatial fields. We also examine the influence of domain size on model performance. For precipitation downscaling, we consider the role of the threshold used to characterize a wet day and apply three approaches (Poisson and Gamma distributions in GLM and ANN) to downscale wet‐day precipitation amounts. While no downscaling formulation is optimal for all predictands and at 10 locations representing diverse U.S. climates, and due to the exclusion of variance inflation all of the downscaling formulations fail to reproduce the range of observed variability, models with larger suites of prospective predictors generally have higher skill. For temperature downscaling, ANNs generally outperform GLM, with greater improvements for Tmin than Tmax. Use of PCA‐derived predictors does not systematically improve model skill, but does improve skill for temperature extremes. Model skill for precipitation occurrence generally increases as the wet‐day threshold increases and models using PCA‐derived predictors tend to outperform those based on grid cell predictors. Each model for wet‐day precipitation intensity overestimates annual total precipitation and underestimates the proportion derived from extreme precipitation events, but ANN‐based models and those with larger predictor suites tend to have the smallest bias. [ABSTRACT FROM AUTHOR]

Weather generators rely on historical meteorological records to simulate time series of synthetic weather sequences, the quality of which has direct influence on model applications. The climate generator CLIGEN’s database has recently been updated to comprise consistent historical records from 1974 to 2013 (updated CLIGEN database, UCD) compared to the current database in which records are of different lengths. In this study, CLIGEN’s performance in estimating precipitation using UCD (eight stations) and the subsequent impact on urban runoff simulations (371 stations) were evaluated in the Great Lakes Region, USA. Generally, UCD-based precipitation could replicate observed daily precipitation up to the 99.5th percentile, but maximum precipitation was underestimated. Results from the Long-Term Hydrologic Impact Assessment model using UCD-based precipitation showed about 0.57 billion cubic meters more (14.9%) average annual runoff being simulated compared with simulations based on the current CLIGEN database. Overall, CLIGEN with the updated database was found suitable for providing precipitation estimates and for use with modeling urban runoff or urbanization effects. [ABSTRACT FROM AUTHOR]

Drivers of alpine plant community composition have been observed to vary with scale. Diversity of alpine tundra across four regions of the Rocky Mountains and among plots within one region was examined relative to temperature and precipitation variables. For regional scale analyses, averages of three metrics of plot-level species diversity relative to environmental variables and regional gamma diversity were examined for a subset of 60 plots from a stratified random sample in each region. For local scale analyses, additional soil and climate variables were included at 96 plots from one of the four regions. Correlations and visual examination of bivariate plots elucidated possible controls of cold temperatures and gamma diversity on average diversity metrics among the four regions and of precipitation and/or location on plot-level metrics within the single region. For the latter, the bivariate graph indicated a triangular distribution in which all levels of diversity exist at low precipitation but only low diversity at higher precipitation. We propose that change in drivers with scale is a general result of the relative importance of temperature and water in seed production (temperature > water) and seedling establishment (vice versa), and the logical priority of seed production over seedling establishment. [ABSTRACT FROM AUTHOR]

The net surface water budget, precipitation minus evaporation (P = E), shows a clear seasonal cycle in the U.S. Southwest with a net gain of surface water (positive P = E) in the cold half of the year (October-March) and a net loss of water (negative P = E) in the warm half (April-September), with June and July being the driest months of the year. There is a significant shift of the summer drying toward earlier in the year under a CO2 warming scenario, resulting in substantial spring drying (March-May) of the U.S. Southwest from the near-term future to the end of the current century, with gradually increasing magnitude. While the spring drying has been identified in previous studies, its mechanism has not been fully addressed. Using moisture budget analysis, it was found that the drying is mainly due to decreased mean moisture convergence, partially compensated by the increase in transient eddy moisture flux convergence. The decreased mean moisture convergence is further separated into components as a result of changes in circulation (dynamic changes) and changes in atmospheric moisture content (thermodynamic changes). The drying is found to be dominated by the thermodynamic-driven changes in column-averaged moisture convergence, mainly due to increased dry zonal advection caused by the climatological land-ocean thermal contrast, rather than by the well-known "dry get drier" mechanism. Furthermore, the enhanced dry advection in the warming climate is dominated by the robust zonal mean atmospheric warming, leading to equally robust spring drying in the southwestern United States. [ABSTRACT FROM AUTHOR]

Long-term changes in North American monsoon (NAM) precipitation intensity in the southwestern United States are evaluated through the use of convective-permitting model simulations of objectively identified severe weather events during 'historical past' (1950-70) and 'present day' (1991-2010) periods. Severe weather events are the days on which the highest atmospheric instability and moisture occur within a long-term regional climate simulation. Simulations of severe weather event days are performed with convective-permitting (2.5 km) grid spacing, and these simulations are compared with available observed precipitation data to evaluate the model performance and to verify any statistically significant model-simulated trends in precipitation. Statistical evaluation of precipitation extremes is performed using a peaks-over-threshold approach with a generalized Pareto distribution. A statistically significant long-term increase in atmospheric moisture and instability is associated with an increase in extreme monsoon precipitation in observations and simulations of severe weather events, corresponding to similar behavior in station-based precipitation observations in the Southwest. Precipitation is becoming more intense within the context of the diurnal cycle of convection. The largest modeled increases in extreme-event precipitation occur in central and southwestern Arizona, where mesoscale convective systems account for a majority of monsoon precipitation and where relatively large modeled increases in precipitable water occur. Therefore, it is concluded that a more favorable thermodynamic environment in the southwestern United States is facilitating stronger organized monsoon convection during at least the last 20 years. [ABSTRACT FROM AUTHOR]

The Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) has provided a valuable precipitation dataset for hydrometeorological studies (1998-2015). However, TMPA shows some differences when compared to the ground-based estimates. In this study, a correction model is developed to improve the accuracy of the TRMM precipitation monthly product by reducing the bias compared to the ground-based estimates. The TRMM 3B43 precipitation product is compared with the Parameter-Elevation Regressions on Independent Slopes Model (PRISM) and with gridded precipitation estimates acquired from the CPC Unified Precipitation Project, two ground-based precipitation estimates, in the conterminous United States. The bias between the satellite and ground-based estimates is compared with mean surface temperature and elevation, respectively. A weak linear relationship is observed between the bias and temperature but a moderate inverse linear relationship is observed between the bias and elevation. Based on these observations, a linear model is developed for the TRMM 3B43-PRISM bias and elevation. The developed model is calibrated and validated using Monte Carlo cross validation with 25% of the available data as a calibration set and the remaining 75% of the data as a validation set. The estimated model parameters are then used in a correction formula for the TRMM 3B43 dataset for elevations above 1500 m above mean sea level. The corrected TRMM 3B43 product is verified for the high-elevation regions over the entire United States as well as in two high-elevation local regions in the western United States. The results show a significant improvement in the accuracy of the monthly satellite product in the high elevations of the United States. [ABSTRACT FROM AUTHOR]

Probabilistic forecasts of U.S. Drought Monitor (USDM) intensification over 2-, 4-, and 8-week time periods are developed based on recent anomalies in precipitation, evapotranspiration, and soil moisture. These statistical forecasts are computed using logistic regression with cross validation. While recent precipitation, evapotranspiration, and soil moisture do provide skillful forecasts, it is found that additional information on the current state of the USDM adds significant skill to the forecasts. The USDM state information takes the form of a metric that quantifies the 'distance' from the next-higher drought category using a nondiscrete estimate of the current USDM state. This adds skill because USDM states that are close to the next-higher drought category are more likely to intensify than states that are farther from this threshold. The method shows skill over most of the United States but is most skillful over the north-central United States, where the cross-validated Brier skill score averages 0.20 for both 2- and 4-week forecasts. The 8-week forecasts are less skillful in most locations. The 2- and 4-week probabilities have very good reliability. The 8-week probabilities, on the other hand, are noticeably overconfident. For individual drought events, the method shows the most skill when forecasting high-amplitude flash droughts and when large regions of the United States are experiencing intensifying drought. [ABSTRACT FROM AUTHOR]

Climate models robustly imply that some significant change in precipitation patterns will occur. Models consistently project that the intensity of individual precipitation events increases by approximately 6%-7% K−1, following the increase in atmospheric water content, but that total precipitation increases by a lesser amount (1%-2% K−1 in the global average in transient runs). Some other aspect of precipitation events must then change to compensate for this difference. The authors develop a new methodology for identifying individual rainstorms and studying their physical characteristics-including starting location, intensity, spatial extent, duration, and trajectory-that allows identifying that compensating mechanism. This technique is applied to precipitation over the contiguous United States from both radar-based data products and high-resolution model runs simulating 80 years of business-as-usual warming. In the model study the dominant compensating mechanism is a reduction of storm size. In summer, rainstorms become more intense but smaller; in winter, rainstorm shrinkage still dominates, but storms also become less numerous and shorter duration. These results imply that flood impacts from climate change will be less severe than would be expected from changes in precipitation intensity alone. However, these projected changes are smaller than model-observation biases, implying that the best means of incorporating them into impact assessments is via 'data-driven simulations' that apply model-projected changes to observational data. The authors therefore develop a simulation algorithm that statistically describes model changes in precipitation characteristics and adjusts data accordingly, and they show that, especially for summertime precipitation, it outperforms simulation approaches that do not include spatial information. [ABSTRACT FROM AUTHOR]

The reply addresses concerns raised by Belmont et al. (2016) on Gupta et al. (2015) through additional analysis of streamflow versus precipitation relationships for the Whetstone and the Redwood Rivers and with data on available soil moisture in prechange and postchange periods in the Cottonwood River watershed. [ABSTRACT FROM AUTHOR]

Gridded precipitation and temperature products are inherently uncertain because of myriad factors, including interpolation from a sparse observation network, measurement representativeness, and measurement errors. Generally uncertainty is not explicitly accounted for in gridded products of precipitation or temperature; if it is represented, it is often included in an ad hoc manner. A lack of quantitative uncertainty estimates for hydrometeorological forcing fields limits the application of advanced data assimilation systems and other tools in land surface and hydrologic modeling. This study develops a gridded, observation-based ensemble of precipitation and temperature at a daily increment for the period 1980-2012 for the conterminous United States, northern Mexico, and southern Canada. This allows for the estimation of precipitation and temperature uncertainty in hydrologic modeling and data assimilation through the use of the ensemble variance. Statistical verification of the ensemble indicates that it has generally good reliability and discrimination of events of various magnitudes but has a slight wet bias for high threshold events (>50 mm). The ensemble mean is similar to other widely used hydrometeorological datasets but with some important differences. The ensemble product produces a more realistic occurrence of precipitation statistics (wet day fraction), which impacts the empirical derivation of other fields used in land surface and hydrologic modeling. In terms of applications, skill in simulations of streamflow in 671 headwater basins is similar to other coarse-resolution datasets. This is the first version, and future work will address temporal correlation of precipitation anomalies, inclusion of other data streams, and examination of topographic lapse rate choices. [ABSTRACT FROM AUTHOR]

ABSTRACT The widespread influence of El Niño-Southern Oscillation ( ENSO) phases on regional climate is well known to contribute to climate anomalies around the world. Understanding the effects of climate variability given the ENSO phases is an important component to improve crop management and reduce climate risk due to potential changes in temperature and precipitation patterns. Recognizing the spatial structure in precipitation and temperature under different ENSO phases is fundamental for comprehensive environmental assessments through decisions and policies that consider climate variability at regional scales. The objective of this study was to investigate if there is evidence of changes to the spatial structure of precipitation and temperature associated with the three categories of ENSO phases: El Niño, La Niña and neutral years in two study areas, the Southeast (Alabama, Florida and Georgia) and the Midwest (Nebraska) United States. Daily data of maximum temperature, minimum temperatures and precipitation (1981-2010) from 407 weather stations were analysed. The spatial structure was measured by analysing the monthly correlation matrices calculated from daily observations. To determine the potential changes to spatial structures of precipitation and temperature, the spatial structure was compared with using the complete 30 year period divided by the three categories of the ENSO phases. The results showed statistically significant differences in the spatial structures of maximum and minimum temperature and precipitation (amount and distribution) among the different ENSO phases and between the combinations of two kinds of different ENSO phases. Statistically significant changes in spatial correlation at seasonal and monthly timescales among the different ENSO phases were also observed in both regions. The statistical differences were not uniform in both regions, and some region-variable-month combinations showed no significance. Knowledge of spatial structure among some combinations of different ENSO phases at each region would be useful to improve further applications of weather/climate data in other disciplines such as geo-spatial weather generator. [ABSTRACT FROM AUTHOR]

Significant increases in summer precipitation have occurred in the midwestern United States over the last century for reasons that remain unclear. It is postulated that the expansion of irrigation and cropland in the central United States over the past 60 yr has been a major contributor to these observed increases in precipitation. As a first step toward attribution of these precipitation changes, a detailed analysis of observed daily summer precipitation frequency and intensity is conducted for the contiguous United States over multiple spatial scales and time periods from 1895 to 2011. Robust increases in precipitation frequency, total precipitation, and moderate to heavy precipitation intensity are identified during July and August in the midwestern United States. Analysis of changes in mean monthly precipitation from the early to late twentieth century initially points to increasing frequency as the source of increasing monthly precipitation in the midwestern United States during the summer, especially during August; however, these large frequency increases are not unique to the summer. On the other hand, changes in precipitation intensity and total precipitation are both greatest during July and August and coincide spatially in the midwestern United States. Additionally, the greatest intensity change occurs downwind of the most heavily irrigated regions, especially for the period between 1950 and 1980 when irrigation rapidly intensified. Thus, the seasonality and location of these regional signatures of increasing precipitation intensity (and total precipitation) are found to be broadly consistent with spatiotemporal trends in irrigation and cropland in the central United States and may be indicative of a causal link. [ABSTRACT FROM AUTHOR]

In this work the authors investigate possible changes in the intensity of rainfall events associated with tropical cyclones (TCs) under idealized forcing scenarios, including a uniformly warmer climate, with a special focus on landfalling storms. A new set of experiments designed within the U.S. Climate Variability and Predictability (CLIVAR) Hurricane Working Group allows disentangling the relative role of changes in atmospheric carbon dioxide from that played by sea surface temperature (SST) in changing the amount of precipitation associated with TCs in a warmer world. Compared to the present-day simulation, an increase in TC precipitation was found under the scenarios involving SST increases. On the other hand, in a CO2-doubling-only scenario, the changes in TC rainfall are small and it was found that, on average, TC rainfall tends to decrease compared to the present-day climate. The results of this study highlight the contribution of landfalling TCs to the projected increase in the precipitation changes affecting the tropical coastal regions. [ABSTRACT FROM AUTHOR]

Freshwater delivery is an important factor determining estuarine character and health and may be influenced by large-scale climate oscillations. Variability in freshwater delivery (precipitation and discharge) to the Altamaha River estuary (GA, USA) was examined in relation to indices for several climate signals: the Bermuda High Index (BHI), the Southern Oscillation Index (SOI), the Improved El Niño Modoki Index (IEMI), the North Atlantic Oscillation (NAO), the Atlantic Multidecadal Oscillation (AMO), the Pacific Decadal Oscillation (PDO), and the Pacific/North American Pattern (PNA). Discharge to this estuary has been linked to key ecosystem properties (e.g., salinity regime, water residence time, nutrient inputs, and marsh processes), so understanding how climate patterns affect precipitation and river discharge will help elucidate how the estuarine ecosystem may respond to climate changes. Precipitation patterns in the Altamaha River watershed were described using empirical orthogonal functions (EOFs) of the combined multidecadal time series of precipitation at 14 stations. The first EOF (67 % of the variance) was spatially uniform, the second EOF (11 %) showed a spatial gradient along the long axis of the watershed (NW-SE), and the third EOF (6 %) showed a NE-SW pattern. We compared the principal components (PCs) associated with these EOFs, monthly standardized anomalies of Altamaha River discharge at the gauge closest to the estuary, and the climate indices. Complex, seasonally alternating patterns emerged. The BHI was correlated with June-January discharge and precipitation PC 1. The SOI was correlated with January-April discharge and precipitation PC 2, and also weakly correlated with PC 1 in November-December. The AMO was correlated with river discharge and precipitation PC 3 mainly in December-February and June. The correlation patterns of precipitation PCs with PDO and PNA were similar to those with SOI, but weaker. There were no consistent relationships with two NAO indices or IEMI. Connections between climate signals and estimates of nutrient loading were consistent with the connections to discharge. The occurrence of tropical storms in the region was strongly related to the BHI but not to the other climate indices, possibly representing the influence of storm tracking more than the rate of storm formation. Comparison with the literature suggests that the patterns found may be typical of southeastern USA estuaries but are likely to be different from those outside the region. [ABSTRACT FROM AUTHOR]

Glyphosate use in the United States increased from less than 5,000 to more than 80,000 metric tons/yr between 1987 and 2007. Glyphosate is popular due to its ease of use on soybean, cotton, and corn crops that are genetically modified to tolerate it, utility in no-till farming practices, utility in urban areas, and the perception that it has low toxicity and little mobility in the environment. This compilation is the largest and most comprehensive assessment of the environmental occurrence of glyphosate and aminomethylphosphonic acid ( AMPA) in the United States conducted to date, summarizing the results of 3,732 water and sediment and 1,018 quality assurance samples collected between 2001 and 2010 from 38 states. Results indicate that glyphosate and AMPA are usually detected together, mobile, and occur widely in the environment. Glyphosate was detected without AMPA in only 2.3% of samples, whereas AMPA was detected without glyphosate in 17.9% of samples. Glyphosate and AMPA were detected frequently in soils and sediment, ditches and drains, precipitation, rivers, and streams; and less frequently in lakes, ponds, and wetlands; soil water; and groundwater. Concentrations of glyphosate were below the levels of concern for humans or wildlife; however, pesticides are often detected in mixtures. Ecosystem effects of chronic low-level exposures to pesticide mixtures are uncertain. The environmental health risk of low-level detections of glyphosate, AMPA, and associated adjuvants and mixtures remain to be determined. [ABSTRACT FROM AUTHOR]

Atmospheric rivers (ARs) have, in recent years, been recognized as the cause of the large majority of major floods in rivers all along the U.S. West Coast and as the source of 30%-50% of all precipitation in the same region. The present study surveys the frequency with which ARs have played a critical role as a common cause of the end of droughts on the West Coast. This question was based on the observation that, in most cases, droughts end abruptly as a result of the arrival of an especially wet month or, more exactly, a few very large storms. This observation is documented using both Palmer Drought Severity Index and 6-month Standardized Precipitation Index measures of drought occurrence for climate divisions across the conterminous United States from 1895 to 2010. When the individual storm sequences that contributed most to the wet months that broke historical West Coast droughts from 1950 to 2010 were evaluated, 33%-74% of droughts were broken by the arrival of landfalling AR storms. In the Pacific Northwest, 60%-74% of all persistent drought endings have been brought about by the arrival of AR storms. In California, about 33%-40% of all persistent drought endings have been brought about by landfalling AR storms, with more localized low pressure systems responsible for many of the remaining drought breaks. [ABSTRACT FROM AUTHOR]

The design of the dams usually considers available historical data for analysis of the flood frequency. The limitation of this approach is the potential shift in flood frequency due to physically plausible factors that cannot be foreseen during design. For example, future flood extremes may change, among other factors, due to strong local atmospheric feedbacks from the reservoir and surrounding land use and land cover (LULC). Probable maximum flood (PMF), which is the key design parameter for hydraulic features of a dam, is estimated from probable maximum precipitation (PMP) and the hydrology of the watershed. Given the nonlinearity of the rainfall-runoff process, a key question that needs to be answered is How do reservoir size and/or LULC modify extreme flood patterns, specifically probable maximum flood via climatic modification of PMP? Using the American River Watershed (ARW) as a representative example of an impounded watershed with a large artificial reservoir (i.e., Folsom Dam), this study applied the distributed variable infiltration capacity (VIC) model to simulate the PMF from the atmospheric feedbacks simulated for various LULC scenarios (predam, current scenario, nonirrigation, and reservoir-double). The atmospheric feedbacks were simulated numerically as PMP using the regional atmospheric modeling system (RAMS). The RAMS-generated PMP scenarios were propagated through the VIC model to simulate the PMFs. Comparison of PMF results for predam and current scenario conditions showed that PMF peak flow can decrease by about , while comparison of current scenario with nonirrigation PMF results showed that irrigation development has increased the PMF by . On the other hand, the reservoir size had virtually no detectable impact on PMP and consequently on PMF results. Where downstream levee capacity is already underdesigned to handle a dam's spillway capacity, such as for the case study, such increases indicate a likely impact on downstream flood risk to which any flood management protocol must adapt. The premise that modern dam design and operations should consider an integrated atmospheric-hydrologic modeling approach for estimating proactively potential extreme precipitation variation due to dam-driven LULC change is well-supported by this case study. [ABSTRACT FROM AUTHOR]

The hourly updating High-Resolution Rapid Refresh (HRRR) model is evaluated with regard to its ability to predict the areal extent of cold-season precipitation and accurately depict the timing and location of regions of snow, rain, and mixed-phase precipitation on the ground. Validation of the HRRR forecasts is performed using observations collected by the Automated Surface Observing System (ASOS) stations across the eastern two-thirds of the United States during the 2010-11 cold season. The results show that the HRRR is able to reliably forecast precipitation extent during the cold season. In particular, the location and areal extent of both snow and rain are very well predicted. Depiction of rain-to-snow transitions and freezing rain is reasonably good; however, the associated evaluation scores are significantly lower than for either snow or rain. The analyses suggest the skill in accurately depicting precipitation extent and phase (i.e., rain, snow, and mixed phase) depends on the size and organization of a weather system. Typically, larger synoptically forced weather systems are better predicted than smaller weather systems, including the associated rain-to-snow transition or freezing-rain areas. Offsets in space or time (i.e., causing misses and false alarms) have a larger effect on the model performance for smaller weather systems. [ABSTRACT FROM AUTHOR]

Using 92-year precipitation data from 400 rain gauge stations located in 48 states throughout the continental United States, the change in precipitation in space and time was investigated. The variability in precipitation was investigated using an entropy-based approach. The Mann-Kendall, Spearman's rho, and Sen slope tests were applied to assess the existence of a trend in precipitation. Analysis showed that annual precipitation exhibited lower temporal variability than did its constituent seasonal series. The fall precipitation had the highest variability, and the spring precipitation the lowest. Not many stations exhibited trends, and they are concentrated in limited areas of the United States. The greatest part of those exhibiting statistically significant trends showed increasing trends both in precipitation and rainy days. Only in a few cases negative trends were found. Finally, analysis showed that no relationship can be established between trend and variability in precipitation. [ABSTRACT FROM AUTHOR]