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The Western Arctic Linkage Experiment (WALE) is aimed at understanding the role of high-latitude terrestrial ecosystems in the response of the Arctic system to global change through collection and comparison of climate datasets and model results. In this paper, a spatiotemporal approach is taken to compare and validate model results from the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) with commonly used analysis and reanalysis datasets for monthly averages of temperature and precipitation in 1992–2000 and for a study area at 55°–65°N, 160°–110°W in northwestern Canada and Alaska. Objectives include a quantitative assessment of similarity between datasets and climate model fields, and identification of geographic areas and seasons that are problematic in modeling, with potential causes that may aid in model improvement. These are achieved by application of algebraic similarity mapping, a simple yet effective method for synoptic analysis of many (here, 45) different spatial datasets, maps, and models. Results indicate a dependence of model–data similarity on seasonality, on climate variable, and on geographic location. In summary, 1) similarity of data and models is better for temperature than for precipitation; and 2) modeling of summer precipitation fields, and to a lesser extent, temperature fields, appears more problematic than that of winter fields. The geographic distribution of areas with best and worst agreement shifts throughout the year, with generally better agreement between maps and models in the northeastern and northern inland areas than in topographically complex and near-coastal areas. The study contributes to an understanding of the geographic complexity of the Arctic system and modeling its diverse climate. [ABSTRACT FROM AUTHOR]

Convection-allowing ensemble forecasts with perturbations to model physics, dynamics, and initial (IC) and lateral boundary conditions (LBC) generated by the Center for the Analysis and Prediction of Storms for the NOAA Hazardous Weather Testbed (HWT) Spring Experiments provide a unique opportunity to understand the relative impact of different sources of perturbation on convection-allowing ensemble diversity. Such impacts are explored in this two-part study through an object-oriented hierarchical cluster analysis (HCA) technique. In this paper, an object-oriented HCA algorithm, where the dissimilarity of precipitation forecasts is quantified with a nontraditional object-based threat score (OTS), is developed. The advantages of OTS-based HCA relative to HCA using traditional Euclidean distance and neighborhood probability-based Euclidean distance (NED) as dissimilarity measures are illustrated by hourly accumulated precipitation ensemble forecasts during a representative severe weather event. Clusters based on OTS and NED are more consistent with subjective evaluation than clusters based on traditional Euclidean distance because of the sensitivity of Euclidean distance to small spatial displacements. OTS improves the clustering further compared to NED. Only OTS accounts for important features of precipitation areas, such as shape, size, and orientation, and OTS is less sensitive than NED to precise spatial location and precipitation amount. OTS is further improved by using a fuzzy matching method. Application of OTS-based HCA for regional subdomains is also introduced. Part II uses the HCA method developed in this paper to explore systematic clustering of the convection-allowing ensemble during the full 2009 HWT Spring Experiment period. [ABSTRACT FROM AUTHOR]

Recent trends in precipitation and streamflow in the United States have become a particular focus of hydroclimatic research. The U.S. Hydro-Climatic Data Network (HCDN) has proven to be especially useful for the analysis of long-term streamflow trends. The U.S. Geological Survey (USGS) scientists selected sites for inclusion in the HCDN from the USGS stream-gauge network on the basis of streamflows being relatively free of nonclimatic anthropogenic influences. Consequently, most previous analyses of flow trends at those sites have either implicitly or explicitly attributed the trends to climate change and variability. In this paper, trends in seasonal and annual precipitation, and annual 7-day low, mean, and peak flows are examined for 48 medium-sized HCDN streams in the upper Mississippi (UM) water-resource region over 1939--2008. Using the concept of precipitation elasticity of flow, it is shown that the observed magnitudes of statistically significant increases in mean and low flows were up to a factor of 3 greater than expected from observed precipitation increases alone. Peak flows increased less than expected, and in the case of the Driftless Area at the center of the UM basin, decreased despite increased precipitation. It is proposed that the differences between expected and observed changes in streamflow can be explained by rural land-use changes in this principally agricultural region. [ABSTRACT FROM AUTHOR]

This paper examines hydrological variability and its changes in two different versions of a coupled ocean–atmosphere general circulation model developed at the National Oceanic and Atmospheric Administration/Geophysical Fluid Dynamics Laboratory and forced with estimates of future increases of greenhouse gas and aerosol concentrations. This paper is the second part, documenting potential changes in variability as greenhouse gases increase. The variance changes are examined using an ensemble of 8 transient integrations for an older model version and 10 transient integrations for a newer model. Monthly and annual data are used to compute the mean and variance changes. Emphasis is placed on computing and analyzing the variance changes for the middle of the twenty-first century and compared with those found in the respective control integrations. The hydrologic cycle intensifies because of the increase of greenhouse gases. In general, precipitation variance increases in most places. This is the case virtually everywhere the mean precipitation rate increases and many places where the precipitation decreases. The precipitation rate variance decreases in the subtropics, where the mean precipitation rate also decreases. The increased precipitation rate and variance, in middle to higher latitudes during late fall, winter, and early spring leads to increased runoff and its variance during that period. On the other hand, the variance changes of soil moisture are more complicated, because soil moisture has both a lower and upper bound that tends to reduce its fluctuations. This is particularly true in middle to higher latitudes during winter and spring, when the soil moisture is close to its saturation value at many locations. Therefore, changes in its variance are limited. Soil moisture variance change is positive during the summer, when the mean soil moisture decreases and is close to the middle of its allowable range. In middle to high northern latitudes, an increase in runoff and its variance during late winter and spring plus the decrease in soil moisture and its variance during summer lend support to the hypothesis stated in other publications that a warmer climate can cause an increasing frequency of both excessive discharge and drier events, depending on season and latitude. [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]

Extreme precipitation can have significant adverse impacts on infrastructure and property, human health, and local economies. This paper examines recent changes in extreme precipitation in the northeast United States. Daily station data from 58 stations missing less than 5% of days for the years 1979–2014 from the U.S. Historical Climatology Network were used to analyze extreme precipitation, defined as the top 1% of days with precipitation. A statistically significant (95% confidence level) increasing trend of the threshold for the top 1% of extreme precipitation events was found (0.3 mm yr−1). This increasing trend was due to both an increase in the frequency of extreme events and the magnitude of extreme events. Rainfall events ≥ 150 mm (24-h accumulation) increased in frequency from 6 events between 1979 and 1996 to 25 events between 1997 and 2014, a 317% increase. The annual daily maximum precipitation, or the highest recorded precipitation amount in a given year, increased by an average of 1.6 mm yr−1, a total increase of 58.0 mm. Decreasing trends in extreme precipitation were observed east of Lake Erie during the warm season. Increasing trends in extreme precipitation were most robust during the fall months of September, October, and November, and particularly at locations further inland. The analysis showed that increases in events that were tropical in nature, or associated with tropical moisture, led to the observed increase in extreme precipitation during the fall months. [ABSTRACT FROM AUTHOR]

This paper uses a case study to explore the potential of Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) and Challenging Minisatellite Payload (CHAMP) global positioning system (GPS) radio occultation (RO) satellite data over the eastern Pacific Ocean to improve analyses and mesoscale forecasts of landfalling atmospheric rivers (ARs) along the U.S. West Coast. The case study is from early November 2006 and was a very high-impact event in the Pacific Northwest where it created torrential rainfall and severe flooding. Recent studies have shown that the COSMIC data offshore have the ability to better define the vertical and horizontal structure of the strong AR. This paper extends the earlier work by assessing the impact of assimilating the COSMIC data into the Advanced Research Weather Research and Forecasting (ARW-WRF) mesoscale numerical model (using a nested mode with 36-, 12-, and 4-km grid sizes) on a key 24-h forecast. The data are assimilated using NCEP''s Gridpoint Statistical Interpolation (GSI), and impacts are evaluated using Special Sensor Microwave Imager (SSM/I) satellite observations over the ocean and precipitation observations over land. The assimilation of GPS RO soundings made use of a local refractivity observation operator as well as an advanced nonlocal excess phase observation operator that considers the effects of atmospheric horizontal gradients. The results show that the assimilation of GPS RO soundings improved the moisture analysis for this AR event. This result supports conclusions from earlier observing systems simulation experiment (OSSE) studies, but in a real event. The use of a nonlocal excess phase observation operator can produce larger and more robust analysis increments. Although this is a single case study, the results are likely representative of the potential impacts of assimilating COSMIC data in other extreme AR and precipitation events and in other regions affected by landfalling ARs, for example, western Europe, western South America, and New Zealand. [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]

Recently the National Aeronautics and Space Administration (NASA) Tropical Rainfall Measuring Mission (TRMM) project office made available a new product called the convective–stratiform heating (CSH). These are the datasets for vertical profiles of diabatic heating rates (the apparent heat source). These observed estimates of heating are obtained from the TRMM satellite’s microwave radiances and the precipitation radar. The importance of such datasets for defining the vertical distribution of heating was largely the initiative of Dr. W.-K. Tao from NASA’s Goddard Laboratory. The need to examine how well some of the current cumulus parameterization schemes perform toward describing the amplitude and the three-dimensional distributions of heating is addressed in this paper. Three versions of the Florida State University (FSU) global atmospheric model are run that utilize different versions of cumulus parameterization schemes; namely, modified Kuo parameterization, simple Arakawa–Schubert parameterization, and Zhang–McFarlane parameterization. The Kuo-type scheme used here relies on moisture convergence and tends to overestimate the rainfall generally compared to the TRMM estimates. The other schemes used here show only a slight overestimate of rain rates compared to TRMM; those invoke mass fluxes that are less stringent in this regard in defining cloud volumes. The mass flux schemes do carry out a total moisture budget for a vertical column model and include all components of the moisture budget and are not limited to the horizontal convergence of moisture. The authors carry out a numerical experimentation that includes over a hundred experiments from each of these models; these experiments differ only in their use of the cumulus parameterization. The rest of the model physics, resolution, and initial states are kept the same for each set of 117 forecasts. The strategy for this experimentation follows the authors’ previous studies with the FSU multimodel superensemble. This includes a 100-day training and a 17-day forecast phase, both of which include a large number of forecast experiments. The training phase provides a useful statistical database for tagging the systematic errors of the respective models. The forecast phase is designed to minimize the collective bias errors of these member models. In these forecasts the authors also include the ensemble mean and the multimodel superensemble. In this paper the authors examine model errors in their representations of the heating (amplitude, vertical level of maximum, and the geographical distributions). The main message of this study is that some cumulus parameterization schemes overestimate the amplitude of heating, whereas others carry lower values. The models also exhibit large errors in the placement of the vertical level of maximum heating. Some significant errors were also found in the geographical distributions of heating. The ensemble mean largely mimics the model features and also carries some large errors. The superensemble is more selective in reducing the three-dimensional collective bias errors of the models and provides the best short range forecasts, through hour 60, for the heating. This study shows that it is possible to diagnose some of the modeling errors in the heating for individual member models and that information can be important for correcting such features. [ABSTRACT FROM AUTHOR]

This paper describes the University of Washington Probability Forecast (PROBCAST), a Web-based portal to probabilistic weather predictions over the Pacific Northwest. PROBCAST products are derived from the output of a mesoscale ensemble system run at the University of Washington, with the fields being post-processed using Bayesian model averaging to produce sharp and reliable probabilistic predictions of temperature and precipitation. Based on research by University of Washington psychologists and human-interface specialists, a Web site has been constructed that allows for access to key elements of the probabilistic information produced by the system. The design approach ofthe PROBCAST system is explained in this paper as well as some of the challenges for future development. PROBCAST is intended to be a prototype for the kind of probabilistic forecast interface that could be used throughout the nation. [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]

This is the second of two papers that examine the organization of the precipitation field during central U.S. cold-season cyclones involving inverted troughs (ITs). The first paper (Part I) used a climatology and composites to find synoptic-scale differences between storms with precipitation located ahead of the IT (ahead cases) and those with precipitation located behind the IT (behind cases). This paper expands the conclusions in Part I through the use of a comparative case study between two cyclones. The first cyclone, on 29 October 1996, was an ahead case that produced heavy rainfall and was associated with a potential vorticity (PV) anomaly moving across the central plains. The IT formed in the lee of the Rockies prior to 0600 UTC 29 October and moved east into the northern plains over the next 18 h. The trough itself was coincident with the limiting streamline, which separated moist air rising over the warm front from dry air subsiding behind the cyclone. The second cyclone, on 17–18 January 1996, had precipitation on both sides of the IT and was associated with heavy snow and blizzard conditions in the northern plains and significant ice accumulation in the western Great Lakes. The IT was associated with large frontogenesis over the snow area. The ascent was further enhanced by a jet streak moving across southern Canada. Dynamically, the IT resembled a warm front, with veering winds with height and a strong frontal inversion. The mechanism that appeared to control the different precipitation organization between the two systems was the orientation of the PV anomalies and the airstreams associated with their secondary circulations. This resulted in a differing orientation of the baroclinicity north and east of the cyclone. In the ahead case, the rising branches of the secondary circulations forced by the northern and southern anomalies remained separate. This allowed the baroclinicity to develop along the traditional warm front, while the IT never developed a thermal gradient as it moved east. In the both sides case, the southern stream anomaly helped to fix the northern anomaly-forced jet streak in place, so that a strong temperature gradient developed along the IT with strong frontogenesis and warm-air advection observed behind the IT. As the frontal circulation developed, the direct circulation associated with the right entrance region of a jet streak enhanced the ascent to the west of the IT. A conceptual model is proposed based upon the case studies and the results of Part I. This model can be used by forecasters to differentiate between the precipitation regimes in cyclones associated with ITs. [ABSTRACT FROM AUTHOR]

Based upon trends in observed climate, extreme events are thought to be increasing in frequency and/or magnitude. This change in extreme events is attributed to enhancement of the hydrologic cycle caused by increased greenhouse gas concentrations. Results are presented of relatively long (50 yr) regional climate model simulations of the western United States examining the sensitivity of climate and extreme events to a doubling of preindustrial atmospheric CO2 concentrations. These results indicate a shift in the temperature distribution, resulting in fewer cold days and more hot days; the largest changes occur at high elevations. The rainfall distribution is also affected; total rain increases as a result of increases in rainfall during the spring season and at higher elevations. The risk of flooding is generally increased, as is the severity of droughts and heat waves. These results, combined with results of decreased snowpack and increased evaporation, could further stress the water supply of the western United States. [ABSTRACT FROM AUTHOR]

Rain-rate retrievals from passive microwave sensors are useful for a number of applications related to weather forecasting. For example, in the United States, such estimates are useful for offshore rainfall systems approaching land and in regions where the Weather Surveillance Radar-1988 Doppler (WSR-88D) network is inadequate. Improvements have been made to the rain-rate retrieval from the Advanced Microwave Sounding Unit (AMSU) on board the National Oceanic and Atmospheric Administration (NOAA) Polar Orbiting Environmental Satellites (POESs). The new features of the improved rain-rate algorithm include a two-stream correction of the satellite brightness temperatures at 89 and 150 GHz, cloud- and rain-type classification for better retrieval of rain rate, and removal of the two ad hoc thresholds in the ice water path (IWP) and effective diameter (De) retrieval where the scattering signals are very small. In this paper, the new algorithm has been compared to the previous NOAA operational algorithm. In particular, the better utilization of the measurements at and above 150 GHz is shown to produce improved sensitivity to light rainfall associated with winter season storm systems. This improvement is demonstrated through a wintertime case study over southern California during February 2003. [ABSTRACT FROM AUTHOR]

The relative contributions of locally evapotranspired (i.e., recycled) moisture versus externally advected water vapor for the growing-season precipitation of the U.S. Corn Belt and surrounding areas (1.23 × 106 km2) are estimated in this paper. Four May–August seasons with highly contrasting precipitation and crop yields (1975, 1976, 1979, and 1988) are investigated. A simple recycling equation—developed from the traditional atmospheric moisture budget and involving regional evapotranspiration and atmospheric water vapor inflow—is applied on daily, monthly, and seasonal time scales. Several atmospheric moisture budget components {moisture flux divergence [MFD], storage change [or change in precipitable water (dPW)], and inflow [IF]} are evaluated for 24-h periods using standard finite difference and line integral methods applied to objectively analyzed U.S. and Canadian rawinsonde data (50-hPa vertical resolution, surface to 300 hPa) for 0000 and 1200 UTC. Daily area-averaged precipitation (P) totals are derived from approximately 600 evenly distributed (but ungridded) recording rain gauges. Evapotranspiration (E) is estimated as the residual of the moisture budget equation for 24-h periods; values compare favorably with the few existing observations. Traditional budget results show the following: E is weakly related to P on monthly and seasonal time scales; there is surprising interannual constancy of seasonal E cycles and averages given the large variation in resulting crop yields; and monthly and seasonal variability of the export of the E - P surplus is determined largely by the horizontal velocity divergence component of MFD. New recycling analyses suggest that the contribution of local E to P (i.e., PE/P) is relatively small and remarkably consistent (largely 0.19–0.24) for monthly and seasonal periods, despite large P and crop yield variations. However, the monthly/seasonal averaging process is found to completely mask a striking decrease of daily PE/P (from approximately 0.30 to 0.15) with increasing P from 0 to 8 mm day-1. Unique and detailed analyses of P-stratified daily moisture budget results provide key insights into apparent contradictions between daily and monthly/seasonal recycling and related results and concomitant interannual variability, especially for the very dry 1988 season. Interpretation is facilitated by the use of modeled daily global radiation values, measured (instantaneous) and modeled (monthly) soil moisture, United States Department of Agriculture (USDA) crop yield estimates, and satellite normalized difference vegetation index (NDVI) imagery. This paper shows that land–atmosphere interactions are intimately involved in pronounced seasonal climate anomalies for the world's richest agricultural region, but apparently with considerable complexity that includes plant behavior, solar radiation forcing, and challenging time-scale interrelations. [ABSTRACT FROM AUTHOR]

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

This paper is the first of two papers that examines the organization of the precipitation field in central U.S. cold-season cyclones involving inverted troughs. The first portion of the study examines the varying precipitation distribution that occurred during a 6-yr synoptic climatology of inverted trough cases. The definition of inverted trough cases has been expanded from the groundbreaking work by Keshishian et al. by 1) not requiring a closed cyclonic isobar along the frontal wave along the conventional surface front and 2) not requiring a surface thermal gradient to be present along the inverted trough. Only 8.5% of the expanded dataset produced the precipitation primarily occurring to the west of the inverted trough ("behind" cases) as seen in Keshishian et al. The largest group of cases, comprising about 40% of the cases, produced precipitation that primarily occurred between the inverted trough and the conventional warm front ("ahead" cases). A composite study compared a subset of the ahead cases with a subset of the behind cases. The ahead cases tended to be more progressive with a stronger jet stream located over the center of the parent low. Broad warm-air advection and frontogenesis in the lower troposphere were observed between the inverted trough and the surface warm front. Cold-air advection to the west of the inverted trough precluded the development of "wraparound precipitation." In contrast, the behind cases had a stronger low-latitude wave couplet with a trough upstream of the surface low and a ridge downstream. The region of warm-air advection and frontogenesis were displaced to the west of the inverted trough and surface cyclone. In addition, the entrance region of a southwest-northeast-oriented jet streak aided the development of ascent to the west of the inverted trough while precluding the development of precipitation to the north of the conventional warm front. Thus, the inverted trough tended to act like a warm front in behind cases,... [ABSTRACT FROM AUTHOR]

This paper describes a publicly available, long-term (1915-2011), hydrologically consistent dataset for the conterminous United States, intended to aid in studies of water and energy exchanges at the land surface. These data are gridded at a spatial resolution of latitude/longitude and are derived from daily temperature and precipitation observations from approximately 20 000 NOAA Cooperative Observer (COOP) stations. The available meteorological data include temperature, precipitation, and wind, as well as derived humidity and downwelling solar and infrared radiation estimated via algorithms that index these quantities to the daily mean temperature, temperature range, and precipitation, and disaggregate them to 3-hourly time steps. Furthermore, the authors employ the variable infiltration capacity (VIC) model to produce 3-hourly estimates of soil moisture, snow water equivalent, discharge, and surface heat fluxes. Relative to an earlier similar dataset by Maurer and others, the improved dataset has 1) extended the period of analysis (1915-2011 versus 1950-2000), 2) increased the spatial resolution from ⅛° to , and 3) used an updated version of VIC. The previous dataset has been widely used in water and energy budget studies, climate change assessments, drought reconstructions, and for many other purposes. It is anticipated that the spatial refinement and temporal extension will be of interest to a wide cross section of the scientific community. [ABSTRACT FROM AUTHOR]

Numerical models have long predicted that the deforestation of the Amazon would lead to large regional changes in precipitation and temperature, but the extratropical effects of deforestation have been a matter of controversy. This paper investigates the simulated impacts of deforestation on the northwest United States December-February climate. Integrations are carried out using the Ocean-Land-Atmosphere Model (OLAM), here run as a variable-resolution atmospheric GCM, configured with three alternative horizontal grid meshes: 1) 25-km characteristic length scale (CLS) over the United States, 50-km CLS over the Andes and Amazon, and 200-km CLS in the far-field; 2) 50-km CLS over the United States, 50-km CLS over the Andes and Amazon, and 200-km CLS in the far-field; and 3) 200-km CLS globally. In the high-resolution simulations, deforestation causes a redistribution of precipitation within the Amazon, accompanied by vorticity and thermal anomalies. These anomalies set up Rossby waves that propagate into the extratropics and impact western North America. Ultimately, Amazon deforestation results in 10%-20% precipitation reductions for the coastal northwest United States and the Sierra Nevada. Snowpack in the Sierra Nevada experiences declines of up to 50%. However, in the coarse-resolution simulations, this mechanism is not resolved and precipitation is not reduced in the northwest United States. These results highlight the need for adequate model resolution in modeling the impacts of Amazon deforestation. It is concluded that the deforestation of the Amazon can act as a driver of regional climate change in the extratropics, including areas of the western United States that are agriculturally important. [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]

Land-atmosphere (L-A) interactions play a critical role in determining the diurnal evolution of land surface and planetary boundary layer (PBL) temperature and moisture states and fluxes. In turn, these interactions regulate the strength of the connection between surface moisture and precipitation in a coupled system. To address model deficiencies, recent studies have focused on development of diagnostics to quantify the strength and accuracy of the land-PBL coupling at the process level. In this paper, a diagnosis of the nature and impacts of local land-atmosphere coupling (LoCo) during dry and wet extreme conditions is presented using a combination of models and observations during the summers of 2006 and 2007 in the U.S. southern Great Plains. A range of diagnostics exploring the links and feedbacks between soil moisture and precipitation is applied to the dry/wet regimes exhibited in this region, and in the process, a thorough evaluation of nine different land-PBL scheme couplings is conducted under the umbrella of a high-resolution regional modeling test bed. Results show that the sign and magnitude of errors in land surface energy balance components are sensitive to the choice of land surface model, regime type, and running mode. In addition, LoCo diagnostics show that the sensitivity of L-A coupling is stronger toward the land during dry conditions, while the PBL scheme coupling becomes more important during the wet regime. Results also demonstrate how LoCo diagnostics can be applied to any modeling system (e.g., reanalysis products) in the context of their integrated impacts on the process chain connecting the land surface to the PBL and in support of hydrological anomalies. [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]

In this paper, the authors verify probability of precipitation (PoP) forecasts provided by the National Weather Service (NWS), The Weather Channel (TWC), and CustomWeather (CW). The n-day-ahead forecasts, where n ranges from 1 to 3 for the NWS, from 1 to 9 for TWC, and from 1 to 14 for CW, are analyzed. The dataset includes almost 13 million PoP forecasts, or about 500 000 PoPs per provider per day of lead time. Data were collected over a 2-yr period (1 November 2008-31 October 2010) at 734 observation stations across the contiguous United States. In addition to verifying these PoP forecasts in an absolute sense, relative comparisons are made between the providers. It is found that, in most cases, TWC does not add skill to NWS forecasts. Perhaps most interestingly, it is also found that CW does have the ability to forecast PoPs at a resolution of 0.01. [ABSTRACT FROM AUTHOR]

The U.S. Climate Reference Network (USCRN) was deployed between 2001 and 2008 for the purpose of yielding high-quality and temporally stable in situ climate observations in pristine environments over the twenty-first century. Given this mission, USCRN stations are engineered to operate largely autonomously with great reliability and accuracy. A triplicate approach is used to provide redundant measurements of temperature and precipitation at each location, allowing for observations at a specific time to be compared for quality control. This approach has proven to be robust in the most extreme environments, from extreme cold (−49°C) to extreme heat (++52°C), in areas of heavy precipitation (4700 mm yr−1), and in locations impacted by strong winds, freezing rain, and other hazards. In addition to a number of stations enduring extreme winter environments in Alaska and the northern United States, seven of the USCRN stations are located at elevations over 2000 m, including stations on Mauna Loa, Hawaii (3407 m) and on Niwot Ridge above Boulder, Colorado (2996 m). The USCRN temperature instruments and radiation shield have also been installed and run successfully at a station on the Quelccaya Ice Cap in Peru (5670 m). This paper reviews the performance of the USCRN station network during its brief lifetime and the potential utility of its triplicate temperature instrument configuration for measuring climate change at elevation. [ABSTRACT FROM AUTHOR]

In an earlier global climate-change study, air temperature and precipitation data for the entire twenty-first century simulated from five general circulation models were used as input to precalibrated watershed models for 14 selected basins across the United States. Simulated daily streamflow and energy output from the watershed models were used to compute a range of statistics. With a side-by-side comparison of the statistical analyses for the 14 basins, regional climatic and hydrologic trends over the twenty-first century could be qualitatively identified. Low-flow statistics (95%% exceedance, 7-day mean annual minimum, and summer mean monthly streamflow) decreased for almost all basins. Annual maximum daily streamflow also decreased in all the basins, except for all four basins in California and the Pacific Northwest. An analysis of the supply of available energy and water for the basins indicated that ratios of evaporation to precipitation and potential evapotranspiration to precipitation for most of the basins will increase. Probability density functions (PDFs) were developed to assess the uncertainty and multimodality in the impact of climate change on mean annual streamflow variability. Kolmogorov--Smirnov tests showed significant differences between the beginning and ending twenty-first-century PDFs for most of the basins, with the exception of four basins that are located in the western United States. Almost none of the basin PDFs were normally distributed, and two basins in the upper Midwest had PDFs that were extremely dispersed and skewed. [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]

Previous research has shown that the impacts of ENSO variability on the winter climate of the western United States vary on decadal time scales. In this study, the relationship between fall season ENSO conditions and winter circulation anomalies over the western United States is shown to vary with phasing of the Pacific decadal oscillation (PDO). During the PDO cold phase of 1948--76, the majority of fall season El Niño events did not precede winter troughing anomalies over the southwestern states that are typically associated with above-normal winter precipitation in this region. In contrast, during the PDO warm phase of 1977--98, fall season El Niño conditions did precede southwestern winter troughing anomalies in all but one instance. Fall season La Niña conditions during both the cold and warm phases of the PDO reliably preceded winter season high pressure ridging centered off the Pacific coast. These results highlight uncertainty on decadal time scales surrounding the use of fall ENSO conditions, particularly El Niño events, as a seasonal climate forecast tool. [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]

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

Model output statistics (MOS) guidance forecasts have been produced at stations and provided to National Weather Service forecasters and private entities for over three decades. As the numerical weather prediction models became more accurate, MOS followed that trend. Up until a few years ago, the MOS produced at observation locations met the basic need for guidance. With the advent of the Interactive Forecast Preparation System and the National Digital Forecast Database, gridded MOS forecasts became needed as guidance for forecasters. One method of providing such grids is to objectively analyze the MOS forecasts for points. A basic successive correction method has been extended to analyze MOS forecasts and surface weather variables. This method is being applied to MOS forecasts to provide guidance for producing grids of sensible weather elements such as temperature, clouds, and snow amount. Guidance forecasts have been implemented for the conterminous United States for most weather elements contained in routine weather forecasts. This paper describes the method applied to daytime maximum temperature over the conterminous United States and gives example results. [ABSTRACT FROM AUTHOR]

The Weather Channel (TWC) is a leading provider of weather information to the general public. In this paper the reliability of their probability of precipitation (PoP) forecasts over a 14-month period at 42 locations across the United States is verified. It is found that PoPs between 0.4 and 0.9 are well calibrated for near-term forecasts. However, overall TWC PoPs are biased toward precipitation, significantly so during the warm season (April–September). PoPs lower than 0.3 and above 0.9 are not well calibrated, a fact that can be explained by TWC’s forecasting procedure. In addition, PoPs beyond a 6-day lead time are miscalibrated and artificially avoid 0.5. These findings should help the general public to better understand TWC’s PoP forecasts and provide important feedback to the TWC so that they may improve future performance. [ABSTRACT FROM AUTHOR]

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

Motivated by the intensity and severity of winds and temperature falls that frequently accompany rapidly developing cold fronts in northern Utah, this paper presents a 25-yr climatology of strong cold frontal passages over the Intermountain West and adjoining western United States. Using conventional surface observations and the North American Regional Reanalysis, strong cold frontal passages are identified based on a temperature fall of 7°C or greater in a 2–3-h period, a concurrent pressure rise of 3 hPa or greater, and the presence of a large-scale 700-hPa temperature gradient of at least 6°C (500 km)-1. The number of strong cold frontal passages exhibits a strong continental signature with very few events (<10) along the Pacific coast and more than 200 events east of the Continental Divide. The number of events increases dramatically from the Cascade Mountains and Sierra Nevada to northern Utah, indicating that the Intermountain West is a frequent cold front breeding ground. A composite of the 25 strongest events at Salt Lake City (based on the magnitude of the temperature fall) reveals that confluent deformation acting on a broad baroclinic zone over central Nevada commonly initiates Intermountain frontogenesis. The confluent deformation develops in southwesterly large-scale flow and appears to be enhanced by flow deflection around the Sierra Nevada. Quasi-stationary development and intensification of the southwest–northeast-oriented cold front then occurs as a mobile upper-level trough approaches from the west. The front becomes mobile as cold advection and ascent associated with the upper-level trough overtake the low-level front. Cloud and precipitation observations suggest that differential diabatic heating contributes to the rapid frontal intensification in many events. [ABSTRACT FROM AUTHOR]

This paper presents two-dimensional (2D) idealized simulations at 1-km grid spacing using the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5) in order to illustrate how a series of ridges along a broad windward slope can impact the precipitation distribution and simulated microphysics. The number of windward ridges for a 2000-m mountain of 50-km half-width is varied from 0 to 16 over a 150-km distance using different stratifications, freezing levels, uniform ambient flows, and ridge amplitudes. A few (200–400 m) windward ridges can enhance the precipitation locally over each ridge crest by a factor of 2–3. Meanwhile, a series of 8–16 ridges that are 200–400 m in height can increase the net precipitation averaged over the windward slope by 10%–35%. This average precipitation enhancement is maximized when the ridge spacing is relatively small (<20 km), since there is less time for subsidence drying within the valleys and the mountain waves become more evanescent, which favors a simple upward and downward motion couplet over each ridge. In addition, small ridge spacing is shown to have a synergistic effect on precipitation over the lower windward slope, in which an upstream ridge helps increase the precipitation over the adjacent downwind ridge. There is little net precipitation enhancement by the ridges for small moist Froude numbers (Fr < 0.8), since flow blocking limits the flow up and over each ridge. For a series of narrow ridges (∼10 km wide), the largest precipitation enhancement for a 500-mb freezing level occurs over lower windward slope of the barrier through warm-rain processes. In contrast, a 1000-mb freezing level has the largest precipitation enhancement over the middle and upper portions of a barrier for a series of narrow (∼10 km wide) ridges given the horizontal advection of snow aloft. [ABSTRACT FROM AUTHOR]

This paper presents and evaluates a method for the construction of long-range and wide-area temporal spatial datasets of daily precipitation and temperature (maximum and minimum). This method combines the interpolation of daily ratios/fractions derived from ground-based meteorological station records and respective fields of monthly estimates. Data sources for the described implementation over the conterminous United States (CONUS) are two independent and quality-controlled inputs: 1) an enhanced compilation of daily observations derived from the National Climatic Data Center digital archives and 2) the Parameter–Elevation Regressions on Independent Slopes Model (PRISM) maps. The results of this study show that this nonconventional interpolation preserves the spatial and temporal distribution of both the PRISM maps (monthly, topography-sensitive patterns) and the original daily observations. Statistics of a preliminary point comparison with the observed values at high-quality and independent reference sites show a reasonable agreement and a noticeable improvement over the nearest station method in orographically sensitive areas. The implemented datasets provide daily precipitation and temperature values at 2.5-min (around 4 km) resolution for 1960–2001. Combining seamless spatial and temporal coverage and topographic sensitivity characteristics, the datasets offer the potential for supporting current and future regional and historical hydrologic assessments over the CONUS. [ABSTRACT FROM AUTHOR]

High-quality, daily meteorological data at high spatial resolution are essential for a variety of hydrologic and ecological modeling applications that support environmental risk assessments and decision making. This paper describes the development, application, and assessment of methods to construct daily high-resolution (∼50-m cell size) meteorological grids for the 2003 calendar year in the Upper South Santiam Watershed (USSW), a 500-km2 mountainous catchment draining the western slope of the Oregon Cascade Mountains. Elevations within the USSW ranged from 194 to 1650 m. Meteorological elements modeled were minimum and maximum temperature; total precipitation, rainfall, and snowfall; and solar radiation and radiation-adjusted maximum temperature. The Parameter–Elevation Regressions on Independent Slopes Model (PRISM) was used to interpolate minimum and maximum temperature and precipitation. The separation of precipitation into rainfall and snowfall components used a temperature-based regression function. Solar radiation was simulated with the Image-Processing Workbench. Radiation-based adjustments to maximum temperature employed equations developed from data in the nearby H. J. Andrews Experimental Forest. The restrictive terrain of the USSW promoted cold-air drainage and temperature inversions by reducing large-scale airflow. Inversions were prominent nearly all year for minimum temperature and were noticeable even for maximum temperature during the autumn and winter. Precipitation generally increased with elevation over the USSW. In 2003, precipitation was nearly always in the form of rain at the lowest elevations but was about 50% snow at the highest elevations. Solar radiation followed a complex pattern related to terrain slope, aspect, and position relative to other terrain features. Clear, sunny days with a large proportion of direct radiation exhibited the greatest contrast in radiation totals, whereas cloudy days with primarily diffuse radiation showed little contrast. Radiation-adjusted maximum temperatures showed similar patterns. The lack of a high-quality observed dataset was a major issue in the interpolation of precipitation and solar radiation. However, observed data available for the USSW were superior to those available for most mountainous regions in the western United States. In this sense, the methods and results presented here can inform others performing similar studies in other mountainous regions. [ABSTRACT FROM AUTHOR]

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

The Commonwealth Scientific and Industrial Research Organisation (CSIRO) Mark 2 global coupled climatic model has been used to generate a 10 000-yr simulation of “present” climate. The resultant dataset has been used to investigate a number of aspects of extremes associated with annual mean rainfall. Multimillennial time series of normalized rainfall amounts for selected points are used to highlight secular variability, spatial variations, and the differences between pluvial and drought conditions. Global distributions are also presented for selected rainfall characteristics, including the frequency of occurrence of specified rainfall anomalies with annual durations, the frequency of occurrence of 5-yr sequences of specified rainfall anomalies, and the maximum and minimum normalized rainfall amounts attained in the simulation. Such features cannot be obtained from observations because of their limited duration. A case study is also made of a megadrought over the southwestern United States, together with an analysis of the associated causal mechanisms. Given the exclusion of all external forcing from the model, it is concluded that the extreme annual mean rainfall extremes presented in the paper are attributable to stochastic events. [ABSTRACT FROM AUTHOR]

This paper examines year-to-year variations in the large-scale summertime hydrologic cycle over the southwestern United States using a suite of regional model simulations and surface- and upper-air-based observations. In agreement with previous results, it is found that observed interannual precipitation variations in this region can be subdivided into two spatiotemporal regimes—one associated with rainfall variability over the southwestern portion of the domain centered on Arizona and the other associated with variations over the southeastern portion centered on western Texas and eastern New Mexico. Because of the limited duration of the model simulation data, it is possible to only investigate one positive rainfall season over the Arizona region and one negative rainfall season over the New Mexico region. From these investigations it appears that for the positive rainfall anomalies over Arizona excess seasonal precipitation is balanced by both enhanced evaporation and vertically integrated large-scale moisture flux convergence. Vertical profiles of these terms indicate that the anomalous large-scale moisture flux convergence is actually related to a decrease in the mean large-scale moisture flux divergence aloft; below 800 mb there is a decrease in the mean moisture flux convergence typically found at these levels, which in turn produces anomalous moisture divergence from the region. For the negative rainfall anomalies over New Mexico similar results, but of opposite sign, are found; one exception is that at the lowest levels there is an additional (negative) contribution to the vertically integrated moisture flux convergence anomaly related to a weakening of the mean low-level moisture flux convergence during the low-rainfall year. Further studies using two different model simulations with the same large-scale dynamic forcing but differing initial soil moisture values indicate that similar balances are also found for rainfall anomalies related to surface soil moisture changes within the domain, suggesting that the changes in large-scale moisture flux convergence described above can be attributed to both year-to-year variations in the regional land–atmosphere interactions as well as variations in the large-scale circulation patterns. [ABSTRACT FROM AUTHOR]

This paper examines the accuracy of high-resolution nested mesoscale model simulations of surface climate. The nesting capabilities of the atmospheric fifth-generation Pennsylvania State University (PSU)–National Center for Atmospheric Research (NCAR) Mesoscale Model (MM5) were used to create high-resolution, 5-yr climate simulations (from 1 October 1994 through 30 September 1999), starting with a coarse nest of 20 km for the western United States. During this 5-yr period, two finer-resolution nests (5 and 1.7 km) were run over the Yampa River basin in northwestern Colorado. Raw and bias-corrected daily precipitation and maximum and minimum temperature time series from the three MM5 nests were used as input to the U.S. Geological Survey’s distributed hydrologic model [the Precipitation Runoff Modeling System (PRMS)] and were compared with PRMS results using measured climate station data. The distributed capabilities of PRMS were provided by partitioning the Yampa River basin into hydrologic response units (HRUs). In addition to the classic polygon method of HRU definition, HRUs for PRMS were defined based on the three MM5 nests. This resulted in 16 datasets being tested using PRMS. The input datasets were derived using measured station data and raw and bias-corrected MM5 20-, 5-, and 1.7-km output distributed to 1) polygon HRUs and 2) 20-, 5-, and 1.7-km-gridded HRUs, respectively. Each dataset was calibrated independently, using a multiobjective, stepwise automated procedure. Final results showed a general increase in the accuracy of simulated runoff with an increase in HRU resolution. In all steps of the calibration procedure, the station-based simulations of runoff showed higher accuracy than the MM5-based simulations, although the accuracy of MM5 simulations was close to station data for the high-resolution nests. Further work is warranted in identifying the causes of the biases in MM5 local climate simulations and developing methods to remove them. [ABSTRACT FROM AUTHOR]

An efficient method for precipitation recycling calculations based on the general bulk recycling model is developed and applied to the Amazon basin region. The results are compared with the estimates provided by simplified recycling models, and with the estimations obtained in other studies. Then the numerical procedure is adjusted to the modified recycling model incorporating some effects of incomplete vertical mixing of water in the tropospheric column as described in Part I of this paper. The modified numerical model is applied to the Amazon basin region and the results are compared with those from the unmodified model. It is seen from the results that the effects related to an incomplete vertical mixing produce significant changes in the level and distribution of precipitation recycling over the Amazon basin. The general changes in the recycling patterns occurring as a result of modifications include a significant increase in the contribution of recycled water to precipitation in the eastern part of the region and weakening continental recycling gradients. The regional recycling ratio values for the Amazon basin estimated by the modified model are significantly higher than the values provided by the unmodified model. The results of application of the models to several other regions on the globe are described in short. In particular, a comparison of the results with the estimates by the modified Budyko model is made for different regions, and it is found that the modified Budyko's model estimates are, in general, closer to the modified model numerical results than it was for the unmodified models. The results for precipitation recycling over the central United States are presented in more detail. For this region, the information on the degree of incomplete vertical mixing available from the GCM tracer transport modeling results has been used in the modified model calculations. It has been found that, as distinct from the Amazon basin, the effects related to an incomplete vertical mixing do not produce significant changes in the level and distribution of precipitation recycling over the central United States. [ABSTRACT FROM AUTHOR]

In this paper, the authors analyze simulations of present and future climates in the western United States performed with four regional climate models (RCMs) nested within two global ocean–atmosphere climate models. The primary goal here is to assess the range of regional climate responses to increased greenhouse gases in available RCM simulations. The four RCMs used different geographical domains, different increased greenhouse gas scenarios for future-climate simulations, and (in some cases) different lateral boundary conditions. For simulations of the present climate, RCM results are compared to observations and to results of the GCM that provided lateral boundary conditions to the RCM. For future-climate (increased greenhouse gas) simulations, RCM results are compared to each other and to results of the driving GCMs. When results are spatially averaged over the western United States, it is found that the results of each RCM closely follow those of the driving GCM in the same region in both present and future climates. This is true even though the study area is in some cases a small fraction of the RCM domain. Precipitation responses predicted by the RCMs in many regions are not statistically significant compared to interannual variability. Where the predicted precipitation responses are statistically significant, they are positive. The models agree that near-surface temperatures will increase, but do not agree on the spatial pattern of this increase. The four RCMs produce very different estimates of water content of snow in the present climate, and of the change in this water content in response to increased greenhouse gases. [ABSTRACT FROM AUTHOR]

Precipitation is arguably the most important meteorological forcing variable in land surface modeling. Many types of precipitation datasets exist (with various pros and cons) and include those from atmospheric data assimilation systems, satellites, rain gauges, ground radar, and merged products. These datasets are being evaluated in order to choose the most suitable precipitation forcing for real-time and retrospective simulations of the Global Land Data Assimilation System (GLDAS). This paper first presents results of a comparison for the period from March 2002 to February 2003. Later, GLDAS simulations 14 months in duration are analyzed to diagnose impacts on GLDAS land surface states when using the Mosaic land surface model (LSM). A comparison of seasonal total precipitation for the continental United States (CONUS) illustrates that the Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP) has the closest agreement with a CPC rain gauge dataset for all seasons except winter. The European Centre for Medium-Range Weather Forecasts (ECMWF) model performs the best of the modeling systems. The satellite-only products [the Tropical Rainfall Measuring Mission (TRMM) Real-time Multi-satellite Precipitation Analysis and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN)] suffer from a few deficiencies—most notably an overestimation of summertime precipitation in the central United States (200–400 mm). CMAP is the most closely correlated with daily rain gauge data for the spring, fall, and winter seasons, while the satellite-only estimates perform best in summer. GLDAS land surface states are sensitive to different precipitation forcing where percent differences in volumetric soil water content (SWC) between simulations ranged from -75% to +100%. The percent differences in SWC are generally 25%–75% less than the percent precipitation differences, indicating that GLDAS and specifically the Mosaic LSM act to generally “damp” precipitation differences. Areas where the percent changes are equivalent to the percent precipitation changes, however, are evident. Soil temperature spread between GLDAS runs was considerable and ranged up to ±3.0 K with the largest impact in the western United States. [ABSTRACT FROM AUTHOR]

This paper reports the results from a regional validation study of monthly precipitation products generated from sensor measurements aboard the Tropical Rainfall Measuring Mission (TRMM) satellite. The study analyzed 4 yr of precipitation estimates (1998–2001) produced from data collected by the TRMM Microwave Imager (TMI) and the precipitation radar (PR) and compared them with corresponding estimates computed using 5-min rain accumulations from 66 rain gauges in the Oklahoma Mesonet. The applied methodology estimated bulk climate-scale sampling and retrieval errors and biases for the TMI and PR at two areal resolutions: 1° × 1° and 2° × 5°. The approach taken in this study generated two gauge-inferred gridded estimates of monthly precipitation over the study period: 1) G[sub 0] , which was computed by performing a complete integration of the monthly gauge time series, and 2) G[sub S] , which consisted of gauge-inferred rain rates subsampled to TRMM overpasses at a gridded resolution of 1° × 1°, with monthly precipitation estimates derived from the bulk statistics collected during each month of the study. The variable G[sub S] depends on the areal swath of each sensor and so yields two sensor-dependent estimates of gauge-inferred precipitation (G[sub TMI] , G[sub PR] ). The advantage of this approach is that it allows for the separation of retrieval and sampling errors, because the subsampled gauge estimates include the temporal sampling errors associated with the satellite sampling. The overall random sampling and retrieval errors for the PR exceeded the TMI errors for the study period, but the PR showed a greater reduction in the errors when the scale was increased to 2° × 5°. Annual coefficients of variation were also generally lower for the PR than the TMI at this scale. This result was consistent with PR retrieval biases, which were positive over all 4 yr, exceeding the TMI biases in every year of the study. [ABSTRACT FROM AUTHOR]

Flash floods are among the most devastating natural weather hazards in the United States, causing an average of more than 225 deaths and $4 billion in property damage annually. As a result, prediction of flash floods in an accurate and timely fashion is one of the most important challenges in weather prediction. Data from geostationary and polar-orbiting satellites are significant sources of information for the diagnosis and prediction of heavy precipitation and flash floods. Geostationary satellites are especially important for their unique ability simultaneously to observe the atmosphere and its cloud cover from the global scale down to the storm scale at high resolution in both time (every 15 min) and space (1–4 km). This capability makes geostationary satellite data ideally suited for estimating and predicting heavy precipitation, especially during flash-flood events. Presented in this paper are current and future efforts in the National Environmental Satellite, Data, and Information Service that support National Weather Service River Forecast Centers and Weather Forecast Offices during extreme-precipitation events. [ABSTRACT FROM AUTHOR]

This paper evaluates the fifth-generation Pennsylvania State University-National Center for Atmospheric Research Mesoscale Model (MM5) precipitation forecasts over the northeastern United States to show the effects of increasing resolution, the spatial variations in model skill, and the impact of convective parameterizations on the MM5 precipitation forecasts. The MM5 is verified during the cool seasons (November-March) of 19992001 and the warm season (May-September) of 2000 using approximately 500 cooperative observer and National Weather Service precipitation sites. During the cool season, the 12-km MM5 produces excessive precipitation immediately downwind of the Great Lakes and along the windward slopes of the Appalachians and too little precipitation in the lee of the barrier. The 36-km MM5 has slightly more skill than at 12-km grid spacing for the light to moderate thresholds, while the 12-km precipitation forecasts are slightly better on average for the heavy precipitation events. During the 2000/01 cool season, two separate MM5 runs were completed twice daily using the National Centers for Environmental Prediction Eta (Eta-MM5) and Aviation (AVN-MM5) Models for initial and boundary conditions. The Eta-MM5 had slightly lower (better) rms errors than the AVN-MM5 for the weak to moderate thresholds (2.54-50.8 mm in 24 h), while for the heavier thresholds the AVN-MM5 had significantly lower rms errors than the Eta-MM5. As a result, the 36-km AVN-MM5 was as skillful as the 12-km Eta-MM5 for these higher thresholds. During the warm season, both the 36- and 12-km grid spacings overpredict precipitation just inland of the coast and significantly underpredict farther inland over the Appalachians. This coastal overprediction originated from an overactive Kain-Fritsch (KF) convective parameterization, while the inland underprediction is associated with a low-level dry bias during the warm season. A representative case study shows that both the Betts-Miller and... [ABSTRACT FROM AUTHOR]

This paper describes a methodology for coupling the Simplified Simple Biosphere Model (SSiB) to the regional Eta Model of the National Centers for Environmental Prediction (NCEP), and presents the application of the coupled system in regional simulation studies. The coupled Eta–SSiB model is used to study the impact of land surface processes and land surface initialization on the regional water and energy cycle in an extreme climate event, by comparing the results from the Eta–SSiB with those from the Eta–bucket model. Simulations from both models spanned 3 months via a succession of 48-hr simulations over June, July, and August 1993, a summer of heavy flooding in the United States. The monthly and seasonal means from the simulations in both model runs are compared. The Eta–SSiB model produces more realistic monthly mean precipitation over the United States and the flood areas. The improvements are mainly manifested in the intensity of the heavy rainfall and its spatial distribution. The results demonstrate that even with a short-term simulation, a more realistic representation of land surface processes and land surface initialization improves the monthly and seasonal means of the simulated regional precipitation for the summer of 1993. In addition to precipitation, the simulations of surface air temperature are also evaluated and they show that the Eta–SSiB model produces reasonable results over most of the United States, with the exception of a cold bias at night in the mountainous western region of the United States. To understand the mechanisms of land surface–atmosphere interactions and the causes for the differences in the Eta–SSiB and the Eta–bucket simulations, the water cycle in the atmosphere–land system and the energy balance at the land surface are analyzed. The changes in (a) spatial distribution and diurnal cycle of surface latent and sensible heat, and (b) low-level moisture flux... [ABSTRACT FROM AUTHOR]