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Precipitation changes the physical and biological characteristics of an ecosystem. Using a precipitation-based conditional sampling technique and a 14 year dataset from a 25 m micrometeorological tower in a high-elevation subalpine forest, we examined how warm-season precipitation affected the above-canopy diel cycle of wind and turbulence, net radiation Rnet, ecosystem eddy covariance fluxes (sensible heat H, latent heat LE, and CO2 net ecosystem exchange NEE) and vertical profiles of scalars (air temperature Ta, specific humidity q, and CO2 dry mole fraction χc). This analysis allowed us to examine how precipitation modified these variables from hourly (i.e., the diel cycle) to multi-day time-scales (i.e., typical of a weather-system frontal passage). During mid-day we found: (i) even though precipitation caused mean changes on the order of 50-70% to Rnet, H, and LE, the surface energy balance (SEB) was relatively insensitive to precipitation with mid-day closure values ranging between 70-80%, and (ii) compared to a typical dry day, a day following a rainy day was characterized by increased ecosystem uptake of CO2 (NEE increased by ≈ 10%), enhanced evaporative cooling (mid-day LE increased by ≈ 30 W m−2), and a smaller amount of sensible heat transfer (mid-day H decreased by ≈ 70 W m−2). Based on the mean diel cycle, the evaporative contribution to total evapotranspiration was, on average, around 6% in dry conditions and 20% in wet conditions. Furthermore, increased LE lasted at least 18 h following a rain event. At night, precipitation (and accompanying clouds) reduced Rnet and increased LE. Any effect of precipitation on the nocturnal SEB closure and NEE was overshadowed by atmospheric phenomena such as horizontal advection and decoupling that create measurement difficulties. Above-canopy mean χc during wet conditions was found to be about 2-3 μmol mol−1 larger than χc on dry days. This difference was fairly constant over the full diel cycle suggesting that it was due to synoptic weather patterns (different air masses and/or effects of barometric pressure). In the evening hours during wet conditions, weakly stable conditions resulted in smaller vertical χc differences compared to those in dry conditions. Finally, the effect of clouds on the timing and magnitude of daytime ecosystem fluxes is described. [ABSTRACT FROM AUTHOR]

The identification and mitigation of anomalous propagation (AP) and normal propagation (NP) ground clutter is an ongoing problem in radar meteorology. Scatter from ground-clutter targets routinely contaminates radar data and masks weather returns causing poor data quality. The problem is typically mitigated by applying a clutter filter to all radar data, but this also biases weather data at near-zero velocity. Modern radar processors make possible the real-time identification and filtering of AP clutter. A fuzzy logic algorithm is used to distinguish between clutter echoes and precipitation echoes and, subsequently, a clutter filter is applied to those radar resolution volumes where clutter is present. In this way, zero-velocity weather echoes are preserved while clutter echoes are mitigated. Since the radar moments are recalculated from clutter-filtered echoes, the underlying weather echo signatures are revealed, thereby significantly increasing the visibility of weather echo. This paper describes the fuzzy logic algorithm, clutter mitigation decision (CMD), for clutter echo identification. A new feature field, clutter phase alignment (CPA), is introduced and described. A detailed discussion of CPA is given in Part I of this paper. The CMD algorithm is illustrated with experimental data from the Denver Next Generation Weather Radar (NEXRAD) at the Denver, Colorado, Front Range Airport (KFTG); and NCAR’s S-band dual-polarization Doppler radar (S-Pol). [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]

Abstract: Floods, droughts, heat waves, and storms have always been part of human lives because they are a normal part of climate variability. However, the observed trends and projected changes in global climate have the potential to alter patterns of these climatic hazards and extreme weather events. Extreme precipitation is one of the factors that contribute to flash floods, but it is the characteristics of the environment, individuals, and society that can turn these natural phenomena into life-threatening disasters. Past decades of disaster risk research and assessments have lead to many innovative approaches to integrating data across disciplinary domains. Recent advancements in integration of meteorological information with other environmental and social data, using Geographic Information Systems (GIS), allow for integrated spatial assessments of societal vulnerability to weather-related hazards. A case study presented in this article builds on the substantial body of previous and ongoing research that is focused on developing improved methods for characterizing and quantifying vulnerabilities to weather hazards, in general, and extreme precipitation events, in particular. Integrating social science into meteorological research and practice has been a key interdisciplinary direction in the meteorological community. Therefore, with specific attention to integrating spatially explicit information on weather and society, this article focuses on interactions between meteorological and social characteristics of an extreme precipitation event that resulted in a flash flood disaster in Fort Collins, Colorado. Using the data from 1997 Fort Collins, Colorado extreme precipitation event, this study constructs a straightforward methodology for integrating meteorological data with readily available societal information into a GIS-based analysis of vulnerable people and places. With the goal of developing specific, targeted interventions and flash flood preparedness and emergency response actions, the analysis of societal vulnerability presented in this paper is specifically focused on the factors affecting population''s response and coping capacities to an extreme precipitation event. Challenges associated with data limitations and integration of meteorological and societal data, with diverse units and scales, into a standardized relative vulnerability measure are discussed. [Copyright &y& Elsevier]

Mean daily to monthly precipitation averages peak in late July over eastern Colorado and some of the most damaging Front Range flash floods have occurred because of extreme 1-day rainfall events during this period. Tree-ring chronologies of adjusted latewood width in ponderosa pine from eastern Colorado are highly correlated with the highest 1-day rainfall totals occurring during this 2-week precipitation maximum in late July. A regional average of four adjusted latewood chronologies from eastern Colorado was used to reconstruct the single wettest day observed during the last two weeks of July. The regional chronology was calibrated with the CPC 0.25° × 0.25° Daily U.S. Unified Gauge-Based Analysis of Precipitation dataset and explains 65% of the variance in the highest 1-day late July precipitation totals in the instrumental data from 1948 to 1997. The reconstruction and instrumental data extend fully from 1779 to 2019 and indicate that the frequency of 1-day rainfall extremes in late July has increased since the late eighteenth century. The largest instrumental and reconstructed 1-day precipitation extremes are most commonly associated with the intrusion of a major frontal system into a deep layer of atmospheric moisture across eastern Colorado. These general synoptic conditions have been previously linked to extreme localized rainfall totals and widespread thunderstorm activity over Colorado during the summer season. Chronologies of adjusted latewood width in semiarid eastern Colorado constitute a proxy of weather time-scale rainfall events useful for investigations of long-term variability and for framing natural and potential anthropogenic forcing of precipitation extremes during this 2-week precipitation maximum in a long historical perspective. [ABSTRACT FROM AUTHOR]

The 2013 federal Colorado River Basin Water Supply and Demand Study projected the water imbalance between future supply and demand to increase. The Colorado water supply (WS) exemplifies a pronounced quasi-decadal oscillation (QDO) of 10-20 years throughout its historical record; however, this QDO feature is unaccounted for in the climate models used to project the future WS. Adjacent to the Colorado River, the large watershed of the Great Salt Lake (GSL) in Utah records the hydrologicQDOsignal in its water surface, leading previous studies to explore the cause of decadal fluctuations in the lake elevation and assess predictability. This study reports a remarkable coherence between the Colorado WS and the GSL elevation at the 10-20-yr time scale. Analysis of precipitation and terrestrial water storage anomalies suggests a crossbasin connection in the climate and hydrometeorological variations of the Colorado WS and the GSL. The 160-yr-long and well-kept GSL elevation record makes it an effective indicator for the Colorado WS. [ABSTRACT FROM AUTHOR]

Radar and disdrometer observations collected during the 2013 Great Colorado Flood are used to diagnose the spatial and vertical structure of clouds and precipitation during episodes of intense rainfall. The analysis focuses on 30 h of intense rainfall in the vicinity of Boulder, Colorado, during 2200-0400 UTC 11-13 September. The strongest rainfall occurred along lower parts of the Colorado Front Range at >1.6 km MSL and on the northern side of the Palmer Divide. The vertical structure of clouds and horizontal distribution of rainfall are strongly linked to upslope flow and low-level forcing, which resulted in surface convergence. During times of weak forcing, shallow convection produced rain at and below the melting layer through collision-coalescence and, to a lesser extent, riming. A mesoscale circulation interacting with the local terrain produced convective rainfall with high cloud tops that favored ice crystal production. During moderate forcing with cloud tops slightly exceeding the 0°C level, both cold- and warm-phase microphysical processes dominated. Less rain with weaker rainfall rates was observed over the higher-elevation stations compared to the lower-elevation stations across the foothills. [ABSTRACT FROM AUTHOR]

The climatology of Colorado's historical extreme precipitation events shows a remarkable degree of seasonal and regional variability. Analysis of the largest historical daily precipitation totals at COOP stations across Colorado by season indicates that the largest recorded daily precipitation totals have ranged from less than 60 mm day−1 in some areas to more than 250 mm day−1 in others. East of the Continental Divide, winter events are rarely among the top 10 events at a given site, but spring events dominate in and near the foothills; summer events are most common across the lower-elevation eastern plains, while fall events are most typical for the lower elevations west of the Divide. The seasonal signal in Colorado's central mountains is complex; high-elevation intense precipitation events have occurred in all months of the year, including summer, when precipitation is more likely to be liquid (as opposed to snow), which poses more of an instantaneous flood risk. Notably, the historic Colorado Front Range daily rainfall totals that contributed to the damaging floods in September 2013 occurred outside of that region's typical season for most extreme precipitation (spring-summer). That event and many others highlight the fact that extreme precipitation in Colorado has occurred historically during all seasons and at all elevations, emphasizing a year-round statewide risk. [ABSTRACT FROM AUTHOR]

The impacts of enhanced CCN concentrations on various cloud and precipitation systems are potentially significant both to the large-scale climate system and local precipitation patterns. Precipitating stable orographic cloud systems are particularly susceptible to increases in CCN as parcel lifetimes within these clouds are typically short compared to clouds of similar depth. As such, even small perturbations to the precipitation efficiency within these clouds can have substantial impacts. In the mountainous regions of the western United States, where water resources are derived primarily from orographic precipitation during the cold season, this effect is of particular interest. The aims of this study are twofold. The first part is focused on the implementation of a simplified aerosol emissions scheme into the Regional Atmospheric Modeling System (RAMS). This scheme uses aerosol output from the Weather Research and Forecast Chemistry model (WRF-Chem) to initialize aerosol sources in RAMS. The second part of this study uses this scheme in the simulation of an orographic snow case that occurred in northwest Colorado during February 2007. The result of this study suggests that atmospheric CCN concentrations can be reasonably simulated using a simplified parameterization of aerosol emissions, despite a lack of explicit secondary aerosol (SA) within the model. Furthermore, the spatial and temporal variations in CCN predicted by this scheme produced a complicated response in the surface distribution of precipitation from the orographic snowstorm, a result not seen in studies where CCN concentrations are set to be horizontally homogenous. [ABSTRACT FROM AUTHOR]

A high-resolution case-based approach for dynamically downscaling climate model data is presented. Extreme precipitation events are selected from regional climate model (RCM) simulations of past and future time periods. Each event is further downscaled using the Weather Research and Forecasting (WRF) Model to storm scale (1.3-km grid spacing). The high-resolution downscaled simulations are used to investigate changes in extreme precipitation projections from a past to a future climate period, as well as how projected precipitation intensity and distribution differ between the RCM scale (50-km grid spacing) and the local scale (1.3-km grid spacing). Three independent RCM projections are utilized as initial and boundary conditions to the downscaled simulations, and the results reveal considerable spread in projected changes not only among the RCMs but also in the downscaled high-resolution simulations. However, even when the RCM projections show an overall (i.e., spatially averaged) decrease in the intensity of extreme events, localized maxima in the high-resolution simulations of extreme events can remain as strong or even increase. An ingredients-based analysis of prestorm instability, moisture, and forcing for ascent illustrates that while instability and moisture tend to increase in the future simulations at both regional and local scales, local forcing, synoptic dynamics, and terrain-relative winds are quite variable. Nuanced differences in larger-scale and mesoscale dynamics are a key determinant in each event's resultant precipitation. Very high-resolution dynamical downscaling enables a more detailed representation of extreme precipitation events and their relationship to their surrounding environments with fewer parameterization-based uncertainties and provides a framework for diagnosing climate model errors. [ABSTRACT FROM AUTHOR]

Precipitation changes between 32-yr periods in the late twentieth and mid-twenty-first centuries are investigated using regional climate model simulations provided by the North American Regional Climate Change Assessment Program (NARCCAP). The simulations generally indicate drier summers in the future over most of Colorado and the border regions of the adjoining states. The decrease in precipitation occurs despite an increase in the surface specific humidity. The domain-averaged decrease in daily summer precipitation occurs in all of the models from the 50th through the 95th percentile, but without a clear agreement on the sign of change for the most extreme (top 1% of) events. [ABSTRACT FROM AUTHOR]

A conference paper on the relation of lightning and rainfall processes which occur in High Plains of Kansas, Colorado and in northern Alabama. It discusses the intrinsic physical link of total lightning activity to ice mass, liquid water mass, and the convictive rainfall amount. It examines the usefulness of zoning in climatological and microphysical studies to determine the roles in precipitation development and water cycle by measuring the warm and cold rain processes in the U.S.

An investigation was conducted on the effects of various physics parameterizations on wintertime precipitation predictions using a high-resolution regional climate model. The objective was to evaluate the sensitivity of cold-season mountainous snowfall to cloud microphysics schemes, planetary boundary layer (PBL) schemes, land surface schemes, and radiative transfer schemes at a 4-km grid spacing applicable to the next generation of regional climate models. The results indicated that orographically enhanced precipitation was highly sensitive to cloud microphysics parameterizations. Of the tested 7 parameterizations, 2 schemes clearly outperformed the others that overpredicted the snowfall amount by as much as ~30%%-60%% on the basis of snow telemetry observations. Significant differences among these schemes were apparent in domain averages, spatial distributions of hydrometeors, latent heating profiles, and cloud fields. In comparison, model results showed relatively weak dependency on the land surface, PBL, and radiation schemes, roughly in the order of decreasing level of sensitivity. [ABSTRACT FROM AUTHOR]

The efficacy of dual-polarization radar for quantitative precipitation estimation (QPE) has been demonstrated in a number of previous studies. Specifically, rainfall retrievals using combinations of reflectivity ( Zh), differential reflectivity ( Zdr), and specific differential phase ( Kdp) have advantages over traditional Z-- R methods because more information about the drop size distribution (DSD) and hydrometeor type are available. In addition, dual-polarization-based rain-rate estimators can better account for the presence of ice in the sampling volume. An important issue in dual-polarization rainfall estimation is determining which method to employ for a given set of polarimetric observables. For example, under what circumstances does differential phase information provide superior rain estimates relative to methods using reflectivity and differential reflectivity? At Colorado State University (CSU), an optimization algorithm has been developed and used for a number of years to estimate rainfall based on thresholds of Zh, Zdr, and Kdp. Although the algorithm has demonstrated robust performance in both tropical and midlatitude environments, results have shown that the retrieval is sensitive to the selection of the fixed thresholds. In this study, a new rainfall algorithm is developed using hydrometeor identification (HID) to guide the choice of the particular rainfall estimation algorithm. A separate HID algorithm has been developed primarily to guide the rainfall application with the hydrometeor classes, namely, all rain, mixed precipitation, and all ice. Both the data collected from the S-band Colorado State University--University of Chicago--Illinois State Water Survey (CSU--CHILL) radar and a network of rain gauges are used to evaluate the performance of the new algorithm in mixed rain and hail in Colorado. The evaluation is also performed using an algorithm similar to the one developed for the Joint Polarization Experiment (JPOLE). Results show that the new CSU HID-based algorithm provides good performance for the Colorado case studies presented here. [ABSTRACT FROM AUTHOR]

Variations in the chemical composition of atmospheric aerosols alter their hygroscopicity and can lead to changes in the cloud-active fraction of the aerosols, or cloud condensation nuclei (CCN) number concentration. To investigate the importance of this effect under different atmospheric conditions, cloud droplet formation was simulated with a Lagrangian parcel model. Initial values of updraft speed and temperature were systematically varied along with aerosol number concentration, size and hygroscopicity (represented by the hygroscopicity parameter, κ). A previous study classifies the sensitivity of CCN activity to compositional changes based on the supersaturation reached in the parcel model. We found that these classifications could not be generalized to a range of aerosol size distribution median radii. Instead, variations in sensitivity with size depend on the location of the dry critical radius for droplet activation relative to the size distribution median radius. The parcel model output was used to construct droplet activation lookup tables based on κ that were implemented in the Regional Atmospheric Modeling System (RAMS) microphysical scheme. As a first application of this system, aerosol hygroscopicity and size were varied in a series of RAMS mesoscale simulations designed to investigate the sensitivity of a mixed-phase orographic cloud case to the parameter variations. Observations from a recent field campaign in northwestern Colorado provided the basis for the aerosol field initializations. Model results show moderate sensitivity in the distribution of total case precipitation to extreme changes in κ, and minimal sensitivity to observed changes in estimated κ. The impact of varying aerosol hygroscopicity diminished with increasing median radius, as expected from the parcel model results. The conclusions drawn from these simulations could simplify similar research in other cloud regimes by defining the need, or lack of need, for detailed knowledge of aerosol composition. [ABSTRACT FROM AUTHOR]

Equilibrium-line altitudes (ELAs) were determined from reconstructions of 22 paleoglaciers at their extent during the local last glacial maximum (LGM) using the accumulation-area method. LGM ELAs thus derived ranged from 2980 to 3560 m and follow a statistically significant regional trend of rising ∼4.5 m km-1 to the east. Two approaches using a degree-day model were used to infer LGM climate by finding plausible combinations of temperature and precipitation change that (I) would be required to lower ELAs to their mean LGM values in both the Taylor Park/eastern Elk Mountains region and western Elk Mountains, and (2) provide steady-state mass balances to maintain individual glaciers. The results of these two approaches are convergent and suggest that in the absence of significant changes in precipitation, mean summer (or mean annual) temperatures within the study area during the LGM were on the order of about 7 °C cooler than at present. The model also suggests that even allowing for modest changes in LGM precipitation (±25%), the required mean summer temperature depressions are within ∼0.5 °C of these values. Furthermore, there appears to be no significant dependence on small potential changes in temperature seasonality (i.e., winter temperatures). The inferred magnitude of LGM temperature change in the study area is consistent with other estimates from the broader Southern and Central Rocky Mountain region. [ABSTRACT FROM AUTHOR]

Statistical evidence supports a relationship between the warm-season thermodynamic environment in northeastern Colorado and the antecedent snow conditions in the upstream higher elevations. Above-normal snow storage and longevity reduces the sensible heating of the elevated terrain by increasing the albedo and exerting a stabilizing influence upon the surface layer. It follows that the residual spring snowpack of the Colorado Rockies represents an energy sink to the prevailing west to southwesterly winds of the free atmosphere, which traverse the uplift of the Western Plateau. The abrupt drop in elevation along the Front Range provides for a setting in which a thermal perturbation induced by mountain snow may be communicated to the downstream plains by cooling and perhaps stratifying the mobile elevated residual layer. Statistical and observational evidence suggest that the May and June months that follow a robust and extended snowmelt season are characterized by cooler 700-mb temperatures over Denver, Colorado. The apparent moist convective response to a positive snowmelt anomaly in the lee plains region is for severe weather to develop on average 45 to 50 min earlier, during May and June, than observed during seasons having a negative snow anomaly. Conversely, late springs, which harbor below-average snow, subject the elevated terrain to greater sensible heating. During the May and June months of these “low snow years,” 700-mb environmental conditions over Denver tend to be warmer and drier. Occurring in tandem with these trends are a reduction in May and June rainfall and a lower number of severe hail days on the lee plains. Observations further suggest that during low snow years substantial precipitation deficits extend to synoptic-scale distances downstream. [ABSTRACT FROM AUTHOR]

Air pollution generated in industrial and urban areas can act to suppress precipitation by creating a narrow cloud droplet spectrum, which inhibits the collision and coalescence process. In fact, precipitation ratios of elevated sites to upwind coastal urban areas have decreased during the twentieth century for locations in California and Israel while pollution emissions have increased. Precipitation suppression by pollution should also be evident in other areas of the world where shallow, orographic clouds produce precipitation. This study investigates the precipitation trends for sites along the Front Range of the Rocky Mountains to determine the effect of air pollution on precipitation in this region. The examination of precipitation trends reveals that the ratio of upslope precipitation for elevated sites west of Denver and Colorado Springs, Colorado, to upwind urban sites has decreased by approximately 30% over the past half-century. Similar precipitation trends were not found for more pristine sites in the region, providing evidence of precipitation suppression by air pollution. [ABSTRACT FROM AUTHOR]

The National Center for Atmospheric Research (NCAR) Land Surface Model (LSM, version 1.0) provides a comprehensive treatment of land surface processes for the NCAR Community Climate Model version 3 (CCM3). It replaces the prescribed surface wetness, prescribed snow cover, surface albedo, and surface flux parameterizations used in the CCM2. A 15-yr simulation of the coupled atmosphere (CCM3) and land (LSM1.0) models using observed sea surface temperatures for the period December 1978--September 1993 is used to document the model's land surface climate. The model simulates many of the observed geographic and seasonal patterns of surface air temperature, precipitation, and soil water. In general, the transition seasons (spring, autumn) are better simulated than winter and summer. Annual precipitation and runoff are well simulated for some river basins and poorly simulated for others. In general, precipitation is better simulated than runoff. The inclusion of net land-atmosphere CO2 exchange is an important component of the land model, allowing it to be used for studies of the global carbon cycle. The model simulates annual net primary production that is consistent with other estimates of annual production. The model also simulates a clearly defined growing season based on temperature and soil water. [ABSTRACT FROM AUTHOR]

Rainwater harvesting (RWH) is a renewable water supply option for nonpotable use, most commonly used for landscaping irrigation. Water rights in Colorado prohibit all RWH except residential rain barrels and a pilot project program that allows centralized rainwater harvesting for new development. Development of a natural catchment creates impervious surfaces, thereby increasing runoff, with a subsequent decrease in infiltration and losses to evapotranspiration; pilot projects are allowed to harvest a volume equal to the predevelopment runoff losses that would have occurred on new impervious areas at the site. To support this administrative policy, a tool was developed for the efficient calculation of daily allowable harvest at nearly any project site in Colorado. A reliable and useful policy tool requires the incorporation of hydrologic science with widely applicable, user-friendly design, a challenging balance of rigor and accessibility that is welcomed by engineers and policymakers alike. The daily allowable harvest is determined for each soil group as a percentage of infiltrated rainfall less the groundwater retur. Horton's infiltration method is used to model rainfall‒runoff for a range of soil parameters (NRCS hydrologic soil groups) and precipitation events (0.25- to 25-year return periods and 15-min to 24-h durations). For most events, the percent infiltration is 90% of the precipitation depth; this ratio decreases when precipitation exceeds the infiltration rate. Results are simplified in a spreadsheet tool for policy application, with allowable harvest rules binned by event duration and frequency. Simulations using the tool for a 2010‒2017 precipitation record from Colorado's Front Range showed that RWH can supply up to 50% of the annual demand for traditional landscaping and over 100% of the water-smart landscaping demand. [ABSTRACT FROM AUTHOR]