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Timothy M. Lahmers, Hoshin Gupta, Christopher L. Castro, David J. Gochis, David Yates, Aubrey Dugger, David Goodrich, and Pieter Hazenberg

Abstract

In August 2016, the National Weather Service Office of Water Prediction (NWS/OWP) of the National Oceanic and Atmospheric Administration (NOAA) implemented the operational National Water Model (NWM) to simulate and forecast streamflow, soil moisture, and other model states throughout the contiguous United States. Based on the architecture of the WRF-Hydro hydrologic model, the NWM does not currently resolve channel infiltration, an important component of the water balance of the semiarid western United States. Here, we demonstrate the benefit of implementing a conceptual channel infiltration function (from the KINEROS2 semidistributed hydrologic model) into the WRF-Hydro model architecture, configured as NWM v1.1. After calibration, the updated WRF-Hydro model exhibits reduced streamflow errors for the Walnut Gulch Experimental Watershed (WGEW) and the Babocomari River in southeast Arizona. Model calibration was performed using NLDAS-2 atmospheric forcing, available from the NOAA National Centers for Environmental Prediction (NCEP), paired with precipitation forcing from NLDAS-2, NCEP Stage IV, or local gauge precipitation. Including channel infiltration within WRF-Hydro results in a physically realistic hydrologic response in the WGEW, when the model is forced with high-resolution, gauge-based precipitation in lieu of a national product. The value of accounting for channel loss is also demonstrated in the Babocomari basin, where the drainage area is greater and the cumulative effect of channel infiltration is more important. Accounting for channel infiltration loss thus improves the streamflow behavior simulated by the calibrated model and reduces evapotranspiration bias when gauge precipitation is used as forcing. However, calibration also results in increased high soil moisture bias, which is likely due to underlying limitations of the NWM structure and calibration methodology.

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Brian E. Mapes, Thomas T. Warner, Mei Xu, and David J. Gochis

Abstract

Several different cumulus parameterizations are compared in a 10-day regional model simulation over the tropical Americas in northern summer. A simple bulk diagnostic test is devised, comparing the model's preferred domain-mean wind divergence profile with “observed” drivergence. The latter is obtained by a line integral of the normal wind component at the model's outer boundary, from the ECMWF reanalysis data used as lateral boundary conditions. The former is obtained from a line integral one grid point in from the boundary, a perimeter that encloses almost exactly the same region. Even though the model fields near the boundary are strongly nudged toward the ECMWF values, the difference is distinct, and indicative of systematic errors in the model's heating field throughout the interior of the domain. Heating reflects the effects of the convection scheme, both direct and indirect (e.g., through its impact on resolved condensation). A useful axis along which to characterize schemes appears to be overactive versus underactive. Underactive convective schemes tend to produce too little low-level convergence and upper-level divergence, while overactive schemes produce too much. This categorization is also reflected in rainfall fields, as overactive schemes produce widespread light convective rain while underactive schemes produce sparse occasional storms. For example, the Kain–Fritsch scheme is overactive with its default entraining-plume radius of 1500 m, a value optimized for midlatitudes over land. A value of 750 m makes the regional divergence magnitude about right, but makes the upper-tropospheric outflow altitude too low, illustrating a classic dilemma of entraining-plume models of convection. Schemes with other conceptual structures give widely varying divergence errors. The largest errors are found with the Anthes–Kuo scheme, while the smallest errors are found with the Betts–Miller–Janjic scheme, which has no consistent divergence bias over time. Diagnosis of other North American monsoon simulations supports the general underactive/ overactive characterization, but shows that the best scheme and parameters may depend on weather regime.

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Giuseppe Mascaro, Enrique R. Vivoni, David J. Gochis, Christopher J. Watts, and Julio C. Rodriguez

Abstract

In this study a temporal statistical downscaling scheme of rainfall is calibrated using observations from 2007 to 2010 at eight sites located along a 14-km topographic transect of 784 m in elevation in northwest Mexico. For this purpose, the rainfall statistical properties over a wide range of temporal scales (3 months–1 min) for the summer (July–September) and winter (November–March) seasons are first analyzed. Rainfall accumulation is found not to be significantly correlated with elevation in either season, and a strong diurnal cycle is found to be present only in summer, peaking in the late afternoon. Winter rainfall events are highly correlated between individual stations across the transect even at short aggregation times (<30 min), and summer storms are more localized in space and time. Spectral and scale invariance analyses showed the presence of three (two) scaling regimes in summer (winter), which are associated with typical meteorological phenomena of the corresponding time scales (frontal systems and relatively isolated convective systems). These analyses formed the basis for calibrating a temporal downscaling model to disaggregate daily precipitation to hourly resolution in the summer season, based on scale invariance and multifractal theory. In this downscaling scheme, a modulation function was used to reproduce the time heterogeneity introduced by the diurnal cycle. The model showed adequate performances in reproducing the small-scale observed precipitation variability. Results of this work are useful for the interpretation of storm-generation mechanisms in the region, and for creating hourly rainfall time series from daily rainfall data, obtained from observations or simulated by climate, meteorological, or other statistical models.

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Yang Hong, David Gochis, Jiang-tao Cheng, Kuo-lin Hsu, and Soroosh Sorooshian

Abstract

Robust validation of the space–time structure of remotely sensed precipitation estimates is critical to improving their quality and confident application in water cycle–related research. In this work, the performance of the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS) precipitation product is evaluated against warm season precipitation observations from the North American Monsoon Experiment (NAME) Event Rain Gauge Network (NERN) in the complex terrain region of northwestern Mexico. Analyses of hourly and daily precipitation estimates show that the PERSIANN-CCS captures well active and break periods in the early and mature phases of the monsoon season. While the PERSIANN-CCS generally captures the spatial distribution and timing of diurnal convective rainfall, elevation-dependent biases exist, which are characterized by an underestimate in the occurrence of light precipitation at high elevations and an overestimate in the occurrence of precipitation at low elevations. The elevation-dependent biases contribute to a 1–2-h phase shift of the diurnal cycle of precipitation at various elevation bands. For reasons yet to be determined, the PERSIANN-CCS significantly underestimated a few active periods of precipitation during the late or “senescent” phase of the monsoon. Despite these shortcomings, the continuous domain and relatively high spatial resolution of PERSIANN-CCS quantitative precipitation estimates (QPEs) provide useful characterization of precipitation space–time structures in the North American monsoon region of northwestern Mexico, which should prove useful for hydrological applications.

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David J. Gochis, Juan-Carlos Leal, W. James Shuttleworth, Christopher J. Watts, and Jaime Garatuza-Payan

Abstract

The purpose of this note is to present preliminary findings from a new event-based surface rain gauge network in the region of northwest Mexico. This region is characterized as semiarid, owing the largest percentage of its annual rainfall to summer convective systems, which are diurnal in nature. Although the existing surface network and satellite-derived precipitation products have clarified some features of convective activity over the core region of the North American monsoon (NAM), a detailed examination of the spatial and temporal structure of such activity has been prohibited by the lack of a surface observation network with adequate temporal and spatial resolution. Specifically, the current network of sparsely spaced climate stations has inhibited a detailed diagnosis of the timing, intensity, and duration of convective rainfall in general, and of the topography–rainfall relationship in particular. In this note, a brief overview of the network and present preliminary analyses from the first monitoring season, summer 2002, is provided. It is shown that the diurnal cycle of precipitation varies with elevation in a way that is consistent with a hypothesis that convective events organize and, occasionally, propagate from high terrain onto lower-elevation plains, but more conclusive statements will require expansion of the network and increased record length. It is also emphasized from these studies that it is essential to evaluate wet-day statistics or rainfall intensities from precipitating periods in parallel, with comparable all-day statistics, when conducting hydrometeorological analyses in semiarid convective regimes where precipitation is infrequent and highly localized.

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David J. Gochis, Alejandro Jimenez, Christopher J. Watts, Jaime Garatuza-Payan, and W. James Shuttleworth

Abstract

Analyses of rainfall characteristics and their linkage to physiographic features are made from the North American monsoon experiment (NAME) Event Rain Gauge Network (NERN) in northwest Mexico. The findings are based on the network configuration for the 2002 and 2003 warm seasons. Despite the relatively short record used, a clearer structure of core-region monsoon rainfall is beginning to emerge. In agreement with earlier, coarser-scale studies, the seasonal precipitation maximum overlies the western slope of the Sierra Madre Occidental but does not strictly parallel a particular elevation band. It is shown that the distance to the Gulf of California and, potentially, the configuration of the terrain profile may also play an important role in determining where the axis of maximum precipitation lies. The diurnal cycles of precipitation frequency and intensity are shown to have distinct relationships to terrain elevation that are qualitatively similar to those observed over the Front Range of the Rocky Mountains in the central-western United States. The relationship between precipitation and gulf surge events occurring during the summer of 2003 is also explored.

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Katja Friedrich, Evan A. Kalina, Joshua Aikins, David Gochis, and Roy Rasmussen

Abstract

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.

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Brant Liebmann, Ileana Bladé, Nicholas A. Bond, David Gochis, Dave Allured, and Gary T. Bates

Abstract

The core region of the North American summer monsoon is examined using spatially averaged daily rainfall observations obtained from gauges, with the objective of improving understanding of its climatology and variability. At most grid points, composite and interannual variations of the onset and end of the wet season are well defined, although, among individual stations that make up a grid average, variability is large. The trigger for monsoon onset in southern and eastern Mexico appears to be related to a change in vertical velocity, while for northwestern Mexico, Arizona, and New Mexico it is related to a reduction in stability, as indicated by a decrease in the lifted index. The wet-season rain rate is a combination of the wet-day rain rate, which decreases with distance from the coast, and the wet-day frequency, which is largest over the Sierra Madre Occidental. Thus the maximum total rate lies slightly to the west of the highest orography. As has been previously noted, onset is not always well correlated with total seasonal precipitation, so in these areas, variations of wet-day frequency and wet-day rain rate must be important. Correlations are small between the wet-day frequency and the wet-day rate, and the former is better correlated than the latter with the seasonal rain rate. Summer rainfall in central to southern Mexico exhibits moderate negative correlations with the leading pattern of sea surface temperature (SST) anomalies in the equatorial Pacific, which projects strongly onto El Niño. The influence of equatorial SSTs on southern Mexico rainfall seems to operate mainly through variability of the wet-day frequency, rather than through variations of the wet-day rain rate.

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Enrique Rosero, Zong-Liang Yang, Lindsey E. Gulden, Guo-Yue Niu, and David J. Gochis

Abstract

The authors introduce and compare the performance of the unified Noah land surface model (LSM) and its augments with physically based, more conceptually realistic hydrologic parameterizations. Forty-five days of 30-min data collected over nine sites in transition zones are used to evaluate (i) their benchmark, the standard Noah LSM release 2.7 (STD); (ii) a version equipped with a short-term phenology module (DV); and (iii) one that couples a lumped, unconfined aquifer model to the model soil column (GW). Their model intercomparison, enhanced by multiobjective calibration and model sensitivity analysis, shows that, under the evaluation conditions, the current set of enhancements to Noah fails to yield significant improvement in the accuracy of simulated, high-frequency, warm-season turbulent fluxes, and near-surface states across these sites. Qualitatively, the versions of DV and GW implemented degrade model robustness, as defined by the sensitivity of model performance to uncertain parameters. Quantitatively, calibrated DV and GW show only slight improvement in the skill of the model over calibrated STD. Then, multiple model realizations are compared to explicitly account for parameter uncertainty. Model performance, robustness, and fitness are quantified for use across varied sites. The authors show that the least complex benchmark LSM (STD) remains as the most fit version of the model for broad application. Although GW typically performs best when simulating evaporative fraction (EF), 24-h change in soil wetness (ΔW 30), and soil wetness, it is only about half as robust as STD, which also performs relatively well for all three criteria. GW’s superior performance results from bias correction, not from improved soil moisture dynamics. DV performs better than STD in simulating EF and ΔW 30 at the wettest site, because DV tends to enhance transpiration and canopy evaporation at the expense of direct soil evaporation. This same model structure limits performance at the driest site, where STD performs best. This dichotomous performance suggests that the formulations that determine the partitioning of LE flux need to be modified for broader applicability. Thus, this work poses a caveat for simple “plug and play” of functional modules between LSMs and showcases the utility of rigorous testing during model development.

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David J. Gochis, Christopher J. Watts, Jaime Garatuza-Payan, and Julio Cesar-Rodriguez

Abstract

Detailed information on the spatial and temporal characteristics of precipitation intensity from the mountainous region of northwest Mexico has, until recently, been lacking. As part of the 2004 North American Monsoon Experiment (NAME) enhanced observing period (EOP) surface rain gauge networks along with weather radar and orbiting satellites were employed to observe precipitation in a manner heretofore unprecedented for this semiarid region. The NAME Event Rain gauge Network (NERN), which has been in operation since 2002, contributed to this effort. Building on previous work, this paper presents analyses on the spatial and temporal characteristics of precipitation intensity as observed by NERN gauges. Analyses from the 2004 EOP are compared with the 2002–04 period and with long-term gauge observations. It was found that total precipitation from July to August of 2004 was similar in spatial extent and magnitude to the long-term average, though substantially wetter than 2003. Statistical analyses of precipitation intensity data from the NERN reveal that large precipitation events at hourly and daily time scales are restricted to coastal and low-elevation areas west of the Sierra Madre Occidental. At 10-min time scales, maximum intensity values equal to those at low elevations could be observed at higher elevations though they were comparatively infrequent. It is also shown that the inclusion of NERN observations in existing operational analyses helps to correct significant biases, which, on the seasonal time scale, are of similar magnitude as the interannual variability in precipitation in key headwater regions of northwest Mexico.

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