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Andrew Newman and Richard H. Johnson

Abstract

Tropical upper-tropospheric troughs (TUTTs), transient summertime disturbances over the Pacific and Atlantic Oceans, are also observed to frequently occur in the North American monsoon (NAM) region. However, unlike TUTTs over the Pacific and Atlantic, which feature a predominance of precipitation on the eastern flank of the disturbances, TUTTs in the NAM region have been shown to enhance precipitation on their western flank as they pass over the mountains of northern Mexico. To investigate this phenomenon, convection-permitting simulations are performed over the core NAM region for the 12–14 July 2004 TUTT event that occurred during the North American Monsoon Experiment (NAME). The effects of the TUTT are isolated using an approach that removes the vorticity anomaly associated with it. Six simulations, three with the TUTT and three without it, are then executed.

It is found that the mean of the TUTT simulations has increased surface–500-hPa and 700–400-hPa shear and that there is incrementally more convective available potential energy (CAPE) along the Sierra Madre Occidental (SMO). These differences lead to convective changes, with the TUTT simulations having more convection, larger maximum updraft velocities, and more precipitation in lower elevations. Overall, the TUTT simulation mean has about 15% more precipitation during the primary period of TUTT interaction with the northern SMO. There are also slight simulated microphysical differences that agree with nearby polarimetric radar observations. Finally, TUTT removal impacts the gulf surge event on 13 July via the convective modifications.

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Andrew J. Newman and Richard H. Johnson

Abstract

Gulf surges are transient disturbances that propagate along the Gulf of California (GoC) from south to north, transporting cool moist air toward the deserts of northwest Mexico and the southwest United States during the North American monsoon. They have been shown to modulate precipitation and have been linked to severe weather and flooding in northern Mexico and the southwest United States. The general features and progression of surge events are well studied, but their detailed evolution is still unclear. To address this, several convection-permitting simulations are performed over the core monsoon region for the 12–14 July 2004 gulf surge event. This surge event occurred during the North American Monsoon Experiment, which allows for extensive comparison to field observations.

A 60-h reference simulation is able to reproduce the surge event, capturing its main characteristics: speed and direction of motion, thermodynamic changes during its passage, and strong northward moisture flux. While the timing of the simulated surge is accurate to within 1–3 h, it is weaker and shallower than observed. This deficiency is likely due to a combination of weaker convection and lack of stratiform precipitation along the western slopes of the Sierra Madre Occidental than observed, hence, weaker precipitation evaporation to aid the surge. Sensitivity simulations show that convective outflow does modulate the intensity of the simulated surge, in agreement with past studies. The removal of gap flows from the Pacific Ocean across the Baja Peninsula into the GoC shows they also impact surge intensity.

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Andrew J. Newman and Richard H. Johnson

Abstract

Gulf surges are transient disturbances that propagate along the Gulf of California (GoC) from south to north, transporting cool moist air toward the deserts of northwest Mexico and the southwest United States during the North American monsoon. They have been shown to modulate precipitation and have been linked to severe weather and flooding in northern Mexico and the southwest United States. The general features and progression of surge events are well documented but their detailed dynamical evolution is still unclear. In this study, a convection-permitting simulation is performed over the core monsoon region for the 12–14 July 2004 gulf surge event and the dynamics of the simulated surge are examined. Initially, convection associated with the tropical easterly wave precursor to Tropical Cyclone Blas creates a disturbance in the southern GoC on early 12 July. This disturbance is a precursor to the gulf surge on 13 July and is a Kelvin shock (internal bore under the influence of rotation) that dissipates in the central GoC. The surge initiates from inflow from the mouth of the GoC along with convective outflow impinging on the southern GoC. Continued convective outflow along the GoC generates multiple gravity currents and internal bores while intensifying the simulated surge as it propagates up the GoC. As the core of the surge reaches the northern GoC, a Kelvin shock is again the best dynamical fit to the phenomenon. Substantial low-level cooling and moistening are associated with the modeled surge along the northern GoC as is observed.

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Hui Ding, Matthew Newman, Michael A. Alexander, and Andrew T. Wittenberg

Abstract

Seasonal forecasts made by coupled atmosphere–ocean general circulation models (CGCMs) undergo strong climate drift and initialization shock, driving the model state away from its long-term attractor. Here we explore initializing directly on a model’s own attractor, using an analog approach in which model states close to the observed initial state are drawn from a “library” obtained from prior uninitialized CGCM simulations. The subsequent evolution of those “model-analogs” yields a forecast ensemble, without additional model integration. This technique is applied to four of the eight CGCMs comprising the North American Multimodel Ensemble (NMME) by selecting from prior long control runs those model states whose monthly tropical Indo-Pacific SST and SSH anomalies best resemble the observations at initialization time. Hindcasts are then made for leads of 1–12 months during 1982–2015. Deterministic and probabilistic skill measures of these model-analog hindcast ensembles are comparable to those of the initialized NMME hindcast ensembles, for both the individual models and the multimodel ensemble. In the eastern equatorial Pacific, model-analog hindcast skill exceeds that of the NMME. Despite initializing with a relatively large ensemble spread, model-analogs also reproduce each CGCM’s perfect-model skill, consistent with a coarse-grained view of tropical Indo-Pacific predictability. This study suggests that with little additional effort, sufficiently realistic and long CGCM simulations provide the basis for skillful seasonal forecasts of tropical Indo-Pacific SST anomalies, even without sophisticated data assimilation or additional ensemble forecast integrations. The model-analog method could provide a baseline for forecast skill when developing future models and forecast systems.

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Andrew J. Newman, Paul A. Kucera, and Larry F. Bliven

Abstract

Herein the authors introduce the Snowflake Video Imager (SVI), which is a new instrument for characterizing frozen precipitation. An SVI utilizes a video camera with sufficient frame rate, pixels, and shutter speed to record thousands of snowflake images. The camera housing and lighting produce little airflow distortion, so SVI data are quite representative of natural conditions, which is important for volumetric data products such as snowflake size distributions. Long-duration, unattended operation of an SVI is feasible because datalogging software provides data compression and the hardware can operate for months in harsh winter conditions. Details of SVI hardware and field operation are given. Snowflake size distributions (SSDs) from a storm near Boulder, Colorado, are computed. An SVI is an imaging system, so SVI data can be utilized to compute diverse data products for various applications. In this paper, the authors present visualizations of frozen particles (i.e., snowflake aggregates as well as individual crystals), which provide insight into the weather conditions such as temperature, humidity, and winds.

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Andrew J. Newman, Naoki Mizukami, Martyn P. Clark, Andrew W. Wood, Bart Nijssen, and Grey Nearing

Abstract

The concepts of model benchmarking, model agility, and large-sample hydrology are becoming more prevalent in hydrologic and land surface modeling. As modeling systems become more sophisticated, these concepts have the ability to help improve modeling capabilities and understanding. In this paper, their utility is demonstrated with an application of the physically based Variable Infiltration Capacity model (VIC). The authors implement VIC for a sample of 531 basins across the contiguous United States, incrementally increase model agility, and perform comparisons to a benchmark. The use of a large-sample set allows for statistically robust comparisons and subcategorization across hydroclimate conditions. Our benchmark is a calibrated, time-stepping, conceptual hydrologic model. This model is constrained by physical relationships such as the water balance, and it complements purely statistical benchmarks due to the increased physical realism and permits physically motivated benchmarking using metrics that relate one variable to another (e.g., runoff ratio). The authors find that increasing model agility along the parameter dimension, as measured by the number of model parameters available for calibration, does increase model performance for calibration and validation periods relative to less agile implementations. However, as agility increases, transferability decreases, even for a complex model such as VIC. The benchmark outperforms VIC in even the most agile case when evaluated across the entire basin set. However, VIC meets or exceeds benchmark performance in basins with high runoff ratios (greater than ~0.8), highlighting the ability of large-sample comparative hydrology to identify hydroclimatic performance variations.

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Andrew J. Monaghan, Martyn P. Clark, Michael P. Barlage, Andrew J. Newman, Lulin Xue, Jeffrey R. Arnold, and Roy M. Rasmussen

Abstract

Weather and climate variability strongly influence the people, infrastructure, and economy of Alaska. However, the sparse observational network in Alaska limits our understanding of meteorological variability, particularly of precipitation processes that influence the hydrologic cycle. Here, a new 14-yr (September 2002–August 2016) dataset for Alaska with 4-km grid spacing is described and evaluated. The dataset, generated with the Weather Research and Forecasting (WRF) Model, is useful for gaining insight into meteorological and hydrologic processes, and provides a baseline against which to measure future environmental change. The WRF fields are evaluated at annual, seasonal, and daily time scales against observation-based gridded and station records of 2-m air temperature, precipitation, and snowfall. Pattern correlations between annual mean WRF and observation-based gridded fields are r = 0.89 for 2-m temperature, r = 0.75 for precipitation, r = 0.82 for snow-day fraction, r = 0.55 for first snow day of the season, and r = 0.71 for last snow day of the season. A shortcoming of the WRF dataset is that spring snowmelt occurs too early over a majority of the state, due partly to positive 2-m temperature biases in winter and spring. Strengths include an improved representation of the interannual variability of 2-m temperature and precipitation and accurately simulated (relative to regional station observations) winter and summer precipitation maxima. This initial evaluation suggests that the 4-km WRF climate dataset robustly simulates meteorological processes and recent climatic variability in Alaska. The dataset may be particularly useful for applications that require high-temporal-frequency weather fields, such as driving hydrologic or glacier models. Future studies will provide further insight on its ability to represent other aspects of Alaska’s climate.

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Matthew Newman, Andrew T. Wittenberg, Linyin Cheng, Gilbert P. Compo, and Catherine A. Smith
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Ryan J. Longman, Andrew J. Newman, Thomas W. Giambelluca, and Mathew Lucas

Abstract

Almost all daily rainfall time series contain gaps in the instrumental record. Various methods can be used to fill in missing data using observations at neighboring sites (predictor stations). In this study, five computationally simple gap-filling approaches—normal ratio (NR), linear regression (LR), inverse distance weighting (ID), quantile mapping (QM), and single best estimator (BE)—are evaluated to 1) determine the optimal method for gap filling daily rainfall in Hawaii, 2) quantify the error associated with filling gaps of various size, and 3) determine the value of gap filling prior to spatial interpolation. Results show that the correlation between a target station and a predictor station is more important than proximity of the stations in determining the quality of a rainfall prediction. In addition, the inclusion of rain/no-rain correction on the basis of either correlation between stations or proximity between stations significantly reduces the amount of spurious rainfall added to a filled dataset. For large gaps, relative median errors ranged from 12.5% to 16.5% and no statistical differences were identified between methods. For submonthly gaps, the NR method consistently produced the lowest mean error for 1- (2.1%), 15- (16.6%), and 30-day (27.4%) gaps when the difference between filled and observed monthly totals was considered. Results indicate that gap filling prior to spatial interpolation improves the overall quality of the gridded estimates, because higher correlations and lower performance errors were found when 20% of the daily dataset is filled as opposed to leaving these data unfilled prior to spatial interpolation.

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Richard H. Johnson, Paul E. Ciesielski, Tristan S. L’Ecuyer, and Andrew J. Newman

Abstract

The diurnal cycle of summer monsoon convection in the coastal, mountainous region of northwestern Mexico is investigated using data from the 2004 North American Monsoon Experiment (NAME). Data from a special sounding network consisting of research and operational sites have been quality controlled and combined with surface, wind profiler, and pibal observations to create a gridded dataset over the NAME domain. This study concentrates on results from the interior portion of the NAME sounding network, where gridded analysis fields are independent of model data. Special attention is given to surface and pibal observations along the western slope of the Sierra Madre Occidental (SMO) in order to obtain an optimal analysis of the diurnally varying slope flows.

Results show a prominent sea-breeze–land-breeze cycle along the western slopes of the SMO. There is a deep return flow above the afternoon sea breeze as a consequence of the elevated SMO immediately to the east. The upslope flow along the western slope of the SMO is delayed until late morning, likely in response to early morning low clouds over the SMO crest and reduced morning insolation over the west-facing slopes. The diurnal cycle of the net radiative heating rate is characterized by a net cooling during most of the daytime except for net heating in the lower and upper troposphere at midday. The diurnal cycle of the apparent heat source Q 1 minus the radiative heating rate QR (providing a measure of net condensational heating) and the apparent moisture sink Q 2 over the SMO is indicative of shallow convection around noon, deep convection at 1800 LT, evolving to stratiform precipitation by midnight, consistent with the radar-observed diurnal evolution of precipitation over this coastal mountainous region as well as the typical evolution of tropical convective systems across a wide range of spatial and temporal scales. Convection over the Gulf of California is strikingly different from that over land, namely, heating and moistening are confined principally to the lower troposphere below 700 hPa, peaking during the nighttime hours.

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