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Brian K. Blaylock
,
John D. Horel
, and
Chris Galli

Abstract

Terabytes of weather data are generated every day by gridded model simulations and in situ and remotely sensed observations. With this accelerating accumulation of weather data, efficient computational solutions are needed to process, archive, and analyze the massive datasets. The Open Science Grid (OSG) is a consortium of computer resources around the United States that makes idle computer resources available for use by researchers in diverse scientific disciplines. The OSG is appropriate for high-throughput computing, that is, many parallel computational tasks. This work demonstrates how the OSG has been used to compute a large set of empirical cumulative distributions from hourly gridded analyses of the High-Resolution Rapid Refresh (HRRR) model run operationally by the Environmental Modeling Center of the National Centers for Environmental Prediction. These cumulative distributions derived from a 3-yr HRRR archive are computed for seven variables, over 1.9 million grid points, and each hour of the calendar year. The HRRR cumulative distributions are used to evaluate near-surface wind, temperature, and humidity conditions during two wildland fire episodes—the North Bay fires, a wildfire complex in Northern California during October 2017 that was the deadliest and costliest in California history, and the western Oklahoma wildfires during April 2018. The approach used here illustrates ways to discriminate between typical and atypical atmospheric conditions forecasted by the HRRR model. Such information may be useful for model developers and operational forecasters assigned to provide weather support for fire management personnel.

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Brian K. Blaylock
,
John D. Horel
, and
Erik T. Crosman

Abstract

During the late afternoon of 18 June 2015, ozone concentrations in advance of a strong lake-breeze front arising from the Great Salt Lake in northern Utah were ~20 ppb lower than those in its wake. The lake-breeze progression and ozone concentrations in the valley were monitored by an enhanced observation network that included automated weather stations, a nearby Terminal Doppler Weather Radar, state air quality measurement sites, and mobile platforms, including a news helicopter. Southerly flow opposing the lake breeze increased convergent frontogenesis and delayed the onset of its passage through the Salt Lake valley. Ozone concentrations were exceptionally high aloft at the lake-breeze frontal boundary. The progression of this lake breeze was simulated using the Weather Research and Forecasting Model at 1-km horizontal grid spacing over northern Utah. The model was initialized using hourly analyses from the High Resolution Rapid Refresh model. Errors in the underlying surface initialization were improved by adjusting the areal extent and surface temperature of the lake to observed lake conditions. An urban canopy parameterization is also included. The opposing southerly flow was weaker in the simulation than that observed such that the simulated lake-breeze front occurred too early. Continuous passive tracers initialized within and ahead of the lake breeze highlight the dispersion and transport of pollutants arising from the lake-breeze front. Tracers within the lake breeze are confined near the surface while tracers in advance of the front are lofted over it.

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John D. Horel
,
Lloyd R. Staley
, and
Timothy W. Barker

An interactive-software package has been developed in the University of Utah's Department of Meteorology to access and display meteorological data received via satellite broadcast. The retrieval of the meteorological data relies upon a test-release version of the Unidata System for Scientific Data Management sponsored by the University Corporation for Atmospheric Research. Output from the National Meteorological Center medium-range forecast and nested-grid models along with surface aviation and upper air raobs are processed and stored for later instructional or research uses.

An overview of the computer hardware and software used in this system is provided. As an example of the capabilities of the software, how output from the medium-range forecast model can be used for real-time monitoring of short-term climate variability and for synoptic instruction on an expanded global scale is demonstrated.

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David T. Myrick
,
John D. Horel
, and
Steven M. Lazarus

Abstract

The terrain between grid points is used to modify locally the background error correlation matrix in an objective analysis system. This modification helps to reduce the influence across mountain barriers of corrections to the background field that are derived from surface observations. This change to the background error correlation matrix is tested using an analytic case of surface temperature that encapsulates the significant features of nocturnal radiation inversions in mountain basins, which can be difficult to analyze because of locally sharp gradients in temperature. Bratseth successive corrections, optimal interpolation, and three-dimensional variational approaches are shown to yield exactly the same surface temperature analysis. Adding the intervening terrain term to the Bratseth approach led to solutions that match more closely the specified analytic solution. In addition, the convergence of the Bratseth solutions to the best linear unbiased estimation of the analytic solution is faster.

The intervening terrain term was evaluated in objective analyses over the western United States derived from a modified version of the Advanced Regional Prediction System Data Assimilation System. Local adjustment of the background error correlation matrix led to improved surface temperature analyses by limiting the influence of observations in mountain valleys that may differ from the weather conditions present in adjacent valleys.

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Kevin J. Dougherty
,
John D. Horel
, and
Jason E. Nachamkin

Abstract

Precipitation forecasts from the High-Resolution Rapid Refresh model (HRRR) of the National Centers for Environmental Prediction (NCEP) and the Navy’s Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS) are examined during heavy precipitation periods in California. Precipitation forecast discrepancies between the two models are examined during a recent heavy winter precipitation episode in California from 6 to 8 December 2019. The skill of initial 12-h precipitation forecasts is examined objectively from 1 December 2018 to 28 February 2019 from the HRRR, COAMPS, and NCEP’s North American Mesoscale Forecast System (NAM-3km). The HRRR exhibited lower seasonal biases and higher skill based on several metrics applied to a sample of 48 12-h periods during California’s second wettest winter season during the past 20 years. Overall, the NAM-3km and COAMPS exhibited a large wet bias over the interior mountain regions while the HRRR model indicated a dry bias along the northern coastal region. All models tended to underestimate precipitation along the coastal mountains of Northern California. To highlight the regional and localized nature of forecast skill, the fraction skill score (FSS) metric is applied across ranges of spatial scales and precipitation values. For the domain as a whole, the HRRR had higher precipitation forecast skill compared to the other two models, particularly within radial distances of 20–30 km and moderate (10–50 mm) precipitation totals. FSS computed locally highlights the HRRR’s overall higher skill as well as enhanced skill in the southern half of the state.

Open access
John D. Horel
,
Judith B. Pechmann
,
John E. Geisler
, and
Andrea N. Hahmann

Abstract

Numerical simulations of the atmospheric circulation over tropical South America are performed with a regional model developed at The Pennsylvania State University and the National Center for Atmospheric Research and commonly referred to as the MM4. The authors focus on a 5-day period beginning at 1200 UTC 27 February 1990. The observed circulation is evaluated in terms of initialized analyses of standard meteorological variables from the National Meteorological Center, outgoing longwave radiation from polar orbiting satellites, and surface observations. The NMC analyses are also used to specify the initial conditions, as well as provide the lateral boundary conditions, for the 5-day simulations.

The impacts on the simulated circulation of major changes to the standard MM4 are assessed. When an improved treatment of radiative processes is included, excessive rainfall develops over the Andes Mountains and over the Amazon Basin. The excessive rainfall is concentrated in “gridpoint” storms that are not eliminated when the surface physical parameterizations are improved. Modifications to the treatment of the vertical transport of moisture are required to diminish the excessive rainfall. Even with these and other changes included in the model, the simulated basin-averaged rainfall continues to exhibit unrealistic features. The improved, though still imperfect, model simulations are used to diagnose the temporal and spatial evolution of the circulation with an emphasis on equatorial-subtropical interactions.

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Daniel P. Tyndall
,
John D. Horel
, and
Manuel S. F. V. de Pondeca

Abstract

A two-dimensional variational method is used to analyze 2-m air temperatures over a limited domain (4° latitude × 4° longitude) in order to evaluate approaches to examining the sensitivity of the temperature analysis to the specification of observation and background errors. This local surface analysis (LSA) utilizes the 1-h forecast from the Rapid Update Cycle (RUC) downscaled to a 5-km resolution terrain level for its background fields and observations obtained from the Meteorological Assimilation Data Ingest System.

The observation error variance as a function of broad network categories and the error variance and covariance of the downscaled 1-h RUC background fields are estimated using a sample of over 7 million 2-m air temperature observations in the continental United States collected during the period 8 May–7 June 2008. The ratio of observation to background error variance is found to be between 2 and 3. This ratio is likely even higher in mountainous regions where representativeness errors attributed to the observations are large.

The technique used to evaluate the sensitivity of the 2-m air temperature to the ratio of the observation and background error variance and background error length scales is illustrated over the Shenandoah Valley of Virginia for a particularly challenging case (0900 UTC 22 October 2007) when large horizontal temperature gradients were present in the mountainous regions as well as over two entire days (20 and 27 May 2009). Sets of data denial experiments in which observations are randomly and uniquely removed from each analysis are generated and evaluated. This method demonstrates the effects of overfitting the analysis to the observations.

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Taylor A. Gowan
,
John D. Horel
,
Alexander A. Jacques
, and
Adair Kovac

Abstract

Numerical weather prediction centers rely on the Gridded Binary Second Edition (GRIB2) file format to efficiently compress and disseminate model output as two-dimensional grids. User processing time and storage requirements are high if many GRIB2 files with size O(100 MB, where B = bytes) need to be accessed routinely. We illustrate one approach to overcome such bottlenecks by reformatting GRIB2 model output from the High-Resolution Rapid Refresh (HRRR) model of the National Centers for Environmental Prediction to a cloud-optimized storage type, Zarr. Archives of the original HRRR GRIB2 files and the resulting Zarr stores on Amazon Web Services (AWS) Simple Storage Service (S3) are available publicly through the Amazon Sustainability Data Initiative. Every hour, the HRRR model produces 18- or 48-hourly GRIB2 surface forecast files of size O(100 MB). To simplify access to the grids in the surface files, we reorganize the HRRR model output for each variable and vertical level into Zarr stores of size O(1 MB), with chunks O(10 kB) containing all forecast lead times for 150 × 150 gridpoint subdomains. Open-source libraries provide efficient access to the compressed Zarr stores using cloud or local computing resources. The HRRR-Zarr approach is illustrated for common applications of sensible weather parameters, including real-time alerts for high-impact situations and retrospective access to output from hundreds to thousands of model runs. For example, time series of surface pressure forecast grids can be accessed using AWS cloud computing resources approximately 40 times as fast from the HRRR-Zarr store as from the HRRR-GRIB2 archive.

Significance Statement

The rapid evolution of computing power and data storage have enabled numerical weather prediction forecasts to be generated faster and with more detail than ever before. The increased temporal and spatial resolution of forecast model output can force end users with finite memory and storage capabilities to make pragmatic decisions about which data to retrieve, archive, and process for their applications. We illustrate an approach to alleviate this access bottleneck for common weather analysis and forecasting applications by using the Amazon Web Services (AWS) Simple Storage Service (S3) to store output from the High-Resolution Rapid Refresh (HRRR) model in Zarr format. Zarr is a relatively new data storage format that is flexible, compressible, and designed to be accessed with open-source software either using cloud or local computing resources. The HRRR-Zarr dataset is publicly available as part of the AWS Sustainability Data Initiative.

Open access
Alexander A. Jacques
,
John D. Horel
,
Erik T. Crosman
, and
Frank L. Vernon

Abstract

Large-magnitude pressure signatures associated with a wide range of atmospheric phenomena (e.g., mesoscale gravity waves, convective complexes, tropical disturbances, and synoptic storm systems) are examined using a unique set of surface pressure sensors deployed as part of the National Science Foundation EarthScope USArray Transportable Array. As part of the USArray project, approximately 400 seismic stations were deployed in a pseudogrid fashion across a portion of the United States for 1–2 yr, then retrieved and redeployed farther east. Surface pressure observations at a sampling frequency of 1 Hz were examined during the period 1 January 2010–28 February 2014 when the seismic array was transitioning from the central to eastern continental United States. Surface pressure time series at over 900 locations were bandpass filtered to examine pressure perturbations on three temporal scales: meso- (10 min–4 h), subsynoptic (4–30 h), and synoptic (30 h–5 days) scales.

Case studies of strong pressure perturbations are analyzed using web tools developed to visualize and track tens of thousands of such events with respect to archived radar imagery and surface wind observations. Seasonal assessments of the bandpass-filtered variance and frequency of large-magnitude events are conducted to identify prominent areas of activity. Large-magnitude mesoscale pressure perturbations occurred most frequently during spring in the southern Great Plains and shifted northward during summer. Synoptic-scale pressure perturbations are strongest during winter in the northern states with maxima located near the East Coast associated with frequent synoptic development along the coastal storm track.

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Alexander A. Jacques
,
John D. Horel
,
Erik T. Crosman
, and
Frank L. Vernon

Abstract

Mesoscale convective phenomena induce pressure perturbations that can alter the strength and magnitude of surface winds, precipitation, and other sensible weather, which, in some cases, can inflict injuries and damage to property. This work extends prior research to identify and characterize mesoscale pressure features using a unique resource of 1-Hz pressure observations available from the USArray Transportable Array (TA) seismic field campaign.

A two-dimensional variational technique is used to obtain 5-km surface pressure analysis grids every 5 min from 1 March to 31 August 2011 from the TA observations and gridded surface pressure from the Real-Time Mesoscale Analysis over a swath of the central United States. Bandpass-filtering and feature-tracking algorithms are employed to isolate, identify, and assess prominent mesoscale pressure perturbations and their properties. Two case studies, the first involving mesoscale convective systems and the second using a solitary gravity wave, are analyzed using additional surface observation and gridded data resources. Summary statistics for tracked features during the period reviewed indicate a majority of perturbations last less than 3 h, produce maximum perturbation magnitudes between 2 and 5 hPa, and move at speeds ranging from 15 to 35 m s−1. The results of this study combined with improvements nationwide in real-time access to pressure observations at subhourly reporting intervals highlight the potential for improved detection and nowcasting of high-impact mesoscale weather features.

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