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Temporal and Spatial Analyses of Pressure Perturbations from the USArray Network: Description of Dissertation ResearchAlex JacquesPh.D. CandidateDept. of Atmospheric Sciences, University of UtahPh.D. General Exam27 February 2015Introduction and MotivationPressure observations used traditionally to assess atmospheric phenomena ranging from high-frequency gravity waves (perturbation period < 20 min) to baroclinic waves (perturbation period > 30 hr)2

Traditional observation networks (e.g., ASOS) have limitationsSampling frequencyData access Network sitingField experiments limited in spatial extent and scopeJacques et al. (2015)Introduction and MotivationNew extended sources for pressure data (e.g., mobile phones) becoming prevalent with potential for scientific use (Mass and Madaus 2014)3

Mass and Madaus (2014)http://www.cumulonimbus.ca/http://www.pressurenet.io/Introduction and MotivationProposed research extends desire for interdisciplinary collaboration, communication, data collection, analysis, and dissemination as described by NSF EarthCube initiative

Candidate involved in other interdisciplinary collaborationsUTA TRAX Air Quality ExperimentGSL Summer 2015 Ozone Field CampaignAnalysis of mobile weather observation platforms (MoPED)MesoWest/National Mesonet Program4

http://earthcube.org/USArray Analysis Project IntroductionEarly 2012: Initial inquiry by the Incorporated Research Institutions for Seismology (IRIS) to MesoWest about 1 and 40 Hz observations from US Transportable Array

Developed procedures to collect, archive, and disseminate 5 min averaged pressure data to NWS Western Region and NOAA Meteorological Automated Data Ingest System (MADIS) in real time

Combination of MesoWests inability to handle 1 Hz data with candidates desire to enter graduate program led to further communication and NSF proposal to explore pressure perturbations in USArray dataset5USArray Analysis Project Introduction6

https://madis-data.noaa.gov/sfc_display/http://preview.weather.gov/edd/http://mesowest.utah.edu/VIDEOFILELINKVIDEOWEBLINKUSArray Network Migration (2010 Present)Stations deployed in pseudo-grid fashion (~70 km spacing)7

USArray Data Collection and DisseminationInitial Goal: Improve access of USArray data to atmospheric communityReal-time operations: accomplished via MesoWestResearch: automated archival and web display capabilities

Daily automated collection of 1 Hz observations into HDF5-formatted archive repositories (2 day latency to allow for data completeness)Compressible format (~50 billion obs = ~60 GB disk space)Fast querying for research applications (repositories optimized for time series and spatial analyses)Objective and subjective QC (Jacques et al. 2015)

Interactive web products (http://meso1.chpc.utah.edu/usarray) for on-demand analysis of current and past pressure perturbation eventsIndividual case studies using filtered/unfiltered dataPerturbation climatologies (Jacques et al. 2015)8Dissertation Research Question #1What are the characteristics and frequencies of large pressure perturbations detected over the period of data collected by the USArray?9USArray Time Series Analyses10Second order Butterworth band-pass filters applied to individual pressure time series (mean-removed and QCd) for entire length of station recordMeso: 10 min 4 h (Koch and Saleeby 2001)Sub-synoptic: 4 30 hSynoptic: 30 h 5 days

Jacques et al. (2015)Jacques et al. (2015)USArray Time Series Analyses1113 Jun 2013: low-end derecho across southeast Virginiaa) Meso- and un-filtered pressure time series from T60Ab) Wind observations from KFAF ASOS station

Jacques et al. (2015)USArray Time Series Analyses12Identify pressure signatures using relative minima/maxima in each filtered time series and store descriptive info (Jacques et al. 2015)Signature duration, pressure change, rate-of-change, etc.Every signature accessible via developed web products

Jacques et al. (2015)Pressure signature histograms help describe tendenciesUSArray Time Series Analyses13Geographic seasonal signature climatologies agree well with previous research on large perturbation occurrences (e.g., Koppel et al. 2000)

Jacques et al. (2015)Seasonal Large Meso (> 3 hPa change) OccurrencesSpring (MAM)Autumn (SON)Winter (DJF)Summer (JJA)

USArray Time Series Analyses14Variances of filtered and unfiltered time series provide further insight into how phenomena at differing scales impact pressure time seriesJacques et al. (2015)USArray Time Series Analyses15Seasonal variance summaries combine both geographic and interannual variability due the movement of the USArray eastward

Jacques et al. (2015)Jacques et al. (2015)Dissertation Research Question #2To what extent can the USArray pressure data detect the spatial patterns, gradients, and temporal evolution of mesoscale, sub-synoptic, and synoptic pressure perturbations?16USArray Spatial Analyses Initial Research17Better understand how the USArray (high temporal resolution, pseudo-grid spacing) may be utilized to improve detection of pressure perturbation features via numerical datasets

University of Utah Two-Dimensional Variational Analysis (UU2DVAR) described by Tyndall and Horel 2013Background first guess grids courtesy 1 h downscaled forecastsSurface observations of temperature, dew point, windBackground and observation errors assumed and assignedFinal analysis grid produced after using 2D variational approach to map observations onto background gridsAnalysis impact and improvement metrics produced from the analysis for each individual observationUSArray Spatial Analyses Initial Research18Step 1: Gather and utilize surface pressure background grids for period of interest (1 h downscaled 2.5 km Rapid Refresh forecasts)Same source as UU2DVAR and RTMA (de Pondeca et al. 2011)Problem: grids only available at hourly intervals

Step 2: Cubic-spline interpolate hourly background grids to 5 min for period of interest to take better advantage of high USArray sampling rate

Step 3: Acquire USArray observations for equivalent period of timeSpline-interpolate to 5 min data to remove observation shockProblem: elevation differences between RAP terrain and observations may lead to mismatches for surface pressureUSArray Spatial Analyses Initial Research19Step 4: Covert interpolated grids/observations to 1 h pressure tendencyEliminates elevation representativeness issuesShown to be useful for data assimilation (Wheatley and Stensrud 2010; Madaus et al. 2014)

Step 5: Generate analysis grids of 1 h pressure tendency using UU2DVAR every 5 min for duration of event

Step 6: Convert analysis back to surface pressure using background from exactly 1 h prior

Step 7: Convert surface pressure grids to altimeter setting using standard conversion formula with RAP terrainUSArray Spatial Analyses Case Example 1201 h Pressure Tendency - 5 Sep 2012 Midwest Convective Systems

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USArray Spatial Analyses Case Example 2231 h Pressure Tendency - 11 Apr 2013 Wisconsin Mesoscale Gravity Wave

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USArray Spatial Analyses Case Example 225

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USArray Spatial Analyses Initial Findings26Cases indicate that USArray spatial deployment and higher temporal resolution could be useful to better resolve mesoscale features in gridded surface pressure analyses

Initial cases assumed independence with time for each analysis (i.e. gridded analyses were only dependent on conditions stated by interpolated first guess fields, not by previous analyses)

Perturbations propagating between USArray stations cause perturbation pulsations in analyses, particularly with large magnitude perturbations that differ significantly from first guess fields

Pressure tendency encompasses all scales, not limited to specific scales of phenomenaUSArray Spatial Analyses Next Steps27Improvement Possibilities for USArray/UU2DVAR Analyses

Estimate horizontal decorrelation length scale through covariance techniques as described by Tyndall et al. (2010)

Tyndall et al. (2010)Tyndall et al. (2010)USArray Spatial Analyses Next Steps28Incorporate time dimension into analyses to aid in filling in gaps due to station spacing in relation to propagating phenomena as well as feature identification

Time-to-space conversion techniquesLow-level wind patterns to estimate propagation properties (Koch and OHandley 1997; Koch and Saleeby 2001)Statistical techniques (e.g., STMAS by Xie et al. 2011)

Objective wave reconstruction techniques (e.g., de Groot-Hedlin et al. 2014) may be beneficial as well for better perturbation and feature identificationUSArray Spatial Analyses Next Steps29Expand upon climatologies from Jacques et al. (2015) with identification and characterization of perturbation features at spatial scales larger than a singular station

Focus on singular season and region of CONUS (e.g., 2011 summer over Great Plains and Midwest due to plentiful MCS-type occurrences)

Seasonal Analysis of background gridded fieldsHigh-pass filter background grids < 30 h to isolate meso and sub-synoptic phenomena (removes ambiguity concerns with long-lived mesoscale systems)Nyquist period limit of 2 h for hourly background grids, short-term mesoscale phenomena likely to be missedUSArray Spatial Analyses Next Steps30Execute UU2DVAR analyses as shown previously (with potential improvements) for same season and geographical limits

Filter 5-min gridded analyses using 10 min 30 h band-pass algorithm

Comparison of filtered background and analysis grids may be useful for isolating mesoscale-type perturbations from longer period phenomena (e.g., thermal tides)

Identify and summarize characteristics of mesoscale perturbationsMaximum/minimum magnitudeSize and durationPropagation speed and directionPerturbation gradient magnitudeCollaborations31Scripps Institution of Oceanography - Array Network Facility (ANF)USArray operations, data collection, and disseminationSeveral meetings already regarding metadata, quality control procedures, research updates, etc.Continued collaboration as deployment of main array in Alaska occurs over next few years

NOAA ESRL MADISMADIS Quality control downstream of MesoWestPressure observations from many stations flagged due to conversion of surface to sea-level pressure (mismatches between observed elevation and gridded terrain used by objective procedures)Discussed in brief with MADIS staff at 2015 AMS Annual meeting, collaboration may continue in timeExpected Timeline32Dissertation to have two primary sections:Section I: Station Time Series AnalysesSection II: Spatial Analyses using Gridded Datasets

Majority of Section I already completed and published in Jacques et al. (2015), but analyses will be expanded beyond end date of 28 Feb 2014

Expected that at least one more publication will be produced from this specific research (likely related to spatial analyses)

Additional lead- or co-authored papers possible related to USArray or other projects candidate is involved withExpected Timeline33Anticipated Completion Semester: Spring 2016TimelineProcess to CompletePrior to General ExamTemporal analyses completed through 28 Feb 2014Initial spatial analyses conductedSpring Early Fall 2015Expand temporal analyses beyond 28 Feb 2014Conduct remainder of spatial analyses researchMid Fall 2015 Spring 2016Dissertation Writing and ReviewSelected References34Brring, L., and K. Fortuniak, 2009: Multi-indices analysis of southern Scandinavian storminess 17802005 and links to interdecadal variations in the NW Europe-North Sea region. Int. J. Climatol., 29, 373384, doi:10.1002/joc.1842. Bosart, L. F., W. E. Bracken, and A. Seimon, 1998: A study of cyclone mesoscale structure with emphasis on a large-amplitude inertia-gravity wave. Mon. Wea. Rev., 126, 14971527, doi:10.1175/1520-0493(1998)126,1497:ASOCMS.2.0.CO;2. Carbone, R. E., J. D. Tuttle, D. A. Ahijevych, and S. B. Trier, 2002: Inferences of predictability associated with warm season precipitation episodes. J. Atmos. Sci., 59, 20332056, doi:10.1175/1520-0469(2002)059,2033:IOPAWW.2.0.CO;2. de Groot-Hedlin, C. D., M. A. Hedlin, and K. T. Walker, 2014: Detection of gravity waves across the USArray: A case study. Earth Planet. Sci. Lett., 402, 346352, doi:10.1016/j.epsl.2013.06.042. de Pondeca, M., and Coauthors, 2011: The Real-Time Mesoscale Analysis at NOAA's National Centers for Environmental Prediction: Current status and development. Wea. Forecasting, 26, 593-612, doi:10.1175/WAF-D-10-05037.1. Jacques, A. A., J. D. Horel, E. T. Crosman, and F. L. Vernon, 2015: Central and eastern United States surface pressure variations derived from the USArray network. Mon. Wea. Rev., In Press, doi:10.1175/MWR-D-14-00274.1 Koch, S. E., and C. OHandley, 1997: Operational forecasting and detection of mesoscale gravity waves. Wea. Forecasting, 12, 253281, doi:10.1175/1520-0434(1997)012,0253:OFADOM.2.0.CO;2. , and S. Saleeby, 2001: An automated system for the analysis of gravity waves and other mesoscale phenomena. Wea. Forecasting, 16, 661679, doi:10.1175/1520-0434(2001)016,0661:AASFTA.2.0.CO;2. Koppel, L. L., L. F. Bosart, and D. Keyser, 2000: A 25-yr climatology of large-amplitude hourly surface pressure changes over the conterminous United States. Mon. Wea. Rev., 128, 5168, doi:10.1175/1520-0493(2000)128,0051:AYCOLA.2.0.CO;2 Li, Y., and R. B. Smith, 2010: The detection and significance of diurnal pressure and potential vorticity anomalies east of the Rockies. J. Atmos. Sci., 67, 27342751, doi:10.1175/2010JAS3423.1. Madaus, L. E., G. J. Hakim, and C. F. Mass, 2014: Utility of dense pressure observations for improving mesoscale analyses and forecasts. Mon Wea Rev., 142, 23982413, doi:10.1175/MWR-D-13-00269.1. Mass, C. F., and L.E. Madaus, 2014: Surface pressure observations from smartphones: A potential revolution for high-resolution weather prediction? Bull. Amer. Meteor. Soc., 95, 13431349, doi:10.1175/BAMS-D-13-00188.1. , W. J. Steenburgh, and D. M. Schultz, 1991: Diurnal surface-pressure variations over the continental United States and the influence of sea level reduction. Mon. Wea. Rev., 119, 28142830, doi:10.1175/1520-0493(1991)119,2814:DSPVOT.2.0.CO;2. Ruppert, J. H., and L. F. Bosart, 2014: A case study of the interaction of a mesoscale gravity wave with a mesoscale convective system. Mon. Wea. Rev., 142, 14031429, doi:10.1175/MWR-D-13-00274.1. Selected References35Tian, W., D. J. Parker, S. Mobbs, M. Hill, C. A. D. Kilburn, and D. Ladd, 2004: Observing coherent boundary layer motions using remote sensing and surface pressure measurement. J. Atmos. Oceanic Technol., 21, 14811490, doi:10.1175/1520-0426(2004)021,1481:OCBLMU.2.0.CO;2. Tyndall, D., and J. Horel, 2013: Impacts of mesonet observations on meteorological surface analyses. Wea. Forecasting, 28, 254-269, doi:10.1175/WAF-D-12-00027.1. , J. D. Horel, and M. S. F. V. de Pondeca, 2010: Sensitivity of surface air temperature analyses to background and observation errors. Wea. Forecasting, 25, 852865. doi:10.1175/2009WAF2222304.1 Wheatley, D. M., and D. J. Stensrud, 2010: The impact of assimilating surface pressure observations on severe weather events in a WRF mesoscale ensemble system. Mon. Wea. Rev., 138, 16731694, doi:10.1175/2009MWR3042.1.