![[logo] US EPA](http://www.epa.gov/epafiles/images/logo_epaseal.gif){kind=logo}
Upper-Air Data Validation and Applications
Note: EPA no longer updates this information, but it may be useful as a reference or resource.
|
Definitions DEFINITIONS
UPPER-AIR DATA PAMS Data
UPPER-AIR INSTRUMENTS
DATA VALIDATION Data Screening Tests
COMMON PROBLEMS ENCOUNTERED IN UPPER-AIR DATA Rawinsonde systems
Use this legend as an aid to interpret plots of winds in this document.
Figure 1 Quality assurance of upper-air
meteorological data is critical. This figure illustrates bird
interference.
Example of bird contamination in radar profiler data collected at New Brunswick, NJ on September 5, 1995. The northerly winds from 2100 and 2300 EST between 500 and 2000 m agl were actually caused by the radar measuring the motion of birds migrating to the south, instead of the northwesterly atmospheric winds. Birds act as large radar "targets," so that signals from birds overwhelm the weaker atmospheric signals. The color plot of signal-to-noise ratio (i.e., reflectivity) shows a region of strong reflectivity that coincides with these northerly winds. Birds generally migrate year-round along preferred flyways, with the peak migrations occurring at night during the Spring and Fall months (Gauthreaux, 1991). Additional information about bird contamination of radar wind profiler data can be found in Wilczak et al. (1995). The top figure is a time-series plot of wind speed and direction at various altitudes. The orientation of the barb indicates wind direction (nose up = wind from north). The number of tails on the barbs indicates wind strength. The bottom figure is a time-series plot of reflectivity at various altitudes. (Red = strong reflectivity, Blue = weak reflectivity.) Figure 2 Another type of natural phenomenon that can invalidate upper-air meteorological data is precipitation.41Example of precipitation interference in radar profiler data collected at New Brunswick, NJ on September 22, 1995. Missing wind data at 1100, 1700-1900, and 2200 EST were caused by precipitation. During precipitation, the radar profiler measures the fall speed of rain drops or snow flakes. In this example, the profiler measured strong, downward motion of -3 to -8 m/s (blue region), which is actually the motion of the rain drops. Missing winds resulted when the radar measured both atmospheric and precipitation motions and the sub-hourly data failed quality control checks (Dye, 1996). Figure 3 Recurrent and excessive ground clutter
can seriously damage data quality. Siting issues are very important.
INVESTIGATE UPPER-AIR METEOROLOGICAL DATA
The depth of the mixed layer is a critical parameter for understanding the formation, dispersion, and transport of ozone and precursors during pollution episodes.
RADAR PROFILER REFLECTIVITY Cn2
Mixing depth = peak in Cn2 profile
Figure 4 Proper interpretation of radar profiler data yields good estimates of mixing depth. 
Figure 5 An understanding of mixing depth and other upper-air parameters enhances air quality analysis. This figure reveals two inversion layers corresponding to the Cn2 maxima. 1993 and profiles of turbulence, ozone concentration, and temperature measured by an aircraft during a descent over the site."> Cn2 profile from the radar profiler at Southeast Houston on August 10, 1993 and profiles of turbulence, ozone concentration, and temperature measured by an aircraft during a descent over the site. The Cn2 profile peaks at 800 and 2400 m, which corresponds to the tops of two polluted layers. The first layer, from the surface to 800 m, was well mixed as indicated by high, uniform ozone concentrations and strong turbulence. This layer was capped by a weak stable layer between 800 and 1000 m, and the peak value of Cn2 closely corresponds to the top of this mixed layer. From 800 to 2300 m, the ozone and turbulence data suggest that mixing had occurred in this layer; these pollutants in this layer were likely vented by updrafts and clouds ahead of a sea-breeze front (Dye, 1996). Figure 6 During growth and at midday, radar
profiler data yield the best estimate of mixing depth. RASS data are
superior when mixing heights are low, below 500 m.
Time series plot of mixing depths estimated from Cn2 and Tv data and from a meteorological model for a radar profiler site in Houston, TX for August 18-20, 1993 (Dye et al., 1995a). The agreement between the model, RASS Tv, and Cn2-derived mixing depths is quite good during the growth of the convective boundary layer (CBL - i.e., between 0800 and 1200 CDT). During the later afternoon and at night, discrepancies among all three estimates occurred. Note that Cn2 mixing depths tend to be more accurate than Tv mixing depths during the day. The opposite is true when the nocturnal inversion layer forms. INVESTIGATE BOUNDARY LAYER STRUCTURE AND EVOLUTION Boundary Layer Features
Figure 7 These upper air data reveal a recurrent nocturnal jet.
Time series cross section of winds, mixing depth, and
inversion conditions measured by the radar profiler on July 12-13, 1994
at Bermudian Valley, PA. The thin solid line denotes the height of the
mixed layer estimated using Cn2 and RASS
temperature data. The thick line denotes the subsidence inversion. The
shaded area indicates the region of the nocturnal low-level wind maxima
(Lindsey et al., 1995b).
ISENTROPIC ANALYSIS
Figure 8 Upper-air data can help elucidate complex structural features of the atmosphere that directly affect air quality and pollutant transport. 
West-to-east isentropic cross section from Rockford,
IL to Muskegon, MI on June 26, 1991 at 0600, 0900, and 1200 CDT.
Isentropes are contoured every 2 K. Aloft winds are plotted every 500 m
at each rawinsonde site. The shaded region in the 0900 CDT figure
denotes aircraft measurements of NOx concentrations from 20
to 40 ppb. The early morning conditions were characterized by a stable
NBL over land and a stable conduction layer over Lake Michigan. The
isentropes show that during the morning, the land breeze and the general
offshore flow in Chicago, Gary, and Milwaukee would transport emissions
offshore into the conduction layer. Hydrocarbon concentrations measured
between 0700-0900 CDT in Chicago and offshore confirmed this type of
transport. (Dye, et al., 1995b)
TRAJECTORY ANALYSIS OF SURFACE AND ALOFT TRANSPORT Analysis Objectives
PERFORMING TRAJECTORY ANALYSES
Figure 9 This figure shows a trajectory for a parcel of air observed at an altitude
of 300 meters. Each symbol represents position in 2-hour increments.
The trajectory indicates an eddy, with very little transport occurring
from 7:00 a.m. to 4:00 p.m. Figure 10 Trajectory analyses performed from
different sites help build consensus about transport phenomena.
100-m backward trajectories starting from Gilchrist at 1300 CST, Texas City at 1300 CST, Seabrook at 1400 CST, and Smith Point at 1400 CST, on September 8, 1993 (SAI et al., 1995). Figure 11 Trajectories should be launched for several hours and from different locations
to arrive at a consensus regarding air parcel origins. 300-m forward trajectories from Galveston, Texas at all hours on August 18 (SAI et al., 1995). Figure 12 The upper-air data in this figure show that inland there is a shallow inversion
at night and deep mixing during the daytime. Time-height cross sections of winds from the Southeast Houston radar profiler on August 19 and 20, 1993. Hourly surface winds are also plotted at 10 m (SAI, et al., 1995). Solid lines indicate the top of the mixed layer. Figure 13 In this figure, over the water, the marine boundary layer shows little diurnal
variation. The meteorological phenomena in the Southeast Houston
and High Island Platform examples are important to consider when
assessing air quality. ANALYSIS OF ALOFT WINDS DURING EPISODES
Figure 14 Light and variable winds during the
daytime allow ozone and its precursors to accumulate.
Example of radar profiler winds during an ozone episode from a site at Holbrook, Pennsylvania on July 31, 1995 (Dye, 1996) Figure 15 Relatively high winds at a regional boundary suggest that ozone and its precursors
can be transported substantial distances. Example of radar profiler winds during an ozone episode from a site at Holbrook, Pennsylvania on July 31, 1995 (Dye, 1996). Wind speeds range from 10 to 13 m/s in the plot which can produce transport distances of 360 km in 10 hours. ANALYSIS OF VENTILATION AND RECIRCULATION
L Resultant (vector) transport distance (km) Q Resultant wind directions in degrees from true N adjusted to the proper quadrant R Recirculation factor (L/S)
R = 0 No net transport R» 1 Good ventilation conditions (for L = few hundred km) R small Stagnation for small S (i.e., low winds) Recirculation for low L (i.e., < 50 km) Figure 16 On days with low ozone concentrations,
aloft winds showed little recirculation.
Vector integrated transport distances, resultant wind directions, and recirculation factors (R), calculated from data collected by the southeast Houston (SHE) radar profiler for the period 0600-1700 CDT on August 16, 1993 (SAI et al., 1995). SUMMARY
UPPER AIR REFERENCES Allwine K.J. and Whiteman C.D. (1994) Single-station integral measures of atmospheric stagnation, recirculation, and ventilation. Atmos. Environ. 28, 713-721. Blumenthal D.L., Lurmann F.W., Roberts P.T., Main H.H., MacDonald C.P., Knuth W.R., and Niccum E.M. (1997) Three-dimensional distribution and transport analyses for SJVAQS/AUSPEX. Draft report prepared for the San Joaquin Valleywide Air Pollution Study Agency, California Air Resources Board, Sacramento, CA by Sonoma Technology, Inc., Santa Rosa, CA, STI-91060-1705-DFR, February. Chang J.C. and Hanna S.R. (1993) Trajectory calculation for selected LMOS periods. Report prepared for Sonoma Technology, Inc., Santa Rosa, CA by Sigma Research Corp., Concord, MA, Report No. 1197-600, May. Dye T.S. (1996) Unpublished data. Sonoma Technology Inc., Santa Rosa, CA. Dye T.S., Lindsey C.G., and Anderson J.A. (1995a) Estimates of mixing depths from "boundary layer" profilers. In Preprints of the 9th Symposium on Meteorological Observations and Instrumentation, Charlotte, NC, March 27-31, STI-94212-1451. Dye T.S., Roberts P.T., and Korc M.E. (1995b) Observations of transport processes for ozone and ozone precursors during the 1991 Lake Michigan Ozone Study. J. Appl. Meteorol. 34, 1877-1889. (STI-1384). Gauthreaux Jr. S.A. (1991) The flight behavior of migrating birds in changing wind fields: radar and visual analyses. Amer. Zool. 31, 187-204. Hanna S.R. and Chang J.C. (1993) Representativeness of 1991 LMOS ozone episodes and relations between ozone episodes and meteorological variables in the Lake Michigan area. Report prepared for Sonoma Technology, Inc., Santa Rosa, CA by Sigma Research Corp., Concord, MA, Report No. 1197-407/411, January. Lindsey C.G., Dye T.S., Blumenthal D.L., Ray S.E., and Arthur M. (1995a) Meteorological aspects of summertime ozone episodes in the Northeast. Paper FA 5.8 to be presented at the 9th Joint Conference on the Applications of Air Pollution Meteorology at the 76th AMS Annual Meeting, Atlanta, GA, January 28-February 2, 1996, (STI-1549). Lindsey C.G., Dye T.S., Roberts P.T., Anderson J.A., and Ray S.E. (1995b) Meteorological aspects of ozone episodes in southeast Texas. Paper No. 95-WP96.02 presented at the 88th Air & Waste Management Association Annual Meeting, San Antonio, TX, June 18-23. Lindsey C.G., Dye T.S., and Baxter R.A. (1995d) Draft guidelines for the quality assurance and management of PAMS upper-air meteorological data. Final report prepared for U.S. Environmental Protection Agency, Research Triangle Park, NC by Sonoma Technology, Inc., Santa Rosa, CA, Work assignment 10-95, EPA Contract No. 68D30020, STI-94611-1556-FR, December. Main H.H., Chinkin L.R., Haste T.L., Roberts P.T., and Reiss R. (1997) Shasta County ozone and ozone precursor transport quantification study. Final report prepared for the Shasta County Department of Resource Management, Redding, CA, STI-95180-1714-FR, March. Roberts P.T. and Main H.H. (1992) Characterization of three-dimensional air quality during the SCAQS. In Southern California Air Quality Study Data Analysis. Proceedings from SCAQS Data Analysis Conference, University of California, Los Angeles, CA, July 21-23, Air & Waste Management Association, Pittsburgh, PA, (STI-1223), VIP-26. Roberts P.T., Main H.H., Smith T.B., Lindsey C.G., and Korc M.E. (1992a) Analysis of 3-D air quality data and carbon, nitrogen, and sulfur species distributions during the Southern California Air Quality Study. Final report prepared for the Coordinating Research Council, Atlanta, GA by Sonoma Technology, Inc., Santa Rosa, CA, STI-99100-1213-FR, October. Roberts P.T., Musarra S., Smith T.B., and Lurmann F.W. (1992c) A study to determine the nature and extent of ozone and ozone precursor transport in selected areas of California. Final report prepared for the California Air Resources Board, Sacramento, CA by Sonoma Technology, Inc., Santa Rosa, CA, STI-90060-1162-FR, December. Roberts P.T., Main H.H., Lindsey C.G., and Korc M.E. (1993a) Ozone and particulate matter case study analysis for the Southern California Air Quality Study. Final report prepared for the California Air Resources Board, Sacramento, CA by Sonoma Technology, Inc., Santa Rosa, CA, STI-90020-1222-FR, May. Roberts P.T., Main H.H., and Korc M.E. (1993b) Comparison of 3-D air quality data with model sensitivity runs for the South Coast Air Basin. Paper No. 93-WP-69B.05 presented at the Air & Waste Management Association Regional Photochemical Measurement and Modeling Studies Conference, San Diego, CA, November 8-12, STI-1244. Roberts P.T., Dye T.S., Korc M.E., and Main H.H. (1994) Air quality data analysis for the 1991 Lake Michigan Ozone Study. Final report prepared for Lake Michigan Air Directors Consortium, Des Plaines, IL by Sonoma Technology, Inc., Santa Rosa, CA, STI-92022-1410-FR. Roberts P., Korc M., Blumenthal D., and Mueller P.K. (1995a) NARSTO-Northeast 1995 summer ozone study. Version 1. Report prepared for Electric Power Research Institute, Palo Alto, CA by Sonoma Technology, Inc., Santa Rosa, CA, STI-95135-1538-WD1; Research project EPRI WO9108-01. Systems Applications International, Sonoma Technology Inc., Earth Tech, and Alpine Geophysics (1995) Gulf of Mexico Air Quality Study. Vol 1: Summary of data analysis and modeling. Draft final report prepared for U.S. Department of the Interior, Minerals Management Service, Gulf of Mexico OCS Region, New Orleans, LA, OCS Study, MMS 94-0046, SYSAPP-95/013d. Tremback C.J. and Lyons W.A. (1993) Trajectory calculation derived from CALRAMS simulations. Report prepared for Lake Michigan Air Directors Consortium, Des Plaines, IL by ASTeR, Inc., Ft. Collins, CO, August. White A.B. (1993) Mixing depth detection using 915 MHz radar reflectivity data. In: Preprints, AMS 8th Symposium on Meteorological Observations and Instruments, Anaheim, CA, January 17-22. Wilczak J.M., Strauch R.G., Weber B.L., Merritt D.A., Ralph F.M., Jordan J.R., Wolfe D.E., Lewis L.K., Wuertz D.B., Gaynor J.E., McLaughlin S., Rogers R., Riddle A., and Dye T. (1995) Contamination of wind profiler data by migrating birds: characteristics of corrupted data and potential solutions. J. of Oceanic and Atmos. Tech., 12, 449-467. Wyngaard J.C. and LeMone M.A. (1980) Behavior of the refractive index structure parameter in the entraining convective boundary layer. J. Atmos. Sci., 37, 1573-1585. [Workbook Table of Contents] [Top of Upper-Air] [Previous Section] [Next Section] |
Example of ground clutter interference from a radar
profiler site at Red Hook, NY on August 25, 1995. Ground clutter is
caused when a transmitted signal is reflected off an object instead of
the atmosphere. In this case, the radar signals were reflected off
distant treets, which produced the light winds between 0600 and 0800 EST
(Dye, 1996).
300-m backward trajectory starting from Aldine, Texas at 1600 CST
on August 19, 1993 (SAI et al., 1995).
Time-height cross sections of winds from the High Island Platform
radar profiler on August 19 and 20, 1993. Hourly surface winds are
also plotted at 10 m (SAI, et al., 1995). Solid lines indicate the
top of the mixed layer.
