Concurrent Radar and Aircraft Measurements of Florida ...€¦ · Concurrent Radar and Aircraft...
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Concurrent Radar and Aircraft Measurements of Florida Thunderstorm Cirrus Clouds Nicholas J. Gapp ([email protected]) 1 , David Delene 1 , Matthew Gilmore 1 , Jerome Schmidt 2 , and Paul Harasti 2 1 University of North Dakota, Grand Forks, North Dakota 2 Naval Research Laboratory Marine Meteorology Division, Monterey, California Introduction The North Dakota Citation Research Aircraft conducted measurements of cirrus cloud particles above 30,000 feet produced by Florida thunderstorms in the summers of 2015 (CAPE2015 field project) and 2019 (CapeEx19 field project). Concurrent with the aircraft measurements, remote sensing observations were made by the United States Navy’s Mid-Course Radar (MCR). Comparison between derived reflectivity factor from in-situ probe data and observed MCR data using the wideband beam is explored. Methodology Ice water content and reflectivity factor are derived assuming spherical ice particles observed by airborne cloud physics instruments. The MCR is a C-band, dual-polarization Doppler radar that alternates transmissions between two wave forms with range resolutions during CAPE2015 of either 37 m (narrowband) or 0.546 m (wideband) (Schmidt et al. 2019). The aircraft’s position (flight tracks below) is downlinked to the MCR, enabling real-time tracking of the aircraft and ensuring concurrent measurements. Reflectivity factor is calculated from aircraft probe data using the following equations (Gapp 2019): where is the total volume of ice from the observed particle size distribution (PSD), subscript n is the PSD bin number, N is the particle concentration, D is the mean diameter, is the effective particle density, is the particle mass observed by the Nevzorov, LED is liquid-equivalent diameter, is the volume of the size bin, is the density of water, and is equivalent derived radar reflectivity factor (Smith 1984). CAPE2015 Aircraft Measurements CAPE2015 Data and Results The cloud physics instruments onboard the Citation Research Aircraft used in this study are shown. The Two-Dimensional Stereo probe (2D-S, top right) provides shadow images of particles (above) using orthogonal lasers that each illuminate an array of 128 10-μm photodiodes. The Nevzorov Water Content Probe (middle right) measures total and liquid water using constant-temperature hot-wire sensors and provides total particle mass after data processing with routines from Airborne Data Processing and Analysis (Delene 2011). The High Volume Precipitation Spectrometer Version 3 (HVPS3, bottom right) provides shadow images of particles (below) using one laser that illuminates an array of 128 150-μm photodiodes. • Hydrometeor attenuation likely has a large role in the disagreement, especially for case AC14 since aircraft is at largest range from MCR. • A single bulk density is applied across entire size spectrum for deriving reflectivity factor, which could also be the reason for the disagreement between derived and MCR reflectivity factors. • Future work will investigate the sensitivity of the relationships to derived particle density, particle diameter, and reflectivity factor. • Delene, D. J., 2011: Airborne data processing and analysis software package. Earth Sci. Inform., 4, 29–44, doi:10.1007/s12145-010-0061-4. • Gapp, N. J., 2019: Comparison of Concurrent Radar and Aircraft Measurements of Cirrus Clouds. M.S. Thesis, Dept. of Atmospheric Sciences, University of North Dakota, 54 pp. • Heymsfield, A. J. and J. L. Parrish, 1978: A Computational Technique for Increasing the Effective Sampling Volume of the PMS Two-Dimensional Particle Size Spectrometer. J. Appl. Meteor., 17, 1566–1572, https://doi.org/10.1175/1520-0450(1978)017<1566:ACTFIT>2.0.CO;2. • Schmidt, J. M., and Coauthors, 2019: Radar detection of individual cloud hydrometeors. Bull. Amer. Meteor. Soc., 100, 0—0, https://doi.org/10.1175/BAMS-D-18-0130.1. • Smith, P., 1984: Equivalent radar reflectivity factors for snow and ice particles. J. Climate Appl. Meteor., 23, 1258–1260. This research is supported under a Naval Surface Warfare Center Dahlgren Division grant to conduct and analyze data from the CAPE2015 and CapeEx19 field projects. The authors would like to thank Martin Schnaiter for the PHIPS data. CapeEx19 Data Conclusions and Future Work References and Acknowledgements The agreement during case AC14 is not as strong as the other three cases even though the cases have similar particle size distributions. Possible reason for the disagreement is investigated by focusing on two AC14 time periods, highlighted by the red and blue boxes below. Comparisons of 1 Hz derived (center-in, area- equivalent; black lines) and MCR wideband (red lines) reflectivity factor from CAPE2015 on 01 August 2015 are shown. The MCR reflectivity factor shown is an average taken within ±5 m of the aircraft’s range. The upper and lower uncertainty (unc.) bounds for the derived (black dotted lines) and MCR- observed (red dotted lines) reflectivity factor values are shown as well (Gapp 2019). Center-in (Heymsfield et al. 1978), area-equivalent particle size distributions (PSDs) averaged across the entirety of each case above are shown. 2D-S particle concentrations are plotted below 1,000 μm and HVPS3 particle concentrations are plotted from 1,000 μm and above (“merged PSD”). The diamonds represent the midpoint of each size bin that makes up the distribution (Gapp 2019). Lower values of reflectivity factor during times of “good” agreement (71837- 71843 sfm; red square and lines) are due to lower ice water content and higher total volume (Eq. 1) than times of “bad” agreement (71845-71851 sfm; blue square and lines); therefore, effective particle densities (Eq. 2), LEDs (Eq. 3), and derived reflectivity factors (Eq. 4) are lower as well. Similarities exist in the PSDs from CapeEx19 (above), but particles are slightly larger than those observed during CAPE2015 (histograms above). Particle Habit Imaging and Polar Scattering (PHIPS) probe images from 03 August 2019 are below. The extra data from CapeEx19 will help to determine the sensitivity between derived particle density, particle diameter, and reflectivity factor. AC13 AC14 AC15 AC16 Average: 0.19 ± 0.02 g/m 3 Average: 321.1 ± 29.7 μm Average: 112.7 ± 32.5 kg/m 3 Average: 7.25 ± 1.67 dBZ Average: 0.29 ± 0.05 g/m 3 Average: 507.1 ± 93.4 μm Average: 397.5 ± 206.6 kg/m 3 Average: 15.08 ± 3.41 dBZ 1,000 μm = n 6 3 , (1) = Τ , (2) = 3 ൗ 6 w , (3) = 0.224 () 6 , (4)
Transcript of Concurrent Radar and Aircraft Measurements of Florida ...€¦ · Concurrent Radar and Aircraft...
Concurrent Radar and Aircraft Measurements of Florida Thunderstorm Cirrus CloudsNicholas J. Gapp ([email protected])1, David Delene1, Matthew Gilmore1, Jerome Schmidt2, and Paul Harasti2
1 University of North Dakota, Grand Forks, North Dakota2 Naval Research Laboratory Marine Meteorology Division, Monterey, California
Introduction
The North Dakota Citation Research Aircraft conducted measurementsof cirrus cloud particles above 30,000 feet produced by Floridathunderstorms in the summers of 2015 (CAPE2015 field project) and 2019(CapeEx19 field project). Concurrent with the aircraft measurements, remotesensing observations were made by the United States Navy’s Mid-CourseRadar (MCR). Comparison between derived reflectivity factor from in-situprobe data and observed MCR data using the wideband beam is explored.
Methodology
Ice water content and reflectivity factor are derived assuming sphericalice particles observed by airborne cloud physics instruments. The MCR is aC-band, dual-polarization Doppler radar that alternates transmissionsbetween two wave forms with range resolutions during CAPE2015 of either37 m (narrowband) or 0.546 m (wideband) (Schmidt et al. 2019). Theaircraft’s position (flight tracks below) is downlinked to the MCR, enablingreal-time tracking of the aircraft and ensuring concurrent measurements.
Reflectivity factor is calculated from aircraft probe data using thefollowing equations (Gapp 2019):
where 𝑉𝑖 is the total volume of ice from theobserved particle size distribution (PSD), subscript n is the PSD bin number, Nis the particle concentration, D is the mean diameter, 𝜌𝑒 is the effectiveparticle density, 𝑚𝑁𝑒𝑣 is the particle mass observed by the Nevzorov, LED isliquid-equivalent diameter, 𝑉𝑛 is the volume of the size bin, 𝜌𝑤 is the densityof water, and𝑍𝑒 is equivalent derived radar reflectivity factor (Smith 1984).
CAPE2015 Aircraft Measurements
CAPE2015 Data and Results
The cloud physics instruments onboard the Citation Research Aircraftused in this study are shown. The Two-Dimensional Stereo probe (2D-S,top right) provides shadow images of particles (above) using orthogonallasers that each illuminate an array of 128 10-µm photodiodes. TheNevzorov Water Content Probe (middle right) measures total and liquidwater using constant-temperature hot-wire sensors and provides totalparticle mass after data processing with routines from Airborne DataProcessing and Analysis (Delene 2011). The High Volume PrecipitationSpectrometer Version 3 (HVPS3, bottom right) provides shadow imagesof particles (below) using one laser that illuminates an array of 128150-µm photodiodes.
• Hydrometeor attenuation likely has a large role in the disagreement,especially for case AC14 since aircraft is at largest range from MCR.
• A single bulk density is applied across entire size spectrum forderiving reflectivity factor, which could also be the reason for thedisagreement between derived and MCR reflectivity factors.
• Future work will investigate the sensitivity of the relationships toderived particle density, particle diameter, and reflectivity factor.
• Delene, D. J., 2011: Airborne data processing and analysis software package. Earth Sci. Inform., 4,29–44, doi:10.1007/s12145-010-0061-4.
• Gapp, N. J., 2019: Comparison of Concurrent Radar and Aircraft Measurements of Cirrus Clouds.M.S. Thesis, Dept. of Atmospheric Sciences, University of North Dakota, 54 pp.
• Heymsfield, A. J. and J. L. Parrish, 1978: A Computational Technique for Increasing the EffectiveSampling Volume of the PMS Two-Dimensional Particle Size Spectrometer. J. Appl. Meteor., 17,1566–1572, https://doi.org/10.1175/1520-0450(1978)017<1566:ACTFIT>2.0.CO;2.
• Schmidt, J. M., and Coauthors, 2019: Radar detection of individual cloud hydrometeors. Bull. Amer.Meteor. Soc., 100, 0—0, https://doi.org/10.1175/BAMS-D-18-0130.1.
• Smith, P., 1984: Equivalent radar reflectivity factors for snow and ice particles. J. Climate Appl.Meteor., 23, 1258–1260.
This research is supported under a Naval Surface Warfare Center Dahlgren Division grant to conductand analyze data from the CAPE2015 and CapeEx19 field projects. The authors would like to thankMartin Schnaiter for the PHIPS data.
CapeEx19 Data
Conclusions and Future Work
References and Acknowledgements
The agreement during case AC14 is not as strong as the other threecases even though the cases have similar particle size distributions.Possible reason for the disagreement is investigated by focusing on twoAC14 time periods, highlighted by the red and blue boxes below.
Comparisons of 1 Hzderived (center-in, area-equivalent; black lines) andMCR wideband (red lines)reflectivity factor fromCAPE2015 on 01 August2015 are shown. The MCRreflectivity factor shown isan average taken within ±5m of the aircraft’s range.The upper and loweruncertainty (unc.) boundsfor the derived (blackdotted lines) and MCR-observed (red dotted lines)reflectivity factor values areshown as well (Gapp 2019).
Center-in (Heymsfield et al. 1978), area-equivalentparticle size distributions (PSDs) averaged across theentirety of each case above are shown. 2D-S particleconcentrations are plotted below 1,000 µm and HVPS3particle concentrations are plotted from 1,000 µm andabove (“merged PSD”). The diamonds represent themidpoint of each size bin that makes up the distribution(Gapp 2019).
Lower values of reflectivityfactor during times of“good” agreement (71837-71843 sfm; red square andlines) are due to lower icewater content and highertotal volume (Eq. 1) thantimes of “bad” agreement(71845-71851 sfm; bluesquare and lines); therefore,effective particle densities(Eq. 2), LEDs (Eq. 3), andderived reflectivity factors(Eq. 4) are lower as well.
Similarities exist in the PSDs from CapeEx19 (above), but particles are slightlylarger than those observed during CAPE2015 (histograms above). ParticleHabit Imaging and Polar Scattering (PHIPS) probe images from 03 August 2019are below. The extra data from CapeEx19 will help to determine thesensitivity between derived particle density, particle diameter, and reflectivityfactor.
AC13
AC14
AC15
AC16
Average:0.19 ± 0.02 g/m3
Average:321.1 ± 29.7 µm
Average:112.7 ± 32.5 kg/m3
Average:7.25 ± 1.67 dBZ
Average:0.29 ± 0.05 g/m3
Average:507.1 ± 93.4 µm
Average:397.5 ± 206.6 kg/m3
Average:15.08 ± 3.41 dBZ
1,000 µm
𝑉𝑖 =n
𝜋6𝑁𝑛𝐷𝑛
3 , (1)
𝜌𝑒 = Τ𝑚𝑁𝑒𝑣 𝑉𝑖 , (2)
𝐿𝐸𝐷 =𝑛
3ൗ6𝑉𝑛𝜌𝑒𝜋𝜌w , (3)
𝑍𝑒 = 0.224𝑛𝑁𝑛 (𝐿𝐸𝐷)𝑛
6 , (4)
