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- Author or Editor: Lawrence D. Carey x
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Abstract
Estimating raindrop size has been a long-standing objective of polarimetric radar–based precipitation retrieval methods. The relationship between the differential reflectivity Z dr and the median volume diameter D 0 is typically derived empirically using raindrop size distribution observations from a disdrometer, a raindrop physical model, and a radar scattering model. Because disdrometers are known to undersample large raindrops, the maximum drop diameter D max is often an assumed parameter in the rain physical model. C-band Z dr is sensitive to resonance scattering at drop diameters larger than 5 mm, which falls in the region of uncertainty for D max. Prior studies have not accounted for resonance scattering at C band and D max uncertainty in assessing potential errors in drop size retrievals. As such, a series of experiments are conducted that evaluate the effect of D max parameterization on the retrieval error of D 0 from a fourth-order polynomial function of C-band Z dr by varying the assumed D max through the range of assumptions found in the literature. Normalized bias errors for estimating D 0 from C-band Z dr range from −8% to 15%, depending on the postulated error in D max. The absolute normalized bias error increases with C-band Z dr, can reach 10% for Z dr as low as 1–1.75 dB, and can increase from there to values as large as 15%–45% for larger Z dr, which is a larger potential bias error than is found at S and X band. Uncertainty in D max assumptions and the associated potential D 0 retrieval errors should be noted and accounted for in future C-band polarimetric radar studies.
Abstract
Estimating raindrop size has been a long-standing objective of polarimetric radar–based precipitation retrieval methods. The relationship between the differential reflectivity Z dr and the median volume diameter D 0 is typically derived empirically using raindrop size distribution observations from a disdrometer, a raindrop physical model, and a radar scattering model. Because disdrometers are known to undersample large raindrops, the maximum drop diameter D max is often an assumed parameter in the rain physical model. C-band Z dr is sensitive to resonance scattering at drop diameters larger than 5 mm, which falls in the region of uncertainty for D max. Prior studies have not accounted for resonance scattering at C band and D max uncertainty in assessing potential errors in drop size retrievals. As such, a series of experiments are conducted that evaluate the effect of D max parameterization on the retrieval error of D 0 from a fourth-order polynomial function of C-band Z dr by varying the assumed D max through the range of assumptions found in the literature. Normalized bias errors for estimating D 0 from C-band Z dr range from −8% to 15%, depending on the postulated error in D max. The absolute normalized bias error increases with C-band Z dr, can reach 10% for Z dr as low as 1–1.75 dB, and can increase from there to values as large as 15%–45% for larger Z dr, which is a larger potential bias error than is found at S and X band. Uncertainty in D max assumptions and the associated potential D 0 retrieval errors should be noted and accounted for in future C-band polarimetric radar studies.
Abstract
Previous studies have demonstrated that rapid increases in total lightning activity (intracloud + cloud-to-ground) are often observed tens of minutes in advance of the occurrence of severe weather at the ground. These rapid increases in lightning activity have been termed “lightning jumps.” Herein, the authors document a positive correlation between lightning jumps and the manifestation of severe weather in thunderstorms occurring across the Tennessee Valley and Washington D.C. A total of 107 thunderstorms from the Tennessee Valley; Washington, D.C.; Dallas, Texas; and Houston, Texas, were examined in this study. Of the 107 thunderstorms, 69 thunderstorms fall into the category of nonsevere and 38 into the category of severe. From the dataset of 69 isolated nonsevere thunderstorms, an average, peak, 1-min flash rate of 10 flashes per minute was determined. A variety of severe thunderstorm types were examined for this study, including a mesoscale convective system, mesoscale convective vortex, tornadic outer rainbands of tropical remnants, supercells, and pulse severe thunderstorms. Of the 107 thunderstorms, 85 thunderstorms (47 nonsevere, 38 severe) were from the Tennessee Valley and Washington, D.C., and these 85 thunderstorms tested six lightning jump algorithm configurations (Gatlin, Gatlin 45, 2σ, 3σ, Threshold 10, and Threshold 8). Performance metrics for each algorithm were then calculated, yielding encouraging results from the limited sample of 85 thunderstorms. The 2σ lightning jump algorithm had a high probability of detection (POD; 87%), a modest false-alarm rate (FAR; 33%), and a solid Heidke skill score (0.75). These statistics exceed current NWS warning statistics with this dataset; however, this algorithm needs further testing because there is a large difference in sample sizes. A second and more simplistic lightning jump algorithm named the Threshold 8 lightning jump algorithm also shows promise, with a POD of 81% and a FAR of 41%. Average lead times to severe weather occurrence for these two algorithms were 23 min. The overall goal of this study is to advance the development of an operationally applicable jump algorithm that can be used with either total lightning observations made from the ground, or in the near future from space using the Geostationary Operational Environmental Satellite Series R (GOES-R) Geostationary Lightning Mapper.
Abstract
Previous studies have demonstrated that rapid increases in total lightning activity (intracloud + cloud-to-ground) are often observed tens of minutes in advance of the occurrence of severe weather at the ground. These rapid increases in lightning activity have been termed “lightning jumps.” Herein, the authors document a positive correlation between lightning jumps and the manifestation of severe weather in thunderstorms occurring across the Tennessee Valley and Washington D.C. A total of 107 thunderstorms from the Tennessee Valley; Washington, D.C.; Dallas, Texas; and Houston, Texas, were examined in this study. Of the 107 thunderstorms, 69 thunderstorms fall into the category of nonsevere and 38 into the category of severe. From the dataset of 69 isolated nonsevere thunderstorms, an average, peak, 1-min flash rate of 10 flashes per minute was determined. A variety of severe thunderstorm types were examined for this study, including a mesoscale convective system, mesoscale convective vortex, tornadic outer rainbands of tropical remnants, supercells, and pulse severe thunderstorms. Of the 107 thunderstorms, 85 thunderstorms (47 nonsevere, 38 severe) were from the Tennessee Valley and Washington, D.C., and these 85 thunderstorms tested six lightning jump algorithm configurations (Gatlin, Gatlin 45, 2σ, 3σ, Threshold 10, and Threshold 8). Performance metrics for each algorithm were then calculated, yielding encouraging results from the limited sample of 85 thunderstorms. The 2σ lightning jump algorithm had a high probability of detection (POD; 87%), a modest false-alarm rate (FAR; 33%), and a solid Heidke skill score (0.75). These statistics exceed current NWS warning statistics with this dataset; however, this algorithm needs further testing because there is a large difference in sample sizes. A second and more simplistic lightning jump algorithm named the Threshold 8 lightning jump algorithm also shows promise, with a POD of 81% and a FAR of 41%. Average lead times to severe weather occurrence for these two algorithms were 23 min. The overall goal of this study is to advance the development of an operationally applicable jump algorithm that can be used with either total lightning observations made from the ground, or in the near future from space using the Geostationary Operational Environmental Satellite Series R (GOES-R) Geostationary Lightning Mapper.
Abstract
A propagation correction algorithm utilizing the differential propagation phase (ϕ dp) was developed and tested on C-band polarimetric radar observations of tropical convection obtained during the Maritime Continent Thunderstorm Experiment. An empirical procedure was refined to estimate the mean coefficient of proportionality a (b) in the linear relationship between ϕ dp and the horizontal (differential) attenuation throughout each radar volume. The empirical estimates of these coefficients were a factor of 1.5–2 times larger than predicted by prior scattering simulations. This discrepancy was attributed to the routine presence of large drops [e.g., differential reflectivity Z dr ≥ 3 dB] within the tropical convection that were not included in prior theoretical studies.
Scattering simulations demonstrated that the coefficients a and b are nearly constant for small to moderate sized drops (e.g., 0.5 ≤ Z dr ≤ 2 dB; 1 ≤ diameter D 0 < 2.5 mm) but actually increase with the differential reflectivity for drop size distributions characterized by Z dr > 2 dB. As a result, large drops 1) bias the mean coefficients upward and 2) increase the standard error associated with the mean empirical coefficients down range of convective cores that contain large drops. To reduce this error, the authors implemented a “large drop correction” that utilizes enhanced coefficients a* and b* in large drop cores.
Validation of the propagation correction algorithm was accomplished with cumulative rain gauge data and internal consistency among the polarimetric variables. The bias and standard error of the cumulative radar rainfall estimator R(Z h ) [R(K dp,Z dr)], where Z h is horizontal reflectivity and K dp is specific differential phase, were substantially reduced after the application of the attenuation (differential attenuation) correction procedure utilizing ϕ dp. Similarly, scatterplots of uncorrected Z h (Z dr) versus K dp substantially underestimated theoretical expectations. After application of the propagation correction algorithm, the bias present in observations of both Z h (K dp) and Z dr(K dp) was removed and the standard errors relative to scattering simulation results were significantly reduced.
Abstract
A propagation correction algorithm utilizing the differential propagation phase (ϕ dp) was developed and tested on C-band polarimetric radar observations of tropical convection obtained during the Maritime Continent Thunderstorm Experiment. An empirical procedure was refined to estimate the mean coefficient of proportionality a (b) in the linear relationship between ϕ dp and the horizontal (differential) attenuation throughout each radar volume. The empirical estimates of these coefficients were a factor of 1.5–2 times larger than predicted by prior scattering simulations. This discrepancy was attributed to the routine presence of large drops [e.g., differential reflectivity Z dr ≥ 3 dB] within the tropical convection that were not included in prior theoretical studies.
Scattering simulations demonstrated that the coefficients a and b are nearly constant for small to moderate sized drops (e.g., 0.5 ≤ Z dr ≤ 2 dB; 1 ≤ diameter D 0 < 2.5 mm) but actually increase with the differential reflectivity for drop size distributions characterized by Z dr > 2 dB. As a result, large drops 1) bias the mean coefficients upward and 2) increase the standard error associated with the mean empirical coefficients down range of convective cores that contain large drops. To reduce this error, the authors implemented a “large drop correction” that utilizes enhanced coefficients a* and b* in large drop cores.
Validation of the propagation correction algorithm was accomplished with cumulative rain gauge data and internal consistency among the polarimetric variables. The bias and standard error of the cumulative radar rainfall estimator R(Z h ) [R(K dp,Z dr)], where Z h is horizontal reflectivity and K dp is specific differential phase, were substantially reduced after the application of the attenuation (differential attenuation) correction procedure utilizing ϕ dp. Similarly, scatterplots of uncorrected Z h (Z dr) versus K dp substantially underestimated theoretical expectations. After application of the propagation correction algorithm, the bias present in observations of both Z h (K dp) and Z dr(K dp) was removed and the standard errors relative to scattering simulation results were significantly reduced.
Abstract
The microphysical properties of mixed-phase altocumulus clouds are investigated using in situ airborne measurements acquired during the ninth Cloud Layer Experiment (CLEX-9) over a midlatitude location. Approximately ⅔ of the sampled profiles are supercooled liquid–topped altocumulus clouds characterized by mixed-phase conditions. The coexistence of measurable liquid water droplets and ice crystals begins at or within tens of meters of cloud top and extends down to cloud base. Ice virga is found below cloud base. Peak liquid water contents occur at or near cloud top while peak ice water contents occur in the lower half of the cloud or in virga. The estimation of ice water content from particle size data requires that an assumption be made regarding the particle mass–dimensional relation, resulting in potential error on the order of tens of percent. The highest proportion of liquid is typically found in the coldest (top) part of the cloud profile. This feature of the microphysical structure for the midlatitude mixed-phase altocumulus clouds is similar to that reported for mixed-phase clouds over the Arctic region. The results obtained for limited cases of midlatitude mixed-phase clouds observed during CLEX-9 may have an implication for the study of mixed-phase cloud microphysics, satellite remote sensing applications, and the parameterization of mixed-phase cloud radiative properties in climate models.
Abstract
The microphysical properties of mixed-phase altocumulus clouds are investigated using in situ airborne measurements acquired during the ninth Cloud Layer Experiment (CLEX-9) over a midlatitude location. Approximately ⅔ of the sampled profiles are supercooled liquid–topped altocumulus clouds characterized by mixed-phase conditions. The coexistence of measurable liquid water droplets and ice crystals begins at or within tens of meters of cloud top and extends down to cloud base. Ice virga is found below cloud base. Peak liquid water contents occur at or near cloud top while peak ice water contents occur in the lower half of the cloud or in virga. The estimation of ice water content from particle size data requires that an assumption be made regarding the particle mass–dimensional relation, resulting in potential error on the order of tens of percent. The highest proportion of liquid is typically found in the coldest (top) part of the cloud profile. This feature of the microphysical structure for the midlatitude mixed-phase altocumulus clouds is similar to that reported for mixed-phase clouds over the Arctic region. The results obtained for limited cases of midlatitude mixed-phase clouds observed during CLEX-9 may have an implication for the study of mixed-phase cloud microphysics, satellite remote sensing applications, and the parameterization of mixed-phase cloud radiative properties in climate models.
Abstract
Rainfall retrieval algorithms often assume a gamma-shaped raindrop size distribution (DSD) with three mathematical parameters N
w
, D
m
, and μ. If only two independent measurements are available, as with the dual-frequency precipitation radar on the Global Precipitation Measurement (GPM) mission core satellite, then retrieval algorithms are underconstrained and require assumptions about DSD parameters. To reduce the number of free parameters, algorithms can assume that μ is either a constant or a function of D
m
. Previous studies have suggested μ–Λ constraints [where Λ = (4 + μ)/D
m
], but controversies exist over whether μ–Λ constraints result from physical processes or mathematical artifacts due to high correlations between gamma DSD parameters. This study avoids mathematical artifacts by developing joint probability distribution functions (joint PDFs) of statistically independent DSD attributes derived from the raindrop mass spectrum. These joint PDFs are then mapped into gamma-shaped DSD parameter joint PDFs that can be used in probabilistic rainfall retrieval algorithms as proposed for the GPM satellite program. Surface disdrometer data show a high correlation coefficient between the mass spectrum mean diameter D
m
and mass spectrum standard deviation σ
m
. To remove correlations between DSD attributes, a normalized mass spectrum standard deviation 
Abstract
Rainfall retrieval algorithms often assume a gamma-shaped raindrop size distribution (DSD) with three mathematical parameters N
w
, D
m
, and μ. If only two independent measurements are available, as with the dual-frequency precipitation radar on the Global Precipitation Measurement (GPM) mission core satellite, then retrieval algorithms are underconstrained and require assumptions about DSD parameters. To reduce the number of free parameters, algorithms can assume that μ is either a constant or a function of D
m
. Previous studies have suggested μ–Λ constraints [where Λ = (4 + μ)/D
m
], but controversies exist over whether μ–Λ constraints result from physical processes or mathematical artifacts due to high correlations between gamma DSD parameters. This study avoids mathematical artifacts by developing joint probability distribution functions (joint PDFs) of statistically independent DSD attributes derived from the raindrop mass spectrum. These joint PDFs are then mapped into gamma-shaped DSD parameter joint PDFs that can be used in probabilistic rainfall retrieval algorithms as proposed for the GPM satellite program. Surface disdrometer data show a high correlation coefficient between the mass spectrum mean diameter D
m
and mass spectrum standard deviation σ
m
. To remove correlations between DSD attributes, a normalized mass spectrum standard deviation
