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13 pages, 7587 KiB  
Article
The Dynamics and Microphysical Characteristics of the Convection Producing the Record-Breaking Hourly Precipitation on 20 July 2021 in Zhengzhou, China
by Kun Zhao, Xin Xu, Ming Xue, Zhe-Min Tan, Hao Huang, Ang Zhou, Xueqi Fan, Qiqing Liu, Chenli Wang, Juan Fang, Wen-Chau Lee, Qinghong Zhang, Fan Zhang, Gang Chen and Ji Yang
Remote Sens. 2023, 15(18), 4511; https://doi.org/10.3390/rs15184511 - 13 Sep 2023
Cited by 3 | Viewed by 1792
Abstract
An hourly rainfall of 201.9 mm fell in Zhengzhou on 20 July 2021, breaking the hourly rainfall record of mainland China and causing severe urban flooding and human casualties. This observation-based study investigates the associated convective-scale and mesoscale dynamics and microphysical processes using [...] Read more.
An hourly rainfall of 201.9 mm fell in Zhengzhou on 20 July 2021, breaking the hourly rainfall record of mainland China and causing severe urban flooding and human casualties. This observation-based study investigates the associated convective-scale and mesoscale dynamics and microphysical processes using disdrometer and polarimetric radar observations aided by retrievals from the Variational Doppler Radar Analysis System. The synoptic flow forcing brought abundant moisture from the oceans and converged at Zhengzhou; then, the extreme rainfall was produced by a slow-moving convective storm that persisted throughout the hour over Zhengzhou. Unusually high concentrations of raindrops of all sizes (showing combined properties of maritime and continental convection) are revealed by the disdrometer data, whereas the polarimetric radar data suggest that both ice-based and warm rain processes were important contributors to the total rainfall. High precipitation efficiency was achieved with an erect updraft at the low levels, whereas enhanced easterly inflows kept the storm moving slowly. The interaction between convective-scale and mesoscale dynamics and microphysical processes within the favorable synoptic conditions led to this extremely heavy rainfall. Full article
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Figure 1
<p>(<b>a</b>) Distribution map of ground instruments on the digital terrain elevation map around ZZ. Triangles denote the locations of four operational radars. Blue, red, and purple contours indicate the 24-h accumulated rainfall over 100, 250, and 400 mm on 20 July, respectively. The time series of the hourly rainfall from the ZZ national surface station (indicated by a red cross) is also shown in the bottom right corner of the figure. (<b>b</b>) Circulation fields at 0800 LST on 20 July 2021. Gray contours represent geopotential height at 500 hPa, and the bold line indicates the location of the western Pacific subtropical high (WPSH). Vectors are vertically integrated water vapor flux (in the layer of 1000–300 hPa) larger than 150 kg m<sup>−1</sup> s<sup>−1</sup>, and black dots indicate the wind divergence at 200 hPa that is larger than 10<sup>−5</sup> s<sup>−1</sup>. Shading represents the 10.8 μm infrared brightness temperature from FY-4A. The black cross indicates the location of the ZZ national surface station.</p>
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<p>(<b>a</b>) Perturbation temperature (shading) and horizontal wind vectors at 0.6 km MSL in the VDRAS analysis at 1200 LST on 20 July 2021. Yellow contours are the horizontal convergence starting from −1 × 10<sup>−3</sup> s<sup>−1</sup> at an interval of −2 × 10<sup>−3</sup> s<sup>−1</sup>. Black lines are 600 m terrain elevation contours and the purple triangle indicates the location of the record-breaking hourly precipitation rain gauge station in ZZ. (<b>b</b>) As (<b>a</b>) but at 3 km. The curved arrow represents the mesoscale vortex. (<b>c</b>) As (<b>a</b>) but at 1600 LST. (<b>d</b>) 0–3 km moisture fluxes through the 4 borders of a 60 × 60 km box region covering the ZZ city (red box in (<b>a</b>,<b>c</b>)) and net flux into the box (positive into the box), based on the VADRS analysis. (<b>e</b>) Inflow environment sounding located at 150 km southeast of ZZ (yellow X in (<b>a</b>)) extracted from the VDRAS analysis at 1200 LST with the red, green, and black solid lines representing the temperature profile, dew point profile, and the parcel ascent curve, respectively. (<b>f</b>) Perturbation temperature (shading) and wind vectors in the west–east vertical cross section through the center of ZZ city (line AB in (<b>c</b>)) in VADRAS analysis at 1600 LST. Red contours denote 40 dBZ radar reflectivity. (<b>g</b>) Evolution of cold pool density current and low-level easterlies between 0 and 1 km calculated within the red and black boxes in (<b>c</b>). (<b>h</b>) Evolution of the southerly and northerly wind components to the south and north of the storm (see the white boxes in (<b>c</b>)) over ZZ, respectively.</p>
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<p>Observed radar reflectivity (color shading; units: dBZ) and horizontal winds at 600 m MSL at (<b>a</b>) 1200 LST, (<b>b</b>) 1500 LST, (<b>c</b>) 1600 LST, and (<b>d</b>) 1700 LST on 20 July 2021. A terrain height of 200 m is indicated by the bold brown contours. The black line represents the urban area of ZZ, and the triangle indicates the national surface station with the hourly extreme rainfall in ZZ. The blue pluses indicate that the lightning occurred 30 min before and after the radar time. SM indicates the Song Mountain. West–east cross sections of (<b>e</b>) Z<sub>H</sub> (color shading) and Z<sub>DR</sub> (contours) and (<b>f</b>) hydrometer types based on hydrometeor classification algorithm (shading) and K<sub>DP</sub> (contours) across the rainfall center at 1600 LST.</p>
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<p>Time series of (<b>a</b>) Z<sub>H</sub>, (<b>b</b>) Z<sub>DR</sub>, and (<b>c</b>) K<sub>DP</sub> fractions in specified bins at 2.0 km MSL in the 30 × 30 km box centered on the CS (labeled in <a href="#remotesensing-15-04511-f003" class="html-fig">Figure 3</a>) from 1200 to 2000 LST. Average profiles of (<b>d</b>) polarimetric radar variables and (<b>e</b>) hydrometer types (shading), LWC (black solid), and IWC (red dashed) for K<sub>DP</sub> values higher than 4° km<sup>−1</sup> near the ZZ national surface station between 1600 and 1700 LST. The label for IWC/LWC is shown at the top of the panel.</p>
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<p>(<b>a</b>) Time series of 1 min DSDs from the OTT disdrometer at the ZZ national surface station. Color shading represents the DSD in logarithmic units of mm<sup>−1</sup> m<sup>−3</sup> and the <span class="html-italic">y</span>-axis indicates the equivalent volume diameter (mm) of raindrops; the instant rain rate is plotted as the red line. (<b>b</b>) Scatters of <span class="html-italic">N<sub>w</sub></span> (<span class="html-italic">D<sub>m</sub></span>) from the ZZ extreme hourly rainfall between 1600 and 1700 LST (red) and DSD samples with an instant rain rate over 20 mm<sup>−1</sup> h<sup>−1</sup> from 1200 to 2000 LST (blue), respectively. The two gray rectangles represent the maritime and continental convective clusters reported by [<a href="#B24-remotesensing-15-04511" class="html-bibr">24</a>]. The blue, green, black, and red crosses represent the mean values of convective rain in different regions from previous studies [<a href="#B50-remotesensing-15-04511" class="html-bibr">50</a>,<a href="#B51-remotesensing-15-04511" class="html-bibr">51</a>,<a href="#B52-remotesensing-15-04511" class="html-bibr">52</a>,<a href="#B53-remotesensing-15-04511" class="html-bibr">53</a>].</p>
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<p>The conceptual model of the maintenance and precipitation microphysics of the convective storm resulting in extreme hourly rainfall in ZZ. The blue line with triangles indicates the cold pool gust front. The red arrows represent the prevalent winds.</p>
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16 pages, 11394 KiB  
Article
Analysis of Extreme Rainstorms Associated with Tropical Cyclone Rammasun (2014) over Guangxi Province of China
by Yufeng Lan, Zhixiang Xiao, Cai Yao, Lei Sun, Junxing Hou and Weiping Lu
Atmosphere 2023, 14(2), 320; https://doi.org/10.3390/atmos14020320 - 6 Feb 2023
Viewed by 1541
Abstract
The tropical cyclone (TC) Rammasun (1409) successively caused three extreme rainstorms centered in Hepu, Fangcheng, and Lingshan in Guangxi. In this study, a set of datasets, including the China Meteorological Administration (CMA) TC best-track data, rain gauge stations, radar products, and the latest [...] Read more.
The tropical cyclone (TC) Rammasun (1409) successively caused three extreme rainstorms centered in Hepu, Fangcheng, and Lingshan in Guangxi. In this study, a set of datasets, including the China Meteorological Administration (CMA) TC best-track data, rain gauge stations, radar products, and the latest ERA5 reanalysis, were used to investigate the spatial–temporal characteristics and causes of the three rainstorms. Overall, there are apparent discrepancies among them regarding the triggering and maintenance mechanisms. Prior to its landfall in Guangxi, the rainstorm surge (hourly precipitation 136.9 mm) around Hepu was triggered by the approaching low-level jet and northward intrusion of convective instability in the context of abundant vapor supply and vigorous convection. At the weakening stage, Rammasun gradually crossed over the Shiwan Mountains, with a strong convection zone in the western eyewall moving to Fangcheng, which enhanced the precipitation there under the help of topographic uplift. The friction effect slowed down the TC, and contributed to accumulation of the precipitation, which, however, was somewhat suppressed (hourly precipitation less than 50 mm) by the low-level stratification. In the decaying stage, the Lingshan rainstorm was triggered under the combined efforts of the high-level divergence due to the expansion of the South Asian high and the surface convergence between the southeasterly and southerly winds, accompanied by the westward expansion of the high-energy zone. Finally, the corresponding conceptual models for the three rainstorms are summarized and should provide important implications to both research and forecasting for TC-related heavy precipitation. Full article
(This article belongs to the Section Meteorology)
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<p>The accumulated precipitation (mm) from 20BST 17th to 20BST 20th July and the track and intensity changes of Rammasun, with the red markers denoting centers of three Guangxi rainstorms defined in <a href="#sec3-atmosphere-14-00320" class="html-sec">Section 3</a>.</p>
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<p>The geographic distribution of observed 6-hour precipitation and track and intensity changes of Rammasun from 20BST 18th to 20BST 19th July (<b>a</b>–<b>d</b>), with the red markers denoting location of maximum precipitation of the corresponding accumulated period (shown as the end time).</p>
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<p>The variations in hourly precipitation at three rainstorm centers (i.e., Hepu, Fangcheng, and Lingshan) from 20BST 18th to 20BST 19th July.</p>
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<p>The geographic distributions of 850 hPa wind (shadings and vectors, shown exceeding 12 m s<sup>−1</sup>), overlaid with 500 hPa (red lines) and 200 hPa (blue lines) geopotential heights (dagpm) from ERA5 at (<b>a</b>) 02BST, (<b>b</b>) 08BST, and (<b>c</b>) 14BST on 19th July, with the red markers denoting three rainstorm centers.</p>
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<p>The geographic distributions of the 925 hPa wind (vectors and shadings, 16 m s<sup>−1</sup>) and divergence (contours, 10<sup>−5</sup> s<sup>−1</sup>) from ERA5 at onset and peak for Hepu (<b>a</b>,<b>b</b>), Fangcheng (<b>c</b>,<b>d</b>), and Lingshan (<b>e</b>,<b>f</b>) rainstorms, with the red markers and black lines denoting the rainstorm centers and convergence lines.</p>
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<p>The horizontal distributions (at 1.5° elevation) and cross sections of radar reflectivity (dBZ) from Doppler weather radar at the peak of three rainstorms centered at (<b>a</b>,<b>d</b>) Hepu, (<b>b</b>,<b>e</b>) Fangcheng, and (<b>c</b>,<b>f</b>) Lingshan.</p>
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<p>Left panels (<b>a</b>,<b>c</b>,<b>e</b>): the time–pressure cross sections of the area-averaged (1° × 1°) vertical velocity (shadings, Pa s<sup>−1</sup>) overlaid with divergence (contours, 10<sup>−5</sup> s<sup>−1</sup>) from ERA5 for three rainstorms centered at Hepu, Fangcheng, and Lingshan. Right panels (<b>b</b>,<b>d</b>,<b>f</b>): the same except for equivalent potential temperature (shadings, K) overlaid with its corresponding gradient (contours, K Pa<sup>−1</sup>).</p>
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<p>Left panels (<b>a</b>,<b>c</b>,<b>e</b>): the geographic distributions of terrain heights (shadings, m) overlaid with 10 m wind (vectors, m s<sup>−1</sup>) at the peak for Hepu, Fangcheng, and Lingshan rainstorms with the red markers denoting the rainstorm centers. Right panels (<b>b</b>,<b>d</b>,<b>f</b>): the corresponding diagnosed hourly orographic precipitation (mm) using ERA5.</p>
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<p>Schematics of conceptual models for (<b>a</b>) Hepu, (<b>b</b>) Fangcheng, and (<b>c</b>) Lingshan rainstorms caused by TC Rammasun.</p>
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22 pages, 7743 KiB  
Article
Extreme Heavy Rainfall Events at Mid-Latitudes as the Outcome of a Slow Quasi-Resonant Ocean—Atmosphere Interaction: 10 Case Studies
by Jean-Louis Pinault
J. Mar. Sci. Eng. 2023, 11(2), 359; https://doi.org/10.3390/jmse11020359 - 5 Feb 2023
Cited by 3 | Viewed by 1691
Abstract
Based on case studies, the development of low-pressure systems leading to extreme precipitation events reveals common characteristics. They highlight the co-evolution of sea surface temperature (SST) anomalies and the clustering of mesoscale convective systems in characteristic period ranges according to harmonic modes of [...] Read more.
Based on case studies, the development of low-pressure systems leading to extreme precipitation events reveals common characteristics. They highlight the co-evolution of sea surface temperature (SST) anomalies and the clustering of mesoscale convective systems in characteristic period ranges according to harmonic modes of the annual declination of the sun. This suggests a quasi-resonance of the heat exchanges of the ocean and the atmosphere during cyclogenesis. The formation of coherent extensive positive SST anomalies in characteristic period ranges, which reflects various interactions from baroclinic waves at mid-latitudes, i.e., Rossby waves especially present where the western boundary currents move away from the continents, may be a precursor of an extreme heavy rainfall event. Fed by warm and humid air coming from coherent SST anomalies, the convective cyclonic system strengthens concomitantly with the formation of cut-off lows, favoring blocks. However, the concentration in space and time of large-amplitude rainfall anomalies requires a relative stability of the atmospheric blocking circulation during the slow maturation processes. Intensification of extratropical cyclones is presumably the consequence of natural and anthropogenic warming, which strengthens the mechanisms leading to the clustering of mesoscale convective systems. The present study should help to refine the prediction of these extreme events while contributing to enrich our understanding of their presumed link with global warming. Full article
(This article belongs to the Section Physical Oceanography)
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<p>The bands in which the data are scale-averaged (the scale in the Morlet wavelet is the period) and, for an optimum representativity of the result, time-averaged over time intervals equal to the mean period. Time is centered on the date of occurrence of the extreme rainfall event. From the top to the bottom, the mean periods of the bands are harmonics 1/6, 1/12, 1/24, 1/48, 1/96, 1/192, and 1/384 of the period of the declination of the sun, i.e., 365.24 days.</p>
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<p>The sea level anomaly (SLA) in red and the sea surface temperature (SST) in blue at 39.875° N, 65.125° W: (<b>a</b>) the raw signals centered and reduced; (<b>b</b>) the Fourier spectrum and the main harmonics of SLA; (<b>c</b>) the Fourier spectrum and the main harmonics of SST; (<b>d</b>) the coherence spectrum of SLA and SST, and the main harmonics; (<b>e</b>) the phase spectrum of SLA and SST, and the main harmonics. To improve the readability of the figures, a confidence level of only 80% is used because only the position of the peaks is relevant, not their height.</p>
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<p>Precipitation height is averaged over the red region covering Georgia, eastern Tennessee, and western North and South Carolina, while SST anomalies are averaged over blue regions 1, 2, 3, and 4, stretching from the Gulf of Mexico to the northeast coast of the United States.</p>
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<p>(<b>a</b>) Daily precipitation depth averaged over the region (86° W, 82° W) × (33° N, 37° N); (<b>b</b>) amplitude of rainfall depth in the 22.8–45.7-day band (1/12-year harmonic); (<b>c</b>) coherence of the precipitation depth and the SST anomalies in the five regions; (<b>d</b>) selection of coherences such that the coherence with respect to SST1 is greater than 0.15.</p>
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<p>Extreme rainfall in Western Europe. The amplitude (<b>a</b>–<b>c</b>,<b>e</b>,<b>g</b>,<b>i,k</b>) and the phase (<b>d</b>,<b>f</b>,<b>h</b>,<b>j,l</b>) of the precipitation depth within the 7 period ranges. The calculation of the amplitude and the phase according to the harmonics 1/384 and 1/192 requires an over-sampling of the data. Each daily time step is divided into 8 equal parts assuming a Gaussian distribution of precipitation. The phases related to those harmonics are not represented, because the periods are close to the sampling step. The amplitudes are expressed in 16 classes each containing the same number of ordered data (quantiles). For each period range, the color of the bar associated with the phase divides the mean period in 18 intervals. Time lags in (<b>b</b>,<b>d</b>,<b>f</b>,<b>h</b>,<b>j</b>) are expressed in relation to 2 June 2016. The time reference is the rainfall depth at 46° N, 6° E. Only the phase corresponding to the 50% quantile of the highest values of the amplitude is displayed.</p>
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<p>SST anomalies involved in extreme rainfall in Western Europe: amplitudes in (<b>a</b>,<b>c</b>,<b>e</b>,<b>g</b>,<b>i</b>) and phases in (<b>b</b>,<b>d</b>,<b>f</b>,<b>h</b>,<b>j</b>).</p>
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<p>Extreme rainfall in Alberta. The amplitude (<b>a</b>–<b>c</b>) and the phase (<b>d</b>) of the precipitation depth, and the amplitude (<b>e</b>) and the phase (<b>f</b>) of the SST anomalies. Time lags in (<b>d</b>,<b>f</b>) are expressed in relation to 6 June 2005. The time reference is the rainfall depth at 50° N, 114° W.</p>
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<p>Extreme rainfall in Japan. Same conventions as in <a href="#jmse-11-00359-f007" class="html-fig">Figure 7</a>. Time lags in (<b>d</b>,<b>f</b>) are expressed in relation to 12 July 2012. The time reference is the rainfall depth at 31° N, 131° E.</p>
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<p>Extreme rainfall in China. Same conventions as in <a href="#jmse-11-00359-f007" class="html-fig">Figure 7</a>. Time lags in (<b>d</b>,<b>f</b>) are expressed in relation to 7 June 2020. The time reference is the rainfall depth at 23° N, 113° E.</p>
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<p>Extreme rainfall in Argentina. Same conventions as in <a href="#jmse-11-00359-f007" class="html-fig">Figure 7</a>. Time lags in (<b>d</b>,<b>f</b>) are expressed in relation to 12 August 2015. The time reference is the rainfall depth at 34° S, 56° W.</p>
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<p>Extreme rainfall in Mozambique. Same conventions as in <a href="#jmse-11-00359-f007" class="html-fig">Figure 7</a>. Time lags in (<b>d</b>,<b>f</b>) are expressed in relation to 22 February 2007. The time reference is the rainfall depth at 22° S, 49° E.</p>
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<p>Extreme rainfall in Victoria, Australia. Same conventions as in <a href="#jmse-11-00359-f007" class="html-fig">Figure 7</a>. Time lags in (<b>d</b>,<b>f</b>) are expressed in relation to 13 January 2011. The time reference is the rainfall depth at 32° S, 143° E.</p>
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<p>Extreme rainfall in Kentucky. Same conventions as in <a href="#jmse-11-00359-f007" class="html-fig">Figure 7</a>. Time lags in (<b>d</b>,<b>f</b>) are expressed in relation to 6 February 2020. The time reference is the rainfall depth at 39° N, 85° W.</p>
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<p>Extreme rainfall in Louisiana, USA. Same conventions as in <a href="#jmse-11-00359-f007" class="html-fig">Figure 7</a>. Time lags in (<b>d</b>,<b>f</b>) are expressed in relation to 29 August 2005. The time reference is the rainfall depth at 29° N, 89° W.</p>
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<p>Extreme rainfall in France. Same conventions as in <a href="#jmse-11-00359-f007" class="html-fig">Figure 7</a>. Time lags in (<b>d</b>,<b>f</b>) are expressed in relation to 4 November 2011. The time reference is the rainfall depth at 42° N, 4° E.</p>
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20 pages, 4865 KiB  
Article
A Comparison of Convective and Stratiform Precipitation Microphysics of the Record-breaking Typhoon In-Fa (2021)
by Zuhang Wu, Yun Zhang, Lifeng Zhang, Hepeng Zheng and Xingtao Huang
Remote Sens. 2022, 14(2), 344; https://doi.org/10.3390/rs14020344 - 12 Jan 2022
Cited by 12 | Viewed by 2799
Abstract
In July 2021, Typhoon In-Fa attacked eastern China and broke many records for extreme precipitation over the last century. Such an unrivaled impact results from In-Fa’s slow moving speed and long residence time due to atmospheric circulations. With the supports of 66 networked [...] Read more.
In July 2021, Typhoon In-Fa attacked eastern China and broke many records for extreme precipitation over the last century. Such an unrivaled impact results from In-Fa’s slow moving speed and long residence time due to atmospheric circulations. With the supports of 66 networked surface disdrometers over eastern China and collaborative observations from the advanced GPM satellite, we are able to reveal the unique precipitation microphysical properties of the record-breaking Typhoon In-Fa (2021). After separating the typhoon precipitation into convective and stratiform types and comparing the drop size distribution (DSD) properties of Typhoon In-Fa with other typhoons from different climate regimes, it is found that typhoon precipitation shows significant internal differences as well as regional differences in terms of DSD-related parameters, such as mass-weighted mean diameter (Dm), normalized intercept parameter (Nw), radar reflectivity (Z), rain rate (R), and intercept, shape, and slope parameters (N0, µ, Λ). Comparing different rain types inside Typhoon In-Fa, convective rain (Nw ranging from 3.80 to 3.96 mm−1 m−3) shows higher raindrop concentration than stratiform rain (Nw ranging from 3.40 to 3.50 mm−1 m−3) due to more graupels melting into liquid water while falling. Large raindrops occupy most of the region below the melting layer in convective rain due to a dominant coalescence process of small raindrops (featured by larger ZKu, Dm, and smaller N0, µ, Λ), while small raindrops account for a considerable proportion in stratiform rain, reflecting a significant collisional breakup process of large raindrops (featured by smaller ZKu, Dm, and larger N0, µ, Λ). Compared with other typhoons in Hainan and Taiwan, the convective precipitation of Typhoon In-Fa shows a larger (smaller) raindrop concentration than that of Taiwan (Hainan), while smaller raindrop diameter than both Hainan and Taiwan. Moreover, the typhoon convective precipitation measured in In-Fa is more maritime-like than precipitation in Taiwan. Based on a great number of surface disdrometer observational data, the GPM precipitation products were further validated for both rain types, and a series of native quantitative precipitation estimation relations, such as ZR and RDm relations were derived to improve the typhoon rainfall retrieval for both ground-based radar and spaceborne radar. Full article
(This article belongs to the Section Environmental Remote Sensing)
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<p>(<b>a</b>) Best track for Typhoon In-Fa (2021) based on observations from China Meteorological Administration (CMA), (<b>b</b>) Location of PARSIVEL disdrometer network (altogether 66 disdrometers) and accumulated precipitation (unit: mm) during the passage of Typhoon In-Fa from 25 July 0000UTC to 30 July 0000UTC. The superimposed red contour in panel (<b>a</b>) represents Jiangsu Province. Four categories of typhoon intensity are classified here following CMA, wherein TD represents tropical depression, TS represents tropical storm, STS represent super tropical storm, and TY represent typhoon.</p>
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<p>GPM instantaneous observation at 1105 UTC: (<b>a</b>) rain types (convective, stratiform, shallow, and other) and (<b>b</b>) maximum equivalent radar reflectivity (dBZ). The paralleled gray lines represent the boundaries of GPM scanning path.</p>
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<p>Time series of rain rate <span class="html-italic">R</span> (mm h<sup>−1</sup>) and its standard deviation <span class="html-italic">σ<sub>R</sub></span> (mm h<sup>−1</sup>) measured by three disdrometers located in (<b>a</b>) Gaoyou, (<b>b</b>) Jiangdong, and (<b>c</b>) Dongtai respectively. The left ordinate (black color) denotes <span class="html-italic">R</span> while the right ordinate (magenta color) denotes <span class="html-italic">σ<sub>R</sub></span>. The rain types are displayed in stripes of different colors. The precipitation collected by three disdrometers during Typhoon In-Fa landfall basically concentrated between 25 July 2021 and 29 July 2021.</p>
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<p>Surface analysis weather maps during Typhoon In-Fa landfall provided by the Japan Meteorological Agency (JMA).</p>
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<p>The composite raindrop spectrum from the PARSIVEL disdrometer data at different rain rates for (<b>a</b>) convective rain and (<b>b</b>) stratiform rain.</p>
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<p><span class="html-italic">µ</span><span class="html-italic">-Λ</span> scatter diagram (gray dots) and corresponding fitting curve (red line) based on PARSIVEL disdrometer observations of Typhoon In-Fa (2021). The dashed line represents the empirical <span class="html-italic">μ-Λ</span> relationship of typhoons over Taiwan from Chang et al. [<a href="#B2-remotesensing-14-00344" class="html-bibr">2</a>]. The dash-dot line represents the empirical <span class="html-italic">μ-Λ</span> relationship of typhoons over continental China from Wen et al. [<a href="#B3-remotesensing-14-00344" class="html-bibr">3</a>]. The gray lines correspond to the <span class="html-italic">D</span><span class="html-italic"><sub>m</sub></span> contour according to <span class="html-italic">Λ</span><span class="html-italic">D</span><span class="html-italic"><sub>m</sub></span> = 4 + <span class="html-italic">μ</span> [<a href="#B30-remotesensing-14-00344" class="html-bibr">30</a>].</p>
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<p>Scatter diagram of <span class="html-italic">log<sub>10</sub></span>(<span class="html-italic">N<sub>w</sub></span>) and <span class="html-italic">D<sub>m</sub></span> observed from the PARSIVEL disdrometers for convective (blue dots) and stratiform precipitation (red dots) in Typhoon In-Fa. The green and magenta rectangles represent the average values (along with ± standard deviation) of convective rain (denoted as AVE.CON) and stratiform rain (denoted as AVE.STR), respectively. The black triangle, diamond, and circle represent results from other typhoon studies for comparison. The two outlined rectangles correspond to the maritime and continental convective clusters observed by Bringi et al. [<a href="#B23-remotesensing-14-00344" class="html-bibr">23</a>]. The black dashed line indicates the fitting curve of stratiform rain in Bringi et al. [<a href="#B23-remotesensing-14-00344" class="html-bibr">23</a>].</p>
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<p>Scatter diagrams and their fitting curves of (<b>a</b>) <span class="html-italic">R</span> (mm h<sup>−1</sup>) with <span class="html-italic">Z</span> (dBZ), and (<b>b</b>) <span class="html-italic">D<sub>m</sub></span> (mm) with <span class="html-italic">R</span> (mm h<sup>−1</sup>) from PARSIVEL disdrometer data for two rain types. The blue and red scatters represent convective and stratiform samples, respectively. The overlaid black line in panel (<b>a</b>) represents the experience relationship <span class="html-italic">Z</span> = 300<span class="html-italic">R</span><sup>1.40</sup> from Fulton et al. [<a href="#B34-remotesensing-14-00344" class="html-bibr">34</a>]. The black dashed (dash-doted) line in panel (<b>b</b>) represents the default relationship <span class="html-italic">R</span> = 1.370<span class="html-italic">D<sub>m</sub></span><sup>5.420</sup> (<span class="html-italic">R</span> = 0.401<span class="html-italic">D<sub>m</sub></span><sup>6.131</sup>) used for the retrieval of convective (stratiform) rain in GPM lev-2 ATBD [<a href="#B19-remotesensing-14-00344" class="html-bibr">19</a>].</p>
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<p>Statistical comparison of PARSIVEL disdrometers and near-surface GPM DPR measurements in terms of probability distribution functions (PDFs) for (<b>a</b>) rain rate (mm h<sup>−1</sup>), and (<b>b</b>) equivalent radar reflectivity (dBZ). Convective rain (solid lines) and stratiform rain (dashed lines) are compared separately. GPM observations are shown in red lines while PARSIVEL observations are shown in blue lines. The median values of convective samples (outside bracket) and stratiform samples (inside bracket) are also shown in each panel.</p>
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<p>The CFADs of Ku-band equivalent radar reflectivity (Z<sub>Ku</sub>) from GPM observation during Typhoon In-Fa landfall for two indicated rain types: (<b>a</b>) convective rain, (<b>b</b>) stratiform rain. The black dashed lines represent median lines. The time and location of GPM observation are shown in <a href="#remotesensing-14-00344-f002" class="html-fig">Figure 2</a>.</p>
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<p>Variations of hydrometeor percentage with altitude in the (<b>a</b>) convective rain and (<b>b</b>) stratiform rain of Typhoon In-Fa (2021). Hydrometeor percentage represents the proportion of the amount of each type of hydrometeor to the total amount of all hydrometeor types. The hydrometeor types are identified based on GPM observation shown in <a href="#remotesensing-14-00344-f002" class="html-fig">Figure 2</a>.</p>
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<p>GPM-derived median profiles (thick lines) for (<b>a</b>) <span class="html-italic">D<sub>m</sub></span> (mm) and (<b>b</b>) <span class="html-italic">log</span><span class="html-italic"><sub>10</sub></span>(<span class="html-italic">N</span><span class="html-italic"><sub>w</sub></span>) (mm<sup>−1</sup> m<sup>−3</sup>) of two rain types during Typhoon In-Fa landfall. The interquartile ranges of each parameter are indicated with shaded area. The GPM observation data used here is shown in <a href="#remotesensing-14-00344-f002" class="html-fig">Figure 2</a>.</p>
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13 pages, 4426 KiB  
Article
Mechanisms for Springtime Onset of Isolated Precipitation across the Southeastern United States
by Rosana Nieto Ferreira and Thomas M. Rickenbach
Atmosphere 2021, 12(2), 213; https://doi.org/10.3390/atmos12020213 - 4 Feb 2021
Cited by 2 | Viewed by 1714
Abstract
This study uses four-year radar-based precipitation organization and reanalysis datasets to study the mechanisms that lead to the abrupt springtime onset of precipitation associated with isolated storms in the Southeast United States (SE US). Although the SE US receives relatively constant precipitation year-round, [...] Read more.
This study uses four-year radar-based precipitation organization and reanalysis datasets to study the mechanisms that lead to the abrupt springtime onset of precipitation associated with isolated storms in the Southeast United States (SE US). Although the SE US receives relatively constant precipitation year-round, previous work demonstrated a “hidden” summertime maximum in isolated precipitation features (IPF) whose annual cycle resembles that of monsoon climates in the subtropics. In the SE US, IPF rain abruptly ramps up in May and lasts until sometime between late August and early October. This study suggests that the onset of the IPF season in the SE US is brought about by a combination of slow thermodynamic processes and fast dynamic triggers, as follows. First, in the weeks prior to IPF onset, a gradual seasonal build-up of convective available potential energy (CAPE) occurs in the Gulf of Mexico. Then, in one-to-two pentads prior to onset, the upper-tropospheric jet stream shifts northward, favoring the presence of slow-moving frontal systems in the SE US. This poleward shift in the jet stream location in turn allows the establishment of the North Atlantic subtropical high western ridge over the SE US which, with associated poleward transport of high CAPE air from the Gulf of Mexico, leads to the establishment of the warm-season regime of IPF precipitation in the SE US. Full article
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Figure 1

Figure 1
<p>Pentad timeseries of (<b>a</b>) IPF rain (mm day<sup>−1</sup>), and North American Regional Reanalysis (NARR), (<b>b</b>) convective available potential energy (CAPE) (J Kg<sup>−1</sup>) and (<b>c</b>) North–South Index (NSI) (m s<sup>1</sup>). All timeseries are averaged over the SE US land domain southward of 40° N and eastward of 90° W. Time is shown in terms of pentads prior to (negative values) and after (positive values) IPF onset, with IPF onset at <span class="html-italic">t</span> = 0 (red arrow). The 4-year average timeseries for each variable is also shown (black line).</p>
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<p>Pentad composites for the four-year period of zonal wind at 200 hPa (m s<sup>−1</sup>) at the (<b>a</b>) <span class="html-italic">onset</span>−5, (<b>b</b>) <span class="html-italic">onset</span>−3, (<b>c</b>) <span class="html-italic">onset</span>−1, (<b>d</b>) onset, (<b>e</b>) <span class="html-italic">onset</span>+3, and (<b>f</b>) <span class="html-italic">onset</span>+5 pentads. Red box marks the SE US domain.</p>
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<p>Pentad composites for the four-year period of CAPE (J Kg<sup>−1</sup>, shaded starting at 200 J Kg<sup>−1</sup> for every 200 J Kg<sup>−1</sup>) and 850 hPa winds (m s<sup>−1</sup>) at the (<b>a</b>) <span class="html-italic">onset</span>−5, (<b>b</b>) <span class="html-italic">onset</span>−3, (<b>c</b>) <span class="html-italic">onset</span>−1, (<b>d</b>) onset, (<b>e</b>) <span class="html-italic">onset</span>+3, and (<b>f</b>) <span class="html-italic">onset</span>+5 pentads. The composite 1560 m geopotential contours (m) are shown for each year (2009 in red, 2010 in green, 2011 in blue, and 2012 in purple) and for the four-year mean (black contours). Red box marks the SE US domain.</p>
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<p>Surface weather maps for (<b>a</b>) 2 May, (<b>b</b>) 4 May, (<b>c</b>) 6 May, (<b>d</b>) 8 May, (<b>e</b>) 10 May, and (<b>f</b>) 12 May 2009 at 1200 UTC (0700 local time in SE US). Sea level pressure (contours), frontal systems and surface station weather observations are also shown. From the Weather Prediction Center at the National Center for Environmental Prediction (available at <a href="https://www.wpc.ncep.noaa.gov/dailywxmap/index.html" target="_blank">https://www.wpc.ncep.noaa.gov/dailywxmap/index.html</a>).</p>
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1998 KiB  
Article
Effect of Spatial Variation of Convective Adjustment Time on the Madden–Julian Oscillation: A Theoretical Model Analysis
by Hui Wang, Yuntao Wei and Fei Liu
Atmosphere 2017, 8(10), 204; https://doi.org/10.3390/atmos8100204 - 20 Oct 2017
Cited by 3 | Viewed by 4145
Abstract
The observed convective adjustment time (CAT) associated with Madden–Julian Oscillation (MJO) precipitation is found to vary significantly in space. Here, we investigate the effect of different spatial distributions of CAT on MJO precipitation based on the frictional coupled dynamics moisture (FCDM) model. The [...] Read more.
The observed convective adjustment time (CAT) associated with Madden–Julian Oscillation (MJO) precipitation is found to vary significantly in space. Here, we investigate the effect of different spatial distributions of CAT on MJO precipitation based on the frictional coupled dynamics moisture (FCDM) model. The results show that a large value of CAT tends to decrease the frequency and growth rate of eastward-propagating MJO-like mode in the FCDM model, delaying the occurrence of MJO deep convection and slowing down its eastward propagation. A large phase lag between circulation and convection decreases convective available potential energy (CAPE). In the observations, a small background vertical moisture gradient (BVMG) tends to increase the frequency associated with cold sea surface temperature (SST), while a large value of CAT tends to decrease the frequency. Due to their competing effect, the simulated frequency and phase speed remain the same when the convection moves from a warm to a cold SST region. The convection is heavily suppressed over the cold SST region due to the decreasing growth rate of unstable wavenumber-one mode with smaller BVMG and longer CAT. This theoretical finding should improve our understanding of MJO dynamics and simulation. Full article
(This article belongs to the Special Issue Madden-Julian Oscillation)
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Figure 1

Figure 1
<p>Climatological mean states. Spatial distributions of annual mean: (<b>a</b>) TRMM 3B42 precipitation <math display="inline"> <semantics> <mrow> <mover accent="true"> <mrow> <msub> <mi>P</mi> <mi>r</mi> </msub> </mrow> <mo stretchy="true">¯</mo> </mover> </mrow> </semantics> </math> (mm day<sup>−1</sup>); (<b>b</b>) column saturation water vapor <math display="inline"> <semantics> <mrow> <mo stretchy="false">〈</mo> <mover accent="true"> <mrow> <msub> <mi>q</mi> <mi>s</mi> </msub> </mrow> <mo stretchy="true">¯</mo> </mover> <mo stretchy="false">〉</mo> <mtext> </mtext> <mrow> <mo>(</mo> <mrow> <mi>mm</mi> </mrow> <mo>)</mo> </mrow> </mrow> </semantics> </math> from ERA-interim; (<b>c</b>) calculated convective relaxation frequency (CRF, day<sup>−1</sup>) defined by <math display="inline"> <semantics> <mrow> <mover accent="true"> <mrow> <msub> <mi>υ</mi> <mi>c</mi> </msub> </mrow> <mo stretchy="true">¯</mo> </mover> <mo>=</mo> <mi>η</mi> <mover accent="true"> <mrow> <msub> <mi>P</mi> <mi>r</mi> </msub> </mrow> <mo stretchy="true">¯</mo> </mover> <mo>/</mo> <mo stretchy="false">〈</mo> <mover accent="true"> <mrow> <msub> <mi>q</mi> <mi>s</mi> </msub> </mrow> <mo stretchy="true">¯</mo> </mover> <mo stretchy="false">〉</mo> </mrow> </semantics> </math>; and (<b>d</b>) non-dimensional tropospheric (900–1000 hPa) background vertical moisture gradient (BVMG) <math display="inline"> <semantics> <mover accent="true"> <mi>Q</mi> <mo>¯</mo> </mover> </semantics> </math> from ERA-interim, overlaid by SST from ERA-interim. The thick dashed contour denotes the SST of 27 °C, and the contour interval is 2 °C. For consistency, all data have been interpolated to 1.5° × 1.5° grid. Adapted from <a href="#atmosphere-08-00204-f002" class="html-fig">Figure 2</a> of Adames [<a href="#B47-atmosphere-08-00204" class="html-bibr">47</a>].</p>
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<p>Sensitivity of FCDM model to different values of convective adjustment time (CAT): (<b>a</b>) frequency (cycles per day); and (<b>b</b>) growth rate (day<sup>−1</sup>) as a function of wavenumber with different CAT values of 2 h, 0.5, 1, 2, and 3 days in the FCDM model.</p>
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<p>Horizontal structures of the eastward-propagating wavenumber-1 mode associated with different values of the spatially uniform CAT. Shown are normalized precipitation anomalies (shading) as well as contours of lower-tropospheric pressure anomalies for different CAT values of (<b>a</b>) <math display="inline"> <semantics> <mi>τ</mi> </semantics> </math> = 1 h, (<b>b</b>) <math display="inline"> <semantics> <mrow> <mi>τ</mi> </mrow> </semantics> </math> = 1 d, and (<b>c</b>) <math display="inline"> <semantics> <mi>τ</mi> </semantics> </math> = 3 d. Positive (negative) anomalies are denoted by solid (dashed) lines. The contour interval is one-fifth of the maximum, and zero contour is not shown. The 0.8 amplitude of the upward PBL Ekman pumping (thick blue contour) is also shown. The thick black lines connect the centers of the convection and PBL moisture convergence. The red “H” denotes the subtropical high pressure center with its font size roughly proportional to the pressure anomaly strength.</p>
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<p>Phase lag and Rossby–Kelvin ratio associated with different values of spatially uniform CAT: (<b>a</b>) phase lag between upward PBL Ekman pumping and precipitation on the equator; and (<b>b</b>) Rossby–Kelvin ratio as a function of wavenumber and spatially uniform CAT in the FCDM model. Phase lag for all waves is compared in a range of 360 degrees by multiplying the wavenumber. The Rossby–Kelvin ratio is defined by the ratio of the maximum pressure anomalies between the off-equatorial (15°–20° N) and equatorial (5° S–5° N) regions.</p>
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<p>Effects of spatially uniform CAT and tropospheric BVMG on eastward-propagating wavenumber one: (<b>a</b>) frequency (cycles per day); and (<b>b</b>) growth rate (day<sup>−1</sup>) as a function of BVMG and spatially uniform CAT for wavenumber one in the FCDM model. The frequencies associated with periods of 30 and 90 d are indicated for references. A scatterplot of calculated band-averaged (10° S–10° N) CAT vs. tropospheric BVMG in <a href="#atmosphere-08-00204-f001" class="html-fig">Figure 1</a> over the Indo-Pacific Ocean from 60° E to 180° E is also represented by the black asterisk in (<b>a</b>). The sample number is 79, and the correlation between the CAT and BVMG is 0.75.</p>
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<p>Mean states of four sensitivity experiments: (<b>a</b>) the CRF (day<sup>−1</sup>); and (<b>b</b>) the non-dimensional tropospheric BVMG used in the four experiments (Exp 1, Exp 2, Exp 3, and Exp 4). In Exp 1, zonally uniform CRF and BVMG are used, while the Warm Pool-like mean state is used in Exp 4. In Exp 2 and Exp 3, the zonally uniform and Warm Pool-like pattern are used for these two fields, respectively.</p>
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<p>Zonal propagation changed by using different mean states. Hovemöller plots of normalized equatorial precipitation in: (<b>a</b>) Exp 1; (<b>b</b>) Exp 2; (<b>c</b>) Exp 3; and (<b>d</b>) Exp 4. The dashed line shows the reference speed of 5 m/s. The equatorial precipitation of each day is normalized by its maximum value.</p>
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<p>Horizontal structures in different experiments. Shown are normalized precipitation (shading) and lower-level wind (vector) at day 30 in: (<b>a</b>) Exp 1; (<b>b</b>) Exp 2; (<b>c</b>) Exp 3; and (<b>d</b>) Exp 4. The 0.8 amplitude of the upward PBL Ekman pumping (thick black contour) is also shown.</p>
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