The Detection of Mesoscale Convective Systems by the GPM Ku-Band Spaceborne Radar

For Peer Review

The Detection of Mesoscale Convective Systems by the GPM Ku-band Spaceborne Radar

Journal: Journal of the Meteorological Society of Japan

Manuscript ID JMSJ-2019-0034.R1

Manuscript Type: Articles

Date Submitted by the n/a Author:

Complete List of Authors: wang, jingyu; Pacific Northwest National Laboratory, Atmospheric Sciences & Global Change Houze, Robert ; University of Washington, Department of Atmospheric Sciences; Pacific Northwest National Laboratory, Atmospheric Sciences and Global Change Division Fan, Jiwen; Pacific Northwest National Laboratory, Atmospheric Sciences and Global Change Division Brodzik, Stacy; University of Washington, Department of Atmospheric Sciences Feng, Zhe; Pacific Northwest National Laboratory, Atmospheric Sciences and Global Change Division Hardin, Joseph; Pacific Northwest National Laboratory, Atmospheric Sciences and Global Change Division

GPM evaluation, 3D reflectivity structure, Mesoscale Convective System Keywords: tracking, MCS feature, Global MCS distribution, Intense convection

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1 The Detection of Mesoscale Convective Systems by the

2 GPM Ku-band Spaceborne Radar

4 Jingyu Wang

5 Pacific Northwest National Laboratory, 6 Richland, Washington, USA

1 7 Robert. A. Houze, Jr

8 University of Washington, 9 Seattle, Washington, USA 10 Pacific Northwest National Laboratory, 11 Richland, Washington, USA

12 Jiwen Fan

13 Pacific Northwest National Laboratory, 14 Richland, Washington, USA

15 Stacy. R. Brodzik

16 University of Washington, 17 Seattle, Washington, USA

18 Zhe Feng

19 Pacific Northwest National Laboratory, 20 Richland, Washington, USA

21 and

22 Joseph C. Hardin

23 Pacific Northwest National Laboratory, 24 Richland, Washington, USA 25 26 27 March 31, 2019 28 ------29 1) Corresponding author: Robert Houze, University of Washington, Seattle, Washington, 30 USA. Email: [email protected] For Peer Review Page 2 of 100

31 Abstract

33 The Global Precipitation Measurement (GPM) core observatory satellite launched in

34 2014 features more extended latitudinal coverage (65S-65N) than its predecessor

35 Tropical Rainfall Measuring Mission (TRMM, 35S-35N). The Ku-band radar onboard of

36 the GPM is known to be capable of characterizing the 3D structure of deep convection

37 globally. In this study, GPM’s capability for detecting mesoscale convective systems

38 (MCSs) is evaluated. Extreme convective echoes seen by GPM are compared against an

39 MCS database that tracks convective entities over the contiguous US. The tracking is

40 based on geostationary satellite and ground-based Next Generation Radar (NEXRAD)

41 network data obtained during the 2014-2016 warm seasons. Results show that more than

42 70% of the GPM-detected Deep-Wide Convective Core (DWC) and Wide Convective Core

43 (WCC) objects are part of NEXRAD identified MCSs, indicating that GPM-classified DWCs

44 and WCCs correlate well with typical MCSs containing large convective features. By

45 applying this method to the rest of the world, a global view of MCS distribution is obtained.

46 This work reveals GPM’s potential in MCS detection at the global scale, particularly over

47 remote regions without dense observation network.

49 Keywords GPM evaluation; 3D reflectivity structure; Mesoscale Convective System

50 tracking; MCS features; Intense convection; Global MCS distribution

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51 1. Introduction

52 As a collaborative effort between the National Aeronautics and Space Administration

53 (NASA) and the Japan Aerospace Exploration Agency (JAXA), the Tropical Rainfall

54 Measuring Mission (TRMM) satellite was equipped with a Ku-band (13.8 GHz) quantitative

55 precipitation radar, together with a variety of sensors including passive microwave, visible,

56 infrared, and lightning (Kummerow et al., 1998; Schumacher et al. 2004; Zipser et al.

57 2006; Houze et al. 2015). Launched in 2014 as the successor of TRMM, the Global

58 Precipitation Measurement (GPM) core observatory satellite carries the first space-borne

59 Dual-frequency Precipitation Radar (DPR) operating at both Ku (13.6 GHz) and Ka (35.5

60 GHz) bands. Compared to TRMM, the DPR system helps improve the accuracy of

61 precipitation measurement and upgrade the detectability of weak rain as low as 0.5 mm hr-

62 1. Another key advancement of GPM is its extended coverage to higher latitudes (65S to

63 65N compared to 35S to 35N for TRMM), providing a near-global view of 3D cloud and

64 precipitation structure every 2-3 hours.

65 Mesoscale Convective Systems (MCSs) are of great importance because of their large

66 area (at least one hundred kilometers in one direction) and intense, long-lasting (up to 24

67 hours) precipitation (Houze 2004, 2018). In midlatitudes, MCSs strongly impact local

68 climate through their precipitation, severe weather, and redistribution of heat and moisture,

69 which further impact the regional to global hydrological cycle and large-scale circulations

70 (Houze et al. 1990; Feng et al. 2016, 2018; Futyan and Del Genio, 2007). Based on the

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71 detailed 3D radar reflectivity field observed by spaceborne precipitation radar, Houze et al.

72 (2007, 2015) developed a convective echo classification algorithm based on the horizontal

73 and vertical dimensions of echoes of a given intensity. The most intense of such 3D

74 convective echo objects are assumed to be strongly associated with MCSs (Houze et al.

75 2015, 2019). However, direct comparison to other independent MCS data sets is needed

76 to support this argument. Identification of MCSs has been traditionally carried out by

77 tracking convective elements in geosynchronous satellite or ground-based radar data,

78 which provide temporally continuous spatial data. For example, satellite imagery that

79 provides cloud radiative properties such as brightness temperature at high temporal

80 resolution has been used to track deep convective clouds at regional to global scale

81 (Schmetz et al. 1993; Machado et al. 1998; Morel and Senesi 2002; Héas and Mémin

82 2008; Escrig et al. 2013; Roca et al. 2014). However, the incapacity of visible and infrared

83 satellite data to show the structure of the clouds in the lower troposphere leaves large

84 uncertainties in satellite-based MCS tracking. Ground-based radar measurements provide

85 3D structure of precipitating deep convection that can complement satellite imagery to

86 more accurately detect MCSs. A recently developed tracking algorithm called FLEXible

87 object TRacKeR (FLEXTRKR, Feng et al. 2018) jointly uses the geostationary satellite

88 brightness temperature and NEXRAD 3D radar reflectivity structure to identify and track

89 MCSs. A comprehensive 13-year (2004-2016) MCS tracking database east of the Rocky

90 Mountains has been developed using FLEXTRKR (Feng et al. 2019). This database is

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91 considered here as ground-truth to evaluate the MCS objects detected by GPM.

92 To determine GPM’s capability of MCS detection, we organize this study as follows. In

93 Section 2, the data sets of GPM Ku-band reflectivity, NEXRAD observations, as well as

94 their corresponding MCS detecting/tracking algorithms are introduced. In Section 3, the

95 quantitative comparison of 3D radar reflectivity fields is performed between the two data

96 sets. This comparison is essential to determine whether the GPM and NEXRAD radar

97 systems detect the 3D radar reflectivity fields in a consistent way. After the consistency

98 between the two data sets is established, the GPM’s snapshots of intense convective echo

99 objects are compared to the NEXRAD MCS tracking database over the Continental United

100 States (CONUS) to quantify GPM’s MCS detection capability. Then, the regionally

101 validated GPM’s MCS detection algorithm is applied elsewhere around the world,

102 presenting a global view of MCS distribution. Finally, conclusions and discussions are

103 provided in Section 4.

104 2. Data and methodology

105 2.1 GPM Data and convective object classification

106 As a major component of the GPM DPR system, the onboard Ku-band precipitation

107 radar features a swath width of 245 km. The horizontal resolution is 0.05, or

108 approximately 5 km. The vertical resolution is 125 m, provided in 176 levels. In this study,

109 the GPM Ku band radar reflectivity data were downloaded from the University of

110 Washington GPM-Ku Data Set located at http://gpm.atmos.washington.edu, which

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111 geolocates and interpolates the DPR Level 2A Ku band version 5B data (Iguchi et al.

112 2017) from “radar coordinates” to a Cartesian grid with the aforementioned resolutions.

113 Detailed methodology regarding the geolocation correction and interpolation can be found

114 in Houze et al. (2007).

115 NASA and JAXA divide the GPM Ku-band radar echoes into convective, stratiform, and

116 other categories. In this study, the echo-object classification scheme developed by Houze

117 et al. (2007, 2015) is applied to the convective echoes. The analysis for evaluating GPM

118 3D radar reflectivity and GPM’s MCS detection capability is conducted in the CONUS

119 region for the warm seasons (April-September) of 2014-2016. The classification scheme

120 defines convective echoes as deep, wide, or deep and wide depending on radar reflectivity

121 intensity thresholds, and criteria for convective echo object height and area. Both strong

122 and moderate criteria (as defined by Houze et al. 2015) are used in this study to examine

123 the sensitivity of the results to the criteria. Strong/moderate deep convective core (DCC)

124 objects contain echoes that are greater than or equal to 40 dBZ/30 dBZ (everywhere in the

125 column) in intensity and have a maximum altitude of at least 10 km/8 km. Strong/moderate

126 wide convective core (WCC) objects contain echoes that are greater than or equal to 40

127 dBZ/30 dBZ and have a maximum horizontal extent of at least 1000 km2/800 km2. The

128 category of DCC has no areal coverage criterion, and is believed to be associated with

129 young, vigorous convection. The WCCs have no height criterion, but rather correspond to

130 where the intense convection has organized horizontally upscale into mesoscale areas of

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131 active, widespread convection. Although the two categories are classified independently,

132 they do have overlap. Deep and wide convective core (DWC) objects meet the criteria for

133 both DCCs and WCCs. The wide categories, DWC and WCC, are thought to be strongly

134 related to the MCSs, as a typical MCS contains a large intense precipitation area with a

135 major axis longer than ~100 km, which is similar to the coverage criteria for the DWC and

136 WCC echo objects.

137 2.2 NEXRAD data and the MCS database

138 Densely distributed across the entire U.S., the NEXRAD network consists of 159 high

139 resolution S-band Doppler radars (WSR-88D) operated by the National Weather Service

140 (NWS). This study uses the mosaic NEXRAD data set named the Gridded Radar data

141 (GridRad, Bowman and Homeyer 2017) that combines all NEXRAD radar data covering

142 the region 155W – 69W, 25N – 49N. The GridRad data set has 2 km horizontal and 1

143 hour temporal resolutions, and a fixed 1 km vertical resolution (24 levels).

144 The 13-year MCS database (2004-2016) developed by Feng et al. (2019) uses both

145 geostationary satellite infrared brightness temperature (Tb) and GridRad radar data to

146 identify and track MCSs east of the Rocky Mountains (110W – 70W, 25N – 49N). The

147 native GridRad radar data were regridded to 4 km resolution to match the geostationary

148 satellite data. In Feng et al. (2019), an MCS was defined when a convective cloud system

4 149 satisfies both criteria in size (cold cloud system with Tb < 241 K area greater than 6 x 10

150 km2, and the radar-observed precipitation feature major axis length exceeds 100 km, with

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151 embedded convective core > 45 dBZ) and duration (persistence longer than 6 hours). The

152 identification of a convective core is based on the storm labeling in three dimensions

153 (SL3D) classification (Starzec et al. 2017). In SL3D, convective core is labeled if any one

154 of the following criteria is satisfied: (1) 25 dBZ echo-top height ≥ 10 km, (2) echo

155 peakedness ≥ 50% of the echo column between the surface and 9 km, and (3) 45 dBZ

156 echo-top height is above the melting-layer height. In the MCS database (Feng et al. 2019),

157 the melting-layer height is determined using the 6-hourly ERA-Interim reanalysis (Dee et

158 al. 2011). After an MCS is tracked, its life cycle is further separated into the initiation,

159 genesis, mature, and dissipation stages. Initiation starts at the first hour when the first

160 MCS-related cold cloud system is detected, followed by the genesis stage when the major

161 axis length of the convective core exceeds 100 km. As the convective core maintains its

162 size, the upscale growth of the MCS’s stratiform rain area concludes the genesis

163 stage. Finally, when the convective core length falls below the 100 km threshold, or the

164 stratiform rain area is lower than the mean value throughout the entire MCS life cycle, the

165 system is classified as in the dissipation stage.

166 2.3 Matching the GPM data to the NEXRAD Coordinate

167 The GPM Ku-band radar operates at a wavelength of 2.2 cm, which is capable of

168 detecting large raindrops or snowflakes (Rauber and Nesbitt 2018), while the 3 GHz

169 ground-based NEXRAD radar has a wavelength is 10 cm. The GPM Ku-band radar has a

170 swath width of 245 km, which is equivalent to the range of a single NEXRAD radar

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171 (maximum operational range of 230 km, although better coverage and resolution is

172 typically provided within 150 km radius of the radar). The GPM radar scans downward,

173 nearly vertically and it has a vertical resolution ~100 m, while the NEXRADs scan quasi-

174 horizontally with a beamwidth of ~1 degree so that at great horizontal distance the vertical

175 resolution is coarse. More importantly, there is a temporal mismatch between the two data

176 sets, as the GPM provides instantaneous snapshots of 3D radar reflectivity field whereas

177 the GridRad temporally averages the observations from different individual NEXRAD

178 radars within a 9-minute window. The different wavelengths, scanning geometries, and

179 data processing of the GPM radar and NEXRAD require that care be taken in comparing

180 data from the two systems.

181 Evaluation of the GPM Ku-band calibration and its attenuation correction has been

182 performed throughout its pre-launch and post-launch stages in multiple field campaigns,

183 such as the 2011 Mid-Continent Convective Clouds Experiment (MC3E, Wang et al. 2015;

184 Jensen et al. 2016), the 2012 GPM Cold-season Precipitation Experiment (GCPEx,

185 Skofronick et al. 2015), the 2014 Integrated Precipitation and Hydrology Experiment

186 (IPHEX, Grecu et al. 2018), and the 2015-2016 Olympic Mountain Experiment

187 (OLYMPEX, Houze et al. 2017, McMurdie et al. 2018; Zagrodnik et al. 2018). These field

188 experiments have found the GPM Ku-band radar data are consistent with the 10 cm

189 wavelength ground-based radar measurements.

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190 In comparing radar datasets from GPM and NEXRAD, differences can result from

191 processing the data at different spatial resolution. It is therefore critical for us to

192 quantitatively examine the consistency of the interpolated 3D radar reflectivity databases.

193 The Earth System Modeling Framework (ESMF) package

194 (https://www.earthsystemcog.org/projects/esmpy/) has been used to bi-linearly re-

195 interpolate the GPM 3D reflectivity data (spatial resolution of 0.05o, vertical resolution of

196 125 m) to the GridRad coordinate (4 km horizontal, 1 km vertical resolution). The hourly

197 NEXRAD data are extracted at the GPM overpass locations and the nearest coincident

198 hour. As described in Bowman and Homeyer (2017), the GridRad data are generated

199 using the 4-dimensional binning (averaging) algorithm that merges multiple individual radar

200 volumes (the mean from 4-minute before to 4-minute after the sample time). To make a

201 fair comparison between the averaged GridRad reflectivity to the instantaneous GPM

202 reflectivity measurements, we apply a Gaussian smoothing technique (Reinhard 2006)

203 vertically to the GPM data to mimic the binning procedure used in GridRad. Following a

204 suggestion from C. Homeyer (2018, personal communication), a 1 km running mean along

205 the vertical direction, and a Gaussian smoothing with kernel width of 9 and sigma value of

206 2.5 are applied to the GPM reflectivity field. After the horizontal regridding and vertical

207 smoothing, the two data sets are in the same coordinates. The smoothing procedure plays

208 a significant role in matching the two observation data sets. If not smoothed, the raw

209 GPM’s reflectivity values are systematically higher than the NEXRAD data by 20-25%.

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210 Note the minimum detectable reflectivity value of GPM Ku-band radar is between 12

211 dBZ (Hou et al. 2014; Toyoshima et al. 2015; Hamada and Takayabu 2016) and 13 dBZ

212 (Olson et al. 2016). Through the examination of all available Ku-band radar data, it is

213 extremely rare to find reflectivity lower than 13 dBZ (with occurrence frequency of 5 x 10-8).

214 As a result, this study adopts the higher threshold of 13 dBZ, and the corresponding

215 NEXRAD reflectivity is truncated accordingly for fair comparison.

216 As revealed in previous studies (e.g., Krajewski et al. 2006; Cui et al. 2016 and 2017),

217 NEXRAD radar beam blockage is a common issue over mountainous regions, making the

218 radar reflectivity comparison with GPM difficult. However, the MCSs commonly initiate

219 from the lee side of the Rocky Mountains (Feng et al. 2019). Therefore we choose the

220 study domain just east of the Rocky Mountains Front Range (eastern Colorado). In

221 addition, NEXRAD radars deployed along the coast have extended observations over the

222 ocean. Those observations lack overlap with other radars and their sample volume is

223 larger than normal (Rinehart 2001), thus the data quality offshore is questionable.

224 Moreover, from the perspective of MCS tracking, convection farther offshore away from

225 the coastal NEXRADs, are prone to have larger uncertainty because of reduced low-level

226 radar coverage. As a result, through the examination of CONUS elevation data

227 (TerrainBase, 1995), the study domain is constrained to the central U.S. as shown in Fig. 1

228 (110W – 85W, 31N – 48N), which avoids most of the complex terrain over Rocky and

229 Appalachian Mountains and excludes all offshore areas.

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230 3. Results

231 3.1 The comparison of 3D radar reflectivity between GPM and NEXRAD

232 Vertical echo structure is indicative of the kinematic, dynamic, and thermodynamic

233 features of convective clouds (Hence and Houze 2008, 2011, 2012a, b), as well as cloud

234 microphysical properties (Leary and Houze 1979; Cetrone and Houze 2011; Rowe and

235 Houze 2014; Fan et al. 2015, 2017; Liu et al. 2015; Barnes and Houze 2016; Han et al.

236 2019). As mentioned in Section 2.3, the GPM data are instantaneous snapshots of 3D

237 radar reflectivity field from above whereas the NEXRAD provides the mean composite

238 data from multiple ground radars. This may result in temporal mismatch between the two

239 measurements, since we have binned the GPM overpasses to the nearest hour, i.e., the

240 hourly NEXRAD data are compared to the GPM overpasses that occur within the time

241 window of +/- 30 minutes for each hour. Previous studies (e.g., Feng et al. 2009, Wang et

242 al. 2016 and 2018) have shown that the time mismatch can significantly impact the

243 reflectivity measurements between different radars, as convective features could change

244 substantially in evolution and/or location on a time scale less than an hour. Therefore, we

245 adopt the statistical method of contoured-frequency-by-altitude-diagrams (CFADs) to

246 compare the vertical distribution of radar reflectivity measurements from the two radar

247 platforms.

248 As shown in Fig. 2, CFADs are generated for GPM (a, d, g, j, m, p) and NEXRAD (b, e,

249 h, k, n, q) from all the collocated data for each month (April - September) within the

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250 sampling area for the 2014-2016 period. The CFADs display the frequency distribution in a

251 coordinate system of reflectivity bins (x-axis) and the altitude (y-axis), and represent the

252 occurrence frequency of reflectivity spectra normalized by the total number of bins in which

253 reflectivity is recorded. The bin sizes in reflectivity and height are 1 dBZ and 1 km,

254 respectively. The frequency is calculated as the number of non-zero echo values in a bin

255 divided by the total number of bins containing reflectivity (all heights). The integral of the

256 frequency over the entire 2D graph equals 1.

257 From the examination of the CFADs structure, the GPM is in good agreement with

258 NEXRAD for each month regardless of the overall shape or magnitude of the frequency at

259 various altitudes. Both data sets demonstrate consistent seasonal variations: the deepest

260 echo top at various reflectivity thresholds increases from spring (April - May) to summer

261 (June - July - August), then decreases towards the early fall (September). This seasonality

262 is also seen for the altitude change of the frequency contour of 0.6% and above. The peak

263 altitudes of this higher frequency are broadly distributed below 6 km in spring, and the

264 altitudes increase to 9 km in the summer, and finally decrease to lower altitude in early fall.

265 The seasonal changes in echo-top heights and the altitude of the higher frequency

266 reflectivity values implies the seasonal variation of the convective intensity. The convective

267 updraft intensity maximum in summer is consistent with the largest convective available

268 potential energy being observed at that time (Xie et al., 2014). Meanwhile, the seasonality

269 in the altitude of higher frequency values is also indicative of the shift in storm types

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270 between seasons. Spring and fall storms tend to have more stratiform clouds with bottom-

271 heavy reflectivity profiles (e.g., strong bright-band signature near the melting level),

272 resulting in higher reflectivity at mid-to-lower levels. During the summer, more occurrence

273 of convective clouds shifts higher reflectivity values aloft.

274 The radar reflectivity distributions normalized at each level are also examined using the

275 box-whisker plots shown in Fig. 2c, f, i, l, o, r. General agreements are also found for the

276 median, interquartile and extreme values between the two data sets at different altitudes

277 for every month. The differences are commonly less than 2 dBZ except for the 95th

278 percentiles above 12 km, where the NEXRAD shows larger reflectivity values than the

279 GPM. From the CFADs, the frequency of occurrence for these extreme values are very

280 low, thus the difference there is not of particular concern.

281 3.2 Evaluation of GPM’s capability in MCS detection

282 Having established that the GPM and WSR-88D reflectivity fields are consistent on the

283 interpolated grids, we now assess GPM’s capability in MCS detection. As mentioned in

284 Section 2.1, it has been suggested that the GPM DWC and WCC echo objects correlate

285 with MCSs, which feature convection organized on larger horizontal scale. Here we test

286 this argument by comparing GPM DWC and WCC echo objects (strong criteria), which are

287 observed as snapshots, to the MCS database, which is unambiguously determined from

288 tracking convective features in time. Figure 3 shows one example of a GPM-defined DWC

289 echo object and the coincident MCS at the nearest hour from the NEXRAD data, as well

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290 as the timing of GPM’s overpass with the tracked MCS’s life cycle. By comparing Fig. 3a

291 (GPM) and Fig. 3b (NEXRAD), the collocated radar reflectivity fields as observed from the

292 two platforms show that they are in as good agreement as could be expected. The GPM-

293 defined DWC snapshot occurred during the time period that the MCS tracked by the

294 FLEXTRKR algorithm was in the genesis stage (Fig. 3c), meaning it was experiencing

295 upscale growth at the time of the GPM observation.

296 In order to form long-term statistics, all 158 DWC objects and 230 WCC objects

297 detected by GPM (strong criteria) within the three warm seasons are evaluated with the

298 NEXRAD MCS database. By overlaying the mask of GPM-defined DWC or WCC object to

299 the mask of the NEXRAD-tracked MCS cold cloud shield, we can determine if the GPM

300 DWC or WCC objects are part of a NEXRAD-defined MCS. If overlap is found, then this

301 GPM-defined DWC or WCC object is considered as a HIT. Otherwise, it is treated as a

302 false alarm (FAR) because the GPM snapshot of the convective echo objects does not

303 align with any NEXRAD-tracked MCS. Note we do not define a missing category (i.e.,

304 MCSs tracked by the NEXRAD but not identified by the GPM) because of the substantial

305 difference in spatial coverage. As described in Section 2, the GPM has a limited swath

306 width of 245 km, where the NEXRAD network covers the entire CONUS. Therefore, the

307 “missing” detection from the GPM is highly possible simply because there is no GPM

308 overpass for a particular MCS.

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309 In addition to the objective comparison, all the GPM DWC/WCC and collocated

310 NEXRAD reflectivity images are manually screened for further confirmation. For the GPM-

311 detected echo objects in the FAR category, there are several cases that the GPM

312 detections are closely located (usually within ~5 km) to the NEXRAD-tracked MCS but

313 without overlap. We suspect that this could be caused by the temporal mismatch between

314 the two data sets as aforementioned and should be treated as HIT instead. As a result, by

315 including the NEXRAD MCS masks from 1 hour before to 1 hour after the coincident hour,

316 overlaps are found for those GPM detections (11 cases).

317 The final result shows that 115 of 158 DWC objects and 158 of 230 WCC objects can

318 be verified as MCSs when compared to the NEXRAD data set, leaving 115 cases as FAR.

319 In summary, 70% of DWC and WCC systems classified by the GPM are MCSs identified in

320 the NEXRAD data set. Interestingly, most of the GPM-detected MCS snapshots are during

321 the genesis (41%) and mature stages (32%) of the tracked MCSs. There are only about

322 21% in the dissipation stage and 6% in the initiation stage. These results are consistent

323 with the finding of Feng et al. (2019) that warm season MCS convective features during

324 the upscale growth stage are the largest and deepest. These convective features are most

325 likely to meet the GPM DWC or WCC criteria.

326 After revisiting the 115 cases in the FAR category by examining the NEXRAD images

327 before and after the GPM detection, 19% of them last more than 6 hours but their major

328 axis length of precipitation area couldn’t exceed 100 km to satisfy the MCS criteria defined

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329 by FLEXTRKR. For the rest of 81% cases, they all dissipate too soon and therefore fail to

330 satisfy the MCS duration requirement of 6 hours. These false alarms reveal the intrinsic

331 limitation of the criteria used to identify MCSs by tracking NEXRAD echoes and the

332 conditions used to define the GPM echo-object categories are arbitrary.

333 Because the majority of false alarms result from the insufficient duration, a question

334 arises as to whether the HIT rate (defined as HITs / (HITs + FARs)) could increase with

335 lowering the MCS duration threshold. Some previous studies defined MCSs in this region

336 using shorter duration of 4 hours (e.g., Geerts 1998; Haberlie and Ashley 2019). To

337 examine the impact of MCS duration criterion to our results, we performed a sensitivity test

338 by reducing the MCS duration threshold from 6 hours to 4 hours in FLEXTRKR, and the

339 total number of MCSs tracked by NEXRAD in the 3 warm seasons increased from 740 to

340 1193. However, only 12 GPM FAR cases are changed to the HIT category. Upon close

341 examination of the FAR cases, we found several reasons that explain this result. First,

342 90% of the additional 453 NEXRAD-tracked MCS cases occur outside the GPM

343 overpasses, thus they have no impact to the GPM statistics. Secondly, for those short-

344 lived false alarms detected by the GPM, their lifespan are commonly less than 3 hours,

345 which fail to meet even the shortened MCS duration criteria. Lastly, short duration and

346 insufficient coverage are not exclusive, i.e., many GPM-detected objects that do not last

347 longer than 6 hours also do not satisfy the size criteria. In this case, it is complex to

348 differentiate the actual causes of false alarms. The fact that changing the MCS duration

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349 threshold produces limited impact to GPM’s MCS statistics may further confirm the

350 correspondence between the majority of GPM-detected DWCs/WCCs and the largest,

351 deepest MCSs, as the latter require longer period of time to form. However, there remain

352 exceptions of short-lived DWCs/WCCs, which raise the necessity of the temporal

353 dimension. More extreme sensitivity test of 2 hours duration threshold is performed, now

354 the systems tracked by the NEXRAD increases to 1671 and 49 false alarms change to the

355 category of HIT, making the HIT rate 83%. However, this comparison may not be

356 meaningful because the 2 hours duration is too short for MCS definition, which makes

357 almost no difference to the direct radar echo comparison between the two platforms.

358 Based on these tests, we conclude that 70% accuracy reached in our first comparison

359 remains a good overall estimate of the capability to determine MCS existence from the

360 GPM radar data.

361 By using the original MCS tracking data set as reference, Fig. 4 and Table 1 show the

362 GPM’s detection skills in each month, where the number of HIT, FAR, as well as the

363 number of NEXRAD-tracked MCSs and their averaged precipitation area coverage and

364 duration are compared. The number of NEXRAD-tracked MCSs demonstrates a strong

365 seasonality, which increases from April to June, then diminishes towards early fall. This

366 variation follows the seasonal change in baroclinic instability over the CONUS. During

367 spring, large-scale forcing brought by the mid-latitude trough frequently occurs, providing a

368 favorable environment for organized convection (Maddox et al 1979; Peters and

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369 Schumacher 2014). In April, MCSs produced under strong baroclinic forcing usually

370 feature broad stratiform rain region, which results in the largest precipitation area, but the

371 number is relatively low compared to the midsummer, when the CONUS has the weakest

372 baroclinic instability and minimal frontal forcing. However, due to the favorable

373 thermodynamic conditions and possible influence from sub-synoptic disturbances, local

374 convection can nevertheless frequently grow upscale into MCSs (Wang et al. 2011; Song

375 et al. 2019; Feng et al. 2019). The combination of spring-like baroclinic waves and a

376 continuously warming surface possibly favors the peak number of MCSs in late spring and

377 early summer (i.e., June), but the MCS precipitation areas are generally smaller than in

378 spring. Although fall has similar large-scale environments as in spring, MCSs are less

379 frequent and no increase in average coverage is found. One possible explanation is the

380 seasonality in surface temperature gradient, which is weaker in fall than in spring, possibly

381 causing weaker baroclinic waves (Feng et al. 2019). The average MCS duration was also

382 calculated for each month, and no seasonal variation was found.

383 From the perspective of MCS detection by the GPM, several factors contribute to a

384 higher HIT rate: more frequent MCS occurrence, larger precipitation area and longer

385 duration (i.e., the propagating nature results in larger area covered by MCSs). The trend of

386 monthly HIT is consistent with the variation of the MCS numbers, indicating that the

387 probability of GPM detected MCSs increases with more frequent MCS occurrence. Spring

388 corresponds to the lowest False Negative Rate (defined as FARs / (HITs + FARs)), which

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389 is consistent with the larger MCS coverage favored by the synoptic forcing. Thus higher

390 accuracy in GPM’s MCS detection can be expected relative to the rest of the months. In

391 contrast, for the August-September period, as the result of poleward expansion of the

392 subtropical ridge (Wang et al. 2019), the baroclinic environment for supporting large MCSs

393 no longer exists, resulting in a higher False Negative Rate.

394 3.3 Global MCS distributions

395 After establishing the GPM’s MCS detection capability of about 70% by validating

396 against the NEXRAD-tracked MCS database over CONUS in the previous section, we can

397 apply the DWC and WCC criteria to the GPM data globally to examine the probably global

398 distribution of MCSs. Because ground-based radar networks like NEXRAD do not exist

399 over most of Earth, GPM is the best available resource for determining the global pattern

400 of MCS occurrence. Figure 5 shows the geographical distribution of MCS occurrence

401 frequency determined from GPM during the boreal summer June-July-August (JJA, Fig 5a)

402 and winter December-January-February (DJF, Fig 5b) over the 5-year period (2014-2018)

403 with available GPM observations. The frequency is computed as the number of pixels

404 identified as either DWC or WCC divided by the total number of pixels sampled by the

405 GPM Ku-band radar within a 0.25 x 0.25 gridbox. It is evident that the occurrence of

406 MCSs is more concentrated over land, which is consistent with previous studies showing

407 that convection over vast oceans is generally less intense than over land (e.g., Futyan and

408 Del Genio 2007; Houze et al. 2015). A comparison between Fig. 5a and Fig. 5b shows that

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409 MCSs occur more frequently during the boreal summer than winter for both hemispheres.

410 In North America during summer, MCSs are densely distributed over the Great Plains

411 (GP), where they are fed by warm moist air transported from the Gulf of Mexico by the

412 climatological low-level jet on the lee side of the Rocky Mountains. In both summer and

413 winter, MCSs frequently occur offshore of the east coast of North America. These MCSs

414 tend to result from the initiation of gravity waves on the lee side of the Appalachian

415 Mountains (Keighton et al. 2007; Letkewicz and Parker 2010). In winter, MCSs are absent

416 over the GP but occur over the southeast and the offshore of the east coast of the U.S.,

417 consistent with NEXRAD-based MCS frequencies reported by Feng et al. (2019).

418 Over the landmass of tropical South America and central equatorial Africa, large

419 clusters of MCSs occur during JJA, but the hot zones of MCSs shift southward to the

420 subtropics and mid-latitudes in DJF, consistent with previous studies (e.g., Romatschke

421 and Houze 2010; Rasmussen and Houze 2011; Rasmussen et al. 2014). The high-

422 frequency MCS areas are displaced from the areas with the most regional rainfall (Houze

423 2015). Over Asia, three hot zones are identified in JJA, namely the Indian monsoon region,

424 east Asian monsoon region, and the maritime continent consisting Indonesia, Malaysia,

425 and Northern Australia (Ramage 1968). The former two regions are strongly influenced by

426 the summer monsoon flow, hence the MCSs peak in JJA but diminish in DJF. In contrast,

427 the maritime continent MCS occurrence is dominated by diurnal forcing associated with

428 the islands and peninsulas of the region, and in DJF the diurnal convection is enhanced by

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429 surges of the boreal winter monsoon (Johnson and Houze 1987) and modulated by

430 passages of the Madden-Julian Oscillation (Madden and Julian, 1971,1972, 1994).

431 Note all of the above MCS analyses are based on the strong thresholds applied to GPM

432 detection as described in Section 2.1. A sensitivity test using moderate thresholds is

433 performed with the corresponding global MCS distributions shown in Fig. 6. As revealed in

434 Houze et al. (2015), strong thresholds better represent the behavior of convection over

435 land, while the weak oceanic convective features can be exhibited more easily using the

436 moderate thresholds. By comparing Fig. 6 with Fig. 5, the locations of high MCS

437 occurrence are seen to be well maintained, but the frequency is greatly increased,

438 potentially including many non-MCS objects. The DWC and WCC objects detected using

439 moderate thresholds are also compared to the FLEXTRKR data set over the CONUS.

440 Although the number of GPM-detected DWC and WCC objects increases from 388 to 620,

441 the MCS HIT rate drops to 49%.

442 The global MCS distribution shown in Fig. 5 extends the MCS detection to higher

443 latitudes (beyond 35S-35N covered by TRMM), where notable MCS occurrence

444 frequency is found above 60N in Siberia, Northern Europe, and Canada. For the high-

445 latitude events, Houze et al. (2019) further found that these high-latitude MCSs occur

446 where global warming has been most intense.

447

448

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449 4. Conclusions and Discussions

450 In this study, the spaceborne GPM Ku-band radar data sets are quantitatively

451 compared to the ground-based NEXRAD radar data sets during a 3-year period (2014-

452 2016) over the CONUS. Based on the morphology of GPM-detected radar echoes, two

453 types of GPM-detected extreme convective echo objects, DWC and WCC, are compared

454 with an MCS database constructed using feature tracking on synthesized geostationary

455 satellite and NEXRAD radar observations. The major findings of this study are

456 summarized as follows.

457 (1) The GPM radar captures consistent 3D distribution of radar reflectivity with

458 NEXRAD across a wide range precipitating cloud systems, including seasonal variations

459 from spring to fall, when the GPM radar and NEXRAD data are interpolated to the same

460 grid and appropriately smoothed. The comparison demonstrates the two independent

461 radar systems observe the 3D radar reflectivity fields in a consistent manner.

462 (2) The GPM classified DWC and WCC objects are compared to the NEXRAD-tracked

463 MCS database. More than 70% of these GPM-defined extreme convective echo objects

464 are collocated with tracked MCSs in the NEXRAD data set, indicating the GPM-detected

465 DWC and WCC objects are highly correlated with MCSs. The GPM’s capability in MCS

466 detection demonstrates strong seasonality, and a better performance is found in spring

467 and summer than fall. This seasonal variation follows the change of large-scale

468 environments that alter MCS characteristics. For the GPM-detected objects not

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469 corresponding to NEXRAD-tracked MCSs, the majority of them have shorter lifespan than

470 6 hours. These false alarms highlight the unavoidable uncertainty associated with the

471 arbitrariness of criteria used to identify MCSs in the two datasets.

472 (3) After validating the GPM’s performance in MCS detection over the CONUS, the

473 DWC and WCC classification algorithm is applied to global GPM observations to obtain a

474 global view of MCS distribution. The well-known MCS hot zones, namely the US GP and

475 the offshore of the east coast in North America, subtropical South America, central

476 equatorial Africa, monsoon regions in Asia, and the maritime continent, are further

477 confirmed in this study. Moreover, MCSs in high-latitude regions (above 60N in Siberia,

478 Northern Europe, and Canada) are revealed, and the relative numbers of MCSs over

479 different parts of Earth are now quantitatively measured.

480

481

482

483

484

485

486

487

488

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489 Acknowledgements

490 We acknowledge the support of the Climate Model Development and Validation (CMDV)

491 project at PNNL. The effort of J. Wang, J. Fan, Z. Feng, and J. Hardin was supported by

492 CMDV. Robert A. Houze was supported by NASA Award NNX16AD75G and by master

493 agreement 243766 between the University of Washington and PNNL. Stacy R. Brodzik

494 was supported by NASA Award NNX16AD75G and subcontracts from the CMDV and

495 Water Cycle and Climate Extreme Modeling (WACCEM) projects of PNNL. PNNL is

496 operated for the US Department of Energy (DOE) by Battelle Memorial Institute under

497 Contract DE-AC05-76RL01830. This research used resources of the National Energy

498 Research Scientific Computing Center (NERSC), a U.S. Department of Energy Office of

499 Science User Facility operated under contract DE‐AC02‐05CH11231. The GPM reflectivity

500 data are download at University of Washington GPM-Ku Data Set at

501 (http://gpm.atmos.washington.edu/), the Global Merged IR dataset is obtained at NASA

502 Goddard Earth Sciences Data and Information Services Center

503 (https://dx.doi.org/10.5067/P4HZB9N27EKU), the GridRad radar dataset is obtained at the

504 Research Data Archive of the National Center for Atmospheric Research (NCAR)

505 (https://rda.ucar.edu/datasets/ds841.0/).

506

507

508

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799 0311.1.

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809 List of Figures

810

811 Fig. 1 The domain for GPM and NEXRAD comparison is marked with the red box (110W

812 – 85W, 31N – 48N) overlaid on the CONUS elevation map.

813 Fig. 2 The contoured-frequency-by-altitude-diagrams (CFADs) normalized by the total

814 number of samples at all altitude levels for GPM (a, d, g, j, m, p) and NEXRAD (b, e, h,

815 k, n, q) for the months from April to September in 2014-2016 period. The box-whisker

816 plots (c, f, i, l, o, r) for GPM (red) and NEXRAD (blue) are calculated using

817 normalization at each individual level, where the center of the box represents the 50%

818 percentile value, the lower quartile (25%) and upper quartile (75%) form the left and

819 right boundary of the box, and whiskers correspond to the 5% and 95% values.

820 Fig. 3 (a) The Deep Wide Convection (DWC) object defined by GPM on 9 June 2014 at

821 04:26:46 UTC and (b) the coincident NEXRAD observed MCS tracked by FLEXTRKR

822 at 04:00 UTC, as well as (c) the intersection of GPM overpass time (dash-line) with

823 respect to the evolution of the MCS precipitation area during its entire life cycle

824 identified by the NEXRAD.

825 Fig. 4 Monthly total number of MCSs HIT (GPM detection validated by NEXRAD, red

826 bars) and false alarm (FAR, not identified as MCS by NEXRAD, blue bars), overlaid by

827 the monthly total number of MCSs detection by NEXRAD (black line) and their average

828 precipitation area coverage (green line).

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829 Fig. 5 Geographical distribution of the probability of MCS occurrence frequency detected

830 by GPM during the months of (a) JJA and (b) DJF. The gray shaded areas inside the

831 continental regions represent the 700 m elevation. The probability is on a scale of 0 to

832 100% and is computed as the number of pixels identified as either DWC or WCC

833 divided by the total number of GPM overpasses within a 0.25 × 0.25 gridbox.

834 Fig. 6 Similar to Figure 5 but using the moderate criteria.

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851

852

853 Fig. 1 The domain for GPM and NEXRAD comparison is marked with the red box (110W

854 – 85W, 31N – 48N) overlaid on the CONUS elevation map.

855

856

857

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858

859 Fig. 2 The contoured-frequency-by-altitude-diagrams (CFADs) normalized by the total

860 number of samples at all altitude levels for GPM (a, d, g, j, m, p) and NEXRAD (b, e, h,

861 k, n, q) for the months from April to September in 2014-2016 period. The box-whisker

862 plots (c, f, i, l, o, r) for GPM (red) and NEXRAD (blue) are calculated using

863 normalization at each individual level, where the center of the box represents the 50%

864 percentile value, the lower quartile (25%) and upper quartile (75%) form the left and

865 right boundary of the box, and whiskers correspond to the 5% and 95% values.

866

867

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869

870 Fig. 3 (a) The Deep Wide Convection (DWC) object defined by GPM on 9 June 2014 at

871 04:26:46 UTC and (b) the coincident NEXRAD observed MCS tracked by FLEXTRKR

872 at 04:00 UTC, as well as (c) the intersection of GPM overpass time (dash-line) with

873 respect to the evolution of the MCS precipitation area during its entire life cycle

874 identified by the NEXRAD.

875

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880 881 Fig. 4 Monthly total number of MCSs HIT (GPM detection validated by NEXRAD, red

882 bars) and false alarm (FAR, not identified as MCS by NEXRAD, blue bars), overlaid by

883 the monthly total number of MCSs detection by NEXRAD (black line) and their average

884 precipitation area coverage (green line).

885

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887

45 Page 47 of 100 For Peer Review

888

889 Fig. 5 Geographical distribution of the probability of MCS occurrence frequency detected

890 by GPM during the months of (a) JJA and (b) DJF. The gray shaded areas inside the

891 continental regions represent the 700 m elevation. The probability is on a scale of 0 to

892 100% and is computed as the number of pixels identified as either DWC or WCC

893 divided by the total number of GPM overpasses within a 0.25 × 0.25 gridbox.

894

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898

899 Fig. 6 Similar to Figure 5 but using the moderate criteria.

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911 List of Tables

912 Table 1 The statistics of GPM’s MCS detection skill in each month.

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937 Table 1 The statistics of GPM’s MCS detection skill in each month. Average Average GPM False GPM GPM GPM HIT NEXRAD MCS MCS Negative HIT (#) FAR (#) Rate MCS (#) Coverage Duration Rate (km2) (hour) April 43 8 84% 16% 104 20,374 21.5 May 47 9 84% 16% 140 13,742 20.8 June 58 26 69% 31% 160 11,961 20.4 July 61 23 73% 27% 137 10,683 20.9 August 37 31 54% 46% 122 10,885 20.6 September 27 18 60% 40% 77 10,141 19.8

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