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Download PDF (3511K) EARLY ONLINE RELEASE This is a PDF of a manuscript that has been peer-reviewed and accepted for publication. As the article has not yet been formatted, copy edited or proofread, the final published version may be different from the early online release. This pre-publication manuscript may be downloaded, distributed and used under the provisions of the Creative Commons Attribution 4.0 International (CC BY 4.0) license. It may be cited using the DOI below. The DOI for this manuscript is DOI:10.2151/jmsj.2019-058 J-STAGE Advance published date: August 6th, 2019 The final manuscript after publication will replace the preliminary version at the above DOI once it is available. 1 The Detection of Mesoscale Convective Systems by the 2 GPM Ku-band Spaceborne Radar 3 4 Jingyu Wang 5 Pacific Northwest National Laboratory, 6 Richland, Washington, USA 7 Robert. A. Houze, Jr1 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] 31 Abstract 32 33 The Global Precipitation Measurement (GPM) core observatory satellite launched in 34 2014 features more extended latitudinal coverage (65S-65N) than its predecessor 35 Tropical Rainfall Measuring Mission (TRMM, 35S-35N). 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. 48 49 Keywords GPM evaluation; 3D reflectivity structure; Mesoscale Convective System 50 tracking; MCS features; Intense convection; Global MCS distribution 1 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 (65S to 63 65N compared to 35S to 35N 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 2 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 3 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 4 111 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 5 131 active, widespread convection. Although the two categories are classified independently, 132 they do have overlap.
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