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ACCOMPLISHMENTS OF NOAA’S AIRBORNE HURRICANE FIELD PROGRAM

AND A BROADER FUTURE APPROACH TO FORECAST IMPROVEMENT

Jonathan Zawislak1,2, Robert F. Rogers1, Sim D. Aberson1, Ghassan J. Alaka, Jr.1, George

Alvey1,2, Altug Aksoy1,2, Lisa Bucci1, Joseph Cione1, Neal Dorst1, Jason Dunion1,2, Michael

Fischer1,2, John Gamache1, Sundararaman Gopalakrishnan1, Andrew Hazelton1,2, Heather M.

Holbach1,3, John Kaplan1, Hua Leighton1,2, Frank Marks1, Shirley T. Murillo1, Paul Reasor1,

Kelly Ryan1,2, Kathryn Sellwood1,2, Jason A. Sippel1, Jun A. Zhang1,2

1 National Oceanographic and Atmospheric Administration / Atlantic Oceanographic and

Meteorological Laboratory / Hurricane Research Division, Miami, FL

2 University of Miami / Cooperative Institute for Marine and Atmospheric Studies, Miami, FL

3 Florida State University / Northern Gulf Institute, Tallahassee, FL

Submitted to the Bulletin of the American Meteorological Society

First Submission: February 11, 2021

Second Submission: May 14, 2021

1 * Corresponding author:

Jonathan Zawislak, [email protected]

4301 Rickenbacker Causeway, Miami, FL, 33149

Early Online Release: This preliminary version has been accepted for publication in Bulletin of the American Meteorological Society, may be fully cited, and has been assigned DOI 10.1175/BAMS-D-20-0174.1. The final typeset copyedited article will replace the EOR at the above DOI when it is published.

2 Since 2005, NOAA has conducted the annual Intensity Forecasting Experiment (IFEX),

3 led by scientists from the Hurricane Research Division at NOAA’s Atlantic Oceanographic and

4 Meteorological Laboratory. They partner with NOAA’s Aircraft Operations Center, who maintain

5 and operate the WP-3D and G-IV Hurricane Hunter aircraft, and NCEP’s National Hurricane

6 Center and Environmental Modeling Center, who task airborne missions to gather data used by

7 forecasters for analysis and forecasting and for ingest into operational numerical weather

8 prediction models. The goal of IFEX is to improve tropical cyclone (TC) forecasts using an

9 integrated approach of analyzing observations from aircraft, initializing and evaluating forecast

10 models with those observations, and developing new airborne instrumentation and observing

11 strategies targeted at filling observing gaps and maximizing the data’s impact in model forecasts.

12 This summary article not only highlights recent IFEX contributions towards improved TC

13 understanding and prediction, but also reflects more broadly on the accomplishments of the

14 program during the 16 years of its existence. It describes how IFEX addresses high-priority

15 forecast challenges, summarizes recent collaborations, describes advancements in observing

16 systems monitoring structure and intensity, as well as in assimilation of aircraft data into

17 operational models, and emphasizes key advances in understanding of TC processes, particularly

18 those that lead to rapid intensification. The article concludes by laying the foundation for the “next

19 generation” of IFEX as it broadens its scope to all TC hazards, particularly rainfall, storm-surge

20 inundation, and tornadoes, that have gained notoriety during the last few years after several

21 devastating landfalling TCs.

Unauthenticated | Downloaded 10/06/21 02:03 PM UTC Accepted for publication in Bulletin of the American Meteorological Society. DOI 10.1175/BAMS-D-20-0174.1. 25 CAPSULE

26 The field program has contributed to improvements in tropical cyclone intensity forecasts through

27 aircraft observations. Future strategies will broaden these efforts by focusing on forecasts of storm

28 hazards.

Unauthenticated | Downloaded 10/06/21 02:03 PM UTC Accepted for publication in Bulletin of the American Meteorological Society. DOI 10.1175/BAMS-D-20-0174.1. 48 MOTIVATION AND OVERVIEW OF IFEX

49 During the past five years, the United States has experienced multiple tropical cyclone (TC)

50 landfalls that caused over a billion dollars in damage (NOAA/National Centers for Environmental

51 Information, NCEI): Hurricanes Matthew (2016), Harvey (2017), Irma (2017), Maria (2017),

52 Florence (2018), Michael (2018), Dorian (2019), Isaias (2020), Laura (2020), Sally (2020), and

53 Tropical Storm Imelda (2019). In 2020 alone, 12 named storms made landfall in the U.S., six of

54 which were hurricanes, during a record-breaking North Atlantic hurricane season.

55 Many of these TCs made landfall as major hurricanes (Categories 3–5 by the Saffir-

56 Simpson Hurricane Wind Scale, or maximum sustained surface wind speed > 95 kt, 1 kt ≈ 0.5 m

57 s-1). Storm-surge inundation, extreme rainfall, high surf, and tornadoes (Table 11) were significant

58 contributors to the damage, in addition to high winds; in fact, flooding from rainfall and surge

59 accounts for the majority of deaths in landfalling storms (Rappaport 2000). A common feature

60 among most of these TCs is that they underwent at least one period of rapid intensification (RI),

61 defined as an intensity increase of 30 kt or more in 24 h (Kaplan and DeMaria 2003).

62 Although improvements in intensity forecast skill have lagged improvements in track

63 forecasts over the last 30 years, there has been a notable decrease in intensity forecast errors and

64 improved model skill over the past 10 years (Cangialosi et al. 2020). That said, forecasters continue

65 to struggle to predict the onset and magnitude of RI, a challenge that is one of the high-priority

1 The impacts from Laura (2020) will not be included in Table 1 as the Tropical Cyclone Report

authored by the National Hurricane Center has not yet been released as of writing.

Unauthenticated | Downloaded 10/06/21 02:03 PM UTC Accepted for publication in Bulletin of the American Meteorological Society. DOI 10.1175/BAMS-D-20-0174.1. 66 objectives of the Weather Research and Forecasting Innovation Act of 20172 and NOAA’s

67 Hurricane Forecast Improvement Project (HFIP; Gall et al 2013; Marks et al. 2019).

68 Improving TC forecasts requires advancements in multiple areas, including developing

69 new observing technologies and strategies that fill existing temporal and spatial gaps in observing

70 networks, improving the assimilation of those observations into numerical models, and improving

71 the models themselves, for instance through the use of observations to evaluate and improve how

72 well a model simulates a TC. Although satellites are one of the primary platforms to observe TC

73 characteristics (Rogers et al. 2019) and improve model initialization through assimilation (e.g.,

74 Goerss 2009; Christophersen et al. 2018; Magnusson et al. 2019), aircraft continue to be a

75 necessary platform for obtaining in-situ measurements at a spatial resolution adequate enough to

76 resolve structures within the inner core.

77 In 2005, the Hurricane Research Division (HRD) at NOAA’s Atlantic Oceanographic and

78 Meteorological Laboratory (AOML) began their Intensity Forecasting Experiment (IFEX), a

79 program dedicated towards using existing airborne tools with new observing strategies, numerical

80 models, and data assimilation to improve intensity prediction. Three primary IFEX goals were

81 defined as (Rogers et al. 2006): 1) collect observations that span the TC life cycle in a variety of

82 environments for model initialization and evaluation; 2) develop and refine measurement

83 strategies and technologies that provide improved real-time monitoring of TC intensity, structure,

84 and environment; and 3) improve the understanding of physical processes important for intensity

85 change for a TC at all stages of its life cycle.

2 https://www.congress.gov/bill/115th-congress/house-bill/353 - Section 104

Unauthenticated | Downloaded 10/06/21 02:03 PM UTC Accepted for publication in Bulletin of the American Meteorological Society. DOI 10.1175/BAMS-D-20-0174.1. 86 IFEX operates as a partnership between AOML/HRD; NOAA’s Office of Marine and

87 Aviation Operations (OMAO) Aircraft Operations Center (AOC), who maintain and operate two

88 Lockheed WP-3D Orion (P-3) turboprops and the Gulfstream IV-SP (G-IV) jet; the 53rd U.S. Air

89 Force Reserve Weather Reconnaissance Squadron (53rd WRS) who also fly TC missions,

90 NOAA/NCEP National Hurricane Center (hereafter, just NHC), who establish the priorities and

91 needs that motivate IFEX; and NOAA/NCEP Environmental Modeling Center (hereafter, just

92 EMC), who develop and implement the operational models, notably the Hurricane Weather

93 Research and Forecasting (HWRF) model.

94 IFEX also partners with HFIP, which was established in 2007 in response to the active and

95 devastating 2004 and 2005 hurricane seasons (Gall et al. 2013; Gopalakrishnan et al. 2020). HFIP

96 contributes to TC forecast improvement though the development of numerical weather prediction

97 models, e.g., HWRF, the “Basin-scale” version of HWRF (HWRF-B; Zhang et al. 2016; Alaka et

98 al. 2017, 2019), and the next-generation Hurricane Analysis and Forecast System (HAFS) (Dong

99 et al. 2020; Hazelton et al. 2020). HFIP invests in forecast improvements not only through

100 advancements of existing models and development of new models, but also in the techniques by

101 which they assimilate observations, and the post-processing, production, and visualization of

102 model products. Given the observational needs for both data assimilation and model evaluation,

103 HFIP benefits from IFEX data collection.

104 The goal of this article is not only to highlight IFEX activities since the previous summaries

105 (Rogers et al. 2006, hereafter R06; Rogers et al. 2013, R13), but to reflect on IFEX

106 accomplishments after 16 years of existence, including how the program contributes to improving

107 TC intensity forecasts through basic research and transition of that research into operations. This

108 summary article serves as a “culmination” of IFEX by reviewing the program’s invaluable

Unauthenticated | Downloaded 10/06/21 02:03 PM UTC Accepted for publication in Bulletin of the American Meteorological Society. DOI 10.1175/BAMS-D-20-0174.1. 109 contributions to understanding and predicting TCs. It will conclude with the path forward,

110 introducing the “next generation” of NOAA’s airborne hurricane field program as it evolves and

111 broadens to meet the future needs of the TC forecasting community, and how it will contribute in

112 a new era of HFIP.

113

114 SUMMARY OF IFEX MISSIONS

115 The primary crewed aircraft used by NOAA for hurricane reconnaissance, surveillance,

116 and research, are the two P-3s and the G-IV. IFEX uses both types of aircraft to sample a variety

117 of spatial and temporal scales (R13). The P-3s primarily sample the structure of the TC inner core

118 (within 200 km of the center) during about a 3–4-h period at altitudes typically around 1.5–10 kft

119 (1 kft ≈ 3.3 km), while the G-IV flies at a much higher altitude (41–45 kft) in the near environment

120 (~150–300 km from the center) to the outer environment (>300 km) around the TC.

121 While the NOAA aircraft are flown for dedicated IFEX research missions, IFEX also

122 benefits from data collection from other missions flown for operational NHC and EMC taskings

123 by integrating research opportunities into those operational missions. For this reason, contributions

124 from all NOAA aircraft missions are considered in IFEX-related studies, regardless of whether or

125 not the missions were “dedicated” to research data collection.

126 NHC-tasked P-3 reconnaissance missions primarily focus on providing real-time guidance

127 on the TC’s intensity (10-m maximum sustained wind speed) and minimum sea level pressure

128 (MSLP), center location, and the radial extent of 34-, 50-, and 64-kt winds; information that

129 contributes to the “TCVitals” dataset, which is one of the inputs for initialization of models. Data

130 gathered from those missions are also used subjectively by NHC to adjust their official intensity

131 forecasts. NHC-tasked G-IV synoptic surveillance missions release dropsondes in the environment

Unauthenticated | Downloaded 10/06/21 02:03 PM UTC Accepted for publication in Bulletin of the American Meteorological Society. DOI 10.1175/BAMS-D-20-0174.1. 132 around TCs to help initialize models and are especially targeted at synoptic flow features that have

133 the potential to interact with a TC during its life cycle and impact a storm’s track forecast (Aberson

134 2003, 2010). EMC-tasked missions will be described in the next section.

135 Table 2 lists some of the notable TCs flown by NOAA since R13. NOAA aircraft sampled

136 all U.S. landfalling hurricanes between 2016 and 2020 in addition to other impactful TCs such as

137 Hurricane Joaquin (2015), a poorly forecasted storm that rapidly intensified unexpectedly and took

138 33 lives in the sinking of the El Faro (Berg 2016), and Hurricane Lorenzo (2020), which despite

139 never making landfall had 19 direct fatalities (11 in the sinking of the tug supply vessel Bourbon

140 Rhode, and eight drownings due to rip currents generated on the East Coast of the U.S.; Zelinsky

141 2019).

142 Other TCs were notable for collaborative sampling conducted by IFEX with aircraft from

143 other agencies (see sidebar on “Field Program Collaborations”). IFEX benefits most when it

144 collaborates with coinciding NOAA field experiments and those of other agencies (such as NASA,

145 ONR, and NSF/NCAR), which bring additional airborne and instrument assets into the field. These

146 collaborative opportunities offer additional temporal, spatial, and vertical sampling with

147 instruments that complement and fill observing gaps that exist within the typical NOAA instrument

148 payloads (an example is shown in Fig. 1 where the P-3 and NASA Global Hawk flew collaborative

149 missions in Hurricane Edouard in 2014).

150 Since the beginning of IFEX, there have been a total of 728 NOAA research and

151 operational missions (P-3s and G-IV combined) into 122 TCs and pre-TCs. Of those, 268 sampled

152 in and around tropical storms (TS), 218 missions in Category-1 and -2 hurricanes, and 165

153 missions in major hurricanes (Fig. 2a). The 2020 season made a large contribution to these

154 numbers as it set records for NOAA and 53rd WRS hurricane flight operations — 86 total NOAA

Unauthenticated | Downloaded 10/06/21 02:03 PM UTC Accepted for publication in Bulletin of the American Meteorological Society. DOI 10.1175/BAMS-D-20-0174.1. 155 missions were flown between the P-3s and G-IV (Table 3), and over 220 missions flown between

156 NOAA and 53rd WRS combined.

157 The relative lack of flights into the formative (pre-TCs) and early stages (defined as TS,

158 tropical depression [TD]) in the pre-IFEX years (Table 3) had been emphasized as an observing

159 gap at the start of IFEX. As Table 3 and Fig. 2a show, filling this gap during IFEX has been a

160 partial success as TS flights have represented the largest percentage of missions flown in the life

161 cycle — especially in 2020 where half of the NOAA flights were in the TS stage. Flights into the

162 formative stage, however, have been far fewer with only 30 TD and 47 pre-TC (i.e., pre-genesis)

163 missions in the IFEX period. And since 2015, the percentage of missions flown at this stage

164 decreased from the first 10 years of IFEX (Table 3). This decrease reflects the opportunities that

165 different seasons offer within the aircraft operating range and the tasking requirements for the

166 aircraft. A considerable proportion of flights since 2015 have been dedicated to flying operational

167 taskings in TCs threatening landfall, and many of those were already at hurricane strength by the

168 first mission.

169 Figure 2b shows the rates of intensity change experienced by TCs 24 h after a given P-3 or

170 G-IV mission, if the storm persists. Only a slight majority of flights were flown in strengthening

171 TCs (>5 kt in 24 h; 241 flights), with a nearly equal amount of flights (226) flown in TCs that

172 subsequently weakened (< -5 kt in 24 h) or remained steady state (-5 to 5 kt in 24 hr). As RI is a

173 priority for IFEX, it is notable that 63 missions were flown in TCs that subsequently rapidly

174 intensified (≥ 30 kt in 24 h), most of which were in TSs and Category 1 hurricanes (Fig. 2a). Also

175 of note are the 57 missions into rapidly weakening (≤ -30 kt in 24 h) events, many of those

176 weakening due to landfall.

177

Unauthenticated | Downloaded 10/06/21 02:03 PM UTC Accepted for publication in Bulletin of the American Meteorological Society. DOI 10.1175/BAMS-D-20-0174.1. 178 IFEX GOAL 1: COLLECT OBSERVATIONS THAT SPAN THE TC LIFE CYCLE IN A

179 VARIETY OF ENVIRONMENTS FOR MODEL INITIALIZATION AND EVALUATION

180 Model initialization

181 IFEX data have been used in three ways in model initialization to improve TC forecasts:

182 1) by assimilation into operational numerical forecast models; 2) through research focused on

183 improving how these data are assimilated into those models; and 3) design of new observing

184 strategies to optimize collection of those data and model impacts.

185 Data assimilation advances in operational HWRF

186 EMC assimilates a suite of aircraft reconnaissance data from the NOAA aircraft and the

187 53rd WRS into its models3. HWRF in particular assimilates all available flight-level, dropsonde,

188 and surface wind speed observations from the Stepped Frequency Microwave Radiometer

189 (SFMR). In addition, the P-3s are tasked by EMC and NHC to provide data from the tail Doppler

190 radar (TDR), an instrument unique to the NOAA aircraft that retrieves 3D Doppler velocity and

191 reflectivity in precipitation. Taskings are governed through close coordination between EMC,

192 NHC, and HRD for potential landfall threats to the U.S.

193 The groundwork for P-3 TDR missions was established around 2010 when advancements

194 in the assimilation of TDR Doppler velocity data using the experimental Penn State University

195 (PSU) data assimilation system resulted in lower track and intensity errors than those from

3 While the global models (e.g., GFS and ECMWF) assimilate some reconnaissance data, the focus

here will be on the impact of reconnaissance data in HWRF, which accepts nearly the complete

suite of real-time reconnaissance data.

Unauthenticated | Downloaded 10/06/21 02:03 PM UTC Accepted for publication in Bulletin of the American Meteorological Society. DOI 10.1175/BAMS-D-20-0174.1. 196 operational NWP at the time (Zhang et al. 2011; Weng and Zhang 2012). These results were later

197 expanded and confirmed using HRD’s Hurricane Experimental Data Assimilation System

198 (HEDAS; Aksoy et al. 2012, 2013; Aberson et al. 2015). Given these successes, real-time

199 transmission and assimilation of TDR data became an operational priority. The real-time data were

200 first assimilated into the operational HWRF during Tropical Storm Karen (2013), where it

201 substantially reduced positive biases in intensity forecasts (Tong et al. 2014).

202 Despite these and other studies (e.g., Aberson et al. 2015; Weng and Zhang 2016) that

203 showed benefits from assimilating the full suite of reconnaissance data, impacts were somewhat

204 mixed in early versions of the operational HWRF (Tong et al. 2018). Though the data did improve

205 forecasts for TSs, they degraded hurricane forecasts due to inadequacies in the HWRF data

206 assimilation system and large biases in the model physics.

207 In response to these inadequacies, since 2013 HWRF has made notable advances in both

208 model physics (Zhang et al. 2015, 2017b, 2018a; Wang et al. 2018) and data assimilation (Pu et

209 al. 2016; Lu et. al 2017; Winterbottom et al. 2018; Tong et al. 2018; Zhang et al. 2018; Zhang et

210 al. 2020), enabling expanded use of reconnaissance data and resulting in a noticeable improvement

211 in the track (Fig. 3a) and intensity (Fig. 3b) forecast skill of HWRF. With system improvements

212 HWRF now makes better use of available data as it assimilates all NOAA and 53rd WRS

213 reconnaissance data transmitted in real time. The result is that, between 2015 and 2019, HWRF

214 was the most accurate TC intensity model in the Atlantic for the 48-h lead time that NHC uses for

215 watch issuances (Cangialosi et al. 2020).

216 Benefits from improvements in model physics and data assimilation were further realized

217 when EMC assessed the impact of reconnaissance data in a 2019 version of HWRF for all high-

218 impact TCs in the Atlantic basin from 2016–2018. They found that including reconnaissance data

Unauthenticated | Downloaded 10/06/21 02:03 PM UTC Accepted for publication in Bulletin of the American Meteorological Society. DOI 10.1175/BAMS-D-20-0174.1. 219 improves the intensity forecast by 10–15% through the 72-h lead time (Fig. 4). This represents a

220 substantial improvement considering that this sample is dominated by many forecast cycles with

221 major hurricanes, for which the impact of reconnaissance was negative in Tong et al. (2018).

222 Recent testing suggests that the positive impact of reconnaissance data in HWRF has further

223 increased since 2019 (personal correspondence, HWRF Implementation Team), and continued

224 advancements are expected as data assimilation and model physics further improve.

225 Data assimilation advancements through research and development

226 Developed at HRD as a unique data assimilation research tool, HEDAS produces high-

227 resolution analyses for storms when aircraft data are available, creating a synthesis of aircraft-,

228 ground-, and satellite-based observations. TC analyses produced using HEDAS can provide input

229 on how to improve the use of existing IFEX observations for model initialization, assess the value

230 of new instrumentation, explore advanced data assimilation techniques, and provide a bridge

231 between research and operational applications. For example, HEDAS demonstrated the

232 importance of accounting for the dropsonde location as the instrument descends (Aberson et al.

233 2017) — a technique that has been transitioned into the operational HWRF.

234 Additional HEDAS developments have been designed to better represent the inner-core

235 vortex structure and evolution. Some examples include: assimilating observations in a storm-

236 relative framework (Aksoy 2013), increasing cycling frequency, varying localization radii (i.e.,

237 distance at which observations are allowed to influence the analysis), testing of new data

238 assimilation methods (Steward et al. 2017, 2018), improving data quality control, and better

239 identifying “outlier” observations.

240

241

Unauthenticated | Downloaded 10/06/21 02:03 PM UTC Accepted for publication in Bulletin of the American Meteorological Society. DOI 10.1175/BAMS-D-20-0174.1. 242 Designing new observing strategies

243 Advances in modeling and data assimilation create opportunities to reassess the use of

244 existing aircraft platforms and distribution of expendables such as dropsondes (expendable

245 instrument platforms deployed from the aircraft that measure pressure, temperature, humidity, and

246 wind as they fall to the ocean). One method for reassessment is an observing system experiment

247 (OSE), where assimilation of existing data can be tested and refined to optimize model analyses

248 and provide insight into how to better integrate the information they provide into an operational

249 model framework. For example, with the recent capability to transmit full-resolution (~1000

250 vertical levels) dropsonde data that contain accurate position information compared to the legacy

251 formatting (only ~20 mandatory and significant pressure levels), OSEs with dropsondes provided

252 preliminary guidance for determining how many and which vertical levels should be assimilated

253 to maximize the impact of these data on HWRF analyses and subsequent forecasts (Sellwood et

254 al. 2020). Another OSE demonstrated that dropsondes deployed at altitudes higher than the P-3

255 (e.g., the GH) improved track and intensity forecasts for Hurricane Edouard (Christophersen et al.

256 2017).

257 Meanwhile, observing system simulation experiments (OSSEs) can demonstrate the

258 potential impacts of hypothetical observations and help optimize sampling strategies. For example,

259 the potential benefits of TDR data on assimilation were first demonstrated in an OSSE by Aksoy

260 et al. (2012). In 2018, an OSSE also motivated a change in the sampling strategies of G-IV synoptic

261 surveillance missions (Ryan et al. 2018). Traditionally, these missions focused on the large-scale

262 environment around TCs for improved performance in track forecasts. The OSSE found that G-

263 IV dropsondes had the largest positive impact on track forecasts when released in the transition

264 region between the inner TC vortex and the near-storm environment (typically ~150 km from the

Unauthenticated | Downloaded 10/06/21 02:03 PM UTC Accepted for publication in Bulletin of the American Meteorological Society. DOI 10.1175/BAMS-D-20-0174.1. 265 center, though storm-size dependent). As a result of this study, NHC implemented an inner

266 circumnavigation (typically ~150 km from the center) of dropsonde releases first during G-IV

267 synoptic surveillance missions around Hurricane Florence (2018), and it has since become

268 standard in surveillance flight tracks.

269

270 Model evaluation and development

271 Observations collected during IFEX provide the means for evaluating models beyond the

272 traditional track and intensity metrics by detailed comparison of observed and modeled TC

273 structures (Hazelton et al. 2018, 2020). For example, an evaluation using near-real-time TDR data

274 has demonstrated the ability of a high-resolution model (e.g., HAFS in Fig. 5) to capture structural

275 features (e.g., of the radius of maximum wind [RMW], eye, eyewall, and precipitation) of the inner

276 core of Hurricane Laura (2020). The availability of TDR data in near-real time has already proven

277 to be an invaluable tool for model evaluation, and future efforts will focus on transitioning real-

278 time evaluation products that will allow forecasters the ability to compare models against

279 observations while storms are ongoing.

280 Model evaluation using observations has also led to improved parameterization schemes,

281 and subsequently forecasts, through the development of a framework to provide and test

282 observation-based estimates of physical parameters (Zhang et al. 2012). This framework was

283 applied to HWRF’s PBL parameterization scheme, in which the vertical eddy diffusivity was

284 modified based on in-situ observations of turbulent fluxes and vertical gradients of wind velocities

285 in the TC PBL collected before (pre-2005) and during IFEX (Gopalakrishnan et al. 2013; Zhang

286 et al. 2015). Likewise, observation-based improvements were also made to the parameterization

287 of horizontal turbulent mixing in HWRF (Zhang and Montgomery 2012). These modifications

Unauthenticated | Downloaded 10/06/21 02:03 PM UTC Accepted for publication in Bulletin of the American Meteorological Society. DOI 10.1175/BAMS-D-20-0174.1. 288 improved vortex-scale, convective-scale, and boundary-layer processes in PBL parameterizations

289 that produced more realistic model TC structures consistent with observations (Zhang et al. 2015,

290 2017b; Bu et al. 2017; Zhang and Rogers 2019), while also reducing HWRF intensity biases

291 (Zhang et al. 2015; Zhang and Marks 2015; Zhang et al. 2018a).

292 Finally, the diabatic heating associated with the formation, growth, and dissipation of

293 hydrometeors is one of the primary driving forces of TC intensity changes, but most microphysics

294 schemes (which represent the collective effects of these processes) are not built for TCs. Data from

295 cloud and precipitation probes on the P-3s obtained above the freezing level (5–7 km) were used

296 to evaluate the ice particle size distribution (PSD) in microphysics schemes and showed that

297 gamma functions more accurately reproduce observations than exponential functions (Leighton et

298 al. 2020). Work is underway to understand how a more accurate parameterization of PSD in

299 numerical models could lead to better intensity and rainfall forecasts.

300

301 IFEX GOAL 2: DEVELOP AND REFINE MEASUREMENT STRATEGIES AND

302 TECHNOLOGIES THAT PROVIDE IMPROVED REAL-TIME MONITORING OF TC

303 INTENSITY, STRUCTURE, AND ENVIRONMENT

304 Near-real-time Doppler Radar Analyses

305 Automation of TDR data quality control, in conjunction with an established wind synthesis

306 method (Gamache 1997; Reasor et al. 2009), has made possible the real-time transmission to

307 NCEP of quality-controlled Doppler velocity data and the instantaneous archival of 3D analyses

308 of TC wind and reflectivity structure. While the routine transmission of coarse-resolution analyses

309 began in 2010, the benefits of these research-quality TDR analysis datasets have been realized in

310 three important new ways within the past five years.

Unauthenticated | Downloaded 10/06/21 02:03 PM UTC Accepted for publication in Bulletin of the American Meteorological Society. DOI 10.1175/BAMS-D-20-0174.1. 311 Increases in computational processing power and satellite communication bandwidth on

312 the NOAA aircraft have led to real-time transmission of high-resolution (2-km horizontal spacing),

313 3D analysis datasets. This includes transmission of TDR data during G-IV surveillance missions

314 since 2013. Through continued improvements in the automated quality-control process, the

315 coverage of analyzed meteorological wind and reflectivity has increased, and non-meteorological

316 artifacts are mostly eliminated in most TC scenarios.

317 In addition to model evaluation (Fig. 5), another benefit of near-real-time TDR analysis is

318 in providing enhanced situational awareness for scientists while missions are ongoing. After each

319 flight segment through (or around) the TC center, publicly available graphics are automatically

320 created highlighting structural aspects of the TC, like radius-height azimuthal-mean structure, flow

321 and precipitation asymmetry, and vortex center tilt with height (examples of real-time TDR

322 analyses for Hurricanes Laura and Teddy in 2020 are shown in Figs. 6 and 7). These real-time

323 graphics can guide modifications to the aircraft sampling strategy that minimize spatial gaps in

324 radar coverage of the circulation and help to achieve mission objectives.

325 Beginning with the 2018 Atlantic hurricane season, the TDR analysis data have also been

326 available operationally for viewing within the AWIPS-II display system (example in Fig. 8) that

327 is used by NWS to synthesize and view observational and model data. Within approximately an

328 hour of a P-3 pass of a TC center, NHC now has access to the full 3D wind structure of the eyewall

329 region and beyond. Although the analyses are currently limited to structure above 0.5-km altitude,

330 and thus cannot provide direct estimates of surface wind, hurricane specialists have cited the TDR

331 data in official NHC forecast discussions of Hurricane Humberto and Tropical Storm Jerry in 2019

332 in assessments of structure (e.g., vortex center tilt with height) and to provide context for flight-

333 level and SFMR measurements that are available only along the flight track. To further meet

Unauthenticated | Downloaded 10/06/21 02:03 PM UTC Accepted for publication in Bulletin of the American Meteorological Society. DOI 10.1175/BAMS-D-20-0174.1. 334 NHC’s needs, one research application in development is the use of TDR to estimate surface wind

335 speed and to provide enhanced guidance on wind-speed radii estimation by taking advantage of

336 the combined coverage of P-3 and G-IV flight patterns.

337

338 Doppler Wind Lidar

339 As the utility of TDR analyses becomes widely recognized, it is important to fill the

340 remaining data-sparse regions temporally and spatially in the TC wind field. These regions include

341 the lowest levels of the PBL and precipitation-free areas that cannot be adequately sampled by the

342 TDR, such as the upshear portion of highly sheared TCs and moats between the eyewall and outer

343 rainbands or secondary eyewalls. A scanning coherent 1.6-µm airborne Doppler wind Lidar

344 (DWL) was flown on the P-3 for portions of the 2015–2018 seasons into 8 TCs (Table 2). The

345 DWL is capable of collecting wind measurements above and below the aircraft when there is a

346 sufficient concentration of aerosols and no optically thick clouds. A comparison of the P-3 DWL

347 observations with other airborne wind observing systems validated the wind retrievals where

348 observations overlapped (Bucci et al. 2018). For example, collocated dropsonde and DWL wind

349 profiles showed a greater than 0.95 correlation for speed and 0.91 for direction. When combined

350 with other observing systems, DWL augmented the observational coverage of the TC wind field

351 (Bucci et al. 2018; Zhang et al. 2018b). Future work will focus on using DWL data to understand

352 the dynamics and physical processes in the sparsely observed regions listed above, and

353 assimilating it in models.

354

355

356

Unauthenticated | Downloaded 10/06/21 02:03 PM UTC Accepted for publication in Bulletin of the American Meteorological Society. DOI 10.1175/BAMS-D-20-0174.1. 357 Dropsondes Equipped with Infrared Sensors for Measuring Sea Surface Temperature

358 Surface fluxes of heat and moisture at the air-sea interface are tied to TC intensity as they are

359 the main energy sources for the storm (e.g., Emanuel 1995; Shay et al. 2000; Ming and Zhang

360 2016). However, direct surface flux measurements in TCs are very difficult to obtain and, so far,

361 have been limited to wind speeds below hurricane force. Alternatively, air-sea fluxes can be

362 estimated using measurements of SST, 10-m wind speed, air temperature and humidity, and

363 exchange coefficients through a bulk parameterization method. Either surface buoys or a

364 combination of dropsondes with ocean expendables and/or other oceanographic instruments (e.g.,

365 underwater gliders) can provide these bulk flux estimates through collocated measurements of the

366 above parameters. A standard dropsonde was modified to integrate an infrared (IR) sensor for SST

367 measurements to collect collocated SST-atmospheric measurements. SST measured with this new

368 type of dropsonde, referred to as an ‘IRsonde,’ showed good agreement with SSTs measured using

369 ocean buoys in regions with weak rainfall (Zhang et al. 2017a).

370

371 Small Uncrewed Aircraft Systems (sUAS)

372 In 2014, the first use of air-deployed sUAS was in major Hurricane Edouard (Cione et al.

373 2016). Two successful deployments of the Coyote sUAS platform sampled the eye/eyewall and

374 boundary layer inflow regions of the TC. These ground-breaking missions collected data in a

375 critically important, yet sparsely-sampled area of the TC where crewed aircraft are unable to fly.

376 More recently, analyses of observations from seven Coyote flights into Hurricanes Maria (2017)

377 and Michael (2018) were also conducted (Cione et al. 2020). A total of nine sUAS missions were

378 flown in three major hurricanes. In all cases, high-resolution PBL measurements of pressure,

379 temperature, relative humidity, wind, and estimates of SST were recorded and will be valuable for

Unauthenticated | Downloaded 10/06/21 02:03 PM UTC Accepted for publication in Bulletin of the American Meteorological Society. DOI 10.1175/BAMS-D-20-0174.1. 380 shedding important insights into air-sea exchange processes, PBL turbulent structures, and

381 optimizing physical parameterizations in coupled ocean-atmosphere TC modeling systems.

382

383 Stepped-Frequency Microwave Radiometer (SFMR)

384 The SFMRs on the P-3 and 53rd WRS aircraft obtain surface wind speed estimates at nadir

385 below the aircraft when flying straight and level. The SFMR relies on comparisons to dropsondes

386 to determine the relationship between the raw observations and wind speed (Klotz and Uhlhorn

387 2014). When dropsondes are released in the eyewall, they are carried by the storm circulation away

388 from where the SFMR collects observations, making direct comparisons between the SFMR and

389 dropsondes difficult. Therefore, a flight pattern was designed to overfly the splash location of

390 dropsondes released in the eyewall to obtain direct spatial collocations of SFMR and dropsondes.

391 These observations will be used to improve the operational SFMR algorithm at high wind speeds

392 in the eyewall of major hurricanes.

393 When flying through a TC it is also common for the aircraft to exceed the SFMR’s

394 thresholds on the aircraft pitch and roll angles, at which point the SFMR does not report useable

395 data. This occurs as the aircraft descends/ascends on a pressure surface into/out of the eyewall of

396 strong hurricanes, a critical location to obtain surface wind-speed estimates. Data have been

397 collected to develop a correction to the SFMR that accounts for pitch and roll angle variations

398 (Holbach et al. 2018). This experiment collected data at constant aircraft roll angles in regions of

399 constant surface conditions over a broad range of surface wind speeds. A second SFMR was also

400 mounted on the P-3 aircraft oriented perpendicular to the operational unit to study the impacts of

401 pitch angle on the SFMR measurements.

402

Unauthenticated | Downloaded 10/06/21 02:03 PM UTC Accepted for publication in Bulletin of the American Meteorological Society. DOI 10.1175/BAMS-D-20-0174.1. 403 IFEX GOAL 3: IMPROVE THE UNDERSTANDING OF PHYSICAL PROCESSES

404 IMPORTANT IN INTENSITY CHANGE FOR A TC AT ALL STAGES OF ITS LIFE

405 CYCLE

406 With several decades worth of TC missions flown by NOAA aircraft, there is a tremendous

407 database of TDR, dropsonde, flight-level, SFMR, and ocean buoy observations that has been

408 accumulated. Such a database allows for analyses of individual cases to provide in-depth

409 exploration of physical processes and their evolution in time, and composite studies to identify

410 statistically robust differences in vortex- and convective-scale structures of TCs encountering a

411 variety of environmental conditions and structure and intensity changes. Examples of the research

412 since R13 are summarized below.

413

414 Characterizing TC Inner-core Structure and Intensity Change

415 A composite analysis of TDR data for intensifying vs. non-intensifying TCs found that

416 intensifying TCs were associated with a ring-like vorticity structure inside the RMW, lower

417 vorticity in the outer core, and a stronger and deeper radial inflow layer (into the center) compared

418 to steady-state TCs (Rogers et al. 2013). Similar relationships were found for composites of flight-

419 level data, along with higher inner- and outer-core moisture for intensifying TCs (Martinez et al.

420 2017). When composited for TCs experiencing vertical wind shear, TDR data also confirmed

421 principal features of the wind shear-induced TC asymmetry documented in prior numerical and

422 observational studies; e.g., circulation center tilted downshear in the vertical, downshear-right

423 (DSR) convective initiation, and a subsequent downshear-left (DSL) precipitation maximum

424 (Reasor et al. 2013). Case-study analyses were conducted for the record-breaking rapidly

425 intensifying East Pacific Hurricane Patricia (Rogers et al. 2017), including a novel analysis of TDR

Unauthenticated | Downloaded 10/06/21 02:03 PM UTC Accepted for publication in Bulletin of the American Meteorological Society. DOI 10.1175/BAMS-D-20-0174.1. 426 and dropsonde data (Martinez et al. 2019) that found that Patricia’s RI was related to the

427 distribution of diabatic heating and mixing across the eye-eyewall interface.

428

429 TC Intensity Change in Vertical Wind Shear

430 The effect of moderate values of vertical wind shear (~5–10 m s-1 between 850 and 200

431 hPa) on TC structure and intensity is a source of considerable forecast uncertainty (Bhatia and

432 Nolan 2013; Zhang and Tao 2013) and has been recognized by NHC as a high priority for research

433 (Sumwalt et al. 2017). Recent IFEX research addressed this priority, focusing on characterizing

434 the multi-scale structures associated with intensity change in shear, with the goal of better

435 understanding the physical processes underlying intensity change in these environments.

436 The emphasis of this work has been on 1) characterizing the structure and distribution of

437 precipitation (and by extension latent heating) in a shear-relative and RMW-relative framework

438 that are associated with TC intensification in shear; and 2) identifying the multiscale structures

439 that modulate precipitation. For example, analyses of P-3 data from individual cases (example

440 from Edouard in Fig. 9a, b from Rogers et al. 2016; Susca-Lopata et al. 2015) and composites

441 (Rogers et al. 2013, 2015; Wadler et al. 2018a) have shown that, for intensifying TCs, the preferred

442 radial location of the strongest convective cores, i.e. convective bursts, is inside the low-level

443 RMW, with a more symmetric coverage of precipitation around the TC center (Alvey et al. 2015;

444 Rogers et al. 2016).

445 Associated with the increase in precipitation symmetry is the preference for intensifying

446 TCs to have deep convection in the upshear-left (USL) quadrant (Fig. 9a, b; Rogers et al. 2016;

447 also observed in Hurricane Michael, Wadler et al. 2020). Composited TDR data showed that

448 convective bursts in the USL quadrant of intensifying TCs had updrafts that peaked with a higher

Unauthenticated | Downloaded 10/06/21 02:03 PM UTC Accepted for publication in Bulletin of the American Meteorological Society. DOI 10.1175/BAMS-D-20-0174.1. 449 magnitude, at a higher altitude, and with higher echo tops, than non-intensifying TCs (Wadler et

450 al. 2018a). The importance of deep convection extending into the USL quadrant was also

451 confirmed in numerical modeling studies (Chen and Gopalakrishnan 2015; Leighton et al. 2018;

452 Rios-Berrios et al. 2018), and in an investigation of vertical mixing in the PBL (Zhang and Rogers

453 2019).

454 Shear-relative distributions of mid-level relative humidity, low-level equivalent potential

455 temperature (θe), SST, and vortex tilt modulate the azimuthal distribution of precipitation and

456 latent heating around the TC center. Dropsonde data (Zawislak et al. 2016; Nguyen et al. 2017)

457 and subsequent modeling studies (e.g., Alvey et al. 2020) showed that a more-humid mid-level

458 upshear environment was an important factor in sustaining precipitation coverage upshear,

459 increasing precipitation symmetry and favoring intensification (Fig. 10). For example, in Edouard

460 an increase in the mid- to upper-level humidity was observed in the USL quadrant as the storm

461 intensified between 12 and 15 September (Fig. 9c, d).

462 The approach of examining factors responsible for modulating precipitation structure and

463 distribution in a sheared environment was also applied to weaker TCs, including tropical

464 depressions, where circulation centers are often misaligned and dry air plays in important role in

465 suppressing convection. Rogers et al. (2020) combined P-3 TDR and deep-layer GH dropsonde

466 data to identify a multiscale cooperative interaction between deep (20 dBZ echo tops > 10 km) and

467 moderate (echo tops between 6–10 km) convection on the downshear side of a TD that became

468 Hurricane Hermine (2016). The local environment had become moistened and stabilized through

469 successive cycles of convective and stratiform precipitation. The interaction between deep and

470 moderate convection lowered the altitude and increased the magnitude of the peak vertical mass

Unauthenticated | Downloaded 10/06/21 02:03 PM UTC Accepted for publication in Bulletin of the American Meteorological Society. DOI 10.1175/BAMS-D-20-0174.1. 471 flux (Fig. 11), fostering a persistently aligned vortex that marked the onset of intensification to

472 hurricane strength.

473

474 Boundary Layer Processes and Air-sea Interaction

475 In addition to modulating the azimuthal distribution of precipitation (diabatic heating),

476 vertical shear can also govern the low-level θe (temperature and moisture) distribution through

477 recovery processes of the PBL. Composite dropsonde analyses of PBL structure were used to show

478 an energy cycling process (Molinari et al. 2013; Zhang et al. 2013) where convection is triggered

479 DSR in the presence a maximum in θe, convective downdrafts bring down cool and dry air to the

480 PBL and lower θe in the DSL and USL quadrants, and air parcels re-acquire high θe from air-sea

481 fluxes as they rotate through the USL and upshear-right (USR) quadrants. Stronger surface fluxes

482 upshear were a key feature distinguishing intensifying from non-intensifying TCs in a composite

483 analysis (Nguyen et al. 2019) and case studies (Jaimes et al. 2015; Zhang et al. 2017a; Wadler et

484 al. 2018b; Zhang and Rogers 2019).

485 Composites of buoy data also showed larger surface fluxes in intensifying storms than in

486 weakening ones due to air-sea humidity differences (Cione et al. 2013). Higher SST favors the

487 increase of surface fluxes, which in turn recover downdraft-induced low-θe air in TCs undergoing

488 RI (Zhang et al. 2017a; Wadler et al. 2020). These impacts of boundary layer recovery on surface

489 fluxes and RI onset are important in statistical-dynamical prediction schemes and numerical

490 models (Kaplan et al. 2015; Zhang et al. 2017a).

491 Kinematic structural differences of the PBL between RI and non-RI TCs have also been

492 identified in IFEX dropsonde data and confirmed by numerical models. Intensifying TCs have a

493 deeper and stronger supergradient PBL jet (i.e., the part of the horizontal wind that exceeds

Unauthenticated | Downloaded 10/06/21 02:03 PM UTC Accepted for publication in Bulletin of the American Meteorological Society. DOI 10.1175/BAMS-D-20-0174.1. 494 gradient wind balance), higher inertial stability (i.e., the resistance of the storm vortex structure to

495 change from other forces), and stronger turbulent mixing in the eyewall region than non-

496 intensifying cases (Montgomery et al. 2014; Rogers et al. 2016; Zhang et al. 2017b; Ahern et al.

497 2019). Composites of HWRF forecasts have shown that intensifying TCs have stronger PBL

498 inflow in the eyewall region, with stronger PBL convergence closer to the center, than non-

499 intensifying TCs (Zhang et al. 2017b) — consistent with the notion that forced convection and

500 strong updrafts are preferentially observed inside the RMW in intensifying TCs (Rogers et al.

501 2013). In a sheared storm, the azimuthal distribution of PBL inflow and convergence also affects

502 the extent of asymmetry of shallow and moderately deep convection, with inflow being more

503 symmetric for TCs undergoing RI (Zhang and Rogers 2019). The stronger inflow also acts to

504 advect more angular momentum toward the center and spin up the primary circulation in the PBL,

505 favoring RI (Smith et al. 2017; Zhang and Rogers 2019).

506 The transition of the TC boundary-layer structure that occurs at and immediately after

507 landfall was investigated in Hurricane Irene (Alford et al. 2020). Irene’s boundary layer structure

508 evolved differently in the eyewall and rainband regions, which may have implications for where

509 the strongest overland wind occurs in a landfalling TC.

510

511 Secondary Eyewall Formation and Eyewall Replacement Cycles

512 Another period of the TC life cycle is the mature stage, when secondary eyewall formation

513 (SEF), typically followed by an eyewall replacement cycle (ERC), can occur. TDR observations

514 were used to hypothesize that SEF can result from PBL convergence driven by a mesoscale

515 descending inflow jet originating from a stratiform precipitation region (Didlake et al. 2018), while

516 dropsonde observations have shown that SEF can occur in a region characterized by a local

Unauthenticated | Downloaded 10/06/21 02:03 PM UTC Accepted for publication in Bulletin of the American Meteorological Society. DOI 10.1175/BAMS-D-20-0174.1. 517 maximum in the PBL inflow associated with agradient forcing (i.e., a force imbalance between the

518 inward-directed pressure gradient force and outward-directed centrifugal and Coriolis forces)

519 (Abarca et al. 2016). Other studies have hypothesized that changes to the azimuthal-mean flow

520 brought on by the interaction between wave-like perturbations of vorticity, referred to as vortex

521 Rossby waves, with the mean flow can lead to SEF (Dougherty et al. 2018; Guimond et al. 2020).

522 TDR observations of two consecutive periods of SEF in Hurricane Irma (2017) showed that

523 multiple processes produce SEFs (Fischer et al. 2020). The first SEF event (Fig. 12) was driven

524 by both a mesoscale descending inflow jet and vortex Rossby waves interacting with the mean

525 vortex structure, while the second SEF occurred in a region of enhanced convergence that arose

526 from processes consistent with a force imbalance in the PBL as well as continued vortex Rossby

527 wave activity.

528 While TCs typically weaken during ERCs, recent work has found that in some cases the

529 TC will fail to weaken as the ERC completes (Dougherty et al. 2018), or even rapidly intensify

530 (Fischer et al. 2020). The variability of the temporal scales of ERCs has also been noted, as the

531 duration of an ERC can range from less than 12 h to multiple days (Molinari et al. 2019; Fischer

532 et al. 2020). Furthermore, the nature of TC convective asymmetries during ERCs has been assessed

533 (Didlake et al. 2017; Dougherty et al. 2018; Didlake et al. 2018; Fischer et al. 2020). For example,

534 Didlake et al. (2017) discovered that updrafts in the outer eyewall tend to occur downwind of

535 updrafts in the inner eyewall.

536

537 LOOKING TO THE FUTURE

538 During the past 16 years, IFEX has contributed to substantial advancements in our ability

539 to observe, understand, and predict TC structure and intensity. More effective assimilation of

Unauthenticated | Downloaded 10/06/21 02:03 PM UTC Accepted for publication in Bulletin of the American Meteorological Society. DOI 10.1175/BAMS-D-20-0174.1. 540 airborne data into numerical models, along with the use of these data to improve the representation

541 of physical processes in these models, have led to demonstrable improvements in intensity forecast

542 guidance. New observing technologies and the improved ability to quality control, analyze, and

543 visualize aircraft observations have significantly enhanced the ability to assess TC structure and

544 intensity in real time. Finally, case-study and composite-based research studies have led to new

545 insights into TC structure and intensity change processes, laying the groundwork for forecast

546 improvements now and in the future.

547 These advancements in our understanding and prediction of TCs have been made possible

548 through partnerships between the operational requirements and research opportunities within and

549 outside NOAA that have formed the foundation of IFEX. As these partnerships continue to

550 strengthen, there is an even greater emphasis on using airborne observations to address the broad

551 array of forecast challenges facing the operational community. For example, many of the

552 significant landfalling TCs of the past several years (Table 1) formed and intensified, some rapidly,

553 within a day or two of landfall. These “short-fuse” storms offer emergency managers and the public

554 little time for preparation. As highlighted in Table 1, they bring dangerous hazards — notably,

555 storm surge inundation and extreme rainfall that are responsible for the majority loss of life in

556 storms — beyond just high winds and tornadoes. While there has been much progress during the

557 IFEX period, forecast guidance continues to be challenged to predict these hazards better, and the

558 path to solving these challenges continue to be through improved skill in resolving TC

559 characteristics themselves.

560 The partnership between IFEX and HFIP will continue to strengthen as each progresses

561 into its “next generation.” During the next five years, HFIP will continue to seek progress toward

562 improved TC prediction, but with a specific focus on more skillful forecasts of TC track, intensity,

Unauthenticated | Downloaded 10/06/21 02:03 PM UTC Accepted for publication in Bulletin of the American Meteorological Society. DOI 10.1175/BAMS-D-20-0174.1. 563 and structure out to 7 days (previously 5 days), more accurate genesis and RI forecasts, and

564 improved probabilistic forecast guidance for all hazards, including better communication to the

565 public of those risks.

566 Guided by these goals, IFEX will also evolve into its own “next generation” as it continues

567 its support of HFIP and operations at NHC and EMC. This evolution will include a continued

568 prioritization on flying TCs earlier in their life cycles, such as the pre-formation and depression

569 stages where the observational record is lacking compared to the mature stage (Table 3, Fig. 2),

570 and in TCs in marginal environments for intensification, where intensity change is less predictable

571 (Bhatia and Nolan 2013). Observations from these flights will continue to fill gaps in our

572 understanding of the interactions between a TC’s environment and inner core, and the physical

573 processes that drive genesis and intensification.

574 Likewise, at its core, IFEX will maintain the approach that has worked so well: the use of

575 aircraft observations to improve the understanding of physical processes and their use in advancing

576 data assimilation, numerical modeling, and OSE/OSSE systems that contribute to better TC

577 forecasting. For example, observations will play a collaborative role in the development of HAFS

578 — NOAA’s next generation hurricane model and data assimilation package that will offer an

579 operational analysis and 7-day guidance for TC genesis, track, intensity (including RI), storm size,

580 rainfall, and tornadoes. Observations will be critical in the production of a HAFS operational

581 analysis as all available airborne and satellite data will be assimilated using new data assimilation

582 advancements developed for HAFS. In turn, HAFS can be a tool for advancing knowledge of TC

583 processes and theory through a combination of observations and model analyses. As for

584 OSE/OSSEs, they will further evolve and determine the potential impact of new instrumentation

585 and targeted observing strategies in model forecasts and resource utilization.

Unauthenticated | Downloaded 10/06/21 02:03 PM UTC Accepted for publication in Bulletin of the American Meteorological Society. DOI 10.1175/BAMS-D-20-0174.1. 586 New observing strategies and instruments will continue to play a key role in sampling

587 relatively under-sampled storm processes and structures (e.g., PBL, microphysics, and turbulence).

588 For example, remote sensors can be adopted on the P-3s and G-IV (or the Gulfstream G550 that

589 NOAA is acquiring) to provide a more complete 3D picture of the TC structure, and to understand

590 what modulates storm size; e.g., extent of the 34-, 50-, and 64-kt surface wind-speed radii that get

591 integrated into storm-surge models like the statistical Probabilistic Hurricane Storm Surge (P-

592 Surge) model (Taylor and Glahn 2008). sUAS platforms released from the P-3s will fly longer (up

593 to 4 hr), providing invaluable situational awareness and real-time monitoring, and carry

594 instruments that better characterize and model the typically under-sampled processes in the PBL.

595 The Wide-Swath Radar Altimeter (WSRA; PopStefanija et al. 2020), an already proven

596 technology on the P-3, will transmit real-time observations of the ocean surface wave field, which

597 help to understand the impacts of waves on PBL processes and coastal impacts, and improve

598 situational awareness for marine forecasters during TCs. Data from cloud microphysics probes can

599 be used to improve physics parameterizations which drive model representation of precipitation

600 structure and, subsequently, rainfall forecasts. And coordination with ground-based landfall

601 observing teams and their array of instrumentation, such as mobile radars, profilers, towers, and

602 ground weather stations, can aid with studying many aspects of landfalling TCs, such as the

603 documentation of convective bands as they move inland (potentially causing tornadoes), as well

604 as changes in the wind field as the TC comes ashore.

605 Observing strategies will also focus on validation of satellite measurements. Since 2018,

606 IFEX has collaborated with the NOAA Joint Polar Satellite System (JPSS) program to release

607 dropsondes from the G-IV for validating satellite retrievals of temperature and moisture soundings

608 from the NOAA Unique Combined Atmospheric Processing System (NUCAPS). The need to

Unauthenticated | Downloaded 10/06/21 02:03 PM UTC Accepted for publication in Bulletin of the American Meteorological Society. DOI 10.1175/BAMS-D-20-0174.1. 609 understand, and improve upon, limitations in satellite observations of TCs will continue in the

610 future as satellites will remain the primary means of observing TCs globally.

611 New and more-frequent in-situ ocean-profiling instruments are also necessary to

612 understand the role of the upper-ocean thermal structure and air-sea interaction processes,

613 especially when they coincide with atmospheric measurements (Fig. 13), and can be assimilated

614 into atmosphere-ocean coupled models. For example, underwater ocean gliders are being deployed

615 in the North Atlantic and Caribbean in a multi-institutional effort that includes AOML’s Physical

616 Oceanography Division. The gliders are underwater autonomous vehicles that provide targeted,

617 real-time ocean profiling observations of temperature and salinity in the path of TCs (Domingues

618 et al. 2019). Their operating locations make them easy targets for coordinating aircraft overflights,

619 as was accomplished for the first time in Dorian (Fig. 13). Gliders not only provide valuable data

620 for process studies, but they have also proven to improve operational TC forecasts (Dong et al.

621 2017), especially when their assimilation into coupled models are complemented by other standard

622 ocean observations from buoys, ships, drifters and floats.

623

624 APHEX: NOAA’s Next Airborne Hurricane Field Program

625 With the above considerations in mind, the focus on “intensity forecasting” at the inception

626 of IFEX is now a narrow scope within a broad expanse of forecast challenges and knowledge gaps

627 that must be addressed at all stages of the TC life cycle, though especially in the genesis and early

628 stages. With the IFEX priorities broadened beyond intensity to include structure and hazards, and

629 with the focus of improving model analyses with observations (prioritized by HFIP through

630 HAFS), the following modifications (highlighted in bold, italics) to the IFEX Goals will become:

631 1) Collect observations that span the TC life cycle in a variety of environments for model

632 initialization and evaluation; 26

Unauthenticated | Downloaded 10/06/21 02:03 PM UTC Accepted for publication in Bulletin of the American Meteorological Society. DOI 10.1175/BAMS-D-20-0174.1. 633 2) Develop and refine measurement strategies and technologies that provide improved

634 real-time analysis of TC intensity, structure, environment, and hazard assessment;

635 3) Improve the understanding of physical processes that affect TC formation, intensity

636 change, structure, and associated hazards

637 To reflect this broader emphasis, the IFEX acronym will be retired in favor of a new acronym:

638 APHEX, Advancing the Prediction of Hurricanes Experiment.

639

640 ACKNOWLEDGEMENTS

641 Data collection for the hurricane field program would not have been possible without the

642 incredible and heroic efforts of all the pilots, flight directors, navigators, engineers, technicians,

643 mechanics, program managers, and leadership at NOAA/OMAO and their Aircraft Operations

644 Center (AOC). We especially want to acknowledge the late Jim McFadden, former Chief of

645 Programs at AOC, whose decades of leadership and dedication paved the way for the success and

646 accomplishments of this program. IFEX also appreciates the courageous and tireless efforts of 53rd

647 Air Force Reserve Weather Reconnaissance Squadron and the data they provide each season, as

648 well as the staff at the unit of the Chief, Aerial Reconnaissance Coordination, All Hurricanes

649 (CARCAH). Michael Brennan, James Franklin, and Ed Rappaport at NHC, and Avichal Mehra

650 and Vijay Tallapragada at EMC, have also been instrumental and supportive partners who guide

651 the priorities of this program, and have allowed IFEX to benefit through piggybacking research on

652 operationally-tasked flights. Many scientists both within and outside of NOAA — too many to

653 fairly convey here — have played integral roles in the development, execution, and success of

654 IFEX. Several funding sources have provided support to this project: NOAA base funds (AOC

655 base-funds for flight hours and AOML base funds for HRD manpower and travel), CIMAS,

Unauthenticated | Downloaded 10/06/21 02:03 PM UTC Accepted for publication in Bulletin of the American Meteorological Society. DOI 10.1175/BAMS-D-20-0174.1. 656 NOAA’s Joint Hurricane Testbed (JHT), HFIP (for flight hours, expendables and travel), and the

657 2018 and 2019 Hurricane Supplementals (for flight hours and expendables). We thank Michael

658 Brennan (NHC), Ricardo Domingues (UM/CIMAS, AOML/PhOD), Ed Zipser, Jeff Halverson,

659 and an anonymous reviewer for their helpful comments that improved this manuscript.

660

661 DATA AVAILABILITY STATEMENT

662 AOML/HRD collects and maintains quality-controlled, research-quality NOAA P-3 and G-IV

663 aircraft data for the hurricane field program, in partnership with OMAO/AOC, and makes that data

664 available on its webpage: https://www.aoml.noaa.gov/data-products/#hurricanedata. AOML/HRD

665 also provides real-time products and model fields from operational and experimental forecast

666 models (e.g., HWRF, Basin-scale HWRF, and HAFS), which is available at

667 https://storm.aoml.noaa.gov/. AOML/HRD is committed to facilitate full and open access to data

668 in a timely manner during and after the annual hurricane field program.

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969 Rogers, R. F., S. Aberson, M. M. Bell, D. J. Cecil, J. D. Doyle, T. B. Kimberlain, J. Morgerman,

970 L. K. Shay, and C. Velden, 2017: Re-Writing the Tropical Record Books: The

971 Extraordinary Intensification of Hurricane Patricia (2015). Bull. Amer. Meteor. Soc., 98,

972 2091–2112, https://doi.org/10.1175/BAMS-D-16-0039.1.

973 Rogers R. F., C. S. Velden, J. A. Zawislak, and J. A. Zhang, 2019: Tropical Cyclones and

974 Hurricanes: Observations. Reference Module in Earth Systems and Environmental

975 Sciences, Elsevier, 25 pp., https://doi.org/10.1016/B978-0-12-409548-9.12065-2.

976

Unauthenticated | Downloaded 10/06/21 02:03 PM UTC Accepted for publication in Bulletin of the American Meteorological Society. DOI 10.1175/BAMS-D-20-0174.1. 977 Rogers, R. F., P. D. Reasor, J. A. Zawislak, and L. T. Nguyen, 2020: Precipitation Processes and

978 Vortex Alignment during the Intensification of a Weak Tropical Cyclone in Moderate

979 Vertical Shear. Mon. Wea. Rev., 148, 1899–1929, https://doi.org/10.1175/MWR-D-19-

980 0315.1.

981 Ryan, K., L. Bucci, J. Delgado, R. Atlas, and S. Murillo, 2018: Impact of Gulfstream-IV

982 Dropsondes on Tropical Cyclone Prediction in a Regional OSSE System. Mon. Wea. Rev.,

983 147(8), 2961–2977, https://doi.org/10.1175/MWR-D-18-0157.1.

984 Sellwood, K. J., J. A. Sippel, and A. Aksoy, 2020: Optimizing Dropsonde Levels for Data

985 Assimilation. 20th Symposium on Meteorological Observation and Instrumentation.

986 Boston MA., Amer. Meteor. Soc. 5.6 [ abstract

987 https://ams.confex.com/ams/2020Annual/meetingapp.cgi/Paper/365847 ]

988 Shay, L. K., G. J. Goni, and P. G. Black, 2000: Effects of a warm oceanic feature on Hurricane

989 Opal. Mon. Wea. Rev., 128, 1366–1383, https://doi.org/10.1175/1520-

990 0493(2000)128<1366:EOAWOF>2.0.CO;2.

991 Smith, R. K., J. A. Zhang, and M. T. Montgomery, 2017: The dynamics of intensification in an

992 HWRF simulation of Hurricane Earl (2010). Q. J. R. Meteorol. Soc., 143, 297–308,

993 https://doi.org/10.1002/qj.2922.

994 Steward, J. L., A. Aksoy, and Z. S. Haddad, 2017: Parallel Direct Solution of the Ensemble Square

995 Root Kalman Filter Equations with Observation Principal Components. J. Atmos. Oceanic

996 Technol., 34, 1867–1884, https://doi.org/10.1175/JTECH-D-16-0140.1.

997 Steward, J. L., J. E. Roman, A. L. Daviña, and A. Aksoy, 2018: Parallel Direct Solution of the

998 Covariance-Localized Ensemble Square Root Kalman Filter Equations with Matrix

999 Functions. Mon. Wea. Rev., 146, 2819–2836, https://doi.org/10.1175/MWR-D-18-0022.1.

Unauthenticated | Downloaded 10/06/21 02:03 PM UTC Accepted for publication in Bulletin of the American Meteorological Society. DOI 10.1175/BAMS-D-20-0174.1. 1000 Stewart, S. R., 2017: National Hurricane Center Tropical Cyclone Report: Hurricane Matthew

1001 (AL142016). National Hurricane Center Rep., 96 pp.,

1002 https://www.nhc.noaa.gov/data/tcr/AL142016_Matthew.pdf.

1003 Stewart, S. R., and R. Berg, 2019: National Hurricane Center Tropical Cyclone Report: Hurricane

1004 Florence (AL062018). National Hurricane Center Rep., 98 pp.,

1005 https://www.nhc.noaa.gov/data/tcr/AL062018_Florence.pdf.

1006 Sumwalt III, R. L., C. A. Hart, E. F. Weener, and T. B. Dinh-Zahr, 2017: Safety recommendation

1007 report: Tropical cyclone information for mariners. National Transportation Safety Board

1008 Tech. Rep. MSR-17/02, 21 pp.,

1009 https://www.ntsb.gov/investigations/AccidentReports/Reports/MSR1702.pdf.

1010 Susca-Lopata, G., J. Zawislak, E. J. Zipser, and R. F. Rogers, 2015: The role of observed

1011 environmental conditions and precipitation evolution in the rapid intensification of

1012 Hurricane Earl (2010). Mon. Wea. Rev., 143, 2207–2223,

1013 doi:https://doi.org/10.1175/MWR-D-14-00283.1.

1014 Taylor, A., and B. Glahn, 2008: Probabilistic guidance for hurricane storm surge. Preprints, 19th

1015 Conf. on Probability and Statistics, New Orleans, LA, Amer. Meteor. Soc., 7.4. [Available

1016 online at https://ams.confex.com/ams/pdfpapers/132793.pdf.]

1017 Tong, M., V. Tallapragada, E. Liu, B. Zhang, W. Wang, C. Kieu, and S. Liu, 2014. Assimilation

1018 of Aircraft Reconnaissance Observations with HWRF Hybrid Data Assimilation System.

1019 31st Conf. on Hurricanes and Tropical Meteorology, San Diego, CA, Amer. Meteor. Soc.,

1020 14D.4, https://ams.confex.com/ams/31Hurr/webprogram/Paper245188.html.

1021 Tong, M., J. A. Sippel, V. Tallapragada, E. Liu, C. Kieu, I.-H. Kwon, W. Wang, Q. Liu, Y. Ling,

1022 and B. Zhang, 2018: Impact of Assimilating Aircraft Reconnaissance Observations on

Unauthenticated | Downloaded 10/06/21 02:03 PM UTC Accepted for publication in Bulletin of the American Meteorological Society. DOI 10.1175/BAMS-D-20-0174.1. 1023 Tropical Cyclone Initialization and Prediction Using Operational HWRF and GSI

1024 Ensemble-Variational Hybrid Data Assimilation. Mon. Wea. Rev., 146(12), 4155–4177,

1025 https://doi.org/10.1175/MWR-D-17-0380.1.

1026 Wadler, J. B., R. F. Rogers, and P. D. Reasor, 2018a: The Relationship between Spatial Variations

1027 in the Structure of Convective Bursts and Tropical Cyclone Intensification as Determined

1028 by Airborne Doppler Radar. Mon. Wea. Rev., 146, 761–780,

1029 https://doi.org/10.1175/MWR-D-17-0213.1.

1030 Wadler, J. B., J. A. Zhang, B. Jaimes, and L. K. Shay, 2018b: Downdrafts and the Evolution of

1031 Boundary Layer Thermodynamics in Hurricane Earl (2010) before and during Rapid

1032 Intensification. Mon. Wea. Rev., 46(11), 3545–3565, https://doi.org/10.1175/MWR-D-18-

1033 0090.1.

1034 Wadler, J. B., J. A. Zhang, R. F. Rogers, B. Jaimes, and L. K. Shay, 2020: The Rapid

1035 Intensification of Hurricane Michael (2018): Storm Structure and the Relationship to

1036 Environmental and Air-Sea Interactions. Mon. Wea. Rev., EOR,

1037 https://doi.org/10.1175/MWR-D-20-0145.1.

1038 Wang, W., Sippel, J. A., Abarca, S., Zhu, L., Liu, B., Zhang, Z., Mehra, A., & Tallapragada, V.

1039 (2018). Improving NCEP HWRF Simulations of Surface Wind and Inflow Angle in the

1040 Eyewall Area, Wea. Forecasting, 33, 887-898, https://doi.org/10.1175/WAF-D-17-0115.1

1041 Weng, Y., and F. Zhang, 2012: Assimilating airborne Doppler radar observations with an ensemble

1042 Kalman filter for convection-permitting hurricane initialization and prediction: Katrina

1043 (2005). Mon. Wea. Rev., 140, 841–859, doi:10.1175/2011MWR3602.1.

1044

1045

Unauthenticated | Downloaded 10/06/21 02:03 PM UTC Accepted for publication in Bulletin of the American Meteorological Society. DOI 10.1175/BAMS-D-20-0174.1. 1046 Weng, Y., and F. Zhang, 2016: Advances in convection-permitting tropical cyclone analysis and

1047 prediction through EnKF assimilation of reconnaissance aircraft observations. J. Meteor.

1048 Soc. Japan, 94(4), 345–358, https://doi.org/10.2151/jmsj.2016-018.

1049 Wick, G. A., J. P. Dunion, P. G. Black, J. R. Walker, R. D. Torn, A. C. Kren, A. Aksoy, H.

1050 Christophersen, L. Cucurull, B. Dahl, J. M. English, K. Friedman, T. R. Peevey, K.

1051 Sellwood, J. A. Sippel, V. Tallapragada, J. Taylor, H. Wang, R. E. Hood, and P. Hall, 2020:

1052 NOAA’s Sensing Hazards with Operational Unmanned Technology (SHOUT) Experiment

1053 Observations and Forecast Impacts. Bull. Amer. Meteor. Soc., 101(7), E968–E097,

1054 https://doi.org/10.1175/BAMS-D-18-0257.1.

1055 Winterbottom, H., J. A. Sippel, A. Mehra, B. Zhang, and Z. Zhang, 2018: Recent and Planned

1056 Upgrades to Operational HWRF Data Assimilation. 33rd Conf. on Hurricanes and

1057 Tropical Meteorology, Ponte Vedra Beach, FL, Amer. Meteor. Soc., 3C.5,

1058 https://ams.confex.com/ams/33HURRICANE/webprogram/Paper338953.html.

1059 Zawislak, J., H. Jiang, G. R. Alvey III, E. J. Zipser, R. F. Rogers, J. A. Zhang, and S. N. Stevenson,

1060 2016: Observations of the structure and evolution of Hurricane Edouard (2014) during

1061 intensity change. Part I: Relationship between the thermodynamic structure and

1062 precipitation. Mon. Wea. Rev., 144, 3333–3354, doi:https://doi.org/10.1175/MWR-D-16-

1063 0018.1.

1064 Zelinsky, D. A., 2019: National Hurricane Center Tropical Cyclone Report: Hurricane Lorenzo

1065 (AL132019). National Hurricane Center Rep., 22 pp.,

1066 https://www.nhc.noaa.gov/data/tcr/AL132019_Lorenzo.pdf.

1067 Zhang, F., Y. Weng, J. F. Gamache, and F. D. Marks, 2011: Performance of convection-permitting

1068 hurricane initialization and prediction during 2008–2010 with ensemble data assimilation

Unauthenticated | Downloaded 10/06/21 02:03 PM UTC Accepted for publication in Bulletin of the American Meteorological Society. DOI 10.1175/BAMS-D-20-0174.1. 1069 of inner-core airborne Doppler radar observations. Geophys. Res. Lett., 38, L15810,

1070 https://doi.org/10.1029/2011GL048469.

1071 Zhang, B., J. A. Sippel, B. Liu, M. Tong, Z. Zhang, V. Tallapragada, and A. Mehra, 2018: Impact

1072 of Assimilating Aircraft Reconnaissance Observations in Operational HWRF on

1073 Improving Hurricane Forecast for the 2017 Atlantic High-Impact Storms. 33rd Conf. on

1074 Hurricanes and Tropical Meteorology, Ponte Vedra Beach, FL, Amer. Meteor. Soc., 6A.3.

1075 https://ams.confex.com/ams/33HURRICANE/webprogram/Paper340147.html

1076 Zhang, F., and D. Tao, 2013: Effects of Vertical Wind Shear on the Predictability of Tropical

1077 Cyclones. J. Atmos. Sci., 70(3), 975–983, https://doi.org/10.1175/JAS-D-12-0133.1.

1078 Zhang, J. A., and M. T. Montgomery, 2012: Observational estimates of the horizontal eddy

1079 diffusivity and mixing length in the low-level region of intense hurricanes. J. Atmos. Sci.,

1080 69, 1306–1316, https://doi.org/10.1175/JAS-D-11-0180.1.

1081 Zhang, J. A., S. G. Gopalakrishnan, F. D. Marks, R. F. Rogers, and V. Tallapragada, 2012: A

1082 Developmental Framework for Improving Hurricane Model Physical Parameterizations

1083 Using Aircraft Observations. Trop. Cycl. Res. Rev, 1(4), 419–429.

1084 Zhang, J. A., R. F. Rogers, P. Reasor, E. Uhlhorn, and F. D. Marks, Jr., 2013: Asymmetric

1085 hurricane boundary layer structure from dropsonde composites in relation to the

1086 environmental wind shear. Mon. Wea. Rev., 141, 3968–3984,

1087 https://doi.org/10.1175/MWR-D-12-00335.1.

1088 Zhang, J. A., D. S. Nolan, R. F. Rogers, and V. Tallapragada, 2015: Evaluating the Impact of

1089 Improvements in the Boundary Layer Parameterization on Hurricane Intensity and

1090 Structure Forecasts in HWRF. Mon. Wea. Rev., 143, 3136–3155,

1091 https://doi.org/10.1175/MWR-D-14-00339.1.

Unauthenticated | Downloaded 10/06/21 02:03 PM UTC Accepted for publication in Bulletin of the American Meteorological Society. DOI 10.1175/BAMS-D-20-0174.1. 1092 Zhang, J. A., and F. D. Marks, 2015: Effects of horizontal diffusion on tropical cyclone intensity

1093 change and structure in idealized three-dimensional numerical simulations. Mon. Wea.

1094 Rev., 143, 3981–3995, https://doi.org/10.1175/MWR-D-14-00341.1.

1095 Zhang, J. A., J. J. Cione, E. A. Kalina, E. W. Uhlhorn, T. Hock, J. A. Smith, 2017a: Observations

1096 of Infrared Sea Surface Temperature and Air-Sea Interaction in Hurricane Edouard (2014)

1097 Using GPS Dropsondes. J. Atmos. Ocean. Tech., 34(6), 1333–1349,

1098 https://doi.org/10.1175/JTECH-D-16-0211.1.

1099 Zhang, J. A., R. F. Rogers, and V. Tallapragada, 2017b: Impact of Parameterized Boundary Layer

1100 Structure on Tropical Cyclone Rapid Intensification Forecasts in HWRF. Mon. Wea. Rev.,

1101 145, 1413–1426, doi: 10.1175/MWR-D-16-0129.1.

1102 Zhang, J. A., F. D. Marks, J. A. Sippel, R. F. Rogers, X. Zhang, S. G. Gopalakrishnan, Z. Zhang,

1103 and V. Tallapragada, 2018a: Evaluating the Impact of Improvement in the Horizontal

1104 Diffusion Parameterization on Hurricane Prediction in the Operational Hurricane Weather

1105 Research and Forecast (HWRF) Model. Wea. Forecasting, 33, 317–329,

1106 https://doi.org/10.1175/WAF-D-17-0097.1.

1107 Zhang, J. A., R. Atlas, G. D. Emmitt, L. Bucci, and K. Ryan, 2018b: Airborne Doppler Wind Lidar

1108 Observations of the Tropical Cyclone Boundary Layer. Remote Sens., 10(6), 825, 15 pp.,

1109 https://doi.org/10.3390/rs10060825.

1110 Zhang, J. A. and R. F. Rogers, 2019: Effects of parameterized boundary layer structure on

1111 hurricane rapid intensification in shear. Mon. Wea. Rev., 147, 853–871,

1112 https://doi.org/10.1175/MWR-D-18-0010.1.

1113 Zhang, X., S. G. Gopalakrishnan, S. Trahan, T. S. Quirino, Q. Liu, Z. Zhang, G. J. Alaka, and V.

1114 Tallapragada, 2016: Representing multiple scales in the Hurricane Weather Research and

Unauthenticated | Downloaded 10/06/21 02:03 PM UTC Accepted for publication in Bulletin of the American Meteorological Society. DOI 10.1175/BAMS-D-20-0174.1. 1115 Forecasting modeling system: Design of multiple sets of movable multilevel nesting and

1116 the basin-scale HWRF forecast application. Wea. Forecasting, 31, 2019–2034,

1117 doi:10.1175/ WAF-D-16-0087.1.

1118 Zhang, Z. and coauthors, 2020: The Impact of Stochastic Physics-Based Hybrid GSI/EnKF Data

1119 Assimilation on Hurricane Forecasts Using EMC Operational Hurricane Modeling System.

1120 Atmosphere, 11, 801. https://doi.org/10.3390/atmos11080801.

1121

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Unauthenticated | Downloaded 10/06/21 02:03 PM UTC Accepted for publication in Bulletin of the American Meteorological Society. DOI 10.1175/BAMS-D-20-0174.1. 1138 SIDEBARS

1139 Field Program Collaborations

1140 IFEX continues to partner with the NOAA/NESDIS/STAR (Center for Satellite

1141 Application and Research) Ocean Winds program, which uses P-3 instrumentation — notably the

1142 Imaging Wind and Rain Airborne Profiler (IWRAP) (Fernandez et al. 2005; Guimond et al. 2014)

1143 — in hurricanes and winter storms to calibrate and validate satellite-borne remote sensors that

1144 measure the ocean surface-wind field and to explore new technologies for future satellites.

1145 From 2012–2014, IFEX partnered with NASA’s Hurricane and Severe Storm Sentinel

1146 (HS3; Braun et al. 2016). The goal of HS3 was to understand the relative roles of inner-core and

1147 environmental processes in the formation and intensification of TCs. HS3 used two Global Hawks

1148 (GH), uncrewed aircraft capable of long duration sampling (up to 24 h) at high altitudes (55–65

1149 kft). The biggest success of this collaboration was the sampling of Hurricane Edouard (2014) with

1150 coinciding GH, P-3, and G-IV flights (Fig. 1), as well as Coyote small Uncrewed Aircraft System

1151 (sUAS) released from the P-3 (Cione et al. 2016). IFEX subsequently partnered with NOAA’s

1152 Sensing Hazards with Operational Unmanned Technology (SHOUT) in 2015–2016, a program

1153 that used the GH to evaluate how observations from high-altitude UAS improve TC forecasts and

1154 other high-impact weather (Wick et al. 2020). IFEX-SHOUT flew several collaborative missions

1155 into Hermine, Karl, and Matthew, which optimized sampling of the TC inner core and surrounding

1156 environment.

1157 IFEX also partnered with the ONR Tropical Cyclone Intensity Experiment (TCI) in 2015,

1158 whose primary goal was to explore the TC upper-level outflow layer and its relationship to rapid

1159 changes in TC intensity and structure (Doyle et al. 2017). TCI used the NASA’s high-altitude WB-

1160 57 aircraft, along with a High-Definition Sounding System (Black et al. 2017) onboard, capable

Unauthenticated | Downloaded 10/06/21 02:03 PM UTC Accepted for publication in Bulletin of the American Meteorological Society. DOI 10.1175/BAMS-D-20-0174.1. 1161 of deploying dropsondes at a relatively high sampling rate (e.g., dozens of dropsondes launched at

1162 ~20-s intervals). Successful collaborations between IFEX and TCI were carried out during three

1163 consecutive days into Hurricane Patricia (21–23 October 2015). During this period, observations

1164 from the WB-57 and P-3 documented a period of extreme RI when Patricia’s maximum sustained

1165 wind speed increased by 105 kt in just 24 h (minimum central pressure decrease of 95 hPa), the

1166 most rapid intensification on record (Rogers et al. 2017). Beginning in 2020, IFEX began

1167 collaborating with ONR once again for their Departmental Research Initiative (DRI) Tropical

1168 Cyclone Rapid Intensification (TCRI) program, which leverages NOAA aircraft data to better

1169 understand and predict RI. Highlights from TCRI 2020 included high-density dropsonde

1170 observations during RI in Sally and Delta (Table 2)

1171 In 2019, IFEX partnered with the NSF/NCAR Organization of Tropical East Pacific

1172 Convection (OTREC) experiment (Fuchs-Stone et al. 2020). The objective of OTREC was to use

1173 measurements from the NCAR/NSF G-V, along with enhanced radiosonde coverage in the region,

1174 to determine how the large-scale environment modulates the characteristics of deep convection in

1175 the southwest Caribbean and eastern North Pacific. OTREC and IFEX flew a 5-day series of

1176 missions from Costa Rica into the disturbance that eventually developed into Tropical Storm Ivo.

1177

1178 Measures of Success

1179 The success of IFEX has primarily been measured using three metrics. The first is the

1180 impact IFEX has had within the operational community through research-to-operational

1181 transitions. Many of these impacts were highlighted in IFEX Goals 1 and 2, and reflect key

1182 contributions to the notable improvement in intensity forecasts over the past decade (Cangialosi et

1183 al. 2020).

Unauthenticated | Downloaded 10/06/21 02:03 PM UTC Accepted for publication in Bulletin of the American Meteorological Society. DOI 10.1175/BAMS-D-20-0174.1. 1184 The second measure gauges the breadth and impact of publications that have used NOAA

1185 aircraft data. Figure SB1 shows the number of published manuscripts since 2006 that included

1186 AOML/HRD authors and that used NOAA aircraft data (collected through IFEX research missions

1187 and operational flights), linked to each IFEX Goal. Goal 3 (“physical processes”) represents the

1188 largest number of manuscripts, followed by Goal 1 (“model evaluation/initialization”), and then

1189 Goal 2 (“real-time monitoring”). Goal 3 publications are further separated by various aspects of

1190 TC processes, both within their inner core and environment (Fig. SB1). The most frequently

1191 published topics include those related to TC structure, intensity change, ocean and air-sea

1192 interaction, PBL, and precipitation. Fewer studies have been published on TC-environment

1193 interactions, turbulence, and microphysics — suggesting a need for additional focus on these topics

1194 in the future.

1195 Though not entirely quantified in Fig. SB1 (as that figure only includes papers with HRD

1196 authors), the third metric is the extent to which IFEX data are used by researchers and forecasters

1197 outside of AOML/HRD. The airborne hurricane program is a service to the entire research and

1198 operational community with a common goal of improving forecasts for the public. AOML/HRD

1199 maintains a decades-long archive of raw and quality-controlled, research-quality NOAA aircraft

1200 TC data on its webpage (https://www.aoml.noaa.gov/hurricane-research-division/), and is

1201 continually trying to make data more useful to its users. Users are encouraged to contact

1202 AOML/HRD scientists to help facilitate their data needs and to collaborate on an experiment

1203 design.

1204

1205

1206

Unauthenticated | Downloaded 10/06/21 02:03 PM UTC Accepted for publication in Bulletin of the American Meteorological Society. DOI 10.1175/BAMS-D-20-0174.1. 1207 TABLES

1208 Table 1. A summary of the hazards experienced during recent landfalling TCs. Landfall locations

1209 are provided for each storm, as well as the peak rainfall measured, observed or estimated maximum

1210 storm-surge inundations, the best-track maximum sustained wind speed at landfall, and the number

1211 of tornadoes observed in the U.S. All information is taken from NHC Tropical Cyclone Reports.

1212 In “Rainfall”, bold text indicates > 20 in., while in “Wind” bold text indicates major hurricane

1213 landfalls and italics further indicates a Category 5.

1214

Landfall Rainfall Surge Wind US Storm (Year) Location [in] Inundation [ft] [kt] Tornadoes Haiti 23.80 Unknown 130 Cuba 26.40 13 115 Matthew (2016) 1 2 Bahamas 19.70 8 115 South Carolina 18.95 7.7 75 Barbados Unknown Unknown 40 Harvey (2017) 2 St. Vincent Unknown Unknown 40 52 Texas# 60.58 10 115 Barbuda Unknown 8 155 St. Martin Unknown Unknown 155 British V. I. Unknown Unknown 155 Irma (2017) 3 Bahamas Unknown Unknown 135 25 Cuba 23.90 10 145 Florida Keys 6–10 8 115 Marco Island, FL 21.66 10 100 Dominica 22.80 Unknown 145 Maria (2017) 4 3* Puerto Rico 37.90 9 135 Florence (2018) 5 North Carolina 35.93 11 80 44 Michael (2018) 6 Florida 11.45 14 140 16 Barbados Unknown Unknown 45 St. Lucia Unknown Unknown 45 St. Croix Unknown Unknown 65 Dorian (2019) 7 21 St. Thomas Unknown Unknown 70 Bahamas^ 22.84 20+ 160 North Carolina 15.21 7 85

Unauthenticated | Downloaded 10/06/21 02:03 PM UTC Accepted for publication in Bulletin of the American Meteorological Society. DOI 10.1175/BAMS-D-20-0174.1. Imelda (2019) 8 Texas 44.29 2 40 2 Dominican 8 Unknown 55 Republic Isaias (2020) 9 39 Bahamas Unknown Unknown 70 North Carolina 9.15 6 80 Sally (2020) 10 Alabama~ 29.99 7 95 16 1215

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1224

1225

1226 # Only the first U.S. landfall is shown. A second landfall occurred 4 days later near Cameron, LA. 1227 * Unconfirmed by the National Weather Service 1228 ^ Only the first Bahamas landfall at Elbow Cay is shown. A second landfall occurred 9 h later in 1229 Grand Bahama. 1230 + Eyewitness account, but not an official measurement 1231 ~ Only the hurricane landfall is shown here; Sally also made landfall near Cutler Bay, FL as a 30 1232 kt TD on 12 September 1233 1234 1 Stewart (2017) 1235 2 Blake and Zelinsky (2018) 1236 3 Cangialosi et al. (2018) 1237 4 Pasch et al. (2019) 1238 5 Stewart and Berg (2019) 1239 6 Beven et al. (2019) 1240 7 Avila et al. (2020) 1241 8 Latto and Berg (2020) 1242 9 Latto et al. (2021) 1243 10 Berg and Reinhart (2021)

Unauthenticated | Downloaded 10/06/21 02:03 PM UTC Accepted for publication in Bulletin of the American Meteorological Society. DOI 10.1175/BAMS-D-20-0174.1. 1244 Table 2. List of notable pre-TC, tropical storms (TS), and hurricanes (HU) flown by NOAA

1245 aircraft in the Atlantic, East Pacific (EP), and Central Pacific (CP) since 2013 and the last IFEX

1246 summary (R13), including the takeoff dates of NOAA flights, NOAA aircraft involved, the number

1247 of missions by each airplane (N42 and N43 are the P-3s, and N49 is the G-IV), number of

1248 dropsondes released by NOAA aircraft, number of tail Doppler radar analyses currently available,

1249 and commentary on why these storms are notable. Storms may be notable for unique

1250 instrumentation flown (e.g., Doppler wind Lidar, DWL, and small uncrewed aircraft systems,

1251 sUAS) and/or collaborations with NESDIS, HS3 (Hurricane and Severe Storm Sentinel), TCI

1252 (Tropical Cyclone Intensity), TCRI (Tropical Cyclone Rapid Intensification), SHOUT (Sensing

1253 Hazards with Operational Unmanned Technology), and OTREC (Organization of Tropical East

1254 Pacific Convection), all described further in the sidebar on, “Field Program Collaborations.”

1255

Storm Dates of NOAA NOAA Number of Comments NOAA aircraft Dropsondes radar Missions analyses

TS Gabrielle 31 Aug–10 N42 (1), 86 0 Genesis research, Sep 2013 N43 (2), collaborative with N49 (2) HS3

HU Cristobal 23–27 Aug N42 (3), 168 24 Collaborative with 2014 N43 (5), HS3 and NESDIS N49 (2) Ocean Winds

HU Edouard 11–19 Sep N42 (3), 120 13 High density of 2014 N43 (6), ocean N49 (1) measurements; Coyote sUAS; collaborative with HS3 and NESDIS Ocean Winds during RI

TS Erika 25–28 Aug N43 (5), 155 19 Saharan Air Layer

Unauthenticated | Downloaded 10/06/21 02:03 PM UTC Accepted for publication in Bulletin of the American Meteorological Society. DOI 10.1175/BAMS-D-20-0174.1. 2015 N49 (3) Experiment; sampled with DWL; collaborative with HS3 and TCI; RI null case

HU Joaquin 29 Sep–2 N49 (5) 145 0 Collaborative with Oct 2015 TCI

HU Patricia 21–23 Aug N43 (3) 29 6 Collaborative with (EP) 2015 TCI and NESDIS Ocean Winds; sampling during RI

HU Earl 2-3 Aug N43 (3) 34 8 Sampled with 2016 DWL on P-3

HU Hermine 25 Aug–1 N43 (11) 116 46 Collaborative with Sep 2016 SHOUT; sampled pre-RI and during RI, landfall

HU Matthew 29 Sep–8 N43 (9), 349 21 Collaborative with Oct 2016 N49 (8) SHOUT and NESDIS Ocean Winds; sampled during RI

TS Javier 8–9 Aug N43 (2) 17 5 Sampled with (EP) 2016 DWL on P-3; Coyote sUAS

HU Harvey 22–25 Aug N42 (4), 171 8 Sampled during RI 2017 N49 (4)

HU Irma 3–9 Sep N42 (8), 325 25 Sampled during RI 2017 N49 (8) and Eyewall Replacement Cycles

HU Maria 19–26 Sep N42 (8), 210 28 Sampled with 2017 N49 (4) DWL; Coyote sUAS

HU Nate 5–10 Oct N42 (5), 187 12 Sampled with 2017 N49 (4) DWL; Sampled RI event with ocean

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HU Lane 19–22 Aug N42 (4), 189 13 Sampled with (EP/CP) 2018 N49 (4) DWL; sampled RI; first central Pacific case flown by NOAA P-3

Pre-Sergio 26–28 Sep N42 (3) 50 13 Genesis research (EP) 2018

HU Florence 8–13 Sep N42 (3), 368 10 Collaborative with 2018 N49 (9) NESDIS Ocean Winds; sampled RI

HU Michael 8–10 Oct N42 (6), 193 16 Sampled RI with 2018 N49 (3) extensive ocean measurements; Coyote sUAS

Pre-Ivo (EP) 17–20 Aug N42 (4) 67 14 Collaborative with 2019 OTREC during genesis

HU Dorian 25 Aug–5 N42 (15), 624 70 Sampled RI; Sep 2019 N49 (10) collaborative with NESDIS Ocean Winds

HU Lorenzo 27–29 Sep N42 (1), 120 10 Sampled RI; 2019 N43 (2), collaborative with N49 (3) NESDIS Ocean Winds

HU Laura 20–26 Aug N42 (6) 407 38 Sampled RI 2020 N43 (5) N49 (6)

HU Sally 13–15 Sep N42 (1) 226 18 Sampled RI; 2020 N43 (3) collaborative with N49 (3) ONR TCRI

HU Teddy 15–22 Sep N42 (3) 98 22 Collaborative with 2020 N43 (2) ONR TCRI, N49 (3) NESDIS Ocean Winds, Stanford Univ.

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Unauthenticated | Downloaded 10/06/21 02:03 PM UTC Accepted for publication in Bulletin of the American Meteorological Society. DOI 10.1175/BAMS-D-20-0174.1. 1277 Table 3. Number of NOAA flights flown in pre-IFEX years, during IFEX in 5-year increments

1278 (2005–2009, 2010–2014, and 2015–2019), and in 2020, and in subsequent rows, the percentage of

1279 the total flights for each period flown across various intensities in the storm life cycle. The

1280 representative intensity for each flight is defined from the best track. The “Pre-IFEX” percentages

1281 are taken from Table 2 in R06.

1282

Pre-IFEX IFEX IFEX IFEX IFEX ’05 – ‘09 ’10 - ‘14 ’15 - ‘19 ‘20

Number of 230 180 232 86 Flights

Pre-TC 4.3 6.5 10.0 5.6 0.0

TD 7.2 5.2 3.4 3.9 3.5

TS 26.8 37.0 36.1 33.6 47.7

Categories 1–2 31.6 27.4 41.1 22.0 34.9

Categories 3–5 30.0 23.9 9.4 34.9 13.9

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Unauthenticated | Downloaded 10/06/21 02:03 PM UTC Accepted for publication in Bulletin of the American Meteorological Society. DOI 10.1175/BAMS-D-20-0174.1. 1291 FIGURES

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1295 Figure 1. Storm-relative flight tracks for the P-3s (“NOAA42”, blue, and “NOAA43”, red) and

1296 NASA Global Hawk (“GH AV6”, green) into Hurricane Edouard on (a) 14 September 2014 (when

Unauthenticated | Downloaded 10/06/21 02:03 PM UTC Accepted for publication in Bulletin of the American Meteorological Society. DOI 10.1175/BAMS-D-20-0174.1. 1297 the storm was intensifying) and (b) 16 September 2014 (storm was steady state) and near-surface

1298 winds from dropsondes (wind barbs) overlaid over visible satellite imagery. Figure is adapted from

1299 Rogers et al. 2016.

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1322 Figure 2. Number of NOAA flights (includes both the G-IV and P-3s) for the IFEX period (2005–

1323 2020) categorized by, (a) storm intensity spanning the storm life cycle, including the pre-genesis

1324 (“Pre-TC”) period, with the number of RI flights at those intensities indicated in red; and (b)

1325 intensity change [kt] 24 hr after the mission, with intensification highlighted in red (rapid

1326 intensification in dark red), weakening in blue (rapid weakening in dark blue), and steady state in

1327 white. Storm intensity is defined as the best-track maximum wind speed representative during the

1328 mission, while the change is defined by the difference between the best-track intensity 24 h later

1329 and the representative intensity during the mission.

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1338 Figure 3. Evolution of 12–120-h (a) track and (b) intensity skill for HWRF relative to NHC’s

1339 climatology-persistence skill baseline (OCD5), composited for cases for three 3-year periods:

1340 2011–2013, 2014–2016, 2017–2019.

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1342 Figure 4. The combined impact of all reconnaissance data on the intensity skill in a pre-

1343 implementation version of the 2019 operational HWRF model. The baseline (red) has no

1344 reconnaissance assimilation, while the operational version (black) has assimilation of TDR,

1345 dropsondes, flight-level, and SFMR data.

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1357 Figure 5. a) 2-km reflectivity (dBZ) and wind barbs (kt) from a 72-h forecast of the Hurricane

1358 Analysis and Forecast System (HAFS) of Hurricane Laura, initialized 0000 UTC 24 August 2020.

1359 The blue arrow shows the 850–200-hPa wind shear vector. b) 2-km wind speed (kt), 2-km

1360 streamlines (black) and 5-km streamlines (gray) from the same 72-h forecast. The blue arrow

1361 shows the 850–200-hPa wind shear vector. c) P-3 TDR observed 2-km reflectivity (dBZ) and wind

1362 barbs (kt), valid during the period from 2238 UTC 26 August 2020 to 0224 UTC 27 August 2020.

Unauthenticated | Downloaded 10/06/21 02:03 PM UTC Accepted for publication in Bulletin of the American Meteorological Society. DOI 10.1175/BAMS-D-20-0174.1. 1363 d) P-3 TDR observed 2-km wind speed (kt), 2-km streamlines (black) and 5-km streamlines (gray).

1364 The green arrow shows the 850–200-hPa wind shear vector from SHIPS.

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1388 Figure 6. Real-time TDR analyses of Hurricane Laura (2020) from P-3 missions during RI. (a)

1389 Flight-composite wind speed [kt] at 2 km (shaded), streamlines at 2/5 km (black/gray), and

1390 locations of updrafts > 3 m s-1 (hatched areas); and (b) azimuthal-mean tangential wind speed (kt)

1391 versus height for the period from 2230 UTC 25 August to 0155 UTC 26 August. (c–d) as in (a–b)

1392 but 12 h later, from 1016–1449 UTC 26 August.

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1402 Figure 7. Real-time TDR analyses from a pass of Hurricane Teddy from 0058–0211 UTC 18

1403 September 2020 showing, (a) 2-km reflectivity (dBZ) and wind (barbs, kt), (b) the precipitation

1404 type classification for convective (“deep”, “moderate”, “shallow”) and stratiform, (c) the vertical

1405 profile cross-section of reflectivity (shading, dBZ) and vertical velocity (contour, m s-1; solid is

1406 upward motion, dashed is downward), and (d) the vertical profile cross-section of storm-relative

1407 radial velocity (m s-1). The red ‘S’ indicates the start of the cross section and the red ‘E’ indicates

1408 the end, with the arrow in (a) showing the direction of the cross section and aircraft center pass.

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1415 Figure 8. (a) Example from AWIPS-II display of TDR data from Hurricane Lane in the central

1416 Pacific at 1700 UTC 20 August 2018, overlaid with TDR 0.5-km wind speed (kt, shading), GOES-

1417 15 infrared satellite imagery, and 1-h cloud-to-ground lightning from the Global Lightning

1418 Detection Network (GLD360) (courtesy Stephanie Stevenson, NHC). This was one of the first

1419 opportunities for hurricane specialists at NHC and the Central Pacific Hurricane Center to view

1420 TDR data in AWIPS-II while the P-3 was still in flight. (b) Sequence of 2-km earth-relative wind

1421 analyses (m s-1) from the TDR showing the RI of Hurricane Delta (2020) for three passes over ~3

1422 h, produced from the files formatted for AWIPS-II.

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1429 Figure 9. For Hurricane Edouard (Fig. 1), accompanying locations of peak updrafts of greater than

1430 3 m s-1 (green) and 5 m s-1 (black), i.e., “convective bursts”, between the 8–16-km layer from the

1431 TDR for passes during the (a) 14 (intensifying) and (b) 16 (steady state) September missions

1432 relative to the 850–200-hPa vertical wind shear (downshear right [DSR], downshear left [DSL],

1433 upshear left [USL], and upshear right [USR]) and in an Earth-relative coordinate system

1434 normalized by the low-level (2-km) RMW (x*, y*) (adapted from Rogers et al. 2016). Panels (c)

1435 and (d) show the mean relative humidity profiles and number of contributing samples within 200

1436 km of the center of Edouard for the DSL and USL quadrants, respectively, from the four GH

1437 missions (adapted from Zawislak et al. 2016).

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1443 Figure 10. Three-dimensional schematics summarizing the hypothesized hindrances to

1444 precipitation symmetry in TCs. The top panel depicts a case with more-asymmetric precipitation

1445 (Cristobal 2014), which is less favorable for intensification, while the bottom panel depicts a case

1446 with more-symmetric precipitation (Bertha 2014), which is favorable for intensification. Lateral

1447 advection of environmental dry air on the upshear side (USL, USR) of TCs, subsidence upshear,

1448 and weak surface fluxes upshear that inhibit PBL recovery all contribute to hinder symmetrization

1449 of precipitation in a sheared TC. Adapted from Nguyen et al. (2017).

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1452 Figure 11. (a) Vertical profile of total circulation (x 10-4 s-1 km-2) per center pass from two

1453 missions by the P-3 in a 1.25° x 1.25° box incorporating low-level circulation center and

1454 downshear environment in Hermine. Values averaged from three center passes during missions

1455 centered at ~2000 UTC 30 August 2016 in Tropical Depression #9 (Mission 160830I1; blue line)

1456 and ~2000 UTC 31 August 2016 in Tropical Storm Hermine (Mission 160831I1; red line). (b)

1457 Contributions to aggregate vertical mass flux (x 109 kg s-1) per center pass from regions classified

Unauthenticated | Downloaded 10/06/21 02:03 PM UTC Accepted for publication in Bulletin of the American Meteorological Society. DOI 10.1175/BAMS-D-20-0174.1. 1458 as deep (solid) and moderate convection (dashed) for Mission 160830I1 (blue) and Mission

1459 160831I1 (red). (c) As in (b), but for the sum of mass flux from deep and moderate convection for

1460 each mission. Note pronounced lowering in altitude and increase in magnitude of peak mass flux

1461 between two missions, with primary contribution from moderate convection during the later

1462 mission. Adapted from Rogers et al. (2020).

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1482 Figure 12. Radius-height depiction of SEF/ERC in Hurricane Irma (2017). Azimuthally-averaged

-1 1483 tangential (VT; m s ; shaded) and vertical wind speed (contours) obtained from six center passes

1484 by the P-3 on 3–4 September. Positive vertical velocity values are shown in the solid contours

1485 every 0.5 m s-1, while negative vertical velocity values are shown in the dashed contours every 0.2

1486 m s-1; solid arrows indicate the direction (upward/downward) of the vertical motion. The inset of

1487 each panel depicts the magnitude of the maximum azimuthally-averaged tangential wind speed at

-1 1488 a height of 3 km (VT3; m s ) and the radius of VT3 (RMW; km). For each flight, only radii with at

1489 least 33% azimuthal coverage are shown. Note the development and contraction of outer maximum

Unauthenticated | Downloaded 10/06/21 02:03 PM UTC Accepted for publication in Bulletin of the American Meteorological Society. DOI 10.1175/BAMS-D-20-0174.1. 1490 in tangential wind and vertical wind speed (the secondary eyewall, denoted by the white dashed

1491 lines) in panels (c)–(e), beginning at ~50–55-km radius and contracting to ~40-km radius over ~3

1492 h period. Adapted from Fischer et al. (2020).

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1514 Figure 13. P-3 (red) and G-IV (blue) flight tracks, P-3 dropsonde release locations (teal ‘D’), and

1515 hurricane glider locations (labeled in yellow with flags) overlaid with IR satellite imagery from

1516 Hurricane Dorian on 28 August 2019. Shaded circles indicate the coordinated glider-dropsonde

1517 measurements. Image is developed using a screenshot from NASA’s Mission Tools Suite (MTS),

1518 which is used for real-time mission situational awareness during the field program.

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1527 Figure SB1. (a) Number of publications (totaling 152) from 2006–2020 that include AOML/HRD

1528 and affiliated scientists, separated for IFEX Goals #1 (24%; “model initialization/evaluation”), #2

1529 (13%; “real-time monitoring”), and #3 (63%; “physical processes”); and (b) the total number of

1530 publications within Goal 3 that are published on various aspects of TC processes.

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