dataesr/scientific-paragraphs-categorization huggingface dataset:
1. The time periods and parameters included in operational center best track records vary. To address this, the Extended Best Track Dataset  (Demuth et al. , 2006) , used extensively by the research community, provides an amalgam of best track position and intensity with RMW, R34, R50, and R64  (pre-2004 ) in NHC's area of responsibility from the 1980s onward. JTWC best tracks include wind radii starting in the early 2000s, but post-storm analysis of R34 for western North Pacific tropical cyclones, and coincident regression of R50 and R64 estimates based on R34, only began in 2016  (Sampson et al. , 2017) dindicated by a blue line in Fig. 1. RSMC Tokyo maintains the longest best track record of TC surface wind structure, which includes the longest and shortest axis of R30 and R50 in geographic octants around western North Pacific tropical cyclones from 1979 onward  (JMA, 2020).
The Australian Bureau of Meteorology also maintains a best-track database that includes wind radii for tropical cyclones in its area of responsibility (Bureau of Meteorology, 2020). Also, they have recorded wind radii in best track data archives since the 2003e2004 season, though operational estimates exist intermittently back to 1983. Finally, RSMC La Reunion maintains a best track database for the South Indian Ocean that includes radii data from the 2003e2004 season onward. Other RSMCs do not provide wind radii in their best track data archives  (Knapp et al. , 2018). Finally, since analysis of surface winds can be a timeconsuming process, quality and consistency of estimates also depend on staffing and staffing priorities. The centers that created the best track file are a good source for such information.
Objective estimates of TC surface winds Since the estimates are from disparate data sources, forecasters have a great deal to interpret when applying these estimates to their TC surface wind best tracking process. This section discusses the objective estimates used to construct the subjective best track datasets described. And, to assist with the process, this section also discusses wind sampling and averaging considerations, outlines objective methods, relevant archives, and finishes with a summary section with a figure intended for use as a quick reference in operations.
These SFMR retrievals are based on relationships identified in mean dropwindsonde profiles. While the aircraft and dropwindsonde are moving independently in space, a 10-s average SFMR wind observation is trained to the characteristic dropwindsonde surface. SFMR relationships are empirical and have gone through various revisions as data improves. Recently, NHC noticed a discrepancy between SFMR wind speeds and surface winds estimated from flight-level observations on the aircraft, particularly in TCs with intensities above 115 kt (60 m s À1 ) in 2017 and following years. Investigation with a larger dropwindsonde-based near surface winds database shows high biases of 1 kt, 5 kt, 7 kt, and 10 kt at 80 kt, 120 kt, 140 kt, and 165 kt surface winds, respectively (Heather Holbach, Personal Communication, 2021).
Aircraft reconnaissance Aircraft reconnaissance has provided decades of data for select storms  (Guard et al. , 1992; Williams, 2015). Prior to upgraded instrumentation on reconnaissance aircraft in the late 1970's (NOAA's research aircraft) and early 1990's (U. S. Air Force Aircraft), accurate flight-level winds were unavailable. For historical insight on aircraft observations, see  Sheets, (1990) ,  Guard et al. (1992) ,  Rappaport et al. (2009) ,  Hagen and Landsea (2012) , and  Landsea and Franklin (2013). Today's aircraft reconnaissance employs high-quality navigation and instrumentation (OMAO, 2020) and provides flight-level wind data for operational reconnaissance use  (OFCM, 2019). NHC formalized methods to estimate surface winds from flight level by 2000  (Franklin et al. , 2003; Rappaport et al.
, 2015) , two of which are available in operations: NOAA's Center for Satellite Applications and Research  (Chang et al. , 2015; Alsweiss et al. , 2018)  and the Japan Aerospace Exploration Agency's (JAXA) all-weather Sea Surface Wind Speed algorithm  (Shibata, 2002 (Shibata, , 2006)). Each algorithm provides wind speed estimates at a resolution of 25 km. The JAXA algorithm is trained using buoy data, and the NOAA algorithm is trained using buoy and SFMR data gathered in tropical and extra-tropical cyclones. AMSR2 winds are flagged in areas of sun glint and radio interference. An inter-comparison of these products  (Alsweiss et al. , 2018)  suggests that both are applicable for TC operations for wind speeds up to 140 kt (70 m s À1 ), although the reported applicability ranges are product dependent as are reported errors, which could be as high as 14 kt (7m s À1 ).
S. operational TC forecast centers. ATCF system graphical user interfaces present fix deck file information to forecasters to aid real-time assessment, postseason analysis and other tasks. The fix deck file data are retained as a historical archive of the time, location, and surface wind structure elements extracted from satellite, aircraft, and algorithm/analysis. More information on the fix deck is available at (https://www. nrlmry. navy. mil/atcf_web/docs/ database/new/newfdeck. txt). Other international forecast centers have developed similar databases, and users should check with those centers to determine availability and specifics on their individual records.
The main limitation of SAR is its sporadic availability. Unlike most satellites in low Earth orbit that continuously collect and broadcast data, SARs are operated following predefined acquisition plans as these radars are operated in different exclusive modes. Any modifications of this plan requires specific request to change the mission, typically two to three days ahead of any acquisition. Data policy is different depending on agency. Radarsat-2 data is fee for service while Sentinel-1 data are free. To collect Sentinel-1 SAR passes over TCs, requests must be submitted through the Copernicus Emergency Management Service. Similarly, RadarSat-2 and RCM data have to be ordered through the Canadian Space Agency via MDA Ltd. Efforts within the WMO and Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER) are underway to formalize SAR acquisitions over TCs as done for  Mouche et al.
(2017) , where TC observations from the Satellite Hurricane Observations Campaign collected SAR data over TCs from 2016 to 2018. While SAR passes will become more common in the future, latency 3  and scheduling issues will continue to limit operational use. Archived RadarSat-2, RMC, and Sentinel-1 passes are available publicly for post-storm analysis and research from both NOAA and IFREMER, as described in the data availability statement.
Archive data sets Algorithm development, climatology studies, and other basic research all rely on archive data sets, and archive owners often seek partnerships with prospective users. Surface wind data archives are particularly useful given their operational relevance and the long history of surface wind measurements in both operations and experiments. NOAA's Hurricane Research Division (HRD) maintains an archive of both NOAA research data and operationallycollected data from both NOAA and U. S. Air Force hurricane aircraft reconnaissance. Several reconnaissance datasets, including reprocessed SFMR, flight-level meteorological observations, dropwindsondes, and unmanned aerial vehicle observations, as well as derived products, are available from HRD's website. These datasets and products complement ATCF forecast, advisory, and satellite fix databases in tropical cyclone analysis and research applications.
HRD also developed a surface wind analysis called H*Wind that synthesized ship, buoy, SFMR, and flight level wind observations  (Powell et al. , 1998). In 2014, H*Wind was transferred to Hwind Scientific, which is now part of Risk Management Systems. Analyses are available by request for scientific use (https://www. rms. com/ event-response/hwind). A number of remotely sensed datasets are available in addition to the previously cited SAR archives. NASA hosts a TC-focused archive for QuikScat data on the Jet Propulsion Laboratory's Tropical Cyclone Information System web site, which also includes experimental data from several other instruments (when available). Data in the QuikScat archive are reprocessed version 3 of the science quality data  (SeaPAC, 2013; Fore et al. , 2014) , which have been further processed  (Stiles et al.
, 2014)  to capture higher TC winds and transform them into azimuthal averages  (Chavas and Vigh, 2015). Other archived remotely sensed data sources include KNMI's scatterometer database and NOAA NESDIS's Ocean Surface Winds Team archive of scatterometer and radiometer surface wind display products. RSS maintains a reprocessed radiometer and scatterometer archive. The website URLs for accessing archived datasets are provided in data availability statements from each of the hosting organizations. We recognized there are many other Universities and organizations that provide similar datasets, but have limited this section to the most used of datasets.
Historical perspective To understand the quality of best track wind radii information, we need to examine observation availability through time (Fig. 8 ). Prior to 1988, only wind speed observations of opportunity were available to operational TC forecasting centers. The Special Sensor Microwave Imager (SSM/I), which provided passive microwave data applicable for estimating wind speeds in rain free regions, began retrievals in 1987. The following year, aircraft data transmission latency decreased as satellite-based transmission of aircraft reconnaissance information such as center fixes, which provided central pressure estimates. Flight-level wind measurements remained unreliable in this period. By the 1990s, scatterometer winds became available to operational forecasters. The European Space agency launched the first C-band scatterometers on ERS-1 and -2, each with narrow swaths of approximately 500 km and 25 km grid spacing.
SCATSAT launched in 2016, and employs the same type of Ku-band scatterometer as its OceanSat predecessor. OceanScat-2 scatterometer data were recently calibrated for TCs, but still present interpretation challenges. In 2019 and 2020, wind speed algorithms for L-band radiometers such as SMOS and SMAP gained acceptance at JTWC and other operational centers. Starting in 2019, RadarSat-2, Sentinel-1, and the RCM SARs all now provide high-resolution surface winds for operational centers. While many operational centers are evaluating the plethora of recently available satellite capabilities, they all still rely heavily or entirely (NHC) on ASCAT.
Recommendations Current capabilities are insufficient to support specification of the entire TC surface wind field every 6 h, as TC forecasting centers require. And, forecasters need more timely data with higher temporal and spatial resolution (on the order of 2e4 km) to identify features such as secondary wind maxima and eyewall replacement cycles. Researchers and product developers also need accurate records of all relevant TC surface wind parameters in best track and other operationallygenerated datasets in order to develop high-quality analysis guidance and conduct climatological studies. These requirements lead us to provide four recommendations, indented and italicized in boldface below. Current TC wind field estimates, while improved, are insufficient to address the requirements described above.
Postseason reanalysis (of best track datasets) is inherently subjective, and the type and quality of historical data available to inform surface wind field estimates, even since 2000, are highly variable. On the bright side, observation availability and operational capabilities have improved in the last 15e20 years, so the more recent estimates in the best tracks are more likely to be based on observations. In fact, many TC centers are just beginning to conduct rigorous post-season analysis of their best track wind radii. Documentation on post-season analyses of wind radii has been provided for North Atlantic and eastern North Pacific  (Landsea and Franklin, 2013) , the Australian Region (Bureau of Meteorology, 2020;  Courtney et al. , 2020) , and western North Pacific TCs  (Sampson et al.
, 2017; JMA, 2020). Still, there are homogeneity issues in the analyses and users need to understand the quality and background of these best track analyses before using them for research and development. Uncertainties in wind radii estimates have been discussed in  Sampson et al. (2017 Sampson et al. ( , 2018) )  and  Combot et al. (2020). It is also important that while these data were available for post-storm analyses (Fig. 8 ), the availability of data on operational workstations, which implies it could have been used for such analyses, varies greatly from agency to agency. Organizational staffing and priorities also may impact the quality of such analyses. Thus, Fig. 8  presents a record of what could be used in a thorough reanalysis from all the past data. Because of these many issues, Best track users should contact organizations that created the records in order to properly assess whether datasets are appropriate for their use.
Despite recent and ongoing efforts to quality control surface wind best track records, there is a pressing need to acquire quality databases of wind speed estimates (section 3e) for use in research and development. However, the dearth of oceanic wind data prior to the early 2000s (Fig. 8 ) suggests that, with the data currently available that Wind radii reanalysis efforts should begin no earlier than the early 2000s. This statement finds further support in recent efforts to reanalyze wind radii at the Bureau of Meteorology, which concluded that their highest quality best tracks exist for periods following the 2003e2004 season (Bureau of Meteorology, 2020;  Courtney et al. , 2020).
Summary and concluding remarks In this review, we have described operationally-relevant TC wind structures, presented a brief history of operational practices and the associated climatological datasets, discussed tools available for estimating TC surface winds, and documented records available for studies. Optimal surface wind estimation techniques would estimate VMAX within the 10 kt (5 m s À1 ) uncertainty observed in the best track  (Landsea and Franklin, 2013; Torn and Snyder, 2012; Combot et al. , 2020)  and wind radii to within an 20% mean error  (Sampson et al. , 2017; Combot et al. , 2020). Many of the remote sensing sensors cited in this review provide data useful to achieving these goals, but human intervention is typically required to discern the quality and capabilities of each.
g. , the Dvorak methods) are uncertain. We have also discussed emerging data sources that augment spatial and temporal coverage of the wind fields. These include SAR, WindSat all-weather winds, and GNSS-R. SAR provides a nearly instantaneous snapshot of the surface wind speeds at up to 50 m resolution, along with a high-resolution snapshot of storm structure, including the RMW. More SAR data are becoming available to operations with the recent launch of the RCM, RadarSat-2, and ESA's Sentinel 1 A and B satellites, a current total six SAR platforms. And, Sentinel 1 C/ D, and NISAR will provide two more in the near future. IFREMER and NOAA are acting to formalize agreements to collect and process SAR data over TCs on a routine basis. Thanks to ongoing efforts by those organizations, SAR data latency has dropped to as little as 2 h.
However, SAR data acquisition reservations must be placed at least 48 h in advance, and are thus depend on forecast track accuracy to target the correct locations. RSS has demonstrated the capability to produce all-weather wind speed estimates from WindSat, which bodes well for future efforts with similar sensors. Though at present, the still-functioning WindSat's future is uncertain as the data is no longer available. NASA's GNSS-R demonstration has demonstrated that GNSS satellite constellations can be used to provide wind speed estimates over large areas on a small budget. Variability in GPS signals has challenged NASA's GNSS-R demonstration, and future efforts would need to mitigate this issue for all GNSS satellite constellations, but the outlook is promising. It is quite possible that GNSS-R winds will soon be available in operations from commercial sources.
Acknowledgments J. Knaff  thanks  NOAA/Center for Satellite Applications and Research  for providing the time to compile this review, and to attend the 9th International Workshop on Tropical Cyclones, where this idea has its origins. ONR has provided funding  C. Sampson  through Program Element  0603207N. M. Morris  gratefully acknowledges support from the  CYGNSS Science Team. Her work was supported as part of the CYGNSS Competed Science and a part of the work described in this paper was carried out by the  Jet Propulsion Laboratory, California Institute of Technology  under a contract with the  NASA , Government sponsorship acknowledged. L. Ricciardulli  and  T. Meissner  acknowledge funding by the  NASA   Team Science Utilization of the Soil Moisture Active-Passive Mission  (SUSMAP, contract  NNH17CA04C ) and the  NASA Ocean Vector Wind Science Team (OVWST , contract  80HQTR19C0003 ).
Data availability statement This publication is supported by multiple datasets, which are openly available, though for cyber security registration is often required. The following are data types followed by locations where data availability can either be directly accessed or registration for access can be initiated: AMSR2 wind speeds from JAXA at https://suzaku. eorc. jaxa. jp/ GCOM_W/data/data_w_dpss. html, AMSR2 wind speed data from NOAA https://satepsanone. nesdis. noaa. gov/pub/ product/nde/amsr2/L2/(contact Paul Chang, paul. s. chang@ noaa. gov), AMSR2 wind speeds from Remote Sensing Systems at http://www. remss. com/missions/amsr, SMAP wind speeds from NASA at https://podaac. jpl. nasa. gov/ dataset/SMAP_JPL_L2B_NRT_SSS_CAP_V42, SMAP wind speed from Remote Sensing Systems at http://www.
remss. com/missions/smap/, SMOS wind speeds at https:// www. smosstorm. org/Data2/SMOS-NRT-wind-Productsaccess, ASCAT, QuikScat, SCATSAT, HSCAT and other scatterometer data from http://projects. knmi. nl/ scatterometer/home/, CYGNSS wind speed data at https:// podaac. jpl. nasa. gov/CYGNSS?sections¼data, SAR data from NOAA at https://www. star. nesdis. noaa. gov/socd/mecb/ sar/AKDEMO_products/APL_winds/tropical/, SAR data from IFERMER at https://cyclobs. ifremer. fr/app/, Aircraft data including SFMR at https://www. aoml. noaa. gov/hrd/ data_sub/hurr. html, and climate quality QuikScat data from NASA at https://tropicalcyclone. jpl. nasa. gov/.
g. ,  Go FAIR Initiative, 2020; RDA-IWG, 2017; Bahim et al. , 2019; Clare et al. , 2019; Martinez et al. , 2019; Wu et al. , 2019). Due to such activity and the gradual increase of awareness that the research landscape as well as organizations are experiencing, the field of Research Data Management (RDM) has been gaining considerable momentum over the last decade. Management of RD for future reuse has become an integral part of the research and publication process and the obligation of fulfilling such management tasks is starting to become hardwired into proposals and manuscripts submitted to research foundations and publishers, respectively. Repositories, such as figshare (www. fig share. com), zenodo (www. zenodo. org) or PANGAEA (www. pangaea. de) have been set up to respond to the need by providing shareable data products and access to material which remains citable, shareable, and discoverable by institutions, publishers and researchers alike.
The strategies and developments of RDIs can be assigned to another larger structure that has been established at the end of last century. Due to a rapidly growing need for spatial data management and analysis, and the increasing number of user groups involved in dealing with spatial data, Spatial Data Infrastructures (SDIs) have become a major subfield of infrastructure research and development (e. g. ,  Williamson et al. , 2003). A SDI "goes far beyond surveying and mapping, it provides an environment within which organizations and/or nations interact with technologies to foster activities for using, managing and producing geographic data. […]  They allow the sharing of data, which is extremely useful, as it enables spatial data users and producers to save their efforts when trying to acquire new datasets.
Section 4 presents and discusses results from the development process along with a number of use cases. Section 5 finally concludes with a discussion and recommendation on implementation aspects and the expected gain from this process. While an encompassing discussion about research data will be needed in the future in order to enable RDM, each research dataset and each discipline have their own data specifications and established workflows. We here therefore concentrate on maps as one of the higher-level research products due to their complexity and nature of integrating various data sources within one research product. In particular when we discuss potential scenarios, the selection of research maps will turn out to be beneficial in the overall process and definition of potential interfaces.
Planetary mapping, maps and map use In order to facilitate our discussion, we need to establish the relationship between research data in general and the concepts of planetary mapping and planetary (geologic) maps, in particular as the latter are the focus in this investigation. The concept of RD encompasses a wide range of datasets that have been generated for, or were derived from, a process focused on research and the derivation of data through observation and analysis, and which are intended to be shared for re-use in the research community (e. g. ,  Borgman, 2012). This understanding focuses solely on purpose and shall suffice for the further discussion in this work. Planetary mapping as a concept is neither well defined nor constrained but its related activity has resulted in a large volume and variety of research data of the last decades.
When talking about maps and map products we refer to an abstract visual model which itself refers either partially or completely to entities in space. The abstraction comes partially from simplification in to form a model of reality on which a map can focus. A major motivation in the creation of a map is its specific purpose which governs the selection of objects on display. When presented as map sheet or as a digital map additional map elements, such as a legend (key), scale representation, explanatory text are included (cf. Robinson et al. , 1995). The focus of this work is on this last-named form of the map. These can in turn be divided into the following types: Classical (ortho-) photo-and image maps and derived products such as albedo and airbrush maps are developed from photos and data obtained by imaging instruments, and are eventually assembled to image mosaics of varying degree of internal control.
Image maps and map atlasses have been published systematically for a small selection of missions (e. g. ,  Roatsch et al. , 2016a; b, 2017 , 2018). Map data for these missions are accessible via the central planetary archives PDS and PSA  (Besse et al. , 2018). More historical examples are maps developed and published in the Surveyor Lunar Photomap and Map Series or the Voyager 1 and 2 Atlas  (Batson, 1984). Maps with uncontrolled but map-projected data form the lowest level of derivation as these constitute visualization of spatially referenced basic measurement data. More common are semi-controlled and controlled image (or photo) mosaics that may be used as raster images in subsequent studies as basemaps. Despite being mono-thematic in nature, image maps are best described as stand-alone reference base maps and are unsuitable for further development due to inherent limitations associated with the particular format.