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October, these geostationary and polar-orbiting satellite-based positions agree well with the NHC best tracks, albeit with a consistent bias to the west. However, there are two times (0600 and 1200 UTC 4 October) when Joaquin was rapidly moving northeastward that the SATCON positions are highly biased to the east. An Air Force reconnaissance mission 305 was presumably responsible for that NHC best track position at 1200 UTC 4 October (and similarly mission 303 for 0000 UTC 4 October and mission 304
October, these geostationary and polar-orbiting satellite-based positions agree well with the NHC best tracks, albeit with a consistent bias to the west. However, there are two times (0600 and 1200 UTC 4 October) when Joaquin was rapidly moving northeastward that the SATCON positions are highly biased to the east. An Air Force reconnaissance mission 305 was presumably responsible for that NHC best track position at 1200 UTC 4 October (and similarly mission 303 for 0000 UTC 4 October and mission 304
mission on a NASA flight to monitor a satellite launch from Vandenberg Air Force Base, California. The sonde deployments were made on the return leg of a north–south flight leg from 12° to 21°N, approximately along 119°W, directly along a dry air intrusion to the east of the former Tropical Storm Cosme, and extending across a strong SSTir gradient of 22°–27°C, as shown in Figs. 8a,b . Three fast-fall sondes (light blue, magenta, and light gray symbols) with sea level fall speeds of 17 m s −1 were
mission on a NASA flight to monitor a satellite launch from Vandenberg Air Force Base, California. The sonde deployments were made on the return leg of a north–south flight leg from 12° to 21°N, approximately along 119°W, directly along a dry air intrusion to the east of the former Tropical Storm Cosme, and extending across a strong SSTir gradient of 22°–27°C, as shown in Figs. 8a,b . Three fast-fall sondes (light blue, magenta, and light gray symbols) with sea level fall speeds of 17 m s −1 were
1. Introduction In recent decades, research has shown that the tropical cyclone (TC) outflow layer is critically related to the TC structure evolution and intensity change rather than just a mechanism to export TC energy at the upper troposphere. The outflow layer relative to the low- and midtroposphere has weaker inertial stability and thus is more susceptible to the environmental forcing ( Holland and Merrill 1984 ; Rappin et al. 2011 ). For example, the outflow can interact with a
1. Introduction In recent decades, research has shown that the tropical cyclone (TC) outflow layer is critically related to the TC structure evolution and intensity change rather than just a mechanism to export TC energy at the upper troposphere. The outflow layer relative to the low- and midtroposphere has weaker inertial stability and thus is more susceptible to the environmental forcing ( Holland and Merrill 1984 ; Rappin et al. 2011 ). For example, the outflow can interact with a
appropriate SAMURAI wind increments. For each of these three domain 2 time steps, three time steps are taken on the 5-km domain 3 centered on the TC forecast position (both domains 2 and 3 are moved with the storm). In each time step on each domain, the mass fields will be adjusted to the SAMURAI wind increment forcing derived for that 15-min AMV dataset. Since the time steps for domains 1, 2, and 3 are 90, 30, and 10 s, the SCDI with 15-min AMV datasets will have 10, 30, and 90 time steps, respectively
appropriate SAMURAI wind increments. For each of these three domain 2 time steps, three time steps are taken on the 5-km domain 3 centered on the TC forecast position (both domains 2 and 3 are moved with the storm). In each time step on each domain, the mass fields will be adjusted to the SAMURAI wind increment forcing derived for that 15-min AMV dataset. Since the time steps for domains 1, 2, and 3 are 90, 30, and 10 s, the SCDI with 15-min AMV datasets will have 10, 30, and 90 time steps, respectively
systems are known, the mean NLLE can be directly obtained from the error evolution equations of the systems. However, the error evolution equations are not known explicitly for the real atmosphere, because of imprecisely known parameters and external forcing terms ( Ding and Li 2007 ). In this case, the NLLE may be estimated from the atmospheric observational data using the LDAs algorithm ( Ding et al. 2010 , 2011 ; Li and Ding 2011 ; Ding et al. 2016 ; Liu et al. 2016 ). In this study, the NLLE
systems are known, the mean NLLE can be directly obtained from the error evolution equations of the systems. However, the error evolution equations are not known explicitly for the real atmosphere, because of imprecisely known parameters and external forcing terms ( Ding and Li 2007 ). In this case, the NLLE may be estimated from the atmospheric observational data using the LDAs algorithm ( Ding et al. 2010 , 2011 ; Li and Ding 2011 ; Ding et al. 2016 ; Liu et al. 2016 ). In this study, the NLLE
Temporal and Spatial Autocorrelations from Expendable Digital Dropsondes (XDDs) in Tropical Cyclones: Convective forcing in the intertropical convergence zone of the eastern Pacific . J. Atmos. Sci. , 60 , 2064 – 2082 , https://doi.org/10.1175/1520-0469(2003)060<2064:CFITIC>2.0.CO;2 . 10.1175/1520-0469(2003)060<2064:CFITIC>2.0.CO;2 Rizvi , S. R. H. , Z. Liu , and X.-Y. Huang , 2012 : Generation of WRF-ARW background errors (BE) for GSI. NCAR Rep., 29 pp., https://dtcenter.org/com-GSI/users/docs/write_ups/WRF-ARW-GSI_BE.pdf . Rogers , R. F. , and Coauthors , 2017 : Rewriting the tropical
: Convective forcing in the intertropical convergence zone of the eastern Pacific . J. Atmos. Sci. , 60 , 2064 – 2082 , https://doi.org/10.1175/1520-0469(2003)060<2064:CFITIC>2.0.CO;2 . 10.1175/1520-0469(2003)060<2064:CFITIC>2.0.CO;2 Rizvi , S. R. H. , Z. Liu , and X.-Y. Huang , 2012 : Generation of WRF-ARW background errors (BE) for GSI. NCAR Rep., 29 pp., https://dtcenter.org/com-GSI/users/docs/write_ups/WRF-ARW-GSI_BE.pdf . Rogers , R. F. , and Coauthors , 2017 : Rewriting the tropical
1. Introduction Until recently, a single ER-2 flight over Hurricane Erin (2001) provided the only direct dropsonde observations through the full depth of the tropical cyclone (TC) outflow layer ( Halverson et al. 2006 ). Conventional aircraft observations of TCs, such as by the U.S. Air Force C-130s and the NOAA P-3s, tend to be limited to the middle to lower levels of the cyclone with a typical flight level of 700 hPa ( Aberson et al. 2006 ). Synoptic observations provided by the NOAA G-IV are
1. Introduction Until recently, a single ER-2 flight over Hurricane Erin (2001) provided the only direct dropsonde observations through the full depth of the tropical cyclone (TC) outflow layer ( Halverson et al. 2006 ). Conventional aircraft observations of TCs, such as by the U.S. Air Force C-130s and the NOAA P-3s, tend to be limited to the middle to lower levels of the cyclone with a typical flight level of 700 hPa ( Aberson et al. 2006 ). Synoptic observations provided by the NOAA G-IV are
-0151.1 . 10.1175/WAF-D-18-0151.1 Zhu , P. , B. Tyner , J. A. Zhang , E. Aligo , S. Gopalakrishnan , F. D. Marks , A. Mehra , and V. Tallapragada , 2019 : Role of eyewall and rainband eddy forcing in tropical cyclone intensification . Atmos. Chem. Phys. , 19 , 14 289 – 14 310 , https://doi.org/10.5194/ACP-19-14289-2019 . 10.5194/acp-19-14289-2019 Zhu , Y. , and Coauthors , 2016 : All-sky microwave radiance assimilation in NCEP’s GSI analysis system . Mon. Wea. Rev. , 144
-0151.1 . 10.1175/WAF-D-18-0151.1 Zhu , P. , B. Tyner , J. A. Zhang , E. Aligo , S. Gopalakrishnan , F. D. Marks , A. Mehra , and V. Tallapragada , 2019 : Role of eyewall and rainband eddy forcing in tropical cyclone intensification . Atmos. Chem. Phys. , 19 , 14 289 – 14 310 , https://doi.org/10.5194/ACP-19-14289-2019 . 10.5194/acp-19-14289-2019 Zhu , Y. , and Coauthors , 2016 : All-sky microwave radiance assimilation in NCEP’s GSI analysis system . Mon. Wea. Rev. , 144
