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modeling. One is the bogus technique ( Heming et al. 1995 ), which consists in forcing the assimilation of pseudo-observations (wind or pressure) into the model initial state. The pseudo-observations are deduced from an idealized cyclone structure observed by satellite imagery. These specific techniques generally improve cyclone forecasts ( Heming 2009 ). Section 2 recalls the general principle of the L82 method and presents the data. Section 3 shows the results for three numerical models. Before
modeling. One is the bogus technique ( Heming et al. 1995 ), which consists in forcing the assimilation of pseudo-observations (wind or pressure) into the model initial state. The pseudo-observations are deduced from an idealized cyclone structure observed by satellite imagery. These specific techniques generally improve cyclone forecasts ( Heming 2009 ). Section 2 recalls the general principle of the L82 method and presents the data. Section 3 shows the results for three numerical models. Before
. During each mission, the G-IV released 25–30 dropwindsondes to sample the atmosphere below flight level (near 150 hPa) at 150–200-km intervals. In those cases in which one or two P-3 or Air Force C-130 aircraft supplemented the G-IV data, 20–25 dropwindsondes were released at the same horizontal resolution from around 400 (P-3) or 300 hPa (C130). The G-IV did not penetrate the inner core of any of the tropical cyclones during surveillance missions, though when the P-3s flew, at least one usually
. During each mission, the G-IV released 25–30 dropwindsondes to sample the atmosphere below flight level (near 150 hPa) at 150–200-km intervals. In those cases in which one or two P-3 or Air Force C-130 aircraft supplemented the G-IV data, 20–25 dropwindsondes were released at the same horizontal resolution from around 400 (P-3) or 300 hPa (C130). The G-IV did not penetrate the inner core of any of the tropical cyclones during surveillance missions, though when the P-3s flew, at least one usually
field campaign was to address short-range TC dynamics and forecast skill in one region and the downstream impacts of TCs on medium-range dynamics and forecast skill in another region ( Elsberry and Harr 2008 ; Parsons et al. 2008 ). This was the first time that four aircraft [the DOTSTAR Astra jet, the German Aerospace Center (DLR) Falcon 20, a U.S. Navy P-3, and a U.S. Air Force C-130] were used simultaneously to observe typhoons. DOTSTAR Astra and DLR Falcon sampled the TC environment, especially
field campaign was to address short-range TC dynamics and forecast skill in one region and the downstream impacts of TCs on medium-range dynamics and forecast skill in another region ( Elsberry and Harr 2008 ; Parsons et al. 2008 ). This was the first time that four aircraft [the DOTSTAR Astra jet, the German Aerospace Center (DLR) Falcon 20, a U.S. Navy P-3, and a U.S. Air Force C-130] were used simultaneously to observe typhoons. DOTSTAR Astra and DLR Falcon sampled the TC environment, especially
improve the manuscript. Mike Jankulak contributed his proofreading and editing expertise. The author thanks the NOAA Aircraft Operations Center (AOC) flight crews, AOC G-IV project manager, Jack Parrish, and HRD personnel who participated in the flights. Additional thanks are due to Air Force C-130 crews that provided additional surveillance data in many cases, as well as the flight crews and scientists who have run the various programs in the west Pacific (DOTSTAR, T-PaRC, TCS-08). REFERENCES Aberson
improve the manuscript. Mike Jankulak contributed his proofreading and editing expertise. The author thanks the NOAA Aircraft Operations Center (AOC) flight crews, AOC G-IV project manager, Jack Parrish, and HRD personnel who participated in the flights. Additional thanks are due to Air Force C-130 crews that provided additional surveillance data in many cases, as well as the flight crews and scientists who have run the various programs in the west Pacific (DOTSTAR, T-PaRC, TCS-08). REFERENCES Aberson
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
same observations for a comparison with the EnKF analysis discussed above. The WRF-3DVAR method used here was developed primarily at NCAR and is now operational at the Air Force Weather Agency ( Barker et al. 2004 ). Its configuration is based on an incremental formulation, producing a multivariate analysis in the model space. Its incremental cost function is minimized in a preconditioned control variable space where the errors of different control variables are largely uncorrelated. As in any
same observations for a comparison with the EnKF analysis discussed above. The WRF-3DVAR method used here was developed primarily at NCAR and is now operational at the Air Force Weather Agency ( Barker et al. 2004 ). Its configuration is based on an incremental formulation, producing a multivariate analysis in the model space. Its incremental cost function is minimized in a preconditioned control variable space where the errors of different control variables are largely uncorrelated. As in any
index is defined as where f is the Coriolis force, V is the magnitude of the vector wind, and is the Brunt–Väisälä frequency. Here, the Eady index is calculated over the 300–1000-hPa layer. Midlatitude baroclinic instability is relatively weak in July and August during which recurving storms WP04, WP09, and WP11 have relatively small 5-day perturbation growth. Midlatitude baroclinic instability strengthens in September and October, during which recurving storms CP01, WP14, WP16, and WP21 have
index is defined as where f is the Coriolis force, V is the magnitude of the vector wind, and is the Brunt–Väisälä frequency. Here, the Eady index is calculated over the 300–1000-hPa layer. Midlatitude baroclinic instability is relatively weak in July and August during which recurving storms WP04, WP09, and WP11 have relatively small 5-day perturbation growth. Midlatitude baroclinic instability strengthens in September and October, during which recurving storms CP01, WP14, WP16, and WP21 have
. , 134 , 2971 – 2988 . Barkmeijer , J. , 1996 : Constructing fast-growing perturbations for the nonlinear regime. J. Atmos. Sci. , 53 , 2838 – 2851 . Barkmeijer , J. , R. Buizza , T. N. Palmer , K. Puri , and J-F. Mahfouf , 2001 : Tropical singular vectors computed with linearized diabatic physics. Quart. J. Roy. Meteor. Soc. , 127 , 685 – 708 . Barkmeijer , J. , T. Iversen , and T. N. Palmer , 2003 : Forcing singular vectors and other sensitive model structures
. , 134 , 2971 – 2988 . Barkmeijer , J. , 1996 : Constructing fast-growing perturbations for the nonlinear regime. J. Atmos. Sci. , 53 , 2838 – 2851 . Barkmeijer , J. , R. Buizza , T. N. Palmer , K. Puri , and J-F. Mahfouf , 2001 : Tropical singular vectors computed with linearized diabatic physics. Quart. J. Roy. Meteor. Soc. , 127 , 685 – 708 . Barkmeijer , J. , T. Iversen , and T. N. Palmer , 2003 : Forcing singular vectors and other sensitive model structures
) approach. The vertical localization is configured to force the covariances to 0 at a distance of (a) 2, (b) 4, or (c) 100 scale heights. Fig . 2. As in Fig. 1 , but for a single observation of AMSU-A channel 10. Fig . 3. The analysis increment of temperature from assimilating either (a) the full set of AMSU-A channels (4–10) at the same location used for Figs. 1 and 2 or (b) the vertical profile of temperature observations from a
) approach. The vertical localization is configured to force the covariances to 0 at a distance of (a) 2, (b) 4, or (c) 100 scale heights. Fig . 2. As in Fig. 1 , but for a single observation of AMSU-A channel 10. Fig . 3. The analysis increment of temperature from assimilating either (a) the full set of AMSU-A channels (4–10) at the same location used for Figs. 1 and 2 or (b) the vertical profile of temperature observations from a
