Search Results
The NCAR–NOAA Global Hawk Dropsonde System1. Introduction Detailed measurements of the kinematic and thermodynamic structure of the atmosphere are important for numerical weather prediction (NWP) and physical process studies. While satelliteborne microwave and infrared sounders retrieve global thermodynamic profiles at relatively coarse vertical resolution, radiosondes and dropsondes (also called dropwindsondes) provide high-quality high-vertical-resolution in situ measurements of atmospheric wind, temperature, and relative humidity
1. Introduction Detailed measurements of the kinematic and thermodynamic structure of the atmosphere are important for numerical weather prediction (NWP) and physical process studies. While satelliteborne microwave and infrared sounders retrieve global thermodynamic profiles at relatively coarse vertical resolution, radiosondes and dropsondes (also called dropwindsondes) provide high-quality high-vertical-resolution in situ measurements of atmospheric wind, temperature, and relative humidity
water sparsity ( Dettinger 2013 ) but can also be hazardous as a cause of major flooding events ( Ralph et al. 2006 ; Ralph and Dettinger 2011 ; Henn et al. 2020 ). To better observe ARs and to improve the forecast skill over the U.S. West, Atmospheric River Reconnaissance (AR Recon) campaigns have been conducted to collect observations, including dropsondes from 1 to 3 aircraft ( Ralph et al. 2020 ), extra drifting buoys relative to the existing network of ocean buoys ( Reynolds et al. 2023
water sparsity ( Dettinger 2013 ) but can also be hazardous as a cause of major flooding events ( Ralph et al. 2006 ; Ralph and Dettinger 2011 ; Henn et al. 2020 ). To better observe ARs and to improve the forecast skill over the U.S. West, Atmospheric River Reconnaissance (AR Recon) campaigns have been conducted to collect observations, including dropsondes from 1 to 3 aircraft ( Ralph et al. 2020 ), extra drifting buoys relative to the existing network of ocean buoys ( Reynolds et al. 2023
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
stratocumulus—and only at the level of the aircraft. Though not well suited to characterizing the vertical profile of divergence, these measurements raise the question if, by measuring the vertical profile of the horizontal wind using dropsondes launched from an aircraft, one could estimate the vertical profile of large-scale mass divergence. Presuming that it is possible in principle, the important practical question then becomes how many sondes would be required to get an estimate of the divergence that
stratocumulus—and only at the level of the aircraft. Though not well suited to characterizing the vertical profile of divergence, these measurements raise the question if, by measuring the vertical profile of the horizontal wind using dropsondes launched from an aircraft, one could estimate the vertical profile of large-scale mass divergence. Presuming that it is possible in principle, the important practical question then becomes how many sondes would be required to get an estimate of the divergence that
troposphere. As a proxy, Tang and Emanuel (2012a) devised a ventilation index that is assumed to scale with the actual ventilation. The ventilation index has been primarily used to model and understand bulk TC genesis and intensity behaviors ( Lin et al. 2017 ; Hoogewind et al. 2020 ; Hsieh et al. 2020 ). In contrast, here, we demonstrate a way to diagnose ventilation via dropsonde observations, which may be useful for monitoring and anticipating ventilation-induced intensity changes in individual TCs
troposphere. As a proxy, Tang and Emanuel (2012a) devised a ventilation index that is assumed to scale with the actual ventilation. The ventilation index has been primarily used to model and understand bulk TC genesis and intensity behaviors ( Lin et al. 2017 ; Hoogewind et al. 2020 ; Hsieh et al. 2020 ). In contrast, here, we demonstrate a way to diagnose ventilation via dropsonde observations, which may be useful for monitoring and anticipating ventilation-induced intensity changes in individual TCs
1. Introduction Tropical cyclones (TCs) usually develop over data-sparse regions of the tropical oceans. The limited number of observations and the rapid development of TCs increases uncertainties of the model analysis in these regions, which can lead to significant forecast errors ( Langland 2005 ). Surveillance programs deploying dropsonde observations in and around TCs have been operated for the Atlantic ( Burpee et al. 1996 ; Aberson 2002 ) and the western North Pacific basin ( Wu et al
1. Introduction Tropical cyclones (TCs) usually develop over data-sparse regions of the tropical oceans. The limited number of observations and the rapid development of TCs increases uncertainties of the model analysis in these regions, which can lead to significant forecast errors ( Langland 2005 ). Surveillance programs deploying dropsonde observations in and around TCs have been operated for the Atlantic ( Burpee et al. 1996 ; Aberson 2002 ) and the western North Pacific basin ( Wu et al
Temporal and Spatial Autocorrelations from Expendable Digital Dropsondes (XDDs) in Tropical Cyclones1. Introduction The Office of Naval Research conducted the Tropical Cyclone Intensity (TCI) experiment in 2015 ( Doyle et al. 2017 ). Three of the tropical cyclones (TCs) that were sampled during TCI are Marty (27–28 September), Joaquin (2–5 October), and Patricia (20–23 October). A total of 725 global positioning system (GPS) dropwindsondes (hereinafter referred to as “dropsondes”) were launched into these three TCs. The dropsondes used were the Expendable Digital Dropsondes (XDDs
1. Introduction The Office of Naval Research conducted the Tropical Cyclone Intensity (TCI) experiment in 2015 ( Doyle et al. 2017 ). Three of the tropical cyclones (TCs) that were sampled during TCI are Marty (27–28 September), Joaquin (2–5 October), and Patricia (20–23 October). A total of 725 global positioning system (GPS) dropwindsondes (hereinafter referred to as “dropsondes”) were launched into these three TCs. The dropsondes used were the Expendable Digital Dropsondes (XDDs
strong impact on tropical cyclone predictions ( Andreas and Emanuel 2001 ; Wang et al. 2001 ; Bao et al. 2011 ), emphasizing the need for better understanding the physics behind heat and momentum transfer at the air–sea interface. In one of the earliest attempts to estimate C D within tropical cyclones, Powell et al. (2003 , hereafter P03 ) used dropsondes to construct mean velocity profiles, and from these, surface stresses (and thus C D ) were calculated using Monin–Obukhov similarity theory
strong impact on tropical cyclone predictions ( Andreas and Emanuel 2001 ; Wang et al. 2001 ; Bao et al. 2011 ), emphasizing the need for better understanding the physics behind heat and momentum transfer at the air–sea interface. In one of the earliest attempts to estimate C D within tropical cyclones, Powell et al. (2003 , hereafter P03 ) used dropsondes to construct mean velocity profiles, and from these, surface stresses (and thus C D ) were calculated using Monin–Obukhov similarity theory
) involved consecutive and coordinated aircraft missions. Participating dropsonde-equipped aircraft include the NASA DC-8 (GRIP), NCAR/NSF G-V (PREDICT), NOAA P-3s and G-IV, and U.S. Air Force (USAF) C-130s. Collaborative investigations during PGI include the rapid intensification and mature stages of Hurricane Earl, the nonredevelopment of Tropical Storm Gaston, the genesis of Tropical Storm Matthew, and perhaps most impressive, the entire life cycle of Hurricane Karl starting 4 days before genesis, to
) involved consecutive and coordinated aircraft missions. Participating dropsonde-equipped aircraft include the NASA DC-8 (GRIP), NCAR/NSF G-V (PREDICT), NOAA P-3s and G-IV, and U.S. Air Force (USAF) C-130s. Collaborative investigations during PGI include the rapid intensification and mature stages of Hurricane Earl, the nonredevelopment of Tropical Storm Gaston, the genesis of Tropical Storm Matthew, and perhaps most impressive, the entire life cycle of Hurricane Karl starting 4 days before genesis, to
Impact of Atmospheric River Reconnaissance Dropsonde Data on the Assimilation of Satellite Radiance Data in GFS) proposed to use radar reflectivity as the anchoring observation for Advanced Baseline Imager (ABI) all-sky radiance assimilation. In light of these studies, this research seeks to address the existing gap by investigating the role of reconnaissance (e.g., Majumdar 2016 ; English et al. 2018 ) dropsonde data in affecting the assimilation of satellite radiance data. We are particularly interested in the dropsonde observations collected during the Atmospheric River (AR) Reconnaissance (AR Recon
) proposed to use radar reflectivity as the anchoring observation for Advanced Baseline Imager (ABI) all-sky radiance assimilation. In light of these studies, this research seeks to address the existing gap by investigating the role of reconnaissance (e.g., Majumdar 2016 ; English et al. 2018 ) dropsonde data in affecting the assimilation of satellite radiance data. We are particularly interested in the dropsonde observations collected during the Atmospheric River (AR) Reconnaissance (AR Recon
