Comparison of Tropical Cyclone Center Positions Determined from Satellite Observations at Infrared and Microwave Frequencies

Y. Hu Joint Center of Data Assimilation for Research and Application, Nanjing University of Information and Science and Technology, Nanjing, China

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X. Zou Joint Center of Data Assimilation for Research and Application, Nanjing University of Information and Science and Technology, Nanjing, China

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Abstract

Determining tropical cyclone (TC) center positions is of interest to many researchers who conduct TC analysis and forecasts. In this study, we develop and apply a TC centering technique to Cross-Track Infrared Sounder (CrIS) and Advanced Technology Microwave Sounder (ATMS) observations of brightness temperature and report on an improvement of accuracy by adding a TC spectral analysis to the state of the art [Automated Rotational Center Hurricane Eye Retrieval (ARCHER)], especially for ATMS. We show that the ARCHER TC center-fixing algorithm locates TC centers more successfully based on the infrared channel with center frequency at 703.75 cm−1 (channel 89) of the CrIS than the ATMS channel 22 (183.31 ± 1.0 GHz) due to small-scale features in ATMS channel’s brightness temperature field associated with strong convective clouds. We propose to first apply the ARCHER TC center-fixing algorithm to ATMS channel 4 (51.76 GHz) that is less affected by small-scale convective clouds, and then to perform a set of the azimuthal spectral analysis of the ATMS channel-22 observations with tryout centers within a squared box centered at the ATMS channel-4-determined center. The center that gives the largest symmetric component is the final ATMS-determined center. Compared to the National Hurricane Center best track, the root-mean-square center-fixing errors determined from the two ATMS channels (one single CrIS channel) are 29.9 km (35.8 km) and 28.0 km (30.9 km) for 104 tropical storm and 81 hurricane cases, respectively, in the 2019 hurricane season.

© 2020 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Xiaolei Zou, xzou@nuist.edu.cn

Abstract

Determining tropical cyclone (TC) center positions is of interest to many researchers who conduct TC analysis and forecasts. In this study, we develop and apply a TC centering technique to Cross-Track Infrared Sounder (CrIS) and Advanced Technology Microwave Sounder (ATMS) observations of brightness temperature and report on an improvement of accuracy by adding a TC spectral analysis to the state of the art [Automated Rotational Center Hurricane Eye Retrieval (ARCHER)], especially for ATMS. We show that the ARCHER TC center-fixing algorithm locates TC centers more successfully based on the infrared channel with center frequency at 703.75 cm−1 (channel 89) of the CrIS than the ATMS channel 22 (183.31 ± 1.0 GHz) due to small-scale features in ATMS channel’s brightness temperature field associated with strong convective clouds. We propose to first apply the ARCHER TC center-fixing algorithm to ATMS channel 4 (51.76 GHz) that is less affected by small-scale convective clouds, and then to perform a set of the azimuthal spectral analysis of the ATMS channel-22 observations with tryout centers within a squared box centered at the ATMS channel-4-determined center. The center that gives the largest symmetric component is the final ATMS-determined center. Compared to the National Hurricane Center best track, the root-mean-square center-fixing errors determined from the two ATMS channels (one single CrIS channel) are 29.9 km (35.8 km) and 28.0 km (30.9 km) for 104 tropical storm and 81 hurricane cases, respectively, in the 2019 hurricane season.

© 2020 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Xiaolei Zou, xzou@nuist.edu.cn

1. Introduction

Locating the center of a tropical cyclone (TC) is the initial step in many TC-retrieval algorithms whose overall accuracy heavily relies on the accuracy of the center fix. A precise center fix of a TC is also critical for vortex initialization and operational forecasting and intensity estimation (Olander et al. 2004; Velden et al. 2006), TC visualization (Wimmers and Velden 2007), TC-structure retrievals (Sitkowski et al. 2011), and the detection of TC rapid intensification (Rozoff et al. 2015). TC centers determined subjectively by experienced forecasters have less consistency (Dvorak 1984; Velden et al. 1998). Since TCs occur and intensify mostly over the oceans where only a handful of in situ measurements are available, meteorological satellite remote sensing observations are the primary source of information for locating TC centers.

The Automated Rotational Center Hurricane Eye Retrieval (ARCHER; Wimmers and Velden 2010, 2016) is an automated center-fixing algorithm, and was used in the advanced Dvorak technique (ADT; Olander and Velden 2007) for determining the best track of hurricanes. The ADT was developed to objectively determine the TC intensity using geostationary satellite infrared imagery. The ARCHER determines a TC center position mainly based on the structure and organization of cloud features in visible and infrared imagery in most applications, such as the ADT. However, results may not be accurate in situations where the hurricane eye appears ragged or is partially obscured or if the TC center is totally covered by a central dense overcast cloud field (Wimmers and Velden 2010). Microwave observations from polar-orbiting satellites can penetrate clouds except for heavy precipitation and can well reveal convective organizations and eyewall structures, beneficial for locating TC centers. The ARCHER was applied to 37- and 85–92-GHz microwave observations from imagers, e.g., the Special Sensor Microwave Imager, the Special Sensor Microwave Imager/Sounder, the Tropical Rainfall Measuring Mission Microwave Imager, the Global Change Observation Mission—Water Satellite 1 Advanced Microwave Scanning Radiometer 2, the Global Precipitation Measurement Microwave Imager, and the WindSat imager (Wimmers and Velden 2016). Explored in this study are satellite microwave observations from temperature and humidity sounders.

We compare the ARCHER-determined TC centers from both infrared and microwave satellite observations and examine if the infrared-determined and microwave-determined centers are consistent. Specifically, the Advanced Technology Microwave Sounder (ATMS) on board the Suomi National Polar-Orbiting Partnership (SNPP) satellite will be used to determine the TC centers. ATMS observations have been widely used in numerical weather prediction and tropical cyclone applications, such as the track and intensity forecasts of landfall hurricanes (Zou et al. 2013), hurricane warm-core retrievals (Tian and Zou 2016; Zou and Tian 2018), and the examination of the diurnal variability of hurricanes’ warm cores (Tian and Zou 2018). There are several reasons for using ATMS observations to determine TC centers. First, microwave radiation can penetrate most clouds beyond the top layers, a beneficial feature for monitoring TCs obscured by cloud tops. Second, the resolutions of ATMS temperature-sounding channels 3–16 and humidity channels 17–22 are 32 and 16 km at nadir, respectively, sufficient to resolve hurricane eyes whose diameters range from 8 to over 200 km and are mostly between 30 and 60 km (Weatherford and Gray 1988). Third, the ATMS has a wider scan swath and leaves almost no data gaps even near the equator, important for monitoring TCs, especially in their early stages. Furthermore, compared to its predecessors, the Advanced Microwave Sounding Unit-A (AMSU-A) and the Microwave Humidity Sounder (MHS), the ATMS has a new temperature-sounding channel and two new water-vapor-sounding channels. These new features allow the ATMS to provide more detailed information about vertical thermal and water vapor structures in the lower troposphere, which is especially important for TC studies (Zou et al. 2013). Although ATMS observations from a single polar-orbiting satellite set a limit on a low temporal resolution (up to twice daily for a TC), the ATMS on board the NOAA-20 satellite, and the microwave temperature sounder-2 and microwave humidity sounder on board the Chinese Fengyun satellites C and D have similar observation resolutions and will provide supplemental information about TC positions.

Specifically, the lower-frequency ATMS temperature-sounding channel-4 observations are less affected by small-scale convection due to its lower resolution (32 km) than the ATMS humidity channels (16 km). Therefore, the ARCHER algorithm is first applied to ATMS temperature channel 4 (51.76 GHz) to avoid the complication of small-scale convection and TC centers if the water vapor channel 22 (183.31 ± 1.0 GHz) was used. A set of the azimuthal spectral analysis (Zou et al. 2010; Tian and Zou 2019) is then carried out on the channel-22 brightness temperature observations with assumed TC centers near the channel-4-determined TC center. The center that achieves the largest axisymmetric component is selected as the final ATMS-determined TC center. In other words, two ATMS microwave channels 4 and 22 are combined to determine the TC centers. As a comparison, the ARCHER algorithm is also applied to the longwave infrared channel 89 (703.75 cm−1) of the Cross-Track Infrared Sounder (CrIS), which is also on board the SNPP satellite, to locate the TC centers. The weighting function peak of CrIS channel 89 is similar to the ATMS channel 22 (~300 hPa) under clear-sky conditions (Li and Zou 2017; Weng et al. 2012).

This paper is organized as follows. Section 2 briefly describes the ATMS and CrIS instrument channel characteristics, the best track from the National Hurricane Center (NHC), and the TC cases. Section 3 summarizes the three major components of the ARCHER algorithm. Section 4 presents the results from applying the ARCHER algorithm to a single ATMS or CrIS channel. In section 5, an azimuthal spectral analysis method is presented and applied to further improve the ARCHER-determined ATMS TC center-fixing results. Section 6 provides a summary and conclusions.

2. Data and TC case description

a. ATMS, CrIS, and best track data description

The ATMS and CrIS are on board the SNPP satellite, which was successfully launched on 28 October 2011 into a near-polar orbit at an altitude of 824 km above Earth and with an inclination angle of 98.7° ± 0.05° to the equator. The ATMS is a cross-track scanning microwave radiometer with 22 channels at frequencies ranging from 23.8 to 190.31 GHz, which allows for probing the atmospheric temperature and moisture profiles under all weather conditions except for heavy precipitation (Weng et al. 2012). It scans a range of ±52.725° from nadir to complete a total of 96 fields of view (FOVs) along each scanline and has a swath width of 2700 km. Specifically, ATMS channels 1–16 are primarily for temperature soundings from the surface to about 1 hPa (~45 km), and channels 17–22 are for humidity soundings from the surface to about 200 hPa (~10 km). ATMS channels 1–3 and 5–16 are similar to those of the AMSU-A, and the ATMS channel 4 is a new channel whose central frequency is at 51.76 GHz to provide additional temperature information from the lower troposphere. ATMS channels 17, 18, 20, and 22 are similar to MHS channels 2–5, and ATMS channels 19 and 21 are new, with central frequencies located at 183 ± 4.5 and 183 ± 1.8 GHz. In this study, ATMS channels 4 and 22 (Table 1) are selected for determining the TC centers. The FOV sizes of channels 4 and 22 are 32 and 16 km at nadir, respectively. Figure 1a shows the weighting functions of these two channels. The weighting functions are largest at 950 and 300 hPa for channels 4 and 22, respectively.

Table 1.

Channel number, center frequency or wavenumber, nadir field of view (FOV) size, number of FOVs per scan, and weighting function (WF) peaks of ATMS channels 4 and 22 and CrIS channel 89.

Table 1.
Fig. 1.
Fig. 1.

(a) Weighting functions for ATMS channels 4 and 22 and CrIS channel 89. (b) Spatial distribution of ATMS channel-22 brightness temperature observations (color shading; unit: K) and the best track (typhoon symbol) for Typhoon Trami at around 0512 UTC 24 Sep 2018. The ATMS footprints along two consecutive scan lines that passed through Trami’s center are indicated by black and purple crosses.

Citation: Journal of Atmospheric and Oceanic Technology 37, 11; 10.1175/JTECH-D-20-0049.1

The CrIS infrared longwave temperature channel 89 (Table 1) is used for locating TC centers. The CrIS is a hyperspectral Fourier transform spectrometer (Han et al. 2013; Li and Zou 2017). A scanline of CrIS consists of 30 fields of regard (FORs), with each FOR containing nine FOVs. The nadir FOV size of CrIS is 14 km. The weighting function peak of CrIS channel 89 is at about 300 hPa (Fig. 1a). Brightness temperature observations at this channel are mostly radiation from the atmospheric layer around 300 hPa in clear-sky conditions or from cloud tops in the presence of high-level clouds.

NHC best track data are used as a reference for comparing the ATMS- and CrIS-determined TC centers. It is the poststorm analyses of the intensities, central pressures, positions, and sizes of TCs at 0000, 0600, 1200, and 1800 UTC during the lifespans of the TCs (Landsea and Franklin 2013). It utilizes all available observations of the TCs, including satellite observations, scatterometer observations, aircraft reconnaissance data, and in situ observations. The best track position uncertainty is about 55.5, 37.4, and 19.8 km for tropical storms, category 1 and 2 hurricanes, and major hurricanes, respectively. The best track position uncertainty reduces to 35.4, 24, and 18 km if limited to those times with aircraft reconnaissance observations. These uncertainties respectively decrease to about 22 n mi (35.4 km), 14.9 n mi (24 km), and 11.2 n mi (18 km) for aircraft reconnaissance and satellite measurements, even further decrease to about 18 n mi (29 km), 12 n mi (19.3 km), and 7.8 n mi (12.6 km) For U.S. landfalling tropical cyclones, for which airborne radar observations are available, the uncertainty further reduces to 29.0, 19.3, and 12.6 km (Landsea and Franklin 2013).

b. TC case description

In general, the SNPP satellite can observe the same geolocation twice a day. Due to much smaller orbital gaps of ATMS than AMSU-A at low latitudes (Weng et al. 2012), fewer TCs may have their center located in the gaps or near the swath edges. Figure 1b shows the spatial distribution of ATMS channel-22 brightness temperature observations where the hurricane center is located at the 94th FOV. The ARCHER algorithm requires a TC storm to have a clear evidence of rotation, such as shear, eyewall, or banding. In this study, we only select TC cases with their maximum sustained wind speed greater than or equal to 34 kt (17 m s−1). A total of 161 TC cases in 2012, 2017, and 2018 are arbitrarily selected for developing the center fixing algorithm and illustrating relevant results, of which 82 cases are over the Atlantic (Fig. 2a), and 79 cases are over the Pacific (Fig. 2b). Most of the selected TC cases were located south of 40°N to serve as a coarse exclusion of extratropical transition cases.

Fig. 2.
Fig. 2.

Spatial distributions of (a) Atlantic Hurricanes Sandy in 2012, Irma, Maria, and Jose in 2017, Oscar, Chris, Helene, and Isaac in 2018, and (b) Pacific Typhoons Cimaron, Jebi, Jelawat, Kong-rey, Maria, Soulik, Trami, and Yutu in 2018. The intensity category is indicated by colors.

Citation: Journal of Atmospheric and Oceanic Technology 37, 11; 10.1175/JTECH-D-20-0049.1

3. A brief description of the ARCHER algorithm

The TC centering technique to be applied to CrIS and ATMS observations of brightness temperature consists of two major steps. The first step adopts the empirical and calibrated parameters of the weight coefficients of the spiral score relative to the ring score provided by Wimmers and Velden (2010). The second step carries out an azimuthal spectrum analysis to further improve the TC center-fixing accuracy.

The ARCHER algorithm objectively determines the rotational center of a TC by automatically matching the horizontal gradients of brightness temperature observed by a satellite instrument with a given spiral unit-vector field and fitting the hurricane eye with rings of different radii. This sectionc presents a brief synopsis of the methodology and components of the ARCHER algorithm. To better illustrate how different procedures of the ARCHER algorithm operate, the ATMS microwave channel 22 is used as an example. Wimmers and Velden (2010) give more detailed descriptions of the ARCHER algorithm.

a. Preprocessing

The brightness temperature observations are first interpolated to a regular 0.2° × 0.2° grid for input into the center-fixing algorithm. The resolution of the grid is equivalent to that of the ATMS channel 22 (16 km). Cubic interpolation is adopted to ensure the accuracy of brightness temperatures and the authenticity of the gradients of the brightness temperature field. A first-guess position is also required to identify the relevant satellite data and establish a limited-size domain of the calculations. Assuming that the hurricane has a constant moving speed and direction in a short period of time, the position extrapolated from the NHC best track position at two times just before the time of the ATMS scan swath is used as the first-guess position for determining a data domain within which to search for a TC center.

b. Spiral centering

A spiral centering score is calculated to measure how well the gradients of a brightness temperature field align with a given spiral pattern representative of an overall TC structure. Numerically, the cross products between the gradients of the brightness temperature field and the spiral unit-vector field that is centered at different grid points are calculated. Figure 3 shows the norms (i.e., lengths) of the cross product vectors between the gradients of ATMS channel-22 brightness temperature observations at 1720 UTC 4 September 2017 and the spiral pattern of unit vectors given by Wimmers and Velden (2010) centered on two different positions. Overall, the norms of the cross product vectors in Fig. 3b are lower than those in Fig. 3a, indicating a better alignment of the horizontal gradients of brightness temperature with the corresponding spiral unit-vector field in Fig. 3a. A fine spiral score (FSS) on a grid point is defined as the average of cross-product vector norms on all points when the spiral unit-vector field is centered on this grid point. Figure 4a shows the horizontal distribution of ATMS channel-22 brightness temperature observations and the FSS for Hurricane Irma at around 1720 UTC 4 September 2017. The point where the FSS is the largest (indicated by the diamond symbol) is close to the location of the maximum brightness temperature observations and slightly deviates from the best track TC position (hurricane symbol).

Fig. 3.
Fig. 3.

Norms of the cross-product vectors (color shading; unit: 10−2) between the horizontal gradients of ATMS channel-22 brightness temperature observations at around 1720 UTC 4 Sep 2017 and a hurricane circulation field (black arrows) centered at (a) the diamond and (b) the cross symbols. Wimmers and Velden (2010) provide the hurricane circulation field.

Citation: Journal of Atmospheric and Oceanic Technology 37, 11; 10.1175/JTECH-D-20-0049.1

Fig. 4.
Fig. 4.

(a) Spatial distribution of ATMS channel-22 (183.31 ± 1.0 GHz, 300 hPa) brightness temperature observations (color shading; unit: K) for Hurricane Irma at around 1720 UTC 4 Sep 2017. The contours are the FSS (unit: 10−2) computed at 0.2° × 0.2° grid boxes (black dots) in the 4° × 4° calculation domain centered on the first-guess position (plus symbol). Also shown are(b) RS (unit: 10−2) and the (c) CS (unit: 10−2) computed at 0.2° × 0.2° grid boxes (black dots) in the 1.2° × 1.2° calculation domain [dashed box in (a)] centered at the point with a maximum FSS. The maximum FSS, RS, and CS are indicated by the diamond, open circle, and triangle symbols. The best track is indicated by a hurricane symbol.

Citation: Journal of Atmospheric and Oceanic Technology 37, 11; 10.1175/JTECH-D-20-0049.1

c. Ring centering

Ring centering defines a ring score (RS) that measures how well gradients of a brightness temperature field fit to a given ring pattern representing a TC eyewall inner edge if it appears. The dot products between the brightness temperature gradients and a ring of radially oriented unit vectors with different radii are calculated at all grid points at a 0.2° × 0.2° resolution in a limited domain (1.2° × 1.2°) centered on the maximum FSS point. The RS at a grid point is defined as the highest average dot product value from a range of ring sizes centered on the grid point. Figure 4b shows the horizontal distribution of the RS in the 1.2° × 1.2° domain centered on the maximum FSS point. In this example, the maximum RS point (the open circle in Fig. 4b) coincides neither with the maximum FFS center nor the best track due to the irregular hurricane eye of this example.

d. Optimized combination of spiral and ring centering

The weighted sum of the FSS and RS defines the combined score (CS). Figure 4c shows the horizontal distribution of ATMS channel-22 brightness temperature observations and the CS. The final TC center position identified by the ARCHER algorithm is the location where the CS reaches a maximum value. The ARCHER-determined center of Hurricane Irma (indicated by the triangle symbol in Fig. 4c) at 1720 UTC 4 September 2017 is about 0.45° to the east of the initial position (plus symbol) and very close to the best track (hurricane symbol). At the ARCHER-determined center of Irma, ATMS channel-22 brightness temperatures show a local warm anomaly, a feature of the clear eye of Irma at this time.

4. Applying the ARCHER algorithm to an ATMS or CrIS channel’s brightness temperature observations

We select ATMS channel 22 because the weighting function peak of channel 22 is around 300 hPa, which is the highest among all ATMS humidity channels 17–22, and is thus less affected by the lower level and surface features. ATMS channel 4 is a temperature-sounding channel whose weighting function peaks around 950 hPa, which is the lowest among all ATMS temperature sounding channels above the surface. The pair of ATMS channels 22 and 4 is thus selected to have the least correlation between ATMS temperature and humidity channels pairs.

Figure 5 compares the CrIS channel-89-determined TC centers (Figs. 5a,b) with the ATMS channel-22-determined TC centers (Figs. 5c,d) of Hurricane Irma at 0640 UTC 8 September 2017 (Figs. 5a,c) and at 0739 UTC 10 September 2017 (Figs. 5b,d). At both times, the centers of Hurricane Irma determined by CrIS infrared channel 89 and the ATMS microwave channel 22 are consistent, located at the local maximum of brightness temperature observations, and deviate slightly from the best track. At 0636 UTC 8 September 2017, Hurricane Irma’s center is characterized by a clear local maximum of CrIS observations of brightness temperatures. The ATMS-determined center (triangle in Fig. 5c) is the same as CrIS-determined center. Their distance to the best track center is 38.6 km. This could be a case that the best track is in more error than the result from our algorithm. Figure 6 shows two more examples, namely, Typhoon Trami at 1640 UTC 22 September 2018 (Figs. 6a,c) and Hurricane Irma at 0405 UTC 31 August 2017 (Figs. 6b,d). The centers determined by CrIS channel 89 are closer to the best track data than the ATMS channel-22-determined centers of both examples. The ATMS-determined centers are away from the best track and seem to be affected by the small-scale features of convective clouds reflected in the ATMS channel-22 brightness temperature observations. These do not exhibit the effects of rotational shear, and can mislead ARCHER with their distribution patterns that have little relationship to rotation.

Fig. 5.
Fig. 5.

Horizontal distributions of brightness temperature observations (unit: K) for (a),(b) CrIS channel 89 and (c),(d) ATMS channel 22 around Hurricane Irma at (a),(c) 0636 UTC 8 Sep and (b),(d) 0739 UTC 10 Sep 2017. Also shown are the best track (hurricane symbol), CrIS center (open circle), and ATMS center (open triangle).

Citation: Journal of Atmospheric and Oceanic Technology 37, 11; 10.1175/JTECH-D-20-0049.1

Fig. 6.
Fig. 6.

Horizontal distributions of brightness temperature observations (unit: K) for (a),(b) CrIS channel 89 and (c),(d) ATMS channel 22 around Typhoon Trami at (a),(c) 1640 UTC 22 Sep 2018 and (b),(d) Hurricane Irma at 0405 UTC 31 Aug 2017. Also shown are the best track (hurricane symbol), CrIS center (open circle), and ATMS center (open triangle).

Citation: Journal of Atmospheric and Oceanic Technology 37, 11; 10.1175/JTECH-D-20-0049.1

Statistical results of the TC centers determined by ATMS channel-22 and CrIS channel-89 brightness temperature observations for the 161 TC cases are provided in Figs. 7a and 7b, respectively, as well as in Table 2.

Fig. 7.
Fig. 7.

Scatterplots of the deviations of the TC centers derived from brightness temperature observations of (a) ATMS channel 22 and (b) CrIS channel 89 from the best track in the east–west (E–W) and north–south (N–S) directions for the 161 TC cases. Also indicated are the means (cross symbols) and the ±1 standard deviations (square boxes).

Citation: Journal of Atmospheric and Oceanic Technology 37, 11; 10.1175/JTECH-D-20-0049.1

Table 2.

Statistics of TC center-fixing results derived from ATMS channel 22, ATMS channel 4, the azimuthal spectral analysis with channels 4 and 22 combined, and CrIS channel 89. The negative sign indicates the direction to the west.

Table 2.

ATMS channel-22-determined TC centers (Fig. 7a) deviate from the best track more than the CrIS channel-89 results (Fig. 7b) in both the east–west and north–south directions (Table 2). Although at a lower resolution (32 km at nadir), ATMS channel-4 observations are less affected by small-scale convection. We thus propose to first apply the ARCHER algorithm to ATMS channel-4 brightness temperature observations, then carry out an azimuthal spectral analysis on ATMS channel-22 observations centered around the ATMS channel-4, ARCHER-determined TC center. The TC center point around which the azimuthal spectral analysis yields the largest axisymmetric component is used as the ATMS-determined TC center. The next section provides details about the procedure and presents numerical results.

5. An azimuthal spectral analysis for determining TC centers

a. Azimuthal spectral analysis

An azimuthal spectral analysis method can extract the axisymmetric component (wavenumber-0 component) and other wavenumbers of a given field encompassing a TC (Zou et al. 2010). Here, an azimuthal spectral analysis is carried out on ATMS channel-22 (183.31 ± 1.0 GHz) brightness temperature observations. Three examples are given to illustrate the process of locating the TC center by the azimuthal spectral analysis and its effectiveness. Figure 8a shows the spatial distribution of ATMS channel-22 brightness temperature observations at 1640 UTC 22 September 2018 and the tryout centers (all symbols in Fig. 8a except for the hurricane symbol) within a 0.8° × 0.8° square box. Carried out is a set of the azimuthal spectral analysis assuming different centers. Figure 8b shows the variations of azimuthal wavenumber-0 amplitude with radial distance from different tryout centers. The maximum axisymmetric component, indicated by the square symbol, accounts for more than 90% of the total spectral power near the center and more than 80% within a 274-km radial distance, significantly higher than that centered on the original ARCHER-determined center indicated by the triangle. Besides, the closer the tryout centers are to the best track, the larger the wavenumber-0 amplitude. The amplitude of wavenumber 0 is calculated from 0.6° to 2.6° radial distances at 0.2° interval from the tryout TC centers.

Fig. 8.
Fig. 8.

(a) Spatial distributions of ATMS channel-22 brightness temperature observations (gray-scale shading; unit: K) around Typhoon Trami at 1640 UTC 22 Sep 2018 and tentative tryout centers of the azimuthal spectral analysis (crosses; color indicates the distance from the best track). (b) Radial variations in the azimuthal wavenumber-0 spectra of ATMS channel-22 brightness temperature observations, assuming the different TC centers shown in (a). The red squares indicates the final TC center determined by the spectrum analysis.

Citation: Journal of Atmospheric and Oceanic Technology 37, 11; 10.1175/JTECH-D-20-0049.1

The spectrum analysis method involves three major steps. The first step is to determine the center of a 0.8° × 0.8° box of tryout TC centers. The spectral analysis is carried out at a set of tryout TC centers in a box centered at a TC guess position. This guess position is determined by ATMS channel-4 observations of brightness temperature using the ARCHER in the first step. The reason for using the ATMS channel-4 observations to determine the guess TC center is that this channel less affected by small-scale convective clouds due to its lower frequency and coarser resolution (32 km) than ATMS channel 22. The second step is to determine a range of radial distances in which the spectral analysis is carried out. The azimuthal spectral analysis is carried out in radial distances from 0.6° and 2.6° longitudes long in the west–east direction from the tryout centers. Close to the TC center (<0.6°), there are very little data for the azimuthal spectral analysis. Beyond 2.6° longitudes from the center, the wavenumber-0 amplitudes become flat. The third step is to set criteria for determining the final TC center, which is that tryout center for which following three conditions are met: 1) the wavenumber-0 amplitude at the smallest radial distance (i.e., 0.6°) of the spectral analysis are greater than the average of wavenumber-0 amplitudes the smallest radial distance from all tryout centers, 2) the wavenumber-0 amplitude at the largest radial distance (i.e., 2.6°) of the spectral analysis are greater than the average wavenumber-0 amplitudes at the largest radial distances from all tryout centers, and 3) the value of wavenumber-0 amplitudes averaged from the six smaller radial distances is the largest among those from all tryout centers. These criteria are based on a hypothesis that the closer the tryout centers are to the real TC center, the larger the wavenumber-0 amplitude. The 2.6° radial distance is used in the second criteria is because the wavenumber-0 amplitudes obtained by spectral analysis become flat beyond this radial distance. The reason for averaging over the six smaller radial distances as the third condition in step 3 is because the resulting root-mean-square error (RMSE) of TC-centering results is the smallest among different averaging radial distances (see Fig. 9).

Fig. 9.
Fig. 9.

Variations of RMSE of ATMS-determined TC center with the averaging length of wavenumber-0 amplitudes from 0.6° to 0.6° + n × 0.2° from tryout TC centers, where n is the number shown on the x axis.

Citation: Journal of Atmospheric and Oceanic Technology 37, 11; 10.1175/JTECH-D-20-0049.1

Figure 10 shows a second example. Figure 10a shows the CrIS-determined center of Hurricane Irma at 0405 UTC 31 August 2017, the ATMS channel-22-determined hurricane center and the hurricane center determined by the azimuthal spectral analysis (Fig. 10b). Figure 10a also shows the horizontal distribution of ATMS channel-22 brightness temperature observations. The axisymmetric components centered on the ARCHER-determined, ATMS or CrIS centers and the best track are significantly lower than the maximum axisymmetric component indicated by the square symbol in Fig. 10a. When the azimuthal spectral analysis is applied to CrIS channel-89 observations, the axisymmetric component centered at the ARCHER-determined center is higher than that centered at the best track.

Fig. 10.
Fig. 10.

(a) Spatial distribution of ATMS channel-22 brightness temperature observations (color shading) around Hurricane Irma at 0405 UTC 31 Aug 2017, with the CrIS center (open circle), the ATMS centers (triangle and square), and the best track (hurricane symbol) indicated. The black, dashed square box indicates the tryout box for the azimuthal spectral analysis. (b) Radial variations in the azimuthal wavenumber-0 spectra of ATMS channel-22 (solid curve) and CrIS channel-89 (dashed curve) brightness temperature observations.

Citation: Journal of Atmospheric and Oceanic Technology 37, 11; 10.1175/JTECH-D-20-0049.1

The third example shows that the spectral analysis does not alter the change in the ARCHER-determined center if it is already quite accurate compared with the best track and the result from the spectral analysis (Fig. 11). Figure 11a shows the spatial distribution of ATMS channel-22 brightness temperature observations for Hurricane Irma at 0739 UTC 10 September 2017 and different TC centers. The center determined by the maximum axisymmetric component coincides with the center determined by the ARCHER algorithm and the local maximum brightness temperature anomaly. The axisymmetric component with the best track as the spectral analysis center is significantly lower than the maximum symmetric component (Fig. 11b).

Fig. 11.
Fig. 11.

As in Fig. 10, but for Hurricane Irma at 0739 UTC 10 Sep 2017.

Citation: Journal of Atmospheric and Oceanic Technology 37, 11; 10.1175/JTECH-D-20-0049.1

b. Statistical results of the modified ATMS-determined TC centers

Figure 12 shows the deviations of TC centers determined by ATMS channel 4 (Fig. 12a) and the combination of ATMS microwave channels 4 and 22 (Fig. 12b) where the ARCHER algorithm is first applied to channel 4, and the azimuthal spectral analysis is applied to ATMS channel 22 afterward. A comparison among ATMS channel 22 only (Fig. 7a), ATMS channel 4 only (Fig. 12a), and ATMS channels 4 and 22 combined (Fig. 12b) reveals that the center-fixing result of the two ATMS channels combined yields the smallest differences from the best track. The average distance from the best track is 23.5 km, the standard deviations in both the east–west and north–south directions are 18.7 and 20.4 km, respectively, and the RMSE is 27.8 km (see also Table 2), all lower than that of applying ARCHER to either ATMS channel 4 or channel 22. The center-fixing accuracy using the two ATMS microwave channels is higher than the single CrIS infrared channel results (Table 2). Overall, the azimuthal spectral analysis method further improves the accuracy of the center-fixing results of the ARCHER algorithm using microwave observations.

Fig. 12.
Fig. 12.

As in Fig. 7, but for (a) ATMS channel 4 and (b) ATMS channels 4 and 22 combined. Also indicated are the means (crosses) and standard deviations (square boxes) for ATMS channel 22 only (blue), ATMS channel 4 only (black), and ATMS channels 4 and 22 combined (red).

Citation: Journal of Atmospheric and Oceanic Technology 37, 11; 10.1175/JTECH-D-20-0049.1

Figure 13 shows the frequency distribution of the distances between the best track and the TC centers determined by ATMS and CrIS channels. About 88% of the center-fixing deviations of ATMS channels 4 and 22 with an azimuthal spectral analysis are less than 40 km. The number of cases with smaller (larger) distance errors is the highest (lowest) for the two ATMS microwave channels combined than the single ATMS channel 22 and CrIS channel 89. Also, the stronger the TC, the more accurate the center fix because a high-intensity TC often has a well-organized structure and a clear eye.

Fig. 13.
Fig. 13.

The total number of TC cases with different distances (10-km interval) between the best track and TC centers determined by the ATMS channel 22 only (cyan), CrIS channel 89 only (yellow), and ATMS channels 4 and 22 combined in the azimuthal spectral analysis (green) for the 161 TC cases. The tropical storm category is indicated by hatched bars.

Citation: Journal of Atmospheric and Oceanic Technology 37, 11; 10.1175/JTECH-D-20-0049.1

We calculated the statistical significance of the differences in TC-centering error between ATMS channel 4 versus channel 22, channels 4 and 22 versus channel 22, channels 4 and 22 versus channel 4 using the 95% confidence interval as the significance delimiter. The probability (p) values are listed in Table 3. It is seen that all the statistics are significant at the 95% confidence level, with their p values less than 0.05. The significant difference in TC-centering error of ATMS channel 4 from channel 22 suggests the appropriateness of using ATMS channel 4 for determining the guess center for the spectral analysis. TC-centering error of channel 22 is significantly greater than that of channel 4, which may be caused by the small-scale features of convective clouds reflected in the ATMS channel-22 brightness temperature observations. The significant differences in TC-centering error between channels 4 and 22 combined versus single channel 22 or single channel 4 implies that both steps 1 and 2 of the proposed TC centering notably improve the center-fixing accuracy.

Table 3.

The probability values calculated from the differences in positioning error for ATMS channel 4 vs channel 22, channels 4 and 22 vs channel 22, and channels 4 and 22 vs channel 4 using a Student’s t test.

Table 3.

c. Validation of the TC centering algorithm on cases in 2019 hurricane season

To avoid a possible overfitting of the algorithm to the dataset used in the development of the algorithm, we apply the proposed TC centering algorithm to TS and hurricane cases over Atlantic Ocean in 2019. This is done so to provide an independent validate (“test”) of the algorithm, and to account for the error in a more generalized setting.

Figure 14 provides a spatial distribution of 2019 Atlantic tropical storms and hurricanes. The maximum sustained wind speed for every case (dot in Fig. 14) is greater than 34 kt. The differences in performance of the TC-centering algorithm are evaluated on 2019 Atlantic hurricanes grouped by intensity. Statistic results are provided in Table 4. The RMSEs of the TC centers are 29.2 and 33.8 km based on ATMS and CrIS observations, respectively (Table 4). If we divide the 185 TC cases into the TS (34 ≤ Vmax < 64 kt) group of 104 cases and the category 1–5 (Vmax ≥ 64 kt) group of 81 hurricanes, we find that the center-fixing results for hurricanes are slightly more accurate than the TS group.

Fig. 14.
Fig. 14.

Spatial distribution of 2019 Atlantic tropical storms and hurricanes. The maximum sustained wind speed for every case is greater than 34 kt.

Citation: Journal of Atmospheric and Oceanic Technology 37, 11; 10.1175/JTECH-D-20-0049.1

Table 4.

The RMSE (km) of TC center-fixing results based on CrIS channel-89 and ATMS channel-4 and -22 observations for all cases, tropical storms only, and hurricanes only in 2019 Atlantic hurricane season.

Table 4.

The last but not least important thing to show is that the algorithm is also able to a center of TC in a sheared system with reasonable accuracy. Figure 15 shows the algorithm performance for Hurricane Sandy in a sheared system at 0652 UTC 27 October 2012. The vertical wind shear is about 22 m s−1, which is calculated as the difference of the averaged 850- and 200-hPa wind vectors over an annulus between radii 200 and 800 km from the center of Sandy (DeMaria et al. 2005) from the fifth-generation European Centre for Medium-Range Weather Forecasts reanalysis (ERA5; Hoffmann et al. 2019). From Fig. 15 we find that both the ATMS- and the CrIS-determined centers are close to the best track. The best track is located between the CrIS- and ATMS-determined centers. The difference between ATMS-determined and CrIS-determined centers could be associated with a possible tilting of Sandy due to strong upper level winds. Considering that infrared observations cannot but microwave observations can penetrate through clouds, the CrIS-determined and ATMS-determined centers might be the center of Hurricane Sandy in upper and low levels, respectively. Further investigation is required to substantiate this hypothesis.

Fig. 15.
Fig. 15.

Spatial distributions of (a) CrIS channel 89 (703.75 cm−1, 307 hPa) and (b) ATMS channel 22 (183.31 ± 1.0 GHz, 300 hPa) brightness temperature observations (color shading; unit: K) for Hurricane Sandy at 0652 UTC 27 Oct 2012. CrIS channel 89–determined center (open circle in both panels), ATMS channels 4 and 22 determined center [triangle in (b)], and the best track (hurricane symbol) are also shown. The arrow vector represents the averaged vertical wind shear of Sandy. The two rings represent the annulus of 200–800 km from the best track position.

Citation: Journal of Atmospheric and Oceanic Technology 37, 11; 10.1175/JTECH-D-20-0049.1

6. Summary and conclusions

Observations from the ATMS and CrIS instruments on board the SNPP satellite have been widely used in numerical weather prediction and tropical cyclones studies. In this study, a TC center-fixing algorithm is proposed and tested that is suited for real-time use for infrared- and microwave-based TC diagnostics and visualization, as well as for analyses of large retrospectively processed TC datasets. An azimuthal spectral analysis is augmented to the ARCHER algorithm, which was applied to two ATMS microwave channels (51.76 and 183.31 ± 1.0 GHz) and a single CrIS infrared channel (703.75 cm−1) for determining TC centers. Difficulties were encountered when the ARCHER algorithm was applied to ATMS channel 22 (183.31 ± 1.0 GHz) due to the impact of small-scale cloud features on brightness temperature observations of this channel. This problem is bypassed by the azimuthal spectral analysis which extract the axisymmetric component of TC observations. In other words, the ARCHER center-fixing algorithm works reasonably with the observations of ATMS and CrIS in TC center fixing, and the spectral analysis method further improves the accuracy of the ARCHER algorithm.

The RMSEs (with respect to best track) of CrIS-determined centers and ATMS-determined centers are about 34 and 29 km, respectively. An intrinsic limitation to the level of skill that can be accomplished by the applying the TC centering algorithm is probably the resolution of CrIS and ATMS observations. We may increase the resolution for calculating the dot products between the brightness temperature gradients and a ring of radially oriented unit vectors with different radii from 0.2° × 0.2° to 0.1° × 0.1° to see if more accurate results could be obtained for determining TC centers. Other factors that could limit the level of skill include vortex tilt caused by vertical shear and the error of the best track itself. We plan to apply the proposed TC centering algorithm to all TCs from 2012 to 2019 so that the algorithm can be thoroughly validated. With a large sample of TC cases, we may analyze the differences in performance across various TC types, grouping by intensity category (TD, TS, H1 to H5), by ocean basin, and by vertical wind shear, etc. We will reevaluate the accuracy of the TC center positions obtained by the proposed TC centering algorithm using the reconnaissance-observed locations or best track limited to reconnaissance times, which are more accurate than the best track. In the future, more satellite observations with high spatial and temporal resolutions will become available for locating TC centers that are required for the vortex initialization and intensity estimation of TCs.

Acknowledgments

This research is supported by the National Key R&D Program of China Grants 2017YFC1501603 and 2018YFC1507004.

REFERENCES

  • DeMaria, M., M. Mainelli, L. Shay, J. Knaff, and J. Kaplan, 2005: Further improvements to the Statistical Hurricane Intensity Prediction Scheme (SHIPS). Wea. Forecasting, 20, 531543, https://doi.org/10.1175/WAF862.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dvorak, V. F., 1984: Tropical cyclone intensity analysis using satellite data. NOAA Tech. Rep. NESDIS 11, 47 pp., http://satepsanone.nesdis.noaa.gov/pub/Publications/Tropical/Dvorak_1984.pdf.

  • Han, Y., and Coauthors, 2013: Suomi NPP CrIS measurements, sensor data record algorithm, calibration and validation activities, and record data quality. J. Geophys. Res. Atmos., 118, 12 73412 748, https://doi.org/10.1002/2013JD020344.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hoffmann, L., and Coauthors, 2019: From ERA-Interim to ERA5: The considerable impact of ECMWF’s next-generation reanalysis on Lagrangian transport simulations. Atmos. Chem. Phys., 19, 30973124, https://doi.org/10.5194/acp-19-3097-2019.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Landsea, C. W., and J. L. Franklin, 2013: Atlantic hurricane database uncertainty and presentation of a new database format. Mon. Wea. Rev., 141, 35763592, https://doi.org/10.1175/MWR-D-12-00254.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Li, X., and X. Zou, 2017: Bias characterization of CrIS radiances at 399 selected channels with respect to NWP model simulations. Atmos. Res., 196, 164181, https://doi.org/10.1016/j.atmosres.2017.06.007.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Olander, T., and C. S. Velden, 2007: The advanced Dvorak technique (ADT)—Continued development of an objective scheme to estimate tropical cyclone intensity using geostationary infrared satellite imagery. Wea. Forecasting, 22, 287298, https://doi.org/10.1175/WAF975.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Olander, T., C. S. Velden, and J. Kossin, 2004: The advanced objective Dvorak technique (AODT): Latest upgrades and future directions. Proc. 26th Hurricane and Tropical Meteorology Conf., Miami, FL, Amer. Meteor. Soc., P1.19, https://ams.confex.com/ams/26HURR/techprogram/paper_75417.htm.

  • Rozoff, C. M., C. S. Velden, J. Kaplan, J. P. Kossin, and A. Wimmers, 2015: Improvements in the probabilistic prediction of tropical cyclone rapid intensification with passive microwave observations. Wea. Forecasting, 30, 10161038, https://doi.org/10.1175/WAF-D-14-00109.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sitkowski, M., J. P. Kossin, and C. M. Rozoff, 2011: Intensity and structure changes during hurricane eyewall replacement cycles. Mon. Wea. Rev., 139, 38293847, https://doi.org/10.1175/MWR-D-11-00034.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tian, X., and X. Zou, 2016: ATMS- and AMSU-A-derived hurricane warm core structures using a modified retrieval algorithm. J. Geophys. Res. Atmos., 121, 12 63012 646, https://doi.org/10.1002/2016JD025042.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tian, X., and X. Zou, 2018: Capturing size and intensity changes of Hurricanes Irma and Maria (2017) from polar-orbiting satellite microwave radiometers. J. Atmos. Sci., 75, 25092522, https://doi.org/10.1175/JAS-D-17-0315.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tian, X., and X. Zou, 2019: A comprehensive 4D-Var vortex initialization using a nonhydrostatic axisymmetric TC model with convection accounted for. Tellus, 71A, 1653138, https://doi.org/10.1080/16000870.2019.1653138.

    • Search Google Scholar
    • Export Citation
  • Velden, C. S., T. L. Olander, and R. M. Zehr, 1998: Development of an objective scheme to estimate tropical cyclone intensity from digital geostationary satellite infrared imagery. Wea. Forecasting, 13, 172186, https://doi.org/10.1175/1520-0434(1998)013<0172:DOAOST>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Velden, C. S., and Coauthors, 2006: The Dvorak tropical cyclone intensity estimation technique: A satellite-based method that has endured for over 30 years. Bull. Amer. Meteor. Soc., 87, 11951210, https://doi.org/10.1175/BAMS-87-9-1195.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Weatherford, C. L., and W. M. Gray, 1988: Typhoon structure as revealed by aircraft reconnaissance. Part II: Structural variability. Mon. Wea. Rev., 116, 10441056, https://doi.org/10.1175/1520-0493(1988)116<1044:TSARBA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Weng, F., X. Zou, X. Wang, S. Yang, and M. D. Goldberg, 2012: Introduction to Suomi National Polar-Orbiting Partnership Advanced Technology Microwave Sounder for numerical weather prediction and tropical cyclone applications. J. Geophys. Res., 117, D19112, https://doi.org/10.1029/2012JD018144.

    • Search Google Scholar
    • Export Citation
  • Wimmers, A., and C. S. Velden, 2007: MIMIC: A new approach to visualizing satellite microwave imagery of tropical cyclones. Bull. Amer. Meteor. Soc., 88, 11871196, https://doi.org/10.1175/BAMS-88-8-1187.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wimmers, A., and C. S. Velden, 2010: Objectively determining the rotational center of tropical cyclones in passive microwave satellite imagery. J. Appl. Meteor. Climatol., 49, 20132034, https://doi.org/10.1175/2010JAMC2490.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wimmers, A., and C. S. Velden, 2016: Advancements in objective multi-satellite tropical cyclone center fixing. J. Appl. Meteor. Climatol., 55, 197212, https://doi.org/10.1175/JAMC-D-15-0098.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zou, X., and X. Tian, 2018: Hurricane warm-core retrievals from AMSU-A and remapped ATMS measurements with rain contamination eliminated. J. Geophys. Res. Atmos., 123, 10 81510 829, https://doi.org/10.1029/2018JD028934.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zou, X., Y. Wu, and P. S. Ray, 2010: Verification of a high-resolution model forecast using airborne Doppler radar analysis during the rapid intensification of Hurricane Guillermo. J. Appl. Meteor. Climatol., 49, 807820, https://doi.org/10.1175/2009JAMC2182.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zou, X., F. Weng, B. Zhang, L. Lin, Z. Qin, and V. Tallapragada, 2013: Impacts of assimilation of ATMS data in HWRF on track and intensity forecasts of 2012 four landfall hurricanes. J. Geophys. Res. Atmos., 118, 11 55811 576, https://doi.org/10.1002/2013JD020405.

    • Crossref
    • Search Google Scholar
    • Export Citation
Save
  • DeMaria, M., M. Mainelli, L. Shay, J. Knaff, and J. Kaplan, 2005: Further improvements to the Statistical Hurricane Intensity Prediction Scheme (SHIPS). Wea. Forecasting, 20, 531543, https://doi.org/10.1175/WAF862.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dvorak, V. F., 1984: Tropical cyclone intensity analysis using satellite data. NOAA Tech. Rep. NESDIS 11, 47 pp., http://satepsanone.nesdis.noaa.gov/pub/Publications/Tropical/Dvorak_1984.pdf.

  • Han, Y., and Coauthors, 2013: Suomi NPP CrIS measurements, sensor data record algorithm, calibration and validation activities, and record data quality. J. Geophys. Res. Atmos., 118, 12 73412 748, https://doi.org/10.1002/2013JD020344.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hoffmann, L., and Coauthors, 2019: From ERA-Interim to ERA5: The considerable impact of ECMWF’s next-generation reanalysis on Lagrangian transport simulations. Atmos. Chem. Phys., 19, 30973124, https://doi.org/10.5194/acp-19-3097-2019.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Landsea, C. W., and J. L. Franklin, 2013: Atlantic hurricane database uncertainty and presentation of a new database format. Mon. Wea. Rev., 141, 35763592, https://doi.org/10.1175/MWR-D-12-00254.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Li, X., and X. Zou, 2017: Bias characterization of CrIS radiances at 399 selected channels with respect to NWP model simulations. Atmos. Res., 196, 164181, https://doi.org/10.1016/j.atmosres.2017.06.007.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Olander, T., and C. S. Velden, 2007: The advanced Dvorak technique (ADT)—Continued development of an objective scheme to estimate tropical cyclone intensity using geostationary infrared satellite imagery. Wea. Forecasting, 22, 287298, https://doi.org/10.1175/WAF975.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Olander, T., C. S. Velden, and J. Kossin, 2004: The advanced objective Dvorak technique (AODT): Latest upgrades and future directions. Proc. 26th Hurricane and Tropical Meteorology Conf., Miami, FL, Amer. Meteor. Soc., P1.19, https://ams.confex.com/ams/26HURR/techprogram/paper_75417.htm.

  • Rozoff, C. M., C. S. Velden, J. Kaplan, J. P. Kossin, and A. Wimmers, 2015: Improvements in the probabilistic prediction of tropical cyclone rapid intensification with passive microwave observations. Wea. Forecasting, 30, 10161038, https://doi.org/10.1175/WAF-D-14-00109.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sitkowski, M., J. P. Kossin, and C. M. Rozoff, 2011: Intensity and structure changes during hurricane eyewall replacement cycles. Mon. Wea. Rev., 139, 38293847, https://doi.org/10.1175/MWR-D-11-00034.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tian, X., and X. Zou, 2016: ATMS- and AMSU-A-derived hurricane warm core structures using a modified retrieval algorithm. J. Geophys. Res. Atmos., 121, 12 63012 646, https://doi.org/10.1002/2016JD025042.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tian, X., and X. Zou, 2018: Capturing size and intensity changes of Hurricanes Irma and Maria (2017) from polar-orbiting satellite microwave radiometers. J. Atmos. Sci., 75, 25092522, https://doi.org/10.1175/JAS-D-17-0315.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tian, X., and X. Zou, 2019: A comprehensive 4D-Var vortex initialization using a nonhydrostatic axisymmetric TC model with convection accounted for. Tellus, 71A, 1653138, https://doi.org/10.1080/16000870.2019.1653138.

    • Search Google Scholar
    • Export Citation
  • Velden, C. S., T. L. Olander, and R. M. Zehr, 1998: Development of an objective scheme to estimate tropical cyclone intensity from digital geostationary satellite infrared imagery. Wea. Forecasting, 13, 172186, https://doi.org/10.1175/1520-0434(1998)013<0172:DOAOST>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Velden, C. S., and Coauthors, 2006: The Dvorak tropical cyclone intensity estimation technique: A satellite-based method that has endured for over 30 years. Bull. Amer. Meteor. Soc., 87, 11951210, https://doi.org/10.1175/BAMS-87-9-1195.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Weatherford, C. L., and W. M. Gray, 1988: Typhoon structure as revealed by aircraft reconnaissance. Part II: Structural variability. Mon. Wea. Rev., 116, 10441056, https://doi.org/10.1175/1520-0493(1988)116<1044:TSARBA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Weng, F., X. Zou, X. Wang, S. Yang, and M. D. Goldberg, 2012: Introduction to Suomi National Polar-Orbiting Partnership Advanced Technology Microwave Sounder for numerical weather prediction and tropical cyclone applications. J. Geophys. Res., 117, D19112, https://doi.org/10.1029/2012JD018144.

    • Search Google Scholar
    • Export Citation
  • Wimmers, A., and C. S. Velden, 2007: MIMIC: A new approach to visualizing satellite microwave imagery of tropical cyclones. Bull. Amer. Meteor. Soc., 88, 11871196, https://doi.org/10.1175/BAMS-88-8-1187.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wimmers, A., and C. S. Velden, 2010: Objectively determining the rotational center of tropical cyclones in passive microwave satellite imagery. J. Appl. Meteor. Climatol., 49, 20132034, https://doi.org/10.1175/2010JAMC2490.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wimmers, A., and C. S. Velden, 2016: Advancements in objective multi-satellite tropical cyclone center fixing. J. Appl. Meteor. Climatol., 55, 197212, https://doi.org/10.1175/JAMC-D-15-0098.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zou, X., and X. Tian, 2018: Hurricane warm-core retrievals from AMSU-A and remapped ATMS measurements with rain contamination eliminated. J. Geophys. Res. Atmos., 123, 10 81510 829, https://doi.org/10.1029/2018JD028934.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zou, X., Y. Wu, and P. S. Ray, 2010: Verification of a high-resolution model forecast using airborne Doppler radar analysis during the rapid intensification of Hurricane Guillermo. J. Appl. Meteor. Climatol., 49, 807820, https://doi.org/10.1175/2009JAMC2182.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zou, X., F. Weng, B. Zhang, L. Lin, Z. Qin, and V. Tallapragada, 2013: Impacts of assimilation of ATMS data in HWRF on track and intensity forecasts of 2012 four landfall hurricanes. J. Geophys. Res. Atmos., 118, 11 55811 576, https://doi.org/10.1002/2013JD020405.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fig. 1.

    (a) Weighting functions for ATMS channels 4 and 22 and CrIS channel 89. (b) Spatial distribution of ATMS channel-22 brightness temperature observations (color shading; unit: K) and the best track (typhoon symbol) for Typhoon Trami at around 0512 UTC 24 Sep 2018. The ATMS footprints along two consecutive scan lines that passed through Trami’s center are indicated by black and purple crosses.

  • Fig. 2.

    Spatial distributions of (a) Atlantic Hurricanes Sandy in 2012, Irma, Maria, and Jose in 2017, Oscar, Chris, Helene, and Isaac in 2018, and (b) Pacific Typhoons Cimaron, Jebi, Jelawat, Kong-rey, Maria, Soulik, Trami, and Yutu in 2018. The intensity category is indicated by colors.

  • Fig. 3.

    Norms of the cross-product vectors (color shading; unit: 10−2) between the horizontal gradients of ATMS channel-22 brightness temperature observations at around 1720 UTC 4 Sep 2017 and a hurricane circulation field (black arrows) centered at (a) the diamond and (b) the cross symbols. Wimmers and Velden (2010) provide the hurricane circulation field.

  • Fig. 4.

    (a) Spatial distribution of ATMS channel-22 (183.31 ± 1.0 GHz, 300 hPa) brightness temperature observations (color shading; unit: K) for Hurricane Irma at around 1720 UTC 4 Sep 2017. The contours are the FSS (unit: 10−2) computed at 0.2° × 0.2° grid boxes (black dots) in the 4° × 4° calculation domain centered on the first-guess position (plus symbol). Also shown are(b) RS (unit: 10−2) and the (c) CS (unit: 10−2) computed at 0.2° × 0.2° grid boxes (black dots) in the 1.2° × 1.2° calculation domain [dashed box in (a)] centered at the point with a maximum FSS. The maximum FSS, RS, and CS are indicated by the diamond, open circle, and triangle symbols. The best track is indicated by a hurricane symbol.

  • Fig. 5.

    Horizontal distributions of brightness temperature observations (unit: K) for (a),(b) CrIS channel 89 and (c),(d) ATMS channel 22 around Hurricane Irma at (a),(c) 0636 UTC 8 Sep and (b),(d) 0739 UTC 10 Sep 2017. Also shown are the best track (hurricane symbol), CrIS center (open circle), and ATMS center (open triangle).

  • Fig. 6.

    Horizontal distributions of brightness temperature observations (unit: K) for (a),(b) CrIS channel 89 and (c),(d) ATMS channel 22 around Typhoon Trami at (a),(c) 1640 UTC 22 Sep 2018 and (b),(d) Hurricane Irma at 0405 UTC 31 Aug 2017. Also shown are the best track (hurricane symbol), CrIS center (open circle), and ATMS center (open triangle).

  • Fig. 7.

    Scatterplots of the deviations of the TC centers derived from brightness temperature observations of (a) ATMS channel 22 and (b) CrIS channel 89 from the best track in the east–west (E–W) and north–south (N–S) directions for the 161 TC cases. Also indicated are the means (cross symbols) and the ±1 standard deviations (square boxes).

  • Fig. 8.

    (a) Spatial distributions of ATMS channel-22 brightness temperature observations (gray-scale shading; unit: K) around Typhoon Trami at 1640 UTC 22 Sep 2018 and tentative tryout centers of the azimuthal spectral analysis (crosses; color indicates the distance from the best track). (b) Radial variations in the azimuthal wavenumber-0 spectra of ATMS channel-22 brightness temperature observations, assuming the different TC centers shown in (a). The red squares indicates the final TC center determined by the spectrum analysis.

  • Fig. 9.

    Variations of RMSE of ATMS-determined TC center with the averaging length of wavenumber-0 amplitudes from 0.6° to 0.6° + n × 0.2° from tryout TC centers, where n is the number shown on the x axis.

  • Fig. 10.

    (a) Spatial distribution of ATMS channel-22 brightness temperature observations (color shading) around Hurricane Irma at 0405 UTC 31 Aug 2017, with the CrIS center (open circle), the ATMS centers (triangle and square), and the best track (hurricane symbol) indicated. The black, dashed square box indicates the tryout box for the azimuthal spectral analysis. (b) Radial variations in the azimuthal wavenumber-0 spectra of ATMS channel-22 (solid curve) and CrIS channel-89 (dashed curve) brightness temperature observations.

  • Fig. 11.

    As in Fig. 10, but for Hurricane Irma at 0739 UTC 10 Sep 2017.

  • Fig. 12.

    As in Fig. 7, but for (a) ATMS channel 4 and (b) ATMS channels 4 and 22 combined. Also indicated are the means (crosses) and standard deviations (square boxes) for ATMS channel 22 only (blue), ATMS channel 4 only (black), and ATMS channels 4 and 22 combined (red).

  • Fig. 13.

    The total number of TC cases with different distances (10-km interval) between the best track and TC centers determined by the ATMS channel 22 only (cyan), CrIS channel 89 only (yellow), and ATMS channels 4 and 22 combined in the azimuthal spectral analysis (green) for the 161 TC cases. The tropical storm category is indicated by hatched bars.

  • Fig. 14.

    Spatial distribution of 2019 Atlantic tropical storms and hurricanes. The maximum sustained wind speed for every case is greater than 34 kt.

  • Fig. 15.

    Spatial distributions of (a) CrIS channel 89 (703.75 cm−1, 307 hPa) and (b) ATMS channel 22 (183.31 ± 1.0 GHz, 300 hPa) brightness temperature observations (color shading; unit: K) for Hurricane Sandy at 0652 UTC 27 Oct 2012. CrIS channel 89–determined center (open circle in both panels), ATMS channels 4 and 22 determined center [triangle in (b)], and the best track (hurricane symbol) are also shown. The arrow vector represents the averaged vertical wind shear of Sandy. The two rings represent the annulus of 200–800 km from the best track position.

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