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Temperature anomalies at 250 hPa at 0643 UTC 2 October 2016 calculated from ATMS observations (a) without and (b) with remapping. (c) Temperature anomalies calculated with ECMWF interim reanalysis data. The black cross shows the location of the observed hurricane center.
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Due to a shorter effective integration time for each field‐of‐view (FOV) of the Advanced Microwave Temperature Sounder (ATMS) onboard the Suomi National Polar‐orbiting Partnership (S‐NPP) satellite than that for the Advanced Microwave Sounding Unit‐A (AMSU‐A) onboard previous National Oceanic and Atmospheric Administration (NOAA) polar‐orbiting sat...
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... anomalies at 250 hPa on 0600 UTC 2 October 2016 calculated from ATMS BT observations with- out and with remapping are presented in Figures 8a and 8b, respectively. Here the temperature anomalies are calculated by subtracting the mean temperature averaged within a 15° latitude/longitude box centered at the storm center but outside of the 34-knot wind radius circle. ...
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... the temperature anomalies are calculated by subtracting the mean temperature averaged within a 15° latitude/longitude box centered at the storm center but outside of the 34-knot wind radius circle. The temperature retrieval without applying the remapping algorithm (Figure 8a) contains significant noise (~1 K), which is successfully mitigated by the remapping algorithm (Figure 8b). The warm-core center of Hurricane Matthew has a temperature anomaly of about 5 K and is centered at the observed hurricane location. ...
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... the temperature anomalies are calculated by subtracting the mean temperature averaged within a 15° latitude/longitude box centered at the storm center but outside of the 34-knot wind radius circle. The temperature retrieval without applying the remapping algorithm (Figure 8a) contains significant noise (~1 K), which is successfully mitigated by the remapping algorithm (Figure 8b). The warm-core center of Hurricane Matthew has a temperature anomaly of about 5 K and is centered at the observed hurricane location. ...
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... warm-core center of Hurricane Matthew has a temperature anomaly of about 5 K and is centered at the observed hurricane location. The warm-core in the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-interim temperature analysis (Figure 8c) is misplaced to the east by more than 2 degrees of longitude, and is too broad and too weak, which is existent in global large-scale analyses (Dee et al., 2011). ...
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... temperature anomalies at 250 hPa (Figure 9a) contain a rainband structure that is in phase with the LWP distribution at 0600 UTC 2 October 2016 ( Figure 9b). The maximum temperature anomaly at the warm-core center (Figure 9a) is more than 3 K higher than that in Figure 8b. The vertical cross section of the temperature anomalies ( Figure 9c) shows a series of cold anomalies in the lower troposphere, with larger magnitudes over areas with stronger LWPs. ...
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... anomalies at 250 hPa on 0600 UTC 2 October 2016 calculated from ATMS BT observations without and with remapping are presented in Fig. 8a and 8b, respectively. Here, the temperature anomalies are calculated by subtracting the mean temperature averaged within a 15˚latitude15˚latitude/longitude box centered at the storm center but outside of the 34-knot wind radius circle. The temperature retrieval without applying the remapping algorithm (Fig. 8a) contains significant noise (~1 ...
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... and with remapping are presented in Fig. 8a and 8b, respectively. Here, the temperature anomalies are calculated by subtracting the mean temperature averaged within a 15˚latitude15˚latitude/longitude box centered at the storm center but outside of the 34-knot wind radius circle. The temperature retrieval without applying the remapping algorithm (Fig. 8a) contains significant noise (~1 K), which is successfully mitigated by the remapping algorithm (Fig. 8b). The warm-core center of Hurricane Matthew has a temperature anomaly of about 5 K and is centered at the observed hurricane ...
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... calculated by subtracting the mean temperature averaged within a 15˚latitude15˚latitude/longitude box centered at the storm center but outside of the 34-knot wind radius circle. The temperature retrieval without applying the remapping algorithm (Fig. 8a) contains significant noise (~1 K), which is successfully mitigated by the remapping algorithm (Fig. 8b). The warm-core center of Hurricane Matthew has a temperature anomaly of about 5 K and is centered at the observed hurricane ...
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... temperature analysis (Fig. 8c) is misplaced to the east by more than two degrees of longitude, and is too broad and too weak, which is existent in global large-scale analyses (Dee et al. ...
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... temperature anomalies at 250 hPa ( Fig. 9a) contain a rainband structure that is in phase with the LWP distribution at 0600 UTC 2 October 2016 (Fig. 9b). The maximum temperature anomaly at the warm-core center (Fig. 9a) is more than 3 K higher than that in Fig. 8b. The vertical cross-section of the temperature anomalies (Fig. 9c) shows a series of cold anomalies in the lower troposphere, with larger magnitudes over areas with stronger LWPs. Such unrealistic cold anomalies below the upper-level warm core is not seen in the retrieval that neglects channels 5, 6, and 7 in cloudy conditions (Fig. ...
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... The typhoon warm cores were retrieved from brightness temperature observations from AMSU-A (Demuth et al., 2004;Tian & Zou, 2016;Zhu et al., 2002), ATMS (Zou & Tian, 2018) and MWTS-3 (Niu & Zou, 2022) using traditional linear regression algorithms in previous studies. This paper contributes to a similar research effort by using an ML neural network (NN) approach to obtain atmospheric temperature and thus typhoon warm cores from FY-3E MWTS-3 observations. ...
... Since MWTS-3 is a cross-track scanning instrument, the limb effects are taken care of by setting the regression coefficients as functions of θ. Temperature profiles are retrieved by using MWTS-3 channels 9-17 in clear-sky areas and MWTS-3 channels 4-17 in cloudy areas (LWP ≥ 0.01 kg m 2 ), based on if liquid water path (LWP) is less than 0.01 kg m 2 or not respectively (Niu et al., 2020;Zou & Tian, 2018). The LWP is derived from MWTS-3 window channels 1 and 2 as follows: ...
Machine learning has gained an increasing popularity in the fields of satellite retrieval and numerical weather modeling. In this study, machine‐learning (ML) neural‐network (NN) models are utilized to retrieve atmospheric temperatures from observations of brightness temperature from the Microwave Temperature Sounder‐3 (MWTS‐3) onboard the China's first dawn‐dusk polar‐orbiting satellite Fengyun (FY)‐3E, with the ERA5 reanalysis serving as training data sets. The root mean square errors of the ML‐retrieved temperatures at all pressure levels are smaller than those obtained by a previously used traditional linear regression method compared to the ERA5 reanalysis over global oceans as well as radiosonde observations over land. Less than a 1‐week period of training data is usually sufficient for an ML NN model to converge in less than 50–100 iterations. The shortest time period of training data is 3‐days right before the testing data period. While the horizontal patterns and temporal evolutions of the ML‐retrieved warm cores of Typhoon Malakas (2022) and Typhoon Haikui (2023) in the upper troposphere compared favorably with those obtained by traditional regression methods as well as the ERA5 reanalysis in the testing periods. The vertical structures of ML‐retrieved warm cores extend further down to the middle and lower troposphere while those from the traditional regression method are confined in the upper troposphere. A comparison among ML results with additional training data sets across different seasons confirms the above conclusion.
... In terms of passive microwave measurements, there is a large body of work on characterizing TCs from space-borne microwave radiometers such as the Advanced Microwave Sounding Unit (AMSU-A), the Microwave Humidity Sounder (MHS), and the Advanced Technology Microwave Sounder (ATMS) [7][8][9]. Much of this work has focused on the warm core feature and linking it to TC intensity [10][11][12][13][14]. A direct estimation of surface wind speed using microwave observations was demonstrated in Hong et al. [15]. ...
... Intuitively, surface pressure and surface wind speed of the TC eyewall region can be retrieved with a single latitude and longitudinal point for every ATMS measurement sample, which has been demonstrated by previous studies [10][11][12][13][14][15]. However, this is not an ideal situation because the atmosphere and surface states surrounding TCs are also crucial factors affecting rapid changes near TC areas. ...
A U-Net algorithm was used to retrieve surface pressure and wind speed over the ocean within tropical cyclones (TCs) and their neighboring areas using NOAA-20 Advanced Technology Microwave Sounder (ATMS) reprocessed Sensor Data Record (SDR) brightness temperatures (TBs) and geolocation information. For TC locations, International Best Track Archive for Climate Stewardship (IBTrACS) data have been used over the North Atlantic Ocean and West Pacific Ocean between 2018 and 2021. The European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5) surface pressure and wind speed were employed as reference labels. Preliminary results demonstrated that the visualizations for wind speed and pressure matched the prediction and ERA5 location. The residual biases and standard deviations between the predicted and reference labels were about 0.15 m/s and 1.95 m/s, respectively, for wind speed and 0.48 hPa and 2.67 hPa, respectively, for surface pressure, after applying cloud screening for each ATMS pixel. This indicates that the U-Net model is effective for surface wind speed and surface pressure estimates over general ocean conditions.
... The microwave radiance is approximately a linear function of the atmospheric temperature at frequencies <200 GHz, larger than all SSMIS and AMSU-A channel frequencies. Based on this physical consideration, TC warm-core anomalies can be retrieved based on TB observations from these microwave instruments [18][19][20][21][22]. The assimilation of satellite microwaves retrieved TC warm-core temperatures improved 48-h forecasts of intensifications and vertical structures of all model state variables (e.g., temperature, water vapor mixing ratio, liquid water content mixing ratio, tangential and radial wind components, and vertical velocity) for Hurricane Florence (2018) and Typhoon Mangkhut (2018) [23]. ...
... Irma's warm core was maximized at about 250 hPa. As Irma's intensity increased from 31 August to 5 September 2017 (see Figure 1b in Tian and Zou [22]), the TB warm-core depth and intensity continually increased from 5 September to 8 September, during which Irma was an H5 hurricane. We noticed that the warm core extended to the surface around 0805 UTC on 5 September and 0910 UTC on 8 September due to Irma's large clear eye, as shown in Figures 13 and 14 for the 0910 UTC on 8 September. ...
The Special Sensor Microwave Imager Sounder (SSMIS) onboard the Defense Meteorological Satellite Program (DMSP) F16, launched on 18 October 2003, was the first conical-scanning radiometer to combine the Special Sensor Microwave/Imagers (SSM/I), Special Sensor Microwave/Temperature Sounder (SSM/T), and the Special Sensor Microwave/Water Vapor Sounder (SSM/T2). Nearly 20 years of F16 SSMIS data are available to the general public, providing many opportunities to study the atmosphere at both the synoptic and decadal scales. However, data noise from complicated structures has occurred in the brightness temperature (TB) observations of lower atmospheric sounding (LAS) channels since 25 April 2013. We used a two-dimensional Fast Fourier Transform to analyze the characteristic features of data noise in cross-track and along-track directions. We found that the data noise is around 1–2 K and occurs at certain cross-track wavelengths (Δλ)noise. A latitudinal variation was found for (Δλ)noise. Due to noise interference, TB observations reflecting rain, clouds, tropical cyclone warm core, temperature, and water vapor distributions are not readily distinguishable, especially in channels above the middle troposphere (channels 4–7 and 24), whose dynamic TB range is smaller than low tropospheric channels 1–3. Examples are provided to show the impact of the proposed noise mitigation for conical-scanning TB observations to capture 3D structures of hurricanes directly. Once the noise in F16 SSMIS LAS channels from 25 April 2013to the present is eliminated, we may investigate the decadal change of many features of tropical cyclones derivable from these TB observations.
... By analyzing the invariant intensity distribution at the mature stage, they showed that the stochastic forcing associated with tangential wind and warm-core anomaly has the largest contribution to TC intensity variability. This theoretical result is consistent with previous modeling studies, which captured strong sensitivity of TC intensity to vortex initialization schemes and warm-core retrieval in TC models (Kurihara et al., 1995;Liu et al., 2000;Van Nguyen and Chen, 2011;Rappin et al., 2013;Zou and Tian, 2018). How the effects of stochastic forcing are compared to those caused by random parameters or the vortex initial condition have not been addressed. ...
Proper representations of stochastic processes in tropical cyclone (TC) models are critical for capturing TC intensity variability in real-time applications. In this study, three different stochastic parameterization methods, namely, random initial conditions, random parameters, and random forcing, are used to examine TC intensity variation and uncertainties. It is shown that random forcing produces the largest variability of TC intensity at the maximum intensity equilibrium and the fastest intensity error growth during TC rapid intensification using a fidelity-reduced dynamical model and a cloud-resolving model (CM1). In contrast, the random initial condition tends to be more effective during the early stage of TC development but becomes less significant at the mature stage. For the random parameter method, it is found that this approach depends sensitively on how the model parameters are randomized. Specifically, randomizing model parameters at the initial time appears to produce much larger effects on TC intensity variability and error growth compared to randomizing model parameters every model time step, regardless of how large the random noise amplitude is. These results highlight the importance of choosing a random representation scheme to capture proper TC intensity variability in practical applications.
... Therefore, atmospheric temperatures can be retrieved from MWTS observations by expressing the atmospheric temperature at a specified pressure level ( T(p) ) as a weighted sum of different MWTS channels' brightness temperature observations. Therefore, TC warm cores can be directly retrieved from microwave temperature sounding channels [50,51]. Since the FOV sizes of MWTS and ATMS sounding channels are nearly twice smaller than those of AMSU-A, a remapping algorithm could be applied to make MWTS and ATMS observations of the same FOVs as AMSU-A so that the MWTS and ATMS retrieved warm cores are consistent to those obtained from AMSU-A onboard NOAA-15, NOAA-18, MetOp-A and MetOp-B [51]. ...
... Therefore, TC warm cores can be directly retrieved from microwave temperature sounding channels [50,51]. Since the FOV sizes of MWTS and ATMS sounding channels are nearly twice smaller than those of AMSU-A, a remapping algorithm could be applied to make MWTS and ATMS observations of the same FOVs as AMSU-A so that the MWTS and ATMS retrieved warm cores are consistent to those obtained from AMSU-A onboard NOAA-15, NOAA-18, MetOp-A and MetOp-B [51]. By doing so, the impact of ATMS's large observational noise on warm-core retrievals can also be reduced. ...
With the rapid advances and abundant observations from Chinese Fengyun-3 (FY-3) meteorological satellites, it is of great interest to summarize a decade of quality assessments of FY-3 observations. The topics covered are noise characterization, bias estimation, striping noise detection and mitigation of striping noise, radio frequency interference detection, geolocation accuracy estimation and improvement, data assimilation cloud detection and quality control for observations from the MicroWave Temperature Sounder (MWTS), the MicroWave Humidity Sounder (MWHS), the MicroWave Radiation Imager (MWRI) and the Hyperspectral Infrared Atmospheric Sounder (HIRAS) instruments on board FY-3A/B/C/D. Whether and how much FY-3 data assimilation could improve the numerical weather forecast skill strongly depends on how well the FY-3 data characteristics and errors listed above are known. This review article shall contribute to promoting internal and national usages of FY-3 observations for weather and climate studies.
... Winterbottom and Xiao (2010) showed that the quality and the horizontal resolution of RO was high enough to study TCs, even before the Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC) six-satellite mission was launched. However, thanks to the higher number of RO observations provided from COSMIC after 2006, it has been possible to get a better understanding of the TCs' thermal structure: a warm core in the troposphere (Zou and Tian, 2018), a cooling corresponding to the TC anvil top height (Biondi et al., 2013;Rivoire et al., 2016) and an increase in WV in the lower stratosphere (LS) above the outermost rainbands (Venkat . Vergados et al. (2013) for the first time used more than 1500 RO temperature, water vapour and refractivity profiles to study the moist thermodynamic structure in the lower and the upper troposphere of 42 North Atlantic TCs in the period 2002-2010. ...
Tropical cyclones (TC) are natural destructive phenomena, which affect wide tropical and subtropical areas every year. Although the correct prediction of their tracks and intensity has improved over recent years, the knowledge about their structure and development is still insufficient. The Global Navigation Satellite System (GNSS) radio occultation (RO) technique can provide a better understanding of the TC because it enables us to probe the atmospheric vertical structure with high accuracy, high vertical resolution and global coverage in any weather conditions. In this work, we create an archive of co-located TC best tracks and RO profiles covering the period 2001–2018 and providing a complete view of the storms since the pre-cyclone status to the cyclone disappearance. We collected 1822 TC best tracks from the International Best Track Archive for Climate Stewardship and co-located them with 48 313 RO profiles from seven satellite missions processed by the Wegener Center for Climate and Global Change. We provide information about location and intensity of the TC, RO vertical profiles co-located within 3 h and 500 km from the TC eye centre, and exact information about temporal and spatial distance between the TC centre and the RO mean tangent point. A statistical analysis shows how the archive covers all the ocean basins and all the intensity categories well. We finally demonstrate the application of this dataset to investigate the vertical structure for one TC example case. All the data files, separately for each TC, are publicly available in NetCDF format at 10.25364/WEGC/TC-RO1.0:2020.1 (Lasota et al., 2020).
... However, the application of these spaceborne, multispectral measurements from multiple sensors is often plagued with the problem of nonuniform spatial resolution caused by the limited size of the satellite instrument antenna and the frequency-dependent microwave emission from the earth-atmosphere system. The mismatch in resolution becomes a critical issue when observations from different frequencies and different sensors are combined to retrieve geophysical parameters like wind speed, cloud liquid water, total precipitation water, and snow depth [3][4][5][6]. It could also lead to uncertainty in the cross-calibration between similar satellite radiometer instruments [2,4,7,8]. ...
... LetĜ source (ξ) andĜ target (ξ) be the Fourier transforms of the original and expected gain functions, respectively. In the frequency domain, the AFA can easily manipulate the beam width by first dividing the original antenna pattern and then multiplying the target one:T a target (ξ) =Ta source (ξ)·Ĝ target (ξ) G source (ξ) (6) The second term on the right side of the equation is called the manipulating term. For resolution enhancement, to suppress the noise level, a cutoff factor c is added to attenuate the high-frequency components in the signal. ...
One of the limitations in using spaceborne, microwave radiometer data for atmospheric remote sensing is the nonuniform spatial resolution. Remapping algorithms can be applied to the data to ameliorate this limitation. In this paper, two remapping algorithms, the Backus–Gilbert inversion (BGI) technique and the filter algorithm (AFA), widely used in the operational data preprocessing of the Advanced Technology Microwave Sounder (ATMS), are investigated. The algorithms are compared using simulations and actual ATMS data. Results show that both algorithms can effectively enhance or degrade the resolution of the data. The BGI has a higher remapping accuracy than the AFA. It outperforms the AFA by producing less bias around coastlines and hurricane centers where the signal changes sharply. It shows no obvious bias around the scan ends where the AFA has a noticeable positive bias in the resolution-enhanced image. However, the BGI achieves the resolution enhancement at the expense of increasing the noise by 0.5 K. The use of the antenna pattern instead of the point spread function in the algorithm causes the persistent bias found in the AFA-remapped image, leading not only to an inaccurate antenna temperature expression but also to the neglect of the geometric deformation of the along-scan field-of-views.
... In this study, we propose to replace the MM5 by the RE87 model and the bogus SLP by satellite-derived warm-core temperature anomalies and total precipitable water (TPW) fields. The novelty of this work involves developing a new 4D-Var VI system, making use of satellite retrieval products of warm core and TPW generated by the authors based on their past research Zou, 2016a, 2016b;Zou and Tian, 2018), and demonstrating of a promising, improved TC intensity prediction. The article is arranged as follows. ...
... Ninety-six ATMS scene resolution cells, symmetrically distributed on both sides of the sub-satellite path, are sampled. Since neighbouring ATMS FOVs overlap significantly, a remap algorithm is applied to convert ATMS FOVs at the original 2.2 beam width to a new 3.3 beam width (Zou and Tian, 2018). ...
... The warm-core algorithm developed by Tian and Zou (2016) and further refined by Zou and Tian (2018) is applied to ATMS and MWTS2 brightness temperature observations of Hurricane Florence (2018). The regression coefficients are obtained separately for both clearsky and cloudy conditions from the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 reanalysis (Hoffmann et al., 2018) during the two-week training period prior to the occurrence of Hurricane Florence, i.e. 15-30 August 2018. ...
A four-dimensional variational (4D-Var) vortex initialization (VI) system is developed for a nonhydrostatic axisymmetric numerical model with convection accounted for (the RE87 model). Derivations of the tangent linear and adjoint models of the RE87 model and the correctness checks are presented. As an initial evaluation of the 4D-Var VI system, a cost function that measures the model fit to satellite microwave retrievals of tropical cyclone (TC), warm-core temperatures and total precipitable water (TPW) from the following four polar-orbiting satellites within a slightly longer than 1-h assimilation window is minimized using the limited-memory quasi-Newton minimization algorithm: the Suomi National Polar-orbiting Partnership, NOAA-20, Fengyun-3D and Global Change Observation Mission-Water Satellite 1. An azimuthal spectral analysis in cylindrical coordinates centred on the TC centres shows that the warm core and TPW fields within TCs are dominated by the axisymmetric component. The 4D-Var VI results assimilating the axisymmetric component of the above satellite retrievals produced a significant reduction in the cost function and the norm of the gradient as the minimization process is completed. The gradient of the cost function is accurately computed by a single integration of the RE87 adjoint model. In the cases of Hurricane Florence and Typhoon Mangkhut, improved forecast of intensifications and more realistic vertical structures of all model state variables (e.g. temperature, water vapour mixing ratio, liquid water content mixing ratio, tangential and radial wind components and vertical velocity) are obtained when compared with a parallel run initialized simply by the European Centre for Medium-Range Weather Forecasts ERA5 reanalysis.
... This capability was confirmed by independent analyses (e.g. Davis et al., 2014), and was also useful in training other satellite sensors to retrieve the TC warm core (Zou and Tian, 2018). The cloud top cooling often results in a double tropopause in TC (Biondi et al., 2011b;Biondi et al., 2013;Vergados et al., 2014). ...
In this paper we review the contributions of GNSS ground-based and radio occultation receivers to the understanding and prediction of severe weather phenomena around the world. These ground- and space-based GNSS observations, which are complementary to other in-situ and remotely sensed observations, are sensitive to the temperature and water vapor content of the atmosphere, both important parameters that characterize the structure and evolution of heavy rainfall and convective storms, atmospheric rivers, tropical cyclones, and droughts and heat waves. With the first ground-based GPS observations reported in the early 1990s and the first radio occultation observations of Earth's atmosphere derived from the GPS/MET proof-of-concept mission (1995–1997), these GNSS-based observations are still relatively new contributors to the research and operational suite of technologies.
... Microwave instruments like the ATMS, however, can observe radiances of the atmospheric profile in almost all weather conditions except for heavy precipitation. Microwave temperature sounder data have been used to estimate TC size and intensity and to improve hurricane forecasts [1], [2], [12]- [15]. Typhoon Jelawat (2018) was the first Category 4 super-typhoon of 2018 that was developed over the West Pacific Ocean and reached a maximum 1-min sustained wind of 130 kn (67 ms −1 ) [16]. ...
... The regression coefficients have to be trained using the TB at each field-of-view (FOV) position. Temperature profiles from GPS ROs were used as the training dataset to obtain better accuracies [2]. However, the geographical distribution of RO profiles is random around the globe, and the sample number of RO profiles collocated with NOAA-20 ATMS observations is small. ...
... The destriping method effectively removes the impacts of striping noise on the ATMS temperature retrievals. In order to investigate the impact of not only the striping noise but also the random noise on the warm core retrieval, the footprint remapping method described in [2] and [21] is also applied to environment stopped the typhoon from intensifying further. The storm then weakened rapidly to Category 1. Vertical wind shear is an important inhibitive factor for TC development [22]- [24]. ...
The Advanced Technology Microwave Sounder (ATMS) is onboard both the National Oceanic and Atmospheric Administration (NOAA)-20 and the Suomi National Polar-Orbiting Partnership (S-NPP) satellites. NOAA-20 has the same sun-synchronous orbit as that of the S-NPP, but is 50 min (i.e., half orbit) ahead. The striping noise is found in ATMS brightness temperature observations from both NOAA-20 and S-NPP. In this study, first, a striping noise detection and mitigation algorithm that was previously developed for striping noise mitigation in ATMS observations from S-NPP is adopted to characterize the striping noise in NOAA-20 ATMS brightness temperature measurements. It combines a principal component analysis and an ensemble empirical mode decomposition method. It is found that the magnitudes of both the striping noise and the random noise in NOAA-20 ATMS data are smaller than those in S-NPP ATMS data. Second, global positioning system radio occultation retrieved temperature profiles are used as the training dataset for ATMS hurricane warm core retrievals in order to investigate the impacts of the data noise. Numerical results are demonstrated using the case of Typhoon Jelawat (2018), which rapidly intensified from a Category 1 to a Category 4 super typhoon and weakened back to Category 1 within 24 h. Finally, we show that a half-orbit separation of NOAA-20 from S-NPP enables the rapidly evolving vertical structures of Typhoon Jelawat. This suggests an enhanced tropical cyclone monitoring capability offered by NOAA-20 and S-NPP for this hurricane season and a few following years.
