An Algorithm Using DBSCAN to Solve the Velocity Dealiasing Problem

Velocity dealiasing is an essential task for correcting the radial velocity data collected by Doppler radar. To improve the accuracy of velocity dealiasing, traditional dealiasing algorithms usually set a series of empirical thresholds, combine three- or four-dimensional data, or introduce other observation data as a reference. In this study, we transform the velocity dealiasing problem into a clustering problem and solve this problem using the density-based spatial clustering of applications with noise (DBSCAN) method. This algorithm is verified with a case study involving radar data on the tropical cyclone Mangkhut in 2018. The results show that the accuracy of the proposed algorithm is close to that of the four-dimensional dealiasing (4DD) method proposed by James and Houze; yet, it only requires two-dimensional velocity data and eliminates the need for other reference data. The results of the case study also show that the 4DD algorithm filters out many observation gates close to the missing data or radar center, whereas the proposed algorithm tends to retain and correct these gates.

Another Superdense Sub-Neptune in K2-182 b and Refined Mass Measurements for K2-199 b and c*

We combine multiple campaigns of K2 photometry with precision radial velocity measurements from Keck-HIRES to measure the masses of three sub-Neptune-sized planets. We confirm the planetary nature of the massive sub-Neptune K2-182 b (P b = 4.7 days, R b = 2.69 R ⊕) and derive refined parameters for K2-199 b and c (P b = 3.2 days, R b = 1.73 R ⊕ and P c = 7.4 days, R c = 2.85 R ⊕). These planets provide valuable data points in the mass–radius plane, especially as TESS continues to reveal an increasingly diverse sample of sub-Neptunes. The moderately bright (V = 12.0 mag) early K dwarf K2-182 (EPIC 211359660) was observed during K2 campaigns 5 and 18. We find that K2-182 b is potentially one of the densest sub-Neptunes known to date (20 ± 5 M ⊕ and 5.6 ± 1.4 g cm−3). The K5V dwarf K2-199 (EPIC 212779596; V = 12.3 mag), observed in K2 campaigns 6 and 17, hosts two recently confirmed planets. We refine the orbital and planetary parameters for K2-199 b and c by modeling both campaigns of K2 photometry and adding 12 Keck-HIRES measurements to the existing radial velocity data set (N = 33). We find that K2-199 b is likely rocky, at 6.9 ± 1.8 M ⊕ and 7.2 − 2.0 + 2.1 g cm−3, and that K2-199 c has an intermediate density at 12.4 ± 2.3 M ⊕ and 2.9 − 0.6 + 0.7 g cm−3. We contextualize these planets on the mass–radius plane, discuss a small but intriguing population of “superdense” sub-Neptunes (R p < 3 R ⊕, M p >20 M ⊕), and consider our prospects for the planets’ atmospheric characterization.

Chemical abundances of three new Ba stars from the Keck/HIRES spectra

Based on high resolution, high signal-to-noise (S/N) ratio spectra from Keck/HIRES, we have determined abundances of 20 elements for 18 Ba candidates. The parameter space of these stars is in the range of 4880 ≤ T eff ≤ 6050 K, 2.56 ≤ log g ≤ 4.53 dex and − 0.27 ≤ [Fe/H] ≤ 0.09 dex. It is found that four of them can be identified as Ba stars with [s/Fe] > 0.25 dex (s: Sr, Y, Zr, Ba, La, Ce and Nd), and three of them are newly discovered, which include two Ba giants (HD 16178 and HD 22233) and one Ba subgiant (HD 2946). Our results show that the abundances of α, odd and iron-peak elements (O, Na, Mg, Al, Si, Ca, Sc, Ti, Mn, Ni and Cu) for our program stars are similar to those of the thin disk, while the distribution of [hs/ls] (hs: Ba, La, Ce and Nd, ls: Sr, Y and Zr) ratios of our Ba stars is similar to those of the known Ba objects. None of the four Ba stars show clear enhancement in carbon including the known CH subgiant HD 4395. It is found that three of the Ba stars present clear evidence of hosting stellar or sub-stellar companions from the radial velocity data.

An account of low level wind shear over Chennai airport - Part II : Turbulence and eddy dissipation

In-flight reports on Low Level Wind Shear (LLWS) received from aircrafts are used to issue wind shear alerts for all subsequent landing aircrafts as per standing guidelines of International Civil Aviation Organisation (ICAO). In this paper, winds reported by aircrafts at 1000 and 1800 ft. are used to validate the wind estimated from DWR measured radial wind data employing standard algorithms. Turbulence indices and parameters have been computed independently using conventional (RS/RW) upper air data, aircraft measured winds and DWR estimated winds and compared these with wind shear induced turbulence reported by aircrews. Mean power law (wind escalation law) profiles in the boundary layer have been arrived at for unstable and stable atmospheric conditions. Three dimensional shear (3DS) upto 600 m a.g.l. has been worked out from DWR measured radial velocity data and compared with wind shear computed from RS/RW and aircraft measured winds and DWR estimated winds. It is found that 3DS values of more than 16 * 10-3 s-1 predict well the occurrence of moderate turbulence. Contrary to the general belief that wind shear is a short lived phenomenon which may last for a few minutes only, it has been observed that incidences of LLWS and induced moderate turbulence lasting more than 10 hrs are not at all uncommon over Chennai aircraft.

SCExAO/CHARIS Direct Imaging of A Low-mass Companion At A Saturn-like Separation from an Accelerating Young A7 Star

We present the SCExAO direct imaging discovery and characterization of a low-mass companion to the nearby young A7IV star, HD 91312. SCExAO/CHARIS JHK (1.1–2.4 μm) spectra and SCExAO/HiCIAO H-band imaging identify the companion over a two year baseline in a highly inclined orbit with a maximum projected separation of 8 au. The companion, HD 91312 B, induces an 8.8σ astrometric acceleration on the star as seen with the Gaia & Hipparcos satellites and a long-term radial-velocity trend as previously identified by Borgniet et al. HD 91312 B’s spectrum is consistent with that of an early-to-mid M dwarf. Hipparcos and Gaia absolute astrometry, radial-velocity data, and SCExAO/CHARIS astrometry constrain its dynamical mass to be 0.337 − 0.044 + 0.042 M ⊙, consistent with - but far more precise than - masses derived from spectroscopy, and favors a nearly edge-on orbit with a semimajor axis of ∼9.7 au. This work is an example of precisely characterizing properties of low-mass companions at solar system-like scales from a combination of direct imaging, astrometry, and radial-velocity methods.

Detecting Wave Features in Doppler Radial Velocity Radar Observations

Mesoscale, wave-like perturbations in horizontal air motions in the troposphere (velocity waves) are associated with vertical velocity, temperature, and pressure perturbations that can initiate or enhance precipitation within clouds. The ability to detect velocity waves from horizontal wind information is an important tool for atmospheric research and weather forecasting. This paper presents a method to routinely detect velocity waves using Doppler radial velocity data from a scanning weather radar. The method utilizes the difference field between consecutive PPI scans at a given elevation angle. Using the difference between fields a few minutes apart highlights small scale perturbations associated with waves because the larger scale wind field changes more slowly. Image filtering retains larger contiguous velocity bands and discards noise. Wave detection scales are limited by the size of the temporal difference relative to the wave motion and the radar resolution volume size.

Revisiting theKeplerfield withTESS: Improved ephemerides usingTESS2 min data

ABSTRACTUp to date planet ephemerides are becoming increasingly important as exoplanet science moves from detecting exoplanets to characterizing their architectures and atmospheres in depth. In this work, ephemerides are updated for 22 Kepler planets and 4 Kepler planet candidates, constituting all Kepler planets and candidates with sufficient signal to noise in the TESS 2 min data set. A purely photometric method is utilized here to allow ephemeris updates for planets even when they do not posses significant radial velocity data. The obtained ephemerides are of very high precision and at least seven years ‘fresher’ than archival ephemerides. In particular, significantly reduced period uncertainties for Kepler-411d, Kepler-538b, and the candidates K00075.01/K00076.01 are reported. O–C diagrams were generated for all objects, with the most interesting ones discussed here. Updated TTV fits of five known multiplanet systems with significant TTVs were also attempted (Kepler-18, Kepler-25, Kepler-51, Kepler-89, and Kepler-396), however these suffered from the comparative scarcity and dimness of these systems in TESS. Despite these difficulties, TESS has once again shown itself to be an incredibly powerful follow-up instrument as well as a planet-finder in its own right. Extension of the methods used in this paper to the 30 min-cadence TESS data and TESS extended mission has the potential to yield updated ephemerides of hundreds more systems in the future.

Precipitation cloud identification based on faster-RCNN for Doppler weather radar

Precipitation clouds are visible aggregates of hydrometeor in the air that floating in the atmosphere after condensation, which can be divided into stratiform cloud and convective cloud. Different precipitation clouds often accompany different precipitation processes. Accurate identification of precipitation clouds is significant for the prediction of severe precipitation processes. Traditional identification methods mostly depend on the differences of radar reflectivity distribution morphology between stratiform and convective precipitation clouds in three-dimensional space. However, all of them have a common shortcoming that the radial velocity data detected by Doppler Weather Radar has not been applied to the identification of precipitation clouds because it is insensitive to the convective movement in the vertical direction. This paper proposes a new method for precipitation clouds identification based on deep learning algorithm, which is according the distribution morphology of multiple radar data. It mainly includes three parts, which are Constant Altitude Plan Position Indicator data (CAPPI) interpolation for radar reflectivity, Radial projection of the ground horizontal wind field by using radial velocity data, and the precipitation clouds identification based on Faster-RCNN. The testing result shows that the method proposed in this paper performs better than the traditional methods in terms of precision. Moreover, this method boasts great advantages in running time and adaptive ability.

Assimilation of Radial Velocity from Coastal NEXRAD into HWRF for Improved Forecasts of Landfalling Hurricanes

The benefits of assimilating NEXRAD (Next Generation Weather Radar) radial velocity data for convective systems have been demonstrated in previous studies. However, impacts of assimilation of such high spatial and temporal resolution observations on hurricane forecasts has not been demonstrated with the NCEP (National Centers for Environmental Prediction) HWRF (Hurricane Weather and Research Forecasting) system. This study investigates impacts of NEXRAD radial velocity data on forecasts of the evolution of landfalling hurricanes with different configurations of data assimilation. The sensitivity of data assimilation results to influencing parameters within the data assimilation system, such as the maximum range of the radar data, super-observations, horizontal and vertical localization correlation length scale, and weight of background error covariances, is examined. Two hurricane cases, Florence and Michael, that occurred in the summer of 2018 are chosen to conduct a series of experiments. Results show that hurricane intensity, asymmetric structure of inland wind and precipitation, and quantitative precipitation forecasting are improved. Suggestions for implementation of operational configurations are provided.