In this study, we estimated the angular diameter distances to two high-redshift active galactic nuclei (AGNs), OJ 248 (z = 0.939) and 4C +38.41 (z = 1.814), using 43 GHz radio light curves. The aim of this work is to extend AGN variability–based distance measurement methods to the high-redshift regime. The distance estimates were analyzed under two assumptions for the maximum intrinsic brightness temperature TB, int: (1) the equipartition temperature, and (2) the sample-averaged TB, int. As a representative result, when adopting the equipartition temperature, the angular diameter distances to OJ 248 and 4C +38.41 are estimated to be 7,592.7±396.7 Mpc and 11,069.8±1,216.9 Mpc, respectively. In addition, Doppler factors were calculated using the inverse-Compton method, and additional distance estimates were obtained based on these values. Our error budget analysis shows that the most significant systematic uncertainty arises from epoch selection. These results indicate that systematic effects have a substantial impact on the derived distance estimates and limit their reliability, particularly in the high-redshift regime. Reducing these uncertainties will require improved observational cadence and reduced post-fit noise. AGN variability provides an alternative approach to distance estimation. However, our results reveal significant limitations in the current methodology and indicate that further methodological and observational improvements are required before its cosmological applicability can be reliably assessed.

To understand the characteristics of low-level clouds (CLs), environmental variables are composited on each CL using individual surface observations and six-hourly upper-air meteorologies around the globe. Individual CLs has its own distinct environmental conditions. Over the eastern subtropical and western North Pacific Ocean in JJA, stratocumulus (CL5) has a colder sea surface temperature (SST), stronger and lower inversion, and more low-level cloud amount (LCA) than the climatology whereas cumulus (CL12) has the opposite characteristics. Over the eastern subtropical Pacific, CL5 and CL12 are influenced by cold and warm advection within the PBL, respectively but have similar cold advection over the western North Pacific. This indicates that the fundamental physical process distinguishing CL5 and CL12 is not the horizontal temperature advection but the interaction with the underlying sea surface, i.e., the deepening-decoupling of PBL and the positive feedback between shortwave radiation and SST. Over the western North Pacific during JJA, skyobscuring fog (CL11), no low-level cloud (CL0), and fair weather stratus (CL6) are associated with anomalous warm advection, surface-based inversion, mean upward flow, and moist mid-troposphere with the strongest anomalies for CL11 followed by CL0. Over the western North Pacific during DJF, bad weather stratus (CL7) occurs in the warm front of the extratropical cyclone with anomalous upward flow while cumulonimbus (CL39) occurs on the rear side of the cold front with anomalous downward flow. Over the tropical oceans, CL7 has strong positive (negative) anomalies of temperature in the upper troposphere (PBL), relative humidity, and surface wind speed in association with the mesoscale convective system while CL12 has the opposite anomalies and CL39 is in between.

To understand the genesis of tropical cyclones (TC), we computed TC genesis probability (GPr) by partitioning a highly localized genesis frequency (GFq) into nearby grid boxes in proportion to the spatial coherence of genesis potential index (GPI). From the analysis of TCs simulated by the Seoul National University Atmosphere Model Version 0 and the observed TCs, it was shown that GPr reasonably converges to GFq when averaged over a long-term period in a decent grid size, supporting its validity as a proxy representing a true TC GPr. The composite anomalies of the gridded GPr in association with the Asia summer monsoon, El Nino-Southern Oscillation (ENSO), and the Madden-Julian Oscillation (MJO) are much less noisy than those of GFq, and consequently are better interpretable. In summary, GPr converges to GFq, varies more smoothly than GFq, represents the spatiotemporal variations of GFq better than GPI, and depicts GFq with greater spatial details than other spatially smoothed GFqs.