Major, sudden midwinter stratospheric warmings (SSWs) are large and rapid temperature increases in the winter polar stratosphere are associated with a complete reversal of the climatological westerly winds (i.e., the polar vortex). These extreme events can have substantial impacts on winter surface climate, including increased frequency of cold air outbreaks over North America and Eurasia and anomalous warming over Greenland and eastern Canada. Here we present a SSW Compendium (SSWC), a new database that documents the evolution of the stratosphere, troposphere, and surface conditions 60 days prior to and after SSWs for the period 1958–2014. The SSWC comprises data from six different reanalysis products: MERRA2 (1980–2014), JRA-55 (1958–2014), ERA-interim (1979–2014), ERA-40 (1958–2002), NOAA20CRv2c (1958–2011), and NCEP-NCAR I (1958–2014). Global gridded daily anomaly fields, full fields, and derived products are provided for each SSW event. The compendium will allow users to examine the structure and evolution of individual SSWs, and the variability among events and among reanalysis products. The SSWC is archived and maintained by NOAA's National Centers for Environmental Information (NCEI, doi:10.7289/V5NS0RWP).

Initial data from the Formosa Satellite‐7/Constellation Observing System for Meteorology Ionosphere and Climate (FORMOSAT‐7/COSMIC‐2, hereafter C2), a recently launched equatorial constellation of six satellites carrying advanced radio occultation receivers, exhibit high signal‐to‐noise ratio, precision, and accuracy, and the ability to provide high vertical resolution profiles of bending angles and refractivity, which contain information on temperature and water vapor in the challenging tropical atmosphere. After an initial calibration/validation phase, over 100,000 soundings of bending angles and refractivity that passed quality control in October 2019 are compared with independent data, including radiosondes, model forecasts, and analyses. The comparisons show that C2 data meet expectations of high accuracy, precision, and capability to detect superrefraction. When fully operational, the C2 satellites are expected to produce ~5,000 soundings per day, providing freely available observations that will enable improved forecasts of weather, including tropical cyclones, and weather, space weather, and climate research.

The amplitude of upward-propagating tropospherically forced planetary waves is known to be of first-order importance in producing sudden stratospheric warmings (SSWs). This forcing amplitude is observed to undergo strong temporal fluctuations. Characteristics of the resulting transient forcing leading to SSWs are studied in reanalysis data and in highly truncated simple models of stratospheric wave–mean flow interaction. It is found in both the reanalysis data and the simple models that SSWs are preferentially generated by transient forcing of sufficiently long time scales (on the order of 1 week or longer). The time scale of the transient forcing is found to play a stronger role in producing SSWs than the strength of the forcing. In the simple models it is possible to fix the amplitude of the tropospheric forcing but to vary the time scale of the forcing. The resulting frequency of occurrence of SSWs shows dramatic reductions for decreasing forcing time scales.

Each spring, the climatological westerly winds of the Northern Hemisphere (NH) stratospheric polar vortex turn easterly as the stratospheric equator‐to‐pole temperature gradient relaxes. The timing of this event is dictated both by the annual return of sunlight to the pole and by dynamical influences from the troposphere. Here we consider the predictability of NH final stratospheric warmings in multimodel hindcasts from the Subseasonal to Seasonal project database. We evaluate how well the Subseasonal to Seasonal prediction systems perform in capturing the timing of final warmings. We compare the predictability of early warmings (which are more strongly driven by wave forcing) and late warmings and find that late warmings are more skillfully predicted at longer lead times. Finally, we find significantly increased predictive skill of NH near‐surface temperature anomalies at Weeks 3–4 lead times following only early final warmings.

Global navigation satellite system (GNSS) radio occultation (RO) receivers onboard the recently-launched COSMIC-2 (C2) satellite constellation provide an unprecedented number of high vertical resolution moisture profiles throughout the tropical and subtropical atmosphere. In this study, the distribution and variability of water vapor was investigated using specific humidity retrievals from C2 observations and compared to collocated ERA5 and MERRA-2 reanalysis profiles within 40°N to 40°S from September to December 2019, which is prior to the assimilation of C2 in the reanalyses. Negative C2 moisture biases are evident within the boundary layer, so we focused on levels above the boundary layer in this study. Overall, C2 specific humidity shows excellent agreement with that of ERA5 and has larger differences with that of MERRA-2. In the tropical mid-troposphere, C2 shows positive biases compared to ERA5 (6–12%) and larger negative biases with MERRA-2 (15–30%). Strong correlations are observed between C2 and reanalysis specific humidity in the subtropics (>0.8) whereas correlations are slightly weaker in the deep tropics, especially for MERRA-2. Profile pairs with large moisture differences often occur in areas with sharp moisture gradients, highlighting the importance of measurement resolution. Locations which demonstrated weaker humidity correlations in active convection regions show that ERA5 has a negative specific humidity bias at 3 km in higher moisture environments, whereas MERRA-2 displays a large positive bias at 7 km. However, additional explanations for profile pairs with large moisture differences remain unclear and require further study.

The three-cornered hat (3CH) method, which was originally developed to assess the random errors of atomic clocks, is a means for estimating the error variances of three different datasets. Here we give an overview of the historical development of the 3CH and select other methods for estimating error variances that use either two or three datasets. We discuss similarities and differences between these methods and the 3CH method. This study assesses the sensitivity of the 3CH method to the factors that limit its accuracy, including sample size, outliers, different magnitudes of errors between the datasets, biases, and unknown error correlations. Using simulated datasets for which the errors and their correlations among the datasets are known, this analysis shows the conditions under which the 3CH method provides the most and least accurate estimates. The effect of representativeness errors caused by differences in vertical resolution of datasets is investigated. These representativeness errors are generally small relative to the magnitude of the random errors in the datasets, and the impact of this source of errors can be reduced by appropriate filtering.