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Cristina L. Archer, Sicheng Wu, Yulong Ma, and Pedro Jiménez

This corrigendum addresses two issues in the paper by Archer et al. (2020): an incorrect version number and a clarification.

The first issue is the incorrect version number of the Weather Research and Forecasting (WRF) Model in which the code bug occurred for the first time, which is v3.6, not v3.3 as stated or implied in the manuscript. The following is a brief history of the treatment of QKE (a WRF array variable that is defined as twice the turbulence kinetic energy), QKE_ADV (a second array that stores QKE to be advected), and the namelist flag bl_mynn_tkeadvect

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Keith D. Sherburn and Matthew D. Parker

Through another research effort, it was recently discovered that the wind profiles used to initialize the Cloud Model 1 (CM1) simulations in Sherburn and Parker (2019) were specified with incorrect units (mistaking meters per second for knots). The following corrected versions of Fig. 1 and Table 1 represent the actual initial conditions for the simulations reported in Sherburn and Parker (2019).

Control base-state environment in HSLC matrix of simulations. Hodograph axes are labeled in knots (1 kt = 0.51444 m s−1) and contoured at 10-kt intervals. Half

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Aaron J. Hill, Christopher C. Weiss, and Brian C. Ancell

In Hill et al. (2016), there were errors in the reporting of covariance localization half-widths, both in the horizontal and vertical. The horizontal half-widths were reported as 600 and 300 km and the vertical half-widths were reported as 0.075 and 0.025 km for the outermost and inner domains, respectively. These half-width values of the Gaspari–Cohn localization function (Gaspari and Cohn 1999) come from a Data Assimilation Research Testbed (Anderson et al. 2009) namelist, which was incorrectly interpreted. The horizontal half-widths should have been calculated as the product of the namelist cutoff value (e.g., 0.05)

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George N. Kiladis, Juliana Dias, Katherine H. Straub, Matthew C. Wheeler, Stefan N. Tulich, Kazuyoshi Kikuchi, Klaus M. Weickmann, and Michael J. Ventrice

In an earlier study (Kiladis et al. 2014), we introduced an all-season OLR-based MJO index (OMI). We recently became aware of a factor of 2 plotting error in Fig. 1 of that paper. The time series of variance explained by the first two principal components of the EOF analysis should be about half of what is displayed in the figure. Those values are mentioned in the text once and this should to be corrected as follows:

These track each other well, differing by only 1%–2% throughout, and peak during mid-January at greater than 65% (33%) of the total

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Anthony W. Lyza and Kevin R. Knupp

An error has been found in the calculation of Froude numbers (Fr H and Fr L ) presented in Fig. 9 of Lyza and Knupp (2018). The corrected Fig. 9 is shown below. This error was discovered during preparation for the submission of an upcoming article that utilizes this dataset. The code for calculating the values of Fr H and Fr L did not account for the wind speed data being provided in knots in the RAP sounding files. Correction of this error and utilizing the correct units reveals no meaningful changes to

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Rae-Seol Park, Jung-Hyo Chae, and Song-You Hong

Because of a production error, a term in Eq. (5b) in Park et al. (2016) was mistakenly left out of the equation. The corrected equation appears below: The staff of Monthly Weather Review regrets any inconvenience this error may have caused.

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Alexandre O. Fierro, Jidong Gao, Conrad L. Ziegler, Kristin M. Calhoun, Edward R. Mansell, and Donald R. Macgorman

In all the lightning data assimilation (LDA) experiments of Fierro et al. (2016, hereafter F16), it is stated that, outside the observed lightning areas, the pseudo-observations for water vapor mass (q υ ) were not created because these grid points are considered as nonupdraft areas. This implied that pseudo-q υ outside the lightning areas were treated as missing values, which was the original intent. In the 3DVAR package, however, the missing pseudo-q υ outside the lightning areas were inadvertently assigned a zero innovation instead of a missing value. Because of this,

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Stephanie N. Stevenson, Kristen L. Corbosiero, and Sergio F. Abarca

An error was discovered in the direction of the deep-layer vertical wind shear vector obtained from the Statistical Hurricane Intensity Prediction Scheme (SHIPS) database in Stevenson et al. (2016). In the original submission, postprocessed files were used for 2005–13, and real-time text files were used for 2014 because the postprocessed files were not available for 2014 prior to submission. The authors were unaware of the differences in shear direction between the two datasets: the postprocessed files use the shear heading (i.e., the direction the shear is going toward), while the real-time text files use the direction

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Chia-Ying Lee and Shuyi S. Chen

The following sentence, which appeared in the accepted version of Lee and Chen (2014), was incorrectly removed during page proofs by the authors:

Although the composite inflow angle in a storm-relative coordinate using dropsondes collected in the Atlantic hurricanes by Zhang and Uhlhorn (2012) showed that the inflow angles are larger in the front-right quadrant in fast-moving storms, variability from storm to storm is large.

This sentence should appear in section 6c on p. 1939, following “The horizontal pattern of inflow angles can vary from storm to storm.”

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Mabrouk Abaza, François Anctil, Vincent Fortin, and Richard Turcotte

Spread-skill plots in Abaza et al. (2013) are in error because the RMSE is compared to the average standard deviation instead of the square root of the average variance. Indeed, two different and inconsistent methodologies have been used over the last few years in the meteorological and hydrological literature to compute the average ensemble spread: in some cases, the square root of the average ensemble variance is used; in other cases the average of the ensemble standard deviation is computed instead. The second option, used in Abaza et al. (2013), is incorrect and may lead to

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