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On 28 April 2019, hourly forecasts from the operational High-Resolution Rapid Refresh (HRRR) model consistently predicted an isolated supercell storm late in the day near Dodge City, Kansas, that subsequently was not observed. Two convection-allowing model (CAM) ensemble runs are created to explore the reasons for this forecast error and implications for severe weather forecasting. The 40-member CAM ensembles are run using the HRRR configuration of the WRF-ARW Model at 3-km horizontal grid spacing. The Gridpoint Statistical Interpolation (GSI)-based ensemble Kalman filter is used to assimilate observations every 15 min from 1500 to 1900 UTC, with resulting ensemble forecasts run out to 0000 UTC. One ensemble only assimilates conventional observations, and its forecasts strongly resemble the operational HRRR with all ensemble members predicting a supercell storm near Dodge City. In the second ensemble, conventional observations plus observations of WSR-88D radar clear-air radial velocities, WSR-88D diagnosed convective boundary layer height, and GOES-16 all-sky infrared brightness temperatures are assimilated to improve forecasts of the preconvective environment, and its forecasts have half of the members predicting supercells. Results further show that the magnitude of the low-level meridional water vapor flux in the moist tongue largely separates members with and without supercells, with water vapor flux differences of 12% leading to these different outcomes. Additional experiments that assimilate only radar or satellite observations show that both are important to predictions of the meridional water vapor flux. This analysis suggests that mesoscale environmental uncertainty remains a challenge that is difficult to overcome. Significance Statement: Forecasts from operational numerical models are the foundation of weather forecasting. There are times when these models make forecasts that do not come true, such as 28 April 2019 when successive forecasts from the operational High-Resolution Rapid Refresh (HRRR) model predicted a supercell storm late in the day near Dodge City, Kansas, that subsequently was not observed. Reasons for this forecast error are explored using numerical experiments. Results suggest that relatively small changes to the prestorm environment led to large differences in the evolution of storms on this day. This result emphasizes the challenges to operational severe weather forecasting and the continued need for improved use of all available observations to better define the atmospheric state given to forecast models. [ABSTRACT FROM AUTHOR]

Rainfall is an important variable to be able to monitor and forecast across Africa, due to its impact on agriculture, food security, climate‐related diseases and public health. Numerical weather models (NWMs) are an important component of this work, due to their complete spatial coverage, high resolution and ability to forecast into the future. In this study, the spatio‐temporal skill of short‐term forecasts of rainfall across Africa from 2016 through 2018 is evaluated. Specifically, the European Centre for Medium‐Range Weather Forecasts (ECMWF) and the National Centers for Environmental Prediction‐Global Forecast System (NCEP‐GFS) forecast models are verified by Rainfall Estimates 2.0 (RFE2) and African Rainfall Climatology Version 2 (ARC2), which are fused products of satellite and in situ observations and are commonly used in analysis of African rainfall. Model rainfall forecasts show good consistency with the satellite rainfall observations in spatial distribution over Africa on the seasonal timescale. Evaluation metrics of daily and weekly forecasts show high spatial and seasonal variations over the African continent, including a strong link to the location of the inter‐tropical convergence zone (ITCZ) and topographically enhanced precipitation. The rainfall forecasts at 1 week aggregation time are improved against daily forecasts. [ABSTRACT FROM AUTHOR]

With the serial treatment of observations in the ensemble Kalman filter (EnKF), the assimilation order of observations is usually assumed to have no significant impact on analysis accuracy. However, Nerger derived that analyses with different assimilation orders are different if covariance localization is applied in the observation space. This study explores whether the assimilation order can be optimized to systematically improve the filter estimates. A mathematical demonstration of a simple two-dimensional case indicates that different assimilation orders can cause different analyses, although the differences are two orders of magnitude smaller than the analysis increments if two identical observation error variances are the same size as the two identical state error variances. Numerical experiments using the Lorenz-96 40-variable model show that the small difference due to different assimilation orders could eventually result in a significant difference in analysis accuracy. Several ordering rules are tested, and the results show that an ordering rule that gives a better forecast relative to future observations improves the analysis accuracy. In addition, the analysis is improved significantly by ordering observations from worse to better impacts using the ensemble forecast sensitivity to observations (EFSO), which estimates how much each observation reduces or increases the forecast error. With the EFSO ordering rule, the change in error during the serial assimilation process is similar to that obtained by the experimentally found best sampled assimilation order. The ordering has more impact when the ensemble size is smaller relative to the degrees of freedom of the dynamical system. [ABSTRACT FROM AUTHOR]

The bred vector (BV) technique applied to the Geophysical Fluid Dynamics Laboratory (GFDL) Mars General Circulation Model (MGCM) identifies regions and seasons of instability of the Martian atmosphere, and a kinetic energy equation applied to the control and perturbed states elucidates their physical origins. Instabilities prominent in the late autumn to early spring seasons of each hemisphere along the polar temperature front result from baroclinic conversions from BV potential to BV kinetic energy near the surface, whereas both baroclinic and barotropic conversions play a role for the westerly jets aloft. The low-level tropics and the northern hemisphere summer are relatively stable, with negative bred vector growth rates. Bred vector growth precedes initiation of travelling wave activity in the midlatitudes during the transition seasons, and their structure relates to the eddy field. Topography plays a role in determining favoured locations for near-surface instabilities. Bred vectors are also linked to forecast ensemble spread in data assimilation and help explain the growth of forecast errors. We finally note that the ability to use breeding to identify instabilities as well as their physical origin depends on the fact that both the control and the perturbed solutions that give rise to bred vectors satisfy exactly the model's governing equations. As a result, this approach can be used with any dynamical system represented by a model. Copyright © 2012 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

Spatial and temporal variations in planetary boundary layer height (PBLH) over the Korean Peninsula and its surrounding oceans are investigated using a regional grid model operated at the Korea Meteorological Administration (KMA). Special attention is placed on daily maximum mixing height for evaluation against two radiosonde observation datasets. In order to construct a new high-resolution PBLH database with 3-hour time and 10 km spatial resolution, short-term integrations with the regional model are carried out for a one-year period from June 2010 to May 2011. The resulting dataset is then utilized to explore the seasonal patterns of horizontal PBLH distribution over the peninsula for one year. Frequency distributions as well as monthly and diurnal variations of PBLH at two selected locations are examined. This study reveals specific spatiotemporal structure of boundary layer depth over the Korean Peninsula for the first time at a relatively high-resolution scale. The results are expected to provide insights into the direction for operational tuning and future development in the model boundary layer schemes at KMA. [ABSTRACT FROM AUTHOR]