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Christopher D. Roberts
,
Magdalena A. Balmaseda
,
Laura Ferranti
, and
Frederic Vitart

Abstract

This study combines operational reforecasts (2001–21) with results from a lower-resolution 41-yr reforecast (1980–2020) to provide a robust assessment of wintertime Euro-Atlantic regimes and their modulation by tropospheric and stratospheric teleconnection pathways in the European Centre for Medium-Range Weather Forecasts (ECMWF) Subseasonal to Seasonal Prediction project (S2S). In both operational and lower-resolution reforecasts, the climatological properties of wintertime Euro-Atlantic regimes, including regime structures, frequencies, and transition probabilities, are accurately simulated at S2S lead times. However, the 41-yr reforecasts allow us to diagnose substantial errors in regime statistics when conditioned on modes of intraseasonal-to-interannual variability. In particular, ECMWF reforecasts underestimate the response of the North Atlantic Oscillation (NAO) to the Madden–Julian oscillation (MJO) and fail to reproduce the modulation of MJO–NAO teleconnections by El Niño–Southern Oscillation (ENSO). Teleconnection and atmospheric wave diagnostics highlight two specific issues that are likely to contribute to these conditional errors in ECMWF reforecasts: (i) insufficient propagation of Rossby wave activity from the Pacific to the Atlantic following MJO phase 3 during El Niño conditions, when the direct tropospheric teleconnection pathway is most active; and (ii) an underestimated response of the stratospheric polar vortex following MJO phase 8 during La Niña conditions, when the indirect stratospheric teleconnection pathway is most active. Improving the representation of tropospheric and stratospheric teleconnection pathways is thus a priority for improving ECMWF forecasts of extratropical weather regimes and their associated surface impacts.

Significance Statement

Subseasonal to Seasonal Prediction project (S2S) forecasts are used operationally at ECMWF to provide early warning of cold conditions in Europe associated with persistent large-scale circulation patterns known as weather regimes. On average, ECMWF reforecasts accurately simulate wintertime Euro-Atlantic regime structures and frequencies at S2S lead times. However, regime forecasts show substantial errors when we restrict our analysis to certain phases of intraseasonal and interannual variability, such as El Niño–Southern Oscillation (ENSO). These errors are related to deficiencies in the simulated response of weather regimes to well-predicted variability in the tropics. Improving the representation of such tropical–extratropical teleconnections will improve predictions of extratropical weather regimes and their associated surface impacts.

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Jeffrey L. Anderson

Abstract

Traditional ensemble Kalman filter data assimilation methods make implicit assumptions of Gaussianity and linearity that are strongly violated by many important Earth system applications. For instance, bounded quantities like the amount of a tracer and sea ice fractional coverage cannot be accurately represented by a Gaussian that is unbounded by definition. Nonlinear relations between observations and model state variables abound. Examples include the relation between a remotely sensed radiance and the column of atmospheric temperatures, or the relation between cloud amount and water vapor quantity. Part I of this paper described a very general data assimilation framework for computing observation increments for non-Gaussian prior distributions and likelihoods. These methods can respect bounds and other non-Gaussian aspects of observed variables. However, these benefits can be lost when observation increments are used to update state variables using the linear regression that is part of standard ensemble Kalman filter algorithms. Here, regression of observation increments is performed in a space where variables are transformed by the probit and probability integral transforms, a specific type of Gaussian anamorphosis. This method can enforce appropriate bounds for all quantities and deal much more effectively with nonlinear relations between observations and state variables. Important enhancements like localization and inflation can be performed in the transformed space. Results are provided for idealized bivariate distributions and for cycling assimilation in a low-order dynamical system. Implications for improved data assimilation across Earth system applications are discussed.

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Samuel K. Degelia
,
Xuguang Wang
,
Yongming Wang
, and
Aaron Johnson

Abstract

The Advanced Baseline Imager (ABI) aboard the GOES-16 and GOES-17 satellites provides high-resolution observations of cloud structures that could be highly beneficial for convective-scale DA. However, only clear-air radiance observations are typically assimilated at operational centers due to a variety of problems associated with cloudy radiance data. As such, many questions remain about how to best assimilate all-sky radiance data, especially when using hybrid DA systems such as EnVar wherein a nonlinear observation operator can lead to cost function gradient imbalance and slow minimization. Here, we develop new methods for assimilating all-sky radiance observations in EnVar using the novel Rapid Refresh Forecasting System (RRFS) that utilizes the Finite-Volume Cubed-Sphere (FV3) model. We first modify the EnVar solver by directly including brightness temperature (Tb ) as a state variable. This modification improves the balance of the cost function gradient and speeds up minimization. Including Tb as a state variable also improves the model fit to observations and increases forecast skill compared to utilizing a standard state vector configuration. We also evaluate the impact of assimilating ABI all-sky radiances in RRFS for a severe convective event in the central Great Plains. Assimilating the radiance observations results in better spinup of a tornadic supercell. These data also aid in suppressing spurious convection by reducing the snow hydrometeor content near the tropopause and weakening spurious anvil clouds. The all-sky radiance observations pair well with reflectivity observations that remove primarily liquid hydrometeors (i.e., rain) closer to the surface. Additionally, the benefits of assimilating the ABI observations continue into the forecast period, especially for localized convective events.

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Nathalie G. Rivera-Torres
,
Kristen L. Corbosiero
, and
Brian H. Tang

Abstract

The conditions associated with tropical cyclones undergoing downshear reformation are explored for the North Atlantic basin from 1998 to 2020. These storms were compared to analog tropical cyclones with similar intensity, vertical wind shear, and maximum potential intensity, but did not undergo downshear reformation. Storm-centered, shear-relative composites were generated using ERA5 and GridSat-B1 data. Downshear reformation predominately occurs for tropical cyclones of tropical storm intensity embedded in moderate vertical wind shear. A comparison between composites suggests that reformed storms are characterized by greater low-level and midtropospheric relative humidity downshear, larger surface latent heat fluxes downshear and left of shear, and larger low-level equivalent potential temperatures and CAPE right of shear. These factors increase thermodynamic favorability, building a reservoir of potential energy and decreasing dry air entrainment, promoting sustained convection downshear, and favoring the development of a new center.

Significance Statement

The development of a new low-level circulation center in tropical cyclones that replaces the original center, called downshear reformation, can affect the structure and intensity of storms, representing a challenge in forecasting tropical cyclones. While there have been a handful of case studies on downshear reformation, this study aims to more comprehensively understand the conditions that favor downshear reformation by comparing a large set of North Atlantic tropical cyclones that underwent reformation with a similar set of tropical cyclones that did not undergo reformation. Tropical cyclones that undergo reformation have a moister environment, larger surface evaporation, and higher low-level instability in specific regions that help sustain deep, downshear convection that favors the development of a new center.

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Xiaoxing Wang
,
Kinya Toride
, and
Kei Yoshimura

Abstract

Old descriptive diaries are important sources of daily weather conditions before modern instrumental measurements were available. A previous study demonstrated the potential of reconstructing historical weather at a high temporal resolution by assimilating cloud cover converted from descriptive diaries. However, cloud cover often exhibits a non-Gaussian distribution, which violates the basic assumptions of most data assimilation schemes. In this study, we applied a Gaussian transformation (GT) approach to cloud cover data assimilation and conducted observing system simulation experiments (OSSEs) using 20 observation points over Japan. We performed experiments to assimilate cloud cover with large observational errors using the Global Spectral Model (GSM) and a local ensemble transform Kalman filter (LETKF). Without GT, meridional wind and temperature exhibited deteriorations in the lower troposphere compared with the experiment with no observations. In contrast, GT reduced the 2-month root-mean-square errors (RMSEs) by 5%–15% throughout the troposphere for wind, temperature, and specific humidity fields. Significant improvements include zonal wind at 500 hPa and temperature at 850 hPa with 6.4% and 7.3% improvements by GT, respectively, compared with the experiment without GT. We further demonstrate that the additional GT application to the precipitation background field improves precipitation estimation by 12.2%, with pronounced improvements over regions with monthly precipitation of less than 150 mm. We also explored the impact of cloud cover GT on a global scale and confirmed improvements extending from around the observation sites. Our results demonstrate the potential of GT in high-resolution historical weather reconstruction using old descriptive diaries.

Significance Statement

To reconstruct the historical weather, cloud cover information from old diaries can be used by incorporating high-resolution model simulations. However, cloud cover is not normally distributed and violates an important assumption when combining cloud cover observations with model simulations. Our results demonstrate that transforming the cloud cover distribution into a normal distribution could improve wind speed, temperature, and humidity fields in the model. We demonstrate the critical role of the transformation in a nonnormally distributed variable when combined with models and show the potential of diary-based weather information to reconstruct historical weather.

Open access
Letícia O. dos Santos
,
Ernani L. Nascimento
, and
John T. Allen

Abstract

Severe storms produce hazardous weather phenomena, such as large hail, damaging winds, and tornadoes. However, relationships between convective parameters and confirmed severe weather occurrences are poorly quantified in south-central Brazil. This study explores severe weather reports and measurements from newly available datasets. Hail, damaging wind, and tornado reports are sourced from the PREVOTS project from June 2018 to December 2021, while measurements of convectively induced wind gusts from 1996 to 2019 are obtained from METAR reports and from Brazil’s operational network of automated weather stations. Proximal convective parameters were computed from ERA5 reanalysis for these reports and used to perform a discriminant analysis using mixed-layer CAPE and deep-layer shear (DLS). Compared to other regions, thermodynamic parameters associated with severe weather episodes exhibit lower magnitudes in south-central Brazil. DLS displays better performance in distinguishing different types of hazardous weather, but does not discriminate well between distinct severity levels. To address the sensitivity of the discriminant analysis to distinct environmental regimes and hazard types, five different discriminants are assessed. These include discriminants for any severe storm, severe hail only, severe wind gust only, and all environments but broken into “high” and “low” CAPE regimes. The best performance of the discriminant analysis is found for the “high” CAPE regime, followed by the severe wind regime. All discriminants demonstrate that DLS plays a more important role in conditioning Brazilian severe storm environments than other regions, confirming the need to ensure that parameters and discriminants are tuned to local severe weather conditions.

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Falko Judt
,
Rosimar Rios-Berrios
, and
George H. Bryan

Abstract

Tropical cyclones that intensify abruptly experience “rapid intensification.” Rapid intensification remains a formidable forecast challenge, in part because the underlying science has not been settled. One way to reconcile the debates and inconsistencies in the literature is to presume that different forms (or modes) of rapid intensification exist. The present study provides evidence in support of this hypothesis by documenting two modes of rapid intensification in a global convection-permitting simulation and the HURDAT2 database. The “marathon mode” is characterized by a moderately paced and long-lived intensification period, whereas the “sprint mode” is characterized by explosive and short-lived intensification bursts. Differences between the modes were also found in initial vortex structure (well defined versus poorly defined), nature of intensification (symmetric versus asymmetric), and environmental conditions (weak shear versus strong shear). Collectively, these differences indicate that the two modes involve distinct intensification mechanisms. Recognizing the existence of multiple intensification modes may help to better understand and predict rapid intensification by, for example, explaining the lack of consensus in the literature, or by raising awareness that rapid intensification in strongly sheared cyclones is not just an exception to a rule, but a typical process.

Significance Statement

Hurricanes are serious threats to society—in particular those that suddenly and quickly intensify before striking land. Forecasting these “rapid intensification” events is a challenge, in part because we do not fully understand the science behind rapid intensification. This study furthers our understanding of hurricane rapid intensification by documenting that rapid intensification comes in different types. Specifically, we show that one type of rapid intensification happens under conditions that meteorologists have thought would lessen the chances of intensification. Awareness of such a type of rapid intensification could lead to better predictions of hurricane intensity because forecasters are more cognizant of this type of event and the conditions in which they occur.

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Abby Hutson
and
Christopher Weiss

Abstract

This study aims to objectively identify storm-scale characteristics associated with tornado-like vortex (TLV) formation in an ensemble of high-resolution supercell simulations. An ensemble of 51 supercells is created using Cloud Model version 1 (CM1). The first member is initialized using a base state populated by the Rapid Update Cycle (RUC) proximity sounding near El Reno, Oklahoma, on 24 May 2011. The other 50 ensemble members are created by randomly perturbing the base state after a supercell has formed. There is considerable spread between ensemble members, with some supercells producing strong, long-lived TLVs, while others do not produce a TLV at all. The ensemble is analyzed using the ensemble sensitivity analysis (ESA) technique, uncovering storm-scale characteristics that are dynamically relevant to TLV formation. In the rear flank, divergence at the surface southeast of the TLV helps converge and contract existing vertical vorticity, but there is no meaningful sensitivity to rear-flank outflow temperature. In the forward flank, warm temperatures within the cold pool are important to TLV production and magnitude. The longitudinal positioning of strong streamwise vorticity is also a clear indicator of TLV formation and strength, especially within 5 min of when the TLV is measured.

Significance Statement

Tornadoes that form in supercell thunderstorms (long-lived storms with a rotating updraft) are heavily influenced by the features created by the storm itself, such as the temperature of a downdraft. In this study, many different iterations of a strong supercell thunderstorm are simulated, in which tornado-like features are formed at different times with widely different strengths. A statistical method is used to identify what the storms had in common when they produced a tornado-like feature, and what they had in common when one failed to form. This study is important because it highlights which storm features are most influential to tornado formation using an objective method, with results that can be used when observing supercells in the field.

Open access
Cameron R. Homeyer
,
Elisa M. Murillo
, and
Matthew R. Kumjian

Abstract

Supercell storms are commonly responsible for severe hail, which is the costliest severe storm hazard in the United States and elsewhere. Radar observations of such storms are common and have been leveraged to estimate hail size and severe hail occurrence. However, many established relationships between radar-observed storm characteristics and severe hail occurrence have been found using data from few storms and in isolation from other radar metrics. This study leverages a 10-yr record of polarimetric Doppler radar observations in the United States to evaluate and compare radar observations of thousands of severe hail–producing supercells based on their maximum hail size. In agreement with prior studies, it is found that increasing hail size relates to increasing volume of high (≥50 dBZ) radar reflectivity, increasing midaltitude mesocyclone rotation (azimuthal shear), increasing storm-top divergence, and decreased differential reflectivity and copolar correlation coefficient at low levels (mostly below the environmental 0°C level). New insights include increasing vertical alignment of the storm mesocyclone with increasing hail size and a Doppler velocity spectrum width minimum aloft near storm center that increases in area with increasing hail size and is argued to indicate increasing updraft width. To complement the extensive radar analysis, near-storm environments from reanalyses are compared and indicate that the greatest environmental differences exist in the middle troposphere (within the hail growth region), especially the wind speed perpendicular to storm motion. Recommendations are given for future improvements to radar-based hail-size estimation.

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Takumi Honda
,
Yousuke Sato
, and
Takemasa Miyoshi

Abstract

Lightning flash observations are closely associated with the development of convective clouds and have a potential for convective-scale data assimilation with high-resolution numerical weather prediction models. A main challenge with the ensemble Kalman filter (EnKF) is that no ensemble members have nonzero lightning flashes in the places where a lightning flash is observed. In this situation, different model states provide all zero lightning, and the EnKF cannot assimilate the nonzero lightning data effectively. This problem is known as the zero-gradient issue. This study addresses the zero-gradient issue by adding regression-based ensemble perturbations derived from a statistical relationship between simulated lightning and atmospheric variables in the whole computational domain. Regression-based ensemble perturbations are applied if the number of ensemble members with nonzero lightning flashes is smaller than a prescribed threshold (N min). Observing system simulation experiments for a heavy precipitation event in Japan show that regression-based ensemble perturbations increase the ensemble spread and successfully induce the analysis increments associated with convection even if only a few members have nonzero lightning flashes. Furthermore, applying regression-based ensemble perturbations improves the forecast accuracy of precipitation although the improvement is sensitive to the choice of N min.

Significance Statement

This study develops an effective method to use lightning flash observations for weather prediction. Lightning flash observations include precious information of the inner structure of clouds, but their effective use for weather prediction is not straightforward since a weather prediction model often misses observed lightning flashes. Our new method uses ensemble-generated statistical relationships to compensate for the misses and successfully improves the forecast accuracy of heavy rains in a simulated case. Our future work will test the method with real observation data.

Open access