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Yue Yang
and
Xuguang Wang

Abstract

The sensitivity of convection-allowing forecasts over the continental United States to radar reflectivity data assimilation (DA) frequency is explored within the Gridpoint Statistical Interpolation (GSI)-based ensemble–variational (EnVar) system. Experiments with reflectivity DA intervals of 60, 20, and 5 min (RAIN60, RAIN20, and RAIN5, respectively) are conducted using 10 diverse cases. Quantitative verification indicates that the degree of sensitivity depends on storm features during the radar DA period. Five developing storms show high sensitivity, whereas five mature or decaying storms do not. The 20-min interval is the most reliable given its best overall performance compared to the 5- and 60-min intervals. Diagnostics suggest that the differences in analyzed cold pools (ACPs) among RAIN60, RAIN20, and RAIN5 vary by storm features during the radar DA period. Such ACP differences result in different forecast skills. In the case where RAIN20 outperforms RAIN60 and the case where RAIN5 outperforms RAIN20, assimilation of reflectivity with a higher frequency commonly produces enhanced and widespread ACPs, promoting broader storms that match better with reality than a lower frequency. In the case where RAIN5 performs worse than RAIN20, the model imbalance of RAIN5 overwhelms information gain associated with frequent assimilation, producing overestimated and spuriously fast-moving ACPs. In the cases where little sensitivity to the reflectivity DA frequency is found, similar ACPs are produced.

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Free access
Philip Tuckman
,
Vince Agard
, and
Kerry Emanuel

Abstract

We analyze the evolution of convective available potential energy (CAPE) and convective inhibition (CIN) in the days leading up to episodes of high CAPE in North America. The widely accepted theory for CAPE buildup, known as the advection hypothesis, states that high moist static energy (MSE) parcels of air moving north from the Gulf of Mexico become trapped under warm but dry parcels moving east from over elevated dry terrain. If and when the resulting CIN erodes, severe convection can occur due to the large energy difference between the boundary layer parcels and cool air aloft. However, our results, obtained via backward Lagrangian tracking of parcels at locations of peak CAPE, show that large values of CAPE are generated mainly via boundary layer moistening in the days leading up to the time of peak CAPE, and that a large portion of this moisture buildup happens on the day of peak CAPE. On the other hand, the free-tropospheric temperature above these tracked parcels rarely changes significantly over the days leading up to such occurrences. In addition, the CIN that allows for this buildup of CAPE arises mostly from unusually strong boundary layer cooling the night before peak CAPE, and has a contribution from differential advection of unusually warm air above the boundary layer to form a capping inversion. These results have important implications for the climatology of severe convective events, as it emphasizes the role of surface properties and their gradients in the frequency and intensity of high CAPE occurrences.

Significance Statement

Severe convective events, such as thunderstorms, tornadoes, and hail storms, are among the most deadly and destructive weather systems. Although forecasters are quite good at predicting the probability of these events a few days in advance, there is currently no reliable seasonal prediction method of severe convection. We show that the buildup of energy for severe convection relies on both strong surface evaporation during the day of peak energy and anomalous cooling the night before. This progress represents a step toward understanding what controls the frequency of severe convective events on seasonal and longer time scales, including the effect of greenhouse gas–induced climate change.

Open access
Naveen Goutham
,
Riwal Plougonven
,
Hiba Omrani
,
Alexis Tantet
,
Sylvie Parey
,
Peter Tankov
,
Peter Hitchcock
, and
Philippe Drobinski

Abstract

Owing to the increasing share of variable renewable energies in the electricity mix, the European energy sector is becoming more weather sensitive. In this regard, skillful subseasonal predictions of essential climate variables can provide considerable socioeconomic benefits to the energy sector. The aim of this study is therefore to improve the European subseasonal predictions of 100-m wind speed and 2-m temperature, which we achieve through statistical downscaling. We employ redundancy analysis (RDA) to estimate spatial patterns of variability from large-scale fields that allow for the best prediction of surface fields. We compare explanatory powers between the patterns obtained using RDA against those derived using principal component analysis (PCA), when used as predictors in multilinear regression models to predict surface fields, and show that the explanatory power of the former is superior to that of the latter. Subsequently, we employ the estimated relationship between RDA patterns and surface fields to produce statistical probabilistic predictions of gridded surface fields using dynamical ensemble predictions of RDA patterns. We finally demonstrate how a simple combination of dynamical and statistical predictions of surface fields significantly improves the accuracy of subseasonal predictions of both variables over a large part of Europe. We attribute the improved accuracy of these combined predictions to improvements in reliability and resolution.

Open access
Michael S. Fischer
,
Paul D. Reasor
,
Brian H. Tang
,
Kristen L. Corbosiero
,
Ryan D. Torn
, and
Xiaomin Chen

Abstract

The multiscale nature of tropical cyclone (TC) intensity change under moderate vertical wind shear was explored through an ensemble of high-resolution simulations of Hurricane Gonzalo (2014). Ensemble intensity forecasts were characterized by large short-term (36-h) uncertainty, with a forecast intensity spread of over 20 m s−1, due to differences in the timing of rapid intensification (RI) onset. Two subsets of ensemble members were examined, referred to as early-RI and late-RI members. The two ensemble groups displayed significantly different vortex evolutions under the influence of a nearby upper-tropospheric trough and an associated dry-air intrusion. Mid-to-upper-tropospheric ventilation in late-RI members was linked to a disruption of inner-core diabatic heating, a more tilted vortex, and vortex breakdown, as the simulated TCs transitioned from a vorticity annulus toward a monopole structure. A column-integrated moist static energy (MSE) budget revealed the important role of horizontal advection in depleting MSE from the TC core, while mesoscale subsidence beneath the dry-air intrusion acted to dry a deep layer of the troposphere. Eventually, the dry-air intrusion retreated from late-RI members as vertical wind shear weakened, the magnitude of vortex tilt decreased, and late-RI members began to rapidly intensify, ultimately reaching a similar intensity as early-RI members. Conversely, the vortex structures of early-RI members were shown to exhibit greater intrinsic resilience to tilting from vertical wind shear, and early-RI members were able to fend off the dry-air intrusion relatively unscathed. The different TC intensity evolutions can be traced back to differences in the initial TC vortex structure and intensity.

Significance Statement

Despite recent advances, tropical cyclone intensity forecasts struggle to accurately predict episodes of rapid intensification. Such forecasts become increasingly challenging when a storm is embedded within an environment of moderate vertical wind shear. This study uses an ensemble of high-resolution simulations to examine how environmental influences can affect the tropical cyclone vortex and precipitation structure, which, in turn, modulate the intensity of the storm and the onset of rapid intensification. We propose a feedback that exists where slightly weaker and less resilient vortices are more susceptible to ventilation from dry, environmental air, aided in part by differential advection from the tilted circulation, resulting in a degradation of vortex organization and a delayed onset of rapid intensification.

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Kuo-Feng Chang
,
Chun-Chieh Wu
, and
Kosuke Ito

Abstract

This work investigates the rapid weakening (RW) processes of Typhoon Trami (2018) by examining sea surface temperature (SST) cooling based on air–sea coupled simulations during typhoon passage. The cold wake and Trami’s RW occurred as the storm was moving at a very slow translation speed. A marked structural change of Trami is found in a three-dimensional ocean-coupled model experiment during the RW stage, in which the convective clouds and convective bursts in the inner core of the simulated TC dramatically decrease, resulting in the loss of diabatic heating and leading to weakening of the TC. In the simulation, the enthalpy flux dramatically decreases in the inner core because of the SST cooling during the RW period, while a stable boundary layer (SBL) is formed in the TC’s inner-core region. The expanding SBL coverage stabilizes the atmosphere and suppresses convection in the inner core, leading to weakening of the storm. A more stable atmosphere in the cold wake is also identified by the inner-core dropsonde data from the field program of Tropical Cyclones-Pacific Asian Research Campaign for Improvement of Intensity Estimations/Forecasts. The strong SST cooling also changes the evolution of Trami’s eyewall replacement cycle (ERC) and limits the eyewall contraction after the ERC.

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Takeshi Horinouchi
,
Satoki Tsujino
,
Masahiro Hayashi
,
Udai Shimada
,
Wataru Yanase
,
Akiyoshi Wada
, and
Hiroyuki Yamada

Abstract

Dynamics of low-level flows in the eye of Typhoon Haishen (2020) in its late phase of intensification are investigated with a special rapid-scan observation of the Himawari-8 geosynchronous satellite conducted every 30 s. This is accomplished by deriving storm-relative atmospheric motion vectors at an unprecedentedly high spatiotemporal resolution by tracking clouds across five consecutive visible-light reflectivity. The overall low-level circulation center was situated several kilometers away from the storm center defined in terms of the inner edge of the lower part of eyewall clouds. The shift direction is rearward of the storm translation, consistently with a numerical study of the tropical cyclone (TC) boundary layer. Over the analysis period of 10 h, azimuthal-mean tangential wind around this center was increased at each radius within the eye, and the rotational angular velocity was nearly homogenized. The instantaneous low-level circulation center is found to orbit around the overall circulation center at distances around 5 km. Its orbital angular speed was close to the maximum angular speed of azimuthal-mean tangential winds. This rotating transient disturbance is found to transport angular momentum inward, which explains the tangential wind increase and the angular velocity homogenization in the eye. These features are consistent with an algebraically growing wavenumber-1 barotropic instability, whose impact on TC structures has not been explored. This instability enhances wavenumber-1 asymmetry in ring-shaped vorticity, which can be induced by various processes such as translation, environmental shear, and exponential barotropic instability. Therefore, it may appear broadly in TCs to affect wind distribution in their eyes.

Significance Statement

Axially asymmetric transient features in the inner cores of tropical storms have been suggested to profoundly affect the structures and the time evolutions of tropical storms. However, the scarcity of observations has hindered studying such processes observationally. By using a specially conducted high-frequency satellite imaging of Typhoon Haishen (2020), we derived atmospheric motion vectors nearly homogeneously at an unprecedentedly high spatiotemporal resolution. Various kinds of asymmetric motions in low-level flows in the eye were found. Of particular interest is a special type of wavenumber-1 instability whose role has not drawn much attention; the instability was found to provide angular momentum transport consistent with the measured homogenization of the rotation.

Open access
Douglas R. Allen
,
Daniel Hodyss
,
Karl W. Hoppel
, and
Gerald E. Nedoluha

Abstract

An essential component of four-dimensional variational data assimilation is the tangent linear model (TLM), which is a linearized version of the full nonlinear forecast model. A relatively new approach to calculating the TLM is a regression model called the ensemble tangent linear model (ETLM). Here we validate the ETLM for linearizing a nonorographic gravity wave drag (NGWD) subgrid-scale model. The regression is applied to an ensemble created by perturbing the atmospheric state and calculating one time step of the NGWD model. The ETLM is validated using independent perturbations based on archived analysis increments. We examine how the skill of the NGWD ETLM depends on the choice of ensemble perturbation, ensemble size, amount of ensemble inflation/deflation, and the size of the localization stencil. After examining the nearly perfect results using a large ETLM ensemble (100 000 members), optimal tuning is then performed for 150–500 members. For smaller ETLM ensembles, spurious noise due to sampling error could be reduced either by downscaling the perturbations or by localizing the ETLM. The impact of localization decreases as the ETLM ensemble size increases. We then validate the ETLM using one year of archived DA analysis increments. The skill varies over time with percentage errors relative to persistence forecasts (where 100% is no skill, 0% is a perfect forecast) generally ranging from ∼50% to 90% (∼40% to 80%) for ETLMs with 150 (500) members. The ETLM is also shown to propagate small increments (1% of the size of analysis increments) with fractional errors of ∼10%.

Open access
Andrew Hazelton
,
Ghassan J. Alaka Jr.
,
Michael S. Fischer
,
Ryan Torn
, and
Sundararaman Gopalakrishnan

Abstract

Hurricane Dorian (2019), a category-5 tropical cyclone (TC), was characterized by a large spread in track forecasts as it moved northwest. A set of 80 ensemble forecasts from the Hurricane Analysis and Forecast System (HAFS) was produced to evaluate Dorian’s track spread and the factors that contributed to it. Track spread was particularly critical at long lead times (5–7 days after initialization near the Lesser Antilles), because of the uncertainty in the location of landfall and hazards. Four clusters of members were analyzed based on the 7-day track, characterized by Dorian moving: 1) slowly near the northern Bahamas (closest to reality), 2) across the Florida Peninsula, 3) slowly into Florida’s east coast, and 4) quickly north of the Bahamas. Ensemble sensitivity techniques were applied to identify areas that were most critical for Dorian’s track. Key differences were found in the strength of the subtropical ridge over the western Atlantic Ocean with a weaker ridge and slower easterly steering flow in the offshore groups. Subtle differences in the synoptic pattern over the United States also appeared to affect the timing of Dorian’s northward turn, specifically the strength of a shortwave trough moving over the Ohio Valley. Despite some early track differences, the correlation between early and late track errors was not significant. An examination of four members further highlights the differences in steering and the strength of the subtropical ridge. This study demonstrates the utility of ensemble datasets for studying TC forecast uncertainty and the importance of medium-range modeling of synoptic-scale steering features to accurately predict the track of tropical cyclones.

Significance Statement

Hurricane Dorian was a catastrophic hurricane for the Bahamas and got very close to Florida without directly impacting the state. Some early forecasts showed the storm moving directly into or across Florida; others correctly showed the storm stalling over the Bahamas and then turning northward. This track forecast uncertainty made preparations in Florida challenging; therefore, we wanted to better understand why Dorian took the track that it did, to see what this tells us about the factors that affect hurricane tracks, and learn for future storms. We looked at an ensemble of 80 runs of a hurricane model, initiated at the same time. Some runs showed a Florida landfall; others showed Dorian stalling over the Bahamas. The strength of the subtropical ridge over the Atlantic north of Dorian and an upper-level trough of low pressure over the United States were key influences on storm path. These two large-scale features were better forecast in the ensemble members that correctly showed Dorian stalling and turning northward. This study shows how useful ensembles can be for understanding the processes driving hurricane motion and also shows that it is critical to forecast multiple synoptic-scale features correctly to accurately predict a hurricane’s track 5–7 days in advance.

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Carolyn A. Reynolds
,
Rebecca E. Stone
,
James D. Doyle
,
Nancy L. Baker
,
Anna M. Wilson
,
F. Martin Ralph
,
David A. Lavers
,
Aneesh C. Subramanian
, and
Luca Centurioni

Abstract

Under the Atmospheric River Reconnaissance (AR Recon) Program, ocean drifting buoys (drifters) that provide surface pressure observations were deployed in the northeastern Pacific Ocean to improve forecasts of U.S. West Coast high-impact weather. We examine the impacts of both AR Recon and non-AR Recon drifter observations in the U.S. Navy’s global atmospheric data assimilation (DA) and forecast system using data-denial experiments and forecast sensitivity observation impact (FSOI) analysis, which estimates the impact of each observation on the 24-h global forecast error total energy. Considering all drifters in the eastern North Pacific for the 2020 AR Recon season, FSOI indicates that most of the beneficial impacts come from observations in the lowest quartile of observed surface pressure values, particularly those taken late in the DA window. Observations in the upper quartile have near-neutral impacts on average and are slightly nonbeneficial when taken late in the DA window. This may occur because the DA configuration used here does not account for model biases, and innovation statistics show that the forecast model has a low pressure bias at high pressures. Case studies and other analyses indicate large beneficial impacts coming from observations in regions with large surface pressure gradients and integrated vapor transport, such as fronts and ARs. Data-denial experiments indicate that the assimilation of AR Recon drifter observations results in a better-constrained analysis at nearby non-AR Recon drifter locations and counteracts the NAVGEM pressure bias. Assimilating the AR Recon drifter observations improves 72- and 96-h Northern Hemisphere forecasts of winds in the lower and middle troposphere, and geopotential height in the lower, middle, and upper troposphere.

Significance Statement

The purpose of this study is to understand how observations of atmospheric pressure at the ocean surface provided by drifting buoys impact weather forecasts. Some of these drifting buoys were deployed under a program to study atmospheric rivers (ARs) to improve forecasts of high-impact weather on the West Coast. We find that these observations are most effective at reducing forecast errors when taken in regions near fronts and cyclones. The additional drifting buoys deployed under the AR Reconnaissance project reduce forecast errors at 72 and 96 h over North America and the Northern Hemisphere. These results are important because they illustrate the potential for improving forecasts by increasing the number of drifting buoy surface pressure observations over the world oceans.

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