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Daniel Galea
,
Kevin Hodges
, and
Bryan N. Lawrence

Abstract

Tropical cyclones (TCs) are important phenomena, and understanding their behavior requires being able to detect their presence in simulations. Detection algorithms vary; here we compare a novel deep learning–based detection algorithm (TCDetect) with a state-of-the-art tracking system (TRACK) and an observational dataset (IBTrACS) to provide context for potential use in climate simulations. Previous work has shown that TCDetect has good recall, particularly for hurricane-strength events. The primary question addressed here is to what extent the structure of the systems plays a part in detection. To compare with observations of TCs, it is necessary to apply detection techniques to reanalysis. For this purpose, we use ERA-Interim, and a key part of the comparison is the recognition that ERA-Interim itself does not fully reflect the observations. Despite that limitation, both TCDetect and TRACK applied to ERA-Interim mostly agree with each other. Also, when considering only hurricane-strength TCs, TCDetect and TRACK correspond well to the TC observations from IBTrACS. Like TRACK, TCDetect has good recall for strong systems; however, it finds a significant number of false positives associated with weaker TCs (i.e., events detected as having hurricane strength but are weaker in reality) and extratropical storms. Because TCDetect was not trained to locate TCs, a post hoc method to perform comparisons was used. Although this method was not always successful, some success in matching tracks and events in physical space was also achieved. The analysis of matches suggested that the best results were found in the Northern Hemisphere and that in most regions the detections followed the same patterns in time no matter which detection method was used.

Open access
S. Kalluri
,
C. Cao
,
A. Heidinger
,
A. Ignatov
,
J. Key
, and
T. Smith
Full access
Susan C. van den Heever
,
Leah D. Grant
,
Sean W. Freeman
,
Peter J. Marinescu
,
Julie Barnum
,
Jennie Bukowski
,
Eleanor Casas
,
Aryeh J. Drager
,
Brody Fuchs
,
Gregory R. Herman
,
Stacey M. Hitchcock
,
Patrick C. Kennedy
,
Erik R. Nielsen
,
J. Minnie Park
,
Kristen Rasmussen
,
Muhammad Naufal Razin
,
Ryan Riesenberg
,
Emily Riley Dellaripa
,
Christopher J. Slocum
,
Benjamin A. Toms
, and
Adrian van den Heever
Full access
Bin Wang
,
Michela Biasutti
,
Michael P. Byrne
,
Christopher Castro
,
Chih-Pei Chang
,
Kerry Cook
,
Rong Fu
,
Alice M. Grimm
,
Kyung-Ja Ha
,
Harry Hendon
,
Akio Kitoh
,
R. Krishnan
,
June-Yi Lee
,
Jianping Li
,
Jian Liu
,
Aurel Moise
,
Salvatore Pascale
,
M. K. Roxy
,
Anji Seth
,
Chung-Hsiung Sui
,
Andrew Turner
,
Song Yang
,
Kyung-Sook Yun
,
Lixia Zhang
, and
Tianjun Zhou
Full access
Cheng Liu
,
Meng Gao
,
Qihou Hu
,
Guy P. Brasseur
, and
Gregory R. Carmichael
Full access
Charles H. White
,
Imme Ebert-Uphoff
,
John M. Haynes
, and
Yoo-Jeong Noh

Abstract

Superresolution is the general task of artificially increasing the spatial resolution of an image. The recent surge in machine learning (ML) research has yielded many promising ML-based approaches for performing single-image superresolution including applications to satellite remote sensing. We develop a convolutional neural network (CNN) to superresolve the 1- and 2-km bands on the GOES-R series Advanced Baseline Imager (ABI) to a common high resolution of 0.5 km. Access to 0.5-km imagery from ABI band 2 enables the CNN to realistically sharpen lower-resolution bands without significant blurring. We first train the CNN on a proxy task, which allows us to only use ABI imagery, namely, degrading the resolution of ABI bands and training the CNN to restore the original imagery. Comparisons at reduced resolution and at full resolution with Landsat-8/Landsat-9 observations illustrate that the CNN produces images with realistic high-frequency detail that is not present in a bicubic interpolation baseline. Estimating all ABI bands at 0.5-km resolution allows for more easily combining information across bands without reconciling differences in spatial resolution. However, more analysis is needed to determine impacts on derived products or multispectral imagery that use superresolved bands. This approach is extensible to other remote sensing instruments that have bands with different spatial resolutions and requires only a small amount of data and knowledge of each channel’s modulation transfer function.

Significance Statement

Satellite remote sensing instruments often have bands with different spatial resolutions. This work shows that we can artificially increase the resolution of some lower-resolution bands by taking advantage of the texture of higher-resolution bands on the GOES-16 ABI instrument using a convolutional neural network. This may help reconcile differences in spatial resolution when combining information across bands, but future analysis is needed to precisely determine impacts on derived products that might use superresolved bands.

Open access
Joshua Chun Kwang Lee
,
Javier Amezcua
, and
Ross Noel Bannister

Abstract

Two aspects of ensemble localization for data assimilation are explored using the simplified nonhydrostatic ABC model in a tropical setting. The first aspect (i) is the ability to prescribe different localization length scales for different variables (variable-dependent localization). The second aspect (ii) is the ability to control (i.e., to knock out by localization) multivariate error covariances (selective multivariate localization). These aspects are explored in order to shed light on the cross-covariances that are important in the tropics and to help determine the most appropriate localization configuration for a tropical ensemble–variational (EnVar) data assimilation system. Two localization schemes are implemented within the EnVar framework to achieve (i) and (ii). One is called the isolated variable-dependent localization (IVDL) scheme and the other is called the symmetric variable-dependent localization (SVDL) scheme. Multicycle observation system simulation experiments are conducted using IVDL or SVDL mainly with a 100-member ensemble, although other ensemble sizes are studied (between 10 and 1000 members). The results reveal that selective multivariate localization can reduce the cycle-averaged root-mean-square error (RMSE) in the experiments when cross-covariances associated with hydrostatic balance are retained and when zonal wind/mass error cross-covariances are knocked out. When variable-dependent horizontal and vertical localization are incrementally introduced, the cycle-averaged RMSE is further reduced. Overall, the best performing experiment using both variable-dependent and selective multivariate localization leads to a 3%–4% reduction in cycle-averaged RMSE compared to the traditional EnVar experiment. These results may inform the possible improvements to existing tropical numerical weather prediction systems that use EnVar data assimilation.

Open access
Fran Morris
,
Juliane Schwendike
,
Douglas J. Parker
, and
Caroline Bain

Abstract

Understanding how mesoscale convection interacts with synoptic-scale circulations over West Africa is crucial for improving regional weather forecasts and developing convection parameterizations to address biases in climate models. A 10-yr pan-African convection-permitting simulation and a corresponding parameterized simulation for current-climate conditions are used to calculate the circulation budget around a synoptic region over the diurnal cycle, splitting processes that modulate circulation tendency (vorticity accumulation and vortex tilting) into diurnal mean and anomalous contributions. Dynamical fields are composited around precipitating grid cells during afternoon and overnight convection to understand how the mesoscale convection modulates synoptic-scale processes, and the composites are compared with an observational case. The dominant process modulating circulation tendency was found to be synoptic-scale vorticity accumulation, which is similar in the two simulations. The greatest difference between the simulated budgets was the tilting term. We propose that the tilting term is affected by convective momentum transport associated with precipitating systems crossing the boundary of the region, whereas the stretching term relies on the convergence and divergence induced by storms within the region. The simulation with parameterized convection captures the heating profile similarly to the simulation with explicit convection, but there are marked differences in convective momentum transport. An accurate vertical convergence structure as well as momentum transport must be simulated in parameterizations to correctly represent the impacts of convection on circulation.

Significance Statement

We used climate simulations with explicit convection and a convection parameterization to interrogate the relationship between mesoscale convection and synoptic-scale circulation over West Africa. We examined the typical behavior of mesoscale precipitating systems in both simulations and compared this with an observation of a storm. We also investigated how synoptic circulation changed over a diurnal cycle in both simulations. The biggest differences between the simulations were caused by how mesoscale systems in each simulation transport momentum when they cross the boundaries of a circulation, but the greatest impact on synoptic circulation was from the patterns of convergence and divergence induced by mesoscale systems, which are very similar in both simulations. Convection parameterizations should prioritize improving the representation of momentum transport.

Open access
Ricardo C. Muñoz
and
Laurence Armi

Abstract

Raco is a local wind occurring in central Chile where the Maipo River Canyon exits into the Santiago valley. The intensification of the easterly down-canyon flow starts at any time during some cold season nights, accompanied by increases in temperature and drops in humidity. The hypothesis of the raco being a gap wind controlled by the narrowest section in the 12-km canyon exit corridor is tested with data from two events in July 2018 and July 2019. The data are analyzed in the framework of hydraulic theory, and a subcritical-to-supercritical transition is documented to occur at the narrows of the gap where the Froude number is close to unity, confirmed by radiosondes launched in the narrows in 2019. For the raco flow, the sum of potential and kinetic energy is conserved upstream of the narrows, while the acceleration occurring farther downstream loses a large fraction of energy to frictional dissipation. The raco events occur under the influence of regional subsidence, but a differential nocturnal warming of the in-canyon air mass is responsible for a pressure gradient driving the raco. In the 2019 case, a ceilometer mounted on an instrumented pickup truck documented the structure and movement of the interface between the raco air and the cold-air pool (CAP) existing over the valley to the west. Together with a radiosonde launched near the CAP–raco surface front, the observations reveal the intense shear-driven mixing taking place at the interface and the factors supporting the establishment of a stationary front.

Open access
Jun Yin
,
Amilcare Porporato
, and
Lamberto Rondoni

Abstract

While the warming trends of Earth’s mean temperature are evident at climatological scales, the local temperature at shorter time scales are highly fluctuating. Here we show that the probabilities of such fluctuations are characterized by a special symmetry typical of small systems out of equilibrium. Their nearly universal properties are linked to the fluctuation theorem and reveal that the progressive warming is accompanied by growing asymmetry of temperature distributions. These statistics allow us to project the global temperature variability in the near future, in line with predictions from climate models, providing original insight about future extremes.

Open access