Atmospheric Cloud Research Articles

Testing Cloud Adjustment Hypotheses for the Maintenance of Earth's Hemispheric Albedo Symmetry With Natural Experiments

AbstractEarth's Northern and Southern Hemispheres reflect essentially equal amounts of sunlight. How—and whether—this hemispheric albedo symmetry is maintained remains a mystery. We decompose Earth's hemispheric albedo symmetry into components associated with the surface, clear‐sky atmosphere, and different cloud types as defined by cloud effective pressure and optical thickness. Climatologically, greater reflection by the surface, aerosols, and high clouds in the Northern Hemisphere is balanced by greater low and mid‐level cloudiness in the Southern Hemisphere. Both hemispheres have darkened at similar rates over the past two decades; whether the darkening from more rapidly declining aerosol in the Northern Hemisphere is causing a departure from all‐sky symmetry remains uncertain. Natural experiments including long‐term trends, sea ice loss, and volcanic eruptions provide strong evidence against the hypothesis that extratropical low clouds compensate changing clear‐sky asymmetries on annual‐to‐decadal timescales but some evidence that tropical high cloud shifts may do so.

Quartz Clouds in the Dayside Atmosphere of the Quintessential Hot Jupiter HD 189733 b

Recent mid-infrared observations with JWST's Mid-Infrared Instrument Low Resolution Spectrometer (MIRI LRS) have resulted in the first direct detections of absorption features from silicate clouds in the transmission spectra of two transiting exoplanets, WASP-17 b and WASP-107 b. In this Letter, we measure the mid-infrared (5–12 μm) dayside emission spectrum of the benchmark hot Jupiter HD 189733 b with MIRI LRS by combining data from two secondary-eclipse observations. We confirm the previous detection of H2O absorption at 6.5 μm from Spitzer's Infrared Spectrograph (IRS) and additionally detect H2S as well as an absorption feature at 8.7 μm in both secondary-eclipse observations. The excess absorption at 8.7 μm can be explained by the presence of small (∼0.01 μm) grains of SiO2[s] in the uppermost layers of HD 189733 b’s dayside atmosphere. This is the first direct detection of silicate clouds in HD 189733 b’s atmosphere, and the first detection of a distinct absorption feature from silicate clouds on the dayside of any hot Jupiter. We find that models including SiO2[s] are preferred by 6–7σ over clear models and those with other potential cloud species. The high-altitude location of these silicate particles is best explained by formation in the hottest regions of HD 189733 b’s dayside atmosphere near the substellar point. We additionally find that HD 189733 b’s emission spectrum longward of 9 μm displays residual features not well captured by our current atmospheric models. When combined with other JWST observations of HD 189733 b’s transmission and emission spectra at shorter wavelengths, these observations will provide us with the most detailed picture to date of the atmospheric composition and cloud properties of this benchmark hot Jupiter.

The presence of nanoparticles in aqueous droplets containing plant-derived biopolymers plays a role in heterogeneous ice nucleation.

Organic matter can initiate heterogeneous ice nucleation in supercooled water droplets, thereby influencing atmospheric cloud glaciation. Predicting the ice nucleation ability of organic matter-containing cloud droplets is challenging due to the unknown mechanism for templating ice. Here, we observed the presence of nanoparticles in aqueous samples of known ice-nucleating biopolymers cellulose and lignin, as well as in newly identified ice-nucleating biopolymers xylan and laminarin. Using our drop Freezing Ice Nuclei Counter (FINC), we measured the median ice nucleation temperature (T50) of xylan and of laminarin droplets of 2 μl to be -14.2 and -20.0 °C, respectively. Next, we characterized these samples using nanoparticle tracking analysis, and we detected and quantified nanoparticles with mean diameters between 132 and 267nm. Xylan contained the largest nanoparticles and froze at higher temperatures. Xylan also dictated the freezing in a 1:1:1:1 mixture with cellulose, lignin, laminarin, and xylan. Filtration experiments down to 300 kDa with the xylan sample indicated that the presence of nanoparticles triggered freezing. Overall, only samples with mean diameters above 150nm froze above -20 °C. Furthermore, we determined the ice-active site densities normalized to particle concentrations, surface area, and mass of the nanoparticles to show that the samples' nucleation site densities are similar to sea spray aerosols and nanometer-sized dust. The identification and characterization of xylan and laminarin as nanometer-sized ice-nucleating substances expands the growing list of organic matter capable of impacting cloud formation and thus climate.

Vapor-Buoyancy Feedback in an Idealized GCM

Abstract Humid air is lighter than dry air at a given temperature and pressure, as the molecular weight of water vapor is less than that of dry air. This effect is known as vapor buoyancy (VB). Although this effect is a well-understood consequence of the ideal gas law, its influence on climate has been under-studied. This study investigates VB’s influence on atmospheric temperature, radiation, and clouds. In mechanism-denial experiments, we remove VB from the dynamics and parameterizations of an idealized general circulation model (GCM). These experiments show that VB warms the tropical free troposphere by approximately 1 K in an Earth-like climate, and the magnitude of this effect increases with warming. This additional atmospheric warming causes greater outgoing longwave radiation (OLR) to be emitted as the climate warms. This constitutes a negative climate feedback which attains a magnitude of 0.1 to 0.2 W/m2/K in the tropics. The simulations further show that, despite warming the atmosphere, VB does not introduce a countervailing positive water vapor feedback. Finally, we find that VB makes the net cloud feedback in the model more negative than it otherwise would be, reinforcing the clear-sky feedback.

Evaluation of WRF-Chem air quality forecasts during the AEROMMA and STAQS 2023 field campaigns

ABSTRACT A real-time air quality forecasting system was developed using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) to provide support for flight planning activities during the NOAA Atmospheric Emissions and Reactions Observed from Megacities to Marine Areas (AEROMMA) and NASA Synergistic TEMPO Air Quality Science (STAQS) 2023 field campaigns. The forecasting system operated on two separate domains centered on Chicago, IL, and New York City, NY, and provided 72-hour predictions of atmospheric composition, aerosols, and clouds. This study evaluates the Chicago-centered forecasting system’s 1-, 2-, and 3-day ozone (O3) forecast skill for Chiwaukee Prairie, WI, a rural area downwind of Chicago that often experiences high levels of O3 pollution. Comparisons to vertical O3 profiles collected by a Tropospheric Ozone Lidar Network (TOLNet) instrument revealed that forecast skill decreases as forecast lead time increases. When compared to surface measurements, the forecasting system tended to underestimate O3 concentrations on high O3 days and overestimate on low O3 days at Chiwaukee Prairie regardless of forecast lead time. Using July 25, 2023, as a case study, analyses show that the forecasts underestimated peak O3 levels at Chiwaukee Prairie during this regionwide bad air quality day. Wind speed and direction data indicates that this underestimation can partially be attributed to lake breeze simulation errors. Surface fine particulate matter (PM2.5) measurements, Geostationary Operational Environmental Satellite-16 (GOES-16) aerosol optical depth (AOD) data, and back trajectories from the NOAA Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model show that transported Canadian wildfire smoke impacted the Lake Michigan region on this day. Errors in the forecasted chemical composition and transport of the smoke plumes also contributed to underpredictions of O3 levels at Chiwaukee Prairie on July 25, 2023. The results of this work help identify improvements that can be made for future iterations of the WRF-Chem forecasting system. Implications: Air quality forecasting is an important tool that can be used to inform the public about upcoming high pollution days so that individuals may plan accordingly to limit their exposure to health-damaging air pollutants. Forecasting also helps scientists make decisions about where to make observations during air quality field campaigns. A variety of observational datasets were used to evaluate the accuracy of an air quality forecasting system that was developed for NOAA and NASA field campaigns that occurred in the summer of 2023. These evaluations inform areas of improvement for future development of this air quality forecasting system.

Impact of Atmospheric Cloud Radiative Effects on Annular Mode Persistence in Idealized Simulations

AbstractThe mechanisms by which clouds impact the variability of the mid‐latitude atmosphere are poorly understood. We use an idealized, dry atmospheric model to investigate the relationship between Atmospheric Cloud Radiative Effects (ACRE) and annular mode persistence. We force the model with time‐varying diabatic heating that mimics the observed ACRE response to the Southern Annular Mode (SAM). Realistic ACRE forcing reduces annular mode persistence by 5 days (−16%), which we attribute to a weakening of low‐frequency eddy forcing via modified low‐level temperature gradients, though this effect is partly compensated by reduced frictional damping due to zonal wind anomalies becoming more top‐heavy. The persistence changes are nonlinear with respect to the amplitude of ACRE forcing, reflecting nonlinearities in the response of the eddy forcing. These results highlight the ACRE's impact on low‐frequency eddy forcing as the dominant cause of changes in annular mode persistence.

Inertial particles in a turbulent/turbulent interface

Turbulent interfaces are ubiquitous in the nature and in many important canonical flows (wakes, jets, mixing layers, boundary layers). In nature, the sharp edges of atmospheric clouds are defined by a turbulent/non-turbulent interface, where the droplet-laden turbulent cloud interior mixes with the unladen outer air. This study presents statistics of sub-Kolmogorov-scale inertial particles, in a parameter range representative of cloud droplets, in a sheared turbulent–turbulent interface. The study focuses on the effect of the inhomogeneous turbulent field on the particles’ clustering properties and settling velocity modification. Wind tunnel experiments were carried out in a well-characterised facility where a grid containing 81 independent gas/liquid injectors generates both high intensity turbulence and a field of small inertial droplets. These atomisers, located at the entrance of the test section, contribute significantly to the carrier phase turbulence and are used to create the sheared turbulent/turbulent interface. Selecting which atomisers are on or off enables us to control the mean velocity profile and the gradient of turbulent intensity through the tunnel cross-section. The resulting carrier flow is characterised by a gradient of mean velocity, turbulent velocity rms, and a sharp interface separating two regions with different turbulent scales. Particles’ velocities and diameters were measured with a Phase Doppler Particle Analyser, at closely-spaced positions across the turbulent interface. In agreement with previous experimental studies on the topic, particles are shown to be transported from the turbulent side, with higher turbulence intensity, to the low-intensity side by large-scale-energetic eddies. Newly observed in our results is an enhancement of particle preferential concentration and settling velocity in the sheared interface region, caused by the interaction between particle trajectories and turbulent structures of different sizes.

Classification of Ice Particle Shapes Using Machine Learning on Forward Light Scattering Images

Abstract Machine learning (ML) has rapidly transitioned from a niche activity to a mainstream tool for environmental research applications including atmospheric science cloud microphysics studies. Two recently developed cloud particle probes measure the light scattered in the near forward direction and save digital images of the scattering light. Scattering pattern images collected by the Particle Phase Discriminator mark 2, Karlsruhe edition (PPD-2K), and the Small Ice Detector, version 3 (SID-3) provide valuable information for particle shape and size characterization. Since different particle shapes have distinctly different light scattering characteristics, the images are ideally suited for ML. Here, results of a ML project to characterize ice particle shapes sampled by the PPD-2K in ice fog and diamond dust during a 3-yr project in Fairbanks, Alaska. About 2.15 million light-scattering pattern images were collected during 3 years of measurements with the PPD-2K. Visual Geometry Group (VGG) convolutional neural network (CNN) was trained to categorize light-scattering patterns into eight categories. Initial training images (120 each category) were selected by human visual examination of data, and the training dataset was augmented using an automated iterative method for image identification of further images which were all visually inspected by a human. Results were well correlated to similar categories identified from previously developed classification algorithms. ML identifies characteristics not included in automated analysis such as sublimation. Of the 2.15 million images analyzed, 1.3% were categorized as spherical (liquid), 43.5% were categorized as having rough surfaces, 15.3% were pristine, 16.3% were categorized as sublimating, and the remaining 23.6% did not fit into any of those categories (irregular or saturated). Significance Statement The shapes and sizes of cloud particles can be extremely important for understanding the conditions that exist in the cloud. In this study, we show that more information about cloud particle characteristics can be identified by using machine learning than by traditional means. To demonstrate this, data are analyzed from a 3-yr study of ice fog and diamond dust events in Fairbanks, Alaska. The Particle Phase Discriminator instrument collected 2.15 million light-scattering pattern images of cloud particles during ground-based measurements. Neither traditional techniques nor machine learning were able to identify all categories, but a combination of both techniques led to a more complete view of ice particle shapes.

Aerosol, Clouds and Trace Gases Research Infrastructure (ACTRIS): The European Research Infrastructure Supporting Atmospheric Science

Abstract The Aerosol, Clouds and Trace Gases Research Infrastructure (ACTRIS) officially became the 33rd European Research Infrastructure Consortium (ERIC) on 25 April 2023 with the support of 17 founding member and observer countries. As a pan-European legal organization, ACTRIS ERIC will coordinate the provision of data and data products on short-lived atmospheric constituents and clouds relevant to climate and air pollution over the next 15–20 years. ACTRIS was designed more than a decade ago, and its development was funded at national and European levels. It was included in the European Strategy Forum on Research Infrastructures (ESFRI) roadmap in 2016 and, subsequently, in the national infrastructure roadmaps of European countries. It became a landmark of the ESFRI roadmap in 2021. The purpose of this paper is to describe the mission of ACTRIS, its added value to the community of atmospheric scientists, providing services to academia as well as the public and private sectors, and to summarize its main achievements. The present publication serves as a reference document for ACTRIS, its users, and the scientific community as a whole. It provides the reader with relevant information and an overview on ACTRIS governance and services, as well as a summary of the main scientific achievements of the last 20 years. The paper concludes with an outlook on the upcoming challenges for ACTRIS and the strategy for its future evolution.

Toward User-Friendly All-Sky Surface Longwave Downward Radiation from Space: General Scheme and Product

Abstract Longwave downward radiation (LWDR) is an important driving parameter in climate and hydrological models. Compared to traditional ground-based measurements, remote sensing has unique advantages in estimating global LWDR. However, for current remote sensing missions, as the typical available satellite-derived LWDR product with global coverage and hourly temporal resolution, the Clouds and the Earth’s Radiant Energy System-Synoptic (CERES-SYN) top of atmosphere and surface fluxes and clouds has a low spatial resolution (1° × 1°). There is still much room for improvement of the existing remote sensing LWDR products in terms of accuracy, spatiotemporal resolutions, and the ability to explain and quantify the changes of longwave radiation at various scales. To overcome these limitations, this paper developed a new global LWDR product with improved accuracy (RMSE < 30 W m−2 over the globe), high temporal resolution (hourly), and spatial resolution (5 km) based on Moderate Resolution Imaging Spectroradiometer (MODIS) measurements. It serves as a LWDR product within the Long-term Earth System spatiotemporally Seamless Radiation budget dataset (referred to as LessRad). As the first long-term high-resolution, spatiotemporally continuous LWDR product (2002–22, 1 h, 5 km), the LessRad reveals its advantages in studying the spatiotemporal variability of LWDR on finer scales. It also provides a valuable data source for various applications, such as analyzing land–atmosphere interactions and quantifying climate feedback, and thus is potentially helpful for understanding Earth’s energy budget and dynamics.

Land Surface Longwave Radiation Retrieval from ASTER Clear-Sky Observations

Surface longwave radiation (SLR) plays a pivotal role in the Earth’s energy balance, influencing a range of environmental processes and climate dynamics. As the demand for high spatial resolution remote sensing products grows, there is an increasing need for accurate SLR retrieval with enhanced spatial detail. This study focuses on the development and validation of models to estimate SLR using measurements from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor. Given the limitations posed by fewer spectral bands and data products in ASTER compared to moderate-resolution sensors, the proposed approach combines an atmospheric radiative transfer model MODerate resolution atmospheric TRANsmission (MODTRAN) with the Light Gradient Boosting Machine algorithm to estimate SLR. The MODTRAN simulations were performed to construct a representative training dataset based on comprehensive global atmospheric profiles and surface emissivity spectra data. Global sensitivity analyses reveal that key inputs influencing the accuracy of SLR retrievals should reflect surface thermal radiative signals and near-surface atmospheric conditions. Validated against ground-based measurements, surface upward longwave radiation (SULR) and surface downward longwave radiation (SDLR) using ASTER thermal infrared bands and surface elevation estimations resulted in root mean square errors of 17.76 W/m2 and 25.36 W/m2, with biases of 3.42 W/m2 and 3.92 W/m2, respectively. Retrievals show systematic biases related to extreme temperature and moisture conditions, e.g., causing overestimation of SULR in hot humid conditions and underestimation of SDLR in arid conditions. While challenges persist, particularly in addressing atmospheric variables and cloud masking, this work lays a foundation for accurate SLR retrieval from high spatial resolution sensors like ASTER. The potential applications extend to upcoming satellite missions, such as the Landsat Next, and contribute to advancing high-resolution remote sensing capabilities for an improved understanding of Earth’s energy dynamics.

Multifrequency radar observations of marine clouds during the EPCAPE campaign

Abstract. The Eastern Pacific Cloud Aerosol Precipitation Experiment (EPCAPE) was a year-round campaign conducted by the US Department of Energy at the Scripps Institution of Oceanography in La Jolla, CA, USA, with a focus on characterizing atmospheric processes at a coastal location. The ground-based prototype of a new Ka-, W-, and G-band (35.75, 94.88, and 238.8 GHz) profiling atmospheric radar, named CloudCube, which was developed at the Jet Propulsion Laboratory, took part in the experiment during 6 weeks in March and April 2023. This article describes the unique data sets that were obtained during the field campaign from a variety of marine clouds and light precipitation. These are, to the best of the authors' knowledge, the first observations of atmospheric clouds using simultaneous multifrequency measurements including 238.8 GHz. These data sets therefore provide an exceptional opportunity to study and analyze hydrometeors with diameters in the millimeter- and submillimeter size range that can be used to better understand cloud and precipitation structure, formation, and evolution. The data sets referenced in this article are intended to provide a complete, extensive, and high-quality collection of G-band data in the form of Doppler spectra and Doppler moments. In addition, Ka-band and W-band reflectivity and Ka-, W-, and G-band reflectivity ratio profiles are included for several cases of interest on 6 different days. The data sets can be found at https://doi.org/10.5281/zenodo.10076227 (Socuellamos et al., 2024).

Monte Carlo simulation of polarized lidar returns for atmospheric clouds sensing

Abstract The paper deals with Monte Carlo simulation of polarized radiative transfer and lidar returns in sensing of atmospheric clouds. The Stokes parameters were used to describe the transfer of polarized radiation, and computation of the lidar returns was based on flux-at-point estimators. We consider modifications of the numerical algorithm with scattering according to the phase function for unpolarized light and with scattering depending on polarization. Numerical experiments were performed for lidar sensing of waterdrop clouds.

On the Application of the Nyström Method to Two-Dimensional Neutral Particle Transport Problems in Heterogeneous Media

Over time, several methods were developed to deal with neutral particle transport problems. The interest in these problems is related to their wide range of applications, from neutron transport and heat transfer in nuclear reactors to radiative transfer in atmospheric clouds. Unlike the discrete ordinates or discrete ordinates–like methods, integral methods do not require discretization of angular variables. Instead, angular variables are completely eliminated by an integration procedure over the solid angle, which allows elimination of the ray effect. That said, this paper presents a new approach to estimate the scalar flux in two-dimensional fixed-source neutron transport problems in a heterogeneous medium, considering isotropic scattering and vacuum and reflective boundary conditions. Here, the Nyström method is combined with the singularity-subtraction technique to present an integral formulation for the scalar flux in a mesh grid over all regions of the domain. The iterative method of the Neumann series is used as an alternative to direct methods to solve the resulting system of equations generated from the domain discretization. Numerical results are given to verify the offered method.

Modifications on the coastal atmospheric sulfur and cloud condensation nuclei along the Eastern China seas by shipping fuel transition

Marine fuel combustion from shipping releases SO2 and forms sulfate particles, which may alter low cloud characteristics. A series of strategies were implemented to control the sulfur content of ship fuel oil from 2018 to 2020, offering insights into the effects of the ship fuel oil transition on sulfur-related pollutants and the consequent cloud condensation nuclei (CCN) in the atmosphere. Compared to 2018 in the southeast China waters, shipping SO2 emission decreased by 78 % in 2020, resulting in a 76 % reduction in ship-related total sulfur concentration, and a decrease of 54 % in CCN number concentration under supersaturation 0.2 % (CCN0.2) contributed by shipping. The response of CCN0.2 to ship-related sulfate modification is more pronounced in relatively clean environments than polluted environments, highlighting the uneven changes in coastal CCN along the Eastern China Sea induced by the ship fuel policies. CCN can trigger the formation of cloud droplets, 2020 fuel regulation may have and will reduce the cooling radiative forcing effect with strong spatial heterogeneity. The study provides insights into the variations in coastal atmospheric sulfur-related pollutants and CCN in uneven response to changes in ship fuel oil, prompting the need for further comprehensive assessments of the climate effects resulting from potential shifts in ship fuel use in the future.

Atmospheric Cloud Image Detection with Convolutional Neural Network (CNN

Atmospheric Cloud Image Detection with Convolutional Neural Network (CNN

A single-photon lidar observes atmospheric clouds at decimeter scales: resolving droplet activation within cloud base

Clouds, crucial for understanding climate, begin with droplet formation from aerosols, but observations of this fleeting activation step are lacking in the atmosphere. Here we use a time-gated time-correlated single-photon counting lidar to observe cloud base structures at decimeter scales. Results show that the air–cloud interface is not a perfect boundary but rather a transition zone where the transformation of aerosol particles into cloud droplets occurs. The observed distributions of first-arriving photons within the transition zone reflect vertical development of a cloud, including droplet activation and condensational growth. Further, the highly resolved vertical profile of backscattered photons above the cloud base enables remote estimation of droplet concentration, an elusive but critical property to understanding aerosol–cloud interactions. Our results show the feasibility of remotely monitoring cloud properties at submeter scales, thus providing much-needed insights into the impacts of atmospheric pollution on clouds and aerosol-cloud interactions that influence climate.

Constraints on atmospheric water abundance and cloud deck pressure in the warm Neptune GJ 3470 b via CARMENES transmission spectroscopy

ABSTRACT Observations of cooler atmospheres of super-Earths and Neptune sized objects often show flat transmission spectra. The most likely cause of this trend is the presence of aerosols (i.e. clouds and hazes) in the atmospheres of such objects. High-resolution spectroscopy provides an opportunity to test this hypothesis by targeting molecular species whose spectral line cores extend above the level of such opaque decks. In this work, we analyse high-resolution infrared observations of the warm Neptune GJ 3470 b taken over two transits using CARMENES (R ∼ 80 000) and look for signatures of H2O (previously detected using Hubble Space Telescope (HST) WFC3 + Spitzer observations) in these transits with a custom pipeline fully accounting for the effects of data cleaning on any potential exoplanet signal. We find that our data are potentially able to weakly detect (∼3σ) an injected signal equivalent to the best-fitting model from previous HST WFC3 + Spitzer observations. However, we do not make a significant detection using the actual observations. Using a Bayesian framework to simultaneously constrain the H2O volume mixing ratio (VMR) and the cloud top pressure level, we select a family of models compatible with the non-detection. These are either very high VMR cloud-free models, solar-abundance models with a high cloud deck, or sub-solar abundance models with a moderate cloud deck. This is a broader range compared to published results from low-resolution spectroscopy, but is also compatible with them at a 1σ level.

Quantifying the dynamical and radiative processes of the drastically weak South Asian summer monsoon circulation in 2015

The South Asian summer monsoon (SASM) circulation in 2015 is the weakest since 2000s, which results in severe drought over broad regions of the Indian peninsula. The 2015 SASM is closely related to the weakened summer meridional thermal contrast between southern Eurasia (SE) and the tropical Indian Ocean (TIO) at the mid–upper troposphere. Based on an updated climate feedback-response analysis method, this study conducts a quantitative attribution analysis of the thermal contrast anomalies associated with the 2015 SASM to multiple dynamical and radiative processes, particular for aerosol process. Result shows that the 2015 weak SASM is mainly attributed to the effect of water vapor (58%), followed by the effects of atmospheric dynamics (18%), clouds (15%), and aerosols (15%), respectively. These positive effects are partially offset by the negative contribution from surface dynamic process (-14%). As the most pronounced factor, the water vapor process weakens the SASM circulation via inducing SE cooling and TIO warming, which is closely linked to the decreased (increased) specific humidity over SE (TIO). Further analysis indicates that the total effect of aerosols is dominated by the changes in black carbon and sea salt. As two important components, the SE cooling and TIO warming separately account for about 51% and 49% to the 2015 SASM. The former is mainly attributed to the cooling effect of clouds, while the latter is mainly induced by the warming effect of atmospheric dynamics. Our result provides a new insight into the 2015 weak SASM from a quantitative perspective.

Local identification of equatorial Kelvin waves in real‐time operational forecasts

AbstractEquatorial Kelvin waves (KWs) are associated with a variety of equatorial atmospheric phenomena, namely, tropical convection, cloud and precipitation variability, tropical cyclogenesis, the onset of monsoon season over Africa and India, and even the quasi‐biennial oscillation and the Madden–Julian oscillation. As such, if operational forecasts are to improve near the Tropics, it is important that they correctly represent KWs. This article aims at developing a novel methodology for identifying KWs in real‐time operational forecasts at specific longitudes using only the meridional structures of the solutions to the free Laplace tidal equations, known as Hough vector functions. The main advantages of this newly proposed methodology are that (a) it can identify KWs at a specific longitude and (b) very low computational cost is needed to apply it to real‐time operational forecasts. The application of the method to 2015–2017 European Centre for Medium‐range Weather Forecasts (ECMWF) analysis and operational forecast data reveals a systematic bias in the representation of KWs by the ECMWF operational forecast model.