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A summer course developed at Purdue University leverages students' intrinsic desire to observe tornadoes as a motivator for learning severe storms forecasting. Relative to previous 'storm chasing' courses, the Students [...]

ABSTRACT Several important developments in the 1960s showed the way to using spectral analysis to identify and describe atmospheric waves predicted by theory. Among these waves were normal-mode Rossby-Haurwitz waves [...]

ABSTRACTS The 2016/17 wildfire season in Chile was the worst on record, burning more than 600,000 ha. While wildfires are an important natural process in some areas of Chile, supporting [...]

The Madden-Julian oscillation (MJO) identified by Madden and Julian in the early 1970s has been well recognized as the most prominent intraseasonal signal in the tropics. Its discovery and its [...]

New York City College of Technology has created a year-round geoscience workforce preparation and geoscience career mentoring program for nongeoscience, minority science, technology, engineering, and math (STEM) students beginning at [...]

From the authors' perspective, this means making optimal use of the comments and recommendations made by the reviewers through careful, comprehensive revisions to the paper and complete, well-reasoned responses to the reviewers and the editor. If you (the author) were the reviewer of somebody else's paper and spent 10 hours (the typical time that reviewers spend with your submitted paper; [13]) reading the paper and writing up your review, you would probably want to see that the authors acknowledged that they read and understood each comment; acknowledged that they took your comment seriously, even if they disagreed with it; and responded to the comment and revised the paper. We editors thank the authors for their continued trust in I Monthly Weather Review i , the reviewers for their essential contributions to the peer-review process, and the AMS staff for their hard work and dedication to ensuring a high-quality issue each month. If editors find that authors are not addressing the reviewers' comments, then they can send the paper back to the authors for further revision or they can reject the paper because it is not progressing toward a publishable outcome. [Extracted from the article]

10-15 January 2021, Virtual Meeting The 2021 AMS Annual Meeting theme is 'Strengthening Engagement with Communities through our Science and Services'. For the full description of the theme, please visit [...]

The National Oceanic and Atmospheric Administration (NOAA) has operated a network of Fischer & Porter gauges providing hourly and subhourly precipitation observations as part of the U.S. Cooperative Observer Program since the middle of the twentieth century. A transition from punched paper recording to digital recording was completed by NOAA's National Weather Service in 2013. Subsequently, NOAA's National Centers for Environmental Information (NCEI) upgraded its quality assurance and data stewardship processes to accommodate the new digital record, better assure the quality of the data, and improve the timeliness by which hourly precipitation observations are made available to the user community. Automated methods for removing noise, detecting diurnal variations, and identifying malfunctioning gauges are described along with quality control algorithms that are applied on hourly and daily time scales. The quality of the hourly observations during the digital era is verified by comparison with hourly observations from the U.S. Climate Reference Network and summary of the day precipitation totals from the Global Historical Climatology Network dataset. [ABSTRACT FROM AUTHOR]

The 2018 AMS Annual Meeting theme is 'Transforming communication in the weather, water, and climate enterprise--Focusing on challenges facing our sciences.' For the full description of the theme, please visit [...]

Educators can enrich their teaching with best practices, share resources, and contribute to the growing atmospheric science education research community by reading and participating in the scholarship of teaching and learning in atmospheric science. This body of scholarship has grown, particularly over the past 15 years, and is now a sizable literature base that documents and exemplifies numerous teaching innovations in undergraduate atmospheric science education. This literature base benefits the entire atmospheric science community because graduates of atmospheric science programs are better prepared to enter the workforce. This literature base has not yet been examined, however, to see how well the evidence supports education practices in the atmospheric science education literature. In this study, we characterized that evidence and show that the majority of papers we reviewed share education innovations with anecdotal or correlational evidence of effectiveness. While providing useful practitioner knowledge and preliminary evidence of the effectiveness of numerous innovative teaching practices, opportunities exist for increasing readers' confidence that the innovations caused the learning gains. Additional studies would also help move conclusions toward generalizability across academic institutions and student populations. We make recommendations for advancing atmospheric science education research and encourage atmospheric science educators to actively use the growing body of education literature as well as contribute to advancing atmospheric science education research. [ABSTRACT FROM AUTHOR]

A method named BTREC, which is in fact an extension of tracking a radar echo by cross-correlation (TREC) method, is proposed based on the Barnes filter in this paper. BTREC is an efficient objective analysis method for smoothing the motion vectors of radar echo patterns. A comparative analysis of the BTREC vectors and the COTREC (another extension of TREC, which is restricted by the 2D continuity equation) vectors is conducted. The results show that the BTREC method corrects the noise and inconsistencies in TREC vectors (often induced by shielding, ground clutter, and rapid morphological changes of the radar patterns) more thoroughly than COTREC. Then the BTREC, COTREC, and TREC methods are applied to extrapolate the real radar echo pattern over Jiangxi Province in China for two cases (1900–2100 UTC 4 June and 0900–1100 UTC 30 June 2018). Results show that the BTREC method performs the best out of the three methods to forecast the radar echo patterns in the following 1 and 2 h, and has the least distortion. To further confirm that, 892 radar composite reflectivity mosaic images with precipitation in summer (June–August) over Jiangxi are collected to test the three methods. The results show that in the 1- and 2-h extrapolations, the mean differences of the threat score (TS), correlation (CORR), and probability of detection (POD) between BTREC and TREC are obviously more than 0, while that of the false alarm ratio (FAR) is remarkably less than 0 (the threshold to identify whether a grid is correctly predicted is set to 10 dBZ). Although the mean difference of TS, CORR, POD, and FAR between COTREC and TREC have similar variation, their magnitudes are obviously decreased (especially for the 2-h extrapolation). This further indicates that the BTREC method obviously improves the forecast of the radar echo pattern within the next 2 h compared to the COTREC and TREC methods. [ABSTRACT FROM AUTHOR]

The 95th AMS Annual Meeting in Phoenix this past January was an outstanding meeting in virtually every way. The scientific conferences were packed with great presentations, both oral and poster; [...]

Weather radars have been widely used to detect and quantify precipitation and nowcast severe weather for more than 50 years. Operational weather radars generate huge three-dimensional datasets that can accumulate to terabytes per day. So it is essential to review what can be done with existing vast amounts of data, and how we should manage the present datasets for the future climatologists. All weather radars provide the reflectivity factor, and this is the main parameter to be archived. Saving reflectivity as volumetric data in the original spherical coordinates allows for studies of the three-dimensional structure of precipitation, which can be applied to understand a number of processes, for example, analyzing hail or thunderstorm modes. Doppler velocity and polarimetric moments also have numerous applications for climate studies, for example, quality improvement of reflectivity and rain rate retrievals, and for interrogating microphysical and dynamical processes. However, observational data alone are not useful if they are not accompanied by sufficient metadata. Since the lifetime of a radar ranges between 10 and 20 years, instruments are typically replaced or upgraded during climatologically relevant time periods. As a result, present metadata often do not apply to past data. This paper outlines the work of the Radar Task Team set by the Atmospheric Observation Panel for Climate (AOPC) and summarizes results from a recent survey on the existence and availability of long time series. We also provide recommendations for archiving current and future data and examples of climatological studies in which radar data have already been used. [ABSTRACT FROM AUTHOR]

The article discusses the importance of peer reviewers in scholarly publishing and expresses gratitude towards those who volunteer their time to review manuscripts. The American Meteorological Society (AMS) analyzed data on peer reviews and found no trend in reviewer acceptance rates or the time taken to complete reviews over the past decade. The article also emphasizes the responsibility of authors to participate in the peer-review process by reviewing papers themselves. The authors of the article acknowledge the time and effort involved in reviewing and encourage those who have not yet embraced their responsibilities as reviewers to volunteer. [Extracted from the article]

The article discusses the importance of peer reviewers in scholarly publishing and expresses gratitude towards those who volunteer their time to review manuscripts. The American Meteorological Society (AMS) analyzed data on peer reviews and found that there is no trend of declining reviewer acceptance rates or longer review times. The article also emphasizes the responsibility of authors to participate in the peer-review process by reviewing papers themselves. The authors of the article acknowledge the time and effort involved in reviewing and encourage those who have not yet embraced their responsibilities as reviewers to volunteer. [Extracted from the article]

The article discusses the importance of peer reviewers in scholarly publishing and the responsibilities of authors in the peer-review process. Peer reviewers are subject-matter experts who provide guidance to editors on the suitability of manuscripts for publication and help authors improve the accuracy and readability of their studies. The American Meteorological Society (AMS) analyzed data on peer reviews and found that there is no trend of declining reviewer acceptance rates or longer review times. The article emphasizes the need for authors to participate in peer reviewing and suggests that authors should review three papers for each one they submit. The AMS expresses gratitude to reviewers and encourages others to volunteer as reviewers. [Extracted from the article]

The article discusses the importance of peer reviewers in scholarly publishing and the responsibilities of authors in the peer-review process. It highlights that editors rely on subject-matter experts to assess the suitability of manuscripts for publication and to provide guidance to authors. The American Meteorological Society (AMS) analyzed data on peer reviews and found no trend in reviewer acceptance rates or review completion time over the past decade. The article emphasizes the need for authors to participate in peer reviewing and suggests that authors should review three papers for each one they submit. The AMS expresses gratitude to reviewers and encourages others to volunteer as reviewers. [Extracted from the article]

The article discusses the importance of peer reviewers in scholarly publishing and the responsibilities of authors in the peer-review process. Peer reviewers are subject-matter experts who provide guidance to editors on the suitability of manuscripts for publication and help authors improve the accuracy and readability of their studies. The American Meteorological Society (AMS) analyzed data on peer reviews and found that there is no trend of declining reviewer acceptance rates or longer review times. The article emphasizes the need for authors to participate in peer reviewing and suggests that authors should review three papers for each one they submit. The AMS expresses gratitude to reviewers and encourages others to volunteer as reviewers. [Extracted from the article]

The article discusses the importance of peer reviewers in scholarly publishing and the responsibilities of authors in the peer-review process. Peer reviewers are subject-matter experts who provide guidance to editors on the suitability of manuscripts for publication and help authors improve the accuracy and readability of their studies. The American Meteorological Society (AMS) analyzed data on peer reviews and found that there is no trend of declining reviewer acceptance rates or longer review times. The article emphasizes the need for authors to participate in peer reviewing and suggests that authors should review three papers for each one they submit. The AMS expresses gratitude to reviewers and encourages others to volunteer as reviewers. [Extracted from the article]

Humidity sounder radiances are currently thinned to 110-km spacing prior to assimilation at ECMWF and used with no averaging applied. In this paper, the thinning scale and possible averaging of all-sky humidity sounder observations into "superobs" are considered. The short- and medium-range forecast impacts of changing the thinning and averaging scales of humidity sounder radiances prior to the data assimilation are investigated separately and then together. Superobbing acts as a low-pass filter and provides smoother images of departures, decreasing the effective sensor noise and thus the standard deviation of background departures, marginally for 183-GHz channels (5%–15%) and significantly for 118-GHz channels (5%–55%). Observations are thus more representative of the model effective resolution, with a better utilization of total information content than thinning native-resolution radiances, as less information is discarded. Whether changed in isolation or combination, the additions of data via superobbing and finer thinning are both shown to markedly improve background fits to independent observations, indicative of better short-range forecasts of humidity and improved winds via the 4D-Var tracer effect. Wind forecasts in the Southern Hemisphere are slightly improved in the medium range. A final configuration is tested at the resolution of the current operational model, with humidity sounder radiances averaged into 50-km superobs with 70-km spacing. This provides about 140% more radiances for assimilation and more finely detailed maps to analyze mesoscale features. The forecast impact of this change is larger in testing with higher model and data assimilation resolutions, showing the scale dependence of such decisions and the expected performance in an operational configuration. Significance Statement: This paper investigates thinning and averaging scales for humidity-sounding microwave observations in the ECMWF data assimilation system. The introduction of spatial averaging shows a positive impact, as does the assimilation of observations with finer spacing. These changes permit more total information on humidity into the system, and both are beneficial for short-range forecasts of humidity and winds in the mid- to upper troposphere. The results highlight the interplay between spatial scales of observations and those of the analysis system, with possibilities for improved utilization in this particular case. This is expected to remain a key consideration in assimilation systems going forward, given the continued increases in the resolution of assimilation systems and forecast models. [ABSTRACT FROM AUTHOR]

Numerical simulations of turbulent moist Rayleigh–Bénard convection driving CCN activation and droplet growth in the laboratory Pi chamber are discussed. Supersaturation fluctuations come from isobaric mixing of warm and humid air rising from the lower boundary with colder air featuring lower water vapor concentrations descending from the upper boundary. Lagrangian particle–based microphysics is used to represent the growth of haze CCN and cloud droplets with kinetic, solute, and surface tension effects included. Dry CCN spectra in the range between 2- and 200-nm radii from field observations are considered. Increasing the total CCN concentration from pristine to polluted conditions results in an increase in the droplet concentration and reduction in the mean droplet radius and spectral width. These are in agreement with Pi chamber observations and numerical simulations, as well as with numerous past studies of CCN cloud-base activation in natural clouds. The key result is that a relatively small fraction of the available CCN is activated in the Pi chamber fluctuating supersaturations, from about a half in the pristine case to only a 10th in the polluted case. The activation fraction as a function of the dry CCN radius is similar in all simulations, close to zero at the CCN small end, increasing to a maximum at CCN radius around 50 nm, and decreasing to close to zero at the large CCN end. This is explained as too small supersaturations to activate small CCN as in natural clouds and insufficient time to allow large CCN reaching the critical radius. Significance Statement: Impact of turbulence on the formation and growth of cloud droplets is an important cloud physics problem. Laboratory experiments in the Michigan Technological University cloud chamber provide key insights into this problem. Numerical simulations of cloud chamber processes discussed in this paper complement laboratory experiments by providing insights difficult or impossible to obtain in the laboratory. The study contrasts the formation and growth of cloud droplets in the laboratory cloud chamber with processes taking place in natural clouds. The differences documented in this paper pose questions concerning the impact of turbulence on the formation and growth of cloud droplets as a result of interactions of clouds with their environment. [ABSTRACT FROM AUTHOR]

Recent articles have shown that the long-portrayed "tornado alley" in the central plains is not an accurate portrayal of current tornado frequency over the United States. The greatest tornado threat now covers parts of the eastern United States. This paper shows that there has been a true spatial shift in tornado frequency, dispelling any misconceptions caused by the better visibility of tornadoes in the Great Plains versus the eastern United States. Using F/EF1+ tornadoes (the dataset least affected by increasing awareness of tornado locations or by changing rating methods), a 1° × 1° grid, and data for the two 35-yr periods 1951–85 and 1986–2020, we show that since 1951, by critical measures (tornadogenesis events, tornado days, and tornado pathlength), tornado activity has shifted away from the Great Plains and toward the Midwest and Southeast United States. In addition, tornadoes have trended away from the warm season, especially the summer, and toward the cold season since 1951. Annual trends in tornadoes by season (winter, spring, summer, and autumn) confirm this. All of the increase in F/EF1+ tornadoes in the eastern United States is due to an increase in cold season tornadoes. Tornadoes in the western United States decreased 25% (from 8451 during 1951–85 to 6307 during 1986–2020), while tornadoes in the eastern United States. increased 12% (from 9469 during 1951–85 to 10 595 during 1986–2020). The cities with the largest increases and decreases in tornado activity since 1951 are determined. Significance Statement: This paper quantifies in many ways (tornadoes, tornado days, and pathlength) the geographical shift in tornadoes from the central to the eastern United States and from the warm season to the cold season, since 1951. Where and when tornadoes most frequently occur is significant not only for the research and operational meteorology communities but also for public perception and risk awareness. Some research studies have shown that tornado casualties are more likely in the eastern United States and the cold season because of preconceived notions of a "tornado alley" in the Great Plains and a "tornado season" in the spring. Publication of the results of this research might help ameliorate this problem. [ABSTRACT FROM AUTHOR]

This paper considers theoretical and observed uncertainties in the estimates of ZDR and ρHV(0) using data from an operational S-band radar and a mobile X-band radar. Cases of widespread uniform precipitation including bright-band, clear air, and ash echoes from forest fires are all considered in order to obtain a wide range of ρHV(0) values as this along with the radar frequency and spectrum width determines the uncertainties. The theoretical uncertainties in these parameters provide a good estimate of the lower bound of the standard deviations of the observed values where these have been estimated using the adjacent data to the target pixel. The implications for the accuracy of precipitation estimation, particle identification, and estimates of drop-size distributions are discussed. Significance Statement: High-quality quantitative precipitation and particle size/classification retrievals using weather radar are strongly dependent on the accuracy of ZDR and ρHV(0). This paper examines the theoretical limits to the measurement accuracy and verifies these limits with radar data at 10- and 3-cm wavelengths. [ABSTRACT FROM AUTHOR]

Between 2014 and 2018, the National Oceanic and Atmospheric Administration conducted the NOAA Satellite Observing System Architecture (NSOSA) study to plan for the next generation of operational environmental satellites. The study generated some important questions that could be addressed by observing system simulation experiments (OSSEs). This paper describes a series of OSSEs in which benefits to numerical weather prediction from existing observing systems are combined with enhancements from potential future capabilities. Assessments include the relative value of the quantity of different types of thermodynamic soundings for global numerical weather applications. We compare the relative impact of several sounding configuration scenarios for infrared (IR), microwave (MW), and radio occultation (RO) observing capabilities. The main results are 1) increasing the revisit rate for satellite radiance soundings produces the largest benefits but at a significant cost by requiring an increase in the number of polar-orbiting satellites from 2 to 12; 2) a large positive impact is found when the number of RO soundings per day is increased well beyond current values and other observations are held at current levels of performance; 3) RO can be used as a mitigation strategy for lower MW/IR sounding revisit rates, particularly in the tropics; and 4) smaller benefits result from increasing the horizontal resolution along the track of the satellites of MW/IR satellite radiances. Furthermore, disaggregating IR and MW instruments into six evenly distributed sun-synchronous orbits is slightly more beneficial than when the same instruments are combined and collocated on three separate orbits. SIGNIFICANCE STATEMENT: The results of this paper are significant because they inform decision-makers about the future configuration of the NOAA's environmental satellite constellation, which serves millions of diverse users. [ABSTRACT FROM AUTHOR]

In recent years, various early warning signals of critical transition have been presented, such as autocorrelation at lag 1 [AR(1)], variance, the propagator based on detrended fluctuation analysis (DFA-propagator), and so on. Many studies have shown that the climate system has the characteristics of long-term memory (LTM). Will the LTM characteristics of the climate system change as it approaches possible critical transition points? In view of this, the present paper first studies whether the LTM of several folding (folded bifurcation) models changes consistently as they approach their critical points slowly by the rescaled range (R/S) analysis. The results of numerical experiments show that when the control parameters of the folding model are close to its critical threshold, the Hurst exponent H exhibits an almost monotonic increase (significance level α = 0.05). We compare the performance of R/S with the existing indicators, including AR(1), variance, and DFA-propagator, and find that R/S is a perfectly valid alternative. When there is no extra false noise, AR(1) and variance have good early warning effects. After the addition of extra Gaussian white noise of different intensities, the values of AR(1) and variance change significantly. As a result, the DFA-propagator based on AR(1) calibration also changed significantly. Compared with the other three indicators, the early warning effect of H has stronger ability to resist the interference of external false signals. To further verify the validity of increasing H, paleoclimate reconstruction of Cariaco Basin sediment core grayscale record with long trends filtered out is studied by R/S analysis. The other three early warning signals are calculated in the same way. The data contain a well-known abrupt climate change: the transition between the Younger Dryas (YD) and the Holocene. We find that approximately 300 years before this abrupt climate change occurred, before 11.7 kyr BP, the LTM exponents for Cariaco Basin deglacial grayscale data present an obvious increasing trend at a significant level of α = 0.05. Meanwhile, the variation trend of H and DFA-propagator is basically similar. This shows that increasing H by R/S analysis is an effective early warning signal, which indicates that a dynamic system is approaching its possible critical transition points; H is a completely valid alternative signal for AR(1) and DFA-propagator. The main conclusion of this paper is based on numerical experiments. The precise relationship between H and the stability of the underlying state approaching the transition needs to be further studied. [ABSTRACT FROM AUTHOR]

In this paper, a data-driven transfer learning (TL) model for locating tropical cyclone (TC) centers from satellite infrared images in the northwest Pacific is developed. A total of 2450 satellite infrared TC images derived from 97 TCs between 2015 and 2018 were used for this paper. The TC center location model (ResNet-TCL) with added residual fully connected modules is built for the TC center location. The MAE of the ResNet-TCL model is 34.8 km. Then TL is used to improve the model performance, including obtaining a pretrained model based on the ImageNet dataset, transferring the pretrained model parameters to the ResNet-TCL model, and using TC satellite infrared imagery to fine-train the ResNet-TCL model. The results show that the TL-based model improves the location accuracy by 14.1% (29.3 km) over the no-TL model. The model performance increases logarithmically with the amount of training data. When the training data are large, the benefit of increasing the training samples is smaller than the benefit of using TL. The comparison of model results with the best track data of TCs shows that the MAEs of TCs center is 29.3 km for all samples and less than 20 km for H2–H5 TCs. In addition, the visualization of the TL-based TC center location model shows that the TL model can accurately extract the most important features related to TC center location, including TC eye, TC texture, and contour. On the other hand, the no-TL model does not accurately extract these features. [ABSTRACT FROM AUTHOR]

Climate changepoint (homogenization) methods abound today, with a myriad of techniques existing in both the climate and statistics literature. Unfortunately, the appropriate changepoint technique to use remains unclear to many. Further complicating issues, changepoint conclusions are not robust to perturbations in assumptions; for example, allowing for a trend or correlation in the series can drastically change changepoint conclusions. This paper is a review of the topic, with an emphasis on illuminating the models and techniques that allow the scientist to make reliable conclusions. Pitfalls to avoid are demonstrated via actual applications. The discourse begins by narrating the salient statistical features of most climate time series. Thereafter, single- and multiple-changepoint problems are considered. Several pitfalls are discussed en route and good practices are recommended. While most of our applications involve temperatures, a sea ice series is also considered. Significance Statement: This paper reviews the methods used to identify and analyze the changepoints in climate data, with a focus on helping scientists make reliable conclusions. The paper discusses common mistakes and pitfalls to avoid in changepoint analysis and provides recommendations for best practices. The paper also provides examples of how these methods have been applied to temperature and sea ice data. The main goal of the paper is to provide guidance on how to effectively identify the changepoints in climate time series and homogenize the series. [ABSTRACT FROM AUTHOR]

High-resolution observations have demonstrated the presence of strong time-mean near-surface wind convergence (NSWC) anchored across oceanic frontal zones, such as the western boundary currents. Initial analyses appeared to show a close association between this time-mean NSWC and time-mean properties of the underlying sea surface temperature (SST), such as the gradients and second derivatives (e.g., Laplacian of SST), acting through pressure-adjustment and vertical-mixing mechanisms. However, a series of recent papers have revealed the instantaneous NSWC to be dominated by atmospheric fronts and have suggested the importance of air–sea processes occurring instead on shorter, synoptic time scales. In this paper, using the ERA5 reanalysis dataset in the Gulf Stream region, we aim to reconcile these viewpoints by investigating the spatial and temporal dependence of NSWC and its relationship to SST. It is revealed that while atmospheric frontal processes govern the day-to-day variability of NSWC, the relatively weak but persistent pressure-adjustment and vertical-mixing mechanisms provide lower-frequency modulations in conditions both with and without atmospheric fronts. In addition to their temporal characteristics, each mechanism is shown through spectral analysis to dominate on specific spatial scales. In light of recent work that has tied remote atmospheric responses to NSWC anomalies in western boundary current regions, these results emphasize the importance of oceanic frontal zones for atmospheric variability on all spatiotemporal scales. [ABSTRACT FROM AUTHOR]

Over the past decade the use of machine learning in meteorology has grown rapidly. Specifically neural networks and deep learning have been used at an unprecedented rate. To fill the dearth of resources covering neural networks with a meteorological lens, this paper discusses machine learning methods in a plain language format that is targeted to the operational meteorological community. This is the second paper in a pair that aim to serve as a machine learning resource for meteorologists. While the first paper focused on traditional machine learning methods (e.g., random forest), here a broad spectrum of neural networks and deep learning methods is discussed. Specifically, this paper covers perceptrons, artificial neural networks, convolutional neural networks, and U-networks. Like the Part I paper, this manuscript discusses the terms associated with neural networks and their training. Then the manuscript provides some intuition behind every method and concludes by showing each method used in a meteorological example of diagnosing thunderstorms from satellite images (e.g., lightning flashes). This paper is accompanied with an open-source code repository to allow readers to explore neural networks using either the dataset provided (which is used in the paper) or as a template for alternate datasets. [ABSTRACT FROM AUTHOR]

This paper investigates the impact of the environmental conditions during the first half of the 2020 mei-yu season (Y20) and the southwest vortex (SWV), as well as their interaction, on heavy precipitation in southern Taiwan during late May 2020, based on a quantitative approach through ensemble simulations. The control experiment successfully replicates observed heavy precipitation in southern and central Taiwan and reveals a positive spatial correlation between precipitation occurrence probabilities and mean accumulated precipitation, emphasizing continuous rainfall accumulation over intermittent extreme events. Comparative analyses with sensitivity experiments elucidate that the Y20, featuring an extended western North Pacific subtropical high, intensify pressure gradients and southwesterly flow near Taiwan, favoring precipitation in windward regions but hindering it in the east. The SWV creates a moist and vortical environment near Taiwan, amplifying moisture supply and westerly winds, promoting precipitation in southern Taiwan, and enhancing frontal activity. The interaction between the SWV and the Y20, though limited in its impact on providing favorable wind and moisture conditions for precipitation southwest of Taiwan, significantly contributes to precipitation in southern Taiwan. The reason is that although the SWV primarily enhances moisture and the Y20 predominantly boost southwesterly flow, creating favorable conditions for rainfall, substantial precipitation occurs only when both factors converge in a nonlinear interaction. The interaction increases frontal activity over the Taiwan Strait and influences the movement and strength of the SWV, enhancing southwesterly flow and moisture flux in southwestern Taiwan. [ABSTRACT FROM AUTHOR]

Existing precipitation-type algorithms have difficulty discerning the occurrence of freezing rain and ice pellets. These inherent biases are not only problematic in operational forecasting but also complicate the development of model-based precipitation-type climatologies. To address these issues, this paper introduces a novel light gradient-boosting machine (LightGBM)-based machine learning precipitation-type algorithm that utilizes reanalysis and surface observations. By comparing it with the Bourgouin precipitation-type algorithm as a baseline, we demonstrate that our algorithm improves the critical success index (CSI) for all examined precipitation types. Moreover, when compared with the precipitation-type diagnosis in reanalysis, our algorithm exhibits increased F1 scores for snow, freezing rain, and ice pellets. Subsequently, we utilize the algorithm to compute a freezing-rain climatology over the eastern United States. The resulting climatology pattern aligns well with observations; however, a significant mean bias is observed. We interpret this bias to be influenced by both the algorithm itself and assumptions regarding precipitation processes, which include biases associated with freezing drizzle, precipitation occurrence, and regional synoptic weather patterns. To mitigate the overall bias, we propose increasing the precipitation cutoff from 0.04 to 0.25 mm h−1, as it better reflects the precision of precipitation observations. This adjustment yields a substantial reduction in the overall bias. Finally, given the strong performance of LightGBM in predicting mixed precipitation episodes, we anticipate that the algorithm can be effectively utilized in operational settings and for diagnosing precipitation types in climate model outputs. Significance Statement: Freezing rain can have significant impacts on transportation and infrastructure, making accurate prediction of precipitation types crucial. In this study, we use a machine learning method known as LightGBM to predict precipitation types. We show that the new algorithm performs better than the existing methods for all precipitation types examined. Additionally, we compute a freezing-rain climatology over the eastern United States. Although the resulting climatology pattern corresponds well to observations, the algorithm overpredicts freezing-rain occurrence. We argue that this bias can be substantially reduced by increasing the precipitation cutoff from 0.04 to 0.25 mm h−1. Overall, this work highlights the potential of the LightGBM algorithm for both weather forecasting and diagnosing precipitation types in climate models. [ABSTRACT FROM AUTHOR]

As the only remaining ice sheet in the Northern Hemisphere, the Greenland ice sheet (GrIS) plays a crucial role in influencing atmospheric circulations, particularly with its rapid melting under global warming. In this paper, the influences of GrIS topography and surface thermal conditions are investigated by a series of aquaplanet experiments. The results show that the GrIS topography induces stationary waves and favors more blocking events through the generation of negative potential vorticity (PV) anomalies, while it tends to suppress local storm activities through the induced stationary waves. The surface cooling center of the GrIS is found to strengthen the jet streams by enhancing the meridional temperature gradient and thermal wind, while it causes the PV and static stability to increase during near-Greenland blocking days, thereby disfavoring blocking onset. Altogether, the topography and surface thermal effects of GrIS appear to compete with each other so that the net effect would determine the final response. Nevertheless, nonlinearity is found in both GrIS-topography alone and GrIS-surface temperature alone experiments, where nonlinear responses of atmospheric circulation are detected when the GrIS topography height or surface temperature exceeds their critical values, respectively. Hence, through this study, the response of the blocking in the vicinity of Greenland to the combined effects of topography and surface thermal conditions may shed light on comprehending the underlying mechanism of blocking alteration in a changing climate. Significance Statement: Although there have been numerous observation-based studies showing that the blocking around Greenland has increased over the past few decades, which is a predominant driver in accelerating the Greenland ice sheet (GrIS) melting, the reasons for this change are still unclear. In addition, the impact of GrIS on blocking still needs investigation. Through idealized aquaplanet simulations, this study examines the effect of GrIS's topography and surface thermal conditions upon blocking onset. It suggests the nonlinearity of the blocking response in the vicinity of Greenland to the combined effects in the variation of the height and surface temperature of GrIS. This study will enhance our understanding of possible changes in blocking due to global warming. [ABSTRACT FROM AUTHOR]

Despite common background La Niña conditions, Australia was very dry in November 2020 and wet in November 2021. This paper aims to provide an explanation for this difference. Large-scale drivers of Australian rainfall, including El Niño–Southern Oscillation, Indian Ocean dipole, Southern Annular Mode, and Madden–Julian oscillation, were examined but did not provide obvious clues for the differences. We found that the absence (in 2020) or presence (in 2021) of an enhanced thermal wind and subtropical jet over the Australian continent contributed to the rainfall anomalies. In general, La Niña sets up warm sea surface temperatures around northern Australia, which enhances the meridional temperature gradient over the continent and hence thermal wind and subtropical jet. In November 2021, these warm sea surface temperatures, coupled with a persistent midlatitude trough, which advected cold air over the Australian continent, led to an enhanced meridional temperature gradient and subtropical jet over Australia. The enhanced jet provided favorable conditions for the development of rain-bearing weather systems across Australia. In 2020, the continent was warm, displacing the latitude of maximum meridional temperature gradient south of the continent, resulting in fewer instances of the subtropical jet over Australia, and little development of weather systems over the continent. We highlight that although La Niña tilts the odds to wetter conditions for Australia, in any given month, variability in temperatures over the continent can contribute to subtropical jet variability and resulting rainfall in ways which confound the normal expectation from La Niña. Significance Statement: Forecasts of El Niño–Southern Oscillation are eagerly awaited, as the state of this climate driver has profound impacts on the likelihood of rainfall in regions around the world. While El Niño and La Niña do change rainfall likelihoods, the actual outcomes of these events are sometimes counter to expectation. This work explores one of the confounding factors to those expectations in the Australian context—the role of the meridional temperature gradient over the continent in modifying the storm track over Australia, which can disrupt the expected El Niño and La Niña teleconnections. We present case studies for two La Niña springs, highlighting that the Australian continent can help shape its own weather toward wetter or drier outcomes. [ABSTRACT FROM AUTHOR]

The South Pacific Oscillation (SPO), characterized by a north–south dipole-like pattern of sea level pressure anomalies, is one of the key factors in understanding tropical–extratropical interactions in the South Pacific. We show that in boreal summer (June–August), the center of the northern lobe sea level pressure anomalies in the SPO is shifted to the east gradually after the 1960–70s. This study focuses on the relationship between the boreal summer SPO and following winter El Niño–Southern Oscillation (ENSO) diversity before and after the eastward shift of the SPO's subtropical lobe. The eastward shift of the SPO's subtropical lobe altered both the seasonal footprint mechanism and the trade wind charging mechanism associated with the SPO and thus profoundly influenced the ENSO diversity. It is revealed that when the northern lobe of the SPO shifts to the west of its average location, it tends to strengthen the eastern Pacific (EP) El Niño mainly via the seasonal footprint mechanism. But after the SPO's northern lobe shifts to the east of its average location, it tends to promote the development of central Pacific (CP) El Niño mainly via the trade wind charging mechanism. The changes in the spatial structure of convection over the tropical Pacific and Indian Oceans may be one of the possible causes for the eastward shift in the SPO's northern lobe. The findings in the present study have implications for a better understanding of ENSO diversity. Significance Statement: Previous studies have demonstrated that the South Pacific Oscillation (SPO), as an important El Niño–Southern Oscillation (ENSO) precursor in the South Pacific, has the potential to provide an enhancement of the prediction of specific ENSO flavor. However, the historical variation in the SPO's spatial structure and related changes in the relationship with the diversity of ENSO are still unclear. In this paper, we show that the subtropical lobe of the boreal summer (June–August) SPO is shifted to the east gradually after the 1960–70s. The changes in the spatial structure have also altered both the seasonal footprint mechanism and the trade wind charging mechanism which play important roles in the developmental processes of different types of ENSO. Our work highlights the importance of the interdecadal changes in the spatial structure of the SPO in understanding the relationship between the SPO and ENSO diversity. [ABSTRACT FROM AUTHOR]

To improve the representation of all relevant scales in initial conditions for large-domain convection-allowing models, a new multiscale ensemble Kalman filter (MEnKF) algorithm is developed and implemented within the Gridpoint Statistical Interpolation analysis system (GSI) data assimilation framework coupled with the Finite-Volume Cubed-Sphere Dynamical Core (FV3) limited-area model. The algorithm utilizes ensemble background error covariances filtered to match the observations assimilated. This is realized in a sequential manner. 1) When assimilating coarse-resolution observations such as radiosondes, ensemble background perturbations are filtered to remove scales smaller than those the observations can represent, along with relatively large horizontal localization radii to ensure low-noise and balanced analysis increments. 2) The resulting ensemble analyses from the first step then serve as the background to assimilate denser observations such as radar data with smaller localization radii. Several passes can be taken to assimilate all observations. In this paper, vertically increasing horizontal filter scales are used when assimilating rawinsonde and surface observations together, while radar data are assimilated in the second step. The algorithm is evaluated through six convective storm cases during May 2021, with cycled assimilation of either conventional data only or with additional radar reflectivity followed by 24-h ensemble forecasts. Overall, positive impacts of the MEnKF on forecasts are obtained regardless of reflectivity data; its advantage over the single-scale EnKF is most significant in surface humidity and temperature forecasts up to at least 12 h. More accurate hourly precipitation forecasts with MEnKF can last up to 24 h for light rain. Furthermore, MEnKF forecasts higher ensemble probabilities for the observed hazardous events. [ABSTRACT FROM AUTHOR]

Dynamical numerical weather prediction has remarkably improved over the last decades. Yet postprocessing techniques are needed to calibrate forecasts which are based on statistical and machine learning techniques. With recent advances in the derivation of year-round, large-scale atmospheric circulations, or weather regimes, the question arises of whether this information can be valuable within forecast postprocessing methods. This paper investigates this by proposing a bias correction scheme to integrate the atmospheric circulation state derived from empirical orthogonal functions, referred to as weather patterns, for deterministic short-term, near-surface temperature forecasts based on least absolute shrinkage and selection operator (LASSO) regression. We propose a computational study which first evaluates different weather pattern definitions (spatial domain) to improve temperature forecasts in Europe. As a bias could be associated with the weather pattern at the model initialization time or at the realization time of the forecast, both variants are tested in this study. We show that forecasted weather patterns with the identical spatial domain as the forecast show best skill reaching mean-square-error skill improvements of up to 3% (day ahead) or 1% (week ahead). Only considering land surface improvements in Europe, improvements of 4%–6% for day-ahead forecasts and 1%–5% for week-ahead forecasts are observable. We believe that this study not only introduces a simple yet effective tool to reduce bias in temperature forecasts but also contributes to the active discussion of how valuable weather patterns are and how to use them within forecast calibration techniques. [ABSTRACT FROM AUTHOR]

The article discusses the condition of Expedited Contributions (ECs) being featured by the American Meteorological Society (AMS) journals. It mentions the reasons by which ECs are features, which include the reduction of time from submission to publication of research papers. It adds that the publication of ECs has declined.

This paper analyzes the relationships between the circulation regimes of the 500-hPa height (z500) and 250-hPa zonal winds (u250) in the Pacific–North America region during boreal winter, and the 45-day Northern Hemisphere oscillation in z500. The regimes were calculated using a k-means clustering applied to the leading 12 principal components of the combined z500–u250 anomaly fields. We divided the oscillation into eight arbitrary phases. The oscillation phase z500 composite maps are spatially well correlated with regime z500 composites: phases 1–2 are best correlated with the Arctic Low, phases 3–5 are best correlated with the Pacific Trough, phase 6 is best correlated with the Arctic High, and phases 7–8 are best correlated with the Alaskan Ridge. We found that these correlations are generally consistent with the regimes that tend to occur during the individual oscillation phases: the Arctic Low occurs above significance in phases 1–2, the Pacific Trough occurs above significance in phase 3, and Alaskan Ridge occurs above significance in phases 7–8. Therefore, the oscillation has a preferred order with respect to the regimes. The regime transitions indicate a pattern that moves through the Pacific Wavetrain, a regime that appears for k = 5 as a mean state. Transitions out of this regime into different regimes are preferred in different phases of the oscillation. These results imply a possible enhancement to regime prediction using the low-frequency oscillations in combination with regimes. Significance Statement: Subseasonal prediction, weather forecasting in the 2–4-week range, is important for many parts of society, e.g., water managers, emergency response units, and farmers. However, current prediction skill in this time range is low. This paper performs an initial analysis of a possible method to increase weather statistic prediction skill beyond 10 days in the winter for the Pacific–North America region. This is done by combining two ways of looking at large, long-lasting patterns of pressure systems in the atmosphere, which are associated with various weather statistics like precipitation extremes and storminess. The results indicate this method holds potential skill for enhancing subseasonal prediction. Further investigation might yield forecasting improvements in this important time range. [ABSTRACT FROM AUTHOR]

The supersaturation equation for a vertically moving adiabatic cloud parcel is analyzed. The effects of turbulent updrafts are incorporated in the shape of a stochastic Lagrangian model, with spatial and time correlations expressed in terms of turbulent kinetic energy. Using the Fokker–Planck equation, the steady-state probability distributions of supersaturation are analytically computed for a number of approximations involving the time-scale separation between updraft fluctuations and phase relaxation, and droplet or ice particle size fluctuations. While the analytical results are presented in general for single-phase clouds, the calculated distributions are used to compute mixed-phase cloud properties—mixed fraction and mean liquid water content in an initially icy cloud—and are argued to be useful for generalizing and constructing new parameterization schemes. Significance Statement: Supersaturation is the fuel for the development of clouds in the atmosphere. In this paper, our goal is to better understand the supersaturation budget of clouds embedded in a turbulent environment by analyzing the basic equations of cloud microphysics. It is found that the turbulent characteristics of an air parcel substantially affect the cloud's supersaturation budget and hence its life cycle. This is also shown in the context of mixed-phase clouds where, depending on the turbulent regime, different liquid-to-ice ratios are found. Consequently, the theoretical approach of this paper is crucial to develop tools to parameterize small-scale atmospheric features, like clouds, into global circulation models to improve climate projections for the future. [ABSTRACT FROM AUTHOR]

The interferometric radar altimeter (IRA) is an innovative remote sensing sensor that enables the observation of mesoscale and submesoscale (meso–submesoscale) ocean dynamic phenomena. The charge-neutral atmosphere introduces path delay and bending in signal propagation. In this study, three types of sea surface height (SSH) errors caused by charge-neutral atmosphere propagation path for IRA were identified: differential delay error (DDE), path delay error (PDE), and path bending error (PBE). Among them, DDE exhibits a proportionality to the negative zenith neutral delay (ZND) and demonstrates a significant increase with the incident angle; PDE is solely reliant on the ZND; PBE is like DDE in trend and magnitude resembling a ramp. Intriguingly, PBE exhibits insensitivity to variations in the charge-neutral atmosphere, behaving more like a systematic error. Theoretically, PBE leads to an increase in the SSH error of about 1.2 cm at far range for SWOT. The ZND spectrum fitted from the Jason-3 zenith delay correction data is additionally utilized to simulate the spatial distribution of ZND anomaly within the SWOT observation swaths. Then, the impact of PDE anomaly (PDEA), PBE, and DDE anomaly (DDEA) on the observation performance of SWOT is also considered in conjunction with SSH data provided by HYCOM. The findings indicate that both PDEA and PBE significantly reduce IRA's performance in oceanic phenomena, while the impact of DDEA can be disregarded. The PBE can distort the sea surface trend and increases the mean sea level within the range, requiring further attention. Significance Statement: This paper focuses on how the signal path affects the accuracy of measuring sea surface height (SSH) using the interferometric radar altimeter (IRA) in the charge-neutral atmosphere. The present paper defines three types of SSH errors caused by propagation path to IRA, namely, differential delay error (DDE), path delay error (PDE), and path bending error (PBE). Three types of SSH errors will make a significant impact on the altimetry performance of IRA. Among them, PDE spatial anomaly and PBE will reduce the expected observation performance of the meso–submesoscale ocean phenomena in IRA's swath, while PBE has not received enough attention in previous studies. Therefore, the research will provide a theoretical basis for IRA to correct the above SSH errors. [ABSTRACT FROM AUTHOR]

Visible satellite imagery is widely used by operational weather forecast centers for tropical and extratropical cyclone analysis and marine forecasting. The absence of visible imagery at night can significantly degrade forecast capabilities, such as determining tropical cyclone center locations or tracking warm-topped convective clusters. This paper documents ProxyVis imagery, an infrared-based proxy for daytime visible imagery developed to address the lack of visible satellite imagery at night and the limitations of existing nighttime visible options. ProxyVis was trained on the VIIRS day/night band imagery at times close to the full moon using VIIRS IR channels with closely matching GOES-16/17/18, Himawari-8/9, and Meteosat-9/10/11 channels. The final operational product applies the ProxyVis algorithms to geostationary satellite data and combines daytime visible and nighttime ProxyVis data to create full-disk animated GeoProxyVis imagery. The simple versions of the ProxyVis algorithm enable its generation from earlier GOES and Meteosat satellite imagery. ProxyVis offers significant improvement over existing operational products for tracking nighttime oceanic low-level clouds. Further, it is qualitatively similar to visible imagery for a wide range of backgrounds and synoptic conditions and phenomena, enabling forecasters to use it without special training. ProxyVis was first introduced to National Hurricane Center (NHC) operations in 2018 and was found to be extremely useful by forecasters becoming part of their standard operational satellite product suite in 2019. Currently, ProxyVis implemented for GOES-16/18, Himawari-9, and Meteosat-9/10/11 is being used in operational settings and evaluated for transition to operations at multiple NWS offices and the Joint Typhoon Warning Center. Significance Statement: This paper describes ProxyVis imagery, a new method for combining infrared channels to qualitatively mimic daytime visible imagery at nighttime. ProxyVis demonstrates that a simple linear regression can combine just a few commonly available infrared channels to develop a nighttime proxy for visible imagery that significantly improves a forecaster's ability to track low-level oceanic clouds and circulation features at night, works for all current geostationary satellites, and is useful across a wide range of backgrounds and meteorological scenarios. Animated ProxyVis geostationary imagery has been operational at the National Hurricane Center since 2019 and is also currently being transitioned to operations at other NWS offices and the Joint Typhoon Warning Center. [ABSTRACT FROM AUTHOR]

Because of its high spatial and temporal variability, rainfall remains one of the most challenging meteorological variables to measure accurately. Obtaining high-quality rainfall products is essential for flood monitoring, disaster warning, and weather forecasting systems, but this is not always possible on the basis of current rainfall observation networks. Innovative alternatives draw inspiration from "citizen science" and "crowdsourcing," allowing for opportunistic sensing of rainfall from existing measurements at a low cost, which has become a popular topic and is beginning to play an important role in developing rainfall observation systems. This paper reviews the current state of new rainfall observation approaches and explores their opportunities to complement more traditional ways of rainfall data collection in a hydrological context. Furthermore, the challenges of each new approach are discussed. Although these new options show great potential in enhancing the current rainfall network, they still face problems in terms of their accuracy, real-time accessibility, and limited applicability when individually employed. In contrast, the fusion of new measurements with traditional observation networks is feasible and will be effective for regional rainfall monitoring. This study also serves as an important reference in developing monitoring techniques for other environmental factors. Significance Statement: New rainfall observation techniques provide a meaningful supplement to current rainfall networks in terms of spatiotemporal resolution and accuracy. In this paper, we present a comprehensive overview of the innovations in rainfall observation and their popularity in different regions around the world. Then, the application value and future opportunities that new techniques bring to hydrological research are analyzed. It is anticipated that this paper will be of value to researchers with an interest in improving the quality of rainfall data, thus paving the way to accelerate these studies, as well as the application and implementation of their findings, to the next stage. Furthermore, we expect to prompt a rethink on utilizing and exploiting these new rainfall products to enhance our understanding and optimization of current rainfall sensing systems. [ABSTRACT FROM AUTHOR]

Does a warming world, where extremely hot summers are becoming more common, mean that cold summers will never again occur? It is crucial to know whether extremely cold summers are still possible, as such knowledge will significantly impact decisions regarding the further adaptation of crops to cold summers. Japan, which has suffered from many extremely cold summers, has managed past agricultural disruptions with emergency rice imports. In this paper, we show that a climate regime shift associated with the positive phase shift of the summer Arctic Oscillation occurred in 2010 in northeast Eurasia, making the occurrence of extremely cold summers highly unlikely as long as this new regime persists. In fact, Japan has not experienced a cold summer since 2010, while extremely hot summers have been frequent. Since 2010, a double-jet structure with subtropical and polar jets has strengthened, and the polar jet has meandered farther north of Japan, resulting in an upper-tropospheric anticyclone. This anticyclone, which extends downward and tilts southward, reaches southern Japan and prevents cold advection of oceanic air over the cold Oyashio. The Okhotsk high, known as the leading cause of cold summers, has occurred frequently in recent years; however, cold summers have not occurred due to the tilting anticyclone. The recent warming of the Oyashio weakens cold advection. The Pacific–Japan pattern, known as a remote tropical influence, has been weakened. A better understanding of the regime shift will help us understand the tilting anticyclone and the associated extreme summers in northeast Eurasia. Significance Statement: Extremely cold summers are among the most destructive natural disasters, both socioeconomically and agriculturally. Historically, food shortages due to cold summers have triggered wars. This paper proposes that a hemispheric-scale climate regime shift occurred in or around 2010. This regime shift has included warmings in the North Pacific and East Eurasian land surface temperatures. The regime shift is accompanied by the positive shift of the Arctic Oscillation (AO), a jet meander, and an upper-tropospheric anticyclone, making eastern Eurasia extremely hot. Our results imply that extremely cold summers are unlikely to occur in eastern Eurasia so long as this regime persists. Moving forward, it is important that the link between this regime shift and global warming be explored. [ABSTRACT FROM AUTHOR]

The spectrum of kinetic energy of vertical motions (VKE) is less well understood compared to the kinetic energy spectrum of horizontal motions (HKE). One challenge that has limited progress in describing the VKE spectrum is a lack of a unified approach to the decomposition of vertical velocities associated with the Rossby motions and inertia–gravity (IG) wave flows. This paper presents such a unified approach using a linear Rossby–IG vertical velocity normal-mode decomposition appropriate for a spherical, hydrostatic atmosphere. New theoretical developments show that for every zonal wavenumber k, the limit VKE is proportional to the total mechanical energy and to the square of the frequency of the normal mode. The theory predicts a VKE ∝ k−5 and a VKE ∝ k1/3 power law for the Rossby and IG waves, assuming a k−3 and a k−5/3 power law for the Rossby and IG HKE spectra, respectively. The Kelvin and mixed Rossby–gravity wave VKE spectra are predicted to follow k−1 and k−5 power laws, respectively. The VKE spectra for ERA5 data from August 2018 show that the Rossby VKE spectra approximately follow the predicted a k−5 power law. The expected k1/3 power law for the gravity wave VKE spectrum is found only in the SH midlatitude stratosphere for k ≈ 10–60. The inertial range IG VKE spectra in the tropical and midlatitude troposphere reflect a mixture of ageostrophic and convection-coupled dynamics and have slopes between −1 and −1/3, likely associated with too steep IG HKE spectra. The forcing by quasigeostrophic ageostrophic motions is seen as an IG VKE peak at synoptic scales in the SH upper troposphere, which gradually moves to planetary scales in the stratosphere. Significance Statement: The spectrum of kinetic energy of vertical motions (VKE) is less well understood compared to the kinetic energy spectrum of horizontal motions. One challenge is a lack of a unified approach to the decomposition of vertical velocities associated with the Rossby motions and inertia–gravity (IG) wave flows. This paper presents such a unified approach using a linear Rossby–IG vertical velocity normal-mode decomposition appropriate for a spherical, hydrostatic atmosphere. It is shown that for every zonal wavenumber, the limit VKE is proportional to the total mechanical energy and to the square of the frequency of the normal mode. The theory is successfully applied to the ERA5 data. It leads the way for a more accurate computation of momentum fluxes. [ABSTRACT FROM AUTHOR]

The coupled nature of the ocean–atmosphere system frequently makes understanding the direction of causality difficult in ocean–atmosphere interactions. This study presents a method to decompose turbulent surface heat fluxes into a component which is directly forced by atmospheric circulation and a residual which is assumed to be primarily "ocean-forced." This method is applied to the North Atlantic in a 500-yr preindustrial control run using the Met Office's HadGEM3-GC3.1-MM model. The method shows that atmospheric circulation dominates interannual to decadal heat flux variability in the Labrador Sea, in contrast to the Gulf Stream where the ocean primarily drives the variability. An empirical orthogonal function analysis identifies several residual heat flux modes associated with variations in ocean circulation. The first of these modes is characterized by the ocean warming the atmosphere along the Gulf Stream and North Atlantic Current and the second by a dipole of cooling in the western subtropical North Atlantic and warming in the subpolar North Atlantic. Lead–lag regression analysis suggests that atmospheric circulation anomalies in prior years partly drive the ocean heat flux modes; however, there is no significant atmospheric circulation response in years following the peaks of the modes. Overall, the heat flux dynamical decomposition method provides a useful way to separate the effects of the ocean and atmosphere on heat flux and could be applied to other ocean basins and to either models or reanalysis datasets. Significance Statement: Variability of the ocean affects atmospheric circulation and provides a source of long-term predictability for surface weather. However, the atmosphere also affects the ocean. This makes the separation of cause and effect in such atmosphere–ocean interactions difficult. This paper introduces a method to separate "turbulent heat fluxes," the primary means by which the atmosphere and ocean influence one another, into a component driven by atmospheric variability and a component which is primarily related to ocean variability. The method is tested by applying it to a climate model simulation and is able to identify regions in which the exchange of heat between the ocean and atmosphere is dominated by atmospheric variability and regions which are dominated by the ocean. [ABSTRACT FROM AUTHOR]

Numerous recent tropical cyclones have caused extreme rainfall and flooding events in the CONUS. Climate change is contributing to heavier extreme rainfall around the world. Modeling studies have suggested that tropical cyclones may be particularly efficient engines for transferring the additional water vapor in the atmosphere into extreme rainfall. This paper develops a new indicator for climate change using the enhanced rainfall metric to evaluate how the frequency and/or intensity of extreme rainfall around tropical cyclones has changed. The enhanced rainfall metric relates the amount of rain from a storm over a given location to the 5-yr return period rainfall in that location to determine the severity of the event. The annual area exposed to tropical-cyclone-related 5-yr rainfall events is increasing, which makes it a compelling climate change indicator. Quantile regression illustrates that the distribution of tropical cyclone rainfall is also changing. For tropical storms, all quantiles are increasing. However, major hurricanes show large increases in their most extreme rainfall. This study does not attempt to make any detection claims (vs natural variability) or attribution of the observed trends to anthropogenic forcing. However, the sensitivity of the results to natural variability in tropical cyclone frequency was somewhat constrained by comparing 2 decades from the previous active era (1951–70) with two from the current era (2001–20). This comparison also shows that both the mean rainfall and the maximum rainfall associated with tropical cyclones are increasing over most areas of the eastern CONUS with the most significant increases from northern Alabama to the southern Appalachians. Significance Statement: The purpose of this study is to analyze the changes in frequency and magnitude of extreme precipitation events associated with tropical cyclones with the goal of developing a new indicator for climate change. This is important because heavy rainfall and associated flooding is one of the primary causes of tropical cyclone destruction and fatalities, especially in inland locations away from where storms initially make landfall. Our results show that both the frequency and magnitude of extreme rainfall events from tropical cyclones have increased over the CONUS. The strongest storms (major hurricanes) also show more of an increase in extreme rainfall than storms of weaker intensities. [ABSTRACT FROM AUTHOR]