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This study examines extratropical cyclone tracks, central pressure, and maximum intensification rates from a widely used automated cyclone tracking scheme and compares them with the manual tracking of five well-known North Atlantic cyclones whose histories are available in the refereed literature. The automated tracking scheme is applied to sea level pressure data from four different reanalyses of varying levels of sophistication to test the sensitivity of the results to input data resolution and quality. Further, we test the tracking scheme using lower-tropospheric vorticity obtained from the most recent reanalysis (ERA5) for four of these cyclone events. Substantial discrepancies in cyclone position, intensity, and maximum intensification rates exist between the manual tracking and the automated tracking and are not eliminated by using higher-resolution reanalyses or by "turning off" the spatial smoothing feature of the automated tracking scheme (needed to reduce spurious cyclone detections). The results point to a particular problem in detecting weaker and earlier stage cyclones and confirm findings from studies that have examined a broad range of cyclone tracking schemes for a range of reanalyses. Notably, this early cyclone stage often involves a smaller-scale secondary cyclogenesis or cyclone wave, which are detected by the automated scheme only after subsequent growth in the ensuing 6–12 h. It is known that these early stages are critical for a comprehensive understanding of rapid intensification events. A discussion of possible future solutions to this problem is presented. Significance Statement: Because of the availability of large modern datasets portraying sea level pressure across the globe, meteorologists have turned to automated detection and tracking of midlatitude cyclones. Detection and tracking are of interest since these storm systems play an important role in weather and climate and potential changes in their location, frequency, and intensity are of considerable societal interest given climate change. This paper compares the results obtained from one commonly used automated tracking method with tracks obtained by human analysts. We find substantial discrepancies in cyclone position, intensity, and intensification rates and that these differences are not eliminated by using improved analyses. A discussion of possible future solutions is presented. [ABSTRACT FROM AUTHOR]

Robert's rising thermal bubble (RRTB) is a benchmark case used to assess atmospheric models. In this paper, RRTB is further studied both using an analytical and a numerical approach, improving to a greater extent the qualitative description found in the literature. The theoretical framework used is that of buoyant thermals and scaling theory that together are able to predict part of the expected behavior of the bubble as it rises and, therefore, can be used to further validate the simulations. For the numerical experiments, we simulate both a two-dimensional and three-dimensional RRTB using a variety of convection schemes under the Boussinesq approximation and with a higher resolution. While the results are in agreement with those presented by previous authors on the same benchmark and also with the theoretical framework established, we add the quantitative measures to validate the underlying physics of the numerical model. Our results also show that, due to its completely chaotic nature when confined in a 2D plane, RRTB becomes a very challenging candidate to be used as a benchmark if only compared in a qualitative way, and when the 3D bubble is simulated, the shape changes significantly. [ABSTRACT FROM AUTHOR]

Data storage and data processing generate significant cost for weather and climate modeling centers. The volume of data that needs to be stored and data that are disseminated to end users increases with increasing model resolution and the use of larger forecast ensembles. If precision of data is reduced, cost can be reduced accordingly. In this paper, three new methods to allow a reduction in precision with minimal loss of information are suggested and tested. Two of these methods rely on the similarities between ensemble members in ensemble forecasts. Therefore, precision will be high at the beginning of forecasts when ensemble members are more similar, to provide sufficient distinction, and decrease with increasing ensemble spread. To keep precision high for predictable situations and low elsewhere appears to be a useful approach to optimize data storage in weather forecasts. All methods are tested with data of operational weather forecasts of the European Centre for Medium-Range Weather Forecasts. [ABSTRACT FROM AUTHOR]

The En4DVar method is designed to combine the flow-dependent statistical covariance information of EnKF into the traditional 4DVar method. However, the En4DVar method is still hampered by its strong dependence on the adjoint model of the underlying forecast model and by its complexity, maintenance requirements, and the high cost of computer implementation and simulation. The primary goal of this paper is to propose an alternative approach to overcome the main difficulty of the En4DVar method caused by the use of adjoint models. The proposed approach, the nonlinear least squares En4DVar (NLS-En4DVar) method, begins with rewriting the standard En4DVar formulation into a nonlinear least squares problem, which is followed by solving the resulting NLS problem by aGauss-Newton iterative method. To reduce the computational and implementation complexity of the proposedNLS-En4DVar method, a few variants of the new method are proposed; thesemodifications make the model cheaper and easier to use than the full NLS-En4DVar method at the expense of reduced accuracy. Furthermore, an improved iterative method based on the comprehensive analysis on the above NLSi-En4DVar family of methods is also proposed. These proposed NLSi-En4DVar methods provide more flexible choices of the computational capabilities for the broader and more realistic data assimilation problems arising from various applications. The pros and cons of the proposed NLSi-En4DVar family of methods are further examined in the paper and their relationships and performance are also evaluated by several sets of numerical experiments based on the Lorenz-96 model and the Advanced Research WRF (ARW) Model, respectively. [ABSTRACT FROM AUTHOR]

An intense mesoscale convective system (MCS) in the Guinea Coast region caused one of the highest ever recorded daily rainfall amounts at the Nigerian station Abakaliki on 12 June 2016 (223.5 mm). This paper provides a detailed analysis of the meso- and synoptic-scale factors leading to this event, including some so far undocumented dynamical aspects for southern West Africa. The MCS formed over the Darfur Mountains due to diurnal heating, then moved southwestward along a mid- to lower-tropospheric trough, and developed into a classical West African squall line in a highly sheared environment with pronounced midlevel dryness. Strong moisture flux convergence over Nigeria prior to the MCS passage led to extreme values in precipitable water and was caused by the formation of a local, short-lived heat low. According to the pressure tendency equation, the latter resulted from tropospheric warming due to MCS-forced subsidence as well as surface insolation in the resulting almost cloud-free atmosphere. In this extremely moist environment, the MCS strongly intensified and initiated the formation of a lower-tropospheric vortex, which resulted in a deceleration of the MCS and high rainfall accumulation at Abakaliki. Following the vorticity equation, the vortex formation was realized through strong low-level vortex stretching and upper-level vertical vorticity advection related to the MCS, which became "dynamically large" compared to the Rossby radius of deformation. Eventually, moisture supply and lifting associated with the vortex are suggested to promote the longevity of the MCS during the subsequent westward movement along the Guinea Coast. [ABSTRACT FROM AUTHOR]

Model error in ensemble prediction systems is often represented by either a tendency perturbation approach or a process-based parameter perturbation scheme. In this paper a novel hybrid stochastically perturbed parameterization (HSPP) scheme is proposed and implemented in the Convection Permitting Limited Area Ensemble Forecasting (C-LAEF) system developed at the Zentralanstalt für Meteorologie und Geodynamik (ZAMG). In HSPP, the individual parameterization tendencies of the physical processes radiation, shallow convection, and microphysics are perturbed stochastically by a spatially and temporally varying pattern. Uncertainties in the turbulence scheme are considered by perturbing key parameters on the process level. The proposed scheme HSPP features several advantages compared to the popular stochastically perturbed parameterization tendencies (SPPT) scheme: it considers a more physically consistent relationship between different parameterization schemes, deals with uncertainties especially adapted to the individual physical processes, respects conservation laws of energy and moisture, and eliminates the tapering function that has to be introduced to the SPPT scheme because of mainly numerical reasons. The hybrid scheme HSPP is evaluated over one summer and one winter month and compared to a reference ensemble without any stochastic physics perturbations and to two versions of the SPPT scheme. The results show that HSPP significantly increases the ensemble spread of temperature, humidity, wind speed, and pressure, especially in the lower levels of the atmosphere where a tapering function is active in the original SPPT approach. Precipitation verification yields a generally improved probabilistic performance of the HSPP scheme in summer when convection is dominating, which has also been demonstrated in a case study. [ABSTRACT FROM AUTHOR]

During the 2015 Plains Elevated Convection at Night (PECAN) field campaign, several nocturnal low-level jets (NLLJs) were observed with integrated boundary layer profiling systems at multiple sites. This paper gives an overview of selected PECAN NLLJ cases and presents a comparison of high-resolution observations with numerical simulations using the Weather Research and Forecasting (WRF) Model. Analyses suggest that simulated NLLJs typically form earlier than the observed NLLJs. They are stronger than the observed counterparts early in the event, but weaker than the observed NLLJs later in the night. However, sudden variations in the boundary layer winds, height of the NLLJ maximum and core region, and potential temperature fields are well captured by the WRF Model. Simulated three-dimensional fields are used for a more focused analysis of PECAN NLLJ cases. While previous studies often related changes in the thermal structure of the nocturnal boundary layer and sudden mixing events to local features, we hypothesize that NLLJ spatial evolution plays an important role in such events. The NLLJ is shown to have heterogeneous depth, wind speed, and wind direction. This study offers detailed documentation of the heterogeneous NLLJ moving down the slope of the Great Plains overnight. As the NLLJ evolves, westerly advection becomes significant. Buoyancy-related mechanisms are proposed to explain NLLJ heterogeneity and down-slope motion. Spatial and temporal heterogeneity of the NLLJ is suggested as a source of the often observed and simulated updrafts during PECAN cases and as a possible mechanism for nocturnal convection initiation. The spatial and temporal characteristics of the NLLJ are interconnected and should not be treated independently. [ABSTRACT FROM AUTHOR]

Monthly Weather Review, one of the oldest continuously published meteorological journals in the world, publishes its 150th volume this year. In January 1873, the U.S. War Department's Army Signal Service began producing this monthly report summarizing weather across the United States. Its first issue consisted of a one-page narrative of weather conditions and storms and one chart depicting tracks of low pressure centers during that month. In 1891, Monthly Weather Review continued as a government publication with the transfer of the nation's weather service from the military to the newly established U.S. Weather Bureau. Over time and sometimes erratically, it grew into a scientific journal. In 1974, Monthly Weather Review was transferred to the American Meteorological Society, who continues to publish it to this day (although a 2003 proposal might have ended it). This Historical Review discusses some of the journal's history and impact, as well as its legacy. This review also compiles for the first time a complete list of Monthly Weather Review editors. The research published within Monthly Weather Review has included highly cited, ground-breaking articles on weather and climate phenomena (e.g., extratropical and tropical cyclones, El Niño–Southern Oscillation, Madden–Julian oscillation), general circulation modeling, and numerical weather prediction. The data published in the early issues have been used—and continue to be used to this day—for a variety of applied research and historical analysis purposes. The composition and content of Monthly Weather Review have changed over the past century and a half, continuing to evolve with the modern publishing landscape, with color figures at no additional cost, open-access articles, open data, and, in the near future, embedded figure animations. [ABSTRACT FROM AUTHOR]

Parameterizations in numerical models account for unresolved processes. These parameterizations are inherently difficult to construct and as such typically have notable imperfections. One approach to account for this uncertainty is through stochastic parameterizations. This paper describes a methodological approach whereby existing parameterizations provide the basis for a simple stochastic approach. More importantly, this paper describes systematically how one can “train” such parameterizations with observations. In particular, a stochastic trigger function has been implemented for convective initiation in the Kain–Fritsch (KF) convective parameterization scheme within the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (Penn State–NCAR) Mesoscale Model (MM5). In this approach, convective initiation within MM5 is modeled by a binary random process. The probability of initiation is then modeled through a transformation in terms of the standard KF trigger variables, but with random parameters. The distribution of these random parameters is obtained through a Bayesian Monte Carlo procedure informed by radar reflectivities. Estimates of these distributions are then incorporated into the KF trigger function, giving a meaningful stochastic (distributional) parameterization. The approach is applied to cases from the International H2O project (IHOP). The results suggest the stochastic parameterization/Bayesian learning approach has potential to improve forecasts of convective precipitation in mesoscale models. [ABSTRACT FROM AUTHOR]

This paper is an examination of 2–3-day convective variability in the tropical Pacific region. The initial focus of the paper is on the western tropical Pacific during the Tropical Ocean Global Atmosphere Coupled Ocean-Atmosphere Response Experiment (TOGA COARE) intensive observation period (IOP); high spatial and temporal resolution outgoing longwave radiation (OLR) data and sea surface temperatures are filtered to eliminate diurnal and lower-frequency variability. The propagation of the 2–3-day convective variability is also studied. Westward propagation appears to be favored in some regions, indicative of the events being influenced by westward-propagating inertio-gravity waves. However, many regions have 2–3-day events that divide fairly equally into eastward and westward propagations, indicating that both eastward- and westward-propagating inertio-gravity waves are influencing the oscillation. The SST data during the 4-month IOP dataset show evidence of a 2–3-day variability during those convective events occurring under low wind speed conditions during the suppressed phases of the MJO, indicative of a possible thermodynamic feedback between the ocean and atmosphere. The results of this 4-month dataset are then expanded by the use of an 11-yr IR brightness temperature dataset that is similarly filtered. Interannual variability in the occurrence of the 2–3-day events is also studied; the results indicate that for those regions in which convection strongly increases or decreases during the ENSO cycle, the occurrence of the 2–3-day variability also increases or decreases. The dependence of the 2–3-day convective episodes on large-scale convection and the intraseasonal oscillation (ISO) is also investigated. Results show that large-scale convection and the ISO are not necessary for these events, consistent with results for the 4-month IOP. [ABSTRACT FROM AUTHOR]

In this paper the standard variational analysis scheme is modified, through a simple transform, to avoid the inversion of the background error covariance matrix. A close inspection of the modified scheme reveals that it is possible to use a filter to replace the multiplication of the covariance matrix. A variational analysis scheme using a filter is then formulated, which does not explicitly involve the covariance matrix. The modified scheme and the filter scheme have the advantage of avoiding the inversion or any usage of the large matrix for analyses using gridpoint representation. To illustrate the use of these schemes, a small-sized and a more realistic analysis problem is considered using real temperature observations. It is found that both the modified scheme and the filter scheme work well. Compared to the standard and modified schemes the storage and computational requirements of the filter scheme can be reduced by several orders of magnitude for realistic atmospheric applications. [ABSTRACT FROM AUTHOR]

This paper describes a new climate model and its potential application to the study of ENSO impacts. The model is a regional spectral model embedded within a global coupled ocean–atmosphere model. The atmospheric part of the model consists of a global spectral model with triangular truncation T63 and a nested regional spectral model. The regional model is a relocatable spectral perturbation model that can be run at any horizontal resolution. In this paper the regional model was run with a resolution of 40 km. The global atmosphere model is coupled to the Max Planck global ocean model. No flux correction or anomaly coupling is used. An ensemble of 120-day integrations was conducted using the coupled nested system for the boreal winters of 1987 and 1988. A control integration was also performed in which observed SSTs were used in both the global and regional models. Two domains were chosen for the regional model: the southeast United States and western North America. Results from the global models show that the models reproduce many of the large-scale ENSO climate variations including the shifts in the Pacific ITCZ and SPCZ along with a Pacific–North America response in the 500-hPa height field. These results are compared against the corresponding ECMWF and Global Precipitation Climatology Centre analysis. Over the southeast United States both the global and regional models captured the precipitation variations between the two years as compared with the monthly mean cooperative station data. It is shown that the regional model solution is consistent with the global model solution, but with more realistic detail. Finally prospects for using this coupled nested ocean–atmosphere regional spectral model for downscaling are discussed. [ABSTRACT FROM AUTHOR]

Spurious, nonamplifying, two-time-step oscillations are present in several numerical models of the atmosphere where the vertical diffusion is parameterized using a nonlinear diffusion equation. The problems become par-ticularly pronounced when the convective activity is studied, since convection is directly influenced by the vertical fluxes and occurs when these fluxes are large. Different numerical formulations for the solution of the diffusion equation in the High Resolution Limited Area Model have been tested in this paper. The results show that a perfect solution has not been found but that there are solutions that should be recommended to replace the traditional formulation with an explicit formulation of the diffusion coefficient. [ABSTRACT FROM AUTHOR]

Densely observed remote sensing data such as radars and satellites generally contain significant spatial error correlations. In data assimilation, the observation error covariance matrix is usually assumed to be diagonal, and the dense data are thinned or spatially averaged to compensate for neglecting the spatial observation error correlation. However, in theory, including the spatial observation error correlation in data assimilation can make better use of the dense data. This study performs perfect model observing system simulation experiments (OSSEs) using the nonhydrostatic icosahedral atmospheric model (NICAM) and the local ensemble transform Kalman filter (LETKF) to assess the impact of assimilating horizontally dense and error-correlated observations. The condition number of the observation error covariance matrix, defined as the ratio of the largest to smallest eigenvalues, is important for the numerical stability of the LETKF computation. A large condition number makes it difficult to compute the ensemble transform matrix correctly. Reducing the condition number by reconditioning is found effective for stable computation. The results show that including the horizontal observation error correlation with reconditioning makes the analysis more accurate but requires 6 times more computations than the case with the diagonal observation error covariance matrix. Significance Statement: It is important to effectively utilize observations in data assimilation for more accurate weather prediction. Spatially dense observations are known to have an error correlation that is ignored in the data assimilation. This study explores assimilating dense observations by explicitly including observation error correlations with an idealized experiment. The results shows that the analysis is improved by including the observation error correlations. Also, the condition number of the observation error covariance matrix is essential for stable computations. [ABSTRACT FROM AUTHOR]

Cloud-to-ground (CG) lightning substantially impacts human health and property. However, the relations between U.S. lightning activity and the Madden–Julian oscillation (MJO) and El Niño–Southern Oscillation (ENSO), two predictable drivers of global climate variability, remain uncertain, in part because most lightning datasets have short records that cannot robustly reveal MJO- and ENSO-related patterns. To overcome this limitation, we developed an empirical model of 6-hourly lightning flash count over the contiguous United States (CONUS) using environmental variables (convective available potential energy and precipitation) and National Lightning Detection Network data for 2003–16. This model is shown to reproduce the observed daily and seasonal variability of lightning over most of CONUS. Then, the empirical model was applied to construct a proxy lightning dataset for the period 1979–2021, which was used to investigate the summer MJO–lightning relationship at daily resolution and the winter–spring ENSO–lightning relationship at seasonal resolution. Overall, no robust relationship between MJO phase and lightning patterns was found when seasonality was taken into consideration. El Niño is associated with increased lightning activity over the coastal Southeast United States during early winter, the Southwest during winter through spring, and the Northwest during late spring, whereas La Niña is associated with increased lightning activity over the Tennessee River valley during winter. Significance Statement: Cloud-to-ground lightning is dangerous for humans via direct strikes or through triggering wildfires, generating air pollution, etc. How lightning activity can be affected by climate remains unclear, and it is challenging to study their links because the data record for lightning is short. With the available lightning record, we developed a model that relates lightning flash counts over the United States to environmental factors. This model well represents observed fluctuations in daily and seasonal lightning over most of the United States. Because the model only needs environmental information to predict lightning flash counts, we were able to construct a longer record of predicted lightning based on the longer data record of environmental variables. With this dataset, we investigated the links between lightning and climate and found that the state of sea surface temperatures in the tropical Pacific (El Niño–Southern Oscillation) is linked to changes in U.S. lightning patterns in winter and spring. [ABSTRACT FROM AUTHOR]

The article comments on the notion that artificial neural networks could accelerate model physics components in an atmospheric general circulation model (AGCM) which was presented in a paper by V. M. Krasnopolsky and colleagues. The prospects and methods used by the author was elaborated. The author pointed that the relevance of neural networks needs to be regularly reevaluated with respect to the particular computational and scientific contents where they are developed and used.

In a recent work, a sensitivity analysis methodology was described that allows for a visual display of forecast sensitivity, with respect to model parameters, across a gridded forecast field. In that approach, sensitivity was assessed with respect to model parameters that are continuous in nature. Here, the analogous methodology is developed for situations involving noncontinuous (discrete or categorical) model parameters. The method is variance based, and the variances are estimated via a random-effects model based on 2k−p fractional factorial designs and Graeco-Latin square designs. The development is guided by its application to model parameters in the stochastic kinetic energy backscatter scheme (SKEBS), which control perturbations at unresolved, subgrid scales. In addition to the SKEBS parameters, the effect of daily variability and replication (both, discrete factors) are also examined. The forecasts examined are for precipitation, temperature, and wind speed. In this particular application, it is found that the model parameters have a much weaker effect on the forecasts as compared to the effect of daily variability and replication, and that sensitivities, weak or strong, often have a distinctive spatial structure that reflects underlying topography and/or weather patterns. These findings caution against fine-tuning methods that disregard 1) sources of variability other than those due to model parameters, and 2) spatial structure in the forecasts. [ABSTRACT FROM AUTHOR]

While GCM horizontal resolution has received the majority of scale improvements in recent years, ample evidence suggests that a model's vertical resolution exerts a strong control on its ability to accurately simulate the physics of the marine boundary layer. Here we show that, regardless of parameter tuning, the ability of a single-column model (SCM) to simulate the subtropical marine boundary layer improves when its vertical resolution is improved. We introduce a novel objective tuning technique to optimize the parameters of an SCM against profiles of temperature and moisture and their turbulent fluxes, horizontal winds, cloud water, and rainwater from large-eddy simulations (LES). We use this method to identify optimal parameters for simulating marine stratocumulus and shallow cumulus. The novel tuning method utilizes an objective performance metric that accounts for the uncertainty in the LES output, including the covariability between model variables. Optimization is performed independently for different vertical grid spacings and value of time step, ranging from coarse scales often used in current global models (120 m, 180 s) to fine scales often used in parameterization development and large-eddy simulations (10 m, 15 s). Uncertainty-weighted disagreement between the SCM and LES decreases by a factor of ∼5 when vertical grid spacing is improved from 120 to 10 m, with time step reductions being of secondary importance. Model performance is shown to converge at a vertical grid spacing of 20 m, with further refinements to 10 m leading to little further improvement. Significance Statement: In successive generations of computer models that simulate Earth's atmosphere, improvements have been mainly accomplished by reducing the horizontal sizes of discretized grid boxes, while the vertical grid spacing has seen comparatively lesser refinements. Here we advocate for additional attention to be paid to the number of vertical layers in these models, especially in the model layers closest to Earth's surface where climatologically important marine stratocumulus and shallow cumulus clouds reside. Our experiments show that the ability of a one-dimensional model to represent physical processes important to these clouds is strongly dependent on the model's vertical grid spacing. [ABSTRACT FROM AUTHOR]

Three-dimensional variational data assimilation (3D-Var) with the first guess at appropriate time (FGAT) appears to be an attractive compromise between accuracy and overall computing time. It is computationally cheaper than four-dimensional (4D)-Var as the increment is not propagated back and forth in time by a model, yet the comparison between the model and the observations is still computed at the right observation time. An interesting feature of the 4D-Var is the iterative process known as the outer loop. This outer-loop approach can also be used in conjunction with 3D-FGAT. But it requires the application of the 3D-FGAT analysis increment at the beginning of the assimilation window. The pros and cons of using this unusual 3D-FGAT variant are illustrated in this paper on two applications focused on the transport, one of the main phenomena governing the atmospheric evolution. The first one is the one-dimensional advection of a passive tracer. By three representative situations, it shows the benefits of the outer loop, except for practical situations driven by very rapid dynamics such as a zonal wind of 50 m s−−1 on the earth''s great circle, when the assimilation window has a size of 3 h. The second application is the 3D-FGAT assimilation of true ozone measurements into a chemical--transport model. It confirms the previous results, showing that the 3D-FGAT analysis with the outer loop produces an overestimation of the ozone increment in regions where the wind speed is high compared to the time length of the assimilation window. [ABSTRACT FROM AUTHOR]

Meridional wind surges from the extratropics into the Tropics strongly regulate tropical convective activity. This paper confirms that extratropical forcing manifested as a meridional surge does modulate the tropical atmosphere over the eastern Indian Ocean, and it describes the tropical–extratropical connection in the region. Surges in the lower atmosphere on submonthly (6–25 days) time scales over the eastern Indian Ocean were examined in tandem with associated tropical convection and large-scale atmospheric fields during the Southern Hemisphere (SH) winter (June–August). Data used in this study are NCEP-2 reanalyses and daily NOAA/Climate Diagnostics Center (CDC) outgoing longwave radiation (OLR) data for 23 yr, from 1979 to 2001. A low-level surge index was calculated using the 850-hPa meridional wind component (υ) averaged over a region where sub–monthly scale υ variance shows a local maximum (17.5°–2.5°S, 87.5°–97.5°E). The surge index defines 62 different surge events. Composites of various components were generated based on the index to define relationships between surge events and large-scale fields. Low-level southerly surges over the eastern Indian Ocean originate from midlatitude Rossby waves with strong baroclinic development in the entrance region of a subtropical jet core off Australia’s west coast. Strengthened low-level wind surges cause cross-equatorial flow stretching from the subtropical eastern Indian Ocean to the southern Bay of Bengal. Surges are accompanied by the advection of cold, dry air from midlatitudes into the Tropics. A cold and dry front develops at the leading surge edge during the surge period. Two to four days later, as the surge peaks, negative OLR anomalies develop near the key region. The OLR anomalies indicate a local blow up of convection over the tropical eastern Indian Ocean. Convection reflects increased instability in the surge region, which is caused by low-level dry air advection and near-surface moistening that is forced by enhanced sea surface evaporation associated with the surge. The southerly surge on submonthly time scales is an important bridge linking the Tropics and midlatitudes over the Indian Ocean. [ABSTRACT FROM AUTHOR]

Seasonal forecasts are subject to various types of errors: amplification of errors in oceanic initial conditions, errors due to the unpredictable nature of the synoptic atmospheric variability, and coupled model error. Ensemble forecasting is usually used in an attempt to sample some or all of these various sources of error. How to build an ensemble forecasting system in the seasonal range remains a largely unexplored area. In this paper, various ensemble generation methodologies for the European Centre for Medium-Range Weather Forecasts (ECMWF) seasonal forecasting system are compared. A series of experiments using wind perturbations (applied when generating the oceanic initial conditions), sea surface temperature (SST) perturbations to those initial conditions, and random perturbation to the atmosphere during the forecast, individually and collectively, is presented and compared with the more usual lagged-average approach. SST perturbations are important during the first 2 months of the forecast to ensure a spread at least equal to the uncertainty level on the SST measure. From month 3 onward, all methods give a similar spread. This spread is significantly smaller than the rms error of the forecasts. There is also no clear link between the spread of the ensemble and the ensemble mean forecast error. These two facts suggest that factors not presently sampled in the ensemble, such as model error, act to limit the forecast skill. Methods that allow sampling of model error, such as multimodel ensembles, should be beneficial to seasonal forecasting. [ABSTRACT FROM AUTHOR]

The purpose of this study is to present the impacts of a fully interactive coupling between an atmospheric and a sea ice model over the Gulf of St. Lawrence, Canada. The impacts are assessed in terms of the atmospheric and sea ice forecasts produced by the coupled numerical system. The ocean-ice model has been developed at the Maurice Lamontagne Institute, where it runs operationally at a horizontal resolution of 5 km and is driven (one-way coupling) by atmospheric model forecasts provided by the Meteorological Service of Canada (MSC). In this paper the importance of two-way coupling is assessed by comparing the one-way coupled version with a two-way coupled version in which the atmospheric model interacts with the sea ice model during the simulation. The impacts are examined for a case in which the sea ice conditions are changing rapidly. Two atmospheric model configurations have been studied. The first one has a horizontal grid spacing of 24 km, which is the operational configuration used at the Canadian Meteorological Centre. The second one is a high-resolution configuration with a 4-km horizontal grid spacing. A 48-h forecast has been validated using satellite images for the ice and the clouds, and also using the air temperature and precipitation observations. It is shown that the two-way coupled system improves the atmospheric forecast and has a direct impact on the sea ice forecast. It is also found that forecasts are improved with a fine resolution that better resolves the physical events, fluxes, and forcing. The coupling technique is also briefly described and discussed. [ABSTRACT FROM AUTHOR]

This paper presents a study of the characteristics of lightning activity during the Cyprus low winter storms over the eastern coast of the Mediterranean. The focus is on changes in the nature of thunderstorms crossing the coastline from the sea into the northern and central parts of Israel, as manifested in their electrical activity. It is based on the Lightning Position and Tracking System (LPATS) measurements of lightning ground strikes during four winter seasons between 1995 and 1999. The spatial distribution shows a maximum of lightning ground strikes over Mount Carmel, possibly due to its topographical forcing. The annual variation shows a major maximum in January with two minor peaks, one in November and another in March, which can be explained by changes in the static instability of the atmosphere throughout the rainy period. The average fraction of positive ground flashes was found to be 6% and their average peak current +41 kA. The average peak current of negative ground flashes was -27 kA. Larger frequencies of ground flashes were detected over the sea than over land during the study period. This is probably due to the large heat and humidity fluxes from the sea surface, which destabilize the colder air above and drive cloud convection. The annual distribution shows that during midwinter (December–January–February) there is higher flash density over the sea, while during autumn and spring the flash density is similar above the two regions. The diurnal variation shows that the maximum in maritime lightning activity was at 0500 LST and over land at 1300 LST. The mean peak current of positive ground flashes was higher over land and of negative ground flashes, over the sea. [ABSTRACT FROM AUTHOR]

The assimilation problem for the coupled ocean–atmosphere system in the tropical Pacific is investigated using an advanced sequential estimator, the extended Kalman filter (EKF). The intermediate coupled model used in this study consists of an upper-ocean model and a steady-state atmospheric response to it. Model errors arise from the uncertainty in atmospheric wind stress. Data assimilation is applied in this idealized context to produce a time-continuous, dynamically consistent description of the model's El Niño–Southern Oscillation, based on incomplete and inaccurate observations. This study has two parts: Part I (the present paper) deals with state estimation for the coupled system, assuming that model parameters are correct, while Part II will deal with simultaneous state and parameter estimation. The dynamical structure of forecast errors is estimated sequentially using a linearized Kalman filter and compared with that of an uncoupled ocean model. The coupling produces large changes in the structure of the error-correlation field. For example, error correlations with opposite signs in the western and eastern part of the model basin are caused by wind stress feedbacks. The full EKF method is used to assimilate various model-generated synthetic oceanic datasets into the coupled model in an identical-twin framework. The assimilated datasets include the sea surface temperature and a combination of wave velocities and thermocline depth anomaly. With the EKF, the model's forecast-assimilation cycle is able to estimate correctly the phase and amplitude of the basic ENSO oscillation while using very few observations. This includes a set of observations that only cover a single meridional section of the ocean, preferably in the eastern basin. [ABSTRACT FROM AUTHOR]

This paper presents a quantitative methodology for evaluating air–sea fluxes related to ENSO from different atmospheric products. A statistical model of the fluxes from each atmospheric product is coupled to an ocean general circulation model (GCM). Four different products are evaluated: reanalyses from the National Centers for Environmental Prediction (NCEP) and the European Centre for Medium-Range Weather Forecasts (ECMWF), satellite-derived data from the Special Sensor Microwave/Imaging (SSM/I) platform and the International Satellite Cloud Climatology Project (ISCCP), and an atmospheric GCM developed at the Geophysical Fluid Dynamics Laboratory (GFDL) as part of the Atmospheric Model Intercomparison Project (AMIP) II. For this study, comparisons between the datasets are restricted to the dominant air–sea mode. The stability of a coupled model using only the dominant mode and the associated predictive skill of the model are strongly dependent on which atmospheric product is used. The model is unstable and oscillatory for the ECMWF product, damped and oscillatory for the NCEP and GFDL products, and unstable (nonoscillatory) for the satellite product. The ocean model is coupled with patterns of wind stress as well as heat fluxes. This distinguishes the present approach from the existing paradigm for ENSO models where surface heat fluxes are parameterized as a local damping term in the sea surface temperature (SST) equation. [ABSTRACT FROM AUTHOR]

A new turbulent mixing scheme, described in Part I of this paper, is tested in the climate and mesoscale configurations of the U.K. Met. Office’s Unified Model (UM). In climate configuration, the scheme is implemented along with increased vertical resolution below 700 hPa (the same as that in the mesoscale model), in order to allow the different boundary layer types and processes to be identified and treated properly. In both configurations, the new boundary layer (PBL-N) mixing scheme produces some improvement over the current boundary layer (PBL-C) scheme. The PBL-N scheme is able to diagnose different boundary layer types that appear to be consistent with the observed conditions, and the boundary layer structure is improved in comparison with observations. In the climate model, the boundary layer and cloud structure in the semipermanent stratocumulus regions of the eastern subtropical oceans are noticeably improved with the PBL-N scheme. The deepening and decoupling of the boundary layer toward the trade cumulus regime is also simulated more realistically. However, the cloud amounts in the stratocumulus regions, which were underestimated with the PBL-C scheme, are reduced further when the PBL-N scheme is included. Tests of the PBL-N scheme in the UM single-column model and in a development version of the UM, where the dynamics, time stepping, and vertical grid are different from the standard version, both show that realistic stratocumulus cloud amounts can be achieved. Thus, it is thought that the performance of the PBL-N scheme in the standard UM may be being limited by other aspects of that model. In the mesoscale model, improvements in the simulation of a convective case are achieved with the PBL-N scheme through reductions in layer cloud amount, while the simulation of a stratocumulus case is improved through better representation of the cloud and boundary layer structure. Other mesoscale model case studies show that there is a consistent... [ABSTRACT FROM AUTHOR]

The Operational Multiscale Environment Model with Grid Adaptivity (OMEGA) and its embedded Atmospheric Dispersion Model is a new atmospheric simulation system for real-time hazard prediction, conceived out of a need to advance the state of the art in numerical weather prediction in order to improve the capability to predict the transport and diffusion of hazardous releases. OMEGA is based upon an unstructured grid that makes possible a continuously varying horizontal grid resolution ranging from 100 km down to 1 km and a vertical resolution from a few tens of meters in the boundary layer to 1 km in the free atmosphere. OMEGA is also naturally scale spanning because its unstructured grid permits the addition of grid elements at any point in space and time. In particular, unstructured grid cells in the horizontal dimension can increase local resolution to better capture topography or the important physical features of the atmospheric circulation and cloud dynamics. This means that OMEGA can readily adapt its grid to stationary surface or terrain features, or to dynamic features in the evolving weather pattern. While adaptive numerical techniques have yet to be extensively applied in atmospheric models, the OMEGA model is the first model to exploit the adaptive nature of an unstructured gridding technique for atmospheric simulation and hence real-time hazard prediction. The purpose of this paper is to provide a detailed description of the OMEGA model, the OMEGA system, and a detailed comparison of OMEGA forecast results with data. [ABSTRACT FROM AUTHOR]

In the conventional quasigeostrophic (QG) form of the ω equation developed by Hoskins et al., the unique forcing of vertical velocity is the geostrophic deformation. As the QG or even the semigeostrophic (SG) hypotheses are not adapted to study the frontal dynamics in the atmospheric boundary layer, this paper proposes a generalized expression of the Hoskins et al. form of the vertical velocity. Two thermal and three dynamical sources of the vertical velocity are identified. These forcings allow for identification of each of the physical processes acting simultaneously on the ageostrophic circulation in the boundary layer. This new form of the ω equation is used to explain wind increase in the atmospheric boundary layer over the warm waters of the sea surface temperature (SST) front observed during a fair anticyclonic day of the SEMAPHORE experiment (1993) and simulated with a nonhydrostatic mesoscale atmospheric model. Since the SST gradients are weak (of the order of 1.5°C 100 km[sup -1] ), the surface turbulent heat forcing is not a dominant factor and all the five forcings of vertical velocity have rather the same intensity. In order to answer the question of how and over what thickness does the oceanic thermal front disturb significantly the atmospheric flow in the marine atmospheric boundary layer in such conditions, the degree of coupling between the turbulent heat forcing and the net forcing directly linked to the atmospheric flow is examined. Their strong anticorrelations (r < -0.9) below 200 m indicate that the ageostrophic circulation and the turbulent heat fluxes are in interregulation in this atmospheric layer, which can be assimilated to an internal boundary layer for the flow. This interregulation works in such a fashion to minimize the atmosphere thermal wind imbalance through an adaptation of the atmospheric flow, but also, to some extent, of the surface turbulent heat fluxes themselves. [ABSTRACT FROM AUTHOR]

This paper documents the development and testing of a new type of atmospheric dynamical core. The model solves the vorticity and divergence equations in place of the momentum equation. The model is discretized in the horizontal using a geodesic grid that is nearly uniform over the entire globe. The geodesic grid is formed by recursively bisecting the triangular faces of a regular icosahedron and projecting those new vertices onto the surface of the sphere. All of the analytic horizontal operators are reduced to line integrals, which are numerically evaluated with second-order accuracy. In the vertical direction the model can use a variety of coordinate systems, including a generalized sigma coordinate that is attached to the top of the boundary layer. Terms related to gravity wave propagation are isolated and an efficient semi-implicit time-stepping scheme is implemented. Since this model combines many of the positive attributes of both spectral models and conventional finite-difference models into a single dynamical core, it represents a distinctively new approach to modeling the atmosphere’s general circulation. The model is tested using the idealized forcing proposed by Held and Suarez. Results are presented for simulations using 2562 polygons (approximately 4.5° × 4.5°) and using 10 242 polygons (approximately 2.25° × 2.25°). The results are compared to those obtained with spectral model simulations truncated at T30 and T63. In terms of first and second moments of state variables such as the zonal wind, meridional wind, and temperature, the geodesic grid model results using 2562 polygons are comparable to those of a spectral model truncated at slightly less than T30, while a simulation with 10 242 polygons is comparable to a spectral model simulation truncated at slightly less than T63. In order to further demonstrate the viability of this modeling approach, preliminary results obtained from a full-physics general... [ABSTRACT FROM AUTHOR]

The virtual temperature used to model moisture-modified tropospheric dynamics is generalized to include a new thermospheric component. The resulting hybrid virtual potential temperature (HVPT) transitions seamlessly with height, from moist virtual potential temperature (MVPT) in the troposphere, to potential temperature in the stratosphere and mesosphere, to thermospheric virtual potential temperature thereafter. For numerical weather prediction (NWP) models looking to extend into the thermosphere, but still heavily invested in retaining MVPT-based dynamical cores for tropospheric prediction, upgrading to HVPT allows the core to capture critical new aspects of variable composition thermospheric dynamics, while leaving the original MVPT-based tropospheric equations and numerics essentially untouched. In this way, HVPT augmentation can both simplify and streamline extension into the thermosphere at little computational cost beyond the inevitable need for more vertical layers and somewhat smaller time steps. To demonstrate, we upgrade the MVPT-based dynamical core of the Navy global NWP model to HVPT, then test its performance in forecasting analytical globally balanced states containing hot or rapidly heated thermospheres and height-varying gas constants. These tests confirm that HVPT augmentation offers an efficient and effective means of extending MVPT-based NWP models into the thermosphere to accelerate development of future ground-to-space NWP models supporting space weather applications. The related issues of variable gravitational acceleration and shallow-atmosphere approximations are also briefly discussed. [ABSTRACT FROM AUTHOR]

Recent idealized simulations have shown that a system of binary tropical cyclones (TCs) induces vertical wind shear (VWS) in each TC, which can subsequently modify the tracks of these TCs through asymmetric diabatic heating. This study investigates these three-dimensional effects in the western North Pacific using the best track and ERA5 reanalysis data. The TC motion was found to deviate systematically from the steering flow. The direction of deviation is clockwise and repelling with respect to the midpoint of the binary TCs with a separation distance of more than 1000 km. The large-scale upper-level anticyclonic and lower-level cyclonic circulations serve as the VWS for each TC in a manner consistent with the idealized simulations. The VWS of a TC tends to be directed to the rear-left quadrant from the direction of the counterpart TC, where the maxima of rainfall and diabatic heating are observed. The potential vorticity budget analysis shows that the actual TC motion is modulated by the diabatic heating asymmetry that offsets the counterclockwise and approaching motion owing to horizontal advection when the separation distance of the binary TCs is 1000–2000 km. With a small separation distance (<1000 km), horizontal advection becomes significant, but the impact of diabatic heating asymmetry is not negligible. The abovementioned features are robust, while there are some dependencies on the TC intensities, size, circulation, duration, and geographical location. This research sheds light on the motion of binary TCs that has not been previously explained by a two-dimensional barotropic framework. [ABSTRACT FROM AUTHOR]

Reliably quantifying uncertainty in precipitation forecasts remains a critical challenge. This work examines the application of a deep learning (DL) architecture, Unet, for postprocessing deterministic numerical weather predictions of precipitation to improve their skills and for deriving forecast uncertainty. Daily accumulated 0–4-day precipitation forecasts are generated from a 34-yr reforecast based on the West Weather Research and Forecasting (West-WRF) mesoscale model, developed by the Center for Western Weather and Water Extremes. The Unet learns the distributional parameters associated with a censored, shifted gamma distribution. In addition, the DL framework is tested against state-of-the-art benchmark methods, including an analog ensemble, nonhomogeneous regression, and mixed-type meta-Gaussian distribution. These methods are evaluated over four years of data and the western United States. The Unet outperforms the benchmark methods at all lead times as measured by continuous ranked probability and Brier skill scores. The Unet also produces a reliable estimation of forecast uncertainty, as measured by binned spread–skill relationship diagrams. Additionally, the Unet has the best performance for extreme events (i.e., the 95th and 99th percentiles of the distribution) and for these cases, its performance improves as more training data are available. Significance Statement: Accurate precipitation forecasts are critical for social and economic sectors. They also play an important role in our daily activity planning. The objective of this research is to investigate how to use a deep learning architecture to postprocess high-resolution (4 km) precipitation forecasts and generate accurate and reliable forecasts with quantified uncertainty. The proposed approach performs well with extreme cases and its performance improves as more data are available in training. [ABSTRACT FROM AUTHOR]

In large-eddy simulations (LES), it is crucial to ensure that discretization errors do not contaminate the subgrid effect of the turbulence model in a wavelength range larger than the effective resolution. Recently, we showed that a seventh- or eighth-order accuracy is required for advection terms in planetary boundary layer simulations when using conventional gridpoint methods. However, a significant amount of communication between parallel computers is necessary to achieve high-order accuracy in gridpoint methods, and this can degrade computational efficiency. The discontinuous Galerkin method (DGM) is a promising approach for overcoming these limitations. Therefore, this study focuses on the numerical criteria of the DGM at LES from the viewpoint of numerical diffusion and dispersion. We extend our earlier study to the DGM framework and clarify the necessary order of the polynomial (p). We find that p = 4 is required based on the numerical criteria at the grid spacing of O(10) m with sufficiently scale-selective modal filters. The examination of temporal accuracy suggests that the fourth-order is sufficient when a fully explicit temporal scheme is used. In addition, we investigate the effect of hyperupwinding that is usually met when the Rusanov flux is employed in the low Mach number flows. It suggests that the choice of numerical flux has little effect on simulation results when the high-order DGM is used. Furthermore, we perform a series of LES in the planetary boundary layer and confirm that the indication obtained from the criteria holds for an actual LES. [ABSTRACT FROM AUTHOR]

Synoptic-scale incursions of cold, midlatitude air that penetrate deep into the Tropics are frequently observed to the east of the Andes cordillera. These incursions are a distinctive year-round feature of the synoptic climatology of this part of South America and exhibit similar characteristics to cold surges observed in the lee of the Rocky Mountains and the Himalayan Plateau. While their large-scale structure has received some attention, details of their mesoscale structural evolution and underlying dynamics are largely unknown. This paper advances our understanding in these matters on the basis of a mesoscale numerical simulation and analysis of the available data during a typical case that occurred in May of 1993. The large-scale environment in which the cold air incursion occurred was characterized by a developing midlatitude wave in the middle and upper troposphere, with a ridge immediately to the west of the Andes and a downstream trough over eastern South America. At the surface, a migratory cold anticyclone over the southern plains of the continent and a deepening cyclone centered over the southwestern Atlantic grew mainly due to upper-level vorticity advection. The surface anticyclone was also supported by midtropospheric subsidence on the poleward side of a jet entrance–confluent flow region over subtropical South America. The northern edge of the anticyclone followed an anticyclonic path along the lee side of the Andes, reaching tropical latitudes 2–3 days after its onset over southern Argentina. The concomitant cold air produced low-level (surface to ∼800 hPa) cooling on the order of 10°C over the subtropical part of the continent (as far north as 10°S). Based on the observations and model results, a three-stage evolution of the cold air incursion is suggested. The initial cooling to the south of 30°S and far from the Andes is mainly produced by the geostrophic southerly winds between the continental anticylone... [ABSTRACT FROM AUTHOR]

Editorial. Introduces a series of articles on ocean and coupled ocean-atmosphere models in the May 1997 issue of 'Monthly Weather Review.'

An improved forecast system has been developed for El Nino--Southern Oscillation (ENSO) prediction at the National Centers for Environmental Prediction. Improvements have been made both to the ocean data assimilation system and to the coupled ocean--atmosphere forecast model. In Part I of a two-part paper the authors describe the new assimilation system. The important changes are 1) the incorporation of vertical variation in the first-guess error variance that concentrates temperature corrections in the thermocline and 2) the overall reduction in the magnitude of the estimated first-guess error. The new system was used to produce a set of retrospective ocean analyses for 1980--95. The new analyses are less noisy than their earlier counterparts and compare more favorably with independent measurements of temperature, currents, and sea surface height variability. Part II of this work presents the results of using these analyses to initialize the coupled forecast model for ENSO prediction. [ABSTRACT FROM AUTHOR]

The high and low pressure transient systems that cross the Andes at various extratropical latitudes are strongly affected by this natural barrier. Available analysis from operational weather services, such as the European Centre for Medium-Range Weather Forecasts (ECMWF), provide a description of these migrating systems, which are often associated with regional features of orographic origin, such as the Northwestern Argentinean Depression. Their propagation is subject to a particularity: for distinct reasons both low and high pressure systems are commonly deflected to the north when crossing the Andes. The purpose of the paper is to test the ability of an atmospheric general circulation model, the LMD (Laboratoire de Mete orologie Dynamique) GCM, to simulate correctly the behavior of these systems. This is done through the analysis of conventional meteorological parameters, but also by duplicating, from the output fields of the model, a series of composite analyses that were previously carried out using both the ECMWF analysis and measurements at local weather stations. The model appears generally successful in reproducing the large-scale features of the circulation. The precipitation patterns are less realistic: they extend too broadly over the eastern side of the mountains, with too little pre-cipitation along the Atlantic coast. Sensitivity experiments are carried out to test the impact of the orography and the role of the model resolution. An experiment with enhanced, and probably more realistic, orography, gives much improved results. An experiment with a lower model resolution shows a degraded but persistent skill in reproducing the observed pattern. [ABSTRACT FROM AUTHOR]

A bulk aerodynamic formula for turbulent heat fluxes has been widely used in land surface process schemes for GCMs. It should have an important impact on GCM simulations since the surface turbulent exchange is a unique approach to linking the atmosphere with the underlying surface. In this paper, long-term recorded tower flux data from the Boreal Ecosystem--Atmosphere Study 1994 field campaigns are used to assess timescale-dependent errors of the widely used traditional and recently modified bulk aerodynamic formulas for sensible heat fluxes over canopy by the standard multiresolution wavelet analysis and recently developed stationary wavelet analysis. The assessed modified bulk formula includes parameterization of the free convection and turbulence in the very stable stratification. It is found that there is an obvious synoptic-timescale variation in the long-term recorded sensible heat fluxes. Unfortunately, neither the traditional bulk formula nor the modified formula can simulate the contribution of this variation to the fluxes. Countergradient phenomena with negative heat fluxes between the canopy and the atmosphere at the synoptic timescale are also discovered. The coarse resolution or large depth used in the bulk formula under the stable condition is mainly responsible for the failure of the bulk aerodynamic formula at the synoptic timescale. Studying and using stability-dependent zero-plane displacement thermal height may be an approach to improve the capability for a GCM to describe the sensible heat fluxes between the canopy and the atmosphere under the stable condition. This analysis also confirms the advantage of the modified bulk formula by including free convection over the traditional formula. However, it is found that both the traditional and modified formulas have significant errors in the stable and weak unstable conditions. Occurrence of the countergradient phenomenon evidently aggravates the incapability of the bulk formula and. [ABSTRACT FROM AUTHOR]

The Hydrological Atmospheric Pilot Experiment in the Sahel (HAPEX-Sahel) was designed to investigate land--atmosphere interactions in the semiarid conditions of southwest Niger. During the intensive observation period (IOP) in 1992, a pronounced mesoscale rainfall gradient developed over the Southern Super Site (SSS). Measurements from a high-resolution rain gauge network indicate that over a distance of 9 km, cumulative rainfall in the final 7 weeks of the wet season (31 July--18 September) ranged from 224 mm in the south to 508 mm in the north. The extreme rainfall gradient is not apparent in other years and evolves through persistent local intensification of convection in passing large-scale storms. This paper assesses the influence of the rainfall variability on the surface and atmosphere, and explores the possibility of a land surface feedback on rainfall at this scale. Soil moisture estimates across the SSS illustrate the importance of rainfall on the water balance and indicate that gradients of soil moisture deficit are likely throughout the IOP. Observations from the three dominant vegetation types reveal the sensitivity of available energy and evaporative fraction to antecedent rainfall. This arises from the high coverage of bare soil and the growth response of Sahelian vegetation to soil moisture. A broad range of evaporation rates are found, while sensible heat fluxes are generally less sensitive to antecedent rainfall. Surface and airborne measurements of temperature and humidity show that rainfall-induced surface variability across the SSS leads to mesoscale gradients in properties of the planetary boundary layer (PBL). On a day with light winds, a thermally induced area of PBL convergence associated with antecedent rainfall conditions is observed. A surface feedback mechanism has been proposed to explain the organization of rainfall... [ABSTRACT FROM AUTHOR]

During the last week of June 2021, the Pacific Northwest region of North America experienced a record-breaking heatwave of historic proportions. All-time high temperature records were shattered, often by several degrees, across many locations, with Canada setting a new national record, the state of Washington setting a new record, and the state of Oregon tying its previous record. Here we diagnose key meteorology that contributed to this heatwave. The event was associated with a highly anomalous midtropospheric ridge, with peak 500-hPa geopotential height anomalies centered over central British Columbia. This ridge developed over several days as part of a large-scale wave train. Back trajectory analysis indicates that synoptic-scale subsidence and associated adiabatic warming played a key role in enhancing the magnitude of the heat to the south of the ridge peak, while diabatic heating was dominant closer to the ridge center. Easterly/offshore flow inhibited marine cooling and contributed additional downslope warming along the western portions of the region. A notable surface thermally induced trough was evident throughout the event over western Oregon and Washington. An eastward shift of the thermal trough, following the eastward migration of the 500-hPa ridge, allowed an inland surge of cooler marine air and dramatic 24-h cooling, especially along the western periphery of the region. Large-scale horizontal warm-air advection played a minimal role. When compared with past highly amplified ridges over the region, this event was characterized by much higher 500-hPa geopotential heights, a stronger thermal trough, and stronger offshore flow. [ABSTRACT FROM AUTHOR]

Convection-allowing model (CAM) ensemble forecasts provide quantitative probabilistic guidance of convective hazards that forecasters would otherwise qualitatively assess. Various initial condition (IC) strategies can be used to generate CAM probabilistic forecasts, but it is still unclear how different configurations perform. Schwartz et al. verified five 10-member IC CAM ensembles over one month of 0000 UTC initializations with a focus on precipitation. Here, we initialize four 42-member IC CAM ensembles every 12 h over 6 weeks and verify forecasts of precipitation, column maximum reflectivity, and hourly maximum updraft helicity. The Texas Tech University real-time EnKF ensemble and three additional IC ensemble modeling systems are verified. Holding the model configuration constant, additional ICs are generated by downscaling time-lagged Global Ensemble Forecast System (GEFS) members, applying correlated random noise to Global Forecast System (GFS) analyses, and recentering EnKF perturbations about GFS analyses. We found that ensemble ICs constructed with correlated random noise and EnKF perturbations about GFS analyses both produced higher-quality precipitation forecasts than downscaled GEFS and EnKF strategies. However, downscaled GEFS and EnKF perturbations about GFS analyses frequently initialized more skillful forecasts of reflectivity than ICs with random perturbations, suggesting that flow-dependent perturbations are important for forecasting deep convection. Even with a suboptimal EnKF configuration, our findings still echo those of Schwartz et al. We extend their work by exploring 1) verification of additional convective hazards and 2) empirical scaling of IC perturbations as a computationally inexpensive method for improving CAM ensemble forecasts. [ABSTRACT FROM AUTHOR]

Precipitation patterns along cold fronts can exhibit a variety of morphologies including narrow cold-frontal rainbands and core-and-gap structures. A three-dimensional primitive equation model is used to investigate alongfront variability of precipitation in an idealized baroclinic wave. Along the poleward part of the cold front, a narrow line of precipitation develops. Along the equatorward part of the cold front, precipitation cores and gaps form. The difference between the two evolutions is due to differences in the orientation of vertical shear near the front in the lower troposphere: at the poleward end the along-frontal shear is dominant and the front is in near-thermal wind balance, while at the equatorward end the cross-frontal shear is almost as large. At the poleward end, the thermal structure remains erect with the front well defined up to the midtroposphere, hence updrafts remain erect and precipitation falls in a continuous line along the front. At the equatorward end, the cores form as undulations appear in both the prefrontal and postfrontal lighter precipitation, associated with vorticity maxima moving along the front on either side. Cross-frontal winds aloft tilt updrafts, so that some precipitation falls ahead of the surface cold front, forming the cores. Sensitivity simulations are also presented in which SST and roughness length are varied between simulations. Larger SST reduces cross-frontal winds aloft and leads to a more continuous rainband. Larger roughness length destroys the surface wind shift and thermal gradient, allowing mesovortices to dominate the precipitation distribution, leading to distinctive and irregularly shaped, quasi-regularly spaced precipitation maxima. [ABSTRACT FROM AUTHOR]

The satellite observational data assimilation community requires consistent hydrometer descriptions—including mass–size relation and particle size distribution—to be used in both the forecast model and observation operator. We develop a microphysics-scheme-consistent snow and graupel single-scattering property database to meet this requirement. In this database, snowflakes are modeled as a mixture of small column and large aggregated ice particles, the mixing ratios of which may be adjusted to satisfy a given mass–size relation. Snow single-scattering properties are computed for four different mass–size relations. Subsequently, the snow description in the Thompson microphysics scheme is used as an example to demonstrate how microphysics-scheme-consistent snow bulk optical properties are derived. The Thompson-scheme-consistent snow bulk optical properties are added to the Community Radiative Transfer Model (CRTM), version 2.4.0. With CloudSat Cloud Profiling Radar (CPR) snow and liquid precipitation retrievals as the inputs, CRTM simulations are performed over global oceans and compared with four collocated Global Precipitation Measurement (GPM) Microwave Imager (GMI) high-frequency channel observations. The CRTM simulated brightness temperatures show agreement with the GMI observed brightness temperatures in cases of light-to-moderate precipitation over extratropical and polar ice-free oceans, with root-mean-square errors of 4.3, 13.0, 1.8, and 3.3 K in the 166-GHz (vertical polarization), 166-GHz (horizontal polarization), 183 ± 3-GHz (vertical polarization), and 183 ± 7-GHz (vertical polarization) channels, respectively. The result demonstrates the potential of using the newly developed microphysics-scheme-consistent snow optical parameterization in data assimilation applications. [ABSTRACT FROM AUTHOR]

Estimation of the radius of maximum wind (RMW) of tropical cyclone (TC) is helpful for the disaster prevention and mitigation. If RMWs are estimated from infrared (IR) imagery taken by geostationary meteorological satellites, their estimation is available densely in time, regardless of the ocean basin. Kossin et al. showed that when TCs have clear eyes, the eye radii estimated from IR images have a high correlation with the RMW estimated from aircraft reconnaissance. The regression of the former onto that latter was shown to have a mean absolute error (MAE) of 4.7 km. We revisit the IR-based RMW estimation by using C-band synthetic aperture radar (SAR) sea surface wind estimates. The criteria for selecting clear-eye cases are simplified. The MAE of the Kossin et al. method is found to be smaller than previously suggested: 3.1 km when the proposed relation is used and 2.7 km when the regression is revised with the SAR-measured RMWs. We further propose an improvement of the IR-based method to estimate the eye radii. The resultant MAE is shown to be 1.7 km, which indicates that the IR-based RMW estimation is more accurate than has been suggested. A strong correlation between eyewall slope and eye size is confirmed. We also investigated cloud features in the eye that may be closely related to RMW and wind structure around RMW. Potential applications of highly accurate RMW estimation are discussed. Significance Statement: The radius of maximum wind (RMW) of tropical cyclone (TC) is an important factor for TC intensity estimation and disaster prevention. A previous study suggested that the RMWs of TCs with clear eyes can be estimated from geostationary satellite images at a mean absolute error (MAE) of 4.7 km. Here we improved the method, reducing the MAE by more than one-half. Since the method does not require aircraft or satellite in low Earth orbit, it helps TC monitoring at high frequency. The method can also improve initialization of models used to predict TC hazards and further our physical understanding and the climatology of the wind structures near the centers of TCs. [ABSTRACT FROM AUTHOR]

Dynamics of low-level flows in the eye of Typhoon Haishen (2020) in its late phase of intensification are investigated with a special rapid-scan observation of the Himawari-8 geosynchronous satellite conducted every 30 s. This is accomplished by deriving storm-relative atmospheric motion vectors at an unprecedentedly high spatiotemporal resolution by tracking clouds across five consecutive visible-light reflectivity. The overall low-level circulation center was situated several kilometers away from the storm center defined in terms of the inner edge of the lower part of eyewall clouds. The shift direction is rearward of the storm translation, consistently with a numerical study of the tropical cyclone (TC) boundary layer. Over the analysis period of 10 h, azimuthal-mean tangential wind around this center was increased at each radius within the eye, and the rotational angular velocity was nearly homogenized. The instantaneous low-level circulation center is found to orbit around the overall circulation center at distances around 5 km. Its orbital angular speed was close to the maximum angular speed of azimuthal-mean tangential winds. This rotating transient disturbance is found to transport angular momentum inward, which explains the tangential wind increase and the angular velocity homogenization in the eye. These features are consistent with an algebraically growing wavenumber-1 barotropic instability, whose impact on TC structures has not been explored. This instability enhances wavenumber-1 asymmetry in ring-shaped vorticity, which can be induced by various processes such as translation, environmental shear, and exponential barotropic instability. Therefore, it may appear broadly in TCs to affect wind distribution in their eyes. Significance Statement: Axially asymmetric transient features in the inner cores of tropical storms have been suggested to profoundly affect the structures and the time evolutions of tropical storms. However, the scarcity of observations has hindered studying such processes observationally. By using a specially conducted high-frequency satellite imaging of Typhoon Haishen (2020), we derived atmospheric motion vectors nearly homogeneously at an unprecedentedly high spatiotemporal resolution. Various kinds of asymmetric motions in low-level flows in the eye were found. Of particular interest is a special type of wavenumber-1 instability whose role has not drawn much attention; the instability was found to provide angular momentum transport consistent with the measured homogenization of the rotation. [ABSTRACT FROM AUTHOR]

A challenge arising from the local Bayesian assimilation of data in an atmospheric flow simulation is the imbalances it may introduce. Acoustic fast-mode imbalances of the order of the slower dynamics can be negated by employing a blended numerical model with seamless access to the compressible and the soundproof pseudo-incompressible dynamics. Here, the blended modeling strategy by Benacchio et al. is upgraded in an advanced numerical framework and extended with a Bayesian local ensemble data assimilation method. Upon assimilation of data, the model configuration is switched to the pseudo-incompressible regime for one time step. After that, the model configuration is switched back to the compressible model for the duration of the assimilation window. The switching between model regimes is repeated for each subsequent assimilation window. An improved blending strategy for the numerical model ensures that a single time step in the pseudo-incompressible regime is sufficient to suppress imbalances coming from the initialization and data assimilation. This improvement is based on three innovations: (i) the association of pressure fields computed at different stages of the numerical integration with actual time levels, (ii) a conversion of pressure-related variables between the model regimes derived from low Mach number asymptotics, and (iii) a judicious selection of the pressure variables used in converting numerical model states when a switch of models occurs. Idealized two-dimensional traveling vortex and buoyancy-driven bubble convection experiments show that acoustic imbalances arising from data assimilation can be eliminated by using this blended model, thereby achieving balanced analysis fields. Significance Statement: Weather forecasting models use a combination of physics-based algorithms and meteorological measurements. A problem with combining outputs from the model with measurements of the atmosphere is that insignificant signals may generate noise and compromise the physical soundness of weather-relevant processes. By selecting atmospheric processes through the toggling of parameters in a mixed model, we propose to suppress the undesirable signals in an efficient way and retain the physical features of solutions produced by the model. The approach is validated here for acoustic imbalances using a compressible/pseudo-incompressible model pair. This development has the potential to improve the techniques used to bring observations into models and with them the quality of atmospheric model output. [ABSTRACT FROM AUTHOR]

To better understand the conditions that favor tropical cyclone (TC) rapid intensification (RI), this study assesses environmental and storm-scale characteristics that differentiate TCs that undergo RI from TCs that undergo slow intensification (SI). This comparison is performed between analog TC pairs that have similar initial intensity, vertical wind shear, and maximum potential intensity. Differences in the characteristics of RI and SI TCs in the North Atlantic and western North Pacific basins are evaluated by compositing and comparing data from the fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA5) and the Gridded Satellite (GridSat) dataset. In the period leading up to the start of RI, RI TCs tend to have a stronger and deeper vortex that is more vertically aligned than SI TCs. Additionally, surface latent heat fluxes are significantly larger in RI TCs prior to the intensity change period, compared to SI TCs. The largest surface latent heat flux differences are initially located to the left of shear; subsequently, upshear and right-of-shear differences amplify, resulting in a more symmetric distribution of surface latent heat fluxes in RI TCs. Increasing azimuthal symmetry of surface latent heat fluxes in RI TCs, together with an increasing azimuthal symmetry of horizontal moisture flux convergence, promote the upshear migration of convection in RI TCs. These differences, and their evolution before and during the intensity change period, are hypothesized to support the persistence and invigoration of upshear convection and, thus, a more symmetric latent heating pattern that favors RI. [ABSTRACT FROM AUTHOR]

Three-dimensional hurricane boundary layer (BL) structure is investigated during secondary eyewall formation, as portrayed in a high-resolution, full-physics simulation of Hurricane Earl (2010). This is the second part of a study on the evolution of BL structure during vortex decay. As in part 1 of this work, the BL's azimuthal structure was linked to vertical wind shear and storm motion. Measures of shear magnitude and translational speed in Earl were comparable to Hurricane Irma (2017) in part 1, but the orientation of one vector relative to the other differed, which contributed to different structural evolutions between the two cases. Shear and storm motion influence the shape of low-level radial flow, which in turn influences patterns of spinup and spindown associated with the advection of absolute angular momentum M. Positive agradient forcing associated with the import of M in the inner core elicits dynamically restorative outflow near the BL top, which in this case was asymmetric and intense at times prior to eyewall replacement. These asymmetries associated with shear and storm motion provide an explanation for BL convergence and spinup at the BL top outside the radius of maximum wind (RMW), which affects inertial stability and agradient forcing outside the RMW in a feedback loop. The feedback process may have facilitated the development of a secondary wind maximum over approximately two days, which culminated in eyewall replacement. Significance Statement: In this second part of a two-part study, a simulation of Hurricane Earl in 2010 is used to analyze the cylindrical structure of the lowest 2.5 km of the atmosphere, which include the boundary layer. Structure at times when Earl weakened prior to and during a secondary eyewall formation is of primary concern. During the secondary eyewall formation, wind and thermal fields had substantial azimuthal structure, which was linked to the state of the environment. It is found that the azimuthal structure could be important to how the secondary eyewall formed in this simulation. A discussion and motivation for further investigating the lower-atmospheric azimuthal structure of hurricanes in the context of storm intensity is provided. [ABSTRACT FROM AUTHOR]