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Research on the interaction between the Madden–Julian oscillation (MJO) and rainfall around Jakarta is limited, although the influence of the MJO on increased rainfall is acknowledged as one of the primary causes of flooding in the region. This paper investigates the local rainfall response around Jakarta to the MJO. We used C-band Doppler radar in October–April during 2009–12 to study rain-rate characteristics at much higher resolution than previous analyses. Results show that the MJO strongly modulates rain rates over the region; however, its effect varies depending on topography. During active phases, MJO induces a high rain rate over the ocean and coast, meanwhile during suppressed phases, it generates a high rain rate mainly over the mountains. In phase 2 of the MJO we find the strongest increase in mean and extreme rain rate, which is earlier in the MJO cycle than most studies reported, based on lower-resolution data. This higher rain rate is likely due to increases in convective and stratiform activities. The MJO promotes more stratiform rain once it resides over Indonesia. In phase 5, over the northwestern coast and western part of the radar domain, the MJO might bring forward the peak of the hourly rain rate that occurs in the early morning. This is likely due to a strong westerly flow arising from MJO superimposed westerly monsoonal flow, blocked by the mountains, inducing a strong wind propagating offshore resulting in convection near the coast in the morning. Our study demonstrates the benefits of using high-resolution radar for capturing local responses to the larger-scale forcing of the MJO in Indonesia. Significance Statement: Rainfall in Jakarta and its surroundings is highly variable and often heavy resulting in devastating floods. In this region, in the wet season, rainfall is influenced by large-scale climate variability including the Madden–Julian oscillation (MJO) characterized by eastward propagation of clouds near the equatorial regions on intraseasonal time scales. The MJO has been known to increase the probability of rainfall occurrence and its magnitude, but we show that the impact differs in varying topography. The frequency and intensity of rainfall increase over land areas including mountains even when MJO has not arrived in Indonesia. Meanwhile, once MJO moves through Indonesia, the frequency and magnitude of the rainfall increases over the northern coast and ocean as well as in the west of the radar domain. [ABSTRACT FROM AUTHOR]

Monitoring precipitation forecast skill in global numerical weather prediction (NWP) models is an important yet challenging task. Rain gauges are inhomogeneously distributed, providing no information over large swathes of land and the oceans. Satellite-based products, on the other hand, provide near-global coverage at a resolution of ∼10–25 km, but limitations on data quality (e.g., biases) must be accommodated. In this paper the stable equitable error in probability space (SEEPS) is computed using a precipitation climatology derived from the Tropical Rainfall Measuring Mission (TRMM) TMPA 3B42 V7 product and a gauge-based climatology and then applied to two global configurations of the Met Office Unified Model (UM). The representativeness and resolution effects on an aggregated SEEPS are explored by comparing the gauge scores, based on extracting the nearest model grid point, with those computed by upscaling the model values to the TRMM grid and extracting the TRMM grid point nearest the gauge location. The sampling effect is explored by comparing the aggregate SEEPS for this subset of ∼6000 locations (dictated by the number of gauges available globally) with all land points within the TRMM region of 50°N and 50°S. The forecast performance over the oceanic areas is compared with performance over land. While the SEEPS computed using the two different climatologies should never be expected to be identical, using the TRMM climatology provides a means of evaluating near-global precipitation using an internally consistent dataset in a climatologically consistent way. [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 local ensemble tangent linear model (LETLM) provides an alternative method for creating the tangent linear model (TLM) and adjoint of a nonlinear model that promises to be easier to maintain and more computationally scalable than earlier methods. In this paper, we compare the ability of the LETLM to predict the difference between two nonlinear trajectories of the Navy's global weather prediction model at low resolution (2.58 at the equator) with that of theTLMcurrently used in the Navy's four-dimensional variational (4DVar) data assimilation scheme. When compared to the pair of nonlinear trajectories, the traditional TLM and the LETLM have improved skill relative to persistence everywhere in the atmosphere, except for temperature in the planetary boundary layer. In addition, the LETLM was, on average, more accurate than the traditional TLM (error reductions of about 20% in the troposphere and 10% overall). Sensitivity studies showed that the LETLM was most sensitive to the number of ensemble members, with the performance gradually improving with increased ensemble size up to the maximum size attempted (400). Inclusion of physics in the LETLM ensemble leads to a significantly improved representation of the boundary layer winds (error reductions of up to 50%), in addition to improved winds and temperature in the free troposphere and in the upper stratosphere/lower mesosphere. The computational cost of the LETLM was dominated by the cost of ensemble propagation. However, the LETLM can be precomputed before the 4DVar data assimilation algorithm is executed, leading to a significant computational advantage. [ABSTRACT FROM AUTHOR]

For the state of Colorado, 10 years (2003-12) of 1 April-31 October cloud-to-ground (CG) lightning stroke data are mapped at 500-m spatial resolution over a 10-m spatial resolution U.S. Geological Survey (USGS) digital elevation model (DEM). Spatially, the 12.5 million strokes that are analyzed represent ground contacts, but translate to density values that are about twice the number of ground contacts. Visual interpretation of the mapped data reveals the general lightning climatology of the state, while geospatial analyses that quantify lightning activity by elevation identify certain topographic influences of Colorado's physical landscape. Elevations lower than 1829 m (6000 ft) and above 3200 m (10 500 ft) show a positive relationship between lightning activity and elevation, while the variegated topography that lies between these two elevations is characterized by a fluctuating relationship. Though many topographic controls are elucidated through the mappings and analyses, the major finding of this paper is the sharp increase in stroke density observed above 3200 m (10 500 ft). Topography's role in this rapid surge in stroke density, which peaks in the highest mountain summits, is not well known, and until now, was not well documented in the refereed literature at such high resolution from a long-duration dataset. [ABSTRACT FROM AUTHOR]

To properly utilize coupled limited-area models (LAMs), the time scales of the cross-boundary fluxes in the available lateral boundary data must be assessed. In current operational practice, the update frequencies of these data are usually determined by common sense guesswork and by technical constraints. This paper quantifies the required temporal resolution of the lateral boundary conditions. For a mesoscale LAM it is concluded that in standard forecast cases, coupling updates of about 3 h are sufficient. However, in rare cases of severe storms, this can lead to errors in the coupling data of about 10 hPa. To avoid such errors, it is found that one should update the coupling fields with the period given by the time step of the model that provides the coupling data. However, in most existing operational applications this is not feasible. For those cases, it is shown that the forecast can be substantially improved by restarting the model run at a forecast range when the storm has entered the domain. The proper restart time can be detected in an operational suite by an existing strategy of monitoring the coupling update frequency. Additionally, it is argued that the forecast should then be initialized by a scale-selective digital filter. [ABSTRACT FROM AUTHOR]

Coupled data assimilation (DA) provides a consistent framework for assimilating satellite observations that are sensitive to several components of the Earth system. In this paper, we focus on low-peaking infrared satellite channels that are sensitive to the lower atmosphere and Earth surface temperature (EST) over both ocean and land. Our atmospheric hybrid-4DVAR system [the Navy Global Environmental Model (NAVGEM)] is extended to include the following: 1) variability in the sea surface temperature (both diurnal variability and climatological perturbations to the ensemble members), 2) the coupled Jacobians of the radiative transfer model for the infrared sensors, and 3) the coupled covariances between the EST and the atmosphere. Our coupling approach is found to improve forecast accuracy and to provide corrections to the EST that are in balance with the atmospheric analysis. The largest impact of the coupling is found on near-surface atmospheric temperature and humidity in the tropics, but the impact extends all the way to the stratosphere. The role of each coupling element on the performance of the global atmospheric circulation model is investigated. Inclusion of variability in the sea surface temperature has the strongest positive impact on the forecast quality. Additional inclusion of the coupled Jacobian and ensemble-based coupled covariances led to further improvements in scores and to modification of the corrections to the ocean boundary layer. Coupled DA had significant impact on latent and sensible heat fluxes over land, locations of western boundary currents, and along the ice edge. [ABSTRACT FROM AUTHOR]

Drylines have been identified as relevant synoptic-scale phenomena that frequently occur in several regions around the world. Despite previous works and the experience of local forecasters that recognizes the occurrence of drylines in Argentina and suggests its possible association with convection initiation, knowledge about the mechanisms leading to the genesis of these features is poor. This paper presents the first synoptic climatology of these drylines as well as a first approach to the understanding of the processes leading to their formation. The climatology is based on an automated algorithm for dryline identification applied to reanalysis data. We found that drylines are more frequent between the northern Patagonia plateau and the central Argentinean plains. A composite analysis is performed to analyze the processes leading to the formation of synoptic-scale drylines within this region. It was found that these drylines form in the confluence between a warm and moist air mass driven by a northwesterly flow and drier air flowing east over the northern Patagonia plateau. The dry air originates on top of the Pacific maritime boundary layer and experiences lee subsidence after crossing the Andes range creating an area of dry and warm air that is advected to the east by the westerly synoptic-scale flow, and transported downward during the day due to strong boundary layer turbulence. At the same time, surface heating over the plateau leads to substantial warming of the originally colder dry air behind the dryline, thus reversing the horizontal temperature gradient across the dryline. [ABSTRACT FROM AUTHOR]

Statistical calibration of forecasts from numerical weather prediction (NWP) models aims to produce forecasts that are unbiased, reliable in ensemble spread, and as skillful as possible. We suggest that the calibrated forecasts should also be coherent in climatology, including seasonality, consistent with observations. This is especially important when forecasts approach climatology as forecast skill becomes low, such as at long lead times. However, it is challenging to achieve these aims when data available to establish sophisticated calibration models are limited. Many NWP models have only a short period of archived data, typically one year or less, when they become officially operational. In this paper, we introduce a seasonally coherent calibration (SCC) model for working effectively with limited archived NWP data. Detailed rationale and mathematical formulations are presented. In the development of the model, three issues are resolved. These are 1) constructing a calibration model that is sophisticated enough to allow for seasonal variation in the statistical characteristics of raw forecasts and observations, 2) bringing climatology that is representative of long-term statistics into the calibration model, and 3) reducing the number of model parameters through sensible reparameterization to make the model workable with short NWP dataset. A case study is conducted to examine model assumptions and evaluate model performance. We find that the model assumptions are sound, and the developed SCC model produces well-calibrated forecasts. [ABSTRACT FROM AUTHOR]

A polar low was observed over the Japan Sea on 21 January 1997. Part I of this paper focuses on the observational analyses by using all available observational data from surface meteorological stations, Geostationary Meteorological Satellite-5 (GMS-5), Sapporo and Niigata radars, 20-km resolution regional objective analysis (RANAL) data, Advanced Earth Observing System/NASA Scatterometer (ADEOS/NSCAT) wind data over oceans, and NOAA-14 Advanced Very High Resolution Radiometer (AVHRR) channel 2 and channel 4 images to document its spatial and temporal structures. The detailed evolutionary process of this polar low is described using GMS-5 data as well as Sapporo and Niigata radar data. This is the first paper to show the detailed evolution of a polar low feature by using radar data. [ABSTRACT FROM AUTHOR]

Hybrid error covariance models that blend climatological estimates of forecast error covariances with ensemble-based, flow-dependent forecast error covariances have led to significant reductions in forecast error when employed in 4DVAR data assimilation schemes. Tangent linear models (TLMs) designed to predict the differences between perturbed and unperturbed simulations of the weather forecast are a key component of such 4DVAR schemes. However, many forecasting centers have found that TLMs and their adjoints do not scale well computationally and are difficult to create and maintain-particularly for coupled ocean-wave-ice-atmosphere models. In this paper, the authors create ensemble-based TLMs (ETLMs) and test their ability to propagate both climatological and flow-dependent parts of hybrid error covariance models. These tests demonstrate that rank deficiency limits the utility of unlocalized ETLMs. High-rank, time-evolving, flow-adaptive localization functions are constructed and tested using recursive application of short-duration ETLMs, each of which is localized using a static localization. Since TLM operators do not need to be semipositive definite, the authors experiment with a variety of localization approaches including step function localization. The step function localization leads to a local formulation that was found to be highly effective. In tests using simple one-dimensional models with both dispersive and nondispersive dynamics, it is shown that practical ETLM configurations were effective at propagating covariances as far as four error correlation scales. [ABSTRACT FROM AUTHOR]

Numerical weather, climate, or Earth system models involve the coupling of components. At a broad level, these components can be classified as the resolved fluid dynamics, unresolved fluid dynamical aspects (i.e., those represented by physical parameterizations such as subgrid-scale mixing), and nonfluid dynamical aspects such as radiation and microphysical processes. Typically, each component is developed, at least initially, independently. Once development is mature, the components are coupled to deliver a model of the required complexity. The implementation of the coupling can have a significant impact on the model. As the error associated with each component decreases, the errors introduced by the coupling will eventually dominate. Hence, any improvement in one of the components is unlikely to improve the performance of the overall system. The challenges associated with combining the components to create a coherent model are here termed physics–dynamics coupling. The issue goes beyond the coupling between the parameterizations and the resolved fluid dynamics. This paper highlights recent progress and some of the current challenges. It focuses on three objectives: to illustrate the phenomenology of the coupling problem with references to examples in the literature, to show how the problem can be analyzed, and to create awareness of the issue across the disciplines and specializations. The topics addressed are different ways of advancing full models in time, approaches to understanding the role of the coupling and evaluation of approaches, coupling ocean and atmosphere models, thermodynamic compatibility between model components, and emerging issues such as those that arise as model resolutions increase and/or models use variable resolutions. [ABSTRACT FROM AUTHOR]

A vertical superposition of the polar and subtropical jet streams constitutes a unique synoptic-scale environment with the potential to induce high-impact weather, including anomalously strong surface cyclones that are accompanied by heavy precipitation and strong winds. Jet superpositions are not always a sufficient condition for the occurrence of high-impact weather, however, so understanding the dynamical and thermodynamic environments that favor the development of high-impact weather in association with jet superpositions is essential for improving sensible weather forecasts during these events. In this study, we pair a climatology of jet superpositions with climatologies of atmospheric rivers and surface cyclones to determine the frequency with which these features accompany jet superpositions. We subsequently construct two subsets of jet superpositions for further analysis. "High-impact" jet superposition cases are defined as those that feature an atmospheric river and a highly anomalous surface cyclone relative to climatology, which can potentially support extreme near-surface winds and precipitation. In contrast, "null" cases are defined as jet superposition cases that are not associated with a surface cyclone and are therefore less likely to support widespread high-impact weather. Composite analyses are then performed to identify discriminating environmental factors between high-impact and null cases, and how these factors influence jet superposition dynamics. We find that stronger environmental baroclinicity and a sufficient moisture supply within the near-jet environment are common characteristics of high-impact cases. These characteristics subsequently support the development of a more vigorous ageostrophic transverse circulation beneath the superposed jet's exit region during high-impact cases and more intense surface cyclogenesis. Significance Statement: A jet superposition event occurs when the normally separate polar and subtropical jets combine to form a single jet. This study aims to understand what factors differentiate jet superposition events that coincide with strong winds and heavy precipitation, or "high-impact weather," from those that are less likely to coincide with such weather conditions. We identified several important environmental characteristics that tend to precede jet superposition events with a large potential to induce high-impact weather, including increased moisture and a strengthened pole-to-equator temperature gradient. These results provide indicators forecasters may consider when predicting the impacts of a jet superposition event. [ABSTRACT FROM AUTHOR]

In the Kalman filter standard algorithm, the computational complexity of the observational update is proportional to the cube of the number y of observations (leading behavior for large y). In realistic atmospheric or oceanic applications, involving an increasing quantity of available observations, this often leads to a prohibitive cost and to the necessity of simplifying the problem by aggregating or dropping observations. If the filter error covariance matrices are in square root form, as in square root or ensemble Kalman filters, the standard algorithm can be transformed to be linear in y, providing that the observation error covariance matrix is diagonal. This is a significant drawback of this transformed algorithm and often leads to an assumption of uncorrelated observation errors for the sake of numerical efficiency. In this paper, it is shown that the linearity of the transformed algorithm in y can be preserved for other forms of the observation error covariance matrix. In particular, quite general correlation structures (with analytic asymptotic expressions) can be simulated simply by augmenting the observation vector with differences of the original observations, such as their discrete gradients. Errors in ocean altimetric observations are spatially correlated, as for instance orbit or atmospheric errors along the satellite track. Adequately parameterizing these correlations can directly improve the quality of observational updates and the accuracy of the associated error estimates. In this paper, the example of the North Brazil Current circulation is used to demonstrate the importance of this effect, which is especially significant in that region of moderate ratio between signal amplitude and observation noise, and to show that the efficient parameterization that is proposed for the observation error correlations is appropriate to take it into account. Adding explicit gradient observations also receives a physical justification. This parameterization is thus proved to be useful to ocean data assimilation systems that are based on square root or ensemble Kalman filters, as soon as the number of observations becomes penalizing, and if a sophisticated parameterization of the observation error correlations is required. [ABSTRACT FROM AUTHOR]

This paper examines the extent to which an empirical correction method can improve forecasts of the National Centers for Environmental Prediction (NCEP) operational Global Forecast System. The empirical correction is based on adding a forcing term to the prognostic equations equal to the negative of the climatological tendency errors. The tendency errors are estimated by a least squares method using 6-, 12-, 18-, and 24-h forecast errors. Tests on independent verification data show that the empirical correction significantly reduces temperature biases nearly everywhere at all lead times up to at least 5 days but does not significantly reduce biases in forecast winds and humidity. Decomposing mean-square error into bias and random components reveals that the reduction in total mean-square error arises solely from reduction in bias. Interestingly, the empirical correction increases the random error slightly, but this increase is argued to be an artifact of the change in variance in the forecasts. The empirical correction also is found to reduce the bias more than traditional “after the fact” corrections. The latter result might be a consequence of the very different sample sizes available for estimation, but this difference in sample size is unavoidable in operational situations in which limited calibration data are available for a given forecast model. [ABSTRACT FROM AUTHOR]

Hakim and Torn (HT) presented a statistical piecewise potential vorticity (PV) regression technique that uses flow-dependent analysis covariances from an ensemble square root filter to statistically infer the relationship between the PV and state fields. This paper illustrates that the PV perturbation effectively regressed by HT’s regression is the projection of the PV perturbation onto the ensemble PV anomalies that define the regression operator. It is shown that the piecewise PV inversion of this effective PV perturbation via the technique presented in Davis and Emanuel yields nearly identical heights to those from an HT regression performed in the subspace of the leading PV singular vectors. [ABSTRACT FROM AUTHOR]

This paper considers implications of different forms of the ensemble transformation in the ensemble square root filters (ESRFs) for the performance of ESRF-based data assimilation systems. It highlights the importance of using mean-preserving solutions for the ensemble transform matrix (ETM). The paper shows that an arbitrary mean-preserving ETM can be represented as a product of the symmetric solution and an orthonormal mean-preserving matrix. The paper also introduces a new flavor of ESRF, referred to as ESRF with mean-preserving random rotations. To investigate the performance of different solutions for the ETM in ESRFs, experiments with two small models are conducted. In these experiments, the performances of two mean-preserving solutions, two non-mean-preserving solutions, and a traditional ensemble Kalman filter with perturbed observations are compared. The experiments show a significantly better performance of the mean-preserving solutions for the ETM in ESRFs compared to non-mean-preserving solutions. They also show that applying the mean-preserving random rotations prevents the buildup of ensemble outliers in ESRF-based data assimilation systems. [ABSTRACT FROM AUTHOR]

This paper studies the impact of different initial condition perturbation methods and horizontal resolutions on short-range limited-area ensemble predictions for two severe winter storms. The methodology consists of 51-member ensembles generated with the global ensemble prediction system (EPS) of the European Centre for Medium-Range Weather Forecasts, which are downscaled with the nonhydrostatic limited-area model Lokal Modell. The resolution dependency is studied by comparing three different limited-area ensembles: (a) 80-km grid spacing, (b) 10-km grid spacing, and (c) 10-km grid spacing with a topography coarse grained to 80-km resolution. The initial condition perturbations of the global ensembles are based on singular vectors (SVs), and the tendencies are not perturbed (i.e., no stochastic physics). Two configurations are considered for the initial condition perturbations: (i) the operational SV configuration: T42 truncation, 48-h optimization time, and dry tangent-linear model, and (ii) the “moist SV” configuration: TL95 truncation, 24-h optimization time, and moist tangent-linear model. Lokal Modell ensembles are analyzed for the European winter storms Lothar and Martin, both occurring in December 1999, with particular attention paid to near-surface wind gusts. It is shown that forecasts using the moist SV configuration predict higher probabilities for strong wind gusts during the storm period compared to forecasts with the operational SV configuration. Similarly, the forecasts with increased horizontal resolution—even with coarse topography—lead to higher probabilities compared with the low-resolution forecasts. Overall, the two case studies suggest that currently developed operational high-resolution limited-area EPSs have a great potential to improve early warnings for severe winter storms, particularly when the driving global EPS employs moist SVs. [ABSTRACT FROM AUTHOR]

In this paper, the variation of adjoint sensitivities as horizontal and vertical resolutions are changed is investigated. The fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5) and its adjoint are used with consistent physics to generate adjoint sensitivities over a 24-h period. The sensitivities are generated with respect to a response function defined as the lowest sigma level perturbation pressure over a region of northwestern Oregon. It is found that the scale, magnitude, and structure of sensitivity with respect to initial temperature varies significantly as grid spacing is decreased from 216 to 24 km. As found in other adjoint studies at relatively coarse resolution, low-level, upshear-tilted, subsynoptic-scale sensitivities were apparent, with the wavelike sensitivity pattern decreasing significantly in scale and spatial extent with increased horizontal resolution. It is also found that perturbation growth rates depend on horizontal resolution, with the adjoint sensitivities predicting larger changes in the response function with increased horizontal resolution. Relatively little change in sensitivity structure and growth rates occurred when the vertical resolution was varied from 10 to 50 vertical levels. It is shown that a majority of the predicted change in the response function comes from the very small proportion of the domain occupied by sensitive regions. Last, the accuracy of the tangent linear approximation is examined, and it is found that for perturbations made in sensitive regions, the tangent linear approximation degrades at finer grid spacing. The implications of these results are discussed for methodologies utilizing adjoint sensitivities, such as four-dimensional variational data assimilation and targeted observations strategies. [ABSTRACT FROM AUTHOR]

Several modeling studies have shown that the south Asian monsoon region has the lowest skill for seasonal forecasts compared with many other domains of the world. This paper demonstrates that a multimodel synthetic superensemble approach, when constructed with any set of coupled atmosphere–ocean models, can provide improved skill in seasonal climate prediction compared with single-member models or their ensemble mean for the south Asian summer monsoon region. However, performance of the superensemble tends to improve when a better set of input member models are used. As many as 13 state-of-the-art coupled atmosphere–ocean models were used in the synthetic superensemble algorithm. The merit of this technique lies in assigning differential weights to the member models. The rms errors, anomaly correlations, case studies of extreme events, and probabilistic skill scores are used here to assess these forecast skills. It was found that over the south Asian region the seasonal forecasts from the superensemble are, in general, superior to the forecasts of the individual member models, and their bias-removed ensemble mean at a significance level of 95% or more (based on a Student's t test) during the 13 yr of forecasts. Moreover, the skill of the superensemble was found to be better than those of the ensemble mean over smaller domains as well as during extreme events that were monitored, especially during the switch on and off of the Indian Ocean dipole, which seems to modulate the Indian monsoon rainfall. The results of this paper suggest that the superensemble provides somewhat consistent forecasts on the seasonal time scale. This methodology needs to be tested for real-time seasonal climate forecasting over the south Asian region. [ABSTRACT FROM AUTHOR]

The stability of constant-coefficients semi-implicit schemes for the hydrostatic primitive equations and the fully elastic Euler equations in the presence of explicitly treated thermal residuals has been theoretically examined in the earlier literature, but only for the case of a flat terrain. This paper extends these analyses to a case in which an orography is present, in the shape of a uniform slope. It is shown, with mass-based coordinates, that for the Euler equations, the presence of a slope reduces furthermore the set of the prognostic variables that can be used in the vertical momentum equation to maintain the robustness of the scheme, compared to the case of a flat terrain. The situation appears to be similar for systems cast in mass-based and height-based vertical coordinates. Still for mass-based vertical coordinates, an optimal prognostic variable is proposed and is shown to result in a robustness similar to the one observed for the hydrostatic primitive equations system. The prognostic variables that lead to robust semi-implicit schemes share the property of having cumbersome evolution equations, and an alternative time treatment of some terms is then required to keep the evolution equation reasonably simple. This treatment is shown not to modify substantially the stability of the time scheme. This study finally indicates that with a pertinent choice for the prognostic variables, mass-based vertical coordinates are equally suitable as height-based coordinates for efficiently solving the compressible Euler equations system. [ABSTRACT FROM AUTHOR]

In gridpoint regional climate models (RCMs), the lateral boundary conditions are usually provided by a procedure called relaxation. The technique was originally studied in the context of numerical weather forecasting. This paper complements the preceding theoretical studies in order to assess the practical choices of model relaxation coefficients. Several profiles of coefficients used in RCMs are then evaluated. The complexity of actual model numerics makes any definite choice of the coefficients out of reach of simple theoretical considerations, but these provide practical guidelines. The latter are confirmed by pragmatic considerations such as minimizing discontinuities and keeping relaxation rates in the range of the represented physical processes. The last part of the paper presents a sensitivity study with the Modèle Atmosphérique Régional (MAR). [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]

Data assimilation schemes combine observational data with a short-term model forecast to produce an analysis. However, many characteristics of the atmospheric states described by the observations and the model differ. Observations often measure a higher-resolution state than coarse-resolution model grids can describe. Hence, the observations may measure aspects of gradients or unresolved eddies that are poorly resolved by the filtered version of reality represented by the model. This inconsistency, known as observation representation error, must be accounted for in data assimilation schemes. In this paper the ability of the ensemble to predict the variance of the observation error of representation is explored, arguing that the portion of representation error being detected by the ensemble variance is that portion correlated to the smoothed features that the coarse-resolution forecast model is able to predict. This predictive relationship is explored using differences between model states and their spectrally truncated form, as well as commonly used statistical methods to estimate observation error variances. It is demonstrated that the ensemble variance is a useful predictor of the observation error variance of representation and that it could be used to account for flow dependence in the observation error covariance matrix. [ABSTRACT FROM AUTHOR]

The skill of weather and climate forecast systems is often assessed by calculating the correlation coefficient between past forecasts and their verifying observations. Improvements in forecast skill can thus be quantified by correlation differences. The uncertainty in the correlation difference needs to be assessed to judge whether the observed difference constitutes a genuine improvement, or is compatible with random sampling variations. A widely used statistical test for correlation difference is known to be unsuitable, because it assumes that the competing forecasting systems are independent. In this paper, appropriate statistical methods are reviewed to assess correlation differences when the competing forecasting systems are strongly correlated with one another. The methods are used to compare correlation skill between seasonal temperature forecasts that differ in initialization scheme and model resolution. A simple power analysis framework is proposed to estimate the probability of correctly detecting skill improvements, and to determine the minimum number of samples required to reliably detect improvements. The proposed statistical test has a higher power of detecting improvements than the traditional test. The main examples suggest that sample sizes of climate hindcasts should be increased to about 40 years to ensure sufficiently high power. It is found that seasonal temperature forecasts are significantly improved by using realistic land surface initial conditions. [ABSTRACT FROM AUTHOR]

The shallow water equations on a rotating sphere form a system of great interest in geophysical fluid dynamics, from meteorology to atmospheric physics and climate studies. Williamson and his collaborators at the National Center for Atmospheric Research introduced a set of numerical experiments of increasing complexity to serve as a standard set for testing proposed numerical schemes. They also provided a software package that solved the equations by methods that are currently state of the art in the field. The authors recently introduced a new method (called 'odd-even splitting') for solving a wide variety of partial differential equations which has several advantages. In this paper a variation of this method, called the 'dual-copy splitting' method of time stepping, is applied to several of the test cases of Williamson et al. using for the spatial discretization the same spectral transform model as they used. The method has the following features that are novel, so far as the authors know. First, the order of accuracy in time can be increased from the usual first or second order, without additional memory requirements to store fields at intermediate time steps. Second, gravity waves, which impose a prohibitively small time step for usual explicit schemes, have been handled here 'exactly' while retaining the advantages of implicit schemes. Indeed, it is believed that this treatment of gravity waves is in fact superior to implicit methods. [ABSTRACT FROM AUTHOR]

This paper shows how to obtain accurate and efficient trajectory calculations for spherical geodesic grids in Cartesian space. Determination of the departure points is essential to characteristic-based methods that trace the value of a function to the foot of the characteristics and then either integrate or interpolate at this location. In this paper, the departure points are all computed in relation to the spherical geodesic grids that are composed of a disjoint set of unstructured equilateral triangles. Interpolating and noninterpolating trajectory calculation approaches are both illustrated and the accuracy of both methods are compared. The noninterpolating method of McGregor results in the most accurate trajectories. The challenge in using McGregor's method on unstructured triangular grids lies in the computation of the derivatives required in the high-order terms of the Taylor series expansion. This paper extends McGregor's method to unstructured triangular grids by describing an accurate and efficient method for constructing the derivatives in an element by element approach typical of finite element methods. An order of accuracy analysis reveals that these numerical derivatives are second-order accurate. [ABSTRACT FROM AUTHOR]

Part I of this paper examined nine trace constituent advection algorithms as applied in channel versions of the University of Wisconsin hybrid isentropic-sigma (Φ-σ) and sigma (σ) models. This paper examines the performance of 12 semi-Lagrangian transport (SLT) algorithms in the same models. The interpolants of trace constituent include second- through twelfth-order Lagrangian, an overlapping polynomial, a quasi-monotonic scheme, and two sixth-order schemes that employ derivative estimates. Additional experiments are performed that emulate the SLT algorithm in the NCAR Community Climate Model 2. As in Part I, these three-dimensional simulations are under adiabatic conditions so that conservation of the initial trace constituent maximum on an isentropic surface, and conservation of the areas between any two constituent contours, can be used as objective measures of SLT algorithm accuracy. Further, the experiments provide comparisons not only between the different SLT formulations but also between their performance in models using Φ and σ coordinates. Similar to Part I, an important result of the experiments is that comparison of algorithms is most revealing under three-dimensional transport within a baroclinic wave in which vertical transport is important. The experiments also show 1) that the 'cascade' interpolation scheme is a reasonable method of greatly reducing computation time in SLT without affecting accuracy; 2) that 'shape-preserving' interpolation schemes reduce accuracy in both the Φ-σ and σ models; and 3) that Lagrangian interpolants of tenth and twelfth order do not significantly improve results. Comparisons to results in Part I suggest that the conservation of second-order moments advection scheme is the most consistent of all options tested. [ABSTRACT FROM AUTHOR]

In this paper, a theoretical framework is described for the representation of surface heterogeneity within complex biophysical surface schemes for use in climate models. The methodology adopts aspects of the mosaic approach and the statistical--dynamical approach. A grid cell is subdivided into fractional areas covered by basic surface types, that is, vegetated, bare soil, snow-covered, and impermeable surfaces, which separately exchange momentum, energy, and water vapor with the overlying atmosphere. Fractional precipitation areas within a grid box, and fractional rainfall and snowfall areas within the precipitation area, can also be specified. Within each surface type, heterogeneity is described by assuming that surface temperatures and soil water content follow continuous analytical probability density functions (PDFs) and by integrating relevant nonlinear terms over the appropriate PDF. Linear and symmetric PDFs are chosen since they allow ready analytical partial and full integration. This heterogeneity representation is implemented within the framework of a surface package, including a multilayer soil model, a one-layer vegetation model, a multilayer snow model, a two-layer impermeable surface model, and a surface hydrology model. The companion paper by Giorgi describes an extensive set of experiments carried out to validate the model and assess its sensitivity to relevant parameters. [ABSTRACT FROM AUTHOR]

This paper discusses a series of sensitivity experiments aimed at testing the surface heterogeneity representation proposed in the companion paper by Giorgi. When driven by observed climatic forcings at three locations and run in point mode, the model shows good performance in reproducing observed surface fluxes. The temperature heterogeneity representation mostly affects the process of snow formation and, therefore, the winter and spring energy and water budgets. The soil water heterogeneity primarily influences the processes of soil water movement and runoff generation, thereby modifying the surface hydrologic budget. In addition, the heterogeneous model results compare reasonably well with aggregated results from point-mode experiments. Model sensitivity to the presence of impermeable surface fractional cover, fractional precipitation area, and a crude delayed runoff formulation is also discussed. As a next phase of model evaluation, it is planned to include this heterogeneity representation within a regional climate model and assess its effect on atmospheric circulations. [ABSTRACT FROM AUTHOR]

This study uses the convective adjustment time scale to identify the climatological frequency of equilibrium and nonequilibrium convection in different parts of the contiguous United States (CONUS) as modeled by the operational convection-allowing High-Resolution Rapid Refresh (HRRR) forecast system. We find a qualitatively different climatology in the northern and southern domains separated by the 40°N parallel. The convective adjustment time scale picks up the fact that convection over the northern domains is governed by synoptic flow (leading to equilibrium), while locally forced, nonequilibrium convection dominates over the southern domains. Using a machine learning algorithm, we demonstrate that the convective adjustment time-scale diagnostic provides a sensible classification that agrees with the underlying dynamics of equilibrium and nonequilibrium convection. Furthermore, the convective adjustment time scale can indicate the model quantitative precipitation forecast (QPF) quality, as it correctly reflects the higher QPF skill for precipitation under strong synoptic forcing. This diagnostic based on the strength of forcing for convection will be employed in future studies across different parts of CONUS to objectively distinguish different weather situations and explore the potential connection to warm-season precipitation predictability. Significance Statement: An objective classification metric that can delineate a wide range of forecasts into distinct scenarios can serve as a valuable tool. This study represents a pioneering effort in utilizing the convective adjustment time scale to identify the climatological frequency of warm-season precipitation under varying levels of synoptic forcing in different parts of the contiguous United States (CONUS). The results demonstrate that the convective adjustment time scale is a robust metric for categorizing precipitation events and establishing a direct link to their predictability. Overall, this study provides a valuable framework for future studies focused on the CONUS domain, offering guidance on how to employ the convective adjustment time scale to classify weather regimes and explore the influence of environmental conditions on predictability of convection. [ABSTRACT FROM AUTHOR]

This paper investigates the tropospheric and stratospheric precursors to a major sudden stratospheric warming (SSW) that began on 6 January 2013. Using the Climate Forecast System Reanalysis dataset, the analysis identified two distinct decelerations of the 10-hPa zonal mean zonal wind at 65°N in December in addition to the major SSW, which occurred on 6 January 2013 when the 10-hPa zonal mean zonal wind at 65°N reversed from westerly to easterly. The analysis shows that the two precursor events preconditioned the stratosphere for the SSW. Analysis of the tropospheric state in the days leading to the precursor events and the major SSW suggests that high-latitude tropospheric blocks occurred in the days prior to the two December deceleration events, but not in the days prior to the SSW. A detailed wave activity flux (WAF) analysis suggests that the tropospheric blocking prior to the two December deceleration events contributed to an anomalously positive 40-day-average 100-hPa zonal mean meridional eddy heat flux prior to the SSW. Analysis of the stratospheric structure in the days prior to the SSW reveals that the SSW was associated with enhanced WAF in the upper stratosphere, planetary wave breaking, and an upper-stratospheric/lower-mesospheric disturbance. These results suggest that preconditioning of the stratosphere occurred as a result of WAF initiated by tropospheric blocking associated with the two December deceleration events. The two December deceleration events occurred in the 40 days prior to the SSW and led to the amplification of wave activity in the upper stratosphere and wave resonance that caused the January 2013 SSW. [ABSTRACT FROM AUTHOR]

Temporal and spatial distributions of the North American monsoon have been studied previously with rainfall and satellite data. In the current study, the monsoon is examined with lightning data from Vaisala's Global Lightning Dataset (GLD360). GLD360 has been operating for over three years and provides sufficient data to develop an exploratory climatology with minimal spatial variation in detection efficiency and location accuracy across the North American monsoon region. About 80% of strokes detected by GLD360 are cloud to ground. This paper focuses on seasonal, monthly, and diurnal features of lightning occurrence during the monsoon season from Mazatlán north-northwest to northern Arizona and New Mexico. The goal is to describe thunderstorm frequency with a dataset that provides uniform spatial coverage at a resolution of 2-5 km and uniform temporal coverage with individual lightning events resolved to the millisecond, compared with prior studies that used hourly point rainfall or satellite data with a resolution of several kilometers. The monthly lightning stroke density over northwestern Mexico increases between May and June, as thunderstorms begin over the high terrain east of the Gulf of California. The monthly lightning stroke density over the entire region increases dramatically to a maximum in July and August. The highest stroke densities observed in Mexico approach those observed by GLD360 in subtropical and tropical regions in Africa, Central and South America, and Southeast Asia. The diurnal cycle of lightning exhibits a maximum over the highest terrain near noon, associated with daytime solar heating, a maximum near midnight along the southern coast of the Gulf, and a gradual decay toward sunrise. [ABSTRACT FROM AUTHOR]

Regional climate models (RCMs) should be evaluated with respect to their ability to downscale large-scale climate information to the local scales, which are sometimes strongly modulated by surface conditions. This is the case for La Réunion (southwest Indian Ocean) because of its island context and its complex topography. Large-scale atmospheric configurations such as tropical cyclones (TCs) may have an amplifying effect on local rainfall patterns that only a very high-resolution RCM, forced by the large scales and resolving finescale processes, may simulate properly. This paper documents the capability of the Weather Research and Forecasting Model (WRF) RCM to regionalize rainfall variability at very high resolution (680 m) over La Réunion island for daily to seasonal time scales and year-to-year differences. Two contrasted wet seasons (November-April) are selected: 2000-01 (abnormally dry) and 2004-05 (abnormally wet). WRF rainfall is compared to a dense network of rain gauge records interpolated onto the WRF grid through the regression-kriging (RK) technique. RK avoids the point-to-grid comparison issue, but produces imperfect estimates due to sampling, so its quality also needs to be tested. Seasonal rainfall amounts and contrasts produced by WRF are fairly realistic. At intraseasonal and daily time scales, differences to RK are more sizable. These differences are not easy to interpret in sectors where the rain gauge network is less dense and the quality of RK more uncertain, as over the eastern slopes of Piton de la Fournaise volcano where WRF seems to simulate more realistic rainfall than RK. Finally, the heavy rainfall associated with TC Ando on 6 January 2001, is documented. WRF shows weak disagreements with RK, indicating its capability to regionalize rainfall during extreme events. [ABSTRACT FROM AUTHOR]

Four-dimensional variational data assimilation (4D-Var) produces unavoidable inaccuracies in the models initial state vector. In this paper the authors investigate a novel variational error estimation method to calculate these inaccuracies. The impacts of model, background, and observational errors on the state estimate produced by 4D-Var are analyzed by applying the variational error estimation method. The structure of the method is similar to the conventional 4D-Var, with the differences in that (i) instead of observations it assimilates observational errors, and (ii) the original model equations (used in 4D-Var as constraints) are first linearized with respect to a small perturbation in the initial state vector and then used as the constraints. The authors then carry out a proof-of-concept study and validate the reliability of this method through multiple twin experiments on the basis of a 2D shallow-water model. All required differentiated models were generated by means of algorithmic differentiation directly from the nonlinear model source code. The experiments reveal that the suggested method works well in a wide range of assimilation windows and types of observational and model errors and can be recommended for error estimation and prediction in data assimilation. [ABSTRACT FROM AUTHOR]

The majority of international climate modeling centers now produce seasonal rainfall forecasts from coupled general circulation models (GCMs). Seasonal rainfall forecasting is highly challenging, and GCM forecast accuracy is still poor for many regions and seasons. Additionally, forecast uncertainty tends to be underestimated meaning that forecast probabilities are statistically unreliable. A common strategy employed to improve the overall accuracy and reliability of GCM forecasts is to merge forecasts from multiple models into a multimodel ensemble (MME). The most widely used technique is to simply pool all of the forecast ensemble members from multiple GCMs into what is known as a superensemble. In Australia, seasonal rainfall forecasts are produced using the Predictive Ocean-Atmosphere Model for Australia (POAMA). In this paper, the authors demonstrate that mean corrected superensembles formed by merging forecasts from POAMA with those from three international models in the ENSEMBLES dataset remain poorly calibrated in many cases. The authors propose and evaluate a two-step process for producing MMEs. First, forecast calibration of the individual GCMs is carried out by using Bayesian joint probability models that account for parameter uncertainty. The calibration leads to satisfactory forecast reliability. Second, the individually calibrated forecasts of the GCMs are merged through Bayesian model averaging (BMA). The use of multiple GCMs results in better forecast accuracy, while maintaining reliability, than using POAMA only. Compared with using equal-weight averaging, BMA weighting produces sharper and more accurate forecasts. [ABSTRACT FROM AUTHOR]

This paper is the second part of a comprehensive study on tropical cyclone (TC) size. In Part I, the climatology of TC size and strength over the western North Pacific (WNP) and the North Atlantic was established based on the Quick Scatterometer (QuikSCAT) data. In this second part, the mechanisms that are likely responsible for TC size changes are explored through analyses of angular momentum (AM) transports and synoptic flow patterns associated with the TC. Changes in AM transport in the upper and lower troposphere appear to be important factors that affect TC intensity and size, respectively. The change in TC intensity is positively related to the change in the upper-tropospheric AM export, while the change in TC size is positively proportional to the change in the lower-tropospheric AM import. An examination of the synoptic flow patterns associated with WNP TCs suggests that changes in the synoptic flow near the TC are important in determining the change in TC size, with developments of the lower-tropospheric anticyclonic flows (one to the east and one to the west) bordering the TC being favorable for TC growth and a weakening of the subtropical high to the southeast for TC size reduction. A recurving TC tends to grow if the lower-tropospheric westerlies to its west increase. Moreover, a northward TC movement is related to the change in TC size. For example, a higher northward-moving speed is found for a larger TC, which also agrees well with the AM transport concept. [ABSTRACT FROM AUTHOR]

Probabilistic forecasts of wind vectors are becoming critical as interest grows in wind as a clean and renewable source of energy, in addition to a wide range of other uses, from aviation to recreational boating. Unlike other common forecasting problems, which deal with univariate quantities, statistical approaches to wind vector forecasting must be based on bivariate distributions. The prevailing paradigm in weather forecasting is to issue deterministic forecasts based on numerical weather prediction models. Uncertainty can then be assessed through ensemble forecasts, where multiple estimates of the current state of the atmosphere are used to generate a collection of deterministic predictions. Ensemble forecasts are often uncalibrated, however, and Bayesian model averaging (BMA) is a statistical way of postprocessing these forecast ensembles to create calibrated predictive probability density functions (PDFs). It represents the predictive PDF as a weighted average of PDFs centered on the individual bias-corrected forecasts, where the weights reflect the forecasts' relative contributions to predictive skill over a training period. In this paper the authors extend the BMA methodology to use bivariate distributions, enabling them to provide probabilistic forecasts of wind vectors. The BMA method is applied to 48-h-ahead forecasts of wind vectors over the North American Pacific Northwest in 2003 using the University of Washington mesoscale ensemble and is shown to provide better-calibrated probabilistic forecasts than the raw ensemble, which are also sharper than probabilistic forecasts derived from climatology. [ABSTRACT FROM AUTHOR]

Seasonal forecasts are most commonly issued as anomalies with respect to some multi-year reference period. However, different seasonal forecasting centres use different reference periods. This paper shows that for near surface temperature, precipitation and mean sea-level pressure, over most regions of the world there is evidence that these differences between reference periods should not be ignored, especially when forecasters combine outputs from several prediction systems. Three methods are presented by which reference periods could be adjusted, and it is shown that the differences between the proposed methods are smaller than the errors due to not correcting for different reference periods. [ABSTRACT FROM AUTHOR]

This paper reviews the current knowledge of the climatological, structural, and organizational aspects of stratocumulus clouds and the physical processes controlling them. More of Earth's surface is covered by stratocumulus clouds than by any other cloud type making them extremely important for Earth's energy balance, primarily through their reflection of solar radiation. They are generally thin clouds, typically occupying the upper few hundred meters of the planetary boundary layer (PBL), and they preferably occur in shallow PBLs that are readily coupled by turbulent mixing to the surface moisture supply. Thus, stratocumuli favor conditions of strong lower-tropospheric stability, large-scale subsidence, and a ready supply of surface moisture; therefore, they are common over the cooler regions of subtropical and midlatitude oceans where their coverage can exceed 50% in the annual mean. Convective instability in stratocumulus clouds is driven primarily by the emission of thermal infrared radiation from near the cloud tops and the resulting turbulence circulations are enhanced by latent heating in updrafts and cooling in downdrafts. Turbulent eddies and evaporative cooling drives entrainment at the top of the stratocumulus-topped boundary layer (STBL), which is stronger than it would be in the absence of cloud, and this tends to result in a deepening of the STBL over time. Many stratocumulus clouds produce some drizzle through the collision-coalescence process, but thicker clouds drizzle more readily, which can lead to changes in the dynamics of the STBL that favor increased mesoscale variability, stratification of the STBL, and in some cases cloud breakup. Feedbacks between radiative cooling, precipitation formation, turbulence, and entrainment help to regulate stratocumulus. Although stratocumulus is arguably the most well-understood cloud type, it continues to challenge understanding. Indeed, recent field studies demonstrate that marine stratocumulus precipitate more strongly, and entrain less, than was previously thought, and display an organizational complexity much larger than previously imagined. Stratocumulus clouds break up as the STBL deepens and it becomes more difficult to maintain buoyant production of turbulence through the entire depth of the STBL. Stratocumulus cloud properties are sensitive to the concentration of aerosol particles and therefore anthropogenic pollution. For a given cloud thickness, polluted clouds tend to produce more numerous and smaller cloud droplets, greater cloud albedo, and drizzle suppression. In addition, cloud droplet size also affects the time scale for evaporation-entrainment interactions and sedimentation rate, which together with precipitation changes can affect turbulence and entrainment. Aerosols are themselves strongly modified by physical processes in stratocumuli, and these two-way interactions may be a key driver of aerosol concentrations over the remote oceans. Aerosol-stratocumulus interactions are therefore one of the most challenging frontiers in cloud-climate research. Low-cloud feedbacks are also a leading cause of uncertainty in future climate prediction because even small changes in cloud coverage and thickness have a major impact on the radiation budget. Stratocumuli remain challenging to represent in climate models since their controlling processes occur on such small scales. A better understanding of stratocumulus dynamics, particularly entrainment processes and mesoscale variability, will be required to constrain these feedbacks. CONTENTS [ABSTRACT FROM AUTHOR]

This paper presents a climatology of the initial eye formations of a broad set of Atlantic tropical cyclones (TCs) during 1989-2008. A new dataset of structure and intensity parameters is synthesized from the vortex data messages transmitted by routine aircraft reconnaissance. Using these data together with satellite imagery and other established datasets, the times when each TC achieved various stages of eye development are tabulated to form the basis of the climatology. About 60% of Atlantic TCs form eyes. Most often, aircraft observe the eye structure before it appears in IR satellite imagery. Eyes tend to form in high potential intensity environments characterized by high sea surface temperatures and low-to-moderate environmental vertical wind shear. A notable discovery is that most (67%) TCs that form eyes tend to do so within 48 h of the cyclone's reaching tropical storm strength. This suggests the existence of an opportune time window during which a TC can readily form an eye. From the lengths of time taken to reach various stages of eye development, the characteristic time scale for eye formation is estimated to be about 36 h. [ABSTRACT FROM AUTHOR]

This paper investigates the effects of spatial filtering on the ensemble-based estimate of the background error covariance matrix in an ensemble-based Kalman filter (EnKF). In particular, a novel kernel smoothing method with variable bandwidth is introduced and its performance is compared to that of the widely used Gaspari-Cohn filter, which uses a fifth-order kernel function with a fixed localization length. Numerical experiments are carried out with the 40-variable Lorenz-96 model. The results of the experiments show that the nonparametric approach provides a more accurate estimate of the background error covariance matrix than the Gaspari-Cohn filter with any localization length. It is also shown that the Gaspari-Cohn filter tends to provide more accurate estimates of the covariance with shorter localization lengths. However, the analyses obtained by using longer localization lengths tend to be more accurate than those produced by using short localization lengths or the nonparametric approach. This seemingly paradoxical result is explained by showing that localization with longer localization lengths produces filtered estimates whose time mean is the most similar to the time mean of both the unfiltered estimate and the true covariance. This result suggests that a better metric of covariance filtering skill would be one that combined a measure of closeness to the sample covariance matrix for a very large ensemble with a measure of similarity between the climatological averages of the filtered and sample covariance. [ABSTRACT FROM AUTHOR]

Skillfully predicting North Atlantic hurricane activity months in advance is of potential societal significance and a useful test of our understanding of the factors controlling hurricane activity. In this paper, a statistical--dynamical hurricane forecasting system, based on a statistical hurricane model, with explicit uncertainty estimates, and built from a suite of high-resolution global atmospheric dynamical model integrations spanning a broad range of climate states is described. The statistical model uses two climate predictors: the sea surface temperature (SST) in the tropical North Atlantic and SST averaged over the global tropics. The choice of predictors is motivated by physical considerations, as well as the results of high-resolution hurricane modeling and statistical modeling of the observed record. The statistical hurricane model is applied to a suite of initialized dynamical global climate model forecasts of SST to predict North Atlantic hurricane frequency, which peaks during the August--October season, from different starting dates. Retrospective forecasts of the 1982--2009 period indicate that skillful predictions can be made from as early as November of the previous year; that is, skillful forecasts for the coming North Atlantic hurricane season could be made as the current one is closing. Based on forecasts initialized between November 2009 and March 2010, the model system predicts that the upcoming 2010 North Atlantic hurricane season will likely be more active than the 1982--2009 climatology, with the forecasts initialized in March 2010 predicting an expected hurricane count of eight and a 50%% probability of counts between six (the 1966--2009 median) and nine. [ABSTRACT FROM AUTHOR]

Most regional numerical models in the atmospheric sciences use temporally interpolated data provided by other low-resolution models, either for a gridpoint coupling at their lateral boundaries or for a spectral nudging of the large scales in the entire domain. In some cases, such as fast-propagating storms, these interpolations can seriously corrupt the meteorological fields. This article shows how to use an operational high-pass filter of the surface pressure field to detect and to localize a propagating storm, and to use this information to locally reinject the available uncorrupted storm in the coupled model. This is achieved by applying a technique of gridpoint nudging in a subarea of the domain, limited to a compact region around the eye of the depression. As an application it is shown that this restores the strength of the storm, while leaving the model state in the rest of the domain quasi intact. It is then discussed how this can improve numerical weather prediction and regional climate models. [ABSTRACT FROM AUTHOR]

In a recent study, Williams introduced a simple modification to the widely used Robert--Asselin (RA) filter for numerical integration. The main purpose of the Robert--Asselin--Williams (RAW) filter is to avoid the undesired numerical damping of the RA filter and to increase the accuracy. In the present paper, the effects of the modification are comprehensively evaluated in the Simplified Parameterizations, Primitive Equation Dynamics (SPEEDY) atmospheric general circulation model. First, the authors search for significant changes in the monthly climatology due to the introduction of the new filter. After testing both at the local level and at the field level, no significant changes are found, which is advantageous in the sense that the new scheme does not require a retuning of the parameterized model physics. Second, the authors examine whether the new filter improves the skill of short- and medium-term forecasts. January 1982 data from the NCEP--NCAR reanalysis are used to evaluate the forecast skill. Improvements are found in all the model variables (except the relative humidity, which is hardly changed). The improvements increase with lead time and are especially evident in medium-range forecasts (96--144 h). For example, in tropical surface pressure predictions, 5-day forecasts made using the RAW filter have approximately the same skill as 4-day forecasts made using the RA filter. The results of this work are encouraging for the implementation of the RAW filter in other models currently using the RA filter. [ABSTRACT FROM AUTHOR]

This paper describes the southerly New York Bight (NYB) jet (11–17 m s−1) that develops primarily during the warm season just above the surface offshore (east) of the northern New Jersey coast and south of Long Island (the NYB). Observations from two offshore buoys are used to develop a 9-yr climatology of 134 jet events from 1997 to 2006. There is a seasonal maximum (2.5 events per month) during June and July, with a skew toward the spring months. The wind directions for the jet trace out a nearly elliptical orbit for the 24-h period around the time of jet maximum at ~2300 UTC [1900 eastern daylight time (EDT)] on average. Composites reveal that the NYB jet occurs on days with southwesterly synoptic flow, and the jet is part of a larger-scale (200–300 km) wind enhancement offshore of the mid-Atlantic and northeast U.S. coasts during the early evening hours. High-resolution observations (surface mesonet, aircraft soundings, and a terminal Doppler weather radar) and Weather Research and Forecasting (WRF) model simulations down to 1.33-km grid spacing are used to diagnose the evolution of the NYB jet on 2 June 2007. The NYB jet at ~150 m MSL occurs within the sloping marine inversion near the coast. Low-level trajectories illustrate low-level diffluence and weak subsidence within the jet. A WRF momentum budget highlights the evolving pressure gradient and accelerations during jet formation. The maximum jet winds occur 1–2 h after the peak meridional pressure gradient is established through a geostrophic adjustment process. Sensitivity experiments show that jet occurrence is dependent on diurnal heating and that the concave bend in the southern New Jersey coast limits the southern extent of the jet. [ABSTRACT FROM AUTHOR]

Objective streamline analyses and digitized high-resolution IR satellite cloud data have been used to examine in detail the changes in the environmental circulation and in the cloud structure that took place in and around Tropical Cyclone Kathy (1984) when it started to intensify, and during its intensification and dissipation stages. The change of low-level circulation around Tropical Cyclone Kathy was measured by the change in the angle of inflow (α4) at a radius of 4° latitude from the cyclone center. When Kathy started to intensify, α4 increased suddenly from 20° to 42.5° in the northerly airstream to the north and northeast of the depression, and decreased to 0° to the south of the depression. At that stage the low-level circulation around the depression appeared as a giant swirl that started some 600 km to the north and northeast of the depression and spiraled inward toward its center, while trade air, which is usually cool, dry, and stable, did not enter the cyclonic circulation. The angle α4 remained the same during intensification. During the dissipation stage, α4 returned to 20° and trade air started to participate in the cyclonic circulation. Satellite cloud data were used to determine the origin, evolution, and importance of the feeder bands in the intensification of the cyclone, to follow the moist near-equatorial air that flowed through them and to estimate the maximum height of cumulonimbi that developed in them, to observe the changes in the convective activity in the central dense overcast (CDO) area, as well as in the area around the CDO. Most of the observed changes in Kathy have also been observed in other tropical cyclones during intensification and dissipation. Using the sequence of observed changes of the circulation and of convective activity in and around the CDO of Kathy, a mathematical model has been developed to forecast the intensity of a tropical cyclone. The model and its application to three tropical cyclones in the Australian region are described in Part II of this paper. [ABSTRACT FROM AUTHOR]

This paper demonstrates how satellite observations of the cloudiness over a complex area such as the European Mediterranean area can be classified into distinct cloud regimes by application of a K-means clustering algorithm to pixel-level cloud properties. The study contrasts with previous approaches in the fact that the clustering is done on the cloud physical properties at the pixel level and not on statistics of these properties over a coarser grid. A method to choose the number of clusters is described. “Shallow cumulus,” “stratocumulus,” and “frontal” clusters are robustly identified, and associated environmental properties are described. The approach helps to refine the diagnosis of errors in model simulations. In addition to isolated classical errors of climate models (lack of midlevel clouds, overestimation of the cloud optical thickness, and underestimation of the stratocumulus) and a dramatic underestimation of the shallow cumulus clouds over land, an underestimation of the boundary layer depth is detected for some regimes as well as an incorrect stratification for the shallow cumulus. The clustering on the individual cloud properties classifies them in a much more homogeneous way than the clustering on the statistics of the cloud properties; the use of individual cloud properties at the pixel level produced by climate models activating simulators combined with subgrid-scale sampling procedures may be considered as an alternative to the use of the statistical products for the evaluation of models. The approach can also be applied to a high-resolution regional climate model. [ABSTRACT FROM AUTHOR]

The isentropic system of equations has particular advantages in the numerical modeling of weather and climate. These include the elimination of the vertical velocity in adiabatic flow, which simplifies the motion to a two-dimensional problem and greatly reduces the numerical errors associated with vertical advection. The mechanism for the vertical transfer of horizontal momentum is simply the pressure drag acting on isentropic coordinate surfaces under frictionless, adiabatic conditions. In addition, vertical resolution is enhanced in regions of high static stability, which leads to better resolution of features such as the tropopause. Negative static stability and isentropic overturning frequently occur in finescale atmospheric motion. This presents a challenge to nonhydrostatic modeling with the isentropic vertical coordinate. This paper presents a new nonhydrostatic atmospheric model based on a generalized vertical coordinate. The coordinate is specified in a manner similar to that of Konor and Arakawa, but “arbitrary Eulerian–Lagrangian” (ALE) methods are used to maintain coordinate monotonicity in regions of negative static stability and to return the coordinate surfaces to their isentropic “targets” in statically stable regions. The model is mass conserving and implements a vertical differencing scheme that satisfies two additional integral constraints for the limiting case of z coordinates. The hybrid vertical coordinate model is tested with mountain-wave experiments including a downslope windstorm with breaking gravity waves. The results show that the advantages of the isentropic coordinate are realized in the model with regard to vertical tracer and momentum transport. Also, the isentropic overturning associated with the wave breaking is successfully handled by the coordinate formulation. [ABSTRACT FROM AUTHOR]