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In this study, a one-step-ahead ensemble Kalman smoother (EnKS) is introduced for the purposes of parameter estimation. The potential for this system to provide new constraints on the surface-exchange coefficients of momentum (Cd) and enthalpy (Ck) is then explored using a series of observing system simulation experiments (OSSEs). The surface-exchange coefficients to be estimated within the data assimilation system are highly uncertain, especially at high wind speeds, and are well known to be important model parameters influencing the intensity and structure of tropical cyclones in numerical simulations. One major benefit of the developed one-step-ahead EnKS is that it allows for simultaneous updates of the rapidly evolving model state variables found in tropical cyclones using a short assimilation window and a long smoother window for the parameter updates, granting sufficient time for sensitivity to the parameters to develop. Overall, OSSEs demonstrate potential for this approach to accurately constrain parameters controlling the amplitudes of Cd and Ck, but the degree of success in recovering the truth model parameters varies throughout the tropical cyclone life cycle. During the rapid intensification phase, rapidly growing errors in the model state project onto the parameter updates and result in an overcorrection of the parameters. After the rapid intensification phase, however, the parameters are correctly adjusted back toward the truth values. Last, the relative success of parameter estimation in recovering the truth model parameter values also has sensitivity to the ensemble size and smoothing forecast length, each of which are explored. Significance Statement Large uncertainty in the surface-exchange coefficients of momentum and heat/moisture exists for hurricane conditions. This is a problem because the numerical weather model predictions of hurricane intensity and storm structure are sensitive to the surface-exchange coefficient values used. In this study we use data assimilation, or the relationships estimated between the surface-exchange coefficients and forecasted observations, to constrain uncertainty in the model’s surface-exchange coefficient values. More specifically, an approach to limit both the rapidly growing errors associated with the hurricane itself and the hurricane’s accumulated response to the surface-exchange coefficient values is presented. Overall, this approach has potential to accurately estimate the surface-exchange coefficients, but the success depends on the number of forecast realizations used and how rapidly the hurricane is changing.

Atmospheric predictability is measured by the average difference (or “error”) within an ensemble of forecasts starting from slightly different initial conditions. The spatial scale of the error field is a fundamental quantity; for meteorological applications, the error field typically varies with latitude and longitude and so requires a two-dimensional (2D) spectral analysis. Statistical predictability theory is based on the theory of homogeneous, isotropic turbulence, in which spectra are circularly symmetric in 2D wavenumber space. One takes advantage of this circular symmetry to reduce 2D spectra to one-dimensional (1D) spectra by integrating around a circle in wavenumber polar coordinates. In recent studies it has become common to reduce 2D error spectra to 1D by computing spectra in the zonal direction and then averaging the results over latitude. It is shown here that such 1D error spectra are generically fairly constant across the low wavenumbers as the amplitude of an error spectrum grows with time and therefore the error spectrum is said grow “up-amplitude.” In contrast computing 1D error spectra in a manner consistent with statistical predictability theory gives spectra that are peaked at intermediate wavenumbers. In certain cases, this peak wavenumber is decreasing with time as the error at that wavenumber increases and therefore the error spectrum is said to grow “upscale.” We show through theory, simple examples, and global predictability experiments that comparisons of model error spectra with the predictions of statistical predictability theory are only justified when using a theory-consistent method to transform a 2D error field to a 1D spectrum.

An ensemble of three-dimensional ensemble-variational (En-3DEnVar) data assimilations is demonstrated with the Joint Effort for Data assimilation Integration (JEDI) with the Model for Prediction Across Scales – Atmosphere (MPAS-A) (i.e., JEDI-MPAS). Basic software building blocks are reused from previously presented deterministic 3DEnVar functionality, and combined with a formal experimental workflow manager in MPAS-Workflow. En-3DEnVar is used to produce an 80-member ensemble of analyses, which are cycled with ensemble forecasts in a 1-month experiment. The ensemble forecasts approximate a purely flow-dependent background error covariance (BEC) at each analysis time. The En-3DEnVar BECs and prior ensemble mean forecast errors are compared to those produced by a similar experiment that uses the Data Assimilation Research Testbed (DART) Ensemble Adjustment Kalman Filter (EAKF). The experiment using En-3DEnVar produces similar ensemble spread to and slightly smaller errors than the EAKF. The ensemble forecasts initialized from En-3DEnVar and EAKF analyses are used as BECs in deterministic cycling 3DEnVar experiments, which are compared to a control experiment that uses 20-member MPAS-A forecasts initialized from Global Ensemble Forecast System (GEFS) initial conditions. The experimental ensembles achieve mostly equivalent or better performance than the off-the-shelf ensemble system in this deterministic cycling setting; although, there are many obvious differences in configuration between GEFS and the two MPAS ensemble systems. An additional experiment that uses hybrid 3DEnVar, which combines the En-3DEnVar ensemble BEC with a climatological BEC, increases tropospheric forecast quality compared to the corresponding pure 3DEnVar experiment. The JEDI-MPAS En-3DEnVar is technically working and useful for future research studies. Tuning of observation errors and spread is needed to improve performance and several algorithmic advancements are needed to improve computational efficiency for larger-scale applications.

On 24th September 2021, JEDI-MPAS 1.0.0, a new data assimilation (DA) system for the Model Prediction Across Scales – Atmosphere (MPAS-A) built on the software framework of the Joint Effort for Data assimilation Integration (JEDI), was publicly released for community use. JEDI-MPAS capabilities include three-dimensional variational (3DVar) and ensemble-variational (EnVar) schemes for deterministic analyses, the ensemble of DAs (EDA) technique to produce ensembles of analyses, and dual resolution, where the analysis increment and the input forecast ensemble are at a lower resolution than that of the background and analysis, thus decreasing computational requirements. Moreover, JEDI-MPAS operates directly on the native MPAS unstructured mesh and can be applied without code modifications to all MPAS meshes, whether uniform, variable resolution, global, or regional. On the observation side, one advanced feature in JEDI-MPAS is the full all-sky approach for satellite radiance DA with the introduction of hydrometeor analysis variables. This paper describes the formulation and implementation of EnVar for JEDI-MPAS. JEDI-MPAS 1.0.0 is evaluated with month-long cycling 3DEnVar experiments with a global 30 km–60 km dual-resolution configuration. The robustness and credible performance of JEDI-MPAS are demonstrated by establishing a benchmark non-radiance DA experiment, then incrementally adding microwave radiances from three sources: AMSU-A temperature sounding channels in clear-sky scenes, AMSU-A window channels in all-sky scenes, and MHS water vapor channels in clear-sky scenes. JEDI-MPAS 3DEnVar behaves well with substantial and significant positive impact obtained for almost all aspects of forecast verification when progressively adding more microwave radiance data. In particular, the day-5 forecast of the best-performing JEDI-MPAS experiment yields an anomaly correlation coefficient (ACC) of 0.8 for 500 hPa geopotential height, a gap of roughly a half day when compared to cold start forecasts initialized from operational analyses of the National Centers for Environmental Prediction, whose ACC does not drop to 0.8 until a lead time of 5.5 days. This indicates JEDI-MPAS’s great potential for both research and operations. Further development of JEDI-MPAS is ongoing, with emphasis on 3DVar, EDA, and cloud analysis from all-sky radiance observations.

We present a new multiscale covariance localisation method for ensemble data assimilation that is based on the estimation of eigenvectors and subsequent projections, together with traditional spatial localisation applied with a range of localisation lengths. In short, we estimate the leading, large-scale eigenvectors from the sample covariance matrix obtained by spatially smoothing the ensemble (treating small scales as noise) and then localise the resulting sample covariances with a large length scale. After removing the projection of each ensemble member onto the leading eigenvectors, the process may be repeated using less smoothing and tighter localizations or, in a final step, using the resulting, residual ensemble and tight localisation to represent covariances in the remaining subspace. We illustrate the use of the new multiscale localisation method in simple numerical examples and in cycling data assimilation experiments with the Lorenz Model III. We also compare the proposed new method to existing multiscale localisation and to single-scale localisation.

The role of vertical mesh spacing in the convergence of full-physics global atmospheric model solutions is examined for synoptic, mesoscale, and convective-scale horizontal resolutions. Using the MPAS-Atmosphere model, convergence is evaluated for three solution metrics: the horizontal kinetic energy spectrum, the Richardson number probability density function, and resolved flow features. All three metrics exhibit convergence in the free atmosphere for a 15-km horizontal mesh when the vertical grid spacing is less than or equal to 200 m. Nonconvergence is accompanied by noise, spurious structures, reduced levels of mesoscale kinetic energy, and reduced Richardson number peak frequencies. Coarser horizontal mesh solutions converge in a similar manner but contain much less noise than the 15-km solutions for coarse vertical resolution. For convective-scale resolution simulations with 3-km cell spacing on a variable-resolution mesh, solution convergence is almost attained with a vertical mesh spacing of 200 m. The boundary layer scheme is the dominant source of vertical filtering in the free atmosphere. Although the increased vertical mixing at coarser vertical mesh spacing depresses the kinetic energy spectra and Richardson number convergence, it does not produce sufficient dissipation to effectively halt scale collapse. These results confirm and extend the results from a number of previous studies, and further emphasize the sensitivity of the energetics to the vertical mixing formulations in the model.

Ensemble‐based data assimilation systems typically use covariance localization to dampen spurious correlations associated with sampling error while increasing the rank of the covariance estimate. Variational methods use model‐space localization, in which localization is applied to ensemble estimates of covariances between model variables and is based on distances between those variables, while ensemble filters apply observation‐space localization to estimates of model‐observation covariances, based on distances between model variables and observations. It has been shown that for nonlocal observations, such as satellite radiances, model‐space localization can be superior. This paper demonstrates a new method for performing model‐space localization in serial ensemble filters using the linearized observation operators (or Jacobians). Results of radiance‐only assimilation in a global forecast system show the benefit of using model‐space localization relative to observation‐space localization. The serial ensemble square root filter with vertical model‐space localization gives results similar to those of the Ensemble Variational system (without outer loops or extra balance constraints) while increasing the runtime compared to the filter with observation‐space localization by a factor between 2 and 8, depending on how sparse the Jacobian matrices are. The results are also similar to another approach to model‐space localization in ensemble filters: ensemble Kalman filter with modulated ensembles.

This study aimed to examine the effects of advice from religious/spiritual leaders and friends/family of a religious/spiritual person with mental health struggles on treatment-seeking. A survey was administered to adult patients of a university-affiliated psychiatric clinic in the Midwest. Participants whose friends/family members' advice conflicted with their psychiatrist's advice were six times more likely to delay seeking mental health treatment (OR: 6.09, 95% CI: 1.37, 27.01). Conflict between religious/spiritual leader's advice and psychiatrist's advice had a significant effect on delay in seeking mental health treatment (OR: 11.73, 95% CI: 2.21, 62.14), with an average delay of just over two years.

During the last few decades, scientific capabilities for understanding and predicting weather and climate risks have advanced rapidly. At the same time, technological advances, such as the Internet, mobile devices, and social media, are transforming how people exchange and interact with information. In this modern information environment, risk communication, interpretation, and decision-making are rapidly evolving processes that intersect across space, time, and society. Instead of a linear or iterative process in which individual members of the public assess and respond to distinct pieces of weather forecast or warning information, this article conceives of weather prediction, communication, and decision-making as an interconnected dynamic system. In this expanded framework, information and uncertainty evolve in conjunction with people’s risk perceptions, vulnerabilities, and decisions as a hazardous weather threat approaches; these processes are intertwined with evolving social interactions in the physical and digital worlds. Along with the framework, the article presents two interdisciplinary research approaches for advancing the understanding of this complex system and the processes within it: analysis of social media streams and computational natural–human system modeling. Examples from ongoing research are used to demonstrate these approaches and illustrate the types of new insights they can reveal. This expanded perspective together with research approaches, such as those introduced, can help researchers and practitioners understand and improve the creation and communication of information in atmospheric science and other fields.

The potential for storm surge to cause extensive property damage and loss of life has increased urgency to more accurately predict coastal flooding associated with landfalling tropical cyclones. This work investigates the sensitivity of coastal inundation from storm tide (surge + tide) to four hurricane parameters—track, intensity, size, and translation speed—and the sensitivity of inundation forecasts to errors in forecasts of those parameters. An ensemble of storm tide simulations is generated for three storms in the Gulf of Mexico, by driving a storm surge model with best track data and systematically generated perturbations of storm parameters from the best track. The spread of the storm perturbations is compared to average errors in recent operational hurricane forecasts, allowing sensitivity results to be interpreted in terms of practical predictability of coastal inundation at different lead times. Two types of inundation metrics are evaluated: point-based statistics and spatially integrated volumes. The practical predictability of surge inundation is found to be limited foremost by current errors in hurricane track forecasts, followed by intensity errors, then speed errors. Errors in storm size can also play an important role in limiting surge predictability at short lead times, due to observational uncertainty. Results show that given current mean errors in hurricane forecasts, location-specific surge inundation is predictable for as little as 12–24 h prior to landfall, less for small-sized storms. The results also indicate potential for increased surge predictability beyond 24 h for large storms by considering a storm-following, volume-integrated metric of inundation.

A global atmospheric analysis and forecast system is constructed based on the atmospheric component of the Model for Prediction Across Scales (MPAS-A) and the Data Assimilation Research Testbed (DART) ensemble Kalman filter. The system is constructed using the unstructured MPAS-A Voronoi (nominally hexagonal) mesh and thus facilitates multiscale analysis and forecasting without the need for developing new covariance models at different scales. Cycling experiments with the assimilation of real observations show that the global ensemble system is robust and reliable throughout a one-month period for both quasi-uniform and variable-resolution meshes. The variable-mesh assimilation system consistently provides higher-quality analyses than those from the coarse uniform mesh, in addition to the benefits of the higher-resolution forecasts, which leads to substantial improvements in 5-day forecasts. Using the fractions skill score, the spatial scale for skillful precipitation forecasts is evaluated over the high-resolution area of the variable-resolution mesh. Skill decreases more rapidly at smaller scales, but the variable mesh consistently outperforms the coarse uniform mesh in precipitation forecasts at all times and thresholds. Use of incremental analysis updates (IAU) greatly decreases high-frequency noise overall and improves the quality of EnKF analyses, particularly in the tropics. Important aspects of the system design related to the unstructured Voronoi mesh are also investigated, including algorithms for handling the C-grid staggered horizontal velocities.

Since its initial release in 2000, the Weather Research and Forecasting (WRF) Model has become one of the world’s most widely used numerical weather prediction models. Designed to serve both research and operational needs, it has grown to offer a spectrum of options and capabilities for a wide range of applications. In addition, it underlies a number of tailored systems that address Earth system modeling beyond weather. While the WRF Model has a centralized support effort, it has become a truly community model, driven by the developments and contributions of an active worldwide user base. The WRF Model sees significant use for operational forecasting, and its research implementations are pushing the boundaries of finescale atmospheric simulation. Future model directions include developments in physics, exploiting emerging compute technologies, and ever-innovative applications. From its contributions to research, forecasting, educational, and commercial efforts worldwide, the WRF Model has made a significant mark on numerical weather prediction and atmospheric science.

Safety compliance issues for operational studies of the atmosphere with balloons require quantifying risks associated with descent and developing strategies to reduce the uncertainties at the location of the touchdown point. Trajectory forecasts are typically computed from weather forecasts produced by an operational center, for example, the European Centre for Medium-Range Weather Forecasts. This study uses past experiments to investigate strategies for improving these forecasts. Trajectories for open stratospheric balloon (OSB) short-term flights are computed using mesoscale simulations with the Weather and Research Forecasting (WRF) Model initialized with ECMWF operational forecasts and are assimilated with radio soundings using the Data Assimilation Research Testbed (DART) ensemble Kalman filter, for three case studies during the Strapolété 2009 campaign in Sweden. The results are very variable: in one case, the error in the final simulated position is reduced by 90% relative to the forecast using the ECMWF winds, while in another case the forecast is hardly improved. Nonetheless, they reveal the main source of forecasting error: during the ceiling phase, errors due to unresolved inertia–gravity waves accumulate as the balloon continuously experiences one phase of a wave for a few hours, whereas they essentially average out during the ascent and descent phases, when the balloon rapidly samples through whole wave packets. This sensitivity to wind during the ceiling phase raises issues regarding the feasibility of such forecasts and the observations that would be needed. The ensemble spread is also analyzed, and it is noted that the initial ensemble perturbations should probably be improved in the future for better forecasts.

Under assumptions of horizontal homogeneity and isotropy, one may derive relations between rotational or divergent kinetic energy spectra and velocities along one-dimensional tracks, such as might be measured by aircraft. Two recent studies, differing in details of their implementation, have applied these relations to the Measurement of Ozone and Water Vapor by Airbus In-Service Aircraft (MOZAIC) dataset and reached different conclusions with regard to the mesoscale ratio of divergent to rotational kinetic energy. In this study the accuracy of the method is assessed using global atmospheric simulations performed with the Model for Prediction Across Scales, where the exact decomposition of the horizontal winds into divergent and rotational components may be easily computed. For data from the global simulations, the two approaches yield similar and very accurate results. Errors are largest for the divergent component on synoptic scales, which is shown to be related to a very dominant rotational mode. The errors are, in particular, sufficiently small so that the mesoscale ratio of divergent to rotational kinetic energy can be derived correctly. The proposed technique thus provides a strong observational check of model results with existing large commercial aircraft datasets. The results do, however, show a significant dependence on the height and latitude ranges considered, and the disparate conclusions drawn from previous applications to MOZAIC data may result from the use of different subsets of the data.

Particle filtering methods for data assimilation may suffer from the “curse of dimensionality,” where the required ensemble size grows rapidly as the dimension increases. It would, therefore, be useful to know a priori whether a particle filter is feasible to implement in a given system. Previous work provides an asymptotic relation between the necessary ensemble size and an exponential function of , a statistic that depends on observation-space quantities and that is related to the system dimension when the number of observations is large; for linear, Gaussian systems, the statistic can be computed from eigenvalues of an appropriately normalized covariance matrix. Tests with a low-dimensional system show that these asymptotic results remain useful when the system is nonlinear, with either the standard or optimal proposal implementation of the particle filter. This study explores approximations to the covariance matrices that facilitate computation in high-dimensional systems, as well as different methods to estimate the accumulated system noise covariance for the optimal proposal. Since may be approximated using an ensemble from a simpler data assimilation scheme, such as the ensemble Kalman filter, the asymptotic relations thus allow an estimate of the ensemble size required for a particle filter before its implementation. Finally, the improved performance of particle filters with the optimal proposal, relative to those using the standard proposal, in the same low-dimensional system is demonstrated.

Miami Township in Montgomery County, Ohio, is a typical suburban community of nearly 30,000 people with a regional shopping mall and a fairly diverse array of offices along with some industrial land uses. It faces many of the same fundamental challenges and opportunities communities across the country face as they wrestle with converging forces that will determine how they grow, adapt, or in some cases decline over the next decades. These forces range from economic and social changes to technological developments that will rapidly affect building and transportation decisions. Communities that can anticipate and adapt to these changes will have the best chance of thriving.

We report the discovery of 29 promising (and 59 total) new lens candidates from the CFHT Legacy Survey (CFHTLS) based on about 11 million classifications performed by citizen scientists as part of the first Space Warps lens search. The goal of the blind lens search was to identify lens candidates missed by robots (the RingFinder on galaxy scales and ArcFinder on group/cluster scales) which had been previously used to mine the CFHTLS for lenses. We compare some properties of the samples detected by these algorithms to the Space Warps sample and find them to be broadly similar. The image separation distribution calculated from the Space Warps sample shows that previous constraints on the average density profile of lens galaxies are robust. SpaceWarps recovers about 65% of known lenses, while the new candidates show a richer variety compared to those found by the two robots. This detection rate could be increased to 80% by only using classifications performed by expert volunteers (albeit at the cost of a lower purity), indicating that the training and performance calibration of the citizen scientists is very important for the success of Space Warps. In this work we present the SIMCT pipeline, used for generating in situ a sample of realistic simulated lensed images. This training sample, along with the false positives identified during the search, has a legacy value for testing future lens finding algorithms. We make the pipeline and the training set publicly available., Comment: 23 pages, 12 figures, MNRAS accepted, minor to moderate changes in this version

Particle filters may suffer from degeneracy of the particle weights. For the simplest “bootstrap” filter, it is known that avoiding degeneracy in large systems requires that the ensemble size must increase exponentially with the variance of the observation log-likelihood. The present article shows first that a similar result applies to particle filters using sequential importance sampling and the optimal proposal distribution and, second, that the optimal proposal yields minimal degeneracy when compared to any other proposal distribution that depends only on the previous state and the most recent observations. Thus, the optimal proposal provides performance bounds for filters using sequential importance sampling and any such proposal. An example with independent and identically distributed degrees of freedom illustrates both the need for exponentially large ensemble size with the optimal proposal as the system dimension increases and the potentially dramatic advantages of the optimal proposal relative to simpler proposals. Those advantages depend crucially on the magnitude of the system noise.

Mesoscale forecasts are strongly influenced by physical processes that are either poorly resolved or must be parameterized in numerical models. In part because of errors in these parameterizations, mesoscale ensemble data assimilation systems generally suffer from underdispersiveness, which can limit the quality of analyses. Two explicit representations of model error for mesoscale ensemble data assimilation are explored: a multiphysics ensemble in which each member’s forecast is based on a distinct suite of physical parameterization, and stochastic kinetic energy backscatter in which small noise terms are included in the forecast model equations. These two model error techniques are compared with a baseline experiment that includes spatially and temporally adaptive covariance inflation, in a domain over the continental United States using the Weather Research and Forecasting (WRF) Model for mesoscale ensemble forecasts and the Data Assimilation Research Testbed (DART) for the ensemble Kalman filter. Verification against independent observations and Rapid Update Cycle (RUC) 13-km analyses for the month of June 2008 showed that including the model error representation improved not only the analysis ensemble, but also short-range forecasts initialized from these analyses. Explicitly accounting for model uncertainty led to a better-tuned ensemble spread, a more skillful ensemble mean, and higher probabilistic scores, as well as significantly reducing the need for inflation. In particular, the stochastic backscatter scheme consistently outperformed both the multiphysics approach and the control run with adaptive inflation over almost all levels of the atmosphere both deterministically and probabilistically.

Four model-error schemes for probabilistic forecasts over the contiguous United States with the WRF-ARW mesoscale ensemble system are evaluated in regard to performance. Including a model-error representation leads to significant increases in forecast skill near the surface as measured by the Brier score. Combining multiple model-error schemes results in the best-performing ensemble systems, indicating that current model error is still too complex to be represented by a single scheme alone. To understand the reasons for the improved performance, it is examined whether model-error representations increase skill merely by increasing the reliability and reducing the bias—which could also be achieved by postprocessing—or if they have additional benefits. Removing the bias results overall in the largest skill improvement. Forecasts with model-error schemes continue to have better skill than without, indicating that their benefit goes beyond bias reduction. Decomposing the Brier score into its components reveals that, in addition to the spread-sensitive reliability, the resolution component is significantly improved. This indicates that the benefits of including a model-error representation go beyond increasing reliability. This is further substantiated when all forecasts are calibrated to have similar spread. The calibrated ensembles with model-error schemes consistently outperform the calibrated control ensemble. Including a model-error representation remains beneficial even if the ensemble systems are calibrated and/or debiased. This suggests that the merits of model-error representations go beyond increasing spread and removing the mean error and can account for certain aspects of structural model uncertainty.

Monthly Notices of the Royal Astronomical Society, 453 (3), ISSN:0035-8711, ISSN:1365-2966, ISSN:1365-8711

In this paper, we present an external pipeline inspection robotic system capable of detecting the surface defects on above-ground pipelines and modeling their degradation. This system consists of two subsystems, the main base and the sensing system. The main base is a self-driving autonomous ground vehicle (AGV) equipped with Lidar, acoustic sensors and motor encoders, which can track the pipeline on uneven terrains. The sensing system includes two cameras attached to a C-arm, which rotates around the pipe. The two cameras are placed 180° from each other and can take pictures at 90° intervals, allowing the system to analyze the full 360° of the pipe with only half of the rotation as compared to a single camera system. When the C-arm encounters a flange or support, it retracts off the pipe, moves past the obstacle, and extends back to continue taking images. These images are used for defect detection. The detected defect data is then used for modeling the defect degradation and predicting when the defects become critical and require maintenance.

Coupled atmosphere–ocean data assimilation (DA) experiments are performed for estimating the Atlantic meridional overturning circulation (AMOC). Recovery of the AMOC with an ensemble Kalman filter is assessed for a range of experiments over observation availability (atmosphere, upper and deep ocean) and for assimilating high-frequency observations compared to time averages. For an idealised low-order coupled climate model, the traditional DA approach using an ensemble of model trajectories to estimate covariances is compared to a simplified “no-cycling” approach involving climatological covariances derived from a single long model integration. Robustness of the no-cycling method is also tested on data from a millennial-scale simulation of a comprehensive coupled atmosphere–ocean climate model. Results show that the no-cycling approach provides a good approximation to the traditional approach, and that assimilation of time-averaged observations improves AMOC recovery using drastically smaller ensembles than would be required for the case of instantaneous observations. Even in the limit of no ocean observations, the no-cycling approach is capable of recovering the low-frequency AMOC with time-averaged observations; assimilation of noisy instantaneous atmospheric observations fails to recover decadal-scale AMOC variability.

Kinetic energy (KE) spectra derived from global high-resolution atmospheric simulations from the Model for Prediction Across Scales (MPAS) are presented. The simulations are produced using quasi-uniform global Voronoi horizontal meshes with 3-, 7.5-, and 15-km mean cell spacings. KE spectra from the MPAS simulations compare well with observations and other simulations in the literature and possess the canonical KE spectra structure including a very-well-resolved shallow-sloped mesoscale region in the 3-km simulation. There is a peak in the vertical velocity variance at the model filter scale for all simulations, indicating the underresolved nature of updrafts even with the 3-km mesh. The KE spectra reveal that the MPAS configuration produces an effective model resolution (filter scale) of approximately 6Δx. Comparison with other published model KE spectra highlight model filtering issues, specifically insufficient filtering that can lead to spectral blocking and the production of erroneous shallow-sloped mesoscale tails in the KE spectra. The mesoscale regions in the MPAS KE spectra are produced without use of kinetic energy backscatter, in contrast to other results reported in the literature. No substantive difference is found in KE spectra computed on constant height or constant pressure surfaces. Stratified turbulence is not resolved with the vertical resolution used in this study; hence, the results do not support recent conjecture that stratified turbulence explains the mesoscale portion of the KE spectrum.

The assimilation of surface observations using an ensemble Kalman filter (EnKF) approach was successfully performed in the Advanced Research version of the Weather Research and Forecasting Model (WRF) coupled with the Data Assimilation Research Testbed (DART) system. The mesoscale cycling experiment for the continuous ensemble data assimilation was verified against independent surface mesonet observations and demonstrated the positive impact on short-range forecasts over the contiguous U.S. (CONUS) domain throughout the month-long period of June 2008. The EnKF assimilation of surface observations was found useful for systematically improving the simulation of the depth and the structure of the planetary boundary layer (PBL) and the reduction of surface bias errors. These benefits were extended above PBL and resulted in a better precipitation forecast for up to 12 h. With the careful specification of observation errors, not only the reliability of the ensemble system but also the quality of the following forecast was improved, especially in moisture. In this retrospective case study of a squall line, assimilation of surface observations produced analysis increments consistent with the structure and dynamics of the boundary layer. As a result, it enhanced the horizontal gradient of temperature and moisture across the frontal system to provide a favorable condition for the convective initiation and the following heavy rainfall prediction in the Oklahoma Panhandle. Even with the assimilation of upper-level observations, the analysis without the assimilation of surface observations simulated a surface cold front that was much weaker and slower than observed.

The role of moist processes in the development of the mesoscale kinetic energy spectrum is investigated with numerical simulations of idealized moist baroclinic waves. Dry baroclinic waves yield upper-tropospheric kinetic energy spectra that resemble a −3 power law. Decomposition into horizontally rotational and divergent kinetic energy shows that the divergent energy has a much shallower spectrum, but its amplitude is too small to yield a characteristic kink in the total spectrum, which is dominated by the rotational part. The inclusion of moist processes energizes the mesoscale. In the upper troposphere, the effect is mainly in the divergent part of the kinetic energy; the spectral slope remains shallow (around −) as in the dry case, but the amplitude increases with increasing humidity. The divergence field in physical space is consistent with inertia–gravity waves being generated in regions of latent heating and propagating throughout the baroclinic wave. Buoyancy flux spectra are used to diagnose the scale at which moist forcing—via buoyant production from latent heating—injects kinetic energy. There is significant input of kinetic energy in the mesoscale, with a peak at scales of around 800 km and a plateau at smaller scales. If the latent heating is artificially set to zero at some time, the enhanced divergent kinetic energy decays over several days toward the level obtained in the dry simulation. The effect of moist forcing of mesoscale kinetic energy presents a challenge for theories of the mesoscale spectrum based on the idealization of a turbulent inertial subrange.

During the spring 2011 season, a real-time continuously cycled ensemble data assimilation system using the Advanced Research version of the Weather Research and Forecasting Model (WRF) coupled with the Data Assimilation Research Testbed toolkit provided initial and boundary conditions for deterministic convection-permitting forecasts, also using WRF, over the eastern two-thirds of the conterminous United States (CONUS). In this study the authors evaluate the mesoscale assimilation system and the convection-permitting forecasts, at 15- and 3-km grid spacing, respectively. Experiments employing different physics options within the continuously cycled ensemble data assimilation system are shown to lead to differences in the mean mesoscale analysis characteristics. Convection-permitting forecasts with a fixed model configuration are initialized from these physics-varied analyses, as well as control runs from 0.5° Global Forecast System (GFS) analysis. Systematic bias in the analysis background influences the analysis fit to observations, and when this analysis initializes convection-permitting forecasts, the forecast skill is degraded as bias in the analysis background increases. Moreover, differences in mean error characteristics associated with each physical parameterization suite lead to unique errors of spatial, temporal, and intensity aspects of convection-permitting rainfall forecasts. Observation bias by platform type is also shown to impact the analysis quality.

Real-time analyses and forecasts using an ensemble Kalman filter (EnKF) and the Advanced Hurricane Weather Research and Forecasting Model (AHW) are evaluated from the 2009 North Atlantic hurricane season. This data assimilation system involved cycling observations that included conventional in situ data, tropical cyclone (TC) position, and minimum SLP and synoptic dropsondes each 6 h using a 96-member ensemble on a 36-km domain for three months. Similar to past studies, observation assimilation systematically reduces the TC position and minimum SLP errors, except for strong TCs, which are characterized by large biases due to grid resolution. At 48 different initialization times, an AHW forecast on 12-, 4-, and 1.33-km grids is produced with initial conditions drawn from a single analysis member. Whereas TC track analyses and forecasts exhibit a pronounced northward bias, intensity forecast errors are similar to (lower than) the NWS Hurricane Weather Research Model (HWRF) and GFDL forecasts for forecast lead times ≤60 h (>60 h), with the largest track errors associated with the weakest systems, such as Tropical Storm (TS) Erika. Several shortcomings of the data assimilation system are addressed through postseason sensitivity tests, including using the maximum 800-hPa circulation to identify the TC position during assimilation and turning off the quality control for the TC minimum SLP observation when the initial intensity is far too weak. In addition, the improved forecast of TS Erika relative to HWRF is shown to be related to having initial conditions that are more representative of a sheared TC and not using a cumulus parameterization.

Ensemble-based techniques have been widely utilized in estimating uncertainties in various problems of interest in geophysical applications. A new cloud retrieval method is proposed based on the particle filter (PF) by using ensembles of cloud information in the framework of Gridpoint Statistical Interpolation (GSI) system. The PF cloud retrieval method is compared with the Multivariate Minimum Residual (MMR) method that was previously established and verified. Cloud retrieval experiments involving a variety of cloudy types are conducted with the PF and MMR methods with measurements of infrared radiances on multi-sensors onboard both geostationary and polar satellites, respectively. It is found that the retrieved cloud masks with both methods are consistent with other independent cloud products. MMR is prone to producing ambiguous small-fraction clouds, while PF detects clearer cloud signals, yielding closer heights of cloud top and cloud base to other references. More collections of small-fraction particles are able to effectively estimate the semi-transparent high clouds. It is found that radiances with high spectral resolutions contribute to quantitative cloud top and cloud base retrievals. In addition, a different way of resolving the filtering problem over each model grid is tested to better aggregate the weights with all available sensors considered, which is proven to be less constrained by the ordering of sensors. Compared to the MMR method, the PF method is overall more computationally efficient, and the cost of the model grid-based PF method scales more directly with the number of computing nodes.

The impact of assimilating radiance observations from the Advanced Microwave Sounding Unit-A (AMSU-A) on forecasts of several tropical cyclones (TCs) was studied using the Weather Research and Forecasting Model (WRF) and a limited-area ensemble Kalman filter (EnKF). Analysis/forecast cycling experiments with and without AMSU-A radiance assimilation were performed over the Atlantic Ocean for the period 11 August–13 September 2008, when five named storms formed. For convenience, the radiance forward operators and bias-correction coefficients, along with the majority of quality-control decisions, were computed by a separate, preexisting variational assimilation system. The bias-correction coefficients were obtained from 3-month offline statistics and fixed during the EnKF analysis cycles. The vertical location of each radiance observation, which is required for covariance localization in the EnKF, was taken to be the level at which the AMSU-A channels’ weighting functions peaked. Deterministic 72-h WRF forecasts initialized from the ensemble-mean analyses were evaluated with a focus on TC prediction. Assimilating AMSU-A radiances produced better depictions of the environmental fields when compared to reanalyses and dropwindsonde observations. Radiance assimilation also resulted in substantial improvement of TC track and intensity forecasts with track-error reduction up to 16% for forecasts beyond 36 h. Additionally, assimilating both radiances and satellite winds gave markedly more benefit for TC track forecasts than solely assimilating radiances.

With the growing use of tropical cyclone (TC) best-track information for weather and climate applications, it is important to understand the uncertainties that are contained in the TC position and intensity information. Here, an attempt is made to quantify the position uncertainty using National Hurricane Center (NHC) advisory information, as well as intensity uncertainty during times without aircraft data, by verifying Dvorak minimum sea level pressure (SLP) and maximum wind speed estimates during times with aircraft reconnaissance information during 2000–09. In a climatological sense, TC position uncertainty decreases for more intense TCs, while the uncertainty of intensity, measured by minimum SLP or maximum wind speed, increases with intensity. The standard deviation of satellite-based TC intensity estimates can be used as a predictor of the consensus intensity error when that consensus includes both Dvorak and microwave-based estimates, but not when it contains only Dvorak-based values. Whereas there has been a steady decrease in seasonal TC position uncertainty over the past 10 yr, which is likely due to additional data available to NHC forecasters, the seasonal TC minimum SLP and maximum wind speed values are fairly constant, with year-to-year variability due to the mean intensity of all TCs during that season and the frequency of aircraft reconnaissance.

An upper-level cold bias in potential temperature tendencies of 10 K day−1, strongest at the top of the model, is observed in Weather Research and Forecasting (WRF) model forecasts. The bias originates from the Rapid Radiative Transfer Model longwave radiation physics scheme and can be reduced substantially by 1) modifying the treatment within the scheme by adding a multilayer buffer between the model top and top of the atmosphere and 2) constraining stratospheric water vapor to remain within the estimated climatology in the stratosphere. These changes reduce the longwave heating rate bias at the model top to ±0.5 K day−1. Corresponding bias reductions are also seen, particularly near the tropopause.

A multiphysics and a stochastic kinetic-energy backscatter scheme are employed to represent model uncertainty in a mesoscale ensemble prediction system using the Weather Research and Forecasting model. Both model-error schemes lead to significant improvements over the control ensemble system that is simply a downscaled global ensemble forecast with the same physics for each ensemble member. The improvements are evident in verification against both observations and analyses, but different in some details. Overall the stochastic kinetic-energy backscatter scheme outperforms the multiphysics scheme, except near the surface. Best results are obtained when both schemes are used simultaneously, indicating that the model error can best be captured by a combination of multiple schemes.

Estimates of three components of an uncertainty budget for a coastal ocean model in a wind-forced regime are made based on numerical simulations. The budget components behave differently in the shelf regime, inshore of the 200-m isobath, and the slope-interior regime, between the 200-m isobath and a fixed longitude (126°W) that is roughly 150 km offshore. The first of the three budget components is an estimate of the uncertainty in the ocean state given only a known history of wind stress forcing, with errors in the wind forcing estimated from differences between operational analyses. It is found that, over the continental shelf, the response to wind forcing is sufficiently strong and deterministic that significant skill in estimating shelf circulation can be achieved with knowledge only of the wind forcing, and no ocean data, for wind fields with these estimated errors. The second involves initial condition error and its influence on uncertainty, including both error growth with time from well-known initial conditions and error decay with time from poorly known initial conditions but with well-known wind forcing. The third component is that of boundary condition error and its influence on the interior solutions, including the dependence of that influence on the specific location along the boundary of the boundary condition error. Boundary condition errors with amplitude comparable to the root-mean-square variability at the boundary lead eventually to errors equal to the root-mean-square variability in the slope-interior regime, and somewhat smaller errors in the shelf regime. Covariance estimates based on differences of the wind-forced solutions from the ensemble mean are not dramatically different from those based on the full fields, and do not show strong state dependence.

Ensemble Kalman filter (EnKF) techniques have been proposed for obtaining atmospheric state estimates on the scale of individual convective storms from radar and other observations, but tests of these methods with observations of real convective storms are still very limited. In the current study, radar observations of the 8 May 2003 Oklahoma City tornadic supercell thunderstorm were assimilated into the National Severe Storms Laboratory (NSSL) Collaborative Model for Multiscale Atmospheric Simulation (NCOMMAS) with an EnKF method. The cloud model employed 1-km horizontal grid spacing, a single-moment bulk precipitation-microphysics scheme, and a base state initialized with sounding data. A 50-member ensemble was produced by randomly perturbing base-state wind profiles and by regularly adding random local perturbations to the horizontal wind, temperature, and water vapor fields in and near observed precipitation. In a reference experiment, only Doppler-velocity observations were assimilated into the NCOMMAS ensemble. Then, radar-reflectivity observations were assimilated together with Doppler-velocity observations in subsequent experiments. Influences that reflectivity observations have on storm-scale analyses were revealed through parameter-space experiments by varying observation availability, observation errors, ensemble spread, and choices for what model variables were updated when a reflectivity observation was assimilated. All experiments produced realistic storm-scale analyses that compared favorably with independent radar observations. Convective storms in the NCOMMAS ensemble developed more quickly when reflectivity observations and velocity observations were both assimilated rather than only velocity, presumably because the EnKF utilized covariances between reflectivity and unobserved model fields such as cloud water and vertical velocity in efficiently developing realistic storm features. Recurring spatial patterns in the differences between predicted and observed reflectivity were noted particularly at low levels, downshear of the supercell’s updraft, in the anvil of moderate-to-light precipitation, where reflectivity in the model was typically lower than observed. Bias errors in the predicted rain mixing ratios and/or the size distributions that the bulk scheme associates with these mixing ratios are likely responsible for this reflectivity underprediction. When a reflectivity observation is assimilated, bias errors in the model fields associated with reflectivity (rain, snow, and hail–graupel) can be projected into other model variables through the ensemble covariances. In the current study, temperature analyses in the downshear anvil at low levels, where reflectivity was underpredicted, were very sensitive both to details of the assimilation algorithm and to ensemble spread in temperature. This strong sensitivity suggests low confidence in analyses of low-level cold pools obtained through reflectivity-data assimilation.

The quality of convective-scale ensemble forecasts, initialized from analysis ensembles obtained through the assimilation of radar observations using an ensemble Kalman filter (EnKF), is investigated for cases whose behaviors span supercellular, linear, and multicellular organization. This work is the companion to Part I, which focused on the quality of analyses during the 60-min analysis period. Here, the focus is on 30-min ensemble forecasts initialized at the end of that period. As in Part I, the Weather Research and Forecasting (WRF) model is employed as a simplified cloud model at 2-km horizontal grid spacing. Various observation-space and state-space verification metrics, computed both for ensemble means and individual ensemble members, are employed to assess the quality of ensemble forecasts comparatively across cases. While the cases exhibit noticeable differences in predictability, the forecast skill in each case, as measured by various metrics, decays on a time scale of tens of minutes. The ensemble spread also increases rapidly but significant outlier members or clustering among members are not encountered. Forecast quality is seen to be influenced to varying degrees by the respective initial soundings. While radar data assimilation is able to partially mitigate some of the negative effects in some situations, the supercell case, in particular, remains difficult to predict even after 60 min of data assimilation.

Premise of the Study Biological collections are uniquely poised to inform the stewardship of life on Earth in a time of cataclysmic biodiversity loss. Efforts to fully leverage collections are impeded by a lack of trained taxonomists and a lack of interest and engagement by the public. We provide a model of a crowd-sourced data collection project that produces quality taxonomic data sets and empowers citizen scientists through real contributions to science. Entitled MicroPlants, the project is a collaboration between taxonomists, citizen science experts, and teachers and students from universities and K–12. Methods We developed an online tool that allows citizen scientists to measure photographs of specimens of a hyper-diverse group of liverworts from a biodiversity hotspot. Results Using the MicroPlants online tool, citizen scientists are generating high-quality data, with preliminary analysis indicating non-expert data can be comparable to expert data. Discussion More than 11,000 users from both the website and kiosk versions have contributed to the data set, which is demonstrably aiding taxonomists working toward establishing conservation priorities within this group. MicroPlants provides opportunities for public participation in authentic science research. The project's educational component helps move youth toward engaging in scientific thinking and has been adopted by several universities into curriculum for both biology and non-biology majors.

Previous simulations of dipole vortices propagating through rotating, stratified fluid have revealed small-scale inertia–gravity waves that are embedded within the dipole near its leading edge and are approximately stationary relative to the dipole. The mechanism by which these waves are generated is investigated, beginning from the observation that the dipole can be reasonably approximated by a balanced quasigeostrophic (QG) solution. The deviations from the QG solution (including the waves) then satisfy linear equations that come from linearization of the governing equations about the QG dipole and are forced by the residual tendency of the QG dipole (i.e., the difference between the time tendency of the QG solution and that of the full primitive equations initialized with the QG fields). The waves do not appear to be generated by an instability of the balanced dipole, as homogeneous solutions of the linear equations amplify little over the time scale for which the linear equations are valid. Linear solutions forced by the residual tendency capture the scale, location, and pattern of the inertia–gravity waves, although they overpredict the wave amplitude by a factor of 2. There is thus strong evidence that the waves are generated as a forced linear response to the balanced flow. The relation to and differences from other theories for wave generation by balanced flows, including those of Lighthill and Ford et al., are discussed.