Search

The intent of this paper is to encourage improved numerical implementation of land models. Our contributions in this paper are twofold. First, we present a unified framework to formulate and implement land model equations. We separate the representation of physical processes from their numerical solution, enabling the use of established robust numerical methods to solve the model equations. Second, we introduce a set of synthetic test cases (the laugh tests) to evaluate the numerical implementation of land models. The test cases include storage and transmission of water in soils, lateral subsurface flow, coupled hydrological and thermodynamic processes in snow, and cryosuction processes in soil. We consider synthetic test cases as "laugh tests" for land models because they provide the most rudimentary test of model capabilities. The laugh tests presented in this paper are all solved with the Structure for Unifying Multiple Modeling Alternatives (SUMMA) model implemented using the Suite of Nonlinear and Differential/Algebraic Equation Solvers (SUNDIALS). The numerical simulations from SUMMA/SUNDIALS are compared against 1) solutions to the synthetic test cases from other models documented in the peer-reviewed literature, 2) analytical solutions, and 3) observations made in laboratory experiments. In all cases, the numerical simulations are similar to the benchmarks, building confidence in the numerical model implementation. We posit that some land models may have difficulty in solving these benchmark problems. Dedicating more effort to solving synthetic test cases is critical in order to build confidence in the numerical implementation of land models. [ABSTRACT FROM AUTHOR]

The priority of an operational forecast center is to issue watches, warnings, and advisories to notify the public about the inherent risks and dangers of a particular event. Occasionally, events occur that do not meet advisory or warning criteria, but still have a substantial impact on human life and property. Short-lived snow bursts are a prime example of such a phenomenon. While these events are typically characterized by small snow accumulations, they often cause very low visibilities and rapidly deteriorating road conditions, both of which are a major hazard to motorists. On the afternoon of 28 January 2010, two such snow bursts moved through the Ottawa River valley and lower St. Lawrence River valley, and created havoc on area roads, resulting in collisions and injuries. Using the National Centers for Environmental Prediction (NCEP) North American Regional Reanalysis (NARR), these snow bursts were found to be associated with an approaching strong upper-tropospheric trough and the passage of an arctic front. While convection or squall lines are not common in January in Canada, snow bursts are shown to be associated with strong quasigeostrophic forcing for ascent and low-level frontogenesis, in the presence of both convective and conditional symmetric instability. Finally, this paper highlights the need for the development of a standard subadvisory criterion warning of short-lived but high-impact winter weather events, which operational forecasters can issue and quickly disseminate to the general public. [ABSTRACT FROM AUTHOR]

The Pacific equatorial cold tongue plays a leading role in Earth's strongest and most predictable climate signals. To illuminate the processes governing cold tongue temperatures, the upper-ocean heat budget is explored using the GFDL-FLOR coupled GCM (the forecast-oriented low ocean resolution version of CM2.5). Starting from the exact temperature budget for layers of time-varying thickness, the layer temperature tendency terms are studied using hourly-, daily-, and monthly-mean output from a 30-yr simulation driven by present-day radiative forcings. The budget is then applied to 1) a surface mixed layer whose temperature is highly correlated with SST, in which the air–sea heat flux is balanced mainly by downward diffusion of heat across the layer base, and 2) a thicker advective layer that subsumes most of the vertical mixing, in which the air–sea heat flux is balanced mainly by monthly-scale advection. The surface warming from shortwave fluxes and submonthly meridional advection and the subsurface cooling from monthly vertical advection are both shown to be essential to maintain the cold tongue thermal stratification against the destratifying effects of vertical mixing. Although layer undulations strongly mediate the tendency terms on diurnal-to-interannual scales, the 30-yr-mean tendencies are found to be well summarized by analogous budgets developed for stationary but spatially varying layers. The results are used to derive practical simplifications of the exact budget, to support the analyses in Part II of this paper, and to facilitate broader application of heat budget analyses when evaluating and comparing climate simulations. [ABSTRACT FROM AUTHOR]

Understanding and forecasting tropical cyclone (TC) intensity change continues to be a paramount challenge for the research and operational communities, partly because of inherent systematic biases contained in model guidance, which can be difficult to diagnose. The purpose of this paper is to present a method to identify such systematic biases by comparing forecasts characterized by large intensity errors with analog forecasts that exhibit small intensity errors. The methodology is applied to the 2015 version of the Hurricane Weather Research and Forecasting (HWRF) Model retrospective forecasts in the North Atlantic (NATL) and eastern North Pacific (EPAC) basins during 2011-14. Forecasts with large 24-h intensity errors are defined to be in the top 15% of all cases in the distribution that underforecast intensity. These forecasts are compared to analog forecasts taken from the bottom 50% of the error distribution. Analog forecasts are identified by finding the case that has 0-24-h intensity and wind shear magnitude time series that are similar to the large intensity error forecasts. Composite differences of the large and small intensity error forecasts reveal that the EPAC large error forecasts have weaker reflectivity and vertical motion near the TC inner core from 3 h onward. Results over the NATL are less clear, with the significant differences between the large and small error forecasts occurring radially outward from the TC core. Though applied to TCs, this analog methodology could be useful for diagnosing systematic model biases in other applications. [ABSTRACT FROM AUTHOR]

This paper proposes a new method to properly define and accurately determine the vortex center of a model-predicted tropical cyclone (TC) from a dynamic perspective. Ideally, a dynamically determined TC vortex center should maximize the gradient wind balance or, equivalently, minimize the gradient wind imbalance measured by an energy norm over the TC vortex. In practice, however, such an energy norm cannot be used to easily and unambiguously determine the TC vortex center. An alternative yet practical approach is developed to dynamically and unambiguously define the TC vortex center. In this approach, the TC vortex core of near-solid-body rotation is modeled by a simple parametric vortex constrained by the gradient wind balance. Therefore, the modeled vortex can fit simultaneously the perturbation pressure and streamfunction of the TC vortex part (extracted from the model-predicted fields) over the TC vortex core area (within the radius of maximum tangential wind), while the misfit is measured by a properly defined cost function. Minimizing this cost function yields the desired dynamic optimality condition that can uniquely define the TC vortex center. Using this dynamic optimality condition, a new method is developed in the form of iterative least squares fit to accurately determine the TC vortex center. The new method is shown to be efficient and effective for finding the TC vortex center that accurately satisfies the dynamic optimality condition. [ABSTRACT FROM AUTHOR]

To study tropical cyclones and generate forecast applications using satellite observations, researchers often consolidate disparate sources of raw and ancillary data. Data consolidation involves obtaining, collocating, and intercalibrating data from different sensors and derived products; calculating environmental diagnostics from a homogeneous source; and standardizing these various products for a straightforward analysis. To alleviate preprocessing issues and provide a long-term, global digital dataset of tropical cyclone satellite observations, we construct the Tropical Cyclone Precipitation, Infrared, Microwave, and Environmental Dataset (TC PRIMED). TC PRIMED contains tropical cyclone–centric 1) intercalibrated, multichannel, multisensor microwave brightness temperatures, 2) retrieved rainfall from NASA's Goddard Profiling Algorithm (GPROF), 3) nearly coincident geostationary satellite infrared brightness temperatures and derived metrics, 4) tropical cyclone position and intensity information, 5) ECMWF fifth-generation reanalysis fields and derived environmental diagnostics, and 6) precipitation radar observations from the TRMM and GPM Core Observatory satellites. TC PRIMED consists of over 176,000 overpasses of 2,101 storms from 1998 to 2019, providing researchers with an analysis-ready dataset to promote and support research into improving our understanding of the relationship between tropical cyclone convective and precipitation structure, intensity, and environment. Here, we briefly describe data sources and processing steps to create TC PRIMED. To demonstrate TC PRIMED's potential utility for studying important tropical cyclone processes and for application development, we present a shear-relative composite analysis of several multisensor satellite variables relative to the tropical cyclone lifetime maximum intensity. The composite analysis provides a simple example of how TC PRIMED can benefit future studies to advance our understanding of tropical cyclones and improve forecasts. [ABSTRACT FROM AUTHOR]

Process-oriented diagnostics for Madden-Julian oscillation (MJO) simulations are being developed to facilitate improvements in the representation of the MJO in weather and climate models. These process-oriented diagnostics are intended to provide insights into how parameterizations of physical processes in climate models should be improved for a better MJO simulation. This paper proposes one such process-oriented diagnostic, which is designed to represent sensitivity of simulated convection to environmental moisture: composites of a relative humidity (RH) profile based on precipitation percentiles. The ability of the RH composite diagnostic to represent the diversity of MJO simulation skill is demonstrated using a group of climate model simulations participating in phases 3 and 5 of the Coupled Model Intercomparison Project (CMIP3 and CMIP5). A set of scalar process metrics that captures the key physical attributes of the RH diagnostic is derived and their statistical relationship with indices that quantify the fidelity of the MJO simulation is tested. It is found that a process metric that represents the amount of lower-tropospheric humidity increase required for a transition from weak to strong rain regimes has a robust statistical relationship with MJO simulation skill. The results herein suggest that moisture sensitivity of convection is closely related to a GCM's ability to simulate the MJO. [ABSTRACT FROM AUTHOR]

In 2008, abundant dropwindsonde data were collected during both reconnaissance and surveillance flights in and around tropical cyclones (TCs) in the western North Pacific basin under the framework of The Observing System Research and Predictability Experiment (THORPEX)-Pacific Asian Regional Campaign (T-PARC). The National Centers for Environmental Prediction Global Forecast System (GFS) showed significant track improvements for Typhoon Sinlaku (2008) after the assimilation of dropwindsonde data. For this particular typhoon, the potential vorticity (PV) diagnosis is adopted to understand the key factors affecting the track. A data denial run initialized at 0000 UTC 10 September is examined to evaluate how the extra data collected during T-PARC improve GFS track forecasts. A quantitative analysis of the steering flow based on the PV diagnosis indicates that the Pacific subtropical high to the east of Sinlaku is a primary factor that advects Sinlaku northwestward, while the monsoon trough plays a secondary role. The assimilation of dropwindsonde data improves the structure and intensity of the initial vortex and maintains the forecast vortex structure in the vertical. The difference in the vertical extent of the vortices could be regarded as a cause for the discrepancy in steering flow between runs with and without the dropwindsonde data. This paper highlights the importance of improved analyses of the vertical TC structure, and thus of a representative steering flow in the deep troposphere during the forecasts. [ABSTRACT FROM AUTHOR]

Even in the absence of external forcing, climate models often exhibit long-term trends that cannot be attributed to natural variability. This so-called climate drift arises for various reasons including the following: perturbations to the climate system on coupling component models together and deficiencies in model physics and numerics. When examining trends in historical or future climate simulations, it is important to know the error introduced by drift so that action can be taken where necessary. This study assesses the importance of drift for a number of climate properties at global and local scales. To illustrate this, the present paper focuses on simulated trends over the second half of the twentieth century. While drift in globally averaged surface properties is generally considerably smaller than observed and simulated twentieth-century trends, it can still introduce nontrivial errors in some models. Furthermore, errors become increasingly important at smaller scales. The direction of drift is not systematic across different models or variables, as such drift is considerably reduced in the multimodel mean. Despite drift being primarily associated with ocean adjustment, it is also apparent in atmospheric variables. For example, most models have local drift magnitudes in surface air and ocean temperatures that are typically between 15% and 35% of the twentieth-century simulation trend magnitudes for 1950-2000. Below depths of 1000-2000 m, drift dominates over any forced trend in most regions. As such steric sea level is strongly affected and for some models and regions the sea level trend direction is reversed. Thus depending on the application, drift may be negligible or may make up an important part of the simulated trend. [ABSTRACT FROM AUTHOR]

This paper applies a new field significance test to establish the existence and consistency of ENSO teleconnection patterns across models and observations. An ENSO teleconnection pattern is defined as a field of regression coefficients between an index of the tropical Pacific sea surface temperature and a field of variables such as surface air temperature or precipitation. The test is applied to boreal winter and summer in six continents using observations and hindcasts from the Development of a European Multimodel Ensemble System for Seasonal-to-Interannual Prediction (DEMETER) and the ENSEMBLE-based predictions of climate changes and their impacts (ENSEMBLES) projects. This comparison represents one of the most comprehensive and up-to-date assessments of the extent to which ENSO teleconnection patterns exist and can be reproduced by coupled models. Statistically significant ENSO teleconnection patterns are detected in both observations and models and in all continents and in both winter and summer seasons, except in two cases: 1) Europe (both seasons and variables), and 2) North America (both variables in boreal summer). Despite many ENSO teleconnection patterns being significant, however, the patterns do not necessarily agree between observations and models. The degree of agreement between models and observations is characterized as 'robust,' 'moderate,' or 'low.' Only Australia and South America are found to have robust agreement between ENSO teleconnection patterns, and then only for limited seasons and variables. Although many of our conclusions regarding teleconnection patterns conform to previous studies, there are exceptions, including the fact that the teleconnection for boreal winter precipitation is generally accepted to exist in Africa but in fact has only low agreement with model simulations, while that in Asia is not widely recognized to exist but is found to be significant and in moderate agreement with model teleconnections. [ABSTRACT FROM AUTHOR]

In 1980 the Holland tropical cyclone (TC) wind profile model was introduced. This simple model was originally intended to estimate the wind profile based on limited surface pressure information alone. For this reason and its relative simplicity, the model has been used in many practical applications. In this paper the potential of a simplified version of the Holland B parameter, which is related to the shape of the tangential wind profile, is explored as a powerful diagnostic tool for monitoring TC structure. The implementation examined is based on the limited information (maximum wind, central pressure, radius and pressure of the outer closed isobar, radii of operationally important wind radii, etc.) that is typically available in operational models and routine analyses of TC structure. This 'simplified Holland B' parameter is shown to be sensitive to TC intensity, TC size, and the rate of radial decay of the tangential winds, but relatively insensitive to the radius of maximum winds. A climatology of the simplified Holland B parameter based on historical best-track data is also developed and presented, providing the expected natural ranges of variability. The relative simplicity, predictable variability, and desirable properties of the simplified Holland B parameter make it ideal for a variety of applications. Examples of how the simplified Holland B parameter can be used for improving forecaster guidance, developing TC structure tools, diagnosing TC model output, and understanding and comparing the climatological variations of TC structure are presented. [ABSTRACT FROM AUTHOR]

In recent years, aircraft data from mountain waves have been primarily analyzed using velocity and temperature power spectrum and momentum flux estimation. Herein it is argued that energy flux wavelets (i.e., pressure--velocity wavelet cross-spectra) provide a more powerful tool for locating and classifying different types of mountain waves. In the first part of the paper, pressure--velocity cross-spectra using various linear mountain-wave solutions are shown to be capable of disentangling collocated waves with different propagation directions and wavelength. A field of group velocity vectors can also be determined. In the second part, the energy flux wavelet technique is applied to five cases of mountain waves entering the stratosphere from the Terrain-Induced Rotor Experiment (T-REX) in 2006. Perturbation pressure along the flight track is determined using aircraft static pressure corrected hydrostatically with GPS altitude. In four of the cases, collocated long up-propagating and short down-propagating waves are seen in the stratosphere. These waves have strong, but opposite, p′′ w′′ cospectra. In one of these cases, a patch of turbulence is collocated with the up and down waves. In two other cases, trapped waves riding on the tropopause inversion layer (TIL) are seen. These trapped waves have p′′ w′′ quadrature spectra that reverse sign across the tropopause. These newly discovered wave types may arise from secondary wave generation (i.e., a nonlinear transfer of energy from the long vertically propagating waves to shorter modes). [ABSTRACT FROM AUTHOR]

This paper describes a technique for quantifying and removing biases from ensemble forecasts of hydrometeorological and hydrologic variables. The technique makes no a priori assumptions about the distributional form of the variables, which is often unknown or difficult to model parametrically. The aim is to estimate the conditional cumulative distribution function (ccdf) of the observed variable given a (possibly biased) real-time ensemble forecast. This ccdf represents the “true” probability distribution of the forecast variable, subject to sampling uncertainties. In the absence of a known distributional form, the ccdf should be estimated nonparametrically. It is noted that the probability of exceeding a threshold of the observed variable, such as flood stage, is equivalent to the expectation of an indicator variable defined for that threshold. The ccdf is then modeled through a linear combination of the indicator variables of the forecast ensemble members. The technique is based on Bayesian optimal linear estimation of indicator variables and is analogous to indicator cokriging (ICK) in geostatistics. By developing linear estimators for the conditional expectation of the observed variable at many thresholds, ICK provides a discrete approximation of the full ccdf. Since ICK minimizes the conditional error variance of the indicator variable at each threshold, it effectively minimizes the continuous ranked probability score (CRPS) when infinitely many thresholds are employed. The technique is used to bias-correct precipitation ensemble forecasts from the NCEP Global Ensemble Forecast System (GEFS) and streamflow ensemble forecasts from the National Weather Service (NWS) River Forecast Centers (RFCs). Split-sample validation results are presented for several attributes of ensemble forecast quality, including reliability and discrimination. In general, the forecast biases were substantially reduced following ICK. Overall, the technique shows significant potential for bias-correcting ensemble forecasts whose distributional form is unknown or nonparametric. [ABSTRACT FROM AUTHOR]

Comparisons of precipitation between general circulation models (GCMs) and observations are often confounded by a mismatch between model output and instrument measurements, including variable type and temporal and spatial resolutions. To mitigate these differences, the radar-simulator Quickbeam within the Cloud Feedback Model Intercomparison Project (CFMIP) Observation Simulator Package (COSP) simulates reflectivity from model variables at the subgrid scale. This work adapts Quickbeam to the dual-frequency precipitation radar (DPR) on board the Global Precipitation Measurement (GPM) satellite. The longer wavelength of the DPR is used to evaluate moderate to heavy precipitation in GCMs, which is missed when Quickbeam is used as a cloud radar simulator. Latitudinal and land–ocean comparisons are made between COSP output from the Community Atmospheric Model version 5 (CAM5) and DPR data. Additionally, this work improves the COSP subgrid algorithm by applying a more realistic, nondeterministic approach to assigning GCM gridbox convective cloud cover when convective cloud is not provided as a model output. Instead of assuming a static 5% convective cloud coverage, DPR convective precipitation coverage is used as a proxy for convective cloud coverage. For example, DPR observations show that convective rain typically only covers about 1% of a 2° grid box, but that the median convective rain area increases to over 10% in heavy rain cases. In our CAM5 tests, the updated subgrid algorithm improved the comparison between reflectivity distributions when the convective cloud cover is provided versus the default 5% convective cloud-cover assumption. [ABSTRACT FROM AUTHOR]

The latest configuration of the Hadley Centre Global Environmental Model, version 3 (HadGEM3), contains significant changes in the formulation of warm rain processes and aerosols. We evaluate the impacts of these changes in the simulation of warm rain formation processes using A-Train observations. We introduce a new model evaluation tool, quartile-based contoured frequency by optical depth diagrams (CFODDs), in order to fill in some blind spots that conventional CFODDs have. Results indicate that HadGEM3 has weak linkage between the size of particle radius and warm rain formation processes, and switching to the new warm rain microphysics scheme causes more difference in warm rain formation processes than switching to the new aerosol scheme through reducing overly produced drizzle mode in HadGEM3. Finally, we run an experiment in which we perturb the second aerosol indirect effect (AIE) to study the rainfall–aerosol interaction in HadGEM3. Since the large changes in the cloud droplet number concentration (CDNC) appear in the AIE experiment, a large impact in warm rain diagnostics is expected. However, regions with large fractional changes in CDNC show a muted change in precipitation, arguably because large-scale constraints act to reduce the impact of such a big change in CDNC. The adjustment in cloud liquid water path to the AIE perturbation produces a large negative shortwave forcing in the midlatitudes. [ABSTRACT FROM AUTHOR]

The physical and dynamical processes associated with warm conveyor belts (WCBs) importantly affect midlatitude dynamics and are sources of forecast uncertainty. Moreover, WCBs modulate the large-scale extratropical circulation and can communicate and amplify forecast errors. Therefore, it is desirable to assess the representation of WCBs in numerical weather prediction (NWP) models in particular on the medium to subseasonal forecast range. Most often, WCBs are identified as coherent bundles of Lagrangian trajectories that ascend in a time interval of 2 days from the lower to the upper troposphere. Although this Lagrangian approach has advanced the understanding of the involved processes significantly, the calculation of trajectories is computationally expensive and requires NWP data at a high spatial [O(~1°)], vertical [O(~10 hPa)], and temporal resolution [O(;3-6 h)]. In this study, we present a statistical framework that derives footprints of WCBs from coarser NWP data that are routinely available. To this end, gridpoint-specific multivariate logistic regression models are developed for the Northern Hemisphere using meteorological parameters from ERA-Interim data as predictors and binary footprints of WCB inflow, ascent, and outflow based on a Lagrangian dataset as predictands. Stepwise forward selection identifies the most important predictors for these three WCB stages. The logistic models are reliable in replicating the climatological frequency of WCBs as well as the footprints of WCBs at instantaneous time steps. The novel framework is a first step toward a systematic evaluation of WCB representation in large datasets such as subseasonal ensemble reforecasts or climate projections. [ABSTRACT FROM AUTHOR]

The interannual variability, trends, and the mean climatology of East African long rains are difficult for models to simulate. This is in part because long rains do not respond in a simple way to large-scale modes of variability such as ENSO and because of interactions with complex topography. Here we focus on the Kenyan regional climate in the ERA-Interim dataset during the long rains to create a set of atmospheric diagnostics that can be applied to the evaluation of climate models. Subseasonal observed rainfall and reanalysis reveal that very wet seasons and very dry seasons develop differently at the beginning of the season. Subseasonal aggregation periods (days 60–80, 80–100, 90–120, 120–150) highlight local (e.g., midtropospheric ascent, moisture flux convergence in the lower to midtroposphere, and midtropospheric moisture) and large-scale (e.g., midtropospheric zonal winds over central Africa, upper-tropospheric velocity potential) diagnostics that are useful to evaluate model atmospheric circulation affecting Kenyan rainfall in mean and wet or dry extremes. [ABSTRACT FROM AUTHOR]

Global gridded precipitation products have proven essential for many applications ranging from hydrological modeling and climate model validation to natural hazard risk assessment. They provide a global picture of how precipitation varies across time and space, specifically in regions where ground-based observations are scarce. While the application of global precipitation products has become widespread, there is limited knowledge on how well these products represent the magnitude and frequency of extreme precipitation—the key features in triggering flood hazards. Here, five global precipitation datasets (MSWEP, CFSR, CPC, PERSIANN-CDR, and WFDEI) are compared to each other and to surface observations. The spatial variability of relatively high precipitation events (tail heaviness) and the resulting discrepancy among datasets in the predicted precipitation return levels were evaluated for the time period 1979–2017. The analysis shows that 1) these products do not provide a consistent representation of the behavior of extremes as quantified by the tail heaviness, 2) there is strong spatial variability in the tail index, 3) the spatial patterns of the tail heaviness generally match the Köppen–Geiger climate classification, and 4) the predicted return levels for 100 and 1000 years differ significantly among the gridded products. More generally, our findings reveal shortcomings of global precipitation products in representing extremes and highlight that there is no single global product that performs best for all regions and climates. [ABSTRACT FROM AUTHOR]

With the recognition that equatorial Pacific precipitation anomalies are fundamental to global teleconnections during ENSO winters, the present research applies vertically integrated moist static energy (MSE) budget analysis to historical simulations of CMIP5 models. Process-based assessment is carried out to understand if the models capture the differing processes that account for regional precipitation anomalies along the equatorial Pacific and to isolate a few leading processes that account for the diversified precipitation response to similar SST forcing and vice versa. To assess SST biases in CMIP5, analysis is also carried out in AMIP5 solutions. Diagnostics reveal that models have limitations in representing the ''sign'' of MSE sources and sinks and, even if they do, compensating errors dominate the budget. The diverse response in precipitation depends on model parameterizations that determine anomalous net radiative flux divergence in the column, free troposphere moisture, and MSE export out of the column, although these processes are not independent. Diagnostics derived from AMIP5 solutions support the findings from CMIP5. The implication is that biases in representing any one of these processes are expected to imprint on others, acknowledging the tight connections among moisture, convection, and radiation. CMIP5 models have limitations in representing the basic states in SST and precipitation over the Niño-3.4 region, and the different convective regimes over the equatorial central and eastern Pacific regions with implications for ENSO. Study limitations are that MSE sources/sinks depend on parameterizations and their interactions, making it difficult to isolate one particular process for attribution. Budgets estimated from monthly anomalies do not capture contributions from high-frequency variability that are vital in closing the budgets. [ABSTRACT FROM AUTHOR]

Mesoscale activities over the equatorial Pacific Ocean are dominated by the Rossby and Yanai modes of tropical instability waves (TIWs). The TIW-induced surface velocity has not been accurately estimated in previous diagnostic models, especially for the meridional component across the equator. This study develops a diagnostic model that retains the acceleration terms to estimate the TIW surface velocity from the satellite-observed sea surface height. Validated against moored observations, the velocity across the equator is accurately estimated for the first time, much improved from existing products. The results identify the Rossby- and Yanai-mode TIWs as the northwest-southeastward (NW-SE) velocity oscillations north of the equator and the northeast-southwestward (NE-SW) velocity oscillations on the equator, respectively. Barotropic instability is the dominant energy source of the two TIW modes. The NE-SW velocity oscillation of the Yanai mode is associated with the counterclockwise shear of the South Equatorial Current on the equator. The two TIW modes induce different sea surface temperature patterns and vertical motions. Accurate estimates of TIW velocity are important for studying equatorial ocean dynamics and climate variability in the tropical Pacific Ocean. [ABSTRACT FROM AUTHOR]

The distinctive monsoon climate over East Asia, which is affected by the vast Eurasian continent and Pacific Ocean basin and the high-altitude Tibetan Plateau, provides arguably the best testbed for evaluating the competence of Earth system climate models. Here, a set of diagnostic metrics, consisting of 14 items and 7 variables, is specifically developed. This physically intuitive set of metrics focuses on the essential features of the East Asian summer monsoon (EASM) and East Asian winter monsoon (EAWM), and includes fields that depict the climatology, the major modes of variability, and unique characteristics of the EASM. The metrics are applied to multimodel historical simulations derived from 20 models that participated in phases 3 and 5 of the Coupled Model Intercomparison Project (CMIP3 and CMIP5, respectively), along with the newly developed Nanjing University of Information Science and Technology Earth System Model, version 3. The CMIP5 models show significant improvements over the CMIP3 models in terms of the simulated East Asian monsoon circulation systems on a regional scale, major modes of EAWM variability, the monsoon domain and precipitation intensity, and teleconnection associated with the heat source over the Philippine Sea. Clear deficiencies persist from CMIP3 to CMIP5 with respect to capturing the major modes of EASM variability, as well as the relationship between the EASM and ENSO during El Niño developing and decay phases. The possible origins that affect models' performance are also discussed. The metrics provide a tool for evaluating the performance of Earth system climate models, and facilitating the assessment of past and projected future changes of the East Asian monsoon. [ABSTRACT FROM AUTHOR]

In agreement with observations, Earth system models participating in phase 5 of the Coupled Model Intercomparison Project (CMIP5) simulate a decline in September Arctic sea ice extent (SIE) over the past decades. However, the spread in their twenty-first-century SIE projections is large and the timing of the first ice-free Arctic summer ranges from 2020 to beyond 2100. The uncertainties arise from three sources (internal variability, model uncertainty, and scenario uncertainty), which are quantified in this study for projections of SIE. The goal is to narrow uncertainties by applying multiple diagnostic ensemble regression (MDER). MDER links future projections of sea ice extent to processes relevant to its simulation under present-day conditions using data covering the past 40 years. With this method, we can reduce model uncertainty in projections of SIE for the period 2020–44 by 30%–50% (0.8–1.3 million km2). Compared to the unweighted multimodel mean, the MDER-weighted mean projects an about 20% smaller SIE and an earlier near-disappearance of Arctic sea ice by more than a decade for a high–greenhouse gas scenario. We also show that two different methods estimating internal variability in SIE differ by 1 million km2. Regardless, the total uncertainties in the SIE projections remain large (up to 3.5 million km2, with irreducible internal variability contributing 30%) so that a precise time estimate of an ice-free Arctic proves impossible. We conclude that unweighted CMIP5 multimodel-mean projections of Arctic SIE are too optimistic and mitigation strategies to reduce Arctic warming need to be intensified. [ABSTRACT FROM AUTHOR]

Deficiencies in forecast models commonly stem from inadequate representation of physical processes; yet, improvement to any single physics component within a model may lead to degradations in other physics components or the model as a whole. In this study, a systematic investigation of physics tendencies is demonstrated to help identify and correct compensating sources of model biases. The model improvement process is illustrated by addressing a commonly known issue in warm-season rainfall forecasts from parameterized convection models: the misrepresentation of the diurnal precipitation cycle over land, especially in its timing. Recent advances in closure assumptions in mass-flux cumulus schemes have made remarkable improvements in this respect. Here, we investigate these improvements in the representation of the diurnal precipitation cycle for a spring period over the United States, and how changes to the cumulus scheme impact the model climate and the behavior of other physics schemes. The modified cumulus scheme improves both the timing of the diurnal precipitation cycle and reduces midtropospheric temperature and moisture biases. However, larger temperature and moisture biases are found in the boundary layer as compared to a predecessor scheme, along with an overamplification of the diurnal precipitation cycle, relative to observations. Guided by a tendency analysis, we find that biases in the diurnal amplitude of the precipitation cycle in our simulations, along with temperature and moisture biases in the boundary layer, originate from the land surface model. [ABSTRACT FROM AUTHOR]

To assess deep convective parameterizations in a variety of GCMs and examine the fast-time-scale convective transition, a set of statistics characterizing the pickup of precipitation as a function of column water vapor (CWV), PDFs and joint PDFs of CWV and precipitation, and the dependence of the moisture–precipitation relation on tropospheric temperature is evaluated using the hourly output of two versions of the GFDL Atmospheric Model, version 4 (AM4), NCAR CAM5 and superparameterized CAM (SPCAM). The 6-hourly output from the MJO Task Force (MJOTF)/GEWEX Atmospheric System Study (GASS) project is also analyzed. Contrasting statistics produced from individual models that primarily differ in representations of moist convection suggest that convective transition statistics can substantially distinguish differences in convective representation and its interaction with the large-scale flow, while models that differ only in spatial–temporal resolution, microphysics, or ocean–atmosphere coupling result in similar statistics. Most of the models simulate some version of the observed sharp increase in precipitation as CWV exceeds a critical value, as well as that convective onset occurs at higher CWV but at lower column RH as temperature increases. While some models quantitatively capture these observed features and associated probability distributions, considerable intermodel spread and departures from observations in various aspects of the precipitation–CWV relationship are noted. For instance, in many of the models, the transition from the low-CWV, nonprecipitating regime to the moist regime for CWV around and above critical is less abrupt than in observations. Additionally, some models overproduce drizzle at low CWV, and some require CWV higher than observed for strong precipitation. For many of the models, it is particularly challenging to simulate the probability distributions of CWV at high temperature. [ABSTRACT FROM AUTHOR]

Recently, the authors proposed a novel diagnostic to quantify the amplitude of Rossby wave packets. This diagnostic extends the local finite-amplitude wave activity (LWA) of N. Nakamura and collaborators to the primitive-equations framework and combines it with a zonal filter to remove the phase dependence. In the present work, this diagnostic is used to investigate the dynamics of upper-tropospheric Rossby wave packets, with a particular focus on distinguishing between conservative dynamics and nonconservative processes. For this purpose, a budget equation for filtered LWA is derived and its utility is tested in a hierarchy of models. Idealized simulations with a barotropic and a dry primitive-equation model confirm the ability of the LWA diagnostic to identify nonconservative local sources or sinks of wave activity. In addition, the LWA budget is applied to forecast data for an episode in which the amplitude of an upper-tropospheric Rossby wave packet was poorly represented. The analysis attributes deficiencies in the Rossby wave packet amplitude to the misrepresentation of diabatic processes and illuminates the importance of the upper-level divergent outflow as a source for the error in the wave packet amplitude. [ABSTRACT FROM AUTHOR]

A new method of determining clear-sky radiative fluxes from satellite observations for climate model evaluation is presented. The method consists of applying adjustment factors to existing satellite clear-sky broadband radiative fluxes that make the observed and simulated clear-sky flux definitions more consistent. The adjustment factors are determined from the difference between observation-based radiative transfer model calculations of monthly mean clear-sky fluxes obtained by ignoring clouds in the atmospheric column and by weighting hourly mean clear-sky fluxes with imager-based clear-area fractions. The global mean longwave (LW) adjustment factor is −2.2 W m−2 at the top of the atmosphere and 2.7 W m−2 at the surface. The LW adjustment factors are pronounced at high latitudes during winter and in regions with high upper-tropospheric humidity and cirrus cloud cover, such as over the west tropical Pacific, and the South Pacific and intertropical convergence zones. In the shortwave (SW), global mean adjustment is 0.5 W m−2 at TOA and −1.9 W m−2 at the surface. It is most pronounced over sea ice off of Antarctica and over heavy aerosol regions, such as eastern China. However, interannual variations in the regional SW and LW adjustment factors are small compared to those in cloud radiative effect. After applying the LW adjustment factors, differences in zonal mean cloud radiative effect between observations and climate models decrease markedly between 60°S and 60°N and poleward of 65°N. The largest regional improvements occur over the west tropical Pacific and Indian Oceans. In contrast, the impact of the SW adjustment factors is much smaller. [ABSTRACT FROM AUTHOR]

Tropical cyclone intensification processes are explored in six high-resolution climate models. The analysis framework employs process-oriented diagnostics that focus on how convection, moisture, clouds, and related processes are coupled. These diagnostics include budgets of column moist static energy and the spatial variance of column moist static energy, where the column integral is performed between fixed pressure levels. The latter allows for the quantification of the different feedback processes responsible for the amplification of moist static energy anomalies associated with the organization of convection and cyclone spinup, including surface flux feedbacks and cloud-radiative feedbacks. Tropical cyclones (TCs) are tracked in the climate model simulations and the analysis is applied along the individual tracks and composited over many TCs. Two methods of compositing are employed: a composite over all TC snapshots in a given intensity range, and a composite over all TC snapshots at the same stage in the TC life cycle (same time relative to the time of lifetime maximum intensity for each storm). The radiative feedback contributes to TC development in all models, especially in storms of weaker intensity or earlier stages of development. Notably, the surface flux feedback is stronger in models that simulate more intense TCs. This indicates that the representation of the interaction between spatially varying surface fluxes and the developing TC is responsible for at least part of the intermodel spread in TC simulation. [ABSTRACT FROM AUTHOR]

This study introduces "Google Trends" as a social data source in monitoring and modeling the dynamics of drought awareness during the 2011–17 California drought. In this study, drought awareness is defined and operationalized as the relative search interest activities within California, using the search term "drought" from Google Trends. First, the 2011–17 California drought is characterized in the duration–intensity curve with other historical California droughts for comparative purposes, using the 12-month standard precipitation index data (1895–2017). Second, the potential triggers of the peaks of drought awareness during the 2011–17 California drought are investigated through Google Trends and Google Search. The Google Trends data show that the first peak of drought awareness occurred when the drought condition reached its peak and the governor declared the drought emergency (January 2014). The other peaks in August 2014, April 2015, and January 2017 are related to public interest in drought recovery driven by the forecast of the strong El Niño winter of 2014/15, the governor's issue of water use rules, and California floods in early 2017, respectively. Last, a power-law decay model of drought awareness is fitted to the Google Trends data. According to the fitted power-law model, Californians remained interested in drought after the social trigger–related peaks longer than they did after the natural trigger–related peaks. The findings of this study suggest that it is necessary to develop a more realistic social dynamic modeling for communities that can respond to natural and human triggers and capture interactions with awareness of related disasters. [ABSTRACT FROM AUTHOR]

The well-known Knudsen relations and the total exchange flow (TEF) analysis framework provide quantifications of exchange flow across an open boundary to the adjacent ocean in terms of bulk values (Knudsen theory: inflow and outflow volume or salinity) or with resolution in salinity space (TEF: profiles of volume and salt flux in salinity coordinates). In the present study, these theories are extended toward mixing of salinity, defined as the decay of salinity variance due to turbulent mixing. In addition to the advective fluxes, diffusive fluxes across the boundary are also considered now. These new Knudsen and TEF relations for mixing are derived by applying Gauss's theorem to the salinity square and salinity variance equations. As a result of the analysis, four different Knudsen relations for the mixing in estuaries are derived. The first one is exact and considers nonperiodicity as well as nonconstancy of the inflow and outflow salinities. The other three formulations are approximate only, in the sense that either nonperiodicity or nonconstancy or both are relaxed. The simplest of those formulations has recently been derived by MacCready et al. and estimates the estuarine mixing as the product of inflow salinity, outflow salinity, and time-averaged river runoff. These four mixing estimates are systematically assessed by means of a number of idealized estuarine test cases. For periodic tidal flow, the simplest estimate still predicts the effective (physical plus numerical) mixing within an error of about 10%. [ABSTRACT FROM AUTHOR]

The diurnal cycle of tropical convection and tropical cyclones (TCs) has been previously described in observational-, satellite-, and modeling-based studies. The main objective of this work is to expand on these earlier studies by identifying signals of the TC diurnal cycle (TCDC) in a hurricane nature run, characterize their evolution in time and space, and better understand the processes that cause them. Based on previous studies that identified optimal conditions for the TCDC, a select period of the hurricane nature run is examined when the simulated storm was intense, in a low shear environment, and sufficiently far from land. When analyses are constrained by these conditions, marked radially propagating diurnal signals in radiation, thermodynamics, winds, and precipitation that affect a deep layer of the troposphere become evident in the model. These propagating diurnal signals, or TC diurnal pulses, are a distinguishing characteristic of the TCDC and manifest as a surge in upper-level outflow with underlying radially propagating tropical squall-line-like features. The results of this work support previous studies that examined the TCDC using satellite data and have implications for numerical modeling of TCs and furthering our understanding of how the TCDC forms, evolves, and possibly impacts TC structure and intensity. [ABSTRACT FROM AUTHOR]

Observations suggest that enhanced turbulent dissipation and mixing over rough topography are modulated by the transient eddy field through the generation and breaking of lee waves in the Southern Ocean. Idealized simulations also suggest that lee waves are important in the energy pathway from eddies to turbulence. However, the energy loss from eddies due to lee wave generation remains poorly estimated. This study quantifies the relative energy loss from the time-mean and transient eddy flow in the Southern Ocean due to lee wave generation using an eddy-resolving global ocean model and three independent topographic datasets. The authors find that the energy loss from the transient eddy flow (0.12 TW; 1 TW = 1012 W) is larger than that from the time-mean flow (0.04 TW) due to lee wave generation; lee wave generation makes a larger contribution (0.12 TW) to the energy loss from the transient eddy flow than the dissipation in turbulent bottom boundary layer (0.05 TW). This study also shows that the energy loss from the time-mean flow is regulated by the transient eddy flow, and energy loss from the transient eddy flow is sensitive to the representation of anisotropy in small-scale topography. It is implied that lee waves should be parameterized in eddy-resolving global ocean models to improve the energetics of resolved flow. [ABSTRACT FROM AUTHOR]

Observation impact studies have received increasing amounts of research attention. The impacts of observations on numerical weather prediction (NWP) are highly dependent on assimilation algorithm, prediction system, and observation source. Therefore, the major NWP centers worldwide have each developed their own diagnostic techniques to assess observation impacts. However, similar diagnostic techniques have not yet been developed in China. In this study, a diagnostic technique was exploited with the randomized perturbation method in the Global/Regional Assimilation and Prediction System (GRAPES) 3DVAR system, and then applied to evaluate observation impacts for various regions of the world. It was found that a reasonable and stable estimation could be obtained when the number of perturbations was greater than 15. Because of differences in observations in the Northern and Southern Hemispheres, refractivity data from GNSS radio occultation (GNSS-RO), satellite radiance, and atmospheric motion vector data had more impact in the Southern Hemisphere than in the Northern Hemisphere. However, radiosonde data, aircraft, and surface data were more important in the Northern Hemisphere. Low-impact observation points were located in data-rich areas, whereas high-impact observation points were located in data-poor areas. In the equatorial region, the contributions of observations to the analysis were smaller than those in the nonequatorial regions because of the lack of proper mass–wind balance relationship. Radiosondes contributed the largest impact in China and its surrounding regions, with contributions of radiosondes and GNSS-RO data exceeding 60% of the total contributions, except for wind speed below 700 hPa. [ABSTRACT FROM AUTHOR]

The relationship between net mixing and the estuarine exchange flow may be quantified using a salinity variance budget. Here “mixing” is defined as the rate of destruction of volume-integrated salinity variance, and the exchange flow is quantified using the total exchange flow. These concepts are explored using an idealized 3D model estuary. It is shown that in steady state (e.g., averaging over the spring–neap cycle) the volume-integrated mixing is approximately given by Mixing ≅ SinSoutQr, where Sin and Sout are the representative salinities of in- and outflowing layers at the mouth and Qr is the river volume flux. This relationship provides an extension of the familiar Knudsen relation, in which the exchange flow is diagnosed based on knowledge of these same three quantities, quantitatively linking mixing to the exchange flow. [ABSTRACT FROM AUTHOR]

Convective transition statistics, which describe the relation between column-integrated water vapor (CWV) and precipitation, are compiled over tropical oceans using satellite and ARM site measurements to quantify the temperature and resolution dependence of the precipitation–CWV relation at fast time scales relevant to convection. At these time scales, and for precipitation especially, uncertainties associated with observational systems must be addressed by examining features with a variety of instrumentation and identifying robust behaviors versus instrument sensitivity at high rain rates. Here the sharp pickup in precipitation as CWV exceeds a certain critical threshold is found to be insensitive to spatial resolution, with convective onset occurring at higher CWV but at lower column relative humidity as bulk tropospheric temperature increases. Mean tropospheric temperature profiles conditioned on precipitation show vertically coherent structure across a wide range of temperature, reaffirming the use of a bulk temperature measure in defining the convective transition statistics. The joint probability distribution of CWV and precipitation develops a peak probability at low precipitation for CWV above critical, with rapidly decreasing probability of high precipitation below and near critical, and exhibits systematic changes under spatial averaging. The precipitation pickup with CWV is reasonably insensitive to time averaging up to several hours but is smoothed at daily time scales. This work demonstrates that CWV relative to critical serves as an effective predictor of precipitation with only minor geographic variations in the tropics, quantifies precipitation-related statistics subject to different spatial–temporal resolution, and provides a baseline for model comparison to apply these statistics as observational constraints on precipitation processes. [ABSTRACT FROM AUTHOR]

Realistic simulations of the Madden-Julian oscillation (MJO) by global climate models (GCMS) remain a great challenge. To evaluate GCM simulations of the MJO, the U.S. CLIVAR MJO Working Group developed a standardized set of diagnostics, providing a comprehensive assessment of statistical properties of the MJO. Here, a suite of complementary diagnostics has been developed that provides discrimination and assessment of MJO simulations based on the perception that the MJO propagation has characteristic dynamic and thermodynamic structures. The new dynamics-oriented diagnostics help to evaluate whether a model produces eastward-propagating MJOs for the right reasons. The diagnostics include 1) the horizontal structure of boundary layer moisture convergence (BLMC) that moistens the lower troposphere to the east of a convection center, 2) the preluding eastward propagation of BLMC that leads the propagation of MJO precipitation by about 5 days, 3) the horizontal structure of 850-hPa zonal wind and its equatorial asymmetry (Kelvin easterly versus Rossby westerly intensity), 4) the equatorial vertical-longitudinal structure of the equivalent potential temperature and convective instability index that reflects the premoistening and predestabilization processes, 5) the equatorial vertical-longitudinal distribution of diabatic heating that reflects the multicloud structure of the MJO, 6) the upper-level divergence that reflects the influence of stratiform cloud heating, and 7) the MJO available potential energy generation that reflects the amplification and propagation of an MJO. The models that simulate better three-dimensional dynamic and thermodynamic structures of MJOs generally reproduce better eastward propagations. This evaluation identifies a number of shortcomings in representing dynamical and heating processes relevant to the MJO simulation and reveals potential sources of the shortcomings. [ABSTRACT FROM AUTHOR]

This study proposes a set of process-oriented diagnostics with the aim of understanding how model physics and numerics control the representation of tropical cyclones (TCs), especially their intensity distribution, in GCMs. Three simulations are made using two 50-km GCMs developed at NOAA's Geophysical Fluid Dynamics Laboratory. The two models are forced with the observed sea surface temperature [Atmospheric Model version 2.5 (AM2.5) and High Resolution Atmospheric Model (HiRAM)], and in the third simulation, the AM2.5 model is coupled to an ocean GCM [Forecast-Oriented Low Ocean Resolution (FLOR)]. The frequency distributions of maximum near-surface wind near TC centers show that HiRAM tends to develop stronger TCs than the other models do. Large-scale environmental parameters, such as potential intensity, do not explain the differences between HiRAM and the other models. It is found that HiRAM produces a greater amount of precipitation near the TC center, suggesting that associated greater diabatic heating enables TCs to become stronger in HiRAM. HiRAMalso shows a greater contrast in relative humidity and surface latent heat flux between the inner and outer regions of TCs. Various fields are composited on precipitation percentiles to reveal the essential character of the interaction among convection, moisture, and surface heat flux. Results show that the moisture sensitivity of convection is higher in HiRAM than in the other model simulations. HiRAM also exhibits a stronger feedback from surface latent heat flux to convection via near-surface wind speed in heavy rain-rate regimes. The results emphasize that the moisture-convection coupling and the surface heat flux feedback are critical processes that affect the intensity of TCs in GCMs. [ABSTRACT FROM AUTHOR]

Several decades after E. Dewan predicted that the shallowing of the atmospheric energy spectrum in mesoscale is produced by the inertia-gravity (IG) waves, global analyses have reached the resolution at which the IG waves across many scales are resolved. The authors discuss the spatial filtering method based on the Hough harmonics that provides the temperature and wind perturbations associated with the IG waves in global analysis data. The method is applied to the ECMWF interim reanalysis and the operational 2014-16 analysis fields. The derived spectrum of IG wave energy is divided into three regimes: a part associated with the large-scale unbalanced circulations that has a slope close to −1 for zonal wavenumbers 1 ≤ k ≤ 6, a synoptic-scale range between 3000 and around 500 km (7 ≤ k ≲ 35) that is characterized by a nearly −5/3 slope, and a mesoscale range below 500 km where the slope of the IG energy spectrum in the 2015/16 analyses is steeper. In contrast, the energy spectrum of the Rossby waves has a −3 slope for all zonal wavenumbers k > 6. Presented results suggest that energy associated with the IG modes exceeds the level of energy associated with the Rossby waves around zonal wavenumber 35. The exact wavenumber depends on the season and considered atmospheric depth and it is suggested as a cutoff scale for studies of gravity waves. [ABSTRACT FROM AUTHOR]

The balanced and unbalanced aspects of tropical cyclone (TC) intensification are revisited with the balanced contribution diagnosed with the outputs from a full-physics model simulation of a TC using the Sawyer-Eliassen (SE) equation. The results show that the balanced dynamics can well capture the secondary circulation in the full-physics model simulation even in the inner-core region in the boundary layer. The balanced dynamics can largely explain the intensification of the simulated TC. The unbalanced dynamics mainly acts to prevent the boundary layer agradient flow in the inner-core region from further intensification. Although surface friction can enhance the boundary layer inflow and make the inflow penetrate more inward into the eye region, contributing to the eyewall contraction, the net dynamical effect of surface friction on TC intensification is negative. The sensitivity of the balanced solution to the procedure used to ensure the ellipticity condition for the SE equation is also examined. The results show that the boundary layer inflow in the balanced response is very sensitive to the adjustment to inertial stability in the upper troposphere and the calculation of radial wind at the surface with relatively coarse vertical resolution in the balanced solution. Both the use of the so-called global regularization and the one-sided finite-differencing scheme used to calculate the surface radial wind in the balanced solution as utilized in some previous studies can significantly underestimate the boundary layer inflow. This explains why the boundary layer inflow in the balanced response is too weak in some previous studies. [ABSTRACT FROM AUTHOR]

An operational weather diagnostics application for automatic generation of wind fields in near-real time from observations delivered by the high-density WegenerNet meteorological station network in the Feldbach region of Austria is introduced. The purpose of the application is to empirically provide near-surface wind fields of very high spatial and temporal resolution for evaluating convection-permitting climate models as well as investigating weather and climate variability on a local scale. The diagnostic California Meteorological Model (CALMET) is used as the core tool. This model computes 3D wind fields based on observational weather data, a digital elevation model, and land-use categories. The application first produces the required input files from the WegenerNet stations and subsequently runs the CALMET model based on this input. In a third step the modeled wind fields are stored in the WegenerNet data archives every 30 min with a spatial resolution of 100 m × 100 m, while also generating averaged weather and climate products during postprocessing. The performance of the modeling against station observations, for which wind speeds were classified into weak and strong wind speeds, is evaluated and reasonably good results were found for both wind speed classes. The statistical agreement for the vector-mean wind speed is slightly better for weak wind speeds than for strong ones while the difference between modeled and observed wind directions is smaller for strong wind speeds than for weak ones. The application is also a valuable tool for other high-density networks. [ABSTRACT FROM AUTHOR]

An evaluation of medium-range forecasts of tropical cyclones (TCs) is performed, covering the eastern North Pacific basin during the period 1 August-3 November 2014. Real-time forecasts from the Model for Prediction Across Scales (MPAS) and operational forecasts from the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) are evaluated. A new TC-verification method is introduced that treats TC tracks as objects. The method identifies matching pairs of forecast and observed tracks, missed and false alarm tracks, and derives statistics using a multicategory contingency table methodology. The formalism includes track, intensity, and genesis. Two configurations of MPAS, a uniform 15-km mesh and a variable-resolution mesh transitioning from 60 km globally to 15 km over the eastern Pacific, are compared with each other and with the operational GFS. The two configurations of MPAS reveal highly similar forecast skill and biases through at least day 7. This result supports the effectiveness of TC prediction using variable resolution. Both MPAS and the GFS suffer from biases in predictions of genesis at longer time ranges; MPAS produces too many storms whereas the GFS produces too few. MPAS better discriminates hurricanes than does the GFS, but the false alarms in MPAS lower overall forecast skill in the medium range relative to GFS. The biases in MPAS forecasts are traced to errors in the parameterization of shallow convection south of the equator and the resulting erroneous invigoration of the ITCZ over the eastern North Pacific. [ABSTRACT FROM AUTHOR]

With the development of convection-permitting numerical weather prediction the efficient use of high-resolution observations in data assimilation is becoming increasingly important. The operational assimilation of these observations, such as Doppler radar radial winds (DRWs), is now common, although to avoid violating the assumption of uncorrelated observation errors the observation density is severely reduced. To improve the quantity of observations used and the impact that they have on the forecast requires the introduction of the full, potentially correlated, error statistics. In this work, observation error statistics are calculated for the DRWs that are assimilated into the Met Office high-resolution U.K. model (UKV) using a diagnostic that makes use of statistical averages of observation-minus-background and observation-minus-analysis residuals. This is the first in-depth study using the diagnostic to estimate both horizontal and along-beam observation error statistics. The new results obtained show that the DRW error standard deviations are similar to those used operationally and increase as the observation height increases. Surprisingly, the estimated observation error correlation length scales are longer than the operational thinning distance. They are dependent both on the height of the observation and on the distance of the observation away from the radar. Further tests show that the long correlations cannot be attributed to the background error covariance matrix used in the assimilation, although they are, in part, a result of using superobservations and a simplified observation operator. The inclusion of correlated error statistics in the assimilation allows less thinning of the data and hence better use of the high-resolution observations. [ABSTRACT FROM AUTHOR]

A method is presented for calculating a complete, numerically closed, mechanical energy budget in a realistic simulation of circulation in a coastal-estuarine domain. The budget is formulated in terms of the 'local' available potential energy (APE; Holliday and McIntyre 1981). The APE may be split up into two parts based on whether a water parcel has been displaced up or down relative to its rest depth. This decomposition clearly shows the different APE signatures of coastal upwelling (particles displaced up by wind) and the estuary (particles displaced down by mixing). Because the definition of APE is local in almost the same sense that kinetic energy is, this study may form meaningful integrals of reservoir and budget terms even over regions that have open boundaries. However, the choice of volume to use for calculation of the rest state is not unique and may influence the results. Complete volume-integrated energy budgets over shelf and estuary volumes in a realistic model of the northeast Pacific and Salish Sea give a new way to quantify the state of these systems and the physical forces that influence that state. On the continental shelf, upwelling may be quantified using APE, which is found to have order-one seasonal variation with an increase due to winds and decrease due to mixing. In the Salish Sea estuarine system, the APE has much less seasonal variation, and the magnitude of the most important forcing terms would take over 7 months to fully drain this energy. [ABSTRACT FROM AUTHOR]

Global data assimilation systems for numerical weather prediction (NWP) are characterized by significant uncertainties in tropical analysis fields. Furthermore, the largest spread of global ensemble forecasts in the short range on all scales is in the tropics. The presented results suggest that these properties hold even in the perfect-model framework and the ensemble Kalman filter data assimilation with a globally homogeneous network of wind and temperature profiles. The reasons for this are discussed by using the modal analysis, which provides information about the scale dependency of analysis and forecast uncertainties and information about the efficiency of data assimilation to reduce the prior uncertainties in the balanced and inertio-gravity dynamics. The scale-dependent representation of variance reduction of the prior ensemble by the data assimilation shows that the peak efficiency of data assimilation is on the synoptic scales in the midlatitudes that are associated with quasigeostrophic dynamics. In contrast, the variance associated with the inertia-gravity modes is less successfully reduced on all scales. A smaller information content of observations on planetary scales with respect to the synoptic scales is discussed in relation to the large-scale tropical uncertainties that current data assimilation methodologies do not address successfully. In addition, it is shown that a smaller reduction of the large-scale uncertainties in the prior state for NWP in the tropics than in the midlatitudes is influenced by the applied radius for the covariance localization. [ABSTRACT FROM AUTHOR]

Pathways to the production of precipitation in two cloud microphysics schemes available in the Weather Research and Forecasting (WRF) Model are investigated in a scenario of tropical cyclone intensification. Comparisons of the results from the WRF Model simulations indicate that the variation in the simulated initial rapid intensification of an idealized tropical cyclone is due to the differences between the two cloud microphysics schemes in their representations of pathways to the formation and growth of precipitating hydrometeors. Diagnoses of the source and sink terms of the hydrometeor budget equations indicate that the major differences in the production of hydrometeors between the schemes are in the spectral definition of individual hydrometeor categories and spectrum-dependent microphysical processes, such as accretion growth and sedimentation. These differences lead to different horizontally averaged vertical profiles of net latent heating rate associated with significantly different horizontally averaged vertical distributions and production rates of hydrometeors in the simulated clouds. Results from this study also highlight the possibility that the advantage of double-moment formulations can be overshadowed by the uncertainties in the spectral definition of individual hydrometeor categories and spectrum-dependent microphysical processes. [ABSTRACT FROM AUTHOR]

Tropical cyclone (TC) forecasts from the NCEP Global Ensemble Forecasting System (GEFS) Reforecast version 2 (1985-2012) were evaluated from the climate perspective, with a focus on tropical cyclogenesis. Although the GEFS captures the climatological seasonality of tropical cyclogenesis over different ocean basins reasonably well, large errors exist on the regional scale. As different genesis pathways are dominant over different ocean basins, genesis biases are related to biases in different aspects of the large-scale or synoptic-scale circulations over different basins. The negative genesis biases over the western North Pacific are associated with a weaker-than-observed monsoon trough in the GEFS, the erroneous genesis pattern over the eastern North Pacific is related to a southward displacement of the ITCZ, and the positive genesis biases near the Cape Verde islands and negative biases farther downstream over the Atlantic can be attributed to the hyperactive Africa easterly waves in the GEFS. The interannual and subseasonal variability of TC activity in the reforecasts was also examined to evaluate the potential skill of the GEFS in providing subseasonal and seasonal predictions. The GEFS skillfully captures the interannual variability of TC activity over the North Pacific and the North Atlantic, which can be attributed to the modulation of TCs by the El Niño-Southern Oscillation (ENSO) and the Atlantic meridional mode (AMM). The GEFS shows promising skill in predicting the active and inactive periods of TC activity over the Atlantic. The skill, however, has large fluctuations from year to year. The analysis presented herein suggests possible impacts of ENSO, the Madden-Julian oscillation (MJO), and the AMM on the TC subseasonal predictability. [ABSTRACT FROM AUTHOR]

The effects of a parameterized linear internal wave drag on the semidiurnal barotropic and baroclinic energetics of a realistically forced, three-dimensional global ocean model are analyzed. Although the main purpose of the parameterization is to improve the surface tides, it also influences the internal tides. The relatively coarse resolution of the model of ~8 km only permits the generation and propagation of the first three vertical modes. Hence, this wave drag parameterization represents the energy conversion to and the subsequent breaking of the unresolved high modes. The total tidal energy input and the spatial distribution of the barotropic energy loss agree with the Ocean Topography Experiment (TOPEX)/Poseidon (TPXO) tidal inversion model. The wave drag overestimates the high-mode conversion at ocean ridges as measured against regional high-resolution models. The wave drag also damps the low-mode internal tides as they propagate away from their generation sites. Hence, it can be considered a scattering parameterization, causing more than 50% of the deep-water dissipation of the internal tides. In the near field, most of the baroclinic dissipation is attributed to viscous and numerical dissipation. The far-field decay of the simulated internal tides is in agreement with satellite altimetry and falls within the broad range of Argo-inferred dissipation rates. In the simulation, about 12% of the semidiurnal internal tide energy generated in deep water reaches the continental margins. [ABSTRACT FROM AUTHOR]

A relatively simple method to estimate tropical cyclone (TC) wind radii from routinely available information including storm data (location, motion, and intensity) and TC size is introduced. The method is based on a combination of techniques presented in previous works and makes an assumption that TCs are largely symmetric and that asymmetries are based solely on storm motion and location. The method was applied to TC size estimates from two sources: infrared satellite imagery and global model analyses. The validation shows that the methodology is comparable with other objective methods based on the error statistics. The technique has a variety of practical research and operational applications, some of which are also discussed. [ABSTRACT FROM AUTHOR]

A deeper analysis of possible errors and inconsistencies in the analysis of vortex asymmetries owing to the placement of centers of tropical cyclones (TCs) in mesoscale models is presented. Previous works have established that components of the 2D and 3D structure of these TCs-primarily radial wind and vertical tilt-can vary greatly depending on how the center of a model TC is defined. This work will seek to expand the previous research on this topic, but only for the 2D structure. To be specific, this work will present how low-wavenumber azimuthal Fourier analyses can vary with center displacement using idealized, parametric TC-like vortices. It is shown that the errors associated with aliasing the mean are sensitive primarily to the difference between the peak of vorticity inside the radius of maximum winds and the average vorticity inside the core. Tangential wind and vorticity aliasing occur primarily in the core; radial wind aliasing spans the whole of the vortex. It is also shown that, when adding low-wavenumber asymmetries, the aliasing is dependent on the placement of the center relative to the location of the asymmetries on the vortex. It is also shown that the primary concern for 2D analysis when calculating the center of a TC is correctly resolving azimuthal wavenumber 0 tangential wind, because errors here will alias onto all higher wavenumbers, the specific structures of which are dependent on the structure of the mean vortex itself. [ABSTRACT FROM AUTHOR]

A Bayesian hidden Markov model (HMM) for climate downscaling of multisite daily precipitation is presented. A generalized linear model (GLM) component allows exogenous variables to directly influence the distributional characteristics of precipitation at each site over time, while the Markovian transitions between discrete states represent seasonality and subseasonal weather variability. Model performance is evaluated for station networks of summer rainfall over the Punjab region in northern India and Pakistan and the upper Yangtze River basin in south-central China. The model captures seasonality and the marginal daily distributions well in both regions. Extremes are reproduced relatively well in the Punjab region, but underestimated for the Yangtze. In terms of interannual variability, the combined GLM-HMM with spatiotemporal averages of observed rainfall as a predictor is shown to exhibit skill (in terms of reduced RMSE) at the station level, particularly for the Punjab region. The skill is largest for dry-day counts, moderate for seasonal rainfall totals, and very small for the number of extreme wet days. [ABSTRACT FROM AUTHOR]

The statistics of model temporal variability ought to be the same as those of the filtered version of reality that the model is designed to represent. Here, simple diagnostics are introduced to quantify temporal variability on different time scales and are then applied to NCEP and CMC global ensemble forecasting systems. These diagnostics enable comparison of temporal variability in forecasts with temporal variability in the initial states from which the forecasts are produced. They also allow for an examination of how day-to-day variability in the forecast model changes as forecast integration time increases. Because the error in subsequent analyses will differ, it is shown that forecast temporal variability should lie between corresponding analysis variability and analysis variability minus 2 times the analysis error variance. This expectation is not always met and possible causes are discussed. The day-to-day variability in NCEP forecasts steadily decreases at a slow rate as forecast time increases. In contrast, temporal variability increases during the first few days in the CMC control forecasts, and then levels off, consistent with a spinup of the forecasts starting from overly smoothed analyses. The diagnostics successfully reflect a reduction in the temporal variability of the CMC perturbed forecasts after a system upgrade. The diagnostics also illustrate a shift in variability maxima from storm-track regions for 1-day variability to blocking regions for 10-day variability. While these patterns are consistent with previous studies examining temporal variability on different time scales, they have the advantage of being obtainable without the need for extended (e.g., multimonth) forecast integrations. [ABSTRACT FROM AUTHOR]