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The filtering and prediction of the Madden-Julian oscillation (MJO) and relevant tropical waves is a contemporary issue with significant implications for extended range forecasting. This paper examines the process of filtering the stochastic skeleton model for the MJO with noisy partial observations. A nonlinear filter, which captures the inherent nonlinearity of the system, is developed and judicious model error is included. Despite its nonlinearity, the special structure of this filter allows closed analytical formulas for updating the posterior states and is thus computationally efficient. A novel strategy for adding nonlinear observational noise to the envelope of convective activity is designed to guarantee its nonnegative property. Systematic calibration based on a cheap single-column version of the stochastic skeleton model provides a practical guideline for choosing the parameters in the full spatially extended system. With these column-tuned parameters, the full filter has a high overall filtering skill for Rossby waves but fails to recover the small-scale fast-oscillating Kelvin and moisture modes. An effectively balanced reduced filter involving a simple fast-wave averaging strategy is then developed, which greatly improves the skill of filtering the moisture modes and other fast-oscillating modes and enhances the total computational efficiency. Both the full and the reduced filters succeed in filtering the MJO and other large-scale features with both homogeneous and warm pool cooling/moistening backgrounds. The large bias in filtering the solutions by running the perfect model with noisy forcing is due to the noise accumulation, which indicates the importance of including judicious model error in designing filters. [ABSTRACT FROM AUTHOR]

Recent observational studies have suggested that negative and positive Indian Ocean dipole (IOD) events (nIOD and pIOD, respectively) favor a transition toward, respectively, El Niño and La Niña events one year later. These statistical inferences are however limited by the length and uncertainties in the observational records. This paper compares observational datasets with twenty-one 155-yr historical simulations from phase 5 of CMIP (CMIP5) to assess IOD and El Niño-Southern Oscillation (ENSO) properties along with their synchronous and delayed relationships. In the observations and most CMIP5 models, it is shown that El Niños tend to be followed by La Niñas but not the opposite, that pIODs co-occur more frequently with El Niños than nIODs with La Niñas, that nIODs tend to be followed by El Niños one year later less frequently than pIODs by La Niñas, and that including an IOD index in a linear prediction based on the Pacific warm water volume improves ENSO peak hindcasts at 14 months lead. The IOD-ENSO delayed relationship partly results from a combination of ENSO intrinsic properties (e.g., the tendency for El Niños to be followed by La Niñas) and from the synchronous IOD-ENSO relationship. The results, however, reveal that this is not sufficient to explain the high prevalence of pIOD-Niña transitions in the observations and 75% of the CMIP5 models, and of nIOD-Niño transitions in 60% of CMIP5 models. This suggests that the tendency of IOD to lead ENSO by one year should be explained by a physical mechanism that, however, remains elusive in the CMIP5 models. The ability of many CMIP5 models to reproduce the delayed influence of the IOD on ENSO is nonetheless a strong incentive to explore extended-range dynamical forecasts of ENSO. [ABSTRACT FROM AUTHOR]

In a strongly coupled data assimilation (DA), a cross-fluid covariance is specified that allows measurements from a coupled fluid (e.g., atmosphere) to directly impact analysis increments in a target fluid (e.g., ocean). The exhaustive solution to this coupled DA problem calls for a covariance where all available measurements can influence all grid points in all fluids. Solution of such a large algebraic problem is computationally expensive, often calls for a substantial rewrite of existing fluid-specific DA systems, and, as shown in this paper, can be avoided. The proposed interface solver assumes that covariances between coupled measurements and target fluid are often close to null (e.g., between stratospheric observations and the deep ocean within a 6-h forecast cycle). In the interface solver, two separate DA solvers are run in parallel: one that produces an analysis solution in the atmosphere, and one in the ocean. Each system uses a coupled observation vector where in addition to resident measurements in the target fluid it also includes nonresident measurements in the coupled fluid that are likely to have significant influence on the analysis in the target fluid (interface measurements). An ensemble-based method is employed and a localization function for coupled ensembles is proposed. Using a coupled model for the Mediterranean Sea (in a twin setting), it is demonstrated that (i) the solution of the interface solver converges to the exhaustive solution and (ii) that in presence of poorly known error covariances, the interface solver can be configured to produce a more accurate solution than an exhaustive solver. [ABSTRACT FROM AUTHOR]

Recently, two analog-based postprocessing methods were demonstrated to reduce the systematic and random errors from Weather Research and Forecasting (WRF) Model predictions of 10-m wind speed over the central United States. To test robustness and generality, and to gain a deeper understanding of postprocessing forecasts with analogs, this paper expands upon that work by applying both analog methods to surface stations evenly distributed across the conterminous United States over a 1-yr period. The Global Forecast System (GFS), North American Mesoscale Forecast System (NAM), and Rapid Update Cycle (RUC) forecasts for screen-height wind, temperature, and humidity are postprocessed with the two analog-based methods and with two time series-based methods-a running mean bias correction and an algorithm inspired by the Kalman filter. Forecasts are evaluated according to a range of metrics, including random and systematic error components; correlation; and by conditioning the error distributions on lead time, location, error magnitude, and day-to-day error variability. Results show that the analog methods are generally more effective than time series-based methods at reducing the random error component, leading to an overall reduction in root-mean-square error. Details among the methods differ and are elucidated upon in this study. The relative levels of random and systematic error in the raw forecasts determine, to a large extent, the effectiveness of each postprocessing method in reducing forecast errors. When the errors are dominated by random errors (e.g., where thunderstorms are common), the analog-based methods far outperform the time series-based methods. When the errors are strictly systematic (i.e., a bias), the analog methods lose their advantage over the time series methods. It is shown that slowly evolving systematic errors rarely dominate, so reducing the random error component is most effective at reducing the error magnitude. The results are shown to be valid for all seasons. The analog methods show similar performance to the operational model output statistics (MOS) while showing greater reduction of random errors at certain lead times. [ABSTRACT FROM AUTHOR]

This expository paper documents an experimental, real-time, 10-member, 3-km, convection-allowing ensemble prediction system (EPS) developed at the National Center for Atmospheric Research (NCAR) in spring 2015. The EPS is particularly unique in that continuously cycling, limited-area, mesoscale ensemble Kalman filter analyses provide diverse initial conditions. In addition to describing the EPS configurations, initial forecast assessments are presented that suggest the EPS can provide valuable severe weather guidance and skillful predictions of precipitation. The EPS output is available to operational forecasters, many of whom have incorporated the products into their toolboxes. Given such rapid embrace of an experimental system by the operational community, acceleration of convection-allowing EPS development is encouraged. [ABSTRACT FROM AUTHOR]

Although previous studies suggest that the Weather Research and Forecasting (WRF) Model can produce physically realistic banded Great Salt Lake-effect (GSLE) precipitation features, the accuracy and reliability of these simulations for forecasting applications remains unquantified. The ability of the WRF to simulate nonbanded GSLE features is also unknown. This paper uses subjective, traditional, and object-based verification to evaluate convection-permitting (1.33-km grid spacing) WRF simulations of 11 banded and 8 nonbanded GSLE events. In all simulations, the WRF was configured with the Thompson microphysics and the Yonsei University (YSU) planetary boundary layer parameterizations. Subjectively, a majority of the simulations of banded GSLE events produce physically realistic precipitation features. In contrast, simulations of nonbanded GSLE events rarely produce physically realistic precipitation features and sometimes erroneously produce banded precipitation features. Simulations of banded GSLE events produce equitable threat scores (ETSs) comparable to other convective-storm verification studies, whereas simulations of nonbanded events exhibit lower ETSs. Object-based verification shows that the WRF tends to generate precipitation to the right (relative to the flow) and downstream of observed. These results, although based on a specific WRF parameterization suite, suggest that deterministic prediction of GSLE using convection-permitting models will prove challenging in practice with current numerical models. In addition, identifying and addressing the causes of the rightward and downstream precipitation bias is necessary to achieve optimal performance from future probabilistic and/or deterministic high-resolution forecast systems. [ABSTRACT FROM AUTHOR]

The Cooperative Program for Operational Meteorology, Education, and Training (COMET)'s mission when it began in 1990 was to deliver professional development opportunities to U.S. government forecasters during the National Weather Service (NWS) modernization program. Since then, COMET has emerged as a worldwide leader in geoscience education. Its original objectives were to provide forecasters with classroom and distance learning training based on state-of-the-art science; support development and testing of new forecast methods; promote collaboration between the forecasting, research, and academic communities; and to advance forecasting and now casting by encouraging research. Over the years, COMET's mission has expanded to disseminating and enhancing scientific knowledge in the environmental sciences, particularly meteorology, but also including diverse areas such as oceanography, hydrology, space weather, and emergency management. This paper reviews COMET's evolution from a primary focus on educating U.S. forecasters on the application of new technologies (such as Doppler radar) to meso-scale meteorology problems into a program with a much broader scope. Those changes include offering learning opportunities that now cover a wider variety of topics and support the educational needs of diverse audiences worldwide. The history of COMET is a story of adaptation to technological changes, funding cycles, partner requirements, and service opportunities as well as taking on a more global mission. We will look at how COMET's activities in geoscience education have changed, how its adaptability has contributed to the longevity of the program that was only supposed to exist until the NWS modernization was complete, and expectations and plans for the future. [ABSTRACT FROM AUTHOR]

The desire to do detailed comparisons between variational and more scalable ensemble-based data assimilation systems in a semioperational environment has led to the development of a state-of-the-art EnKF system at ECMWF. A broad description of the ECMWF EnKF is given in this paper, focusing on highlighting differences compared to standard EnKF practice. In particular, a discussion of the novel algorithm used to control imbalances between the mass and wind fields in the EnKF analysis is given. The scalability and computational properties of the EnKF are reviewed and the implementation choices adopted at ECMWF described. The sensitivity of the ECMWF EnKF to ensemble size, horizontal resolution, and representation of model errors is also discussed. A comparison with 4DVar will be found in Part II of this two-part study. [ABSTRACT FROM AUTHOR]

The use of ensembles in numerical weather prediction models is becoming an increasingly effective method of forecasting. Many studies have shown that using the mean of an ensemble as a deterministic solution produces the most accurate forecasts. However, the mean will eventually lose its usefulness as a deterministic forecast in the presence of nonlinearity. At synoptic scales, this appears to occur between 12- and 24-h forecast time, and on storm scales it may occur significantly faster due to stronger nonlinearity. When this does occur, the question then becomes the following: Should the mean still be adhered to, or would a different approach produce better results? This paper will investigate the usefulness of the mean within a WRF Model utilizing an ensemble Kalman filter for severe convective events. To determine when the mean becomes unrealistic, the divergence of the mean of the ensemble ('mean') and a deterministic forecast initialized from a set of mean initial conditions ('control') are examined. It is found that significant divergence between the mean and control emerges no later than 6 h into a convective event. The mean and control are each compared to observations, with the control being more accurate for nearly all forecasts studied. For the case where the mean provides a better forecast than the control, an approach is offered to identify the member or group of members that is closest to the mean. Such a forecast will contain similar forecast errors as the mean, but unlike the mean, will be on an actual forecast trajectory. [ABSTRACT FROM AUTHOR]

Given the reality of anthropogenic global warming, it is tempting to seek an anthropogenic component in any recent change in the statistics of extreme weather. This paper cautions that such efforts may, however, lead to wrong conclusions if the distinctively skewed and heavy-tailed aspects of the probability distributions of daily weather anomalies are ignored or misrepresented. Departures of several standard deviations from the mean, although rare, are far more common in such a distinctively non-Gaussian world than they are in a Gaussian world. This further complicates the problem of detecting changes in tail probabilities from historical records of limited length and accuracy. A possible solution is to exploit the fact that the salient non-Gaussian features of the observed distributions are captured by so-called stochastically generated skewed (SGS) distributions that include Gaussian distributions as special cases. SGS distributions are associated with damped linear Markov processes perturbed by asymmetric stochastic noise and as such represent the simplest physically based prototypes of the observed distributions. The tails of SGS distributions can also be directly linked to generalized extreme value (GEV) and generalized Pareto (GP) distributions. The Markov process model can be used to provide rigorous confidence intervals and to investigate temporal persistence statistics. The procedure is illustrated for assessing changes in the observed distributions of daily wintertime indices of large-scale atmospheric variability in the North Atlantic and North Pacific sectors over the period 1872-2011. No significant changes in these indices are found from the first to the second half of the period. [ABSTRACT FROM AUTHOR]

The antecedent atmospheric conditions at midmorning (1200 UTC or 0900 LST) related to subsequent squall-line (SL) initiation in the late afternoon or early evening over the northern coast of Brazil (NCB) were obtained for a specific month (July) when the SL initiation is independent of the strong synoptic forcing related to the intertropical convergence zone. The cases of SL and sparse deep convection in the late afternoon or early evening over the NCB were identified both objectively and subjectively, and grouped into either the SL category (SLC) or the no-convection category (NOC). For the central area of the NCB, the vertical profiles at midmorning for SLC and NOC, computed from radiosonde and ERA-Interim data spanning 9 years (2004-12), were compared. By focusing on the significant differences for both datasets, it was found that two midmorning conditions are favorable to SL initiation in July: a moister layer between 850 and 350 hPa, and a northeasterly flow at 350 hPa. These regional conditions are part of a larger-scale pattern: moister (drier) conditions over the whole NCB (southeastern South America) at 700 hPa and more intense anticyclonic circulation over the Atlantic Ocean close to northeastern Brazil at 350 hPa. The paper's findings have the potential to aid weather forecasting activities, such as those focused on the prediction of SL-related precipitation 6-12 h ahead. [ABSTRACT FROM AUTHOR]

Parameterizing deep convection at model resolutions of a few kilometers or less requires diverging from several simplifying assumptions valid at coarser resolutions. The separation or complementarity between the deep-convection scheme and the model cloud scheme must be addressed properly to prevent a double counting of some phenomena, account for evolution in time, and keep consistent results while approaching resolutions where deep convection can be treated explicitly (without parameterization). In this paper, the author formulates and tests a perturbation approach of the bulk mass-flux representation of deep convective updrafts and downdrafts. The subgrid deep-convection scheme represents only the effect of the unresolved part of the real updrafts, complementing an explicit part associated with the mean gridbox vertical velocity. Special attention is paid to the ordering and interactions of the moist parameterizations, the formulation of the closure and of the triggering, and the accounting of time evolution aspects. The multiresolution behavior of the scheme is assessed in the operational numerical prediction model ALARO [a version of the Action de Recherche Petite Echelle Grande Echelle-Aire Limitée Adaptation Dynamique Développement International (ARPEGE-ALADIN) operational limited area model with a revised and modular structure of the physical parameterizations]. Unlike most mass-flux schemes, the parameterized part gradually fades out when the model resolution is increased, allowing the results to approach those of an explicit treatment of deep convection. [ABSTRACT FROM AUTHOR]

The physical mechanisms and predictability associated with extreme daily rainfall in southeastern South America (SESA) are investigated for the December-February season in a two-part study. Through a k-mean analysis, this first paper identifies a robust set of daily circulation regimes that are used to link the frequency of rainfall extreme events with large-scale potential predictors at subseasonal-to-seasonal scales. This represents a basic set of daily circulation regimes related to the continental and oceanic phases of the South Atlantic convergence zone (SACZ) and wave train patterns superimposed on the Southern Hemisphere polar jet. Some of these recurrent synoptic circulation types are conducive to extreme rainfall events in the region through synoptic control of different mesoscale physical features and, at the same time, are influenced by climate phenomena that could be used as sources of potential predictability. Extremely high rainfall (as measured by the 95th and 99th percentiles) is associated with two of these weather types (WTs), which are characterized by moisture advection intrusions from lower latitudes and the Pacific Ocean; another three WTs, characterized by above-normal moisture advection toward lower latitudes or the Andes, are associated with dry days (days with no rain). The analysis permits the identification of several subseasonal-to-seasonal scale potential predictors that modulate the occurrence of circulation regimes conducive to extreme rainfall events in SESA. It is conjectured that a cross-time scale interaction between the different climate drivers improves the predictive skill of extreme precipitation in the region. [ABSTRACT FROM AUTHOR]

This paper studies a new leading averaged coupled covariance (LACC) method for the strongly coupled data assimilation (SCDA). The SCDA not only uses the coupled model to generate the forecast and assimilate observations into multiple model components like the weakly coupled version (WCDA), but also applies a cross update using the coupled covariance between variables from different model components. The cross update could potentially improve the balance and quality of the analysis, but its implementation has remained a great challenge in practice because of different time scales between model components. In a typical extratropical coupled system, the ocean-atmosphere correlation shows a strong asymmetry with the maximum correlation occurring when the atmosphere leads the ocean by about the decorrelation time of the atmosphere. The LACC method utilizes such asymmetric structure by using the leading forecasts and observations of the fast atmospheric variable for cross update, therefore, increasing the coupled correlation and enhancing the signal-to-noise ratio in calculating the coupled covariance. Here it is applied to a simple coupled model with the ensemble Kalman filter (EnKF). With the LACC method, the SCDA reduces the analysis error of the oceanic variable by over 20% compared to the WCDA and 10% compared to the SCDA using simultaneous coupled covariance. The advantage of the LACC method is more notable when the system contains larger errors, such as in the cases with smaller ensemble size, bigger time-scale difference, or model biases. [ABSTRACT FROM AUTHOR]

The National Oceanic and Atmospheric Administration/Earth System Research Laboratory (NOAA/ESRL) generated a multidecadal (from 1985 to present) ensemble reforecast database for the 2012 version of the Global Ensemble Forecast System (GEFS). This dataset includes 11-member reforecasts initialized once per day at 0000 UTC. This GEFS version has a strong cold bias for winter and warm bias for summer in the Northern Hemisphere. Although the operational decaying average bias-correction approach performs well in winter and summer, it sometimes fails during the spring and fall transition seasons at long lead times (>~5 days). In this paper, 24- (1985-2008) and 25-yr (1985-2009) reforecast biases are used to calibrate 2-m temperature forecasts in 2009 and 2010, respectively. The reforecast-calibrated forecasts for both years are more accurate than those adjusted by the decaying average method during transition seasons. A long training period (>5 yr) is necessary to help avoid a large impact on bias correction from an extreme year case and keep a broader diversity of weather scenarios. The improvement from using the full 25-yr, 31-day window, weekly training dataset is almost equivalent to that from using daily training samples. This provides an option to reduce computational expenses while maintaining a desired accuracy. To provide the potential to improve forecast accuracy for transition seasons, reforecast information is added into the current operational bias-correction method. The relative contribution of the two methods is determined by the correlation between the ensemble mean and analysis. This method improves the forecast accuracy for most of the year with a maximum benefit during April-June. [ABSTRACT FROM AUTHOR]

Dual-polarization Doppler radar has proven useful for the estimation of hydrometeor content and the classification of hydrometeor type. Recent studies have leveraged dual-polarization-specific information to produce improved assimilated cloud and precipitation fields from the warm rain (above freezing) portion of deep convective storms. While the strengths of dual-polarization radar observations have been conclusively shown for rain and hail hydrometeors, it is less clear how much information is provided in mixed-phase and ice-only regions. In this paper, a Markov chain Monte Carlo (MCMC) algorithm is used to examine the information content of dual-polarization-specific variables in the ice-phase region of a convective storm. Results are used to quantify how much information is added by specific differential phase and radar correlation coefficient, as well as how this information is degraded when the assumed particle size distribution and particle density are allowed to vary. It is found that dual-polarization-specific observations ( Kdp and ρhv) provide significant information on rimed ice content, and moderate information on pristine ice, especially where snow mass is more than 10% of the total volume hydrometeor mass. There is a significant reduction in information content for rain and a near-complete loss of information for graupel-hail and snow when the particle size distribution and ice particle densities are not well known, and there are systematic changes in radar information gain and loss with changes in hydrometeor mass. The results highlight the need for a thorough exploration of forward model sensitivities prior to performing radar data assimilation. [ABSTRACT FROM AUTHOR]

A total of 163 tropical cyclones (TCs) occurred in the eastern China seas during 1979-2011 with four types of tracks: left turning, right turning, straight moving, and irregular. The left-turning type is unusual and hard to predict. In this paper, 133 TCs from the first three types have been investigated. A generalized beta-advection model (GBAM) is derived by decomposing a meteorological field into climatic and anomalous components. The ability of the GBAM to predict tracks 1-2 days in advance is compared with three classical beta-advection models (BAMs). For both normal and unusual tracks, the GBAM apparently outperformed the BAMs. The GBAM's ability to predict unusual TC tracks is particularly encouraging, while the BAMs have no ability to predict the left-turning and right-turning TC tracks. The GBAM was also used to understand unusual TC tracks because it can be separated into two forms: a climatic-flow BAM (CBAM) and an anomalous-flow BAM (ABAM). In the CBAM a TC vortex is steered by the large-scale climatic background flow, while in the ABAM, a TC vortex interacts with the surrounding anomalous flows. This decomposition approach can be used to examine the climatic and anomalous flows separately. It is found that neither the climatic nor the anomalous flow alone can explain unusual tracks. Sensitivity experiments show that two anomalous highs as well as a nearby TC played the major roles in the unusual left turn of Typhoon Aere (2004). This study demonstrates that a simple model can work well if key factors are properly included. [ABSTRACT FROM AUTHOR]

The Hurricane Weather Research and Forecasting Model (HWRF) was operationally implemented with a 27-km outer domain and a 9-km moving nest in 2007 (H007) as a tropical cyclone forecast model for the North Atlantic and eastern Pacific hurricane basins. During the 2012 hurricane season, a modified version of HWRF (H212), which increased horizontal resolution by adding a third (3 km) nest within the 9-km nest, replaced H007. H212 thus became the first operational model running at convection-permitting resolution. In addition, there were modifications to the initialization, model physics, tracking algorithm, etc. This paper compares H212 hindcast forecasts for the 2010-11 Atlantic hurricane seasons with forecasts from H007 and H3GP, a triply nested research version of HWRF. H212 reduced track forecast errors for almost all forecast times versus H007 and H3GP. H3GP was superior for intensity forecasts, although H212 showed some improvement over H007. Stratifying the cases by initial vertical wind shear revealed that the main weakness for H212 intensity forecasts was for cases with initially high shear. In these cases, H212 over- and under-intensified storms that were initially stronger and weaker, respectively. These results suggest the primary deficiency negatively impacting H212 intensity forecasts, especially in cases of rapid intensification, was that physics calls were too infrequent for the 3-km inner mesh. Correcting this deficiency along with additional modifications in the 2013 operational version yielded improved track and intensity forecasts. These intensity forecasts were comparable to statistical-dynamical models, showing that dynamical models can contribute to a decrease in operational forecast errors. [ABSTRACT FROM AUTHOR]

In data assimilation (DA) schemes for numerical weather prediction (NWP) systems, the estimation of forecast error covariances is a key point to get some flow dependency. As shown in previous studies, ensemble data assimilation methods are the most accurate for this task. However, their huge computational cost raises a strong limitation to the ensemble size. Consequently, covariances estimated with small ensembles are affected by random sampling errors. The aim of this study is to develop a theory of covariance filtering in order to remove most of the sampling noise while keeping the signal of interest and then to use it in the DA scheme of a real NWP system. This first part of a two-part study presents the theoretical aspects of such criteria for optimal filtering based on the merging of the theories of optimal linear filtering and of sample centered moments estimation. Its strength relies on the use of sample estimated quantities and filter output only. These criteria pave the way for new algorithms and interesting applications for NWP. Two of them are detailed here: spatial filtering of variances and covariance localization. Results obtained in an idealized 1D analytical framework are shown for illustration. Applications on real forecast error covariances deduced from ensembles at convective scale are discussed in a companion paper. [ABSTRACT FROM AUTHOR]

An ensemble-based multiple linear regression technique is developed to assess the predictability of regional and national June-September (JJAS) anomalies and local monthly rainfall totals for Ethiopia. The ensemble prediction approach captures potential predictive signals in regional circulations and global sea surface temperatures (SSTs) two to three months in advance of the monsoon season. Sets of 20 potential predictors are selected from visual assessments of correlation maps that relate rainfall with regional and global predictors. Individual predictors in each set are utilized to initialize specific forward stepwise regression models to develop ensembles of equal number of statistical model estimates, which allow quantifying prediction uncertainties related to individual predictors and models. Prediction skill improvement is achieved through error minimization afforded by the ensemble. For retroactive validation (RV), the ensemble predictions reproduce well the observed all-Ethiopian JJAS rainfall variability two months in advance. The ensemble mean prediction outperforms climatology, with mean square error reduction (SSClim) of 62%. The skill of the prediction remains high for leave-one-out cross validation (LOOCV), with the observed-predicted correlation r (SSClim) being +0.81 (65%) for 1970-2002. For tercile predictions (below, near, and above normal), the ranked probability skill score is 0.45, indicating improvement compared to climatological forecasts. Similarly high prediction skill is found for local prediction of monthly rainfall total at Addis Ababa ( r = +0.72) and Combolcha ( r = +0.68), and for regional prediction of JJAS standardized rainfall anomalies for northeastern Ethiopia ( r = +0.80). Compared to the previous generation of rainfall forecasts, the ensemble predictions developed in this paper show substantial value to benefit society. [ABSTRACT FROM AUTHOR]

The need to draw climate-related inferences from historical data makes understanding the biases and errors in these data critical. While climate data are collected at point-level monitoring sites, they are often postprocessed by averaging sites within a geographic area to align the data to a grid, easing analysis and visualization. Although this aggregation generally provides reasonable estimates of the mean, its use can be problematic for characterizing the full distribution of climate measures. Specifically, the process of averaging point-level data up to grid level can lead to inconsistencies, particularly when the grid box is heterogeneous and extremes are of interest. Point-level data are measured at individual points, while gridded data are the averaged product of many measurements within a larger spatial area. Because of this aggregation, point-level and grid-level distributions differ in many fundamental properties, such as their shape, skew, and tail behavior. This paper highlights these differences and their effects on analyses pertaining to current climatological questions. Mathematical relationships are derived to link the distributions of grid-level climate measures to the distributions of point-level climate measures using the notion of effective sample size. Then, these relationships are leveraged to propose a correction factor to use when modeling higher moments and extreme events. [ABSTRACT FROM AUTHOR]

A real-time regional forecasting system for the South China Sea (SCS), called the Experimental Platform of Marine Environment Forecasting (EPMEF), is introduced in this paper. EPMEF consists of a regional atmosphere model, a regional ocean model, and a wave model, and performs a real-time run four times a day. Output from the Global Forecast System (GFS) from the National Centers for Environmental Prediction (NCEP) is used as the initial and boundary conditions of two nested domains of the atmosphere model, which can exert a constraint on the development of small- and mesoscale atmospheric perturbations through dynamical downscaling. The forecasted winds at 10-m height from the atmosphere model are used to drive the ocean and wave models. As an initial evaluation, a census on the track predictions of 44 tropical cyclones (TCs) during 2011-13 indicates that the performance of EPMEF is very encouraging and comparable to those of other official agencies worldwide. In particular, EPMEF successfully predicted several abnormal typhoon tracks including the sharp recurving of Megi (2010) and the looping of Roke (2011). Further analysis reveals that the dynamically downscaled GFS forecasts from the most updated forecast cycle and the optimal combination of different microphysics and PBL schemes primarily contribute to the good performance of EPMEF in TC track forecasting. EPMEF, established primarily for research purposes with the potential to be implemented into operations, provides valuable information not only to the operational forecasters of local marine/meteorological agencies or international TC forecast centers, but also to other stakeholders such as the fishing industry and insurance companies. [ABSTRACT FROM AUTHOR]

This paper documents features that led to major forecast errors on the 12-24-h time scale in the nature and location of severe weather in the southern plains on 30 May 2012. Evidence is presented that the forecast errors were the result of 1) dry air that originated in a region of dissipating, elevated convective storms, and which was advected in a narrow tongue into western Oklahoma, inhibiting convective initiation; 2) the development of a cyclone along the dryline in western Texas, to the east of which several supercells formed; 3) the upscale development of the supercells into a mesoscale convective system (MCS) at nightfall; and 4) the dissipation of an MCS that had formed along a cold front in southwestern Kansas and was propagating into northwestern Oklahoma, as it encountered dry, subsiding air underneath the stratiform precipitation region of the rear portion of the MCS farther south. There was a meridionally oriented swath of high winds in clear air, in between the two MCSs. This swath of high winds may have been associated with a bore triggered at night by the MCSs approaching from the north, as the MCS collapsed, producing a gust front that propagated through stable, low-level air. This case study illustrates how the predictability of severe weather in a region can be extremely sensitive to the details of where nearby convective storms form and how they evolve. It also highlights the likely importance of the accurate representation of cloud microphysics and dynamics in numerical forecast models on predictability. [ABSTRACT FROM AUTHOR]

A new microphysics scheme has been developed based on the prediction of bulk particle properties for a single ice-phase category, in contrast to the traditional approach of separating ice into various predefined species (e.g., cloud ice, snow, and graupel). In this paper, the new predicted particle properties (P3) scheme, described in Part I of this series, is tested in three-dimensional simulations using the Weather Research and Forecasting (WRF) Model for two contrasting well-observed cases: a midlatitude squall line and winter orographic precipitation. Results are also compared with simulations using other schemes in WRF. Simulations with P3 can produce a wide variety of particle characteristics despite having only one free ice-phase category. For the squall line, it produces dense, fast-falling, hail-like ice near convective updraft cores and lower-density, slower-falling ice elsewhere. Sensitivity tests show that this is critical for simulating high precipitation rates observed along the leading edge of the storm. In contrast, schemes that represent rimed ice as graupel, with lower fall speeds than hail, produce lower peak precipitation rates and wider, less distinct, and less realistic regions of high convective reflectivity. For the orographic precipitation case, P3 produces areas of relatively fast-falling ice with characteristics of rimed snow and low- to medium-density graupel on the windward slope. This leads to less precipitation on leeward slopes and more on windward slopes compared to the other schemes that produce large amounts of snow relative to graupel (with generally the opposite for schemes with significant graupel relative to snow). Overall, the new scheme produces reasonable results for a reduced computational cost. [ABSTRACT FROM AUTHOR]

This paper presents the development of a single executable four-dimensional variational data assimilation (4D-Var) system based on the Weather Research and Forecasting (WRF) Model through coupling the variational data assimilation algorithm (WRF-VAR) with the newly developed WRF tangent linear and adjoint model (WRFPLUS). Compared to the predecessor Multiple Program Multiple Data version, the new WRF 4D-Var system achieves major improvements in that all processing cores are able to participate in the computation and all information exchanges between WRF-VAR and WRFPLUS are moved directly from disk to memory. The single executable 4D-Var system demonstrates desirable acceleration and scalability in terms of the computational performance, as demonstrated through a series of benchmarking data assimilation experiments carried out over a continental U.S. domain. To take into account the nonlinear processes with the linearized minimization algorithm and to further decrease the computational cost of the 4D-Var minimization, a multi-incremental minimization that uses multiple horizontal resolutions for the inner loop has been developed. The method calculates the innovations with a high-resolution grid and minimizes the cost function with a lower-resolution grid. The details regarding the transition between the high-resolution outer loop and the low-resolution inner loop are introduced. Performance of the multi-incremental configuration is found to be comparable to that with the full-resolution 4D-Var in terms of 24-h forecast accuracy in the week-long analysis and forecast experiment over the continental U.S. domain. Moreover, the capability of the newly developed multi-incremental 4D-Var system is further demonstrated in the convection-permitting analysis and forecast experiment for Hurricane Sandy (2012), which was hardly computationally feasible with the predecessor WRF 4D-Var system. [ABSTRACT FROM AUTHOR]

The early season rainfall (ESR) over southern China, usually occurring from April to June, is a prominent meteorological phenomenon of the East Asian monsoon system. In this paper, output from the 45-day hindcast by the NCEP Climate Forecast System, version 2 (CFSv2), and various observational datasets are analyzed to assess the predictability of the ESR and associated atmospheric circulation. Results show that CFSv2 can successfully predict the ESR and associated circulation patterns over southern China. The lower-tropospheric convergence and upper-tropospheric divergence as well as the local upward motion over southern China lead to the formation of ESR. Analysis of bias shows small differences and close relationships between the predicted and observed ESR values when the forecast lead time is less than 2 weeks. The skill in the ESR predictions by CFSv2 decreases significantly when the lead time is longer than 2 weeks. Overall, CFSv2 has a higher level of skill when predicting the southern China ESR compared to the rainfall over other Asian regions during the same period of time. [ABSTRACT FROM AUTHOR]

Wind is one of the parameters best predicted by numerical weather models, as it can be directly calculated from the physical equations of pressure that govern its movement. However, local winds are considerably affected by topography, which global numerical weather models, due to their limited resolution, are not able to reproduce. To improve the skill of numerical weather models, statistical and data analysis methods can be used. Machine learning techniques can be applied to train a model with data coming from both the model and observations in the area of interest. In this paper, a new method based on nonparametric multivariate locally weighted regression is studied for improving the forecasted wind speed of a numerical weather model. Wind direction data are used to build different regression models, as a way of accounting for the effect of surrounding topography. The use of this technique offers similar levels of accuracy for wind speed forecasts compared with other machine learning algorithms with the advantage of being more intuitive and easy to interpret. [ABSTRACT FROM AUTHOR]

While traditional verification methods are commonly used to assess numerical model quantitative precipitation forecasts (QPFs) using a grid-to-grid approach, they generally offer little diagnostic information or reasoning behind the computed statistic. On the other hand, advanced spatial verification techniques, such as neighborhood and object-based methods, can provide more meaningful insight into differences between forecast and observed features in terms of skill with spatial scale, coverage area, displacement, orientation, and intensity. To demonstrate the utility of applying advanced verification techniques to mid- and coarse-resolution models, the Developmental Testbed Center (DTC) applied several traditional metrics and spatial verification techniques to QPFs provided by the Global Forecast System (GFS) and operational North American Mesoscale Model (NAM). Along with frequency bias and Gilbert skill score (GSS) adjusted for bias, both the fractions skill score (FSS) and Method for Object-Based Diagnostic Evaluation (MODE) were utilized for this study with careful consideration given to how these methods were applied and how the results were interpreted. By illustrating the types of forecast attributes appropriate to assess with the spatial verification techniques, this paper provides examples of how to obtain advanced diagnostic information to help identify what aspects of the forecast are or are not performing well. [ABSTRACT FROM AUTHOR]

One of the potential impacts of global warming is likely to be experienced through changes in flood frequency and magnitude, which poses a potential threat to the downstream reservoir flood control system. In this paper, the downscaling results of the multimodel dataset from phases 3 and 5 of the Coupled Model Intercomparison Project (CMIP3 and CMIP5, respectively) were coupled with the Variable Infiltration Capacity (VIC) model to evaluate the impact of climate change on the Feilaixia reservoir flood control in the Beijiang River basin for the first time. Four emissions scenarios [A1B and representative concentration pathway (RCP) scenarios RCP2.6, RCP4.5, and RCP8.5] were chosen. Results indicate that annual distribution and interannual variability of temperature and precipitation are well simulated by the downscaling results of the CMIP3 and CMIP5 multimodel dataset. The VIC model, which performs reasonably well in simulating runoff processes with high model efficiency and low relative error, is suitable for the study area. Overall, annual maximum 1-day precipitation in 2020-50 would increase under all the scenarios (relative to the baseline period 1970-2000). However, the spatial distribution patterns of changes in projected extreme precipitation are uneven under different scenarios. Extreme precipitation is most closely associated with extreme floods in the study area. There is a gradual increase in extreme floods in 2020-50 under any of the different emission scenarios. The increases in 500-yr return period daily discharge of the Feilaixia reservoir have been found to be from 4.35% to 9.18% in 2020-50. The reservoir would be likely to undergo more flooding in 2020-50. [ABSTRACT FROM AUTHOR]

The log-linear regression model is one of the most commonly used models to estimate flood quantiles at ungauged sites within the regional frequency analysis (RFA) framework. However, hydrological processes are naturally complex in several aspects including nonlinearity. The aim of the present paper is to take into account this nonlinearity by introducing the generalized additive model (GAM) in the estimation step of RFA. A neighborhood approach using canonical correlation analysis (CCA) is used to delineate homogenous regions. GAMs possess a number of advantages such as flexibility in shapes of the relationships as well as the distribution of the output variable. The regional model is applied on a dataset of 151 hydrometrical stations located in the province of Québec, Canada. A stepwise procedure is employed to select the appropriate physiometeorological variables. A comparison is performed based on different elements (regional model, variable selection, and delineation). Results indicate that models using GAM outperform models using the log-linear regression as well as other methods applied to this dataset. In addition, GAM is flexible and allows for the inclusion and presentation of nonlinear effects of explanatory variables, in particular, basin area effect (scale). Another finding is the reduced effect of CCA delineation when combined with GAM. [ABSTRACT FROM AUTHOR]

In this paper, large-scale aspects for the onset of the extreme cold European weather event in January-February 2012 are investigated. It is shown that the outbreak of this extreme cold weather event may be attributed to the transition from a positive North Atlantic Oscillation (NAO+) event to a long-lasting blocking event over the eastern Atlantic and western Europe (hereafter ENAO−). A persistent decline of the surface air temperature (SAT) is seen over all of Europe during the long-lived ENAO− event, while the main region of enhanced precipitation is located over southern Europe and part of central Europe, in association with the presence of a persistent double storm track: one over the Norwegian and Barents Seas and the other over southern Europe. The NAO+ to NAO− transition events are divided into NAO+ to ENAO− and NAO+ to WNAO− transition events [ENAO− (WNAO−) events correspond to eastward- (westward-) displaced NAO− events whose positive center is defined to be located to the east (west) of 10°W], and a statistical analysis of the NAO+ to ENAO− transition events during 1978-2012 is performed. It is found that there has been a marked increase in the frequency of the NAO+ to ENAO− transition events during the period 2005-12. Composites of SAT anomalies indicate that the marked decline of the SAT observed over much of Europe is primarily associated with NAO+ to ENAO− transition events. Thus, NAO+ to ENAO− transition events may be more favorable for the extreme cold events over Europe observed in recent winters than other types of NAO− events. [ABSTRACT FROM AUTHOR]

The Ross Ice Shelf airstream (RAS), a prominent transport mechanism of cold, continental air to the north, is the most common wind pattern over the Ross Ice Shelf, Antarctica. The forcing mechanisms of the RAS include katabatic drainage, mesoscale forcing, and synoptic forcing. This paper uses the 15-km output from the Antarctic Mesoscale Prediction System (AMPS) and the method of self-organizing maps (SOM) to analyze how the combination of these forcing mechanisms impacts the strength and position of the RAS. It is found that the strength and position of the RAS is mainly driven by the thermal forcing in the region of the Transantarctic Mountains. This forcing includes the pressure gradient associated with cold air pooling at the base of the Transantarctic Mountains, as well as, the pressure gradient associated with the temperature contrast between the cold air located over the East Antarctic Plateau and the warm ambient air over the Ross Ice Shelf. These forcing mechanisms are analyzed in a region near the southern tip of the Ross Ice Shelf. In this region, the pressure gradient associated with the temperature contrast between the East Antarctic Plateau and the ambient air over the ice shelf is usually present during RAS events, while the pressure gradient associated with the cold air pooling varies between RAS events. The analysis shows that, in the region of the southern Ross Ice Shelf, RAS events can occur without the presence of cold air pooling. [ABSTRACT FROM AUTHOR]

A basic question in forecasting is whether one prediction system is more skillful than another. Some commonly used statistical significance tests cannot answer this question correctly if the skills are computed on a common period or using a common set of observations, because these tests do not account for correlations between sample skill estimates. Furthermore, the results of these tests are biased toward indicating no difference in skill, a fact that has important consequences for forecast improvement. This paper shows that the magnitude of bias is characterized by a few parameters such as sample size and correlation between forecasts and their errors, which, surprisingly, can be estimated from data. The bias is substantial for typical seasonal forecasts, implying that familiar tests may wrongly judge that differences in seasonal forecast skill are insignificant. Four tests that are appropriate for assessing differences in skill over a common period are reviewed. These tests are based on the sign test, the Wilcoxon signed-rank test, the Morgan-Granger-Newbold test, and a permutation test. These techniques are applied to ENSO hindcasts from the North American Multimodel Ensemble and reveal that the Climate Forecast System, version 2, and the Canadian Climate Model, version 3 (CanCM3), outperform other models in the sense that their squared error is less than that of other single models more frequently. It should be recognized that while certain models may be superior in a certain sense for a particular period and variable, combinations of forecasts are often significantly more skillful than a single model alone. In fact, the multimodel mean significantly outperforms all single models. [ABSTRACT FROM AUTHOR]

In this paper, theoretical and numerical analyses of the properties of some complex semi-Lagrangian methods are performed to deal with the issues of the instability associated with the treatment of the nonlinear part of the forcing term. A class of semi-Lagrangian semi-implicit schemes is proposed using a modified TR-BDF2 method, which is the combination of the trapezoidal rule (TR) and the second-order backward differentiation formula (BDF2). The process used for the nonlinear term includes two stages as predictor and corrector in the trapezoidal method and one stage for the BDF2 method. For the treatment of the linear term, the implicit trapezoidal method is employed in the first step, the explicit trapezoidal method in the second step, and the implicit BDF2 method in the third step. The combination of these techniques leads to a family of schemes that has a large region of absolute stability, performs well for the purely oscillatory cases, and has good qualities in terms of accuracy and convergence. The use of the explicit method for the linear term in the second step makes the proposed class of schemes competitive in terms of efficiency compared to some well-known schemes that use two steps. Numerical experiments presented herein confirm that the proposed class of schemes performs well in terms of stability, accuracy, convergence, and efficiency in comparison with other, previously known, semi-Lagrangian semi-implicit schemes and semi-implicit predictor-corrector methods. The potential practical application of the proposed class of schemes to a weather prediction model or any other atmospheric model is not discussed and could be the subject of other forthcoming studies. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) techniques have had significant recent successes in multiple fields. These fields and the fields of satellite remote sensing and NWP share the same fundamental underlying needs, including signal and image processing, quality control mechanisms, pattern recognition, data fusion, forward and inverse problems, and prediction. Thus, modern AI in general and machine learning (ML) in particular can be positively disruptive and transformational change agents in the fields of satellite remote sensing and NWP by augmenting, and in some cases replacing, elements of the traditional remote sensing, assimilation, and modeling tools. And change is needed to meet the increasing challenges of Big Data, advanced models and applications, and user demands. Future developments, for example, SmallSats and the Internet of Things, will continue the explosion of new environmental data. ML models are highly efficient and in some cases more accurate because of their flexibility to accommodate nonlinearity and/or non-Gaussianity. With that efficiency, ML can help to address the demands put on environmental products for higher accuracy, for higher resolution—spatial, temporal, and vertical, for enhanced conventional medium-range forecasts, for outlooks and predictions on subseasonal to seasonal time scales, and for improvements in the process of issuing advisories and warnings. Using examples from satellite remote sensing and NWP, it is illustrated how ML can accelerate the pace of improvement in environmental data exploitation and weather prediction—first, by complementing existing systems, and second, where appropriate, as an alternative to some components of the NWP processing chain from observations to forecasts. [ABSTRACT FROM AUTHOR]

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

The scientific community has long acknowledged the importance of high-temporal-resolution radar observations to advance science research and improve high-impact weather prediction. Development of innovative rapid-scan radar technologies over the past two decades has enabled radar volume scans of 10–60 s compared to 3–5 min with traditional parabolic dish research radars and the WSR-88D radar network. This review examines the impact of rapid-scan radar technology, defined as radars collecting volume scans in 1 min or less, on atmospheric science research spanning different subdisciplines and evaluates the strengths and weaknesses of the use of rapid-scan radars. In particular, a significant body of literature has accumulated for tornado and severe thunderstorm research and forecasting applications, in addition to a growing number of studies of convection. Convection research has benefited substantially from more synchronous vertical views, but could benefit more substantially by leveraging multi-Doppler wind retrievals and complementary in situ and remote sensors. In addition, several years of forecast evaluation studies are synthesized from radar testbed experiments, and the benefits of assimilating rapid-scan radar observations are analyzed. Although the current body of literature reflects the considerable utility of rapid-scan radars to science research, a weakness is that limited advancements in understanding of the physical mechanisms behind observed features have been enabled. There is considerable opportunity to bridge the gap in physical understanding with the current technology using coordinated efforts to include rapid-scan radars in field campaigns and expanding the breadth of meteorological phenomena studied. Significance Statement: Recently developed rapid-scan radar technologies, capable of collecting volumetric (i.e., three-dimensional) measurements in 10–60 s, have improved temporal sampling of weather phenomena. This review examines the impact of these radar observations from the past two decades on science research and emerging operational capabilities. Substantial breadth and impact of research is evident for tornado research and forecasting applications, in addition to documentation of other rapidly evolving phenomena associated with deep convection, such as tornadoes, hail, lightning, and tropical cyclones. This review identifies the strengths and weaknesses of how these radars have been used in scientific research to inform future studies, emerging from the increasing availability and capability of rapid-scan radars. In addition, this review synthesizes research that can benefit future operational radar decisions. [ABSTRACT FROM AUTHOR]

In 2009, advancements in NWP and computing power inspired a vision to advance hazardous weather warnings from a warn-on-detection to a warn-on-forecast paradigm. This vision would require not only the prediction of individual thunderstorms and their attributes but the likelihood of their occurrence in time and space. During the last decade, the warn-on-forecast research team at the NOAA National Severe Storms Laboratory met this challenge through the research and development of 1) an ensemble of high-resolution convection-allowing models; 2) ensemble- and variational-based assimilation of weather radar, satellite, and conventional observations; and 3) unique postprocessing and verification techniques, culminating in the experimental Warn-on-Forecast System (WoFS). Since 2017, we have directly engaged users in the testing, evaluation, and visualization of this system to ensure that WoFS guidance is usable and useful to operational forecasters at NOAA national centers and local offices responsible for forecasting severe weather, tornadoes, and flash floods across the watch-to-warning continuum. Although an experimental WoFS is now a reality, we close by discussing many of the exciting opportunities remaining, including folding this system into the Unified Forecast System, transitioning WoFS into NWS operations, and pursuing next-decade science goals for further advancing storm-scale prediction. Significance Statement: The purpose of this research is to develop an experimental prediction system that forecasts the probability for severe weather hazards associated with individual thunderstorms up to 6 h in advance. This capability is important because some people and organizations, like those living in mobile homes, caring for patients in hospitals, or managing large outdoor events, require extended lead time to protect themselves and others from potential severe weather hazards. Our results demonstrate a prediction system that enables forecasters, for the first time, to message probabilistic hazard information associated with individual severe storms between the watch-to-warning time frame within the United States. [ABSTRACT FROM AUTHOR]

Extreme rainfall found in tropical cyclones (TCs) is a risk for human life and property in many low- to midlatitude regions. Probabilistic modeling of TC rainfall in risk assessment and forecasting can be computationally expensive, and existing models are largely unable to model key rainfall asymmetries such as rainbands and extratropical transition. Here, a machine learning–based framework is developed to model overwater TC rainfall for the North Atlantic basin. First, a catalog of high-resolution TC precipitation simulations for 26 historical events is assembled for the North Atlantic basin using the Weather Research and Forecasting (WRF) Model. The simulated spatial distribution of rainfall for these historical events are then decomposed via principal component analysis (PCA), and quantile regression forest (QRF) models are trained to predict the conditional distributions of the first five principal component (PC) weights. Conditional distributions of rain-rate levels are estimated separately using historical satellite data and a QRF model. With these models, probabilistic predictions of rainfall maps can be made given a set of storm characteristics and local environmental conditions. The model is able to capture storm total rainfall compared to satellite observations with a correlation coefficient of 0.96 and r2 value of 0.93. Additionally, the model shows good accuracy in modeling hourly total rainfall compared to satellite observations. Rain-rate maps predicted by the model are also compared to historical satellite observations and to the WRF simulations during cross validation, and the spatial distribution of estimates captures rainfall variability consistent with TC rainbands, wavenumber asymmetries, and possibly extratropical transition. [ABSTRACT FROM AUTHOR]

A new version of the Weather Research and Forecasting (WRF) double-moment 6-class (WDM6) microphysics scheme was developed based on the existing WDM6 scheme by predicting snow and graupel number concentrations. The new WDM6 scheme was tested for summer rainfall and winter snowfall cases to evaluate the effects of prognostic number concentration of snow and graupel on the simulated precipitation. The number concentration of snow decreases at the upper layers and the one of graupel also decreases at all layers in the new WDM6 scheme compared to the diagnosed ones in the original WDM6 scheme. Rain number concentration is remarkably reduced in the new WDM6 scheme due to the newly added and modified sink processes. Therefore, the new scheme produces a larger size of raindrops with a reduced number concentration than the original scheme, which hinders raindrop evaporation and produces more surface rain. Even though the enhanced surface rainfall in the new scheme deteriorates the bias score, the new scheme improves the statistical skill of the equitable threat score and probability of detection in most cases. These scores all improved for warm-type summer cases in the new scheme. The new scheme also shows more comparable features to the observation for the probability density functions of simulated liquid equivalent precipitation rates by alleviating the overprediction problem of precipitation frequencies belonging to heavy precipitation categories. Therefore, the new scheme improves the precipitation forecast for warm-type summer cases, which occur most frequently during the summer season over the Korean Peninsula. [ABSTRACT FROM AUTHOR]

Total solar eclipses (TSEs) are impressive astronomical events that have attracted people's curiosity since ancient times. Their abrupt alterations to the radiation balance have stimulated studies on "eclipse meteorology," most of them documenting events in the Northern Hemisphere while only one TSE (23 November 2003) has been described over Antarctica. On 4 December 2021--just a few days before the austral summer solstice--the moon blocked the sun over the austral high latitudes, with the path of totality arching from the Weddell Sea to the Amundsen Sea, thus producing a ~2-min central TSE. In this work we present high-resolution meteorological observations from Union Glacier Camp (80°S, 83°W), the only location with a working station under totality, and South Pole station. These observations were complemented with meteorological records from 37 surface stations across Antarctica. Notably, the largest cooling (~5°C) was observed over the East Antarctic dome, where obscurity was ~85% while many sectors experienced insignificant temperature changes. This heterogenous cooling distribution, at odds with the seemingly homogeneous land surface of Antarctica, is partially captured by a simple radiative model. To further diagnose the effect of the eclipse on the surface meteorology, we ran multiple pairs of simulations (eclipse enabled and disabled) using the Weather Research and Forecasting (WRF) Model. The overall pattern and magnitude of the simulated cooling agree well with the observations and reveal that, in addition to the solar radiation deficit and cloud cover, low-level winds and the height of the planetary boundary layer are key determinants of the temperature changes and their spatial variability. [ABSTRACT FROM AUTHOR]

A strong coherence of the summer (June–August) surface air temperature (SAT) anomalies exists over eastern Eurasia (EE) and central Eurasia (CE) in association with a remarkable intraseasonal (10–90-day) variability. The intraseasonal SAT over Eurasia shows a negative correlation between EE and CE, which is closely related to an intraseasonal wave train at the upper troposphere over the mid–high latitudes of Eurasia. The wave train propagates eastward and results in the intraseasonal variation of SAT in a seesaw pattern. The column-integrated temperature budget suggests that both the horizontal and vertical advection are important for the intraseasonal SAT dipole. More specifically, the climatological westerly, intraseasonal meridional wind anomaly, and the anomalous vertical motion play dominant roles in perturbing temperature over the CE and EE regions, while the diabatic heating is a negative feedback. The propagation of the Rossby wave train in the upper troposphere helps to maintain the circulation anomalies associated with the SAT seesaw. The leading modes of meridional wind anomalies at 200 hPa over the Eurasian high latitudes resemble the intraseasonal wave train, indicating that the wave train associated with the SAT seesaw is an atmospheric intrinsic mode over the mid–high latitudes. This study also investigates the impact of the intraseasonal SAT seesaw on regional heat wave events. It is suggested that the intraseasonal SAT seesaw has a major influence on the variability of heat wave events over both EE and CE, which may provide great implications for regional subseasonal forecasts of extreme weather events. [ABSTRACT FROM AUTHOR]

We evaluated the ability of the fvGFS with a 13-km resolution in simulating tropical cyclone genesis (TCG) by conducting hindcast experiments for 42 TCG events over 2018–19 in the western North Pacific (WNP). We observed an improved hit rate with a lead time of between 5 and 4 days; however, from 4- to 3-day lead time, no consistent improvement in the temporal and spatial errors of TCG was obtained. More "Fail" cases occurred when and where a low-level easterly background flow prevailed: from mid-August to September 2018 and after October 2019 and mainly in the eastern WNP. In "Hit" cases, 850-hPa streamfunction and divergence, 200-hPa divergence, and genesis potential index (GPI) provided favorable TCG conditions. However, the Hit–Fail case differences in other suggested factors (vertical wind shear, 700-hPa moisture, and SST) were nonsignificant. By contrast, the reanalysis used for validation showed only significant difference in 850-hPa streamfunction. We stratified the background flow of TCG into four types. The monsoon trough type (82%) provided the most favorable environmental conditions for successful hindcasts, followed by the subtropical high (45%), easterly (17%), and others (0%) types. These results indicated that fvGFS is more capable of enhancing monsoon trough circulation and provides a much better environment for TCG development but is less skillful in other types of background flow that provides weaker large-scale forcing. The results suggest that the most advanced high-resolution weather forecast models such as the fvGFS warrant further improvement to properly simulate the subtle circulation features (e.g., mesoscale convection system) that might provide seeds for TCG. Significance Statement: This study provides an evaluation of tropical cyclone genesis prediction skill of fvGFS. Favorable large-scale environmental factors for successful prediction are identified. Skill dependence on environmental factors provides guidance for evaluating the reliability of a genesis forecast in advance. [ABSTRACT FROM AUTHOR]

This study conducts the first large-sample comparison of the impact of dropsondes in the tropical cyclone (TC) inner core, vortex, and environment on NWP-model TC forecasts. We analyze six observing-system experiments, focusing on four sensitivity experiments that denied dropsonde observations within annuli corresponding with natural breakpoints in reconnaissance sampling. These are evaluated against two other experiments detailed in a recent parallel study: one that assimilated and another that denied dropsonde observations. Experiments used a basin-scale, multistorm configuration of the Hurricane Weather Research and Forecasting (HWRF) Model and covered active periods of the 2017–20 North Atlantic hurricane seasons. Analysis focused on forecasts initialized with dropsondes that used mesoscale error covariance derived from a cycled HWRF ensemble, as these forecasts were where dropsondes had the greatest benefits in the parallel study. Some results generally support findings of previous research, while others are novel. Most notable was that removing dropsondes anywhere, particularly from the vortex, substantially degraded forecasts of maximum sustained winds. Removing in-vortex dropsondes also degraded outer-wind-radii forecasts in many instances. As such, in-vortex dropsondes contribute to a majority of the overall impacts of the dropsonde observing system. Additionally, track forecasts of weak TCs benefited more from environmental sampling, while track forecasts of strong TCs benefited more from in-vortex sampling. Finally, inner-core-only sampling strategies should be avoided, supporting a change made to the U.S. Air Force Reserve's sampling strategy in 2018 that added dropsondes outside of the inner core. Significance Statement: This study uses a regional hurricane model to conduct the most comprehensive assessment to date of the impact of dropsondes at different distances away from the center of a tropical cyclone (TC) on TC forecasts. The main finding is that in-vortex dropsondes are most important for intensity and outer-wind-radii forecasts. Particularly notable is the impact of dropsondes on TC maximum wind speed forecasts, as reducing sampling anywhere would degrade those forecasts. [ABSTRACT FROM AUTHOR]

Analyses of cloud-top temperature and lightning characteristics of 48 Weather Research and Forecasting (WRF) Model–simulated ocean-based wind events, with 1-min temporal and 0.5-km horizontal resolution, revealed signatures similar to the corresponding 13 observed events detected by buoys and Coastal-Marine Automated Network (C-MAN) stations as shown in prior research on ocean-based wind events by the first author. These events occurred in the eastern Gulf of Mexico and in the Atlantic Ocean from Florida northward through South Carolina. The coldest WRF cloud-top temperature (WCTT) and peak WRF-estimated lightning flash rate values of the model-simulated events, where each event was required to have a negative vertical velocity of at least 10 m s−1 in the lowest 2 km associated with a convective storm, occurred at an average of 4.2 and 1.1 min prior to the events, respectively. With 36 of the events, the peak estimated flash rate occurred within 5 min of the coldest WCTT. Cloud depth typically increased as the WCTT decreased, and the maximum depth occurred at an average of 2.9 min prior to the events. Thermal cooling and precipitation loading provided negative buoyancy needed to help drive the wind events. Environmental characteristics of the model-simulated ocean-based wind events also resembled those associated with land-based wet downbursts, including moist air near the surface, lapse rates near moist adiabatic, and low cloud bases. [ABSTRACT FROM AUTHOR]

Daily expected wind power production from operating wind farms across North America are used to evaluate capacity factors (CF) computed using simulation output from the Weather Research and Forecasting (WRF) Model and to condition statistical models linking atmospheric conditions to electricity production. In Parts I and II of this work, we focus on making projections of annual energy production and the occurrence of electrical production drought. Here, we extend evaluation of the CF projections for sites in the Northeast, Midwest, southern Great Plains (SGP), and southwest U.S. coast (SWC) using statewide wind-generated electricity supply to the grid. We then quantify changes in the time scales of CF variability and the seasonality. Currently, wind-generated electricity is lowest in summer in each region except SWC, which causes a substantial mismatch with electricity demand. While electricity of residential heating may shift demand, research presented here suggests that summertime CF are likely to decline, potentially exacerbating the offset between seasonal peak power production and current load. The reduction in summertime CF is manifest for all regions except the SGP and appears to be linked to a reduction in synoptic-scale variability. Using fulfillment of 50% and 90% of annual energy production to quantify interannual variability, it is shown that wind power production exhibits higher (earlier fulfillment) or lower (later fulfillment) production for periods of over 10–30 years as a result of the action of internal climate modes. Significance Statement: Electrical power system reassessment and redesign may be needed to aid efficient increased use of variable renewables in the generation of electricity. Currently wind-generated electricity in many regions of North America exhibits a minimum in summertime and hence is not well synchronized with electricity demand, which tends to be maximized in summer. Future projections indicate evidence of reductions in wind power during summer that would amplify this offset. However, electrification of heating may lead to increased wintertime demand, which would lead to greater synchronization. [ABSTRACT FROM AUTHOR]

Considerable effort has gone into building numerical weather and ocean prediction models during the past 50 years. Less effort has gone into the visual representation of output from those forecast models and many of the techniques used are known to be ineffective. The effectiveness of a data display depends on how well critical patterns can be perceived. This paper outlines a set of perceptual principles for what makes a good representation of a 2D vector field and shows how these principles can be used for the portrayal of currents, winds, and waves. Examples are given from a series of evaluation studies that examine the optimal representation of these variables. The results suggest that for static graphic presentations, equally spaced streamlines may be optimal. If wind barbs are curved to follow streamlines, perception of local wind speed and direction as well as the overall pattern is improved. For animated portrayals of model output, animated streamlets can perceptually separate layers of information so that atmospheric pressure and surface temperature can clearly be shown simultaneously with surface winds. [ABSTRACT FROM AUTHOR]

Researchers are producing an ever greater number of web-based climate data and analysis tools in support of natural resource research and management. Yet the apparent absence or underreporting of evaluation in the development of these applications has raised questions as to whether, by whom, and for what they are utilized, and, relatedly, whether they meet the rationale for their development. This paper joins recent efforts to address these questions by introducing one approach to evaluation—developmental evaluation—and reporting on its use in the evaluation of the Southwest Climate and Environmental Information Collaborative (SCENIC). A web interface under development at the Western Regional Climate Center, SCENIC provides access to climate data and analysis tools to environmental scientists in support of natural resource research and management in the southwestern United States. Evaluation findings highlight subtlety in the improvements necessary for ensuring a useful and usable application that could not have been ascertained in the absence of end-user feedback. We therefore urge researchers to systematically evaluate web-based climate data and analysis tools in the interest of ensuring their usefulness, usability, and fulfillment of the proposed rationale. In so doing, we recommend that researchers test and apply established evaluation frameworks, thereby engaging end users directly in the process of application development. [ABSTRACT FROM AUTHOR]

Monitoring changes of precipitation phase from space is important for understanding the mass balance of Earth's cryosphere in a changing climate. This paper examines a Bayesian nearest neighbor approach for prognostic detection of precipitation and its phase using passive microwave observations from the Global Precipitation Measurement (GPM) satellite. The method uses the weighted Euclidean distance metric to search through an a priori database populated with coincident GPM radiometer and radar observations as well as ancillary snow-cover data. The algorithm performance is evaluated using data from GPM official precipitation products, ground-based radars, and high-fidelity simulations from the Weather Research and Forecasting Model. Using the presented approach, we demonstrate that the hit probability of terrestrial precipitation detection can reach to 0.80, while the probability of false alarm remains below 0.11. The algorithm demonstrates higher skill in detecting snowfall than rainfall, on average by 10%. In particular, the probability of precipitation detection and its solid phase increases by 11% and 8%, over dry snow cover, when compared to other surface types. The main reason is found to be related to the ability of the algorithm in capturing the signal of increased liquid water content in snowy clouds over radiometrically cold snow-covered surfaces. [ABSTRACT FROM AUTHOR]