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Sharanya J. Majumdar

Abstract

It has long been conceived that numerical weather forecasts will benefit from the assimilation of supplementary observations that augment the conventional observational network. In particular, the concept of “targeting” observations in selected regions to improve a forecast of a high-impact weather event had been promoted and tested prior to and during the World Meteorological Organization/World Weather Research Programme’s The Observing System Research and Predictability Experiment (THORPEX) era (2005–14), through field campaigns and assimilation experiments. The end of the THORPEX era provided an appropriate opportunity to review the outcomes and, in particular, the evaluations of the influence of assimilating targeted observations on numerical weather predictions. The main outcome in the extratropics was that the influence of the targeted observations was positive though small (typically an average forecast error reduction of less than 10%). In the tropics, the targeted observations usually improved tropical cyclone track forecasts. Significantly, the results from these and other experiments were found to be sensitive to the sample chosen, the method of verification, and the numerical weather prediction system including the data assimilation scheme and the treatment of observations. Recommendations for the future include innovations to optimize the use of the Global Observing System via better exploitation of routinely available resources together with new instrumentation; expanding into the convective scale and mesoscale; investing quantitative evaluations and improving our understanding of how observations affect forecasts; and assessing the socioeconomic value of improved forecasts. A comprehensive bibliography of approximately 200 papers is provided in the online supplement to this paper.

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Munehiko Yamaguchi and Sharanya J. Majumdar

Abstract

Ensemble initial perturbations around Typhoon Sinlaku (2008) produced by ECMWF, NCEP, and the Japan Meteorological Agency (JMA) ensembles are compared using The Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble (TIGGE) data, and the dynamical mechanisms of perturbation growth associated with the tropical cyclone (TC) motion are investigated for the ECMWF and NCEP ensembles. In the comparison, it is found that the vertical and horizontal distributions of initial perturbations as well as the amplitude are quite different among the three NWP centers before, during, and after the recurvature of Sinlaku. In addition, it turns out that those variations cause a difference in the TC motion not only at the initial time but also during the subsequent forecast period. The ECMWF ensemble exhibits relatively large perturbation growth, which results from 1) the baroclinic energy conversion in a vortex, 2) the baroclinic energy conversion associated with the midlatitude waves, and 3) the barotropic energy conversion in a vortex. Those features are less distinctive in the NCEP ensemble. A statistical verification shows that the ensemble spread of TC track predictions in NCEP (ECMWF) is larger than ECMWF (NCEP) for 1- (3-) day forecasts on average. It can be inferred that while the ECMWF ensemble starts from a relatively small amplitude of initial perturbations, the growth of the perturbations helps to amplify the ensemble spread of tracks. On the other hand, a relatively large amplitude of initial perturbations seems to play a role in producing the ensemble spread of tracks in the NCEP ensemble.

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Michael J. Brennan and Sharanya J. Majumdar

Abstract

Sources of dynamical model track error for Hurricane Ike (2008) in the Gulf of Mexico are examined. Deterministic and ensemble model output are compared against National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) analyses to identify potential critical features associated with the motion of Ike and its eventual landfall along the upper Texas coast. Several potential critical features were identified, including the subtropical ridge north of Ike and several synoptic-scale short-wave troughs and ridges over central and western North America, and Tropical Storm Lowell in the eastern North Pacific. Using the NCEP Gridpoint Statistical Interpolation (GSI) data assimilation scheme, the operational GSI analysis from the 0000 UTC 9 September 2008 cycle was modified by perturbing each of these features individually, and then integrating the GFS model using the perturbed initial state. The track of Ike from each of the perturbed runs was compared to the operational GFS and it was found that the greatest improvements to the track forecast were associated with weakening the subtropical ridge north of Ike and strengthening a midlevel short-wave trough over California. A GFS run beginning with an analysis where both of these features were perturbed produced a greater track improvement than either did individually. The results suggest that multiple sources of error exist in the initial states of the operational models, and that the correction of these errors in conjunction with reliable ensemble forecasts would lead to improved forecasts of tropical cyclone tracks and their accompanying uncertainty.

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William A. Komaromi and Sharanya J. Majumdar

Abstract

Several metrics are employed to evaluate predictive skill and attempt to quantify predictability using the ECMWF Ensemble Prediction System during the 2010 Atlantic hurricane season, with an emphasis on large-scale variables relevant to tropical cyclogenesis. These metrics include the following: 1) growth and saturation of error, 2) errors versus climatology, 3) predicted forecast error standard deviation, and 4) predictive power. Overall, variables that are more directly related to large-scale, slowly varying phenomena are found to be much more predictable than variables that are inherently related to small-scale convective processes, regardless of the metric. For example, 850–200-hPa wind shear and 200-hPa velocity potential are found to be predictable beyond one week, while 200-hPa divergence and 850-hPa relative vorticity are only predictable to about one day. Similarly, area-averaged quantities such as circulation are much more predictable than nonaveraged quantities such as vorticity. Significant day-to-day and month-to-month variability of predictability for a given metric also exists, likely due to the flow regime. For wind shear, more amplified flow regimes are associated with lower predictive power (and thereby lower predictability) than less amplified regimes. Relative humidity is found to be less predictable in the early and late season when there exists greater uncertainty of the timing and location of dry air. Last, the ensemble demonstrates the potential to predict error standard deviation of variables averaged in 10° × 10° boxes, in that forecasts with greater ensemble standard deviation are on average associated with greater mean error. However, the ensemble tends to be underdispersive.

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Peter M. Finocchio and Sharanya J. Majumdar

Abstract

A statistical analysis of tropical cyclone (TC) environmental wind profiles is conducted in order to better understand how vertical wind shear influences TC intensity change. The wind profiles are computed from global atmospheric reanalyses around the best track locations of 7554 TC cases in the Northern Hemisphere tropics. Mean wind profiles within each basin exhibit significant differences in the magnitude and direction of vertical wind shear. Comparisons between TC environments and randomly selected “non-TC” environments highlight the synoptic regimes that support TCs in each basin, which are often characterized by weaker deep-layer shear. Because weaker deep-layer shear may not be the only aspect of the environmental flow that makes a TC environment more favorable for TCs, two new parameters are developed to describe the height and depth of vertical shear. Distributions of these parameters indicate that, in both TC and non-TC environments, vertical shear most frequently occurs in shallow layers and in the upper troposphere. Linear correlations between each shear parameter and TC intensity change show that shallow, upper-level shear is slightly more favorable for TC intensification. But these relationships vary by basin and neither parameter independently explains more than 5% of the variance in TC intensity change between 12 and 120 h. As such, the shear height and depth parameters in this study do not appear to be viable predictors for statistical intensity prediction, though similar measures of midtropospheric vertical wind shear may be more important in particularly challenging intensity forecasts.

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William A. Komaromi and Sharanya J. Majumdar

Abstract

The predictability of selected variables associated with tropical cyclogenesis is examined using 10-day ECMWF ensemble forecasts for 21 events from the 2010 Atlantic hurricane season. Variables are associated with the strength of the pregenesis disturbance, quantified via circulation and thickness anomaly, and the favorability of the immediate environment via moisture and vertical wind shear.

For approximately half of the cases, the predicted strength of the genesis signal is directly related to the predicted favorability of the environment. For the remainder of the cases, predictability is more directly associated with the strength and location of the analyzed disturbance. Some commonalities among the majority of the sample are also observed. Forecast joint distributions demonstrate that 700-hPa relative humidity of less than 60% within 300 km of the circulation center is a limiting factor for genesis. Genesis is also predicted and found to occur in the presence of significant wind shear (~15 m s−1), but almost exclusively when the core and environment of the wave are both very moist.

The ensemble also demonstrates the potential to predict error standard deviation of variables averaged within 300- and 1000-km radii about individual tropical waves. Forecasts with greater ensemble standard deviation tend to be, on average, associated with greater mean error, especially for forecasts of less than 7 days. However, model biases, particularly a dry core and weak circulation bias, become pronounced at longer lead times. Overall, these results demonstrate that both the environmental conditions favorable to genesis and the genesis events themselves may be predictable to a week or more.

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Sharanya J. Majumdar and Ryan D. Torn

Abstract

Probabilistic forecasts of tropical cyclogenesis have been evaluated for two samples: a near-homogeneous sample of ECMWF and Weather Research and Forecasting (WRF) Model–ensemble Kalman filter (EnKF) ensemble forecasts during the National Science Foundation’s (NSF) Pre-Depression Investigation of Cloud-systems in the Tropics (PREDICT) field campaign (15 August–30 September 2010) and ECMWF ensemble forecasts during the 2010–12 Atlantic hurricane seasons. Quantitative criteria for tropical cyclone (TC) formation were first determined from model analyses based on threshold values of lower-tropospheric circulation, local thickness anomaly, and minimum sea level pressure. A binary verification was then performed for all ensemble forecasts with initial-time tropical disturbances. During the PREDICT period, the ECMWF and WRF–EnKF had similar verification statistics, with reliability diagrams of positive slope flatter than unity, and relative operating characteristic (ROC) curves that demonstrate skill. For the 2010–12 ECMWF ensemble forecasts, the equitable threat score was small and positive, with skill mostly lost after 5 days. The reliability diagrams for 1–5-day forecasts were monotonic increasing, though an overly large number of short-range ensemble forecasts predicted a low probability of a TC when a TC was verified. The ROC curves exhibited similar skill for forecasts out to 5 days. The reliability curves were sensitive to parameters such as time tolerance and threshold values, and insensitive to cases that originated from African easterly waves versus those that did not. Qualitative investigations revealed case-to-case variability in the probabilistic predictions. While the sample size was limited, the ensembles showed the potential for probabilistic prediction out to 5 days, though it appeared that the model struggled with developing a warm core in the short-range forecast.

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Sharanya J. Majumdar and Peter M. Finocchio

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The ability of ensemble prediction systems to predict the probability that a tropical cyclone will fall within a certain area is evaluated. Ensemble forecasts of up to 5 days issued by the European Centre for Medium-Range Weather Forecasts (ECMWF) and the Met Office (UKMET) were evaluated for the 2008 Atlantic and western North Pacific seasons. In the Atlantic, the ECMWF ensemble mean was comparable in skill to a consensus of deterministic models. Dynamic “probability circles” that contained 67% of the ECMWF ensemble captured the best track in ∼67% of all cases for 24–84-h forecasts, and were slightly underdispersive beyond 96 h. In contrast, the Goerss predicted consensus error (GPCE) was overdispersive. The addition of the UKMET ensemble yielded improvements in the short range and degradations for longer-range forecasts. The ECMWF ensemble performed similarly when the size was reduced from 50 to 20. On average, it produced a lower measure of independence between its members than an ensemble comprising different deterministic models. The 67% circles normally captured the best track during straight-line motion, but less so for sharply turning tracks. In contrast to the Atlantic, the ECMWF ensemble (and GPCE) was unable to capture sufficient verifications within the 67% probability circles in the western North Pacific, in part because of a less skillful ensemble mean (and consensus). Though further evaluations are necessary, the results demonstrate the potential for ensemble prediction systems to enhance probabilistic forecasts, and for The Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble (TIGGE) to be embraced by the operational and research communities.

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Sharanya J. Majumdar, Michael J. Brennan, and Kate Howard

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Because of the threat that Hurricane Irene (2011) posed to the United States, supplemental observations were collected for assimilation into operational numerical models in the hope of improving forecasts of the storm. Synoptic surveillance aircraft equipped with dropwindsondes were deployed twice daily over a 5-day period, and supplemental rawinsondes were launched from all upper-air sites in the continental United States east of the Rocky Mountains at 0600 and 1800 UTC, marking an unprecedented magnitude of coverage of special rawinsondes at the time. The impact of assimilating the supplemental observations on National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) model track forecasts of Irene was evaluated over the period that these observations were collected. The GFS track forecasts possessed small errors even in the absence of the supplemental observations, providing little room for improvement on average. The assimilation of the combined dropwindsonde and supplemental rawinsonde data provided small but statistically significant improvements in the 42–60-h range for GFS forecasts initialized at 0600 and 1800 UTC. The primary improvement from the dropwindsonde data was also within this time range, with an average improvement of 20% for 48-h forecasts. The rawinsonde data mostly improved the forecasts beyond 3 days by modest amounts. Both sets of observations provided small, additive improvements to the average cross-track errors. Investigations of individual forecasts identified corrections to the model analyses of the Atlantic subtropical ridge and an upstream midlatitude short-wave trough over the contiguous United States due to the assimilation of the extra data.

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Craig H. Bishop, Brian J. Etherton, and Sharanya J. Majumdar

Abstract

A suboptimal Kalman filter called the ensemble transform Kalman filter (ET KF) is introduced. Like other Kalman filters, it provides a framework for assimilating observations and also for estimating the effect of observations on forecast error covariance. It differs from other ensemble Kalman filters in that it uses ensemble transformation and a normalization to rapidly obtain the prediction error covariance matrix associated with a particular deployment of observational resources. This rapidity enables it to quickly assess the ability of a large number of future feasible sequences of observational networks to reduce forecast error variance. The ET KF was used by the National Centers for Environmental Prediction in the Winter Storm Reconnaissance missions of 1999 and 2000 to determine where aircraft should deploy dropwindsondes in order to improve 24–72-h forecasts over the continental United States. The ET KF may be applied to any well-constructed set of ensemble perturbations.

The ET KF technique supercedes the ensemble transform (ET) targeting technique of Bishop and Toth. In the ET targeting formulation, the means by which observations reduced forecast error variance was not expressed mathematically. The mathematical representation of this process provided by the ET KF enables such things as the evaluation of the reduction in forecast error variance associated with individual flight tracks and assessments of the value of targeted observations that are distributed over significant time intervals. It also enables a serial targeting methodology whereby one can identify optimal observing sites given the location and error statistics of other observations. This allows the network designer to nonredundantly position targeted observations. Serial targeting can also be used to greatly reduce the computations required to identify optimal target sites. For these theoretical and practical reasons, the ET KF technique is more useful than the ET technique. The methodology is illustrated with observation system simulation experiments involving a barotropic numerical model of tropical cyclonelike vortices. These include preliminary empirical tests of ET KF predictions using ET KF, 3DVAR, and hybrid data assimilation schemes—the results of which look promising. To concisely describe the future feasible sequences of observations considered in adaptive sampling problems, an extension to Ide et al.’s unified notation for data assimilation is suggested.

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