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Anthony G. Barnston
,
Michael K. Tippett
,
Michelle L. L'Heureux
,
Shuhua Li
, and
David G. DeWitt
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John T. Allen
,
Michael K. Tippett
,
Adam H. Sobel
, and
Chiara Lepore
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Gregory W. Carbin
,
Michael K. Tippett
,
Samuel P. Lillo
, and
Harold E. Brooks

Abstract

Two novel approaches to extending the range of prediction for environments conducive to severe thunderstorm events are described. One approach charts Climate Forecast System, version 2 (CFSv2), run-to-run consistency of the areal extent of severe thunderstorm environments using grid counts of the supercell composite parameter (SCP). Visualization of these environments is charted for each 45-day CFSv2 run initialized at 0000 UTC. CFSv2 ensemble-mean forecast maps of SCP coverage over the contiguous United States are also produced for those forecasts meeting certain criteria for high-impact weather. The applicability of this approach to the severe weather prediction challenge is illustrated using CFSv2 output for a series of severe weather episodes occurring in March and April 2014. Another approach, possibly extending severe weather predictability from CFSv2, utilizes a run-cumulative time-averaging technique of SCP grid counts. This process is described and subjectively verified with severe weather events from early 2014.

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Michael K. Tippett
,
Timothy DelSole
,
Simon J. Mason
, and
Anthony G. Barnston

Abstract

There are a variety of multivariate statistical methods for analyzing the relations between two datasets. Two commonly used methods are canonical correlation analysis (CCA) and maximum covariance analysis (MCA), which find the projections of the data onto coupled patterns with maximum correlation and covariance, respectively. These projections are often used in linear prediction models. Redundancy analysis and principal predictor analysis construct projections that maximize the explained variance and the sum of squared correlations of regression models. This paper shows that the above pattern methods are equivalent to different diagonalizations of the regression between the two datasets. The different diagonalizations are computed using the singular value decomposition of the regression matrix developed using data that are suitably transformed for each method. This common framework for the pattern methods permits easy comparison of their properties. Principal component regression is shown to be a special case of CCA-based regression. A commonly used linear prediction model constructed from MCA patterns does not give a least squares estimate since correlations among MCA predictors are neglected. A variation, denoted least squares estimate (LSE)-MCA, is suggested that uses the same patterns but minimizes squared error. Since the different pattern methods correspond to diagonalizations of the same regression matrix, they all produce the same regression model when a complete set of patterns is used. Different prediction models are obtained when an incomplete set of patterns is used, with each method optimizing different properties of the regression. Some key points are illustrated in two idealized examples, and the methods are applied to statistical downscaling of rainfall over the northeast of Brazil.

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Andrew W. Robertson
,
Jian-Hua Qian
,
Michael K. Tippett
,
Vincent Moron
, and
Anthony Lucero

Abstract

The additional value derived from a regional climate model (RCM) nested within general circulation model (GCM) seasonal simulations, over and above statistical methods of downscaling, is compared over the Philippines for the April–June monsoon transition season. Spatial interpolation of RCM and GCM gridbox values to station locations is compared with model output statistics (MOS) correction. The anomaly correlation coefficient (ACC) skill at the station scale of seasonal total rainfall is somewhat higher in the RCM compared to the GCM when using spatial interpolation. However, the ACC skills obtained using MOS of the GCM or RCM wind fields are shown to be generally—and rather equally—superior. The ranked probability skill scores (RPSS) are also generally much higher when using MOS, with slightly higher scores in the GCM case. Very high skills were found for MOS correction of daily rainfall frequency as a function of GCM and RCM seasonal-average low-level wind fields, but with no apparent advantage from the RCM. MOS-corrected monsoon onset dates often showed skill values similar to those of seasonal rainfall total, with good skill over the central Philippines. Finally, it is shown that the MOS skills decrease markedly and become inferior to those of spatial interpolation when the length of the 28-yr training set is halved. The results may be region dependent, and the excellent station data coverage and strong impact of ENSO on the Philippines may be factors contributing to the good MOS performance when using the full-length dataset over the Philippines.

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Chia-Ying Lee
,
Michael K. Tippett
,
Suzana J. Camargo
, and
Adam H. Sobel

Abstract

The authors describe the development and verification of a statistical model relating tropical cyclone (TC) intensity to the local large-scale environment. A multiple linear regression framework is used to estimate the expected intensity of a tropical cyclone given the environmental and storm conditions. The uncertainty of the estimate is constructed from the empirical distribution of model errors. NCEP–NCAR reanalysis fields and historical hurricane data from 1981 to 1999 are used for model development, and data from 2000 to 2012 are used to evaluate model performance. Seven predictors are selected: initial storm intensity, the change of storm intensity over the past 12 h, the storm translation speed, the difference between initial storm intensity and its corresponding potential intensity, deep-layer (850–200 hPa) vertical shear, atmospheric stability, and 200-hPa divergence. The system developed here models storm intensity changes in response to changes in the surrounding environment with skill comparable to existing operational forecast tools. Since one application of such a model is to predict changes in TC activity in response to natural or anthropogenic climate change, the authors examine the performance of the model using data that is most readily available from global climate models, that is, monthly averages. It is found that statistical models based on monthly data (as opposed to daily) with only a few essential predictors, for example, the difference between storm intensity and potential intensity, perform nearly as well at short leads as when daily predictors are used.

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Anthony G. Barnston
,
Nicolas Vigaud
,
Lindsey N. Long
,
Michael K. Tippett
, and
Jae-Kyung E. Schemm

Abstract

The Madden–Julian oscillation (MJO) is known to exert some control on the variations of North Atlantic tropical cyclone (TC) activity within a hurricane season. To explore the possibility of better TC predictions based on improved MJO forecasts, retrospective hindcast data on MJO and on TC activity are examined both in the current operational version of the CFSv2 model (T126 horizontal resolution) and a high-resolution (T382) experimental version of CFS. Goals are to determine how well each CFS version reproduces reality in 1) predicting MJO and 2) reproducing observed relationships between MJO phase and TC activity. For the operational CFSv2, skill of forecasts of TC activity is evaluated directly.

Both CFS versions reproduce MJO behavior realistically and also roughly approximate observed relationships between MJO phase and TC activity. Specific biases in the high-resolution CFS are identified and their causes explored. The high-resolution CFS partially reproduces an observed weak tendency for TC activity to propagate eastward during and following the high-activity MJO phases. The operational (T126) CFSv2 shows useful skill (correlation >0.5) in predicting the MJO phase and amplitude out to ~3 weeks. A systematic error of slightly too slow MJO propagation is detected in the operational CFSv2, which still shows usable skill (correlation >0.3) in predicting weekly variations in TC activity out to 10–14 days. A conclusion is that prediction of intraseasonal variations of TC activity by CFSv2 is already possible and implemented in real-time predictions. An advantage of the higher resolution in the T382 version is unable to be confirmed.

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Suzana J. Camargo
,
Frédéric Vitart
,
Chia-Ying Lee
, and
Michael K. Tippett

Abstract

In this paper we analyze Atlantic Ocean hurricane activity in the European Centre for Medium-Range Weather Forecasts (ECMWF) monthly hindcasts for the period 1998–2017. The main climatological characteristics of Atlantic tropical cyclone (TC) activity are considered at different lead times and across the entire ECMWF ensemble using three diagnostic variables: the number of tropical cyclones, the number of hurricanes, and the accumulated cyclone energy. The impacts of changing horizontal resolution and stochastic parameterization are clear in these diagnostic variables. The model skill scores for the number of tropical cyclones and accumulated cyclone energy by lead time are also computed. Using cluster analysis, we compare the characteristics of the forecast TC tracks with observations. Although four of the ECMWF clusters have similar characteristics to observed ones, one of the ECMWF clusters does not have a corresponding one in observations. We consider the predictability of each of these clusters, as well the modulation of their frequency by climate modes, such as the El Niño–Southern Oscillation and the Madden–Julian oscillation, taking advantage of the very large sample size of TC datasets in these hindcasts.

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Chia-Ying Lee
,
Suzana J. Camargo
,
Fréderic Vitart
,
Adam H. Sobel
, and
Michael K. Tippett

Abstract

Subseasonal probabilistic prediction of tropical cyclone (TC) genesis is investigated here using models from the Seasonal to Subseasonal (S2S) Prediction dataset. Forecasts are produced for basin-wide TC occurrence at weekly temporal resolution. Forecast skill is measured using the Brier skill score relative to a seasonal climatology that varies monthly through the TC season. Skill depends on models’ characteristics, lead time, and ensemble prediction design. Most models show skill for week 1 (days 1–7), the period when initialization is important. Among the six S2S models examined here, the European Centre for Medium-Range Weather Forecasts (ECMWF) model has the best performance, with skill in the Atlantic, western North Pacific, eastern North Pacific, and South Pacific at week 2. Similarly, the Australian Bureau of Meteorology (BoM) model is skillful in the western North Pacific, South Pacific, and across northern Australia at week 2. The Madden–Julian oscillation (MJO) modulates observed TC genesis, and there is a relationship, across models and lead times, between models’ skill scores and their ability to accurately represent the MJO and the MJO–TC relation. Additionally, a model’s TC climatology also influences its performance in subseasonal prediction. The dependence of the skill score on the simulated climatology, MJO, and MJO–TC relationship, however, varies from one basin to another. Skill scores increase with the ensemble size, as found in previous weather and seasonal prediction studies.

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Chia-Ying Lee
,
Suzana J. Camargo
,
Adam H. Sobel
, and
Michael K. Tippett

Abstract

Tropical cyclone (TC) activity is examined using the Columbia Hazard model (CHAZ), a statistical–dynamical downscaling system, with environmental conditions taken from simulations from phase 5 of the Coupled Model Intercomparison Project (CMIP5) for both the historical period and a future scenario under the representative concentration pathway 8.5. Projections of individual global and basin TC frequency depend sensitively on the choice of moisture variable used in the tropical genesis cyclone index (TCGI) component of CHAZ. Simulations using column relative humidity show an increasing trend in the future, while those using saturation deficit show a decreasing trend, although both give similar results in the historical period. While the projected annual TC frequency is also sensitive to the choice of model used to provide the environmental conditions, the choice of humidity variable in the TCGI is more important. Changes in TC frequency directly affect the projected TCs’ tracks and the frequencies of strong storms on both basin and regional scales. This leads to large uncertainty in assessing regional and local storm hazards. The uncertainty here is fundamental and epistemic in nature. Increases in the fraction of major TCs, rapid intensification rate, and decreases in forward speed are insensitive to TC frequency, however. The present results are also consistent with prior studies in indicating that those TC events that do occur will, on average, be more destructive in the future because of the robustly projected increases in intensity.

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