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Andrew W. Robertson
,
Nicolas Vigaud
,
Jing Yuan
, and
Michael K. Tippett

Abstract

Large-scale atmospheric circulation regime structures are used to diagnose subseasonal forecasts of wintertime geopotential height fields over the North American sector, from the NCEP CFSv2 model. Four large-scale daily circulation regimes derived from reanalysis 500-hPa geopotential height data using K-means clustering are used as a low-dimensional basis for diagnosing the model’s forecasts up to 45 days ahead. On average, hindcast skill in regime space is found to be limited to 10–15 days ahead, in terms of anomaly correlation of 5-day averages of regime counts, over the 1999–2010 period. However, skill up to 30 days ahead is identified in individual winters, and intraseasonal episodes of high skill are identified using a forecast-evolution graphical tool. A striking vacillation between the West Coast and Pacific ridge patterns during December–January 2008/09 is shown to be predicted 20–25 days in advance, illustrating the possibility to identify “forecasts of opportunity” when subseasonal forecast skill is much higher than the average. The forecast-evolution tool also provides insight into the poor seasonal forecasts of California precipitation by operational centers during the 2015/16 El Niño winter. The Pacific trough regime is shown to be greatly overpredicted beyond 1–2 weeks in advance during the 2015/16 winter, with weather-scale features dominating the forecast evolution at shorter lead times. A similar though less extreme situation took place during the weaker El Niño of 2009/10, with the Pacific trough overforecast at S2S lead times.

Open access
Chiara Lepore
,
Michael K. Tippett
, and
John T. Allen

Abstract

Climate Forecast System, version 2, predictions of monthly U.S. severe thunderstorm activity are analyzed for the period 1982–2016. Forecasts are based on a tornado environmental index and a hail environmental index, which are functions of monthly averaged storm relative helicity (SRH), convective precipitation (cPrcp), and convective available potential energy (CAPE). Overall, forecast indices reproduce well the annual cycle of tornado and hail events. Forecast index biases are mostly negative and caused by environment values that are low east of the Rockies, although forecast CAPE is higher than the reanalysis values over the High Plains. Skill is diagnosed spatially for the indices and their constituents separately. SRH is more skillfully forecast than cPrcp and CAPE, especially during December–June. The spatial patterns of forecast skill for CAPE and cPrcp are similar, with higher skill for CAPE and less spatial coherence for cPrcp. The indices are forecast with substantially less skill than the environmental parameters. Numbers of tornado and hail events are forecast with modest but statistically significant skill in some NOAA regions and months of the year. Skill tends to be relatively higher for hail events and in climatologically active seasons and regions. Much of the monthly skill appears to be derived from the first 2 weeks of the forecast. El Niño–Southern Oscillation (ENSO) modulates forecasts and, to a lesser extent, forecast skill, during March–May, with more activity and higher skill during cool ENSO conditions.

Open access
Michael K. Tippett
,
Mansour Almazroui
, and
In-Sik Kang

Abstract

The climate of Saudi Arabia is arid–semiarid with infrequent but sometimes intense rainfall, which can cause flooding. Interannual and intraseasonal precipitation variability in the region is related to ENSO and MJO tropical convection. The predictability of these tropical signals gives some expectation of skillful extended-range rainfall forecasts in the region. Here, the extent to which this predictability is realizable in the Climate Forecast System (CFS), version 2, a state-of-the-art coupled global ocean–atmosphere model, is assessed. While there are deficiencies in the forecast climatology likely related to orography and resolution, as well as lead-dependent biases, CFS represents the climatology of the region reasonably well. Forecasts of the areal average of rainfall over Saudi Arabia show that the CFS captures some features of a spring 2013 heavy rainfall event up to 10 days in advance and a transition from dry to wet conditions up to 20 days in advance. Analysis of a 12-yr (1999–2010) reforecast dataset shows that the CFS can skillfully predict the rainfall amount, the number of days exceeding a threshold, and the probability of heavy rainfall occurrence for forecast windows ranging from 1 to 30 days. While the probability forecasts show good discrimination, they are overconfident. Logistic regression based on the ensemble mean value improves forecast skill and reliability. Forecast probabilities have a clear relation with the MJO phase in the wet season, providing a physical basis for the observed forecast skill.

Full access
Qidong Yang
,
Chia-Ying Lee
, and
Michael K. Tippett

ABSTRACT

Rapid intensification (RI) is an outstanding source of error in tropical cyclone (TC) intensity predictions. RI is generally defined as a 24-h increase in TC maximum sustained surface wind speed greater than some threshold, typically 25, 30, or 35 kt (1 kt ≈ 0.51 m s−1). Here, a long short-term memory (LSTM) model for probabilistic RI predictions is developed and evaluated. The variables (features) of the model include storm characteristics (e.g., storm intensity) and environmental variables (e.g., vertical shear) over the previous 48 h. A basin-aware RI prediction model is trained (1981–2009), validated (2010–13), and tested (2014–17) on global data. Models are trained on overlapping 48-h data, which allows multiple training examples for each storm. A challenge is that the data are highly unbalanced in the sense that there are many more non-RI cases than RI cases. To cope with this data imbalance, the synthetic minority-oversampling technique (SMOTE) is used to balance the training data by generating artificial RI cases. Model ensembling is also applied to improve prediction skill further. The model’s Brier skill scores in the Atlantic and eastern North Pacific are higher than those of operational predictions for RI thresholds of 25 and 30 kt and comparable for 35 kt on the independent test data. Composites of the features associated with RI and non-RI situations provide physical insights for how the model discriminates between RI and non-RI cases. Prediction case studies are presented for some recent storms.

Free access
Michelle L. L’Heureux
,
Michael K. Tippett
, and
Emily J. Becker

Abstract

The relation between the El Niño–Southern Oscillation (ENSO) and California precipitation has been studied extensively and plays a prominent role in seasonal forecasting. However, a wide range of precipitation outcomes on seasonal time scales are possible, even during extreme ENSO states. Here, we investigate prediction skill and its origins on subseasonal time scales. Model predictions of California precipitation are examined using Subseasonal Experiment (SubX) reforecasts for the period 1999–2016, focusing on those from the Flow-Following Icosahedral Model (FIM). Two potential sources of subseasonal predictability are examined: the tropical Pacific Ocean and upper-level zonal winds near California. In both observations and forecasts, the Niño-3.4 index exhibits a weak and insignificant relationship with daily to monthly averages of California precipitation. Likewise, model tropical sea surface temperature and outgoing longwave radiation show only minimal relations with California precipitation forecasts, providing no evidence that flavors of El Niño or tropical modes substantially contribute to the success or failure of subseasonal forecasts. On the other hand, an index for upper-level zonal winds is strongly correlated with precipitation in observations and forecasts, across averaging windows and lead times. The wind index is related to ENSO, but the correlation between the wind index and precipitation remains even after accounting for ENSO phase. Intriguingly, the Niño-3.4 index and California precipitation show a slight but robust negative statistical relation after accounting for the wind index.

Full access
Michael K. Tippett
,
Kelsey Malloy
, and
Simon H. Lee

Abstract

Here we examine the relation between U.S. tornado activity and a new year-round classification of North American weather regimes. The regime classification is based on 500-hPa geopotential height anomalies and classifies each day as Pacific Trough, Pacific Ridge, Alaskan Ridge, Greenland High, or No Regime. During the period 1979–2022, we find statistically significant relations between average tornado report numbers and weather regimes in all months except June–August. Tornado activity is enhanced on Pacific Ridge days during late winter and spring, reduced on Pacific Trough days in spring, and reduced on Alaskan Ridge and Greenland High days during fall and early winter. During active regimes, the probability of many tornadoes occurring also increases, and there is greater variability in the number of tornadoes reported each day. A reanalysis-based tornado index reproduces the regional features of the modulation of tornado activity by the weather regimes and attributes them to changes in storm relative helicity, convective available potential energy, and convective precipitation. The phase of the El Niño-Southern Oscillation (ENSO) also plays a role. In winter and spring, Pacific Ridge days occur more often and average more reports per day during cool ENSO conditions. During warm ENSO conditions, Pacific Trough days occur more often and are associated with widespread reduced tornado activity.

Restricted access
Simon J. Mason
,
Michael K. Tippett
,
Andreas P. Weigel
,
Lisa Goddard
, and
Balakanapathy Rajaratnam
Full access
Michael K. Tippett
,
Jeffrey L. Anderson
,
Craig H. Bishop
,
Thomas M. Hamill
, and
Jeffrey S. Whitaker

Abstract

Ensemble data assimilation methods assimilate observations using state-space estimation methods and low-rank representations of forecast and analysis error covariances. A key element of such methods is the transformation of the forecast ensemble into an analysis ensemble with appropriate statistics. This transformation may be performed stochastically by treating observations as random variables, or deterministically by requiring that the updated analysis perturbations satisfy the Kalman filter analysis error covariance equation. Deterministic analysis ensemble updates are implementations of Kalman square root filters. The nonuniqueness of the deterministic transformation used in square root Kalman filters provides a framework to compare three recently proposed ensemble data assimilation methods.

Full access
Qidong Yang
,
Chia-Ying Lee
,
Michael K. Tippett
,
Daniel R. Chavas
, and
Thomas R. Knutson

Abstract

Here we present a machine learning–based wind reconstruction model. The model reconstructs hurricane surface winds with XGBoost, which is a decision-tree-based ensemble predictive algorithm. The model treats the symmetric and asymmetric wind fields separately. The symmetric wind field is approximated by a parametric wind profile model and two Bessel function series. The asymmetric field, accounting for asymmetries induced by the storm and its ambient environment, is represented using a small number of Laplacian eigenfunctions. The coefficients associated with Bessel functions and eigenfunctions are predicted by XGBoost based on storm and environmental features taken from NHC best-track and ERA-Interim data, respectively. We use HWIND for the observed wind fields. Three parametric wind profile models are tested in the symmetric wind model. The wind reconstruction model’s performance is insensitive to the choice of the profile model because the Bessel function series correct biases of the parametric profiles. The mean square error of the reconstructed surface winds is smaller than the climatological variance, indicating skillful reconstruction. Storm center location, eyewall size, and translation speed play important roles in controlling the magnitude of the leading asymmetries, while the phase of the asymmetries is mainly affected by storm translation direction. Vertical wind shear impacts the asymmetry phase to a lesser degree. Intended applications of this model include assessing hurricane risk using synthetic storm event sets generated by statistical–dynamical downscaling hurricane models.

Full access
Melanie Bieli
,
Adam H. Sobel
,
Suzana J. Camargo
, and
Michael K. Tippett

Abstract

This paper introduces a logistic regression model for the extratropical transition (ET) of tropical cyclones in the North Atlantic and the western North Pacific, using elastic net regularization to select predictors and estimate coefficients. Predictors are chosen from the 1979–2017 best track and reanalysis datasets, and verification is done against the tropical/extratropical labels in the best track data. In an independent test set, the model skillfully predicts ET at lead times up to 2 days, with latitude and sea surface temperature as its most important predictors. At a lead time of 24 h, it predicts ET with a Matthews correlation coefficient of 0.4 in the North Atlantic, and 0.6 in the western North Pacific. It identifies 80% of storms undergoing ET in the North Atlantic and 92% of those in the western North Pacific. In total, 90% of transition time errors are less than 24 h. Select examples of the model’s performance on individual storms illustrate its strengths and weaknesses. Two versions of the model are presented: an “operational model” that may provide baseline guidance for operational forecasts and a “hazard model” that can be integrated into statistical TC risk models. As instantaneous diagnostics for tropical/extratropical status, both models’ zero lead time predictions perform about as well as the widely used cyclone phase space (CPS) in the western North Pacific and better than the CPS in the North Atlantic, and predict the timings of the transitions better than CPS in both basins.

Free access