Unified ensemble mean forecasting of tropical cyclones based on the feature-oriented mean method

AbstractOperational and research applications generally use the consensus approach for forecasting the track and intensity of tropical cyclones (TCs) due to the spatial displacement of the TC location and structure in ensemble member forecasts. This approach simply averages the location and intensity information for TCs in individual ensemble members, which is distinct from the traditional pointwise arithmetic mean (AM) method for ensemble forecast fields. The consensus approach, despite having improved skills relative to the AM in predicting the TC intensity, cannot provide forecasts of the TC spatial structure. We introduced a unified TC ensemble mean forecast based on the feature-oriented mean (FM) method to overcome the inconsistency between the AM and consensus forecasts. FM spatially aligns the TC-related features in each ensemble field to their geographical mean positions before the amplitude of their features is averaged.We select 219 TC forecast samples during the summer of 2017 for an overall evaluation of the FM performance. The results show that the TC track consensus forecasts can differ from AM track forecasts by hundreds of kilometers at long lead times. AM also gives a systematic and statistically significant underestimation of the TC intensity compared with the consensus forecast. By contrast, FM has a very similar TC track and intensity forecast skill to the consensus approach. FM can also provide the corresponding ensemble mean forecasts of the TC spatial structure that are significantly more accurate than AM for the low- and upper-level circulation in TCs. The FM method has the potential to serve as a valuable unified ensemble mean approach for the TC prediction.

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Extended Prediction of North Indian Ocean Tropical Cyclones

Weather and Forecasting ◽

10.1175/waf-d-11-00083.1 ◽

2012 ◽

Vol 27 (3) ◽

pp. 757-769 ◽

Cited By ~ 31

Author(s):

James I. Belanger ◽

Peter J. Webster ◽

Judith A. Curry ◽

Mark T. Jelinek

Keyword(s):

Tropical Cyclone ◽

Indian Ocean ◽

Tropical Cyclones ◽

North Indian Ocean ◽

Ensemble Prediction ◽

Tropical Cyclone Genesis ◽

Lead Times ◽

Prediction System ◽

Ensemble Prediction System ◽

The North

Abstract This analysis examines the predictability of several key forecasting parameters using the ECMWF Variable Ensemble Prediction System (VarEPS) for tropical cyclones (TCs) in the North Indian Ocean (NIO) including tropical cyclone genesis, pregenesis and postgenesis track and intensity projections, and regional outlooks of tropical cyclone activity for the Arabian Sea and the Bay of Bengal. Based on the evaluation period from 2007 to 2010, the VarEPS TC genesis forecasts demonstrate low false-alarm rates and moderate to high probabilities of detection for lead times of 1–7 days. In addition, VarEPS pregenesis track forecasts on average perform better than VarEPS postgenesis forecasts through 120 h and feature a total track error growth of 41 n mi day−1. VarEPS provides superior postgenesis track forecasts for lead times greater than 12 h compared to other models, including the Met Office global model (UKMET), the Navy Operational Global Atmospheric Prediction System (NOGAPS), and the Global Forecasting System (GFS), and slightly lower track errors than the Joint Typhoon Warning Center. This paper concludes with a discussion of how VarEPS can provide much of this extended predictability within a probabilistic framework for the region.

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Heavy rainfall in the northern coast of Ecuador in the aftermath of El Niño 2015/2016 and its predictability

10.5194/egusphere-egu21-8559 ◽

2021 ◽

Author(s):

Luis E. Pineda ◽

Juan Changoluisa ◽

Ángel G. Muñoz

Keyword(s):

Heavy Rainfall ◽

Heavy Rain ◽

Rainfall Anomaly ◽

Precipitation Event ◽

Lead Times ◽

Northern Coast ◽

Atmospheric Conditions ◽

The North ◽

Ensemble Mean ◽

Rainfall Anomalies

In January 2016, a high precipitation event (HPE) affected the northern coast of Ecuador leading to devastating flooding in the Esmeraldas&#8217; river basin. The HPE appeared in the aftermath of the 2015/2016 El Ni&#241;o as an early onset of heavy rainfalls otherwise expected in the core rainy season (Mar-Apr). Using gauge data, satellite imagery and reanalysis we investigate the daily and &#8216;weather-within-climate&#8217; characteristics of the HPE and its accompanying atmospheric conditions. The convective storms developed into a mesoscale convective complex (MCC) during nighttime on 24th January. The scale size of the heavy rainfall system was about 250 km with a lifecycle lasting 16 hours for the complete storm with 6 hours of convective showers contributing to the HPE. The genesis of the MCC was related to above-normal moisture and orographic lifting driving convective updrafts; the north-south mountain barrier acted as both a channel boosting upslope flow when it moves over hillslopes; and, as a heavy-rain divide for inner valleys. The above normal moisture conditions were favored by cross-time-scale interactions involving the very strong El Ni&#241;o 2015/2016 event, an unusually persistent Madden&#8211;Julian oscillation (MJO) in phases 3 and 6, remotely forced by tropical synoptic scale disturbances. In the dissipation stage, a moderate low-level easterly shear with wind velocity of about 10 m/s moved away the unstable air and the convective pattern disappear on the shore of the Esmeraldas basin.&#160;We use ECMWF re-forecast from the Sub-seasonal to Seasonal (S2S) prediction project dataset and satellite observations to investigate the predictability of the HPE. Weekly ensemble-mean rainfall anomaly forecasts computed from raw (uncorrected) S2S reforecast initialized on 31st Dec 2015, 7th, 14th and 21st Jan 2016 are used to assess the occurrence of rainfall anomalies over the region. The reforecast represents consistently, over all lead times, the spatial pattern of the HPE. Also, the ensemble-mean forecast shows positive rainfall anomalies at times scales of 1-3 weeks (0-21 days) at nearly all initialization dates and lead times, predicting this way successfully the timing and amplitude of the highest HPE leading the 25th January flood.

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The Unexpected Rapid Intensification of Tropical Cyclones in Moderate Vertical Wind Shear. Part I: Overview and Observations

Monthly Weather Review ◽

10.1175/mwr-d-18-0020.1 ◽

2018 ◽

Vol 146 (11) ◽

pp. 3773-3800 ◽

Cited By ~ 13

Author(s):

David R. Ryglicki ◽

Joshua H. Cossuth ◽

Daniel Hodyss ◽

James D. Doyle

Keyword(s):

Tropical Cyclones ◽

Wind Shear ◽

Vertical Wind Shear ◽

Convective Cloud ◽

Vertical Wind ◽

Rapid Intensification ◽

Brightness Temperatures ◽

Idealized Modeling ◽

Hurricane Prediction ◽

Upper Level

Abstract A satellite-based investigation is performed of a class of tropical cyclones (TCs) that unexpectedly undergo rapid intensification (RI) in moderate vertical wind shear between 5 and 10 m s−1 calculated as 200–850-hPa shear. This study makes use of both infrared (IR; 11 μm) and water vapor (WV; 6.5 μm) geostationary satellite data, the Statistical Hurricane Prediction Intensity System (SHIPS), and model reanalyses to highlight commonalities of the six TCs. The commonalities serve as predictive guides for forecasters and common features that can be used to constrain and verify idealized modeling studies. Each of the TCs exhibits a convective cloud structure that is identified as a tilt-modulated convective asymmetry (TCA). These TCAs share similar shapes, upshear-relative positions, and IR cloud-top temperatures (below −70°C). They pulse over the core of the TC with a periodicity of between 4 and 8 h. Using WV satellite imagery, two additional features identified are asymmetric warming/drying upshear of the TC relative to downshear, as well as radially thin arc-shaped clouds on the upshear side. The WV brightness temperatures of these arcs are between −40° and −60°C. All of the TCs are sheared by upper-level anticyclones, which limits the strongest environmental winds to near the tropopause.

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Tropical Cyclone Diurnal Cycle Signals in a Hurricane Nature Run

Monthly Weather Review ◽

10.1175/mwr-d-18-0130.1 ◽

2019 ◽

Vol 147 (1) ◽

pp. 363-388 ◽

Cited By ~ 10

Author(s):

Jason P. Dunion ◽

Christopher D. Thorncroft ◽

David S. Nolan

Keyword(s):

Tropical Cyclones ◽

Diurnal Cycle ◽

Deep Layer ◽

Squall Line ◽

Distinguishing Characteristic ◽

Optimal Conditions ◽

Work Support ◽

Upper Level ◽

Radiation Thermodynamics ◽

Low Shear

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

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Ensemble long-term soil moisture forecast using hydrological modeling

RBRH ◽

10.1590/2318-0331.252020190036 ◽

2020 ◽

Vol 25 ◽

Author(s):

Bibiana Rodrigues Colossi ◽

Carlos Eduardo Morelli Tucci

Keyword(s):

Soil Moisture ◽

Hydrological Modeling ◽

Hydrological Model ◽

Computing Time ◽

Ensemble Member ◽

Lead Times ◽

Hydrologic Simulation ◽

The Mean ◽

Group B

ABSTRACT Long-term soil moisture forecasting allows for better planning in sectors as agriculture. However, there are still few studies dedicated to estimate soil moisture for long lead times, which reflects the difficulties associated with this topic. An approach that could help improving these forecasts performance is to use ensemble predictions. In this study, a soil moisture forecast for lead times of one, three and six months in the Ijuí River Basin (Brazil) was developed using ensemble precipitation forecasts and hydrologic simulation. All ensemble members from three climatologic models were used to run the MGB hydrological model, generating 207 soil moisture forecasts, organized in groups: (A) for each model, the most frequent soil moisture interval predicted among the forecasts made with each ensemble member, (B) using each model’s mean precipitation, (C) considering a super-ensemble, and (D) the mean soil moisture interval predicted among group B forecasts. The results show that long-term soil moisture based on precipitation forecasts can be useful for identifying periods drier or wetter than the average for the studied region. Nevertheless, estimation of exact soil moisture values remains limited. Forecasts groups B and D performed similarly to groups A and C, and require less data management and computing time.

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Reduced El Niño variability in the PlioMIP2 model ensemble

10.5194/egusphere-egu21-10002 ◽

2021 ◽

Author(s):

Arthur Oldeman ◽

Michiel Baatsen ◽

Anna von der Heydt ◽

Henk Dijkstra ◽

Julia Tindall

Keyword(s):

Pacific Ocean ◽

Spatial Structure ◽

Spectral Power ◽

Tropical Pacific ◽

Tropical Pacific Ocean ◽

Model Ensemble ◽

Ensemble Mean ◽

Mean State ◽

Sst Gradient ◽

The Tropical Pacific

The mid-Piacenzian or mid-Pliocene warm period (mPWP, 3.264 &#8211; 3.025 Ma) is the most recent geological period to see atmospheric CO&#173;2 levels similar to the present-day values (~400 ppm). Some proxy reconstructions for the mPWP show reduced zonal SST gradients in the tropical Pacific Ocean, possibly indicating an El Ni&#241;o-like mean state in the mid-Pliocene. However, past modelling studies do not show the same results. Efforts to understand mPWP climate dynamics have led to the Pliocene Model Intercomparison Project (PlioMIP). Results from the first phase (PlioMIP1) showed clear El Ni&#241;o variability (albeit significantly reduced) and did not show the greatly reduced time-mean zonal SST gradient suggested by some of the proxies.In this work, we study ENSO variability in the PlioMIP2 ensemble, which consists of additional global coupled climate models and updated boundary conditions compared to PlioMIP1. We quantify ENSO amplitude, period and spatial structure as well as the tropical Pacific annual mean state in a mid-Pliocene and pre-industrial reference simulation. Results show a reduced El Ni&#241;o amplitude in the model- ensemble mean, with 11 out of 13 individual models showing such a reduction. Furthermore, the spectral power of this variability considerably decreases in the 3&#8211;7-year band and shifts to higher frequencies compared to pre-industrial. The spatial structure of the dominant EOF shows no particular change in the patterns of tropical Pacific variability in the model-ensemble mean, compared to the pre-industrial. Further analyses that will be presented include the correlation of the zonal SST gradient with the El Ni&#241;o amplitude, investigation of shift in El Ni&#241;o flavour, and a discussion of the coupled feedbacks at play in the mid-Pliocene tropical Pacific Ocean.

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A Dynamical Performance-Ranking Method for Predicting Individual Ensemble Member Performance and Its Application to Ensemble Averaging

Monthly Weather Review ◽

10.1175/mwr-d-10-05007.1 ◽

2011 ◽

Vol 139 (10) ◽

pp. 3284-3303 ◽

Cited By ~ 18

Author(s):

Jun Du ◽

Binbin Zhou

Keyword(s):

Distance Measure ◽

Forecast Error ◽

Ensemble Member ◽

Ranking Method ◽

Weighted Averaging ◽

Ensemble Size ◽

Ensemble Averaging ◽

Ensemble Mean ◽

Magnitude Difference ◽

Performance Ranking

Abstract This study proposes a dynamical performance-ranking method (called the Du–Zhou ranking method) to predict the relative performance of individual ensemble members by assuming the ensemble mean is a good estimation of the truth. The results show that the method 1) generally works well, especially for shorter ranges such as a 1-day forecast; 2) has less error in predicting the extreme (best and worst) performers than the intermediate performers; 3) works better when the variation in performance among ensemble members is large; 4) works better when the model bias is small; 5) works better in a multimodel than in a single-model ensemble environment; and 6) works best when using the magnitude difference between a member and its ensemble mean as the “distance” measure in ranking members. The ensemble mean and median generally perform similarly to each other. This method was applied to a weighted ensemble average to see if it can improve the ensemble mean forecast over a commonly used, simple equally weighted ensemble averaging method. The results indicate that the weighted ensemble mean forecast has a smaller systematic error. This superiority of the weighted over the simple mean is especially true for smaller-sized ensembles, such as 5 and 11 members, but it decreases with the increase in ensemble size and almost vanishes when the ensemble size increases to 21 members. There is, however, little impact on the random error and the spatial patterns of ensemble mean forecasts. These results imply that it might be difficult to improve the ensemble mean by just weighting members when an ensemble reaches a certain size. However, it is found that the weighted averaging can reduce the total forecast error more when a raw ensemble-mean forecast itself is less accurate. It is also expected that the effectiveness of weighted averaging should be improved when the ensemble spread is improved or when the ranking method itself is improved, although such an improvement should not be expected to be too big (probably less than 10%, on average).

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Analysis of Ensemble Mean Forecasts: The Blessings of High Dimensionality

Monthly Weather Review ◽

10.1175/mwr-d-18-0211.1 ◽

2019 ◽

Vol 147 (5) ◽

pp. 1699-1712 ◽

Cited By ~ 3

Author(s):

Bo Christiansen

Keyword(s):

High Dimensional ◽

Lead Times ◽

Ensemble Forecasts ◽

Analytical Results ◽

Weather And Climate ◽

Ensemble Mean ◽

The Individual ◽

Almost All ◽

Community Standard ◽

Better Than

Abstract In weather and climate sciences ensemble forecasts have become an acknowledged community standard. It is often found that the ensemble mean not only has a low error relative to the typical error of the ensemble members but also that it outperforms all the individual ensemble members. We analyze ensemble simulations based on a simple statistical model that allows for bias and that has different variances for observations and the model ensemble. Using generic simplifying geometric properties of high-dimensional spaces we obtain analytical results for the error of the ensemble mean. These results include a closed form for the rank of the ensemble mean among the ensemble members and depend on two quantities: the ensemble variance and the bias both normalized with the variance of observations. The analytical results are used to analyze the GEFS reforecast where the variances and bias depend on lead time. For intermediate lead times between 20 and 100 h the two terms are both around 0.5 and the ensemble mean is only slightly better than individual ensemble members. For lead times larger than 240 h the variance term is close to 1 and the bias term is near 0.5. For these lead times the ensemble mean outperforms almost all individual ensemble members and its relative error comes close to −30%. These results are in excellent agreement with the theory. The simplifying properties of high-dimensional spaces can be applied not only to the ensemble mean but also to, for example, the ensemble spread.

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Ensemble Regression

Monthly Weather Review ◽

10.1175/2008mwr2605.1 ◽

2009 ◽

Vol 137 (7) ◽

pp. 2365-2379 ◽

Cited By ~ 32

Author(s):

David A. Unger ◽

Huug van den Dool ◽

Edward O’Lenic ◽

Dan Collins

Keyword(s):

Linear Regression ◽

Error Variance ◽

Ensemble Member ◽

Ensemble Prediction ◽

Ensemble Spread ◽

Ensemble Forecasts ◽

Ensemble Mean ◽

Standard Linear ◽

Environmental Prediction ◽

Ensemble Regression

A regression model was developed for use with ensemble forecasts. Ensemble members are assumed to represent a set of equally likely solutions, one of which will best fit the observation. If standard linear regression assumptions apply to the best member, then a regression relationship can be derived between the full ensemble and the observation without explicitly identifying the best member for each case. The ensemble regression equation is equivalent to linear regression between the ensemble mean and the observation, but is applied to each member of the ensemble. The “best member” error variance is defined in terms of the correlation between the ensemble mean and the observations, their respective variances, and the ensemble spread. A probability density function representing the ensemble prediction is obtained from the normalized sum of the best-member error distribution applied to the regression forecast from each ensemble member. Ensemble regression was applied to National Centers for Environmental Prediction (NCEP) Climate Forecast System (CFS) forecasts of seasonal mean Niño-3.4 SSTs on historical forecasts for the years 1981–2005. The skill of the ensemble regression was about the same as that of the linear regression on the ensemble mean when measured by the continuous ranked probability score (CRPS), and both methods produced reliable probabilities. The CFS spread appears slightly too high for its skill, and the CRPS of the CFS predictions can be slightly improved by reducing its ensemble spread to about 0.8 of its original value prior to regression calibration.

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The decadal climate prediction skill with focus on the North Atlantic region

10.5194/egusphere-egu2020-11243 ◽

2020 ◽

Author(s):

Shuting Yang ◽

Bo Christiansen

Keyword(s):

North Atlantic ◽

Large Degree ◽

Climate Prediction ◽

Prediction Skill ◽

Lead Times ◽

The North ◽

Ensemble Mean ◽

The North Atlantic ◽

Decadal Climate ◽

Decadal Climate Prediction

The skill of the decadal climate prediction is analyzed based on recent ensemble experiments from the CMIP5 and CMIP6 decadal climate prediction projects (DCPP) and the Community Earth System Model (CESM) Large Ensemble (LENS) Project. The experiments are initialized every year at November 1 for the period of 1960-2005 in the CMIP5 DCPP experiments and 1960-2016 for the CMIP6 DCPP models as well as the CESM LENS decadal prediction. The CMIP5/6 ensemble has 10 members for each model and the CESM ensemble has 40 members. For the considered models un-initialized (historical) ensembles with the same forcings exist. The advantage of initialization is analyzed by comparing these two sets of experiments. We find that the models agree that for lead-times between 4-10 years little effect of initialization is found except in the North Atlantic sub-polar gyre region (NASPG). This well-known result is found for all the models and is robust to temporal and spatial smoothing. In the sub-polar gyre region the ensemble mean of the forecast explains 30-40 % more of the observed variance than the ensemble mean of the historical non-initialized experiments even for lead-times of 10 years. However, the skill in the NASPG seems to a large degree to be related to the shift towards warmer temperatures around 1996. Weak or no skill is found when the sub-periods before and after 1996 are considered. We further analyze the characteristics of other climate indicators than surface temperature as well as the NAO to understand the cause and implication of the prediction skill.

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