A Consolidated CLIPER Model for Improved August–September ENSO Prediction Skill

10.1175/813.1 ◽  
2004 ◽  
Vol 19 (6) ◽  
pp. 1089-1105 ◽  
Author(s):  
Benjamin Lloyd-Hughes ◽  
Mark A. Saunders ◽  
Paul Rockett
Keyword(s):  
Prediction Model ◽  
Seasonal Prediction ◽  
Boreal Summer ◽  
Northwest Pacific ◽  
Tropical Precipitation ◽  
Spring Predictability Barrier ◽  
Forecast Models ◽  
Hurricane Activity ◽  
Initial Results ◽  
Enso Indices

Abstract A prime challenge for ENSO seasonal forecast models is to predict boreal summer ENSO conditions at lead. August–September ENSO has a strong influence on Atlantic hurricane activity, Northwest Pacific typhoon activity, and tropical precipitation. However, summer ENSO skill is low due to the spring predictability barrier between March and May. A “consolidated” ENSO–climatology and persistence (CLIPER) seasonal prediction model is presented to address this issue with promising initial results. Consolidated CLIPER comprises the ensemble of 18 model variants of the statistical ENSO–CLIPER prediction model. Assessing August–September ENSO skill using deterministic and probabilistic skill measures applied to cross-validated hindcasts from 1952 to 2002, and using deterministic skill measures applied to replicated real-time forecasts from 1900 to 1950, shows that the consolidated CLIPER model consistently outperforms the standard CLIPER model at leads from 2 to 6 months for all the main ENSO indices (3, 3.4, and 4). The consolidated CLIPER August–September 1952–2002 hindcast skill is also positive to 97.5% confidence at leads out to 4 months (early April) for all ENSO indices. Optimization of the consolidated CLIPER model may lead to further skill improvements.

scholarly journals The Predictability of Rainfall over the Greater Horn of Africa. Part I: Prediction of Seasonal Rainfall

2014 ◽  
Vol 15 (3) ◽  
pp. 1011-1027 ◽  
Author(s):  
Sharon E. Nicholson
Keyword(s):  
Summer Rainfall ◽  
Equatorial Region ◽  
Forecast Skill ◽  
Boreal Summer ◽  
Horn Of Africa ◽  
Spring Predictability Barrier ◽  
Forecast Models ◽  
Short Rains ◽  
Rainfall Region ◽  
Atmospheric Variables

Abstract The predictability of each of the three rainy seasons affecting the Horn of Africa is examined using multiple linear regression and cross validation. In contrast to most other empirical forecast models, atmospheric dynamics are emphasized. Two geographical sectors are considered: the summer rainfall region with a single rainfall peak in the boreal summer and an equatorial rainfall region with rainy seasons in both the boreal spring and the boreal autumn. These two seasons are termed the “long rains” and “short rains,” respectively, in much of East Africa. Excellent predictability is noted 5 months in advance for both regions during the boreal autumn season and 2 months in advance for the summer season in the summer rainfall region. There is also excellent predictability for the short rains of the equatorial region 2 months in advance. Two notable findings are that atmospheric variables generally provide higher forecast skill than surface variables, such as sea surface temperatures and sea level pressure, and that ENSO and the Indian Ocean dipole provide less forecast skill than atmospheric variables associated with them. As in other studies, the results show that the spring predictability barrier limits the lead time for the forecasting of spring and summer rainfall.

scholarly journals Atlantic Niño/Niña Prediction Skills in NMME Models

Atmosphere ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 803
Author(s):  
Ran Wang ◽  
Lin Chen ◽  
Tim Li ◽  
Jing-Jia Luo
Keyword(s):  
Sea Surface ◽  
Prediction Skill ◽  
Boreal Summer ◽  
Boreal Winter ◽  
Equatorial Atlantic ◽  
Multimodel Ensemble ◽  
Spring Predictability Barrier ◽  
Anomaly Correlation Coefficient ◽  
Earth’S Climate ◽  
Atlantic Niño

The Atlantic Niño/Niña, one of the dominant interannual variability in the equatorial Atlantic, exerts prominent influence on the Earth’s climate, but its prediction skill shown previously was unsatisfactory and limited to two to three months. By diagnosing the recently released North American Multimodel Ensemble (NMME) models, we find that the Atlantic Niño/Niña prediction skills are improved, with the multi-model ensemble (MME) reaching five months. The prediction skills are season-dependent. Specifically, they show a marked dip in boreal spring, suggesting that the Atlantic Niño/Niña prediction suffers a “spring predictability barrier” like ENSO. The prediction skill is higher for Atlantic Niña than for Atlantic Niño, and better in the developing phase than in the decaying phase. The amplitude bias of the Atlantic Niño/Niña is primarily attributed to the amplitude bias in the annual cycle of the equatorial sea surface temperature (SST). The anomaly correlation coefficient scores of the Atlantic Niño/Niña, to a large extent, depend on the prediction skill of the Niño3.4 index in the preceding boreal winter, implying that the precedent ENSO may greatly affect the development of Atlantic Niño/Niña in the following boreal summer.

scholarly journals Tropical Cyclone Count Forecasting Using a Dynamical Seasonal Prediction System: Sensitivity to Improved Ocean Initialization

2011 ◽  
Vol 24 (12) ◽  
pp. 2963-2982 ◽  
Author(s):  
Andrea Alessandri ◽  
Andrea Borrelli ◽  
Silvio Gualdi ◽  
Enrico Scoccimarro ◽  
Simona Masina
Keyword(s):  
Tropical Cyclone ◽  
Indian Ocean ◽  
Wind Shear ◽  
Large Scale ◽  
Initial Conditions ◽  
North Pacific Ocean ◽  
Convective Available Potential Energy ◽  
Seasonal Prediction ◽  
Boreal Summer ◽  
Prediction System

Abstract This study investigates the predictability of tropical cyclone (TC) seasonal count anomalies using the Centro Euro-Mediterraneo per i Cambiamenti Climatici–Istituto Nazionale di Geofisica e Vulcanologia (CMCC-INGV) Seasonal Prediction System (SPS). To this aim, nine-member ensemble forecasts for the period 1992–2001 for two starting dates per year were performed. The skill in reproducing the observed TC counts has been evaluated after the application of a TC location and tracking detection method to the retrospective forecasts. The SPS displays good skill in predicting the observed TC count anomalies, particularly over the tropical Pacific and Atlantic Oceans. The simulated TC activity exhibits realistic geographical distribution and interannual variability, thus indicating that the model is able to reproduce the major basic mechanisms that link the TCs’ occurrence with the large-scale circulation. TC count anomalies prediction has been found to be sensitive to the subsurface assimilation in the ocean for initialization. Comparing the results with control simulations performed without assimilated initial conditions, the results indicate that the assimilation significantly improves the prediction of the TC count anomalies over the eastern North Pacific Ocean (ENP) and northern Indian Ocean (NI) during boreal summer. During the austral counterpart, significant progresses over the area surrounding Australia (AUS) and in terms of the probabilistic quality of the predictions also over the southern Indian Ocean (SI) were evidenced. The analysis shows that the improvement in the prediction of anomalous TC counts follows the enhancement in forecasting daily anomalies in sea surface temperature due to subsurface ocean initialization. Furthermore, the skill changes appear to be in part related to forecast differences in convective available potential energy (CAPE) over the ENP and the North Atlantic Ocean (ATL), in wind shear over the NI, and in both CAPE and wind shear over the SI.

Prediction of August Atlantic Basin Hurricane Activity

10.1175/814.1 ◽  
2004 ◽  
Vol 19 (6) ◽  
pp. 1044-1060 ◽  
Author(s):  
Eric S. Blake ◽  
William M. Gray
Keyword(s):  
Tropical Cyclone ◽  
Subtropical High ◽  
Northwest Pacific ◽  
Seasonal Forecasts ◽  
Reanalysis Dataset ◽  
Atlantic Basin ◽  
The North ◽  
Hurricane Activity ◽  
Environmental Prediction ◽  
The Bering Sea

Abstract Although skillful seasonal hurricane forecasts for the Atlantic basin are now a reality, large gaps remain in our understanding of observed variations in the distribution of activity within the hurricane season. The month of August roughly spans the first third of the climatologically most active part of the season, but activity during the month is quite variable. This paper reports on an initial investigation into forecasting year-to-year variability of August tropical cyclone (TC) activity using the National Centers for Environmental Prediction–National Center for Atmospheric Research reanalysis dataset. It is shown that 55%–75% of the variance of August TC activity can be hindcast using a combination of 4–5 global predictors chosen from a 12-predictor pool with each of the predictors showing precursor associations with TC activity. The most prominent predictive signal is the equatorial July 200-mb wind off the west coast of South America. When this wind is anomalously strong from the northeast during July, Atlantic TC activity in August is almost always enhanced. Other July conditions associated with active Augusts include a weak subtropical high in the North Atlantic, an enhanced subtropical high in the northwest Pacific, and low pressure in the Bering Sea region. The most important application of the August-only forecast is that predicted net tropical cyclone (NTC) activity in August has a significant relationship with the incidence of U.S. August TC landfall events. Better understanding of August-only TC variability will allow for a more complete perspective of total seasonal variability and, as such, assist in making better seasonal forecasts.

scholarly journals Long-Lead Seasonal Prediction of China Summer Rainfall Using an EOF–PLS Regression-Based Methodology*,+

2016 ◽  
Vol 29 (5) ◽  
pp. 1783-1796 ◽  
Author(s):  
Wen Xing ◽  
Bin Wang ◽  
So-Young Yim
Keyword(s):  
Air Temperature ◽  
Dynamic Models ◽  
Principal Component ◽  
Summer Rainfall ◽  
Seasonal Prediction ◽  
Rainfall Anomaly ◽  
Pls Regression ◽  
Rainfall Prediction ◽  
Forecast Validation ◽  
Forecast Models

Abstract Considerable year-to-year variability of summer rainfall exposes China to threats of frequent droughts and floods. Objective prediction of the summer rainfall anomaly pattern turns out to be very challenging. As shown in the present study, the contemporary state-of-the-art dynamical models’ 1-month-lead prediction of China summer rainfall (CSR) anomalies has insignificant skills. Thus, there is an urgent need to explore other ways to improve CSR prediction. The present study proposes a combined empirical orthogonal function (EOF)–partial least squares (PLS) regression method to offer a potential long-lead objective prediction of spatial distribution of CSR anomalies. The essence of the methodology is to use PLS regression to predict the principal component (PC) of the first five leading EOF modes of CSR. The preceding December–January mean surface temperature field [ST; i.e., SST over ocean and 2-m air temperature (T2m) over land] is selected as the predictor field for all five PCs because SST and snow cover, which is reflected by 2-m air temperature, are the most important factors that affect CSR and because the correlation between each mode and ST during winter is higher than in spring. The 4-month-lead forecast models are established by using the data from 1979 to 2004. A 9-yr independent forward-rolling prediction is made for the latest 9 yr (2005–13) as a strict forecast validation. The pattern correlation coefficient skill (0.32) between the observed and the 4-month-lead predicted patterns during the independent forecast period of 2005–13 is significantly higher than the dynamic models’ 1-month-lead hindcast skill (0.04), which indicates that the EOF–PLS regression is a useful tool for improving the current seasonal rainfall prediction. Issues related to the EOF–PLS method are also discussed.

A novel model independence methodology to improve multi-model seasonal forecasts combination

2020 ◽  
Author(s):  
Franco Catalano ◽  
Andrea Alessandri ◽  
Kristian Nielsen ◽  
Irene Cionni ◽  
Matteo De Felice
Keyword(s):  
Seasonal Prediction ◽  
Boreal Summer ◽  
Brier Score ◽  
Boreal Winter ◽  
Seasonal Forecasts ◽  
Surface Solar Radiation ◽  
Model Ensembles ◽  
Prediction Systems ◽  
June July ◽  
Forecasts Combination

<p align="justify">Multi-model ensembles (MMEs) are powerful tools in dynamical climate prediction as they account for the overconfidence and the uncertainties related to single model ensembles. The potential benefit that can be expected by using a MME amplifies with the increase of the independence of the contributing Seasonal Prediction Systems. To this aim, a novel methodology has been developed to assess the relative independence of the prediction systems in the probabilistic information they provide.</p><p align="justify"><span>We considered the Copernicus C3S seasonal forecasts product considering the one-month lead retrospective seasonal predictions for boreal summer and boreal winter seasons (1</span><sup><span>st</span></sup><span> May and 1</span><sup><span>st</span></sup><span> November start dates, i.e. June-July-August, JJA and December-January-February, DJF). We analysed the seasonal hindcasts in terms of deterministic and probabilistic scores with a particular focus on </span><span>continental areas</span><span>, since little evaluation has been performed so far over land domains that is where most of the applications of seasonal forecasts are based. The most relevant target variables of interest for the energy users have been considered and skill differences between the prediction systems have been analysed together with related possible sources of predictability. The analysis evidenced the importance of snow-albedo processes for temperature predictions in DJF and the effect of the atmospheric dynamics through moisture convergence for the prediction of surface solar radiation in JJA. </span><span>A</span><span> new metric, the Brier Score Covariance, designed to quantify the probabilistic independence among the models, has been </span><span>developed and </span><span>applied to optimize model selection and combination strategies with a particular focus on the most relevant variables for energy applications.</span></p>

scholarly journals Multimodel Prediction Skills of the Somali and Maritime Continent Cross-Equatorial Flows

2018 ◽  
Vol 31 (6) ◽  
pp. 2445-2464 ◽  
Author(s):  
Chen Li ◽  
Jing-Jia Luo ◽  
Shuanglin Li ◽  
Harry Hendon ◽  
Oscar Alves ◽  
...  
Keyword(s):  
High Resolution ◽  
Walker Circulation ◽  
Seasonal Prediction ◽  
Atmospheric Model ◽  
Precipitation Anomaly ◽  
Boreal Summer ◽  
Maritime Continent ◽  
Anomaly Correlation Coefficient ◽  
The Difference ◽  
The Individual

Predictive skills of the Somali cross-equatorial flow (CEF) and the Maritime Continent (MC) CEF during boreal summer are assessed using three ensemble seasonal forecasting systems, including the coarse-resolution Predictive Ocean Atmospheric Model for Australia (POAMA, version 2), the intermediate-resolution Scale Interaction Experiment–Frontier Research Center for Global Change (SINTEX-F), and the high-resolution seasonal prediction version of the Australian Community Climate and Earth System Simulator (ACCESS-S1) model. Retrospective prediction results suggest that prediction of the Somali CEF is more challenging than that of the MC CEF. While both the individual models and the multimodel ensemble (MME) mean show useful skill (with the anomaly correlation coefficient being above 0.5) in predicting the MC CEF up to 5-month lead, only ACCESS-S1 and the MME can skillfully predict the Somali CEF up to 2-month lead. Encouragingly, the CEF seesaw index (defined as the difference of the two CEFs as a measure of the negative phase relation between them) can be skillfully predicted up to 4–5 months ahead by SINTEX-F, ACCESS-S1, and the MME. Among the three models, the high-resolution ACCESS-S1 model generally shows the highest skill in predicting the individual CEFs, the CEF seesaw, as well as the CEF seesaw index–related precipitation anomaly pattern in Asia and northern Australia. Consistent with the strong influence of ENSO on the CEFs, the skill in predicting the CEFs depends on the model’s ability in predicting not only the eastern Pacific SST anomaly but also the anomalous Walker circulation that brings ENSO’s influence to bear on the CEFs.

scholarly journals A Dynamical Initialization Scheme for Real-Time Forecasts of Tropical Cyclones Using the WRF Model*

2013 ◽  
Vol 141 (3) ◽  
pp. 964-986 ◽  
Author(s):  
Dong-Hyun Cha ◽  
Yuqing Wang
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Initial Conditions ◽  
Wrf Model ◽  
Forecast Model ◽  
Initial Position ◽  
Northwest Pacific ◽  
Forecast System ◽  
Positive Effects ◽  
Forecast Models

Abstract To improve the initial conditions of tropical cyclone (TC) forecast models, a dynamical initialization (DI) scheme using cycle runs is developed and implemented into a real-time forecast system for northwest Pacific TCs based on the Weather Research and Forecasting (WRF) Model. In this scheme, cycle runs with a 6-h window before the initial forecast time are repeatedly conducted to spin up the axisymmetric component of the TC vortex until the model TC intensity is comparable to the observed. This is followed by a 72-h forecast using the Global Forecast System (GFS) prediction as lateral boundary conditions. In the DI scheme, the spectral nudging technique is employed during each cycle run to reduce bias in the large-scale environmental field, and the relocation method is applied after the last cycle run to reduce the initial position error. To demonstrate the effectiveness of the proposed DI scheme, 69 forecast experiments with and without the DI are conducted for 13 TCs over the northwest Pacific in 2010 and 2011. The DI shows positive effects on both track and intensity forecasts of TCs, although its overall skill depends strongly on the performance of the GFS forecasts. Compared to the forecasts without the DI, on average, forecasts with the DI reduce the position and intensity errors by 10% and 30%, respectively. The results demonstrate that the proposed DI scheme improves the initial TC vortex structure and intensity and provides warm physics spinup, producing initial states consistent with the forecast model, thus achieving improved track and intensity forecasts.

scholarly journals Robust Predictors for Seasonal Atlantic Hurricane Activity Identified with Causal Effect Networks

2020 ◽  
Author(s):  
Peter Pfleiderer ◽  
Carl-Friedrich Schleussner ◽  
Tobias Geiger ◽  
Marlene Kretschmer
Keyword(s):  
Risk Reduction ◽  
Disaster Preparedness ◽  
Lead Time ◽  
Causal Effect ◽  
Atlantic Hurricane ◽  
Operational Forecasts ◽  
Forecast Models ◽  
Hurricane Activity ◽  
Hurricane Forecasting ◽  
Risk Reduction Measures

Abstract. Atlantic hurricane activity varies substantially from year to year and so do the associated damages. Longer-term forecasting of hurricane risks is a key element to reduce damages and societal vulnerabilities by enabling targeted disaster preparedness and risk reduction measures. While the immediate synoptic drivers of tropical cyclone formation and intensification are increasingly well understood, precursors of hurricane activity on longer time-horizons are still not well established. Here we use a causal network-based algorithm to identify physically motivated late-spring precursors of seasonal Atlantic hurricane activity. Based on these precursors we construct seasonal forecast models with competitive skill compared to operational forecasts. We present a skillful model to forecast July to October cyclone activity at the beginning of April. Earlier seasonal hurricane forecasting provides a multi-month lead time to implement more effective disaster risk reduction measures. Our approach also highlights the potential of applying causal effects network analysis in seasonal forecasting.

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