Predictability of Extreme Waves in the Lorenz-96 Model Near Intermittency and Quasi-Periodicity

We introduce a method for quantifying the predictability of the event that the evolution of a deterministic dynamical system enters a specific subset of state space at a given lead time. The main idea is to study the distribution of finite-time growth rates of errors in initial conditions along the attractor of the system. The predictability of an event is measured by comparing error growth rates for initial conditions leading to that event with all possible growth rates. We illustrate the method by studying the predictability of extreme amplitudes of traveling waves in the Lorenz-96 model. Our numerical experiments show that the predictability of extremes is affected by several routes to chaos in a different way. In a scenario involving intermittency due to a periodic attractor disappearing through a saddle-node bifurcation we find that extremes become better predictable as the intensity of the event increases. However, in a similar intermittency scenario involving the disappearance of a 2-torus attractor we find that extremes are just as predictable as nonextremes. Finally, we study a scenario which involves a 3-torus attractor in which case the predictability of extremes depends nonmonotonically on the prediction lead time.

Download Full-text

Dependence of Indian summer monsoon rainfall forecast skill of CFSv2 on initial conditions and the role of bias in SST boundary forcing

10.5194/egusphere-egu21-7046 ◽

2021 ◽

Author(s):

Stella Jes Varghese ◽

Kavirajan Rajendran ◽

Sajani Surendran ◽

Arindam Chakraborty

Keyword(s):

Summer Monsoon ◽

Indian Summer Monsoon ◽

Lead Time ◽

Monsoon Rainfall ◽

Indian Summer Monsoon Rainfall ◽

Initial Conditions ◽

Summer Monsoon Rainfall ◽

Forecast Skill ◽

Boreal Summer ◽

Central Pacific

Indian summer monsoon seasonal reforecasts by CFSv2, initiated from January (4-month lead time, L4) through May (0-month lead time, L0) initial conditions (ICs), are analysed to investigate causes for the highest Indian summer monsoon rainfall (ISMR) forecast skill of CFSv2 with February (3-month lead time, L3) ICs. Although theory suggests forecast skill should degrade with increase in lead-time, CFSv2 shows highest skill with L3, due to its forecasting of ISMR excess of 1983 which other ICs failed to forecast. In contrast to observation, in CFSv2, ISMR extremes are largely decided by sea surface temperature (SST) variation over central Pacific (NINO3.4) associated with El Ni&#241;o-Southern Oscillation (ENSO), where ISMR excess (deficit) is associated with La Ni&#241;a (El Ni&#241;o) or cooling (warming) over NINO3.4. In 1983, CFSv2 with L3 ICs forecasted strong La Ni&#241;a during summer, which resulted in 1983 ISMR excess. In contrast, in observation, near normal SSTs prevailed over NINO3.4 and ISMR excess was due to variation of convection over equatorial Indian Ocean, which CFSv2 fails to capture with all ICs. CFSv2 reforecasts with late-April/early-May ICs are found to have highest deterministic ISMR forecast skill, if 1983 is excluded and Indian monsoon seasonal biases are also reduced. During the transitional ENSO in Boreal summer of 1983, faster and intense cooling of NINO3.4 SSTs in L3, could be due to larger dynamical drift with longer lead time of forecasting, compared to L0. Boreal summer ENSO forecast skill is also found to be lowest for L3 which gradually decreases from June to September. Rainfall occurrence with strong cold bias over NINO3.4, is because of the existence of stronger ocean-atmosphere coupling in CFSv2, but with a shift of the SST-rainfall relationship pattern to slightly colder SSTs than the observed. Our analysis suggests the need for a systematic approach to minimize bias in SST boundary forcing in CFSv2, to achieve improved ISMR forecasts.

Download Full-text

Improving Student Learning Outcomes Through Story Card Media to Find The Main Idea of a Paragraph in Class VI SDN Luwunggede 04

Social, Humanities, and Educational Studies (SHEs): Conference Series ◽

10.20961/shes.v3i4.54371 ◽

2020 ◽

Vol 3 (4) ◽

pp. 640

Author(s):

Kirno Kirno

Keyword(s):

Action Research ◽

Student Learning ◽

Learning Outcomes ◽

Initial Conditions ◽

Main Idea ◽

Student Learning Outcomes ◽

Final Conclusion ◽

Classroom Action Research ◽

Improving Student Learning ◽

Improve Student Learning

The purpose of this study is to improve student learning outcomes in Indonesian lessons in elementary school grade VI by using story cards to find the main idea of a paragraph. The research conducted was Classroom Action Research (CAR) in two cycles, with each cycle having one meeting. The stages of each cycle are the stages of planning, implementation, observation and reflection. Data collection techniques are observation and tests. Based on data analysis, it was found that the use of story card media to find the main idea of paragraphs in Indonesian language lessons to determine the main idea can improve student learning outcomes as indicated by the level of mastery learning in the initial conditions of 40% to 65% in the first cycle, and increased to 80% in cycle II. The final conclusion from the implementation of this classroom action research is that the use of story card media to find the main idea of a paragraph is able to improve student learning outcomes in Indonesian language lessons to determine the main idea at SD Negeri Luwunggede 04, Larangan, Brebes.

Download Full-text

Estimating the Intrinsic Limit of Predictability Using a Stochastic Convection Scheme

Journal of the Atmospheric Sciences ◽

10.1175/jas-d-17-0373.1 ◽

2019 ◽

Vol 76 (3) ◽

pp. 757-765 ◽

Cited By ~ 7

Author(s):

Tobias Selz

Keyword(s):

Initial Conditions ◽

Error Growth ◽

Convection Scheme ◽

Error Level ◽

Computational Costs ◽

Convection Schemes ◽

Potential Improvement ◽

North And South ◽

Data Assimilation System ◽

Growth Experiments

Abstract Global model simulations together with a stochastic convection scheme are used to assess the intrinsic limit of predictability that originates from convection up to planetary scales. The stochastic convection scheme has been shown to introduce an appropriate amount of variability onto the model grid without the need to resolve the convection explicitly. This largely reduces computational costs and enables a set of 12 cases equally distributed over 1 year with five ensemble members for each case, generated by the stochastic convection scheme. As a metric, difference kinetic energy at 300 hPa over the midlatitudes, both north and south, is used. With this metric the intrinsic limit is estimated to be about 17 days when a threshold of 80% of the saturation level is applied. The error level at 3.5 days roughly compares to the initial-condition uncertainty of the current ECMWF data assimilation system, which suggests a potential improvement of 3.5 forecast days through perfecting the initial conditions. Error-growth experiments that use a deterministic convection scheme show smaller errors of about half the size at early forecast times and an estimate of intrinsic predictability that is about 10% longer, confirming the overconfidence of deterministic convection schemes.

Download Full-text

Accounting for three sources of uncertainty in ensemble hydrological forecasting

Hydrology and Earth System Sciences ◽

10.5194/hess-20-1809-2016 ◽

2016 ◽

Vol 20 (5) ◽

pp. 1809-1825 ◽

Cited By ~ 29

Author(s):

Antoine Thiboult ◽

François Anctil ◽

Marie-Amélie Boucher

Keyword(s):

Ensemble Kalman Filter ◽

Lead Time ◽

Initial Conditions ◽

Forecast Horizon ◽

Meteorological Forcing ◽

Total Uncertainty ◽

Sources Of Uncertainty ◽

Forecasting Performance ◽

Respective Contribution ◽

Different Sources

Abstract. Seeking more accuracy and reliability, the hydrometeorological community has developed several tools to decipher the different sources of uncertainty in relevant modeling processes. Among them, the ensemble Kalman filter (EnKF), multimodel approaches and meteorological ensemble forecasting proved to have the capability to improve upon deterministic hydrological forecast. This study aims to untangle the sources of uncertainty by studying the combination of these tools and assessing their respective contribution to the overall forecast quality. Each of these components is able to capture a certain aspect of the total uncertainty and improve the forecast at different stages in the forecasting process by using different means. Their combination outperforms any of the tools used solely. The EnKF is shown to contribute largely to the ensemble accuracy and dispersion, indicating that the initial conditions uncertainty is dominant. However, it fails to maintain the required dispersion throughout the entire forecast horizon and needs to be supported by a multimodel approach to take into account structural uncertainty. Moreover, the multimodel approach contributes to improving the general forecasting performance and prevents this performance from falling into the model selection pitfall since models differ strongly in their ability. Finally, the use of probabilistic meteorological forcing was found to contribute mostly to long lead time reliability. Particular attention needs to be paid to the combination of the tools, especially in the EnKF tuning to avoid overlapping in error deciphering.

Download Full-text

Prediction of Monthly-Mean Temperature: The Roles of Atmospheric and Land Initial Conditions and Sea Surface Temperature

Journal of Climate ◽

10.1175/2009jcli3090.1 ◽

2010 ◽

Vol 23 (3) ◽

pp. 717-725 ◽

Cited By ~ 26

Author(s):

Mingyue Chen ◽

Wanqiu Wang ◽

Arun Kumar

Keyword(s):

Lead Time ◽

Initial Conditions ◽

Lower Boundary ◽

Atmospheric Model ◽

Sea Surface ◽

Forecast System ◽

Ensemble Forecasts ◽

Lower Boundary Condition ◽

Asymptotic Value ◽

Mean Temperature

Abstract Using the retrospective forecasts from the National Centers for Environmental Prediction (NCEP) coupled atmosphere–ocean Climate Forecast System (CFS) and the Atmospheric Model Intercomparison Project (AMIP) simulations from its uncoupled atmospheric component, the NCEP Global Forecast System (GFS), the relative roles of atmospheric and land initial conditions and the lower boundary condition of sea surface temperatures (SSTs) for the prediction of monthly-mean temperature are investigated. The analysis focuses on the lead-time dependence of monthly-mean prediction skill and its asymptotic value for longer lead times, which could be attributed the atmospheric response to the slowly varying SST. The results show that the observed atmospheric and land initial conditions improve the skill of monthly-mean prediction in the extratropics but have little influence in the tropics. However, the influence of initial atmospheric and land conditions in the extratropics decays rapidly. For 30-day-lead predictions, the global-mean forecast skill of monthly means is found to reach an asymptotic value that is primarily determined by the SST anomalies. The lead time at which initial conditions lose their influence varies spatially. In addition, the initial atmospheric and land conditions are found to have longer impacts in northern winter and spring than in summer and fall. The relevance of the results for constructing lagged ensemble forecasts is discussed.

Download Full-text

Towards scale independent hydrological forecasting in regulated semi-arid regions

10.5194/egusphere-egu2020-6047 ◽

2020 ◽

Author(s):

Pallav Kumar Shrestha ◽

Christof Lorenz ◽

Husain Najafi ◽

Stephan Thober ◽

Oldrich Rakovec ◽

...

Keyword(s):

Lead Time ◽

Arid Regions ◽

Initial Conditions ◽

Skill Score ◽

Seasonal Forecasting ◽

Forecasting Model ◽

Reservoir Water ◽

Reservoir Water Level ◽

Model Chain ◽

Semi Arid

Semi-arid regions are characterized by low annual precipitation that exhibit large seasonal fluctuations. While semi-arid regions cover 3.6% of the globe, 13% of world&#8217;s documented reservoirs (GRanD database) are within 100 km of semi-arid regions to fulfill water demand year-round. Reservoirs are known to increase evaporation and significantly change hydrologic regime downstream. Accurate representation of reservoirs and scale independent modeling is indispensable for reliable hydrologic forecasting systems in semi-arid regions. To address this, the mesoscale hydrological model (mHM, git.ufz.de/mhm) is augmented with a new lake/reservoir module (multiscale lake module, mLM). The objective is to measure the performance of a scalable seasonal forecasting model chain with and without reservoirs.The experimental setup constitutes the SaWaM (http://grow-sawam.org/) project study regions encompassing seven semi-arid basins and 15 reservoirs of high significance across three continents (Sao Francisco, Jaguaribe, Piranhas in Brazil, Blue Nile, Atbara in Sudan, Karun in Iran, Chira-Catamayo in Ecuador).The calibration of mHM parameters and its initial conditions for forecsating are obtained using the spatially disaggregated ERA5 (ERA-SD, &#8776; 10 km, starting 1981) climate reanalysis data. The calibrated model is forced with an ensemble of 25 realisations of ECMWF-SEAS5 seasonal hindcasts which are bias corrected and spatially disaggregated (BCSD, &#8776;10 km) using ERA-SD. The 2010&#8211;2016 hindcasting experiment generates hydrological forecasts with lead time of upto six months. The performance of the model chain BCSD-mHM-mLM and BCSD-mHM are evaluated using the Brier Skill Score.Preliminary results show that incorporating reservoirs in the model improves the performance of mHM (average NSE improvement &#8776; +0.1 for the period 1990&#8211;2010) and the overarching forecasting model chain. Sub-grid level lake delineation and in-/outflow calculations of mLM result in scalable reservoir states and fluxes and thus overall scalable basin hydrology. Seamless forecasts for soil moisture, streamflow, reservoir inflow and reservoir water level are achieved across scales (&#8776;10 km to &#8776;1 km) showing skills to up to two months lead time. This study is the first step towards an operational hydrological seasonal forecasting system which has potential to significantly improve water management, specially in semi-arid regions.

Download Full-text

Seasonal Climate Predictability in a Coupled OAGCM Using a Different Approach for Ensemble Forecasts

Journal of Climate ◽

10.1175/jcli3526.1 ◽

2005 ◽

Vol 18 (21) ◽

pp. 4474-4497 ◽

Cited By ~ 184

Author(s):

Jing-Jia Luo ◽

Sebastien Masson ◽

Swadhin Behera ◽

Satoru Shingu ◽

Toshio Yamagata

Keyword(s):

Indian Ocean ◽

Lead Time ◽

Initial Conditions ◽

Surface Wind ◽

Lead Times ◽

Western Coast ◽

Easterly Wind ◽

Skill Scores ◽

Wind Bursts ◽

Sst Anomalies

Abstract Predictabilities of tropical climate signals are investigated using a relatively high resolution Scale Interaction Experiment–Frontier Research Center for Global Change (FRCGC) coupled GCM (SINTEX-F). Five ensemble forecast members are generated by perturbing the model’s coupling physics, which accounts for the uncertainties of both initial conditions and model physics. Because of the model’s good performance in simulating the climatology and ENSO in the tropical Pacific, a simple coupled SST-nudging scheme generates realistic thermocline and surface wind variations in the equatorial Pacific. Several westerly and easterly wind bursts in the western Pacific are also captured. Hindcast results for the period 1982–2001 show a high predictability of ENSO. All past El Niño and La Niña events, including the strongest 1997/98 warm episode, are successfully predicted with the anomaly correlation coefficient (ACC) skill scores above 0.7 at the 12-month lead time. The predicted signals of some particular events, however, become weak with a delay in the phase at mid and long lead times. This is found to be related to the intraseasonal wind bursts that are unpredicted beyond a few months of lead time. The model forecasts also show a “spring prediction barrier” similar to that in observations. Spatial SST anomalies, teleconnection, and global drought/flood during three different phases of ENSO are successfully predicted at 9–12-month lead times. In the tropical North Atlantic and southwestern Indian Ocean, where ENSO has predominant influences, the model shows skillful predictions at the 7–12-month lead times. The distinct signal of the Indian Ocean dipole (IOD) event in 1994 is predicted at the 6-month lead time. SST anomalies near the western coast of Australia are also predicted beyond the 12-month lead time because of pronounced decadal signals there.

Download Full-text

Global meteorological drought – Part 2: Seasonal forecasts

Hydrology and Earth System Sciences ◽

10.5194/hess-18-2669-2014 ◽

2014 ◽

Vol 18 (7) ◽

pp. 2669-2678 ◽

Cited By ~ 40

Author(s):

E. Dutra ◽

W. Pozzi ◽

F. Wetterhall ◽

F. Di Giuseppe ◽

L. Magnusson ◽

...

Keyword(s):

Lead Time ◽

Initial Conditions ◽

Standardized Precipitation Index ◽

Meteorological Drought ◽

Added Value ◽

Lead Times ◽

Seasonal Forecasts ◽

Ensemble Forecasts ◽

Global Precipitation Climatology Centre ◽

The World

Abstract. Global seasonal forecasts of meteorological drought using the standardized precipitation index (SPI) are produced using two data sets as initial conditions: the Global Precipitation Climatology Centre (GPCC) and the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim reanalysis (ERAI); and two seasonal forecasts of precipitation, the most recent ECMWF seasonal forecast system and climatologically based ensemble forecasts. The forecast evaluation focuses on the periods where precipitation deficits are likely to have higher drought impacts, and the results were summarized over different regions in the world. The verification of the forecasts with lead time indicated that generally for all regions the least reduction on skill was found for (i) long lead times using ERAI or GPCC for monitoring and (ii) short lead times using ECMWF or climatological seasonal forecasts. The memory effect of initial conditions was found to be 1 month of lead time for the SPI-3, 4 months for the SPI-6 and 6 (or more) months for the SPI-12. Results show that dynamical forecasts of precipitation provide added value with skills at least equal to and often above that of climatological forecasts. Furthermore, it is very difficult to improve on the use of climatological forecasts for long lead times. Our results also support recent questions of whether seasonal forecasting of global drought onset was essentially a stochastic forecasting problem. Results are presented regionally and globally, and our results point to several regions in the world where drought onset forecasting is feasible and skilful.

Download Full-text

Oscillatory motion of a viscoelastic fluid within a spherical cavity

Journal of Fluid Mechanics ◽

10.1017/jfm.2011.263 ◽

2011 ◽

Vol 685 ◽

pp. 1-22 ◽

Cited By ~ 18

Author(s):

Julia Meskauskas ◽

Rodolfo Repetto ◽

Jennifer H. Siggers

Keyword(s):

Viscoelastic Fluid ◽

Growth Rates ◽

Spherical Cavity ◽

Small Amplitude ◽

Transient Flow ◽

Initial Conditions ◽

Vitreous Humour ◽

Forced Oscillations ◽

Relaxation Behaviour ◽

Rheological Models

AbstractWe study the motion of a viscoelastic fluid within a rigid spherical cavity with the aim of improving understanding of the motion of the vitreous humour in the human eye. The flow of vitreous humour leads to traction on the retina, which, once the retina is torn or damaged, can cause it to detach from the choroid, leading to loss of sight if left untreated. In the first part of the paper we investigate the relaxation behaviour of the fluid, the transient flow that would be observed in the stationary sphere starting from non-stationary initial conditions. For a general viscoelastic fluid we calculate the growth rates and eigenfunctions associated with the system, and we discuss two particular rheological models of the vitreous humour taken from the literature. In the second part of the paper we consider forced oscillations of the fluid, due to small-amplitude rotations of the sphere about a diameter, representing saccades of the eyeball. We conclude with a discussion of the possible occurrence of resonant phenomena and their clinical relevance.

Download Full-text

Adjustment of estimated tree growth rates in northern California conifers for changes in precipitation levels

Canadian Journal of Forest Research ◽

10.1139/x98-090 ◽

1998 ◽

Vol 28 (8) ◽

pp. 1241-1248 ◽

Cited By ~ 6

Author(s):

Lee C Wensel ◽

Eric C Turnblom

Keyword(s):

Tree Growth ◽

Growth Rates ◽

Initial Conditions ◽

Growth Parameters ◽

Forest Growth ◽

Northern California ◽

Growth Data ◽

Short Term ◽

Individual Tree ◽

Periodic Growth

Even with similar initial conditions, observed forest growth rates on permanent sample plots in the conifer region of northern California differ for different periods. Thus, individual-tree growth models built with growth parameters estimated from data from one period may not produce accurate estimates for another period unless some allowance is made for this variation in growth rates. Variation in growth rates of northern California conifers through time has been shown to be correlated with precipitation changes. A method is presented that adjusts periodic growth estimates for variation in precipitation between periods. This provides a basis for adjusting short-term growth data for making long-term growth projections. Perhaps more importantly, short-term inventory updates might be made more accurately.

Download Full-text