Assessing improved CPTEC probabilistic forecasts on medium-range timescale

Cunningham, Christopher; Bonatti, José Paulo; Ferreira, Márcio Borges

doi:10.1002/met.1464

Cited by 9 publications

(8 citation statements)

References 15 publications

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“…The CPTEC/INPE atmospheric global circulation model (AGCM) is used at CPTEC/INPE to perform seasonal predictions on monthly basis since 1995. The model was developed from previous versions of CPTEC/COLA AGCM (Cavalcanti et al, 2002;Marengo et al, 2003) and used in studies such as Mendonça andBonatti (2009) andCunningham et al (2014). Seasonal predictions using this model were well succeeded for three strong droughts in Amazonia (Coelho et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

On the opposite relation between extreme precipitation over west Amazon and southeastern Brazil: observations and model simulations

Cavalcanti

Marengo

Alves

et al. 2016

Intl Journal of Climatology

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During the austral summer of 2014 and 2015, a severe drought occurred in southeastern Brazil at the same time when flooding conditions were registered in the state of Acre in the western Amazonia of Brazil. This study aimed the identification of the atmospheric and oceanic large‐scale characteristics and regional features associated with these conditions, and verification of the Center of Weather Forecasting and Climate Studies/National Institute of Space Research (CPTEC/INPE) atmospheric global circulation model ability in reproducing the observed features. The state of Acre was chosen as a pilot area of the PULSE‐Brazil project – a web platform tool that contains climate, ecological and health data for Brazil. The observational data and model results were analysed for the climatological period of 1981–2010. Correlation analyses between precipitation over west Amazon and atmospheric and oceanic variables indicated the main patterns in both data sets. Extreme wet and dry Januaries over west Amazon were selected to explore the regional and large‐scale associated features. The general results of climate analyses indicated an opposite relation of precipitation between western Amazon and southeastern Brazil, in January, besides the typical precipitation dipole between the south Atlantic convergence zone and southeastern South America. This configuration is related to anomalous humidity flux at low levels and associated anomalous sea level pressure over southeastern South America induced by subsidence. The role of convection anomalies over the Pacific Ocean on South America anomalies is discussed. The wet and dry cases in Acre region display opposite atmospheric anomalies over South America that are linked to wavetrains over the Pacific Ocean, likely related to the opposite conditions of Indonesia‐Pacific tropical convection. Similar patterns of the dry cases were observed during January of 2014 and 2015. The model reproduced some observed atmospheric patterns related to precipitation extremes and the results are discussed in terms of regional and large‐scale climate variability.

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Section: Introductionmentioning

confidence: 99%

On the opposite relation between extreme precipitation over west Amazon and southeastern Brazil: observations and model simulations

Cavalcanti

Marengo

Alves

et al. 2016

Intl Journal of Climatology

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“…A more detailed revision of the EOF‐based perturbation methodology used at CPTEC can be found in Cunningham et al . ().…”

Section: Model Description Datasets and Experimental Configurationsmentioning

confidence: 97%

Configuration and hindcast quality assessment of a Brazilian global sub‐seasonal prediction system

Guimarães

Coelho

Woolnough

et al. 2020

Quart J Royal Meteoro Soc

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This article presents the Centre for Weather Forecast and Climate Studies (CPTEC) developments for configuring a global sub-seasonal prediction system and assessing its ability in producing retrospective predictions (hindcasts) for meteorological conditions of the following 4 weeks. Six Brazilian Global Atmospheric Model version 1.2 (BAM-1.2) configurations were tested in terms of vertical resolution, deep convection and boundary-layer parametrizations, as well as soil moisture initialization. The aim was to identify the configuration with best performance when predicting weekly accumulated precipitation, weekly mean 2 m temperature (T2M) and the Madden-Julian Oscillation (MJO) daily evolution. Hindcasts assessment was performed for 12 extended austral summers (November-March, 1999/2000) with two start dates for each month for the six configurations and two ensemble approaches. The first approach, referred to as Multiple Configurations Ensemble (MCEN), was formed of one ensemble member from each of the six configurations. The second, referred to as Initial Condition Ensemble (ICEN), was composed of six ensemble members produced with the chosen configuration as the best using an empirical orthogonal function (EOF) perturbation methodology. The chosen configuration presented high correlation and low root-mean-squared error (RMSE) for precipitation and T2M anomaly predictions at the first week and these indices degraded as lead time increased, maintaining moderate performance up to week-4 over the tropical Pacific and northern South America. For MJO predictions, this configuration crossed the 0.5 bivariate correlation threshold in 18 days. The ensemble approaches improved the correlation and RMSE of precipitation and T2M anomalies. ICEN improved precipitation and T2M predictions performance over eastern South America at week-3 and over northern South America at week-4. Improvements were also noticed for MJO predictions. The time to cross the above-mentioned threshold increased to 21 days for MCEN and to 20 days for ICEN. K E Y W O R D Sforecast verification, intraseasonal variability, MJO Q J R Meteorol Soc. 2020;146:1067-1084.wileyonlinelibrary.com/journal/qj

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“…Below Figure 1 shows the temporal evolution of ensemble predictions made operationally by CPTEC (Center for Weather Forecasting and Climate Studies), the Brazilian principal weather forecasting institution (Cunningham et al, 2014). As an example, spaghetti plots are presented with predictions for 15, 12, 10, 7, 5, and 3-days for September 17, 2014 at 12 UTC.…”

Section: The Ensemble Predictionmentioning

confidence: 99%

Revisiting the Question of Atmospheric Predictability

Santos¹,

Buchmann²

2015

JGG

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This study seeks to demystify the claim that the 'atmospheric chaos' imposes a two-week limit on reliable weather forecasts. 'Deterministic chaos' indeed occurs due to the use of nonlinear numerical models for these forecasts. This 'deterministic chaos' does impose time limits on valid predictions, but it also facilitates, through the ensemble forecasting technique, the use of interesting statistical indicators that define regions and the duration these predictions are more or less reliable. Recently published articles show that the 'uncertainties' in the initial conditions are an inherent difficulty in meteorological observations and have nothing to do with the atmospheric behavior. These studies demonstrate two important aspects regarding 'uncertainties' in data used to initialize models. First, to achieve improvements in numerical weather forecasts, these 'uncertainties' must be skillfully introduced in the large scale and not in the small scale. Secondly, the numerical models must include equations or parameterizations that reproduce nature's ways that let different scales 'interact', that is, the models should reproduce how the energy of different atmospheric modes 'travels'.In the 1960s and 1970s the academic meteorology community debated if more degrees of freedom could reduce the system instabilities in forecast model equations. With this line of reasoning the articles of Charney (1963) and Lorenz (1963) should be highlighted. Charney thought that with more degrees of freedom, the system of equations could stabilize, and thus extend the effective forecast limits. However, at that time, Lorenz, using a very simple convection model based on an approximate system of nonlinear ordinary differential equations, discovered that two runs of the model starting from slightly different initial conditions gave surprisingly divergent responses after a non-long period of integrations. Lorenz called this unexpected result 'deterministic chaos'. Reinforcing the Lorenz results, Kalnay (2003) stated that nothing could be done to ameliorate the models because, as the atmosphere was 'chaotic', the fourteen-day predictability limit could not be surpassed.Nowadays, nonlinear models are used instead of linear to forecast weather, because nonlinear models produce more 'realistic' or substantiated results. This is probably because nonlinear models take into account the disturbance products in the advection and others nonlinear terms, which are important to simulate the real atmosphere and which have aperiodic solutions, contrary to linear models. This study revisits the question of atmospheric predictability, suggesting the research community should invest its effort in two approaches. The first should endeavor to find modeling strategies that better reproduce the realistic ways large-scale interact with the small-scale in geophysical fluid systems, especially the atmosphere, in what are here named 'better models'. The second approach, more evident to the meteorological community, is to strongly invest to obtain more and 'bette...

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Assessing improved CPTEC probabilistic forecasts on medium-range timescale

Cited by 9 publications

References 15 publications

On the opposite relation between extreme precipitation over west Amazon and southeastern Brazil: observations and model simulations

On the opposite relation between extreme precipitation over west Amazon and southeastern Brazil: observations and model simulations

Configuration and hindcast quality assessment of a Brazilian global sub‐seasonal prediction system

Revisiting the Question of Atmospheric Predictability

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