Seasonal forecasts of tropical cyclone activity in a high-atmospheric-resolution coupled prediction system

Seasonal forecast skill of the basinwide and regional tropical cyclone (TC) activity in an experimental coupled prediction system based on the ECMWF System 4 is assessed. As part of a collaboration between the Center for Ocean–Land–Atmosphere Studies (COLA) and the ECMWF called Project Minerva, the system is integrated at the atmospheric horizontal spectral resolutions of T319, T639, and T1279. Seven-month hindcasts starting from 1 May for the years 1980–2011 are produced at all three resolutions with at least 15 ensemble members. The Minerva system demonstrates statistically significant skill for retrospective forecasts of TC frequency and accumulated cyclone energy (ACE) in the North Atlantic (NA), eastern North Pacific (EP), and western North Pacific. While the highest scores overall are achieved in the North Pacific, the skill in the NA appears to be limited by an overly strong influence of the tropical Pacific variability. Higher model resolution improves skill scores for the ACE and, to a lesser extent, the TC frequency, even though the influence of large-scale climate variations on these TC activity measures is largely independent of resolution changes. The biggest gain occurs in transition from T319 to T639. Significant skill in regional TC forecasts is achieved over broad areas of the Northern Hemisphere. The highest-resolution hindcasts exhibit additional locations with skill in the NA and EP, including land-adjacent areas. The feasibility of regional intensity forecasts is assessed. In the presence of the coupled model biases, the benefits of high resolution for seasonal TC forecasting may be underestimated.

The Climate-system Historical Forecast Project: do stratosphere-resolving models make better seasonal climate predictions in boreal winter?

Using an international, multi-model suite of historical forecasts from the World Climate Research Programme (WCRP) Climate-system Historical Forecast Project (CHFP), we compare the seasonal prediction skill in boreal wintertime between models that resolve the stratosphere and its dynamics (“high-top”) and models that do not (“low-top”). We evaluate hindcasts that are initialized in November, and examine the model biases in the stratosphere and how they relate to boreal wintertime (Dec-Mar) seasonal forecast skill. We are unable to detect more skill in the high-top ensemble-mean than the low-top ensemble-mean in forecasting the wintertime North Atlantic Oscillation, but model performance varies widely. Increasing the ensemble size clearly increases the skill for a given model. We then examine two major processes involving stratosphere-troposphere interactions (the El Niño-Southern Oscillation/ENSO and the Quasi-biennial Oscillation/QBO) and how they relate to predictive skill on intra-seasonal to seasonal timescales, particularly over the North Atlantic and Eurasia regions. High-top models tend to have a more realistic stratospheric response to El Niño and the QBO compared to low-top models. Enhanced conditional wintertime skill over high-latitudes and the North Atlantic region during winters with El Niño conditions suggests a possible role for a stratospheric pathway.

Expert system to calculate the cofficient of friction : an approach to enhance traffic safety

Friction plays a key role in causing slipperiness as a low coefficient of friction on the road may result in slippery and hazardous conditions. Analyzing the strong relation between friction and accident risk on winter roads is a difficult task. Many weather forecasting organizations use a variety of standard and bespoke methods to predict the coefficient of friction on roads. This article proposes an approach to predict the extent of slipperiness by building and testing an expert system. It estimates the coefficient of friction on winter roads in the province of Dalarna, Sweden using the prevailing weather conditions as a basis. Weather data from the road weather information system, Sweden (RWIS) was used. The focus of the project was to use the expert system as a part of a major project in VITSA, within the domain of intelligent transport systems

Modeling and forecasting long-term natural gas (NG) consumption in Iran, using particle swarm optimization (PSO)

The gradual changes in the world development have brought energy issues back into high profile. An ongoing challenge for countries around the world is to balance the development gains against its effects on the environment. The energy management is the key factor of any sustainable development program. All the aspects of development in agriculture, power generation, social welfare and industry in Iran are crucially related to the energy and its revenue. Forecasting end-use natural gas consumption is an important Factor for efficient system operation and a basis for planning decisions. In this thesis, particle swarm optimization (PSO) used to forecast long run natural gas consumption in Iran. Gas consumption data in Iran for the previous 34 years is used to predict the consumption for the coming years. Four linear and nonlinear models proposed and six factors such as Gross Domestic Product (GDP), Population, National Income (NI), Temperature, Consumer Price Index (CPI) and yearly Natural Gas (NG) demand investigated.

Forecasting multivariate time series under present-value-model short- and long-run co-movement restrictions

It is well known that cointegration between the level of two variables (e.g. prices and dividends) is a necessary condition to assess the empirical validity of a present-value model (PVM) linking them. The work on cointegration,namelyon long-run co-movements, has been so prevalent that it is often over-looked that another necessary condition for the PVM to hold is that the forecast error entailed by the model is orthogonal to the past. This amounts to investigate whether short-run co-movememts steming from common cyclical feature restrictions are also present in such a system. In this paper we test for the presence of such co-movement on long- and short-term interest rates and on price and dividend for the U.S. economy. We focuss on the potential improvement in forecasting accuracies when imposing those two types of restrictions coming from economic theory.

Forecasting Multivariate Time Series under Present-Value-Model Short- and Long-run Co-movement Restrictions

This paper has two original contributions. First, we show that the present value model (PVM hereafter), which has a wide application in macroeconomics and fi nance, entails common cyclical feature restrictions in the dynamics of the vector error-correction representation (Vahid and Engle, 1993); something that has been already investigated in that VECM context by Johansen and Swensen (1999, 2011) but has not been discussed before with this new emphasis. We also provide the present value reduced rank constraints to be tested within the log-linear model. Our second contribution relates to forecasting time series that are subject to those long and short-run reduced rank restrictions. The reason why appropriate common cyclical feature restrictions might improve forecasting is because it finds natural exclusion restrictions preventing the estimation of useless parameters, which would otherwise contribute to the increase of forecast variance with no expected reduction in bias. We applied the techniques discussed in this paper to data known to be subject to present value restrictions, i.e. the online series maintained and up-dated by Shiller. We focus on three different data sets. The fi rst includes the levels of interest rates with long and short maturities, the second includes the level of real price and dividend for the S&P composite index, and the third includes the logarithmic transformation of prices and dividends. Our exhaustive investigation of several different multivariate models reveals that better forecasts can be achieved when restrictions are applied to them. Moreover, imposing short-run restrictions produce forecast winners 70% of the time for target variables of PVMs and 63.33% of the time when all variables in the system are considered.

Forecasting multivariate time series under present-value-model short- and long-run co-movement restrictions

Using a sequence of nested multivariate models that are VAR-based, we discuss different layers of restrictions imposed by present-value models (PVM hereafter) on the VAR in levels for series that are subject to present-value restrictions. Our focus is novel - we are interested in the short-run restrictions entailed by PVMs (Vahid and Engle, 1993, 1997) and their implications for forecasting. Using a well-known database, kept by Robert Shiller, we implement a forecasting competition that imposes different layers of PVM restrictions. Our exhaustive investigation of several different multivariate models reveals that better forecasts can be achieved when restrictions are applied to the unrestricted VAR. Moreover, imposing short-run restrictions produces forecast winners 70% of the time for the target variables of PVMs and 63.33% of the time when all variables in the system are considered.

Weed interference on maize (Zea mays) under no tillage system

A field trial was carried out in Brazil in March 2002 with the aim to evaluate the effects of different timing and extension of weedy period on maize productivity. The hybrid Pioneer 30K75 was sowed under 7 t ha(-1) mulching promoted by glyphosate spraying. The treatments were divided in two groups: In the first group, weeds were maintained since the maize sowing until different periods in the crop cycle: 0, 14, 28, 42, 56, 70, and 150 days (harvesting time). In the second group, the maize crop was kept weed free for the same periods of the first group. Weed control was done through hand hoeing. A complete randomized blocks experimental design with five replications was used for plots distribution in the field. Nonlinear regression model was used to study the effects of weedy or weedfree periods on maize productivity. Weed community included 13 families and 31 species. Asteraceae, Poaceae, and Euphorbiaceae were the most abundant families. Results showed that under no tillage condition with 7 t ha-1 mulching at sowing time, the maize crop could cohabit with weed community for 54 days without any yield lost. on the other hand, if the crop was kept weed free for 27 days, the weed interference was not enable to reduce maize production. According to these results one weed control measure between 27 and 54 days after crop emergence could be enough to avoid any reduction in maize productivity.

Influence of irrigation on wheat crop

O uso da irrigação na triticultura tem aumentado significativamente nos últimos anos no Brasil. Neste trabalho, objetivou-se avaliar a influência da irrigação na produtividade, na qualidade tecnológica da farinha e no sistema radicular do trigo. em experimento de campo no IAPAR, em Londrina, Paraná, a cultivar IPR 118 foi cultivada sob irrigação por aspersão convencional (Tratamento Irrigado) e sem irrigação (Tratamento Sequeiro). A produtividade foi determinada, colhendo-se três amostras de 25 m² por tratamento. Nestas mesmas amostras, procedeu-se à análise da qualidade tecnológica da farinha, sendo avaliada, entre outros parâmetros, a força de glúten (W). A avaliação do sistema radicular foi realizada após a colheita, no perfil de 0 a 45 cm de profundidade do solo, amostrando-se oito plantas em cada tratamento. O método da parede do perfil foi utilizado para determinar o número de raízes (NR) e o método do monólito para determinar a massa seca de raízes (MSR). A irrigação aumentou em três vezes a produtividade do trigo, porém diminuiu o W na farinha. Ainda assim, o valor de W encontrado no Tratamento Irrigado (249 10-4 J) foi suficiente para manter a classificação do trigo como tipo-pão, a mesma para o qual a cultivar IPR 118 é classificada. Os valores medidos de NR e a MSR foram iguais ou maiores no Tratamento Sequeiro.

Operational parameters of soybean seeding in santa fe system

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Intercropping corn and kudzu in a rotation system with soybean

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

The effects of inter-crop cultivation Between rows of citrus crop on spreading of Guignardia citricarpa Ascospores and in the citrus black spot occurrence

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Severity of banana leaf spot in an intercropping system in two cycles of banana Prata Ana

Prata Ana is the most planted banana cultivar in northern Minas Gerais, Brazil. It is however susceptible to several pathogens. This study was carried out to evaluate the disease severity of banana leaf spot in the Prata Ana cv. in the first and second cycle under six different planting systems. The randomized block experimental design was used with six treatments and four replications. lit an evaluation of the severity of banana leaf spot, no disease symptoms were found on Thap Maeo and Caipira. The evolution curve of the disease indicated seasonal effects in the first and second cycles. The severity, of banana leaf spot was highest soon after the regional rainy period from November to March. A comparison of the means of the evaluations indicated a reduction in disease severity from the first to the second cycle.

Spatial relationships between soil attributes and corn yield in no-tillage system

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Crop type influences soil aggregation and organic matter under no-tillage

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)