The polar ionosphere at Zhongshan Station on May 11, 1999, the day the solar wind almost disappeared

The solar wind almost disappeared on May 11,1999: the solar wind plasma density and' dynamic pressure were less than 1 cm(-3) and 0.1 nPa respectively, while the interplanetary magnetic field was northward. The polar ionospheric data observed by the multi-instruments at Zhongshan Station in Antarctica on such special event day was compared with those of the control day (May 14). It was shown that geomagnetic activity was very quiet on May 11 at Zhongshan. The magnetic pulsation, which usually occurred at about magnetic noon, did not appear. The ionosphere was steady and stratified, and the F-2 layer spread very little. The critical frequency of dayside F-2 layer, f(0)F(2), was larger than that of control day, and the peak of f(0)F(2) appeared 2 hours earlier. The ionospheric drift velocity was less than usual. There were intensive auroral E-s appearing at magnetic noon. All this indicates that the polar ionosphere was extremely quiet and geomagnetic field was much more dipolar on May 11. There were some signatures of auroral substorm before midnight, such as the negative deviation of the geomagnetic H component, accompanied with auroral E-s and weak Pc3 pulsation.

Ramp and constant power trials produce equivalent critical power estimates

The standard critical power test protocol on the cycle prescribes a series of trials to exhaustion, each at a different but constant power setting. Recently the protocol has been modified and applied to a series of trials to exhaustion each at a different ramp incremental rate. This study was undertaken to compare critical power and anaerobic work capacity estimates in the same group of subjects when derived from the two protocols. Ten male subjects of mixed athletic ability cycled to exhaustion on eight occasions in randomized order over a 3-wk period. Four trials were performed at differing constant power settings and four trials on differing ramp incremental rates. Both critical power and anaerobic work capacity were estimated for each subject by curve fitting of the ramp model and of three versions of the constant power model. After adjusting for inter-subject variability, no significant differences were detected between critical power estimates or between anaerobic work capacity estimates from any model formulation or from the two protocols. It is concluded that both the ramp and constant power protocols produce equivalent estimates for critical power and anaerobic work capacity.

Forecasting regional crop production using SOI phases: an example for the Australian peanut industry

Using peanuts as an example, a generic methodology is presented to forward-estimate regional crop production and associated climatic risks based on phases of the Southern Oscillation Index (SOI). Yield fluctuations caused by a highly variable rainfall environment are of concern to peanut processing and marketing bodies. The industry could profitably use forecasts of likely production to adjust their operations strategically. Significant, physically based lag-relationships exist between an index of ocean/atmosphere El Nino/Southern Oscillation phenomenon and future rainfall in Australia and elsewhere. Combining knowledge of SOI phases in November and December with output from a dynamic simulation model allows the derivation of yield probability distributions based on historic rainfall data. This information is available shortly after planting a crop and at least 3-5 months prior to harvest. The study shows that in years when the November-December SOI phase is positive there is an 80% chance of exceeding average district yields. Conversely, in years when the November-December SOI phase is either negative or rapidly falling there is only a 5% chance of exceeding average district yields, but a 95% chance of below average yields. This information allows the industry to adjust strategically for the expected volume of production. The study shows that simulation models can enhance SOI signals contained in rainfall distributions by discriminating between useful and damaging rainfall events. The methodology can be applied to other industries and regions.