Economic Bias of Weather Forecasting: A Spatial Modeling Approach

The value of accurate weather forecast information is substantial. In this paper we examine competition among forecast providers and its implications for the quality of forecasts. A simple economic model shows that an economic bias geographical inequality in forecast accuracy arises due to the extent of the market. Using the unique data on daily high temperature forecasts for 704 U.S. cities, we find that forecast accuracy increases with population and income. Furthermore, the economic bias gets larger when the day of forecasting is closer to the target day; i.e. when people are more concerned about the quality of forecasts. The results hold even after we control for location-specific heterogeneity and difficulty of forecasting.

Relationship between long-term environmental fluctuations and diving effort of female Australian fur seals

For predators foraging within spatially and temporally heterogeneous marine ecosystems, environmental fluctuations can alter prey availability. Using the proportion of time spent diving and foraging trip duration as proxies of foraging effort, a multi-year dataset was used to assess the response of 58 female Australian fur seals Arctocephalus pusillus doriferus to interannual environmental fluctuations. Multiple environmental indices (remotely sensed ocean colour data and numerical weather predictions) were assessed for their influence on inter-annual variations in the proportion of time spent diving and trip duration. Model averaging revealed strong evidence for relationships between 4 indices and the proportion of time spent diving. There was a positive relationship with effort and 2 yr-lagged spring sea-surface temperature, current winter zonal wind and southern oscillation index, while a negative relationship was found with 2 yr-lagged spring zonal wind. Additionally, a positive relationship was found between foraging trip duration and 1 yr-lagged spring surface chlorophyll a. These results suggest that environmental fluctuations may influence prey availability by affecting the survival and recruitment of prey at the larval and post-larval phases while also affecting current distribution of adult prey.

ANGEL: agent-based scheduling for real-time tasks in virtualized clouds

The success of cloud computing makes an increasing number of real-time applications such as signal processing and weather forecasting run in the cloud. Meanwhile, scheduling for real-time tasks is playing an essential role for a cloud provider to maintain its quality of service and enhance the system's performance. In this paper, we devise a novel agent-based scheduling mechanism in cloud computing environment to allocate real-time tasks and dynamically provision resources. In contrast to traditional contract net protocols, we employ a bidirectional announcement-bidding mechanism and the collaborative process consists of three phases, i.e., basic matching phase, forward announcement-bidding phase and backward announcement-bidding phase. Moreover, the elasticity is sufficiently considered while scheduling by dynamically adding virtual machines to improve schedulability. Furthermore, we design calculation rules of the bidding values in both forward and backward announcement-bidding phases and two heuristics for selecting contractors. On the basis of the bidirectional announcement-bidding mechanism, we propose an agent-based dynamic scheduling algorithm named ANGEL for real-time, independent and aperiodic tasks in clouds. Extensive experiments are conducted on CloudSim platform by injecting random synthetic workloads and the workloads from the last version of the Google cloud tracelogs to evaluate the performance of our ANGEL. The experimental results indicate that ANGEL can efficiently solve the real-time task scheduling problem in virtualized clouds.

Forecasting the onset of the grass pollen season in Melbourne (Australia)

In Melbourne, a southern hemisphere city with a cool temperate climate, the grass pollen season has been monitored using a Burkard spore trap for 12 years (11 pollen seasons, which extend from October through January). The onset of the grass pollen season (OGPS) has been defined in various ways using both arbitrary cumulative scores (Sum 75, Sum 100) and percentages (10% Pollen Fly). OGPS, based on the forecast model of pollen season devised by Lejoly-Gabriel (Acta Geogr. Lovan., 13 (1978) 1–260) has been most widely used in efforts to forecast the beginning of the pollen season. OGPS occurred in Melbourne between 20 October to 24 November (average 6 November), a difference of 35 days. Duration of the pollen season ranged from 46 to 81 days, with a mean of 55 days, one of the longest reported. The relationships between onset and various weather parameters for July have enabled us to modify a model, using linear regression analysis, to predict onset. The prediction model is based on a negative correlation between date of onset and the sum of rainfall for July (a winter month). The error of prediction (Ep) is 24% and predicted day of OGPS was precisely predicted on 2 occasions, and on others with a range of accuracy of 3 to 14 days.

Forecasting bike sharing demand using fuzzy inference mechanism

Forecasting bike sharing demand is of paramount importance for management of fleet in city level. Rapidly changing demand in this service is due to a number of factors including workday, weekend, holiday and weather condition. These nonlinear dependencies make the prediction a difficult task. This work shows that type-1 and type-2 fuzzy inference-based prediction mechanisms can capture this highly variable trend with good accuracy. Wang-Mendel rule generation method is utilized to generate rule base and then only current information like date related information and weather condition is used to forecast bike share demand at any given point in future. Simulation results reveal that fuzzy inference predictors can potentially outperform traditional feed forward neural network in terms of prediction accuracy.