Many studies evaluating model boundary-layer schemes focus either on near-surface parameters or on short-term observational campaigns. This reflects the observational datasets that are widely available for use in model evaluation. In this paper we show how surface and long-term Doppler lidar observations, combined in a way to match model representation of the boundary layer as closely as possib...

[1] The 24-year retrospective forecast data set from the NCEP Climate Forecast System (CFS) is analyzed to study its idealized predictability of precipitation and temperature under its current configuration. The analysis approach assumes the forecasting model and system to be predicted share exactly the same physics so that the idealized predictability is calculated and serves as the upper limi...

In this paper, we will use the time series analysis method to predict the throughput of the research, through the establishment of time series SARIMA model, using the January-February 2017 domestic container throughput statistics of the main container port forecast from March 2017 to December 2020 container throughput data and analyze the trend of the throughput of major container ports in Chin...

Forecasting of time series that have seasonal and other variations remains an important problem for forecasters. This paper presents a neural network (NN) approach along with a fuzzy time series methods to forecasting sugar production in India. The agriculture production and productivity is one of the such processes, which is not governed by any deterministic process due to highly non linearity...

The Atmospheric Infrared Sounder (AIRS) on the EOS Aqua Spacecraft was launched on May 4, 2002. Early in the mission, the AIRS instrument demonstrated its value to the weather forecasting community with better than 6 hours of improvement on the 5 day forecast. Now with over eight years of consistent and stable data from AIRS, scientists are able to examine processes governing weather and climat...

Meteorological and hydrological centres around the world are looking at ways to improve their capacity to be able to produce and deliver skilful and reliable forecasts of high-impact extreme rainfall and flooding events on a range of prediction timescales (e.g. sub-daily, daily, multi-week, seasonal). Making improvements to extended-range rainfall and flood forecast models, assessing forecast s...

This work aims to present a full Bayesian framework to identify, extract and forecast unobserved components in time series. The major novelty is to present a probabilistic framework to analyze the identification conditions. More precisely, informative prior distributions are assigned to the spectral densities of the unobserved components. This entails a interesting feature: the possibility to a...

BACKGROUND Malaria is a vector-borne disease which, despite recent scaled-up efforts to achieve control in Africa, continues to pose a major threat to child survival. The disease is caused by the protozoan parasite Plasmodium and requires mosquitoes and humans for transmission. Rainfall is a major factor in seasonal and secular patterns of malaria transmission along the East African coast. OB...

The Bowen Basin contains the largest coal reserves in Australia. Prolonged heavy rainfall during the 2010-2011 wet-season severely affected industry operations with an estimated economic loss of A$5.7 billion (£3.8 billion). There was no explicit warning of the exceptionally wet conditions in the seasonal forecast from the Australian Bureau of Meteorology, which simply suggested a 50-55% probab...

This paper presents a multi-model predictor called Qualitative Multi-Model Predictor Plus (QMMP+) for demand forecast in water distribution networks. QMMP+ is based on the decomposition of the quantitative and qualitative information of the time-series. The quantitative component (i.e., the daily consumption prediction) is forecasted and the pattern mode estimated using a Nearest Neighbor (NN) ...