Quality Control in Weather Monitoring with Dynamic Linear Models

Decisions in agriculture are frequently based on weather. With an increase in the availability and affordability of off-the-shelf weather stations, farmers able to acquire localised weather information. However, with uncertainty in the sensor and installation quality, farmers are at risk of making poor decisions based on incorrect data. We present an automated approach to perform quality control on weather sensors. Our approach uses time-series modelling and data fusion with Bayesian principles to provide predictions with uncertainty quantification. These predictions and uncertainty are used to estimate the validity of a sensor observation. We test on temperature, wind, and humidity data and achieve error hit rates above 80% and false negative rates below 11%

Burr XII distributions

Metrika manuscript No. (will be inserted by the editor

A new parametric model for survival data with long-term survivors

19 page(s

Statistical power calculation and sample size determination for environmental studies with data below detection limits

Power calculation and sample size determination are critical in designing environmental monitoring programs. The traditional approach based on comparing the mean values may become statistically inappropriate and even invalid when substantial proportions of the response values are below the detection limits or censored because strong distributional assumptions have to be made on the censored observations when implementing the traditional procedures. In this paper, we propose a quantile methodology that is robust to outliers and can also handle data with a substantial proportion of below-detection-limit observations without the need of imputing the censored values. As a demonstration, we applied the methods to a nutrient monitoring project, which is a part of the Perth Long-Term Ocean Outlet Monitoring Program. In this example, the sample size required by our quantile methodology is, in fact, smaller than that by the traditional t-test, illustrating the merit of our method

Knot-Optimizing Spline Networks (KOSNETS) for nonparametric regression

In this paper we present a novel method for short term forecast of time series based on Knot-Optimizing Spline Networks (KOSNETS). The time series is first approximated by a nonlinear recurrent system. The resulting recurrent system is then approximated by feedforward B-spline networks, yielding a nonlinear optimization problem. In this optimization problem, both the knot points and the coefficients of the B-splines are decision variables so that the solution to the problem has both optimal coefficients and partition points. To demonstrate the usefulness and accuracy of the method, numerical simulations and tests using various model and real time series are performed. The numerical simulation results are compared with those from a well-known regression method, MARS. The comparison shows that our method outperforms MARS for nonlinear problems.20 page(s

Recent Decline of Irrigation‐Induced Cooling Effect Over the North China Plain in Observations and Model Simulations

Abstract Irrigation over the North China Plain (NCP) has been demonstrated to lower temperature by altering the surface energy budget. During past decades, the concurrence of irrigated area variation and reduced irrigation intensity prompted our investigation into whether there has been a temporal change in irrigation cooling effect over the NCP, which is largely unknown. Using historical observations in 1979–2018, we detect a shift in the cooling effect occurring around 1995, when the expansion of irrigated area was going to slow down and water‐conserving irrigation technology was boomingly introduced. After this time, the accelerated process of cooling effect (−0.0045°C year−1) switches to a decelerated one (0.0089°C year−1). Regional climate simulations also show a pronounced slowdown in irrigation‐induced cooling with the rate of 0.0081°C year−1. The irrigation‐induced cooling is expected to be weaker with the persistent reduction in agricultural water use and contribute to a more rapid warming

Evaluation of non-uniform groundwater level data using spatiotemporal modeling

Groundwater is one of the main sources of freshwater. To ensure its sustainability, it is important to know its current status and changing pattern over time, through the essential groundwater monitoring program conducted by water management planners, groundwater modelers and urban developers. However, uniformly distributed data is hardly available in most catchments. In this study, the Spatiotemporal Regression Kriging method (Rkriging) was adopted to derive a spatiotemporal pattern for Harvey River Catchment in Western Australia, using the limited groundwater data in the catchment. The accuracy of the estimation was investigated using the Leave-One-Out Cross-Validation approach. Time-series analysis (i.e., auto-correlation and cross-correlation) was then employed to provide a better understanding of the estimated groundwater level change (ΔGWL) over time. To gain insight into the change of groundwater levels, the correlation between groundwater level (GWL) and precipitation pattern with possible time-lag was explored. The results showed that the Rkriging method is satisfactory and the findings were consistent with the previously published results in literature in the area. The estimated decreasing GWL trend matched the precipitation pattern in the catchment. Such shallow groundwater levels in Harvey Catchment resulted in a short time-lag between the precipitation and GWL time-series. The proposed method should be applied to other catchments with limited groundwater data and can be a useful approach for catchments with irregular temporal and spatial data

Quantile regression without the curse of unsmoothness

We consider quantile regression models and investigate the induced smoothing method for obtaining the covariance matrix of the regression parameter estimates. We show that the difference between the smoothed and unsmoothed estimating functions in quantile regression is negligible. The detailed and simple computational algorithms for calculating the asymptotic covariance are provided. Intensive simulation studies indicate that the proposed method performs very well. We also illustrate the algorithm by analyzing the rainfall–runoff data from Murray Upland, Australia