Comparison of spatial interpolation methods for gridded bias removal in surface temperature forecasts

All numerical weather prediction (NWP) models inherently have substantial biases, especially in the forecast of near-surface weather variables. Statistical methods can be used to remove the systematic error based on historical bias data at observation stations. However, many end users of weather forecasts need bias corrected forecasts at locations that scarcely have any historical bias data. To circumvent this limitation, the bias of surface temperature forecasts on a regular grid covering Iran is removed, by using the information available at observation stations in the vicinity of any given grid point. To this end, the running mean error method is first used to correct the forecasts at observation stations, then four interpolation methods including inverse distance squared weighting with constant lapse rate (IDSW-CLR), Kriging with constant lapse rate (Kriging-CLR), gradient inverse distance squared with linear lapse rate (GIDS-LR), and gradient inverse distance squared with lapse rate determined by classification and regression tree (GIDS-CART), are employed to interpolate the bias corrected forecasts at neighboring observation stations to any given location. The results show that all four interpolation methods used do reduce the model error significantly, but Kriging-CLR has better performance than the other methods. For Kriging-CLR, root mean square error (RMSE) and mean absolute error (MAE) were decreased by 26% and 29%, respectively, as compared to the raw forecasts. It is found also, that after applying any of the proposed methods, unlike the raw forecasts, the bias corrected forecasts do not show spatial or temporal dependency.     

Optimal shape design of defense structures for minimizing short wave impact

The paper presents coastal structures design in the presence of waves by the minimization of a cost function. It aims to show that shape optimization can be efficiently applied to ocean engineering. This is an underlying guiding principle for the design of harbors or offshore breakwaters. We compute the solution of a specific simplified boundary value problem describing the short wave propagation toward a vertical sea cliff or vertical wall and modify accordingly the shape of defense structures in order to minimize a pre-defined cost function taking into account the strength (energy) of the water waves. The optimization procedure relies on a global semi-deterministic search algorithm able to escape from local minima.     

Design,manufacture and calibration of the SARI portable rainfall simulator for field and laboratory experiments

The Simulator of Artificial RaInfall (SARI) rainfall simulator (RS) is a newly designed, constructed and calibrated, portable, two-nozzle RS with low water consumption, accurate measurement, easy management and low cost. The raindrop size distribution and velocity and mean rainfall intensity were measured. The best rainfall spatial distribution was achieved with nozzles separated by 50, 60 and 70 cm, and with oscillation angles of 30, 45 and 60°, at a pressure of 60 kPa. The uniformity coefficient varied from 57 to 61% and rainfall intensity from 48 to 101 mm h^?1. The raindrop diameter varied from 0.2 to 9.9 mm. The raindrop velocity at the optimum pressure of 60 kPa, which was measured with high-speed photography, ranged from 1.1 to 7.1 m s^?1. Comparison with other RSs shows that the SARI simulator is a suitable apparatus to research soil erosion and runoff generation under laboratory and field conditions.     

Investigating extreme scenarios with multiple-point geostatistics and variance maximization

Stochastic Environmental Research and Risk Assessment - In many geoscience applications, the data extracted from environmental variables are very limited. Multiple-point geostatistical (MPS)...     

Investigation of meteorological extreme events over coastal regions of Iran

In this study, in order to detect probable trends and effects of climatic extreme events of precipitation and temperature as well as maximum relative humidity, dew point temperature, sunshine hours, and wind speed, 12 stations on the northern and southern coastlines of Iran were investigated from 1977 to 2007. For this purpose, 27 indices of precipitation and temperature, which are specified by the Expert Team of the World Meteorological Organization and Climate Variability and Predictability, were calculated by using RClimDex software. The Mann?CKendall method was also used to detect possible trends in the data time series. The results indicate that temperature indices are absolutely consistent with warming. Warm nights, hot days, and hot day and night frequencies increased, while cold spell and cool day and night frequencies declined. The minimum temperature experienced a considerable rise both in its maximum and minimum values. The minimum temperature had a higher increase than the maximum temperature. Therefore, diurnal temperature ranges have experienced dramatic declines. In the northern coastal sites, hot day frequency and hottest day temperature showed higher magnitudes than those of the southern sites as a result of the significant increase in the maximum sunshine hours in northern stations. This enhancement led to a considerable increase in the maximum wind speed. Consequently, relative humidity declined in the northern sites. Precipitation indices indicate few significant trends over the studied period. Temporal precipitation distribution was different from station to station. Three precipitation patterns were detected at individual stations, although an overall regional rainfall pattern was not detectable. On the whole, the results of this study emphasize that the water resources in the studied area are going to become problematic.     

Regularization of geophysical ill-posed problems by iteratively re-weighted and refined least squares

The iteratively re-weighted least squares (IRLS) is a commonly used algorithm which has received significant attention in geophysics and other fields of scientific computing for regularization of discrete ill-posed problems. The IRLS replaces a difficult optimization problem by a sequence of weighted linear systems. The optimum solution of the original problem is usually determined by computing the solution for various regularization parameters λ, each needing several re-weighted iterations (usually 10–15). In this paper, in order to decrease the required computation time (iterations) while maintaining good properties of the algorithm such as edge-preserving, the IRLS is augmented with a refinement strategy and the value of λ is progressively updated in a geometrical form during the iterations. The new algorithm, called iteratively re-weighted and refined least squares (IRRLS), can be interpreted as a Landweber iteration with a non-stationary shaping matrix which is updated based on the solution obtained from previous iteration. Two main properties of IRRLS are (1) the regularization parameter is the stopping iteration and (2) it is equipped with a tuning parameter which makes it flexible for recovering models with different smoothness. We show numerically that both the residual and regularization norms are monotone functions of iteration and hence well behaved for automatic determination of stopping parameter. The Stain’s unbiased risk estimate (SURE), generalized cross validation (GCV), L-curve analysis, and discrepancy principle (DCP) techniques are employed for automatic determination of optimum iteration. Experimental results from seismic deconvolution and seismic tomography are included showing that the proposed methodology outperforms the conventional IRLS with significantly lower computational burden.     

Database extension for digital soil mapping using artificial neural networks

Cost and time are the two most important factors conditioning soil surveys. Since these surveys provide basic information for modelling and management activities, new methods are needed to speed the soil-mapping process with limited input data. In this study, the polypedon concept was used to extend the spatial representation of sampled pedons (point data) in order to train artificial neural networks (ANNs) for digital soil mapping (DSM). The input database contained 97 soil profiles belonging to 7 different soil series and 15 digital elevation model (DEM) attributes. Pedons were represented in raster format as one-cell areas. The corresponding polypedons were then spatially represented by neighbouring raster cells (e.g. 2 × 2, … up to 6 × 6 cells). The primary database contained 97 pedons (97 cells) that were extended up to 3492 cells (in the case of 6 × 6-cell regions). This approach employed test and validation areas to calculate the respective accuracies of data interpolation and extrapolation. The results showed increased accuracies in training and interpolation (test area) but a poor level of accuracy in the extrapolation process (validation area). However, the overall precision of all predictions increased considerably. Using only topographic attributes for extrapolation was not sufficient to obtain an accurate soil map. To improve prediction, other soil-forming factors, such as landforms and/or geology, should also be considered as input data in the ANN. The proposed method could help to improve existing soil maps by using DSM results in areas with limited soil data and to save time and money in soil survey work.     

Modeling the effects of controlled drainage at a watershed scale using SWATDRAIN

The recently developed SWATDRAIN model was employed to assess the impact of controlled drainage on the water table dynamics, subsurface drainage, and surface runoff in an agricultural watershed in Ontario, Canada. Controlled drainage was defined with a depth of 1.0 m to restrict flow at the drain outlet to maintain the water table at 0.5 m below the surface level during the winter (November–April) and at 0.6 m during the summer (June–August) months. The effects of the absence, or implementation, of drainage water management were predicted for the 3-year period of 1991–1993. Implementing controlled drainage resulted in a 16 % reduction in the mean annual drain flow, while increasing surface runoff by as much as 71 %. This indicates that overall watershed hydrology could be significantly impacted by the implementation of controlled drainage. This research demonstrates the SWATDRAIN model’s ability to predict the controlled drainage in small agricultural watersheds.