Evaluating the Evolution of ECMWF Precipitation Products Using Observational Data for Iran: From ERA40 to ERA5

ECMWF Reanalysis (ERA), one of the most widely used precipitation products, has evolved over time from ERA-40 to ERA-20CM, ERA-20C, ERA-Interim, and ERA5. Studies evaluating the performance of individual ERA precipitation products cannot adequately assess the evolution in the products. Therefore, we compared the performance of five successive ERA precipitation products using data at daily, monthly, and annual scale (1980-2018) from more than 2100 precipitation gauges in Iran, and applied various statistical and categorical metrics and error decomposition methods. The results indicated that ERA-40 performed worst, followed by ERA-20CM, which showed only minor improvements over ERA-40. ERA-20C considerably outperformed its predecessors, benefiting from assimilation of observational data. Although several previous studies have reported full superiority of ERA5 over ERA-Interim, our results revealed several shortcomings compared with ERA-Interim, in ERA5 precipitation estimates for Iran. Both ERA-Interim and ERA5 performed best overall, with ERA-Interim showing better statistical and categorical skill scores, and ERA5 performing better in estimating extreme precipitations. These results suggest that the accuracy of ERA precipitation products improved from ERA-40 to ERA-Interim, but not consistently from ERA-Interim to ERA5. These findings are useful for model development at global scale and for hydrological applications in Iran.

Validation of Satellite-Based Precipitation Products from TRMM to GPM

The global precipitation measurement mission (GPM) has been in operation for seven years and continues to provide a vast quantity of global precipitation data at finer temporospatial resolutions with improved accuracy and coverage. GPM’s signature algorithm, the integrated multisatellite retrievals for GPM (IMERG) is a next-generation of precipitation product expected for wide variety of research and operational applications. This study evaluates the latest version (V06B) of IMERG and its predecessor, the tropical rainfall measuring mission (TRMM) multisatellite precipitation (TMPA) 3B42 (V7) using ground-based and gauge-corrected multiradar multisensor system (MRMS) precipitation products over the conterminous United States (CONUS). The spatial distributions of all products are analyzed. The error characteristics are further examined for 3B42 and IMERG in winter and summer by an error decomposition approach, which partitions total bias into hit bias, biases due to missed precipitation and false precipitation. The volumetric and categorical statistical metrics are used to quantitatively evaluate the performance of the two satellite-based products. All products show a similar precipitation climatology with some regional differences. The two satellite-based products perform better in the eastern CONUS than in the mountainous Western CONUS. The evaluation demonstrates the clear improvement in IMERG precipitation product in comparison with its predecessor 3B42, especially in reducing missed precipitation in winter and summer, and hit bias in winter, resulting in better performance in capturing lighter and heavier precipitation.

Exploring the relationship between Airbnb and traditional accommodation for regional variations of tourism markets

The relationship between Airbnb-based and traditional accommodation is mainly documented for key tourist destinations with a large tourism sector, while there is almost no evidence on this for other destinations. This article focuses on regional variations in the relationship between Airbnb-based and traditional accommodation across primary and secondary tourist destinations in Norway. Through an exploratory cluster analysis and a panel vector autoregressive (PVAR) model with forecast error decomposition of shocks (unobserved effects), it finds evidence of spillovers from Airbnb-based accommodation to traditional accommodation in secondary destinations. The demand for traditional accommodation is positively affected by Airbnb demand in the long run. Interestingly, a smaller effect is found with the supply-side of regional tourism markets in the Norwegian secondary tourist destinations. The growth of Airbnb may, thus, spur growth in the general tourism sector in such less frequented destinations.

Coefficient-based regularization network with variance loss for error

Loss function is the key element of a learning algorithm. Based on the regression learning algorithm with an offset, the coefficient-based regularization network with variance loss is proposed. The variance loss is different from the usual least quare loss, hinge loss and pinball loss, it induces a kind of samples cross empirical risk. Also, our coefficient-based regularization only relies on general kernel, i.e. the kernel is required to possess continuity, boundedness and satisfy some mild differentiability condition. These two characteristics bring essential difficulties to the theoretical analysis of this learning scheme. By the hypothesis space strategy and the error decomposition technique in [L. Shi, Learning theory estimates for coefficient-based regularized regression, Appl. Comput. Harmon. Anal. 34 (2013) 252–265], a capacity-dependent error analysis is completed, satisfactory error bound and learning rates are then derived under a very mild regularity condition on the regression function. Also, we find an effective way to deal with the learning problem with samples cross empirical risk.