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This study represents a pioneering effort to establish a downscaling framework named "land-surfacephysics-based downscaling" (LSP-DS) for agricultural meteorological prediction in the tropical-monsoon region of the Red River Delta, Vietnam. The primary focus of this article is to evaluate the performance of LSP-DS on meteorological variables, specifically temperature and humidity. In details, LSP-DS, which is based on the NCAR's Noah Multi-Parameterizations land surface model, incorporated by recently developed land use/cover data for Vietnam released by JAXA, is forced by ERA5 data for 2013; and the results are compared with ground-based station observations. The results exhibit excellent performance of LSP-DS versus observations with consistently high correlation coefficient between the two, highlighting the high potential of using LSP-DS for real-time operational forecast. The LSP-DS performance varies with different sub land use/cover categories, implying that the proper parameter settings could be key point for improvement. The findings of this research underscore the dual strengths of the LSP-DS approach: its computational efficiency and its remarkable efficacy in predicting spatial heterogeneity of local climates. These attributes render it well-suited for agrometeorological forecasting in a tropical monsoon climate, exemplified by the Red River Delta in Vietnam. [ABSTRACT FROM AUTHOR]

Dramatic increases in climate data underlie a gradual paradigm shift in knowledge-acquisition methods from physical-based models to data-based mining techniques. k-Means is one of the most popular data clustering/mining techniques, and it has been used to detect hidden patterns in climate systems. k-Means is established based on distance metrics for pattern recognition, which is relatively ineffective when dealing with "structured" data that are dominant in climate science, that is, data in time and space domains. Here, we propose (i) a novel structural similarity recognition-based k-means algorithm called structural k-means or S k-means for climate data mining and (ii) a new clustering uncertainty representation / evaluation framework based on the information entropy concept. We demonstrated that the novel S k-means could provide higher-quality clustering outcomes in terms of general silhouette analysis, although it requires higher computational resources compared with conventional algorithms. The results are consistent with different demonstration problem settings using different types of input data, including two-dimensional weather patterns, historical climate change in terms of time series, and tropical cyclone paths. Additionally, by quantifying the uncertainty underlying the clustering outcomes we for the first time evaluated the "meaningfulness" of applying a given clustering algorithm for a given dataset. We expect that this study will constitute a new standard of k-means clustering with "structural" input data, as well as a new framework for uncertainty representation/evaluation of clustering algorithms for (but not limited to) climate science. [ABSTRACT FROM AUTHOR]

In this study, we propose a novel structural self-organizing map (S-SOM) algorithm for synoptic weather typing. A novel feature of the S-SOM compared with traditional SOMs is its ability to deal with input data that have spatial or temporal structures. In detail, the search scheme for the best matching unit (BMU) in a S-SOM is built based upon a structural similarity (S-SIM) index rather than by using the traditional Euclidean distance (ED). S-SIM enables the BMU search to consider the correlation in space between weather states, such as the location of highs of lows, that is impossible when using ED. The S-SOM performance is evaluated by multiple demo simulations of clustering weather patterns over Japan using the ERA-Interim sea-level pressure data. The results show the superiority of the S-SOM compared with a standard SOM with ED (or ED-SOM) in two respects: clustering quality based on silhouette analysis and topological preservation based on topological error analysis. The superior performance of the S-SOM compared with an ED-SOM is probably independent of both the input data and SOM configuration. [ABSTRACT FROM AUTHOR]

Urban development exacerbates urban heat island effects of cities and thermal discomfort of the residents worldwide. However, such urban effects vary with the geographical and climatic conditions of the respective cities. This study is the first to quantitatively estimate the impact of past (1878), present (2012), and future (2050) urbanization on surface air temperatures and human thermal comfort in Sofia, the largest city of Bulgaria. We used the state-of-the-art Weather Research and Forecasting (WRF) climate model, with a 1-km horizontal resolution for three cases: LU1878, LU2012, and LU2050. We first validated the results of the control simulation (case LU2012) against actual observations. The model satisfactorily reproduced the diurnal variation of the observed monthly mean surface air temperatures for July (2011-2013) with a mean bias of ±0.6°C. We then estimated the potential impacts of past and future urbanization on surface temperature. Our results suggest that because of urbanization of Sofia, the surface air temperature at 0600 Local Standard Time (LST) has increased approximately 4.0°C in the past (1878-2012) and approximately 0.4°C in future (2012-2050). We also evaluated the impact of the urban thermal environment on human comfort levels. Considering the LU2050 case in the central part of Sofia, the temperature-humidity index during mid-afternoon (1400-1700 LST) is estimated to be 24-26, indicating that approximately 50% of the residents could feel perceptibly uncomfortable. [ABSTRACT FROM AUTHOR]