Reliable quantification of urban heat island (UHI) can contribute to the effective evaluation of potential heat risk. Traditional methods for the quantification of UHI intensity (UHII) using pairs-measurements are sensitive to the choice of stations or grids. In order to get rid of the limitation of urban/rural divisions, this paper proposes a new approach to quantify surface UHII (SUHII) using the relationship between MODIS land surface temperature (LST) and impervious surface areas (ISA). Given the footprint of LST measurement, the ISA was regionalized to include the information of neighborhood pixels using a Kernel Density Estimation (KDE) method. Considering the footprint improves the LST-ISA relationship. The LST shows highly positive correlation with the KDE regionalized ISA (ISAKDE). The linear functions of LST are well fitted by the ISAKDE in both annual and daily scales for the city of Berlin. The slope of the linear function represents the increase in LST from the natural surface in rural regions to the impervious surface in urban regions, and is defined as SUHII in this study. The calculated SUHII show high values in summer and during the day than in winter and at night. The new method is also verified using finer resolution Landset data, and the results further prove its reliability.

The High Asia Refined analysis (HAR) is a regional atmospheric data set generated by dynamical downscaling of the Final operational global analysis (FNL) using the Weather Research and Forecasting (WRF) model. It has been successfully and widely utilized. A new version (HAR v2) with longer temporal coverage and extended domains is currently under development. ERA5 reanalysis data is used as forcing data. This study aims to find the optimal set‐up for the production of the HAR v2 to provide similar or even better accuracy as the HAR. First, we conducted a sensitivity study, in which different cumulus, microphysics, planetary boundary layer, and land surface model schemes were compared and validated against in situ observations. The technique for order preference by similarity to the ideal solution (TOPSIS) method was applied to identify the best schemes. Snow depth in ERA5 is overestimated in High Mountain Asia (HMA) and causes a cold bias in the WRF output. Therefore, we used Japanese 55‐year Reanalysis (JRA‐55) to correct snow depth initialized from ERA5 based on the linear scaling approach. After applying the best schemes identified by the TOPSIS method and correcting the initial snow depth, the model performance improves. Finally, we applied the improved set‐up for the HAR v2 and computed a one‐year run for 2011. Compared to the HAR, the HAR v2 has a better representation of air temperature at 2 m. It produces slightly higher precipitation amounts, but the spatial distribution of seasonal mean precipitation is closer to observations.

Precipitation is a central quantity of hydrometeorological research and applications. Especially in complex terrain, such as in High Mountain Asia (HMA), surface precipitation observations are scarce. Gridded precipitation products are one way to overcome the limitations of ground truth observations. They can provide datasets continuous in both space and time. However, there are many products available, which use various methods for data generation and lead to different precipitation values. In our study we compare nine different gridded precipitation products from different origins (ERA5, ERA5-Land, ERA-interim, HAR v2 10 km, HAR v2 2 km, JRA-55, MERRA-2, GPCC and PRETIP) over a subregion of the Central Himalaya and the Southwest Tibetan Plateau, from May to September 2017. Total spatially averaged precipitation over the study period ranged from 411 mm (GPCC) to 781 mm (ERA-Interim) with a mean value of 623 mm and a standard deviation of 132 mm. We found that the gridded products and the few observations, with few exceptions, are consistent among each other regarding precipitation variability and rough amount within the study area. It became obvious that higher grid resolution can resolve extreme precipitation much better, leading to overall lower mean precipitation spatially, but higher extreme precipitation events. We also found that generally high terrain complexity leads to larger differences in the amount of precipitation between products. Due to the considerable differences between products in space and time, we suggest carefully selecting the product used as input for any research application based on the type of application and specific research question. While coarse products such as ERA-Interim or ERA5 that cover long periods but have coarse grid resolution have previously shown to be able to capture long-term trends and help with identifying climate change features, this study suggests that more regional applications, such as glacier mass-balance modeling, require higher spatial resolution, as is reproduced, for example, in HAR v2 10 km.