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.

The World Urban Database and Access Portal Tools (WUDAPT) is a community initiative to collect worldwide data on urban form (i.e., morphology, materials) and function (i.e., use and metabolism). This is achieved through crowdsourcing, which we define here as the collection of data by a bounded crowd, composed of students. In this process, training data for the classification of urban structures into Local Climate Zones (LCZ) are obtained, which are, like most volunteered geographic information initiatives, of unknown quality. In this study, we investigated the quality of 94 crowdsourced training datasets for ten cities, generated by 119 students from six universities. The results showed large discrepancies and the resulting LCZ maps were mostly of poor to moderate quality. This was due to general difficulties in the human interpretation of the (urban) landscape and in the understanding of the LCZ scheme. However, the quality of the LCZ maps improved with the number of training data revisions. As evidence for the wisdom of the crowd, improvements of up to 20% in overall accuracy were found when multiple training datasets were used together to create a single LCZ map. This improvement was greatest for small training datasets, saturating at about ten to fifteen sets.

High Altitude Wetlands of the Andes (HAWA) belong to a unique type of wetland within the semi-arid high Andean region. Knowledge about HAWA has been derived mainly from studies at single sites within different parts of the Andes at only small time scales. On the one hand, HAWA depend on water provided by glacier streams, snow melt or precipitation. On the other hand, they are suspected to influence hydrology through water retention and vegetation growth altering stream flow velocity. We derived HAWA land cover from satellite data at regional scale and analysed changes in connection with precipitation over the last decade. Perennial and temporal HAWA subtypes can be distinguished by seasonal changes of photosynthetically active vegetation (PAV) indicating the perennial or temporal availability of water during the year. HAWA have been delineated within a region of 12 800 km² situated in the Northwest of Lake Titicaca. The multi-temporal classification method used Normalized Differenced Vegetation Index (NDVI) and Normalized Differenced Infrared Index (NDII) data derived from two Landsat ETM+ scenes at the end of austral winter (September 2000) and at the end of austral summer (May 2001). The mapping result indicates an unexpected high abundance of HAWA covering about 800 km² of the study region (6 %). Annual HAWA mapping was computed using NDVI 16-day composites of Moderate Resolution Imaging Spectroradiometer (MODIS). Analyses on the relation between HAWA and precipitation was based on monthly precipitation data of the Tropical Rain Measurement Mission (TRMM 3B43) and MODIS Eight Day Maximum Snow Extent data (MOD10A2) from 2000 to 2010. We found HAWA subtype specific dependencies on precipitation conditions. A strong relation exists between perennial HAWA and snow fall (r²: 0.82) in dry austral winter months (June to August) and between temporal HAWA and precipitation (r²: 0.75) during austral summer (March to May). Annual changes in spatial extend of perennial HAWA indicate alterations in annual water supply generated from snow melt.

Urban green infrastructures have been extensively studied for their ability to mitigate the urban heat island (UHI) effect. However, allotment gardens (AGs)—a prominent type of urban green infrastructure within many European cities—have not yet been comprehensively investigated concerning their microclimates. In this study, nocturnal air temperatures ( $T_N$ ) in 13 AG complexes (AGCs) were measured during the summer of 2018 in Berlin, Germany. These were compared to measurements in densely built-up urban areas (URB), two large inner-city parks and rural areas (RUR). On average, the assessed AGCs were 2.7 K cooler at night than URB. Most of the investigated AGCs (11/13) displayed a larger mean $T_N$ difference to URB ( $\overline{\Delta T_{N AGC} }$ ) than the examined urban parks. RUR showed the largest differences to URB ( $\overline{\Delta T_{N RUR}}$ ), indicating a UHI effect. Furthermore, the influence of land surface characteristics of the AGCs on $\overline{\Delta T_{N AGC}}$ was analyzed. $\overline{\Delta T_{N AGC}}$ decreased significantly as the floor space index around AGCs increased. The analysis of the shape complexity also produced a significant positive correlation with $\overline{\Delta T_{N AGC}}$ . In contrast, size and distance to the city center of an AGC decreased significantly with increasing $\overline{\Delta T_{N AGC}}$ . This study provides first insights into the microclimate of AGs and influencing variables concerning $T_N$ .

Landslide is a major natural hazard in Kyrgyzstan and Tajikistan. Knowledge about atmospheric triggering conditions and climatic disposition of landslides in Kyrgyzstan and Tajikistan is limited even though this topic has already been investigated thoroughly in other parts of the world. In this study, the newly developed, high-resolution High Asia Refined analysis version 2 (HAR v2) data set generated by dynamical downscaling was combined with historical landslide inventories to analyze the atmospheric conditions that initialized landslides in Kyrgyzstan and Tajikistan. The results indicate the crucial role of snowmelt in landslide-triggering processes since it contributes to the initialization of 40 % of landslide events. Objective thresholds for rainfall, snowmelt, and the sum of rainfall and snowmelt (rainfall + snowmelt) were defined. Thresholds defined by rainfall + snowmelt have the best predictive performance. Mean intensity, peak intensity, and the accumulated amount of rainfall + snowmelt events show similar predictive performance. Using the entire period of rainfall + snowmelt events results in better predictive performance than just considering the period up to landslide occurrence. Mean annual exceedance maps were derived from defined regional thresholds for rainfall + snowmelt. Mean annual exceedance maps depict climatic disposition and have added value in landslide susceptibility mapping. The results reported in this study highlight the potential of dynamical downscaling products generated by regional climate models in landslide prediction.