Fine-resolution population distribution mapping is necessary for many purposes, which cannot be met by aggregated census data due to privacy. Many approaches utilize ancillary data that are related to population density, such as nighttime light imagery and land use, to redistribute the population from census to finer-scale units. However, most of the ancillary data used in the previous studies of population modeling are environmental data, which can only provide a limited capacity to aid population redistribution. Social sensing data with geographic information, such as point-of-interest (POI), are emerging as a new type of ancillary data for urban studies. This study, as a nascent attempt, combined POI and multisensor remote sensing data into new ancillary data to aid population redistribution from census to grid cells at a resolution of 250 m in Zhejiang, China. The accuracy of the results was assessed by comparing them with WorldPop. Results showed that our approach redistributed the population with fewer errors than WorldPop, especially at the extremes of population density. The approach developed in this study—incorporating POI with multisensor remotely sensed data in redistributing the population onto finer-scale spatial units—possessed considerable potential in the era of big data, where a substantial volume of social sensing data is increasingly being collected and becoming available.

Abstract. Understanding aerosol effects on deep convective clouds and the derived effects on the radiation budget and rain patterns can largely contribute to estimations of climate uncertainties. The challenge is difficult in part because key microphysical processes in the mixed and cold phases are still not well understood. For deep convective clouds with a warm base, understanding aerosol effects on the warm processes is extremely important as they set the initial and boundary conditions for the cold processes. Therefore, the focus of this study is the warm phase, which can be better resolved. The main question is: "How do aerosol-derived changes in the warm phase affect the properties of deep convective cloud systems?" To explore this question, we used a weather research and forecasting (WRF) model with spectral bin microphysics to simulate a deep convective cloud system over the Marshall Islands during the Kwajalein Experiment (KWAJEX). The model results were validated against observations, showing similarities in the vertical profile of radar reflectivity and the surface rain rate. Simulations with larger aerosol loading resulted in a larger total cloud mass, a larger cloud fraction in the upper levels, and a larger frequency of strong updrafts and rain rates. Enlarged mass both below and above the zero temperature level (ZTL) contributed to the increase in cloud total mass (water and ice) in the polluted runs. Increased condensation efficiency of cloud droplets governed the gain in mass below the ZTL, while both enhanced condensational and depositional growth led to increased mass above it. The enhanced mass loading above the ZTL acted to reduce the cloud buoyancy, while the thermal buoyancy (driven by the enhanced latent heat release) increased in the polluted runs. The overall effect showed an increased upward transport (across the ZTL) of liquid water driven by both larger updrafts and larger droplet mobility.These aerosol effects were reflected in the larger ratio between the masses located above and below the ZTL in the polluted runs. When comparing the net mass flux crossing the ZTL in the clean and polluted runs, the difference was small. However, when comparing the upward and downward fluxes separately, the increase in aerosol concentration was seen to dramatically increase the fluxes in both directions, indicating the aerosol amplification effect of the convection and the affected cloud system properties, such as cloud fraction and rain rate.