Simulating the Effects of Land Surface Characteristics on Planetary Boundary Layer Parameters for a Modeled Landfalling Tropical Cyclone

This study examined whether varying moisture availability and roughness length for the land surface under a simulated Tropical Cyclone (TC) could affect its production of precipitation. The TC moved over the heterogeneous land surface of the southeastern U.S. in the control simulation, while the other simulations featured homogeneous land surfaces that were wet rough, wet smooth, dry rough, and dry smooth. Results suggest that the near-surface atmosphere was modified by the changes to the land surface, where the wet cases have higher latent and lower sensible heat flux values, and rough cases exhibit higher values of friction velocity. The analysis of areal-averaged rain rates and the area receiving low and high rain rates shows that simulations having a moist land surface produce higher rain rates and larger areas of low rain rates in the TC’s inner core. The dry and rough land surfaces produced a higher coverage of high rain rates in the outer regions. Key differences among the simulations happened as the TC core moved over land, while the outer rainbands produced more rain when moving over the coastline. These findings support the assertion that the modifications of the land surface can influence precipitation production within a landfalling TC.

Comparison on Land-Use/Land-Cover Indices in Explaining Land Surface Temperature Variations in the City of Beijing, China

The urban thermal environment is closely related to landscape patterns and land surface characteristics. Several studies have investigated the relationship between land surface characteristics and land surface temperature (LST). To explore the effects of the urban landscape on urban thermal environments, multiple land-use/land-cover (LULC) remote sensing-based indices have emerged. However, the function of the indices in better explaining LST in the heterogeneous urban landscape has not been fully addressed. This study aims to investigate the effect of remote-sensing-based LULC indices on LST, and to quantify the impact magnitude of green spaces on LST in the city built-up blocks. We used a random forest classifier algorithm to map LULC from the Gaofen 2 (GF-2) satellite and retrieved LST from Landsat-8 ETM data through the split-window algorithm. The pixel values of the LULC types and indices were extracted using the line transect approach. The multicollinearity effect was excluded before regression analysis. The vegetation index was found to have a strong negative relationship with LST, but a positive relationship with built-up indices was found in univariate analysis. The preferred indices, such as normalized difference impervious index (NDISI), dry built-up index (DBI), and bare soil index (BSI), predicted the LST (R2 = 0.41) in the multivariate analysis. The stepwise regression analysis adequately explained the LST (R2 = 0.44) due to the combined effect of the indices. The study results indicated that the LULC indices can be used to explain the LST of LULC types and provides useful information for urban managers and planners for the design of smart green cities.

Urban Transition and Its Impact on Mesoscale Weather

Urban transition is an unstoppable process. Globally, several planning measures are taken by the city and country administration to control the sprawling process. Despite all the planning, most of the cities experience appreciable impact of urbanization on the localized weather parameters. This chapter summarizes the understanding relating to urban modification of localized weather, that is, changes in precipitation, temperature, and wind speed in the form of increase or decrease, their spatio-temportal distribution, urban heat island (UHI), and urban wind island (UWI). The impacts of the urbanization are primarily because of changes in land-surface characteristics due to the alteration of land use in a city. The urbanization effects on local or mesoscale weather could be studied both through observations and/or numerical modeling. The purpose of this chapter is to provide a review of most of the relevant studies carried out globally and with a special emphasis on India.

Checking actual evapotranspiration model using remotely collected surface data: Case study

<p>Drought is a reoccurring worldwide natural hazard that affects not only food production but also economics, health, and infrastructure. It is also known that regional drought condition is sensitive to the fine particulate matters (PM) and has relationships with future changes in fine dust levels and associated health impacts under climate change. This mode is strongly correlated to evapotranspiration and land surface conditions and drought index might be good when the actual evapotranspiration and the land surface characteristics are implicitly included in the formula. The procedure for estimating actual evapotranspiration is complex and scientists often tend to select simple model that does not require intensive field data. As a preliminary study this study checks the possibility of PT-JPL which is relatively simple and requires minimum number of observations for estimating local actual evapotranspiration. The model has no calibration, tuning, or spin-up for local adjustment. The model was set up for five representative stations in East Asia. The satellite-collected normalized difference vegetation index (NDVI) and soil-adjusted vegetation index (SAVI) were used to describe the land surface characteristics. Meteorological information such as temperature, water vapor, radiation, and actual evapotranspiration was retrieved from AsiaFlux. The results show that the PT-JPL is promising for estimating local actual evapotranspiration. This study will extend to developing a drought index and its relationship to particulate matters (PM) in the near future.</p><p> </p><p><strong>Key words</strong>: Actual evapotranspiration, Particulate matters (PM), Drought, PT-JPL</p><p> </p><p><strong>Acknowledgement</strong></p><p>This work was supported by the National Research Foundation of Korea (NRF-2017-2017001809)</p>