| Land surface-atmospheric interactions influence climate and weather varying spatial scales from local to mesoscale, and even to global. This dissertation deals with several topics: (1) evaluation of various sources of incoming solar radiations, (2) evaluation of land surface process descriptions in the land surface models in both basin-scale and point scale offline model simulations, and (3) inverse estimation of radiation components using net radiation and other meteorological variables.; Incoming solar radiations from various sources were evaluated. This study identified the two sources of errors in the North American Data Assimilation system (NLDAS) solar radiation: One is related to bias inherited from the ETA Data Assimilation System (EDAS) during 2001 and 2003, and the other is software error at NESDIS operational system during 2002.; Land surface processes are treated quite differently in the land surface models used in this study. Over the state of Oklahoma, Common Land Model 2.1 (CLM2.1) estimates more evaporation but less transpiration than the Variable Infiltration Capacity (VIC3L) model. This is due to the difference in the runoff algorithm, which results in more infiltration down to the soil layer and then providing more available water to plant roots in VIC3L. CLM2.1 overestimates ground heat flux in Point scale simulation. CoLM, which employs two stream radiative transfer scheme, shows better agreements to adjusted ground observations (using Bowen-ration closure method) in offline simulations than CLM2.1. CoLM, in addition, shows various model behaviors depending on vegetation cover types.; Inverse radiation estimation methods were developed and evaluated at four AmeriFlux sites. Analysis of observed radiations showed a triangle shape relationship among net radiation, net solar radiation and cloud factor (defined in this study). Clear-sky downward longwave radiation is needed to be calibrated for each site. SCE-UA method was used to calibrate an empirical estimation method for the clear-sky downward longwave radiation ( L↓sky ). The results were very successful. Among four methods that are suggested in this study, Linear Cloud Factor Method (LCFM) is most successful in estimating the clear-sky downward longwave radiation ( L↓cloud ). |
