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Land and atmospheric initial conditions of the Weather\udResearch and Forecasting (WRF) model are often interpolated from a different\udmodel output. We perform case studies during NASA's SEAC<sup>4</sup>RS and\udDISCOVER-AQ Houston airborne campaigns, demonstrating that using land\udinitial conditions directly downscaled from a coarser resolution dataset led\udto significant positive biases in the coupled NASA-Unified WRF (NUWRF,\udversion 7) surface and near-surface air temperature and planetary boundary layer\udheight (PBLH) around the Missouri Ozarks and Houston, Texas, as well as\udpoorly partitioned latent and sensible heat fluxes. Replacing land initial\udconditions with the output from a long-term offline Land Information System (LIS) simulation can effectively reduce the positive biases in NUWRF surface\udair temperature by ∼ 2 °C. We also show that the LIS\udland initialization can modify surface air temperature errors almost 10\udtimes as effectively as applying a different atmospheric initialization\udmethod. The LIS-NUWRF-based isoprene emission calculations by the Model of\udEmissions of Gases and Aerosols from Nature (MEGAN, version 2.1) are at\udleast 20 % lower than those computed using the coarser resolution\uddata-initialized NUWRF run, and are closer to aircraft-observation-derived\udemissions. Higher resolution MEGAN calculations are prone to amplified\uddiscrepancies with aircraft-observation-derived emissions on small scales.\udThis is possibly a result of some limitations of MEGAN's\udparameterization and uncertainty in its inputs on small scales, as well as the\udrepresentation error and the neglect of horizontal transport in deriving\udemissions from aircraft data. This study emphasizes the importance of proper\udland initialization to the coupled atmospheric weather modeling and the\udfollow-on emission modeling. We anticipate it to also be critical to\udaccurately representing other processes included in air quality modeling and\udchemical data assimilation. Having more confidence in the weather inputs is\udalso beneficial for determining and quantifying the other sources of\uduncertainties (e.g., parameterization, other input data) of the models that\udthey drive

geoscientific model development

Biogenic isoprene emissions driven by regional weather predictions using different initialization methods: case studies during the SEAC&amp;lt;sup&amp;gt;4&amp;lt;/sup&amp;gt;RS and DISCOVER-AQ airborne campaigns