Impact of Land Model Calibration on Coupled Land–Atmosphere Prediction

Abstract

Land-atmosphere (L-A) interactions play a critical role in determining the diurnal 2 evolution of both planetary boundary layer (PBL) and land surface heat and moisture budgets, as 3 well as controlling feedbacks with clouds and precipitation that lead to the persistence of dry and 4 wet regimes. Recent efforts to quantify the strength of L-A coupling in prediction models have 5 produced diagnostics that integrate across both the land and PBL components of the system. In 6 this study, we examine the impact of improved specification of land surface states, anomalies, what, how, and when to calibrate land surface models for coupled model prediction.

Land surface spinup for episodic modeling

“…For purposes of atmospheric modeling as opposed to hydrology, the important output of the LSM is not the soil mois- ture itself but the partitioning between sensible and latent heat fluxes (Santanello et al, 2013). Figure 7 shows the sensible heat flux from the Noah LSM at 14:00 UTC on the same days as Fig.…”

Integrated modeling of aerosol, cloud, precipitation and land processes at satellite-resolved scales

“…The advantages of coupling LIS and WRF include the ability to spin-up land surface conditions using multiple input forcings (e.g., NLDAS, The Modern-Era Retrospective Analysis for Research and Applications-Land product, MERRA-Land (Reichle et al, 2011;Reichle, 2012)) on a common grid from which to initialize the regional model. The framework also supports flexible and high-resolution, satellite-based soil and vegetation representation [e.g., real-time MODIS GVF (Case et al, 2014) as produced by the NASA Short-term Prediction Research and Transition Center (SPoRT; Jedlovec, 2013)], additional choices of LSMs, and various LIS plug-in options such as land data assimilation (Kumar et al, 2008b), parameter estimation (Santanello et al, 2007(Santanello et al, , 2013a, and uncertainty analysis (Harrison et al, 2012). Higher-resolution initial land surface conditions have been shown to improve prediction of cumulus convection associated with heterogenous distributions of land-surface fluxes and mesoscale circulations (Pielke, 2001).…”

“…To address refutability, we discuss several science results in the next section resulting from hypotheses that the NU-WRF system simulates observed atmospheric states better than the default WRF model (e.g., Zaitchik et al, 2013;Shi et al, 2014). We did not undertake formal uncertainty quantification, although some work along those lines is ongoing through the LIS Uncertainty Estimation subsystem (e.g., Harrison et al, 2012;Santanello et al, 2013a). These cases were all the subject of previous work using the standard WRF model, so the system tests focused on ensuring that the model behavior was better than and/or comparable to previous results.…”

“…Additionally, a measureable impact of the soil moisture state was demonstrated on the coupled near-surface, PBL, and warm-season precipitation predictions in NU-WRF over the Southeastern U.S., resulting in improvement over that using a default land initialization in WRF (Case et al, 2011). Likewise, the LoCo methodology has been used to quantify the impact of LSM calibration on coupled forecasts, and to show that predictions can be improved by combining mesoscale observations with the optimization subsystem within LIS (Santanello et al, 2013a).…”

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

“…The initialization of soil moisture and other landrelated data fields have also been shown to be important to the modeled atmospheric weather states (e.g., air temperature, humidity, winds, precipitation, and PBLH), as well as latent and sensible heat fluxes. Suitable and sufficient LSM spin-up as well as land data assimilation can benefit land surface modeling and the coupled atmospheric weather prediction (e.g., Rodell et al, 2005;Case et al, 2008Case et al, , 2011Zeng et al, 2014;Angevine et al, 2014;Collow et al, 2014;Lin and Cheng, 2015;Santanello et al, 2013Santanello et al, , 2016. However, the potential benefit of appropriate land initialization of numerical weather models to emission and air quality related studies needs to be better understood.…”