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Land surface models (LSMs) are useful for estimating land surface states and fluxes such as snow water equivalent, soil moisture content, vegetation, and river discharge. Although estimates are continuous in time and space, LSMs are flawed since they lack comprehensive representation of process physics and are dependent on uncertain boundary conditions. One way to improve LSM performance is by conditioning model states on space-borne retrievals using an ensemble Kalman filter framework. In this study, the Noah-MP version 4.0.1 LSM without conditioning (a.k.a., Open Loop; OL) is compared against the same model with conditioning (a.k.a., Data Assimilation; DA). Two different univariate DA experiments are conducted: 1) assimilation using terrestrial water storage (TWS) anomalies from GRACE / GRACE-FO, and 2) assimilation using leaf area index (LAI) retrievals from MODIS. Not only do the experiments assimilate different types of retrievals (i.e., TWS versus LAI), but also, they assimilate products of different spatial (~3° versus ~0.005°) and temporal (~monthly versus ~weekly) resolutions. Experiments are conducted across different watersheds in North America with a particular focus on basins with irrigated agriculture. Modeled states and fluxes from the OL and DA are then compared against independent, ground-based measurement networks including U.S. SNOTEL, Canadian CanSWE product, U.S. SCAN for soil moisture, and USGS measurement gauges for river discharge. Statistical analyses, including bias, RMSE, and normalized information content (NIC) are computed to quantify the marginal improvements via each assimilation experiment. Results provide a basis to better understand the coupling between different state variables (i.e., snow mass, soil moisture, and groundwater) as well as the utility of using coarse-scale and fine-scale retrievals in land data assimilation.

PurposeTo determine the two-year results of small incision lenticule extraction (SMILE) for correcting post-keratoplasty myopia and myopic astigmatism.MethodsIn this case-series study, 10 eyes of 10 patients with a 6-10-year history of successful deep lamellar keratoplasty (DALK) underwent SMILE using VisuMax laser platform. Ophthalmologic examinations and visual acuity and refraction measurement were done pre- and 1, 3, 6, 12, and 24 months postoperatively. Pentacam and Sirius imaging was done in the first and last follow-up session.ResultsThe mean age of the patients was 39.60±7.86 years. Six subjects were male. After two years, uncorrected distance visual acuity did not change in one eye and improved 1-6 lines in other eyes. Corrected distance visual acuity decreased in one eye, did not change in four eyes, and improved 1-8 lines in other eyes. The mean decrease of spherical equivalent, spherical error, and cylinder power was 1.92±1.96 diopter (D) (P=0.013), 0.70±3.05D (P=0.213), and 2.42±2.91D (P=0.024), respectively. The vector mean target induced astigmatism, surgical induced astigmatism, and difference vector was 1.30D@44˚, 1.11D@24˚, and 0.86D@73˚, respectively. Two years after surgery, vertical coma, horizontal coma, and spherical aberration increased by 0.44±0.51, 0.23±0.32, and 0.02±0.16µm respectively (all P>0.05) while trefoil reduced by 0.29±0.75µm (P=0.428).ConclusionSMILE is a successful procedure for reducing refraction and astigmatism after DALK in patients with moderate myopia and moderate to severe astigmatism and improves the visual acuity in these patients. Axis rotation during surgery may result in under-correction of astigmatism. Refinement of SMILE treatment nomogram for post-DALK cases seems necessary.