![]() This assessment of OMI tropospheric NO2 columns, together with the comparison of OMI-retrieved and model-simulated NO2columns, could offer diagnostic evaluation of the model. Monthly mean vertical NO2 profile shapes from the Global Modeling Initiative (GMI) chemistry-transport model (CTM) used in the OMI retrievals are highly consistent with in situ aircraft measurements, but these measured profiles exhibit considerable day-to-day variation, affecting the retrieved daily NO2 columns by up to 40%. Contrasting results between different data sets reveal complexities behind NO2 validation. Overall, OMI retrievals tend to be lower in urban regions and higher in remote areas, but generally agree with other measurements to within ± 20%. Despite considerable sampling differences, NO2 vertical column densities from OMI are modestly correlated (r = 0.3–0.8) with in situ measurements of tropospheric NO2 from aircraft, ground-based observations of NO2 columns from MAX-DOAS and Pandora instruments, in situ surface NO2measurements from photolytic converter instruments, and a bottom-up NOx emission inventory. We assess the standard operational nitrogen dioxide (NO2) data product (OMNO2, version 2.1) retrieved from the Ozone Monitoring Instrument (OMI) onboard NASA’s Aura satellite using a combination of aircraft and surface in situ measurements as well as ground-based column measurements at several locations and a bottom-up NOx emission inventory over the continental US. Future applications include imaging the current systems surrounding auroral arcs in order to distinguish physical mechanisms. The algorithm is tested using synthetic and real data and appears surprisingly robust at estimating the divergence of the field. There exists a unique solution when the average measurement error is smaller than the average measurement amplitude. ![]() The error on the reconstructed image is estimated by mapping the mathematical form to a Bayesian estimate and observing that the Lagrangian method determines an effective a priori covariance matrix from a user-defined regularization metric. The algorithm implements the Lagrange method of undetermined multipliers to regularize the underdetermined problem posed by the radar measurements. The imaged region covers about 4° in magnetic latitude and 8° in magnetic longitude for the specific geometry considered (that of the Poker Flat ISR). The first independent test with a set of RADARSAT-2 and Envisat images from the 2010 Impact of Typhoons on the Ocean in the Pacific (ITOP) experiment reveals a few weaknesses but essentially confirms the feasibility of the concept.Īn algorithm has been developed to image the local structure in the convection electric field using multibeam incoherent scatter radar (ISR) data. We explain how the algorithm works and how the empirical SWH model function has been determined from a set of hurricane images from RADARSAT-1 and reference wave spectra from a numerical wave model. This way, it is possible to obtain SWH estimates for the entire image and to account for the contributions of subresolution-scale waves. We think we have found a promising new technique for wave parameter retrievals from C-band ScanSAR images, which determines peak wavelengths and directions from image spectra where possible but uses an empirically determined relation to estimate significant wave heights (SWHs) from local mean image intensities, which is similar to the method used for wind retrievals. The interpretation of wave patterns that exist in an image is difficult because of the imaging mechanism’s nonlinearities. While C-band ScanSAR images have been demonstrated to be well suitable for wind retrievals, ocean wave retrievals are a more challenging problem: Because of the limited spatial resolution of 100 m (RADARSAT)/150 m (Envisat), only long waves can get imaged directly, and many images of tropical storm scenarios do not exhibit clear signatures of any waves in large areas. Because of their large swath widths of about 400-500 km, the ScanSAR modes of RADARSAT-1 and -2 and of the Advanced SAR (ASAR) system on Envisat have been the preferred modes of operation for hurricane and typhoon observations and similar applications.
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