[1] The numerical weather models (NWMs) developed by the meteorological community are able to provide accurate analyses of the current state of the atmosphere in addition to the predictions of the future state. To date, most attempts to apply the NWMs to estimate the refractivity of the atmosphere at the time of satellite synthetic aperture radar (SAR) data acquisitions have relied on predictive models. We test the hypothesis that performing a final assimilative routine, ingesting all available meteorological observations for the times of SAR acquisitions, and generating customized analyses of the atmosphere at those times will better mitigate atmospheric artifacts in differential interferograms. We find that, for our study area around Mount St. Helens (Amboy, Washington, USA), this approach is unable to model the refractive changes and provides no mean benefit for interferogram analysis. The performance is improved slightly by ingesting atmospheric delay estimates derived from the limited local GPS network; however, the addition of water vapor products from the GOES satellites reduces the quality of the corrections. We interpret our results to indicate that, even with this advanced approach, NWMs are not a reliable mitigation technique for regions such as Mount St. Helens with highly variable moisture fields and complex topography and atmospheric dynamics. It is possible, however, that the addition of more spatially dense meteorological data to constrain the analyses might significantly improve the performance of weather modeling of atmospheric artifacts in satellite radar interferograms.
Numerical weather prediction (NWP) models are now capable of operating at horizontal resolutions in the 100-m to 1-km range, a grid spacing similar in scale to that of the turbulent eddies present in the atmospheric convective boundary layer (CBL). Known as the 'grey zone' of turbulence, this regime is characterized by significant contributions from both the resolved and subgrid components to represent the dominant motions of the system. This study examines how the initiation of resolved turbulence-a concept commonly referred to as 'spin-up'-can be delayed during the evolution of a simulated CBL in the grey zone. We identify the importance of imposed pseudo-random perturbations of potential temperature (θ) for the development of the resolved fields showing that without such perturbations, resolved turbulence does not become established at all. When the perturbations are organized, spin-up can develop more rapidly, and we find that the earliest spin-up times can be achieved by applying an idealized profile of variance to derive the θ perturbation values. The perturbation structures are shown to be most effective when applied at intervals following the mixed-layer time scale, t * , rather than perturbing only at the initial time. We also propose a modification to the three-dimensional Smagorinsky turbulence closure, in which the Smagorinsky constant is replaced by a scale-dependent coefficient. Both the approaches of: (1) applying structured θ perturbations, and (2) using a dynamically-evolving Smagorinsky coefficient are shown to encourage faster spin-up independently of each other, but the best results clearly emerge when the two methods are applied concurrently.
[1] A study of ship-based precipitable water (PW) estimation using the global positioning system (GPS) is presented for a field experiment in the coastal waters of Hawai'i. GPS precipitable water estimates, with a temporal resolution of 30 min, are shown to agree with radiosonde observations with an RMS error of 2.16 mm. The GPS PW time series is shown to possess significant value in identifying atmospheric features at both synoptic and mesoscale resolution throughout the experiment. Examples include influences from an upper level low-pressure system, shear lines, island blocking, and zones of low level convergence. Given PW's high spatial and temporal variability and the fact that it is not dynamically tied to other variables such as pressure or temperature, future PW estimates from ships could provide an important constraint for numerical weather prediction (NWP) models over ocean regions.
Abstract. Novel methods of cloud detection are applied to airborne remote sensing observations from the unique Fennec aircraft dataset, to evaluate the Met Office-derived products on cloud properties over the Sahara based on the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) on-board the Meteosat Second Generation (MSG) satellite. Two cloud mask configurations are considered, as well as the retrievals of cloud-top height (CTH), and these products are compared to airborne cloud remote sensing products acquired during the Fennec campaign in June 2011 and June 2012. Most detected clouds (67 % of the total) have a horizontal extent that is smaller than a SEVIRI pixel (3 km × 3 km). We show that, when partially cloud-contaminated pixels are included, a match between the SEVIRI and aircraft datasets is found in 80 ± 8 % of the pixels. Moreover, under clear skies the datasets are shown to agree for more than 90 % of the pixels. The mean cloud field, derived from the satellite cloud mask acquired during the Fennec flights, shows that areas of high surface albedo and orography are preferred sites for Saharan cloud cover, consistent with published theories. Cloud-top height retrievals however show large discrepancies over the region, which are ascribed to limiting factors such as the cloud horizontal extent, the derived effective cloud amount, and the absorption by mineral dust. The results of the CTH analysis presented here may also have further-reaching implications for the techniques employed by other satellite applications facilities across the world.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.