Now showing 1 - 5 of 6
- ItemOpen AccessGREMLIN CONUS2 Dataset(Colorado State University. Libraries, 2022) Hilburn, KyleThe objective of this research is to develop techniques for assimilating GOES-R series observations in precipitating scenes for the purpose of improving short-term convective-scale forecasts of high-impact weather hazards. Whereas one approach is radiance assimilation, the information content of GOES-R radiances from its Advanced Baseline Imager saturates in precipitating scenes, and radiance assimilation does not make use of lightning observations from the GOES Lightning Mapper. Here, a convolutional neural network (CNN) is developed to transform GOES-R radiances and lightning into synthetic radar reflectivity fields to make use of existing radar assimilation techniques. We find that the ability of CNNs to utilize spatial context is essential for this application and offers breakthrough improvement in skill compared to traditional pixel-by-pixel based approaches. To understand the improved performance, we use a novel analysis method that combines several techniques, each providing different insights into the network’s reasoning. Channel-withholding experiments and spatial information–withholding experiments are used to show that the CNN achieves skill at high reflectivity values from the information content in radiance gradients and the presence of lightning. The attribution method, layerwise relevance propagation, demonstrates that the CNN uses radiance and lightning information synergistically, where lightning helps the CNN focus on which neighboring locations are most important. Synthetic inputs are used to quantify the sensitivity to radiance gradients, showing that sharper gradients produce a stronger response in predicted reflectivity. Lightning observations are found to be uniquely valuable for their ability to pinpoint locations of strong radar echoes.
- ItemOpen AccessMatlab code associated with manuscript "Lognormal and Mixed Gaussian–Lognormal Kalman Filters"(Colorado State University. Libraries, 2022) Fletcher, StevenIn this paper we shall present the derivation of two new forms of the Kalman filter equations; the first is for a pure lognormally distributed random variable, while the second set of Kalman filter equations will be for a combination of Gaussian and lognormally distributed random variables. We shall show that the appearance is similar to that of the Gaussian based equations, but that the analytical state is a multivariate median and not the mean. We shall show results of the mixed distribution Kalman filter with the Lorenz 1963 model with lognormal errors for the background and observations of the $z$ component, and compare them to results and forecasts from a traditional Gaussian based Kalman filter and show that under certain circumstances the new approach produces more accurate results.
- ItemOpen AccessData associated with "Constraining aerosol phase function using dual-view geostationary satellites"(Colorado State University. Libraries, 2021) Bian, Qijing; Kreidenweis, Sonia; Chiu, J. Christine; Miller, Steven D.; Xu, Xiaoguang; Wang, Jun; Kahn, Ralph A.; Limbacher, James A.; Remer, Lorraine A.; Levy, Robert C.Passive satellite observations play an important role in monitoring global aerosol properties and helping quantify aerosol radiative forcing in the climate system. The quality of aerosol retrievals from the satellite platform relies on well-calibrated radiance measurements from multiple spectral bands, and the availability of appropriate particle optical models. Inaccurate scattering phase function assumptions can introduce large retrieval errors. High-spatial resolution, dual-view observations from the Advanced Baseline Imagers (ABI) on board the two most recent Geostationary Operational Environmental Satellites (GOES), East and West, provide a unique opportunity to better constrain the aerosol phase function. Using dual GOES reflectance measurements for a dust event in the Gulf of Mexico in 2019, we demonstrate how a first-guess phase function can be reconstructed by considering the variations in observed scattering angle throughout the day. Using the reconstructed phase function, aerosol optical depth retrievals from the two satellites are self-consistent and agree well with surface-based optical depth estimates. We evaluate our methodology and reconstructed phase function against independent retrievals made from low-Earth-orbit multi-angle observations for a different dust event in 2020. Our new aerosol optical depth retrievals have a root-mean-square-difference of 0.019– 0.047. Furthermore, the retrievals between the two geostationary satellites for this case agree within about 0.059±0.072, as compared to larger discrepancies between the operational GOES products at times, which do not employ the dual-view technique.
- ItemOpen AccessDataset associated with "Detection of non-Gaussian behaviour using machine learning techniques"(Colorado State University. Libraries, 2019) Goodliff, MichaelAn important assumption made in most variational, ensemble and hybrid based data assimilation systems is that all minimised errors are Gaussian random variables. There has been theory developed at the Cooperative Institute for Research in the Atmosphere (CIRA) that enables for the Gaussian assumption for the different types of errors to be relaxed to a lognormally distributed random variable. While this is a first step towards using more consistent distributions to model the errors involved in numerical weather/ocean prediction, we still need to be able to identify when we need to assign a lognormal distribution in a mixed Gaussian-lognormal approach. In this paper, we present some machine learning techniques and experiments with the Lorenz 63 model. Using these machine learning techniques, we show detection of non-Gaussian distributions can be done using two methods; a support vector machine, and a neural network. This is done by training past data to classify 1) differences with the distribution statistics (means and modes) and 2) the skewness of the probability density function.
- ItemOpen AccessDataset associated with "A tale of two dust storms: analysis of a complex dust event in the Middle East"(Colorado State University. Libraries, 2019) Miller, StevenLofted mineral dust over data-sparse regions presents considerable challenges to satellite-based remote sensing methods and numerical weather prediction alike. The Southwest Asia domain is replete with such examples, with its diverse array of dust sources, dust mineralogy, and meteorologically-driven lofting mechanisms on multiple spatial and temporal scales. A microcosm of these challenges occurred over 3-4 August 2016 when two dust plumes, one lofted within an inland dry air mass and another embedded within a moist air mass, met over the Southern Arabian Peninsula. Whereas conventional infrared-based techniques readily detected the dry air mass dust plume, they experienced marked difficulties in detecting the moist air mass dust plume, which only became apparent when visible reflectance revealed it crossing over an adjacent dark water background. In combining information from numerical modelling, multi-satellite/multi-sensor observations of lofted dust and moisture profiles, and idealized radiative transfer simulations, we develop a better understanding of the environmental controls of this event, characterizing the sensitivity of infrared-based dust detection to column water vapor, dust vertical extent, and dust optical properties. Differences in assumptions of dust complex refractive index translate to variations in the sign and magnitude of the split-window brightness temperature difference commonly used for detecting mineral dust. A multi-sensor technique for mitigating the radiative masking effects of water vapor via modulation of the split-window dust-detection threshold, predicated on idealized simulations tied to these driving factors, is proposed and demonstrated. The new technique, indexed to an independent description of the surface-to-500 hPa atmospheric column moisture, reveals parts of the missing dust plume embedded in the moist air mass, with best performance over land surfaces.