Browsing UA Faculty Research by Subjects
Now showing items 1-3 of 3
Algorithm for Improved QPE over Complex Terrain Using Cloud-to-Ground Lightning OccurrencesLightning and deep convective precipitation have long been studied as closely linked variables, the former being viewed as a proxy, or estimator, of the latter. However, to date, no single methodology or algorithm exists for estimating lightning-derived precipitation in a gridded form. This paper, the third in a series, details the specific algorithm where convective rainfall was estimated with cloud-to-ground lightning occurrences from the U.S. National Lightning Detection Network (NLDN), for the North American Monsoon region. Specifically, the authors present the methodology employed in their previous studies to get this estimation, noise test, spatial and temporal neighbors and the algorithm of the Kalman filter for dynamically derived precipitation from lightning.
Eigenvector-spatial localisationWe present a new multiscale covariance localisation method for ensemble data assimilation that is based on the estimation of eigenvectors and subsequent projections, together with traditional spatial localisation applied with a range of localisation lengths. In short, we estimate the leading, large-scale eigenvectors from the sample covariance matrix obtained by spatially smoothing the ensemble (treating small scales as noise) and then localise the resulting sample covariances with a large length scale. After removing the projection of each ensemble member onto the leading eigenvectors, the process may be repeated using less smoothing and tighter localizations or, in a final step, using the resulting, residual ensemble and tight localisation to represent covariances in the remaining subspace. We illustrate the use of the new multiscale localisation method in simple numerical examples and in cycling data assimilation experiments with the Lorenz Model III. We also compare the proposed new method to existing multiscale localisation and to single-scale localisation. © 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
Exploring analog-based schemes for aerosol optical depth forecasting with WRF-ChemWe implement and test an analog-based post-processing method to improve short range forecasts of aerosol optical depth (AOD) using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem). Model postprocessing of AOD is performed using historical analog forecasts and a Kalman Filter (KF). Analog forecasts are selected from WRF-Chem simulations based on a set of environmental predictors (AOD, wind speed, precipitable water, and particulate matter) that exhibit past values similar to the current forecasts. Space-borne AOD from Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard Terra and Aqua satellites corresponding to the analogs are used to build the analog ensemble. This study focuses on a spatial domain covering the AERONET sites in contiguous United States. We use the analog ensemble weighted mean (AN) and Kalman filter analog (KFAN) algorithms, which are both trained using WRF-Chem AOD forecasts for the months of June to August during 2008–2011 and tested during the same months for 2012. Overall, the AOD forecast are more skillful when the forecast errors are corrected using a combination of analogs and Kalman filter in KFAN. This is especially true for the western US where the correlation of AOD with PM2.5, PM10, and surface horizontal wind speed are higher than those for other predictors. In fact, the overall biases in AOD are significantly reduced close to zero, with KFAN AOD being statistically indistinguishable to MODIS. However, both methods show mixed results (albeit still showing overall improvements) in eastern and central U.S., where AOD and its variability are highest. We find that, during the summer, PM is not the only predominant factor driving AOD in these regions, unlike western United States (U.S.) (except New Mexico and Arizona). We note, however, that the quality of the analogs depends on the model's capability to accurately simulate total precipitable water, which in turn influences aerosol sources and sinks.