i
An Evaluation of Non-Gaussian Data Assimilation Methods in Moist Convective Regimes
-
2023
-
-
Source: Monthly Weather Review, 151(7), 1609-1629
[PDF-5.41 MB]
Details:
-
Journal Title:Monthly Weather Review
-
Personal Author:
-
NOAA Program & Office:
-
Description:Obtaining a faithful probabilistic depiction of moist convection is complicated by unknown errors in subgrid-scale physical parameterization schemes, invalid assumptions made by data assimilation (DA) techniques, and high system dimensionality. As an initial step toward untangling sources of uncertainty in convective weather regimes, we evaluate a novel Bayesian data assimilation methodology based on particle filtering within a WRF ensemble analysis and forecasting system. Unlike most geophysical DA methods, the particle filter (PF) represents prior and posterior error distributions nonparametrically rather than assuming a Gaussian distribution and can accept any type of likelihood function. This approach is known to reduce bias introduced by Gaussian approximations in low-dimensional and idealized contexts. The form of PF used in this research adopts a dimension-reduction strategy, making it affordable for typical weather applications. The present study examines posterior ensemble members and forecasts for select severe weather events between 2019 and 2020, comparing results from the PF with those from an ensemble Kalman filter (EnKF). We find that assimilating with a PF produces posterior quantities for microphysical variables that are more consistent with model climatology than comparable quantities from an EnKF, which we attribute to a reduction in DA bias. These differences are significant enough to impact the dynamic evolution of convective systems via cold pool strength and propagation, with impacts to forecast verification scores depending on the particular microphysics scheme. Our findings have broad implications for future approaches to the selection of physical parameterization schemes and parameter estimation within preexisting data assimilation frameworks.
-
Keywords:
-
Source:Monthly Weather Review, 151(7), 1609-1629
-
DOI:
-
ISSN:0027-0644;1520-0493;
-
Format:
-
Publisher:
-
Document Type:
-
Rights Information:Other
-
Compliance:Submitted
-
Main Document Checksum:
-
Download URL:
-
File Type:
