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Adjoint sensitivity of North Pacific atmospheric river forecasts

TitleAdjoint sensitivity of North Pacific atmospheric river forecasts
Publication TypeJournal Article
Year of Publication2019
AuthorsReynolds C.A, Doyle J.D, Ralph FM, Demirdjian R.
JournalMonthly Weather Review
Date Published2019/06
Type of ArticleArticle
ISBN Number0027-0644
Accession NumberWOS:000467262200002
Keywordsanalysis; dropsonde; ensemble-based; extratropical cyclones; extreme precipitation; forecasting; impact; initial condition sensitivity; mesoscale; Meteorology & Atmospheric Sciences; midlatitude cyclones; model; numerical; Numerical weather prediction; observations; singular-vector structure; water-vapor transport; weather prediction

The initial-state sensitivity and optimal perturbation growth for 24- and 36-h forecasts of low-level kinetic energy and precipitation over California during a series of atmospheric river (AR) events that took place in early 2017 are explored using adjoint-based tools from the Coupled Ocean-Atmosphere Mesoscale Prediction System (COAMPS). This time period was part of the record-breaking winter of 2016-17 in which several high-impact ARs made landfall in California. The adjoint sensitivity indicates that both low-level winds and precipitation are most sensitive to mid- to lower-tropospheric perturbations in the initial state in and near the ARs. A case study indicates that the optimal moist perturbations occur most typically along the subsaturated edges of the ARs, in a warm conveyor belt region. The sensitivity to moisture is largest, followed by temperature and winds. A 1 g kg(-1) perturbation to moisture may elicit twice as large a response in kinetic energy and precipitation as a 1 m s(-1) perturbation to the zonal or meridional wind. In an average sense, the sensitivity and related optimal perturbations are very similar for the kinetic energy and precipitation response functions. However, on a case-by-case basis, differences in the sensitivity magnitude and optimal perturbation structures result in substantially different forecast perturbations, suggesting that optimal adaptive observing strategies should be metric dependent. While the nonlinear evolved perturbations are usually smaller (by about 20%, on average) than the expected linear perturbations, the optimal perturbations are still capable of producing rapid nonlinear perturbation growth. The positive correlation between sensitivity magnitude and wind speed forecast error or precipitation forecast differences supports the relevance of adjoint-based calculations for predictability studies.

Short TitleMon. Weather Rev.
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