Moist adjoint‐based forecast sensitivities for a heavy snowfall event over the Korean Peninsula on 4–5 March 2004

Abstract

[1] Adjoint sensitivity analyses are applied to a heavy snowfall event on the Korean Peninsula using the MM5 Adjoint Modeling System. To evaluate the effects of initial conditions on forecast error, adjoint integrations are performed using forecast error as a response function. Initial adjoint sensitivities are located in the middle to lower troposphere with horizontally isolated upshear-tilted structures in the upstream regions (i.e., southern Mongolia). In addition, due to the effect of moist physics in adjoint model integration, vertically striped structures in the lower troposphere are also detected in the southern sea of the Korean Peninsula. Both initial adjoint sensitivity structures match the individual singular vector structures. The new 36-h forecast using the initial condition perturbed with scaled adjoint sensitivities (i.e., key analysis error) shows much improvement in the intensity and location of the surface cyclone as well as the upper trough, and reduces 42.9% of the forecast error compared to the control forecast. In addition, the forecasts using the initial condition perturbed by the key analysis error deviate slightly from the observations than the forecasts using the control analysis in the first 12 h and are much closer to the observations than the control forecasts in the later 12 h. The linear and nonlinear evolutions of temperature perturbations over the large adjoint sensitivity regions show similar growth rates and spatial distributions. Additional experiments with and without specific moist physics for linear model integrations show that disagreement of moist physics in the nonlinear and linear model integrations degrades the linearity.

Adjoint-Derived Observation Impact Using WRF in the Western North Pacific

“…where M T represents the adjoint model. There have been many previous studies using adjointderived forecast sensitivity analysis for idealized cyclogenesis (Rabier et al 1992;Langland et al 1995;Kim and Beare 2011), real cyclogenesis (Errico and Vukicevic 1992;Rabier et al 1996;Klinker et al 1998;Zou et al 1998;Langland et al 2002;Kleist and Morgan 2005a,b;Ancell andMass 2006, 2008;Jung and Kim 2009), tropical cyclones (Kim and Jung 2006;Wu et al 2007;Chu et al 2011), and Asian dust transport events (Kim et al 2008;Kim and Kay 2010). A detailed introduction to adjoint theory and applications can be found in Errico (1997).…”

Spatial‐temporal features of intense snowfall events in China and their possible change

“…Many of the previous studies on the ISE focused on case studies [ Sanderson and Mason , 1958; Rosenblum and Sanders ,1974; Sanders and Bosart , 1985; Marwitz and Toth , 1993; Mote et al , 1997; Market and Cissell , 2002; Heo and Ha , 2008; Jung and Kim , 2009; Lin et al , 2009]. Through analysis of the synoptic settings from both observational data and numerical modeling, these studies revealed the physical processes that favor the ISE, consequently providing valuable information for a better understanding of the formation of ISEs and improving their operational forecasts.…”

Effect of Observation Network Design on Meteorological Forecasts of Asian Dust Events

“…Adjoint sensitivities, which represent the gradient of the response function with respect to the initial condition, have been used to identify sensitive regions for various meteorological features (Kim and Jung 2006;Jung and Kim 2009;Kim and Kay 2010;Kim et al 2013). The forecast can be expressed as…”