First simulations with a whole atmosphere data assimilation and forecast system: The January 2009 major sudden stratospheric warming

Abstract

A Whole atmosphere Data Assimilation System (WDAS) is used to simulate the January 2009 sudden stratospheric warming (SSW). WDAS consists of the Whole Atmosphere Model (WAM) and the 3‐dimensional variational (3DVar) analysis system GSI (Grid point Statistical Interpolation), modified to be compatible with the WAM model. An incremental analysis update (IAU) scheme was implemented in the data assimilation cycle to overcome the problem of excessive damping by digital filter in WAM of the important tidal waves in the upper atmosphere. IAU updates analysis incrementally into the model, thus avoids the initialization procedure (i.e., digital filter) during the WAM forecast stage. The WDAS simulation of the January 2009 SSW shows a significant increase in TW3 (terdiurnal, westward propagating, zonal wave number 3) and a decrease in SW2 (semidiurnal, westward propagating, zonal wave number 2) wave amplitudes in the E region during the warming, which can be attributed likely to the nonlinear wave‐wave interactions between SW2, TW3 and DW1 (diurnal, westward propagating, zonal wave number 1). There is a delayed increase in SW2 in the E region after the warming, indicating a modulation by the changing large‐scale planetary waves in the loweratmosphere during the SSW. These tidal wave responses during SSW appeared to be global in scale. An extended WAM forecast initialized from WDAS analysis shows remarkably consistent tidal wave responses to SSW, indicating a potential forecasting capability of several days in advance of the effects of the large‐scale tropospheric and stratospheric dynamics on the thermospheric and ionospheric variability.

Evaluation of SSMIS Upper Atmosphere Sounding Channels for High-Altitude Data Assimilation

“…Therefore, the close correspondence between mesospheric results from the UAS-only and MLS1SABER experiments indicates a that a reliable operational mesospheric analysis can be obtained by assimilating all the SSMIS UAS channels. This is an important finding, since NWP systems are being developed to extend much higher, initially through the entire mesosphere (e.g., Eckermann et al 2009) and eventually through the thermosphere (e.g., Wang et al 2011). Yet, as our NoMesoObs experiment attests, operational versions of such systems must have real-time mesospheric observations to assimilate if they are to fully exploit their potential to forecast the mesosphere.…”

“…9a,b, is that the NoMesoObs experiment, which assimilated no mesospheric observations at all, produces tidal amplitudes that are similar to those from the other three experiments assimilating various subsets of the MLS, SABER, and SSMIS mesospheric observations. The NoMesoObs experiment is similar in many ways to the standard configurations of other prototype NWP systems that have extended upper model boundaries through the mesosphere yet continue to assimilate only operationally available observations from the troposphere and lower-middle stratosphere (Polavarapu et al 2005;Wang et al 2011). If care is taken to minimize the filtering of tidal structures in the data assimilation algorithm (Sankey et al 2007), the forecast models in these systems consistently reproduce reasonable mesospheric migrating tidal structures despite the absence of any local mesospheric data assimilation constraint (Sankey et al 2007;Xu et al 2011Xu et al , 2012Wang et al 2011), consistent with the findings here.…”

“…Yet observations and modeling studies indicate that these circulation anomalies often form first in the mesosphere and lower thermosphere (MLT) up to a week before impacting the stratosphere (Dowdy et al 2004;Coy et al 2011), providing a potential for both advanced warning and improved predictability of these deep nonlinear dynamical features. Second, 0-100-km NWP systems are a necessary step toward integrated systems for ground-to-space weather prediction (e.g., Wang et al 2011), an important initiative given emerging evidence that tropospheric and stratospheric meteorological disturbances have unexpectedly large impacts on space weather and its prediction (e.g., Goncharenko et al 2010). Finally, there are emerging defense and civilian technologies that operate in the ''near space'' environment at altitudes ;20-100 km and require forecast guidance for their operations.…”

Vortex Preconditioning due to Planetary and Gravity Waves prior to Sudden Stratospheric Warmings

“…In particular, they found that some orographic GWD schemes led to the enhancement of planetary wave activity prior to the SSW, while the implementation of other schemes eliminated the SSW altogether. In light of these results, and the fact that the 2009 warming has been extensively analyzed in the literature (e.g., Manney et al 2009;Harada et al 2010;Coy et al 2011;Wang et al 2011;Ayarzaguena et al 2011), the 2009 SSW makes an ideal case study for examining the relationship between gravity waves, planetary waves, and the two SSW-triggering scenarios outlined in the introduction.…”

Evaluating Different Techniques for Constraining Lower Atmospheric Variability in an Upper Atmosphere General Circulation Model: A Case Study During the 2010 Sudden Stratospheric Warming

“…Recent advancements in middle and upper atmospheric modeling have led to self‐consistent, coupled numerical models of the global stratosphere, mesosphere, thermosphere, and ionosphere that can generally incorporate the day‐to‐day variability in lower and middle atmospheric wave activity by constraining the relevant dynamical fields using observational and/or reanalysis data sets. Typically, observational data sets are incorporated into physics‐based models through data assimilation (DA; e.g., Codrescu et al, ; Fuller‐Rowell et al, ; Matsuo et al, ; Pedatella et al, ; Sutton, ; H. Wang et al, , and references therein), while reanalysis data sets are incorporated into physics‐based models via nudging. More formally nudging is referred to as Newtonian relaxation, where model fields are relaxed using a user‐specified time constant (e.g., Liu et al, ; Marsh, ; Smith, Pedatella, et al, ; Siskind & Drob, ; J. C. Wang et al, , and references therein).…”