David Derieg Kuhl

ANCHOR is a novel assimilative model developed at the U.S. Naval Research Laboratory. It extracts ionospheric parameters from RO and ionosonde data and assimilates them as point measurements into the maps of the background parameters using Kalman Filter approach. This paper introduces the ANCHOR algorithm, discusses its coordinate system and background, explains the background covariance formation, discusses the extraction of the ionospheric parameters from the data and the assimilation process, and, finally, shows the results of the observing system simulation experiment.

Whitepaper #376 in the Decadal Survey for Solar and Space Physics (Heliophysics) 2024-2033. Main topics: space weather applications; infrastructure/workforce/other programmatic. Additional topics: space weather research/operations/research loop; space weather […]

Recent advances in data processing from the Cloud Imaging and Particle Size (CIPS) instrument on the NASA Aeronomy of Ice in the Mesosphere satellite allow observation of bright mesospheric clouds at mid‐latitudes (<60°). When adjusted for the evolving local time (LT) of the CIPS observations during its mission we find that the frequencies of these bright clouds in the northern hemisphere show no trend from 2007 to 2021 and no dependence on the solar cycle, although the interannual variability is extreme. Rather we investigate the possible link with propellant exhaust from orbital vehicles, typically launched at lower latitudes. By filtering the launch record equatorward of 60°N using only those launches between 23 and 10 LT, we find a strong correlation with the observed mid‐latitude mesospheric cloud frequency variability between 56° and 60°N. Meridional winds at 92 km from a meteorological analysis system reveal that these morning launches occurred at the time of maximum northward transport. Based upon this combination of high correlation between the cloud frequency and the launch record plus favorable transport conditions, it is likely that space traffic has a strong influence on the interannual variability of these bright mesospheric clouds.

Whole atmosphere models that fully capture the propagation of wave dynamics from lower to upper atmosphere are believed sufficient to reproduce the type of short-term variability in the neutral upper atmosphere that produces observed variations in ionospheric parameters. However, recent studies suggest that upper atmospheric observations are needed to accurately represent short-term variability in both planetary-scale mass transport and tidal behavior crucial to representing the structure of the thermosphere and the wind-dynamo coupling in the ionosphere. To address this, we use atmospheric specifications from the prototype High-Altitude Navy Global Environmental Model (HA-NAVGEM) from the ground to 92 km to nudge the Whole Atmosphere Community Climate Model extended version (WACCM-X) coupled to the Navy Highly Integrated Thermosphere Ionosphere Demonstration System (Navy-HITIDES) ionospheric model. The HA-NAVGEM data assimilation/forecast system is run in two configurations: a reference experiment for the time period December 2012-March 2013, where satellite-based middle atmospheric observations (SABER temperature retrievals; Aura MLS temperature, ozone, and water vapor retrievals; and SSMIS microwave radiances) are included between 20-90 km; and a perturbed experiment, during the same time period, in which the middle atmospheric observations are removed. The resulting nudged simulations using WACCM-X coupled to Navy-HITIDES are used to study the impact of upper atmospheric observations in reproducing the observed short-term variability in the thermosphere-ionosphere system, both in terms of the thermospheric structure and the ionospheric response via wind-dynamo coupling. The role of solar thermal and lunar gravitational tides is discussed, as well as the impact of observations on the weather of the day in the lower thermosphere.

A data assimilation system (DAS) is described for global atmospheric reanalysis from 0- to 100-km altitude. We apply it to the 2014 austral winter of the Deep Propagating Gravity Wave Experiment (DEEPWAVE), an international field campaign focused on gravity wave dynamics from 0 to 100 km, where an absence of reanalysis above 60 km inhibits research. Four experiments were performed from April to September 2014 and assessed for reanalysis skill above 50 km. A four-dimensional variational (4DVAR) run specified initial background error covariances statically. A hybrid-4DVAR (HYBRID) run formed background error covariances from an 80-member forecast ensemble blended with a static estimate. Each configuration was run at low and high horizontal resolution. In addition to operational observations below 50 km, each experiment assimilated 105 observations of the mesosphere and lower thermosphere (MLT) every 6 h. While all MLT reanalyses show skill relative to independent wind and temperature measurements, HYBRID outperforms 4DVAR. MLT fields at 1-h resolution (6-h analysis and 1–5-h forecasts) outperform 6-h analysis alone due to a migrating semidiurnal (SW2) tide that dominates MLT dynamics and is temporally aliased in 6-h time series. MLT reanalyses reproduce observed SW2 winds and temperatures, including phase structures and 10–15-day amplitude vacillations. The 0–100-km reanalyses reveal quasi-stationary planetary waves splitting the stratopause jet in July over New Zealand, decaying from 50 to 80 km then reintensifying above 80 km, most likely via MLT forcing due to zonal asymmetries in stratospheric gravity wave filtering.

The local ensemble tangent linear model (LETLM) provides an alternative method for creating the tangent linear model (TLM) and adjoint of a nonlinear model that promises to be easier to maintain and more computationally scalable than earlier methods. In this paper, we compare the ability of the LETLM to predict the difference between two nonlinear trajectories of the Navy’s global weather prediction model at low resolution (2.5° at the equator) with that of the TLM currently used in the Navy’s four-dimensional variational (4DVar) data assimilation scheme. When compared to the pair of nonlinear trajectories, the traditional TLM and the LETLM have improved skill relative to persistence everywhere in the atmosphere, except for temperature in the planetary boundary layer. In addition, the LETLM was, on average, more accurate than the traditional TLM (error reductions of about 20% in the troposphere and 10% overall). Sensitivity studies showed that the LETLM was most sensitive to the number of ensemble members, with the performance gradually improving with increased ensemble size up to the maximum size attempted (400). Inclusion of physics in the LETLM ensemble leads to a significantly improved representation of the boundary layer winds (error reductions of up to 50%), in addition to improved winds and temperature in the free troposphere and in the upper stratosphere/lower mesosphere. The computational cost of the LETLM was dominated by the cost of ensemble propagation. However, the LETLM can be precomputed before the 4DVar data assimilation algorithm is executed, leading to a significant computational advantage.

Extraction of wind and temperature information from stratospheric ozone assimilation is examined within the context of the Navy Global Environmental Model (NAVGEM) hybrid 4-D variational assimilation (4D-Var) data assimilation (DA) system. Ozone can improve the wind and temperature through two different DA mechanisms: (1) through the flow-of-the-day ensemble background error covariance that is blended together with the static background error covariance and (2) via the ozone continuity equation in the tangent linear model and adjoint used for minimizing the cost function. All experiments assimilate actual conventional data in order to maintain a similar realistic troposphere. In the stratosphere, the experiments assimilate simulated ozone and/or radiance observations in various combinations. The simulated observations are constructed for a case study based on a 16-day cycling truth experiment (TE), which is an analysis with no stratospheric observations. The impact of ozone on the analysis is evaluated by comparing the experiments to the TE for the last 6 days, allowing for a 10-day spin-up. Ozone assimilation benefits the wind and temperature when data are of sufficient quality and frequency. For example, assimilation of perfect (no applied error) global hourly ozone data constrains the stratospheric wind and temperature to within ∼ 2 m s⁻¹ and ∼ 1 K. This demonstrates that there is dynamical information in the ozone distribution that can potentially be used to improve the stratosphere. This is particularly important for the tropics, where radiance observations have difficulty constraining wind due to breakdown of geostrophic balance. Global ozone assimilation provides the largest benefit when the hybrid blending coefficient is an intermediate value (0.5 was used in this study), rather than 0.0 (no ensemble background error covariance) or 1.0 (no static background error covariance), which is consistent with other hybrid DA studies. When perfect global ozone is assimilated in addition to radiance observations, wind and temperature error decreases of up to ∼ 3 m s⁻¹ and ∼ 1 K occur in the tropical upper stratosphere. Assimilation of noisy global ozone (2 % errors applied) results in error reductions of ∼ 1 m s⁻¹ and ∼ 0.5 K in the tropics and slightly increased temperature errors in the Northern Hemisphere polar region. Reduction of the ozone sampling frequency also reduces the benefit of ozone throughout the stratosphere, with noisy polar-orbiting data having only minor impacts on wind and temperature when assimilated with radiances. An examination of ensemble cross-correlations between ozone and other variables shows that a single ozone observation behaves like a potential vorticity (PV) charge, or a monopole of PV, with rotation about a vertical axis and vertically oriented temperature dipole. Further understanding of this relationship may help in designing observation systems that would optimize the impact of ozone on the dynamics.

Data assimilation schemes combine observational data with a short-term model forecast to produce an analysis. However, many characteristics of the atmospheric states described by the observations and the model differ. Observations often measure a higher-resolution state than coarse-resolution model grids can describe. Hence, the observations may measure aspects of gradients or unresolved eddies that are poorly resolved by the filtered version of reality represented by the model. This inconsistency, known as observation representation error, must be accounted for in data assimilation schemes. In this paper the ability of the ensemble to predict the variance of the observation error of representation is explored, arguing that the portion of representation error being detected by the ensemble variance is that portion correlated to the smoothed features that the coarse-resolution forecast model is able to predict. This predictive relationship is explored using differences between model states and their spectrally truncated form, as well as commonly used statistical methods to estimate observation error variances. It is demonstrated that the ensemble variance is a useful predictor of the observation error variance of representation and that it could be used to account for flow dependence in the observation error covariance matrix.