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Under the Atmospheric River Reconnaissance (AR Recon) Program, ocean drifting buoys (drifters) that provide surface pressure observations were deployed in the northeastern Pacific Ocean to improve forecasts of U.S. West Coast high-impact weather. We examine the impacts of both AR Recon and non-AR Recon drifter observations in the U.S. Navy’s global atmospheric data assimilation (DA) and forecast system using data-denial experiments and forecast sensitivity observation impact (FSOI) analysis, which estimates the impact of each observation on the 24-h global forecast error total energy. Considering all drifters in the eastern North Pacific for the 2020 AR Recon season, FSOI indicates that most of the beneficial impacts come from observations in the lowest quartile of observed surface pressure values, particularly those taken late in the DA window. Observations in the upper quartile have near-neutral impacts on average and are slightly nonbeneficial when taken late in the DA window. This may occur because the DA configuration used here does not account for model biases, and innovation statistics show that the forecast model has a low pressure bias at high pressures. Case studies and other analyses indicate large beneficial impacts coming from observations in regions with large surface pressure gradients and integrated vapor transport, such as fronts and ARs. Data-denial experiments indicate that the assimilation of AR Recon drifter observations results in a better-constrained analysis at nearby non-AR Recon drifter locations and counteracts the NAVGEM pressure bias. Assimilating the AR Recon drifter observations improves 72- and 96-h Northern Hemisphere forecasts of winds in the lower and middle troposphere, and geopotential height in the lower, middle, and upper troposphere. Significance Statement The purpose of this study is to understand how observations of atmospheric pressure at the ocean surface provided by drifting buoys impact weather forecasts. Some of these drifting buoys were deployed under a program to study atmospheric rivers (ARs) to improve forecasts of high-impact weather on the West Coast. We find that these observations are most effective at reducing forecast errors when taken in regions near fronts and cyclones. The additional drifting buoys deployed under the AR Reconnaissance project reduce forecast errors at 72 and 96 h over North America and the Northern Hemisphere. These results are important because they illustrate the potential for improving forecasts by increasing the number of drifting buoy surface pressure observations over the world oceans.

In this review, data assimilation (DA) techniques used for tropical cyclones (TCs) are briefly overviewed. The strength and weakness of variational methods, ensemble methods, hybrid methods, and particle filter methods are also discussed. Several global numerical weather prediction models and their corresponding DA systems frequently used for TC forecasting and verification are described first. The DA research and development efforts in the operational regional model from the National Centers for Environmental Prediction's Hurricane Weather Research and Forecasting are then discussed in greater detail. Focused remarks on TC observations from reconnaissance, ground-based radar, enhanced satellite-derived atmospheric motion vectors and all-sky satellite radiances and their impacts on TC analyses and forecasts are addressed. Recent TC DA advancements and challenges on better use of observations and more advanced DA methods for TC application are also briefly reviewed.

A new multiscale, ensemble-based data assimilation (DA) method, multiscale local gain form ensemble transform Kalman filter (MLGETKF), is introduced. MLGETKF allows simultaneous update of multiple scales for both the ensemble mean and perturbations through assimilating all observations at once. MLGETKF performs DA in independent local volumes, which lends the algorithm a high degree of computational scalability. The multiscale analysis is enabled through the rapid creation of many pseudoensemble perturbations via a multiscale ensemble modulation procedure. The Kalman gain that is used to update the raw background ensemble mean and perturbations is based on this modulated ensemble, which intrinsically includes multiscale model space localization. Experiments with a noncycled statistical model show that the full background covariance estimated by MLGETKF more accurately resembles the shape of the true covariance than a scale-unaware localization. The mean analysis from the best-performing MLGETKF is statistically significantly more accurate than the best-performing scale-unaware LGETKF. The accuracy of the MLGETKF analysis is more sensitive to small-scale band localization radius than large-scale band. MLGETKF is further examined in a cycling DA context with a surface quasigeostrophic model. The root-mean-square potential temperature analysis error of the best-performing MLGETKF is 17.2% lower than that of the best-performing LGETKF. MLGETKF reduces analysis errors measured in kinetic energy spectra space by 30%–80% relative to LGETKF with the largest improvement at large scales. MLGETKF deterministic and ensemble mean forecasts are more accurate than LGETKF for full and large scales up to 5–6-day lead time and for small scales up to 3–4-day lead time, gaining ~12 h–1 day of predictability.

Using the Vector LInearized Discrete Ordinate Radiative Transfer (VLIDORT) code as the main driver for forward model simulations, a first-of-its-kind data assimilation scheme has been developed for assimilating Ozone Monitoring Instrument (OMI) aerosol index (AI) measurements into the Naval Aerosol Analysis and Predictive System (NAAPS). This study suggests that both root mean square error (RMSE) and absolute errors can be significantly reduced in NAAPS analyses with the use of OMI AI data assimilation when compared to values from NAAPS natural runs. Improvements in model simulations demonstrate the utility of OMI AI data assimilation for aerosol model analysis over cloudy regions and bright surfaces. However, the OMI AI data assimilation alone does not outperform aerosol data assimilation that uses passive-based aerosol optical depth (AOD) products over cloud-free skies and dark surfaces. Further, as AI assimilation requires the deployment of a fully multiple-scatter-aware radiative transfer model in the forward simulations, computational burden is an issue. Nevertheless, the newly developed modeling system contains the necessary ingredients for assimilation of radiances in the ultraviolet (UV) spectrum, and our study shows the potential of direct radiance assimilation at both UV and visible spectrums, possibly coupled with AOD assimilation, for aerosol applications in the future. Additional data streams can be added, including data from the TROPOspheric Monitoring Instrument (TROPOMI), the Ozone Mapping and Profiler Suite (OMPS), and eventually the Plankton, Aerosol, Cloud and ocean Ecosystem (PACE) mission.

We analyzed the scale features of satellite infrared (IR) water vapor (WV) brightness temperature observations of tropical cyclones (TCs). This is to characterize the storm information at dominate ...

Atmospheric rivers, often associated with impactful weather along the west coast of North America, can be a challenge to forecast even on short time scales. This is attributed, at least in part, to the scarcity of eastern Pacific in situ observations. We examine the impact of assimilating dropsonde observations collected during the Atmospheric River (AR) Reconnaissance 2018 field program on the Navy Global Environmental Model (NAVGEM) analyses and forecasts. We compare NAVGEM’s representation of the ARs to the observations, and examine whether the observation–background difference statistics are similar to the observation error variance specified in the data assimilation system. Forecast sensitivity observation impact is determined for each dropsonde variable, and compared to the impacts of the North American radiosonde network. We find that the reconnaissance soundings have significant beneficial impact, with per observation impact more than double that of the North American radiosonde network. Temperature and wind observations have larger total and per observation impact than moisture observations. In our experiment, the 24-h global forecast error reduction from the reconnaissance soundings can be comparable to the reduction from the North American radiosonde network for the field program dates that include at least two flights.

The Committee on Radio Frequencies (CORF) is an independent committee of experts convened by the U.S. National Academies of Sciences, Engineering, and Medicine to consider the use of radio frequency spectrum for scientific applications and how such use may be protected amidst rising needs for the use of spectrum for numerous other purposes. This talk will provide an overview of a range of CORF activities with focus on the work of the committee in the past year.

Using the Vector LInearized Discrete Ordinate Radiative Transfer (VLIDORT) code as the main driver for forward model simulations, a first-of-its-kind data assimilation scheme has been developed for assimilating Ozone Monitoring Instrument (OMI) aerosol index (AI) measurements into the Naval Aerosol Analysis and Predictive System (NAAPS). This study suggests both RMSE and absolute errors can be significantly reduced in NAAPS analyses with the use of OMI AI data assimilation, when compared to values from NAAPS natural runs. Improvements in model simulations demonstrate the utility of OMI AI data assimilation for improving the accuracy of aerosol model analysis over cloudy regions and bright surfaces. However, the OMI AI data assimilation alone does not out-perform aerosol data assimilation that uses passive-based aerosol optical depth (AOD) products over cloud free skies and dark surfaces. Further, as AI assimilation requires the deployment of a fully-multiple-scatter-aware radiative transfer model in the forward simulations, computational burden is an issue. Nevertheless, the newly-developed modeling system contains the necessary ingredients for assimilation of radiances in the ultra-violet (UV) spectrum, and our study shows the potential of direct radiance assimilation at both UV and visible spectrums, possibly coupled with AOD assimilation, for aerosol applications in the future. Additional data streams can be added, including data from TROPOspheric Monitoring Instrument (TROPOMI), Ozone Mapping and Profiler Suite (OMPS) and eventually with the Plankton, Aerosol, Cloud and ocean Ecosystem (PACE) mission.

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.

Appropriate specification of the error statistics for both observational data and short-term forecasts is necessary to produce an optimal analysis. Observation error stems from instrument error, forward model error, and error of representation. All sources of observation error, particularly error of representation, can lead to nonzero correlations. While correlated forecast error has been accounted for since the early days of atmospheric data assimilation, observation error has typically been treated as uncorrelated until relatively recently. Thinning, averaging, and/or inflation of the assigned observation error variance have been employed to compensate for unaccounted error correlations, especially for high-resolution satellite data. In this study, the benefits of accounting for nonzero vertical (interchannel) correlation for both the Advanced Technology Microwave Satellite (ATMS) and Infrared Atmospheric Sounding Interferometer (IASI) in the NRL Atmospheric Variational Data Assimilation System-Accelerated Representer (NAVDAS-AR) are assessed. The vertical observation error covariance matrix for the ATMS and IASI instruments was estimated using the Desroziers method. The results suggest lowering the assigned error variance and introducing strong correlations, especially in the moisture-sensitive channels. Strong positive impact on forecast skill (verified against both the ECMWF analyses and high-quality radiosonde data) is shown in both the ATMS and IASI instruments. Additionally, the convergence of the iterative solver in NAVDAS-AR can be improved by small modifications to the observation error covariance matrices, resulting in further reduction in RMS error.

Recent studies have shown that day-to-day variability in thermospheric winds (100–300 km altitude) driven by meteorological variability from below affects ionospheric E and lower F regions, highlighting the need for accurate, continuous specification of day-to-day variability throughout the entire atmosphere for geospace weather prediction systems. To better understand the nature of forcing from below on the coupled thermosphere/ionosphere system, this study uses the Specified Dynamics Whole Atmosphere Community Climate Model eXtended (SD-WACCM-X) to quantify how the meteorology of the underlying atmosphere impacts the thermosphere. For this study, global meteorological specifications are produced by a high-altitude version of the Navy Global Environmental Model (NAVGEM-HA), which assimilates standard meteorological observations from the surface through the lower atmosphere, and satellite-based observations of temperature and constituents in middle atmosphere (MA) region 10–90 km altitude. Two SD-WACCM-X simulations for the January–February 2013 period are performed using NAVGEM-HA specifications produced with and without assimilation of MA observations. Results show that the availability of MA observations strongly constrains the modeled spectrum of planetary scale waves (zonal wavenumbers 1–3) in the thermosphere. Specifically, the amplitudes of the solar non-migrating DE3 tide and westward quasi-two day wave (Q2DW) are nearly twice as large in SD-WACCM-X simulations without MA observations compared to simulations with MA observations. Model diagnostics show that these differences are related to non-linear wave-wave interactions impacting the DE3 mode and to sources of baroclinic/barotropic instability near the summer mesospheric easterly jet impacting the Q2DW. This study highlights the importance of MA observations for constraining whole atmosphere models needed for next-generation space weather prediction capabilities.

We present a study of horizontal winds in the mesosphere and lower thermosphere (MLT) during the boreal winters of 2009–2010 and 2012–2013 produced with a new high-altitude numerical weather prediction (NWP) system. This system is based on a modified version of the Navy Global Environmental Model (NAVGEM) with an extended vertical domain up to ∼116 km altitude coupled with a hybrid four-dimensional variational (4DVAR) data assimilation system that assimilates both standard operational meteorological observations in the troposphere and satellite-based observations of temperature, ozone and water vapor in the stratosphere and mesosphere. NAVGEM-based MLT analyzed winds are validated using independent meteor radar wind observations from nine different sites ranging from 69°N–67°S latitude. Time-averaged NAVGEM zonal and meridional wind profiles between 75 and 95 km altitude show good qualitative and quantitative agreement with corresponding meteor radar wind profiles. Wavelet analysis finds that the 3-hourly NAVGEM and 1-hourly radar winds both exhibit semi-diurnal, diurnal, and quasi-diurnal variations whose vertical profiles of amplitude and phase are also in good agreement. Wavelet analysis also reveals common time-frequency behavior in both NAVGEM and radar winds throughout the Northern extratropics around the times of major stratospheric sudden warmings (SSWs) in January 2010 and January 2013, with a reduction in semi-diurnal amplitudes beginning around the time of a mesospheric wind reversal at 60°N that precedes the SSW, followed by an amplification of semi-diurnal amplitudes that peaks 10–14 days following the onset of the mesospheric wind reversal. The initial results presented in this study demonstrate that the wind analyses produced by the high-altitude NAVGEM system accurately capture key features in the observed MLT winds during these two boreal winter periods. © 2016. This is the authors’ accepted and refereed manuscript to the article. Locked until 24.12.2018 due to copyright restrictions. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/

In this paper we discuss the application of observability as a measurement of observation quality for data assimilation in numerical weather prediction (NWP ). Observability is a measure of well-posedness of a dynamical system and provides a flexible framework to address questions in data assimilation. We review the concept of observability for differential equations and high dimensional numerical models. We discuss the relationship of observability to observation impact . We conclude with a discussion of various applications of observability to data assimilation including optimal sensor placement and data thinning .