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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.

On February 13, 2013, the US Navy's weather forecast system reached a milestone when the NAVy Global Environmental Model (NAVGEM) replaced the Navy Operational Global Atmospheric Prediction System (NOGAPS) for operational global weather prediction. The new operational system NAVGEM 1.1 combines a semi-Lagrangian/semi-implicit dynamical core together with advanced parameterizations of subgrid-scale moist processes, convection, ozone, and radiation. The NAVGEM dynamical core allows for much higher spatial resolutions without the need for the small time steps that would be necessary in NOGAPS. The increased computational efficiency is expected to enable significant increases in resolution in future NAVGEM releases. Model physics improvements in the NAVGEM 1.1 transition include representations of cloud liquid water, cloud ice water, and ozone as fully predicted constituents. Following successful testing of a new mass flux scheme, a second transition to NAVGEM 1.2 occurred on November 6, 2013. Addition of this mass flux parameterization to the eddy diffusion vertical mixing parameterization resulted in a reduction of the cold temperature bias of the lower troposphere over ocean and further increased the forecast skill of NAVGEM.

Upper atmosphere sounding (UAS) channels of the Special Sensor Microwave Imager/Sounder (SSMIS) were assimilated using a high-altitude version of the Navy Global Environmental Model (NAVGEM) in order to investigate their potential for operational forecasting from the surface to the mesospause. UAS radiances were assimilated into NAVGEM using the new Community Radiative Transfer Model (CRTM) that accounts for Zeeman line splitting by geomagnetic fields. UAS radiance data from April 2010 to March 2011 are shown to be in good agreement with coincident temperature measurements from the Sounding of the Atmosphere Using Broadband Emission Radiometry (SABER) instrument that were used to simulate UAS brightness temperatures. Four NAVGEM experiments were performed during July 2010 that assimilated (i) no mesospheric observations, (ii) UAS data only, (iii) SABER and Microwave Limb Sounder (MLS) mesospheric temperatures only, and (iv) SABER, MLS, and UAS data. Zonal mean temperatures and observation − forecast differences for the UAS-only and SABER+MLS experiments are similar throughout most of the mesosphere, and show large improvements over the experiment assimilating no mesospheric observations, proving that assimilation of UAS radiances can provide a reliable large-scale constraint throughout the mesosphere for operational, high-altitude analysis. This is confirmed by comparison of solar migrating tides and the quasi-two-day wave in the mesospheric analyses. The UAS-only experiment produces realistic tidal and two-day wave amplitudes in the summer mesosphere in agreement with the experiments assimilating MLS and SABER observations, whereas the experiment with no mesospheric observations produces excessively strong mesospheric winds and two-day wave amplitudes.

The effect on weather forecast performance of incorporating ensemble covariances into the initial covariance model of the four-dimensional variational data assimilation (4D-Var) Naval Research Laboratory Atmospheric Variational Data Assimilation System-Accelerated Representer (NAVDAS-AR) is investigated. This NAVDAS-AR-hybrid scheme linearly combines the static NAVDAS-AR initial background error covariance with a covariance derived from an 80-member flow-dependent ensemble. The ensemble members are generated using the ensemble transform technique with a (three-dimensional variational data assimilation) 3D-Var-based estimate of analysis error variance. The ensemble covariances are localized using an efficient algorithm enabled via a separable formulation of the localization matrix. The authors describe the development and testing of this scheme, which allows for assimilation experiments using differing linear combinations of the static and flow-dependent background error covariances. The tests are performed for two months of summer and two months of winter using operational model resolution and the operational observational dataset, which is dominated by satellite observations. Results show that the hybrid mode data assimilation scheme significantly reduces the forecast error across a wide range of variables and regions. The improvements were particularly pronounced for tropical winds. The verification against radiosondes showed a greater than 0.5% reduction in vector wind RMS differences in areas of statistical significance. The verification against self-analysis showed a greater than 1% reduction from verifying against analyses between 2- and 5-day lead time at all eight vertical levels examined in areas of statistical significance. Using the Navy's summary of verification results, the Navy Operational Global Atmospheric Prediction System (NOGAPS) scorecard, the improvements resulted in a score (+1) that justifies a major system upgrade.

Time-dependent variational data assimilation allows the possibility of extracting wind information from observations of ozone or other trace gases. Since trace gas observations are not available at sufficient resolution for deriving feature-track winds, they must be combined with model background information to produce an analysis. If done with time-dependent variational assimilation, wind information may be extracted via the adjoint of the linearized tracer continuity equation. This paper presents idealized experiments that illustrate the mechanics of tracer–wind extraction and demonstrate some of the limitations of this procedure. We first examine tracer–wind extraction using a simple one-dimensional advection equation. The analytic solution for a single trace gas observation is discussed along with numerical solutions for multiple observations. The limitations of tracer–wind extraction are then explored using highly idealized ozone experiments performed with a development version of the Navy Global Environmental Model (NAVGEM) in which globally distributed hourly stratospheric ozone profiles are assimilated in a single 6 h update cycle in January 2009. Starting with perfect background ozone conditions, but imperfect dynamical conditions, ozone errors develop over the 6 h background window. Wind increments are introduced in the analysis in order to reduce the differences between background ozone and ozone observations. For "perfect" observations (unbiased and no random error), this results in root-mean-square (RMS) vector wind error reductions of up to ~4 m s−1 in the winter hemisphere and tropics. Wind extraction is more difficult in the summer hemisphere due to weak ozone gradients and smaller background wind errors. The limitations of wind extraction are also explored for observations with imposed random errors and for limited sampling patterns. As expected, the amount of wind information extracted degrades as observation errors or data voids increase. In the case of poorly specified observation error covariances, assimilation of ozone data with imposed errors may result in increased RMS wind error, since the assimilation is constrained too tightly to the noisy observations.

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 .

The forecast model and three-dimensional variational data assimilation components of the Navy Operational Global Atmospheric Prediction System (NOGAPS) have each been extended into the upper stratosphere and mesosphere to form an Advanced Level Physics High Altitude (ALPHA) version of NOGAPS extending to ~100 km. This NOGAPS-ALPHA NWP prototype is used to assimilate stratospheric and mesospheric temperature data from the Microwave Limb Sounder (MLS) and the Sounding of the Atmosphere using Broadband Emission Radiometry (SABER) instruments. A 60-day analysis period in January and February 2006, was chosen that includes a well documented stratospheric sudden warming. SABER and MLS temperatures indicate that the SSW caused the polar winter stratopause at ~40 km to disappear, then reform at ~80 km altitude and slowly descend during February. The NOGAPS-ALPHA analysis reproduces this observed stratospheric and mesospheric temperature structure, as well as realistic evolution of zonal winds, residual velocities, and Eliassen-Palm fluxes that aid interpretation of the vertically deep circulation and eddy flux anomalies that developed in response to this wave-breaking event. The observation minus forecast (O-F) standard deviations for MLS and SABER are ~2 K in the mid-stratosphere and increase monotonically to about 6 K in the upper mesosphere. Increasing O-F standard deviations in the mesosphere are expected due to increasing instrument error and increasing geophysical variance at small spatial scales in the forecast model. In the mid/high latitude winter regions, 10-day forecast skill is improved throughout the upper stratosphere and mesosphere when the model is initialized using the high-altitude analysis based on assimilation of both SABER and MLS data.

The measurement uncertainty requirements imposed by numerical weather prediction (NWP) data assimilation applications for temperature sounding radiances are very demanding. For an ensemble of observations collected during an orbit, (postbias correction) measurement uncertainties of ~ 0.2 K (at 1 sigma) or better are required in tropospheric sounding channels to improve analyses, and hence forecasts, from current NWP models. A significant fraction of F-16 Special Sensor Microwave Imager/Sounder (SSMIS) observations are affected by calibration errors caused by solar intrusions into the warm calibration load and by thermal emission from the main reflector. The magnitude of these effects is as large as 1.5 K for the lower atmospheric temperature sounding channels. This paper describes the approach to correct for these effects, which involves data averaging, flagging solar intrusions, and modeling reflector emission. The resulting quality of the radiances is improved by a factor of three to four for mid-tropospheric temperature sounding channels. Observation minus background field differences are reduced from 0.5-0.8 K (at one standard deviation) for uncorrected data to 0.2 K for corrected data. Although localized biases remain in the corrected data, assimilation experiments using SSMIS data at four operational NWP centers (Met Office, ECMWF, NCEP, and NRL) show a neutral-to-positive impact on forecast quality in the Southern Hemisphere with, for example, mean sea-level pressure forecast errors at days 1-4 reduced by 0.5%-2.5%. Impacts in the Northern Hemisphere are neutral in most assimilation experiments.

Recent observation-targeting field experiments, such as the Fronts and Atlantic Storm-Track Experiment (FASTEX) and the NORth Pacific Experiment (NORPEX), have demonstrated that by using objective adjoint techniques it is possible, in advance, to identify regions of the atmosphere where forecast-error growth in numerical forecast models is maximally sensitive to the error in the initial conditions. 'Qpically, such techniques produce a field of the sensitivity of some aspect of the forecast to the analysis field. This analysis sensitivity field is then used to identify promising targets for the deployment of additional observations during the flight-planning phase of field experiments. While FASTEX and, particularly. NORPEX had a number of successful ‘hits’, where the addition of dropsondes reduced the forecast error, there were also failures. None of the objective techniques have involved any consideration of the characteristics of the data-assimilation systems used in the analysis of the targeted observations. In particular, the interaction with the background field, interactions with other observations, and the background- and observationerror characteristics have been ignored. This can lead to potential mis-sampling, conflict with other observations, and inefficient use of aircraft and expendables. In this study, the adjoint of a simplified data-assimilation system is used to determine directly the sensitivity of the forecast aspect to the observations and the background field. The procedure is illustrated by using simplified linear contexts such as the one- and two-dimensional horizontal univariate problem and the one-dimensional direct radiance assimilation problem. Adaptive targeting tools, such as a single-observation sensitivity map and a marginal observation sensitivity vector, are devised and tested. The possibility of determining when the forecast would be sensitive to the background field and/or the observations is demonstrated. Such dependencies are shown to be a function of the specified observation- and background-error variances, the characteristic scales of analysis sensitivity vector and background-error correlations, and the properties of forward (observation) operators. Although the present experiments concerned simplified assimilation systems and observation networks, extension of the technique to real situations is quite feasible. Obtaining the adjoint of a full three-dimensional variational assimilation system is straightforward; moreover, the target areas are small and contain relatively few observations so the computational requirements are modest. Finally, the data-assimilation adjoint theory can be used for a posteriori assessment of those sources of forecast error which are due to errors in the initial analysis.

The Marine Meteorology Division of the Naval Research Laboratory (NRL), assisted by the Fleet Numerical Meteorology and Oceanography Center, has performed global and mesoscale reanalyses to support the study of Gulf War illness. Realistic and quantitatively accurate atmospheric conditions are needed to drive dispersion models that can predict the transport and dispersion of chemical agents that may have affected U.S. and other coalition troops in the hours and days following the demolition of chemical weapons at Khamisiyah, Iraq, at approximately 1315 UTC 10 March 1991. The reanalysis was conducted with the navy’s global and mesoscale analysis and prediction systems: the Navy Operational Global Atmospheric Prediction System and the NRL Coupled Ocean–Atmosphere Mesoscale Prediction System. A comprehensive set of observations has been collected and used in the reanalysis, including unclassified and declassified surface reports, ship and buoy reports, observations from pibal and rawinsonde, and retr...

Data assimilation with representer-based algorithms (also called “dual space” algorithms) are currently being used for weak-constraint four-dimensional variational data assimilation (W4D-Var) atmospheric prediction, distributed parameter estimation, and other hydrodynamic data assimilation problems. The iterative linear solvers at the core of these systems may display non-monotonic convergence in the norm defined by the primal objective function, and this behavior makes problematic the development of practical stopping criteria. One approach to this problem is described, namely an implementation of the inner solver using the generalized conjugate residual(GCR) algorithm. Additional elements of data assimilation systems are error model for the background, model forcings, and observations. An implementation of a posterior analysis method for diagnosing the error variances is described, and representative results from an atmospheric data assimilation systems are shown.

Time-dependent variational data assimilation allows the possibility of extracting wind information from observations of long-lived trace gases. Since trace gas observations are not available at sufficient resolution for deriving feature-track winds, they must be combined with model background information to produce an analysis. If done with time-dependent variational assimilation, wind information may be extracted via the adjoint of the linearized tracer continuity equation. This paper presents idealized experiments that illustrate the mechanics of tracer-wind extraction and demonstrate some of the limitations of this procedure. We first examine tracer-wind extraction using a simple one-dimensional advection equation. The analytic solution for a single trace gas observation is discussed along with numerical solutions for multiple observations. The limitations of tracer-wind extraction are then explored using highly idealized ozone experiments performed with a development version of the Navy Global Environmental Model (NAVGEM) in which stratospheric globally-distributed hourly stratospheric ozone profiles are assimilated in a single 6-h update cycle in January 2009. Starting with perfect background ozone conditions, but imperfect dynamical conditions, ozone errors develop over the 6-h background window. Wind increments are introduced in the analysis in order to reduce the differences between background ozone and ozone observations. For "perfect" observations (unbiased and no random error), this results in root mean square (RMS) vector wind error reductions of up to ∼ 3 m s−1 in the winter hemisphere and tropics. Wind extraction is more difficult in the summer hemisphere due to weak ozone gradients and smaller background wind errors. The limitations of wind extraction are also explored for observations with imposed random errors and for limited sampling patterns. As expected, the amount of wind information extracted degrades as observation errors or data voids increase. In the case of poorly specified observation error covariances, assimilation of ozone data with imposed errors may result in erroneous wind increments, since the assimilation is constrained too tightly to the noisy observations.

Accurate aerosol and visibility forecasts are critical to both civilian and military applications. To improve the Navy's electro-optical propagation forecast capability, the world's first operational aerosol assimilation model was developed for improving the short and long-term forecasts of NAAPS through the assimilation of the over-water MODIS aerosol products. In this project, the capability of over-land aerosol data assimilation was studied, a multi-sensor data assimilation technique was evaluated, and a prototype of a system that assimilates MODIS fine model aerosol optical depth ratio was developed for improving model representation of aerosol speciation. Extending beyond the scope of this study, a 3-D aerosol assimilation package was constructed, which is designed to improve the accuracy of NAAPS aerosol vertical profiles by assimilating data from CALIPSO. With knowledge gained from the multi-sensor analysis, the long-term aerosol trend was also studied for climate related applications. This research project yielded a total often authored or coauthored peer-reviewed journal papers and more than twenty conference presentations.

[1] Coupled two/three-dimensional variational (2D/3DVAR) assimilation of aerosol physical properties retrieved from the Moderate Resolution Imaging Spectroradiometer (MODIS), Multi-angle Imaging Spectroradiometer (MISR) and Cloud Aerosol Lidar with Orthogonal Polarization (CALIOP) satellite-borne instruments is described for the U. S. Navy Aerosol Analysis and Prediction System (NAAPS) global aerosol mass transport model. Coupled 2D/3DVAR assimilation for NAAPS is evaluated for 48-hr forecast cycles, computed four times daily in six-hour intervals, versus stand-alone 2DVAR assimilation of MODIS and MISR aerosol optical depths (AOD). Both systems are validated against AERONET ground-based sun photometer measurements of AOD. Despite a narrow nadir viewing swath and more than 2700 km of equatorial separation between orbits, satellite lidar data assimilation elicits a positive model response. Improvements in analysis and forecast AOD absolute errors are found over both land and maritime AERONET sites. The primary impact to the model from 3DVAR assimilation is the redistribution of aerosol mass into the boundary layer, though the process is sensitive to parameterization of vertical error correlation lengths.

[1] We have studied the mesospheric response to two recent stratospheric warmings by performing short-term forecasts at medium (1.5°) and high (0.5°) spatial resolution under different gravity wave drag (GWD) scenarios. We validated our models with our high-altitude analysis that extends from 0 to 90 km. For the minor warming of January 2008, reduced upper-level orographic GWD weakened the downward residual circulation and cooled the mesosphere. Parameterized nonorographic GWD increased the simulated mesospheric cooling. For the prolonged major warming of 2006, heavily attenuated orographic GWD led to pronounced cooling near 50 km. During the extended phase of this event, an unusually strong westerly polar vortex reformed in the lower mesosphere, which allowed westward propagating nonorographic gravity waves to reach the mesosphere and break, with net westward accelerations of over 50 m s−1 d−1. This, in turn, forced a strong residual circulation, yielding descent velocities over 2 cm s−1 between 65°N and 85°N, consistent with previous reports of enhanced downward transport of trace constituents. The resulting adiabatic heating, as evidenced by the unusually vertically displaced stratopause at 80 km, is likely a direct consequence of this enhanced gravity wave driven descent. High-resolution simulations without parameterized GWD were closer to the analysis than medium-resolution simulations with parameterized orographic GWD only, but still did not fully simulate the mesospheric thermal response. Specifically, the 80 km temperature enhancement was still underestimated in these simulations. This suggests that higher spatial resolution is needed to adequately resolve extratropical gravity wave momentum fluxes.

The adjoints of the numerical weather prediction (NWP) model and data assimilation system may be used together to objectively determine the observation impact – or whether a given observation platform or observing system improves or degrades the subsequent NWP forecast.

[1] In this study, we present an aerosol data assimilation system destined for operational use at the Fleet Numerical Meteorological and Oceanographic Center (FNMOC). The system is an aerosol physics version of the Naval Research Laboratory (NRL) Atmospheric Variational Data Assimilation System (NAVDAS) that is already operational. The purpose of this new system, NAVDAS-Aerosol Optical Depth (NAVDAS-AOD) is to improve the NRL Aerosol Analysis and Prediction System (NAAPS)'s forecasting capability by assimilating observational data sources with NAAPS forecast fields. This will allow for not only improved aerosol forecasting but also for dramatically enhanced global scale research capabilities for the study of aerosol-meteorology interaction. NAVDAS-AOD assimilates a newly developed over-water Moderate-Resolution Imaging Spectroradiometers (MODIS) level 3 aerosol product with NAAPS. This paper is the second in a series which describes NRL's program to realistically monitor global aerosol distributions. Here we explain the reasons and procedures for constructing the over-water level 3 MODIS aerosol product, describe the theoretical basis for NAVDAS-AOD, and provide a thorough statistical error analysis for both the MODIS observations and the NAAPS model background fields that are critical to aerosol data assimilation. Using 5 months of analysis, our study shows that by carefully screening over-water satellite observations to ensure only the best quality data are used in the aerosol assimilation process, the NAVDAS-AOD can significantly improve the NAAPS global aerosol optical depth analysis as well as improve the aerosol forecast skill.

The U.S. Navy's new three-dimensional variational analysis system NAVDAS became operational at Fleet Numerical Meteorology and Oceanography Center (FNMOC) on October 1, 2003, paving the way for the direct assimilation of NOAA AMSU-A radiances with the Navy Operational Global Atmospheric Prediction System (NOGAPS). AMSU-A radiance assimilation, which became operational at FNMOC on June 9, 2004, leads to significant improvement in forecast skill, as compared with assimilation of NESDIS ATOVS retrievals. The two- to five-day forecast skill at 500 hPa is increased by 3-10 hours in the Northern Hemisphere, and by 12-20 hrs in the Southern Hemisphere, with similar improvements at 1000 hPa. Forecasts with AMSU-A are consistently better, with fewer forecast "busts", fewer synoptic errors and a general strengthening of the circulations in both hemispheres. Overall, NAVDAS analyses and forecasts with AMSU-A exhibit better fit with radiosondes and other observations. Observations from AMSU-B, which are sensitive to the vertical distribution of water vapor in the troposphere, are used to compute 1DVAR humidity retrievals. NAVDAS assimilation of AMSU-B retrievals into NOGAPS dries out the middle and upper troposphere, and strengthens moisture gradients such as the Intertropical Convergence Zone, correcting known model tendencies. Tropical cyclone track and intensity predictions are slightly improved. Transition of AMSU-B retrieval assimilation to operations at FNMOC is targeted for early 2005.© (2005) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Temperature retrievals from polar-orbiting satellites are clearly beneficial in the Southern Hemisphere and the stratosphere, due to lack of conventional data, but have neutral impact on Northern Hemisphere forecasts. An alternative to retrievals is the direct assimilation of radiance data. The NRL Variational Data Assimilation System (NAVDAS), coupled with the Navy Operational Global Atmospheric Prediction System (NOGAPS) NWP model, constitute a system capable of three-dimensional variational assimilation (3DVar) of radiance data. In particular, the assimilation of microwave radiance data from the Advanced Microwave Sounding Unit (AMSU-A) has shown clear positive impact on 5-day forecasts in both hemispheres. One requirement for successful radiance assimilation is bias correction. Biases are due both to the satellite instrument, and the underlying airmass, resulting from inaccuracies in the fast radiative transfer model that converts NWP fields into simulated radiances. Our approach to airmass bias correction uses multilinear regression of fifteen days of observed minus computed radiances, with selected NWP fields as predictors. Research into hybrid methods, which add the radiances themselves as predictors, is being pursued. Moisture retrievals from AMSU-B can also benefit from bias correction. Preliminary results comparing uncorrected and bias-corrected AMSU-B moisture retrievals are presented. The need for bias correction is universal. Our methodology is robust and general, and should be applicable to current and future satellites.

This study focuses on microwave land surface emissivity estimation over Northern Africa and the Middle East and the related impact on temperature and moisture retrievals. Land surface temperature retrievals are performed using a plane-parallel radiative transfer model, analyses from the Navy Operational Global Atmospheric Prediction System (NOGAPS) and data from the High Resolution Infrared Radiation Sounder Version 3 (HIRS/3). Infrared surface emissivity is indexed to each location using soil and vegetation databases provided by the Global Land Data Assimilation System (GLDAS), and spectral reflectance libraries of soil and vegetation. Initial microwave land emissivity estimates are made using a plane-parallel radiative transfer model, the infrared retrieved land surface temperatures, analyses from NOGAPS, and data from the Advanced Microwave Sounding Unit (AMSU). Perturbations of the atmospheric profiles and land surface temperatures provide estimates of the microwave emissivity error covariances necessary for retrievals and radiance assimilation. The error estimation is used in both the Naval Research Laboratory (NRL) 1DVAR retrieval, and for future use in 3DVAR radiance assimilation with the NRL Atmospheric Variational Data Assimilation System (NAVDAS). The window channels on AMSU/A have shown sensitivity to both temperature and moisture in the lowest five kilometers of the atmospheric profile, with these sensitivities strongly correlated to the estimate of the microwave land emissivity. Though the sensitivities are strongly correlated in the vertical dimension, an ability to extract meaningful profiling information from the microwave data is displayed. Further, the atmospheric sensitivity is linked to the precision to which the microwave radiances are estimated.

This report provides a technical description of the adjoint code for the NRL Atmospheric Variational Data Assimilation System (NAVDAS). The output of the NAVDAS adjoint is an array that contains the sensitivity of a user-defined forecast cost function with respect to the complete set of observations assimilated by NAVDAS at a particular time. The input for the NAVDAS adjoint is an analysis sensitivity vector provided by the adjoint of a forecast model such as NOGAPS. Step by step guidelines for using the NAVDAS adjoint system to compute observation sensitivity are given. The basic theory of observation sensitivity is outlined, and features of the adjoint code and main subroutines are described in detail. Examples of observation sensitivity applications are provided, including the impact of innovations on short-range forecast error, and targeted observing.

Remotely-sensed observations from satellites hold great promise for providing a wealth of high-temporal and spatial resolution data, particularly over the oceans. However, it has been very difficult for numerical weather prediction systems to show consistent, positive forecast improvement attributable to satellite retrievals obtained in the conventional manner. By developing techniques for the direct assimilation of the satellite measured radiances, this project will lay the foundation for improved three- and four-dimensional data assimilation systems for global and mesoscale applications. These systems may be run at operational central-site forecast centers or run on-scene to provide operational mission support.

On 29 November 1991 a series of collisions involving 164 vehicles occurred on Interstate 5 in the San Joaquin Valley in California in a dust storm that reduced the visibility to near zero. The accompanying high surface winds are hypothesized to result from intense upper-tropospheric downward motion that led to the formation of a strong upper front and tropopause fold and that transported high momentum air downward to midlevels where boundary layer processes could then mix it to the surface. The objectives of the research presented in this paper are to document the event, to provide support for the hypothesis that both upper-level and boundary layer processes were important, and to determine the structure of the mesoscale circulations in this case for future use in evaluating the navy's mesoscale data assimilation system. The strong upper-level descent present in this case is consistent with what one would expect for jet streak and frontal circulations in combination with quasigeostrophic processes. During...

An adjoint-based procedure for assessing the impact of observations on the short-range forecast error in numerical weather prediction is described. The method is computationally inexpensive and allows observation impact to be partitioned for any set or subset of observations, by instrument type, observed variable, geographic region, vertical level or other category. The cost function is the difference between measures of 24-h and 30-h global forecast error in the Navy Operational Global Atmospheric Prediction System (NOGAPS) during June and December 2002. Observations are assimilated at 00UTC in the Naval Research Laboratory (NRL) Atmospheric Variational Data Assimilation System (NAVDAS). The largest error reductions in the Northern Hemisphere are produced by rawinsondes, satellite wind data, and aircraft observations. In the Southern Hemisphere, the largest error reductions are produced by Advanced TIROS Operational Vertical Sounder (ATOVS) temperature retrievals, satellite wind data and rawinsondes. Approximately 60% (40%) of global observation impact is attributed to observations below (above) 500 hPa. A significant correlation is found between observation impact and cloud cover at the observation location. Currently, without consideration of moisture observations and moist processes in the forecast model adjoint, the observation impact procedure accounts for about 75% of the actual reduction in 24-h forecast error.

The Holton (1982) wave-mean flow interaction model's primitive equation is presently adapted in order to clarify the interaction between the semidiurnal tide and the thermally driven mean meridional circulation near cloud top level. The model is found to produce midlatitude jets whose structure is insensitive to vertical shear of the background angular velocity, both above and below cloud top level; a sensitivity to background angular velocity at cloud top level, however, is noted. Agreement between the model tide and either the observed one or that generated by the more detailed calculations of Pechmann and Ingersoll (1984) is found to be greatest when the background angular velocity at the jet level is of the order of 30 percent larger than that which is observed.