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[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.

[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 highaltitude 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. 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.

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.

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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.

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 dataassimilation 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.

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.