NRL) Atmospheric Variational Data Assimilation System Research Articles

On the Impact of Unmanned Aerial System Observations on Numerical Weather Prediction in the Coastal Zone

Abstract The Trident Warrior observational field campaign conducted off the U.S. mid-Atlantic coast in July 2013 included the deployment of an unmanned aerial system (UAS) with several payloads on board for atmospheric and oceanic observation. These UAS observations, spanning seven flights over 5 days in the lowest 1550 m above mean sea level, were assimilated into a three-dimensional variational data assimilation (DA) system [the Naval Research Laboratory Atmospheric Variational Data Assimilation System (NAVDAS)] used to generate analyses for a numerical weather prediction model [the Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS)] with a coupled ocean model [the Naval Research Laboratory Navy Coastal Ocean Model (NCOM)]. The impact of the assimilated UAS observations on short-term atmospheric prediction performance is evaluated and quantified. Observations collected from 50 radiosonde launches during the campaign adjacent to the UAS flight paths serve as model forecast verification. Experiments reveal a substantial reduction of model bias in forecast temperature and moisture profiles consistently throughout the campaign period due to the assimilation of UAS observations. The model error reduction is most substantial in the vicinity of the inversion at the top of the model-estimated boundary layer. Investigations reveal a consistent improvement to prediction of the vertical position, strength, and depth of the boundary layer inversion. The relative impact of UAS observations is explored further with experiments of systematic denial of data streams from the NAVDAS DA system and removal of individual measurement sources on the UAS platform.

Reduction of Middle-Atmospheric Forecast Bias through Improvement in Satellite Radiance Quality Control

Abstract This article discusses a practical problem faced in operational atmospheric forecasting and data assimilation, and efforts to improve forecast quality through the choice of quality control parameters. The need to utilize as much data as possible must be carefully balanced against the need to reject observations deemed erroneous because they are far from the background value. Alleviation of forecast bias in the middle atmosphere for a global atmospheric prediction system is attempted via improvement of the quality control and bias correction of the satellite radiance data; in particular, the sensitivity of the analysis to the satellite radiance outlier check parameters for the Naval Research Laboratory’s three-dimensional variational data assimilation system [Naval Research Laboratory Atmospheric Variational Data Assimilation System (NAVDAS)] is investigated. A series of forecast experiments are performed with an extended-top (0.04 hPa or ∼65 km) version of the U.S. Navy’s Operational Global Atmospheric Prediction System (NOGAPS) for the month of January 2007. The experiments vary the prescribed radiance observation error variance for the Advanced Microwave Sounding Unit-A (AMSU-A) and the tolerance factors for the AMSU-A and NAVDAS quality control processes. The biases of geopotential height, temperature, and wind in the middle atmosphere are significantly reduced when the observation error limit for the highest-altitude AMSU-A channel (i.e., 14) is relaxed from 0.95 to 3 K and the tolerance factors for the AMSU-A and NAVDAS quality control processes are relaxed from 3 to 4. The improvement is due to assimilation of more high quality AMSU-A radiance data from the highest-peaking channel.

Regional Ensemble Forecasts Using the Ensemble Transform Technique

AbstractA computationally inexpensive ensemble transform (ET) method for generating high-resolution initial perturbations for regional ensemble forecasts is introduced. The method provides initial perturbations that (i) have an initial variance consistent with the best available estimates of initial condition error variance, (ii) are dynamically conditioned by a process similar to that used in the breeding technique, (iii) add to zero at the initial time, (iv) are quasi-orthogonal and equally likely, and (v) partially respect mesoscale balance constraints by ensuring that each initial perturbation is a linear sum of forecast perturbations from the preceding forecast. The technique is tested using estimates of analysis error variance from the Naval Research Laboratory (NRL) Atmospheric Variational Data Assimilation System (NAVDAS) and the Navy’s regional Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS) over a 3-week period during the summer of 2005. Lateral boundary conditions are provided by a global ET ensemble. The tests show that the ET regional ensemble has a skillful mean and a useful spread–skill relationship in mass, momentum, and precipitation variables. Diagnostics indicate that ensemble variance was close to, but probably a little less than, the forecast error variance for wind and temperature variables, while precipitation ensemble variance was significantly smaller than precipitation forecast error variance.

A system for operational aerosol optical depth data assimilation over global oceans

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.

Evaluation of the Ensemble Transform Analysis Perturbation Scheme at NRL

Abstract The ensemble transform (ET) scheme changes forecast perturbations into analysis perturbations whose amplitudes and directions are consistent with a user-provided estimate of analysis error covariance. A practical demonstration of the ET scheme was undertaken using Naval Research Laboratory (NRL) Atmospheric Variational Data Assimilation System (NAVDAS) analysis error variance estimates and the Navy Operational Global Atmospheric Prediction System (NOGAPS) numerical weather prediction (NWP) model. It was found that the ET scheme produced forecast ensembles that were comparable to or better in a variety of measures than those produced by the Fleet Numerical and Oceanography Center (FNMOC) bred-growing modes (BGM) scheme. Also, the demonstration showed that the introduction of stochastic perturbations into the ET forecast ensembles led to a substantial improvement in the agreement between the ET and NAVDAS analysis error variances. This finding is strong evidence that even a small-sized ET ensemble is capable of obtaining good agreement between the ET and NAVDAS analysis error variances, provided that NWP model deficiencies are accounted for. Last, since the NAVDAS analysis error covariance estimate is diagonal and hence ignores multivariate correlations, it was of interest to examine the ET analysis perturbations’ spatial correlation. Tests showed that the ET analysis perturbations exhibited statistically significant, realistic multivariate correlations.

NOGAPS-ALPHA Simulations of the 2002 Southern Hemisphere Stratospheric Major Warming

Abstract A high-altitude version of the Navy Operational Global Atmospheric Prediction System (NOGAPS) spectral forecast model is used to simulate the unusual September 2002 Southern Hemisphere stratospheric major warming. Designated as NOGAPS-Advanced Level Physics and High Altitude (NOGAPS-ALPHA), this model extends from the surface to 0.005 hPa (∼85 km altitude) and includes modifications to multiple components of the operational NOGAPS system, including a new radiative heating scheme, middle-atmosphere gravity wave drag parameterizations, hybrid vertical coordinate, upper-level meteorological initialization, and radiatively active prognostic ozone with parameterized photochemistry. NOGAPS-ALPHA forecasts (hindcasts) out to 6 days capture the main features of the major warming, such as the zonal mean wind reversal, planetary-scale wave amplification, large upward Eliassen–Palm (EP) fluxes, and splitting of the polar vortex in the middle stratosphere. Forecasts beyond 6 days have reduced upward EP flux in the lower stratosphere, reduced amplitude of zonal wavenumbers 2 and 3, and a middle stratospheric vortex that does not split. Three-dimensional EP-flux diagnostics in the troposphere reveal that the longer forecasts underestimate upward-propagating planetary wave energy emanating from a significant blocking pattern over the South Atlantic that played a large role in forcing the major warming. Forecasts of less than 6 days are initialized with the blocking in place, and therefore are not required to predict the blocking onset. For a more thorough skill assessment, NOGAPS-ALPHA forecasts over 3 weeks during September–October 2002 are compared with operational NOGAPS 5-day forecasts made at the time. NOGAPS-ALPHA forecasts initialized with 2002 operational NOGAPS analyses show a modest improvement in skill over the NOGAPS operational forecasts. An additional, larger improvement is obtained when NOGAPS-ALPHA is initialized with reanalyzed 2002 fields produced with the currently operational (as of October 2003) Naval Research Laboratory (NRL) Atmospheric Variational Data Assimilation System (NAVDAS). Thus the combination of higher model top, better physical parameterizations, and better initial conditions all yield improved forecasting skill over the NOGAPS forecasts issued operationally at the time.

Development of NAVDAS-AR: non-linear formulation and outer loop tests

NAVDAS-AR is the 4DVAR extension of the United States Navy’s operational 3DVAR data assimilation system, NAVDAS (Naval Research Laboratory Atmospheric Variational Data Assimilation System), where AR stands for accelerated representer. Like NAVDAS, NAVDAS-AR is cast in observation space, and is an observation space formulation of the 4DVAR algorithm, in contrast to the European Centre for Medium-Range Weather Forecasts 4DVAR system, which is an analysis space algorithm. In this paper we show how an inner loop linear solution can be augmented by an outer loop iteration procedure that introduces non-linear effects in the form of a first-order Taylor series expansion. The non-linear problem is then solved iteratively as a sequence of linear problems.We show results of the outer loop strategy, where the first loop, the ‘linear’ solution, is modified in the second outer loop by both a Taylor series term in the calculation of the observation innovations, and a linearized form of the background model that is linearized about an analysis ‘trajectory’ from the solution of the first loop. We also show results of how the outer loop strategy allows better assimilation of observations of surface wind speed and precipitable water, compared to the operational NAVDAS.

Observation Impact during the North Atlantic TReC—2003

Abstract An adjoint-based method is used to calculate the impact of observation data on a measure of short-range forecast error in the Navy Operational Global Atmospheric Prediction System (NOGAPS) during November and December 2003. The evaluated observations include all regular satellite and in situ data assimilated in the Naval Research Laboratory (NRL) Atmospheric Variational Data Assimilation System (NAVDAS) at 1800 UTC, and also targeted dropsonde profiles provided by the North Atlantic Observing-System Research and Predictability Experiment (THORPEX) Regional Campaign (NA-TReC) field program. Commerical aircraft observations account for 46% of the total forecast error reduction by observations in the NA-TReC domain, which includes the North Atlantic and adjacent regions of North America and Europe. Targeted dropsonde data have high impact per observation, but the impact of all dropsonde data is less than 2% of the total during the 2-month study period. Eight of 12 targeted dropsonde cases reduce forecast error. The percent of total impact for other observations assimilated at 1800 UTC in the NA-TReC domain is as follows: Advanced Microwave Sounding Unit-A (AMSU-A) radiances (16%), satellite winds (14%), land surface data (9%), radiosondes (8%), and ship-surface data (5%). If observations over the entire global domain are evaluated, the largest impact of data provided at 1800 UTC during November and December 2003 is provided by AMSU-A radiance data (48% of total).

Development of NAVDAS-AR: formulation and initial tests of the linear problem

A 4-D implementation of an observation space variational data assimilation system is under development at the Marine Meteorology Division of the Naval Research Laboratory (NRL). The system is an extension of the current US Navy 3-D operational data assimilation system, the NRL Atmospheric Variational Data Assimilation System (NAVDAS). The new system, NAVDAS-AR, where AR stands for accelerated representer, is similar in many respects to the European Centre for Medium-Range Weather Forecasts (ECMWF) 4DVAR system. However, NAVDAS-AR is based on a weak constraint observation space, while the ECMWF system is based on a strong constraint model space. In this paper the formulation of NAVDAS-AR is described in detail and preliminary results with a perfect model assumption and comparisons with the operational NAVDAS are presented.

Estimation of observation impact using the NRL atmospheric variational data assimilation adjoint system

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.

NAVDAS: Formulation and Diagnostics

Abstract The Naval Research Laboratory Atmospheric Variational Data Assimilation System (NAVDAS) is a three-dimensional variational data assimilation suite for generating atmospheric state estimates to satisfy a variety of navy needs. These needs range from global initial conditions for navy global prediction models to environmental input into forward-deployed shipboard tactical decision aids. In common with many other U.S. Navy applications, the NAVDAS system has been designed to be robust, flexible, and portable. In particular, it can perform central site global assimilation on massively parallel machines as well as local data assimilation on workstations with the same code. NAVDAS is an observation space algorithm. The preconditioned conjugate gradient method is used as the descent algorithm to minimize the three-dimensional cost function. The number of iterations required to reach convergence is minimized through the use of dual block diagonal preconditioners with Choleski decomposition. Vertical eige...