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Global AVHRR Winds Assimilation at Fleet Numerical Meteorology and Oceanography Center/Naval Research Laboratory, Monterey, CA

From the Spring 2017 issue of the JCSDA Quarterly, DOI: 10.7289/V5V98648

Atmospheric Motion Vectors (AMVs) from polar-orbiting satellites have been in operational use at Fleet Numerical Meteorology and Oceanography Center (FNMOC) since winds from the Moderate-resolution Imaging Spectro- radiometer (MODIS) produced at the University of Wisconsin were introduced in October 2004. Traditionally, such polar winds are based on imagery from overlapping swaths in successive orbits from a single satellite. Their dependence on overlapping swaths also limits the data to polar regions, typically poleward of 60 degrees. However, the European Organisation for the Exploitation of Meteorological Satel lites (EUMETSAT) is taking advantage of having Metop-A and Metop-B in the same orbit (separated by half an orbit) and began operational production of two-satellite AMVs using image pairs in February 2015 (Borde et al. 2016), and using image triplets (A-B-A or B-A-B) in January 2016 (EUMETSAT 2016). The "dual Metop" AMVs are available globally, while the "triplet Metop" join "single Metop" AMVs in polar regions. (Note that at FNMOC, we form superobs for these data without differentiating among single Metop, dual Metop, and triplet Metop wind vectors, instead treating them as a single observation type.) This article describes results from tests of Global AVHRR AMVs in the U.S. Navy's global modeling system, where they have been used operationally since February 2016.

Atmospheric motion vectors have had large beneficial forecast impact in the U.S. Navy's global operational numerical weather prediction system for many years, so the U.S. Naval Research Laboratory (NRL) and FNMOC continue to aggressively pursue testing and assimilation of new satellite winds datasets. The U.S. Navy's global forecast system is composed of NAVDAS-AR (NRL Atmospheric Variational Data Assimilation System-Accelerated Representer), a hybrid ensemble/4DVAR (four-dimensional variational) global data assimilation system in observation space (Xu et al. 2005; Rosmond and Xu 2006; Chua et al. 2009; Kuhl et al. 2013), and NAVGEM (Navy Global Environmental Model), a global atmospheric model currently run with a resolution of 425 spectral waves with triangular truncation and 60 levels (Hogan et al. 2014). Global AVHRR testing by NRL/FNMOC began in November 2015 and immediately showed beneficial impacts, so these winds were introduced into operations in February 2016. Soon thereafter it was noted (Stone et al. 2016) that some of the data excluded by standard QC procedures looked as if it might also be beneficial, motivating a fresh look at some of the QC measures and a set of test runs which relaxed some of the QC measures.

The control run for our experiment emulated operations as closely as possible. In our tests, we relaxed two routine QC checks that are used in operations. One is a check which screens out incoming AMVs (prior to being superobbed) based on observation-minus-background (OmB) vector differences; the OmB limit ranges from 8–12 m/s, depending on the pressure level of the observation. The other is a check which screens out observations based on their pressure level; all AMVs above 175 hPa, below 975 hPa, and almost all AMVs between 425 and 675 hPa are excluded from the assimilation. Our test run Hnorejvec bypassed the routine which screens based on OmB vector difference, while the test run Hnocutout also bypassed the check against the background, but, in addition, allowed through observations above 175 hPa and between 425 and 675 hPa.

We applied the relaxed QC measures to all sources of AMVs, but Global AVHRR and JMA's Himawari-8 winds were responsible for the great majority of newly admitted data. Most of the mid-level data excluded from the control is in polar and near-polar regions, while most of the upper- level data excluded from the control is in the tropics. Fig. 1 shows data distributions for a typical six-hour data window.

Figure 1. Meridional slice of zonally averaged data counts Figure 1. Meridional slice of zonally averaged data counts of superobbed Global AVHRR winds in the control (a, upper left) and the experiment with relaxed quality control screening (b, upper right). Plotted geographic positions of AMVs assimilated in the experiment but not the control above 175 hPa (c, lower left) and 425-675 hPa (d, lower right) for a single 6-hour data window.

The mean vector difference (MVD) between the observations and the background, one indication of data quality, is plotted in profile in Fig. 2a. The vector differences of AMVs with heights between 425–675 hPa indicate that the data in these mid-levels are comparable in accuracy to the AMVs in the levels above and below. Fig. 2b shows the impact of the data using the Forecast Sensitivity Observation Impact (FSOI) method of Langland and Baker, 2004. Again, the data in mid-levels is comparable in impact, and perhaps even more beneficial than the data above and below because the mid-levels were a relative data void. At upper levels, the newly admitted data has significantly larger MVDs, and while we cannot determine how much of the increase is due to data quality as opposed to background quality, we do see that the counts above 175 hPa are small enough that the beneficial impact at those levels is quite small.

Figure 2. Mean Vector Difference Figure 2. Mean Vector Difference (a, upper left) and Forecast Sensitivity Observation Impact (b, upper right) of the control and both experiments, binned by pressure levels. Time series of total FSOI for all vertical levels (c, lower left). Ranking of observation types by total contribution to FSOI during July 2016 (d, lower right).

Fig. 2c shows the FSOI due to Global AVHRR AMVs (all levels) for each six-hour analysis data window during the test period July 2016. There were no instances of non-beneficial impacts (as occasionally happens with other instruments, particularly when data counts are low), and the beneficial impact in the Hnocutout run was greater than in the control in all but two six-hour windows. In our tests, Global AVHRR's contribution to total FSOI was greater than the contributions from all but one of the geostationary satellite sources. Global AVHRR winds provide approximately 2.3 percent of the total FSOI, which is more than the combined surface satellite-derived winds from ASCAT, SSMIS, and WindSat (Figure 2d).

Because of these positive results, the mid- level cutout for Global AVHRR AMVs was eliminated from the operational suite, allowing these data into the operational analysis, beginning with the update that went in on January 25, 2017. Global AVHRR's upper level cutout and its screening against background values remain in place pending further testing.

Rebecca Stone (SAIC, U.S. Naval Research Laboratory, Monterey, CA), Patricia Pauley (U.S. Naval Research Laboratory, Monterey, CA), Nancy Baker (U.S. Naval Research Laboratory, Monterey, CA), Randal Pauley (Fleet Numerical Meteorology and Oceanography Center, Monterey, CA), Bryan Karpowicz (Devine Consulting, U.S. Naval Research Laboratory, Monterey, CA)


The authors gratefully acknowledge the support from the Office of Naval Research through Program Element PE0603207N. This is NRL contribution NRL/PU/7530-17-074, which is approved for public release and distribution is unlimited.


Borde, R., O. Hautecoeur, and M. Carranza, 2016. EUMETSAT Global AVHRR Wind Product. J. Atmos. Oceanic Technol., 33, 429–438, DOI: 10.1175/JTECH-D-15-0155.1.

Chua, B., L. Xu, T. Rosmond, and E. Zaron, 2009. Preconditioning representer-based variational data assimilation systems: Application to NAVDAS-AR. Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications. Springer-Verlag, pp. 307–320.

EUMETSAT, 2016. The new AVHRR Triplet Mode Polar Winds are available on EUMETCast and the GTS from today (21 January). Accessed February 14, 2017. [Available online at]

Hogan, T.F., M. Liu, J.A. Ridout, M.S. Peng, T.R. Whitcomb, B.C. Ruston, C.A. Reynolds, S.D. Eckermann, J.R. Moskaitis, N.L. Baker, J.P. McCormack, K.C. Viner, J. G. McLay, M.K. Flatau, L. Xu, C. Chen, and S.W. Chang, 2014. The Navy Global Environmental Model. Oceanography, 27(3), 116–125, DOI: 10.5670/oceanog.2014.73.

Kuhl, D.D, T.E. Rosmond, C.H. Bishop, N.L. Baker, and J. McLay, 2013. Comparison of hybrid ensemble/4D-Var and 4D-Var within the NAVDAS-AR data assimilation framework. Mon. Wea. Rev., 141, 2740-2758.

Langland, R.H., and Baker, N.L. (2004). Estimation of observation impact using the NRL atmospheric variational data assimilation adjoint system. Tellus A, 56: 189–201.

Rosmond, T., and L. Xu, 2006. Development of NAVDAS-AR: Non-linear formulation and outer loop tests. Tellus A., 58, 45–58.

Stone, R.E., N. Baker, P. Pauley and B. Karpowicz, 2016. Comparison and Impact of Newly Available Atmospheric Motion Vectors in NAVGEM. 14th JCSDA Technical Review Meeting & Science Workshop on Satellite Data Assimilation, Moss Landing, CA, Joint Center for Satellite Data Assimilation, Poster Session. [Available online at].

Xu, L., T. Rosmond, and R. Daley, 2005. Development of NAVDAS-AR: Formulation and initial tests of the linear problem. Tellus A, 57, 546–559.

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