Use of Suomi-NPP in NAVDAS-AR * Benjamin Ruston 1, Steve Swadley 1, Nancy Baker 1, Rolf Langland 1 1 NRL, Monterey, CA *Navy Atmospheric Variational Data Assimilation System – Accelerated Representer

Suomi-NPP in NAVDAS-AR ATMS Operationally assimilated using JCSDA CRTM Improved coverage compared to AMSU-A/MHS Improved performance of water vapor channels compared to MHS, both spectrally and in noise performance OMPS OMPS Nadir Profiler (NP) assimilation only First use of passive tracer assimilation capability in NAVDAS-AR Transferred along with SBUV/2 Inactive due to missing stratospheric photochemistry CrIS Awaiting promotion with NAVGEM v1.3.1 (Aug2015) Using JCSDA CRTM Following McNally and Watts cloud detection (also used for IASI and AIRS) 84 LW (CO 2 temperature) and 49 MW (H 2 O humidity) channels VIIRS AMV direct broadcast from Fairbanks via CIMSS implemented Jul 2014 AMV feed from NESDIS finally sorted late Nov2014 operational Jan2015

NCODA NRL Coupled Ocean Data Assimilation System Multivariate Analysis of ocean u,v,T,s,ice,SSH,SWH. Global, Regional, Local Ocean Data Assimilation. NAVDAS NRL Atmospheric Variational Data Assimilation System 3D Variational Analysis, Observation Space. Global, Regional, or Local Application. NAVDAS-AR NAVDAS Accelerated Representer 4D Variational Analysis, Weak Constraint, Model Space. Global or Regional Application. High Altitude DA. ADJOINTS NAVDAS(–AR) Adjoints of 3D & 4D Data Assimilation Systems NOGAPS TLM; Moist Adjoint COAMPS ® TLM; Moist Adjoint, including explicit moist physics NAVOBS NAVDAS-Adjoint OBservation Monitoring System Real-time monitoring of all data assimilated. Identification of observation quality problems. Real-time data selection and data targeting. Ensemble DA Ensemble Kalman Filter Algorithm Testing for COAMPS ® using real observations. EnKF/4DVAR Hybrid for the NAVDAS-AR framework. Navy’s Data Assimilation Tools

Outline VIIRS Atmospheric Motion Vector Assimilation Polar winds impact on global system Ozone in the Navy global model and OMPS assimilation Navy Global Environmental Model (NAVGEM) Ozone analysis Passive tracers in NAVGEM and NAVDAS-AR Impacts of OMPS-NP and SBUV/2 on NAVDAS-AR increment Current ATMS operational performance How does the impact of ATMS compare with other Microwave (MW) and Infrared (IR) sensors How has ATMS looked from a stability standpoint What components could be added for additional ATMS impacts Current development work with CrIS Channel selection and questions Data thinning, current strategy and concepts Observation errors as diagnosed from innovation statistics Summary and Future Directions

VIIRS Atmospheric Motion Vectors VIIRS AMVs have been added in addition to winds from AVHRR, MODIS, Geostationary, and combined LeoGeo winds Impacts are similar to if not slightly improved from those of MODIS Vertical distribution of impact shows greatest benefit in the mid to upper troposphere

OMPS Ozone Assimilation 8.7 hPa 0.91 hPa hPa hPa hPa hPa NAVGEM currently uses a linearized ozone photochemistry parameterization based on diurnally averaged odd-oxygen (O 3 +O) production and loss rates in the stratosphere. It does not account for diurnal cycle in ozone present above 1 hPa. A new generalized ozone photochemistry parameterization has been tested in NOGAPS-ALPHA (above), and is slated for testing in L74 NAVGEM.

OMPS Ozone Assimilation Capability added to operational system; however, it is not active This is largely due to missing photochemistry in stratosphere Also a bias correction between OMPS and SBUV/2 should be examined Increments to Ozone

Operational Use of ATMS ATMS is treated as a primary sensor and has a first priority weighting along with MetOp-B, DMSP-F19, and NOAA-19. A 36km Gaussian 100pt filter is used to scene average the data The ATMS data are thinned to ~135km before assimilation In addition to quality flags in the data itself; a sea ice, cloud liquid water and scattering index is generated and applied to water vapor and tropospheric temperature channels Operational bias correction is variational; however, a Harris- Kelley offline type is available

Operational Use of ATMS N = 200* pre-computed closest points *note: a 100-point filter is used operationally Gaussian Average Boxcar Average s: Scan Position, b: Beam position ChannelFull Res3x3 Boxcar Gaussian σ=25km Gaussian σ=36km Gaussian σ=50km ATMS Spatial Smoothing Effects on OB-BK StDv DTG:

Operational Use of ATMS Monitoring of operational data streams at FNMOC – Radgrams Global mean and stdv of innovation Zonal Innovation Latitudinal dependence of mean and stdv Observation Impact Reduction of NWP error due to observation

Monitoring of ATMS ATMS contributes close to 5% of total FSOI Water vapor channels impact are higher than MHS or SSMIS Also exhibit best noise characteristics (lowest [O-B] RMS) NAVGEM v1.2 06Nov2013 NAVGEM v Jul2014 From initial implementation in NAVGEM v1.2 ATMS biases have generally seen a reduction

CrIS is treated as a primary sensor and has a first priority weighting along with MetOp-B, DMSP-F19, and NOAA-19. Data is selected for only a single FOV of the 9 per “golf ball” The CrIS data is thinned to ~135km before assimilation A Hamming apodization is performed on the radiances before conversion to brightness temperature to match coefficients in JCSDA CRTM In addition to quality flags in the data itself; a cloud screen is applied using the innovation similar to IASI and AIRS, if a cloud is detected water vapor channels will not be assimilated for that pixel Experimental bias correction is variational; however, a Harris- Kelley offline type will be developed before delivery to operations CrIS Assimilation

CrIS performance continues to meet expectations for the sensor Advanced planning beginning for full resolution CrIS which impacts mid- and short-wave bands Ingest, quality control and assimilation tests have been completed. Promotion planned with NAVGEM v1.3.1 release. No correlated error is included in the initial release for either the longwave temperature or mid-wave (humidity channels).

Monitoring of CrIS  Evaluate CrIS against currently assimilated AIRS and IASI  Jacobians from CrIS were evaluated on channel-by- channel basis

CrIS Assimilation  8-day summary for STDV of Innovation for 5x5-degree box  CrIS has very good noise performance in this band

CrIS Assimilation  Current channel selection:  84 longwave CO 2 channels and 49 H 2 0 channels  Cloud screening uses the McNally and Watts (2003) CrIS consistently shows a lower innovation standard deviation than that seen for AIRS or IASI. This is due in part to the coarser spectral resolution of CrIS FY15 transfer: NPP CrIS

Per Ob Impact x FY15 transfer: NPP CrIS 30-day Forecast Error Reduction (IR/MW Sounders) ` CrIS Assimilation

Summary Ozone from OMPS-NP is awaiting activation Assimilation capability was delivered with NAVGEM v1.2.1, it does not have a dramatic impact on the global atmospheric forecasts, particularly in the troposphere. Decision made to wait for photochemistry update. VIIRS Atmospheric Motion Vectors (AMV) are operational A small subset was initially assimilated real time (via direct broadcast Fairbanks, AK) beginning July of 2014, but global real-time feed established Nov2014 and operationally promoted in February ATMS has positive impact on Navy Global NWP ATMS is consistently showing a positive impact on Navy global NWP via the NAVGEM/NAVDAS-AR system. The impact is very similar to that of SSMIS and is an improvement over a combined AMSU-A/MHS sensor suite from the NOAA or MetOp satellite series. CrIS will be operationally assimilated All pre-operational testing is showing the CrIS sensor has beneficial impact on the NAVGEM/NAVDAS-AR system. It is being prepared as one of the updates with the NAVGEM v1.3.1 system, and will include both temperature and moisture channel assimilation.

Future Directions Aid in promotion of CrIS assimilation to operations Explore more dynamic thinning for ATMS, rather than evenly spaced Test any striping mitigation strategies which are proposed and provided Correlated error (see Campbell et al) for both ATMS and CrIS Channel down-selection based on condition number of correlation matrix Investigate impacts of improved normalization of moisture variable (such as Hölm transform) Re-evaluate assimilation after stratospheric Ozone photochemistry update Investigate potential “bias” correction between SBUV/2 and OMPS-NP