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The FORMOSAT‐7/COSMIC‐2 GNSS‐RO mission was launched on June 25, 2019, and it has provided a large increase in the number of GNSS‐RO observations available for operational numerical weather prediction (NWP) in the latitude band between ±40°. A key aim of this mission has been to improve the GNSS‐RO measurement quality in the lower and middle troposphere. In this study, we summarize the impact of the FORMOSAT‐7/COSMIC‐2 measurements in two independent NWP systems, which are now assimilating these measurements operationally. These are the United States Navy Global Environmental Model (NAVGEM) and the European Center for Medium‐Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS). Both systems employ a 4‐dimensional variational system (4D‐Var), and assimilate GNSS‐RO bending angles. The experiments cover the period January to March 2020. The impact of the FORMOSAT‐7/COSMIC‐2 measurements is assessed using improvements in short‐range forecast departures to other observations such as radiosonde and radiances, forecast error statistics against a verifying analysis, and adjoint based Forecast Sensitivity to Observation Impact (FSOI) estimates. The FORMOSAT‐7/COSMIC‐2 measurement has a clear impact on stratospheric temperatures and winds in the tropics. A novel finding is that the measurements also improve the tropical tropospheric humidity fit to radiosondes, and the fit to tropospheric radiances sensitive to humidity. To date, the impact of GNSS‐RO on humidity has been difficult to demonstrate in well constrained, operational NWP systems assimilating the full suite of observations. The results are achieved with a conservative assimilation approach which extended the quality control and observation error assignments used for the previous COSMIC receivers; further, possible improvements to the assimilation strategy are noted. Two parallel observing system experiments were carried out at the U.S. Naval Research Laboratory and European Centre for Medium Range Forecasts. These assessed the impact FORMOSAT‐7/COSMIC‐2 GNSS Radio occultation measurements had on analyses and forecasts. The experiments showed clear impact on stratospheric temperatures and winds in the tropics, and reductions were also seen in the RMS of departures for the Advanced Technology Microwave Sensor (ATMS). A novel finding was an improvement in the tropical tropospheric humidity for both systems.

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.

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.

The assimilation of hyperspectral infrared sounders (HIS) observations aboard Earth-observing satellites has become vital to numerical weather prediction, yet this assimilation is predicated on the assumption of clear-sky observations. Using collocated assimilated observations from the Atmospheric Infrared Sounder (AIRS) and the Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP), it is found that nearly 7.7% of HIS observations assimilated by the Naval Research Laboratory Variational Data Assimilation System–Accelerated Representer (NAVDAS-AR) are contaminated by cirrus clouds. These contaminating clouds primarily exhibit visible cloud optical depths at 532 nm (COD 532nm ) below 0.10 and cloud-top temperatures between 240 and 185 K as expected for cirrus clouds. These contamination statistics are consistent with simulations from the Radiative Transfer for TOVS (RTTOV) model showing a cirrus cloud with a COD 532nm of 0.10 imparts brightness temperature differences below typical innovation thresholds used by NAVDAS-AR. Using a one-dimensional variational (1DVar) assimilation system coupled with RTTOV for forward and gradient radiative transfer, the analysis temperature and moisture impact of assimilating cirrus-contaminated HIS observations is estimated. Large differences of 2.5 K in temperature and 11 K in dewpoint are possible for a cloud with COD 532nm of 0.10 and cloud-top temperature of 210 K. When normalized by the contamination statistics, global differences of nearly 0.11 K in temperature and 0.34 K in dewpoint are possible, with temperature and dewpoint tropospheric root-mean-squared errors (RMSDs) as large as 0.06 and 0.11 K, respectively. While in isolation these global estimates are not particularly concerning, differences are likely much larger in regions with high cirrus frequency.

The fluctuation of radio occultation (RO) signals in the presence of refractivity irregularities in the moist lower troposphere results in uncertainties of retrieved bending angle and refractivity profiles. In this study the local spectral width (LSW) of RO signals, transformed to impact parameter representation, is used for the characterization of the uncertainty (random error) of retrieved bending angle and refractivity profiles. A large LSW has some correlation with the large mean difference (bias) of retrieved refractivity and bending angle from radiosondes and European Centre for Medium-Range Weather Forecasts analyses based on data from 2008 to 2014. An LSW-based quality control (QC) procedure is developed to eliminate low-quality (large random errors and biases) profiles from data assimilation. The LSW-based QC procedure is tested and evaluated in the assimilation of Constellation Observing System for Meteorology, Ionosphere and Climate RO data using the NCAR Data Assimilation Research Testbed and the Weather Research and Forecasting Model. Preliminary results, based on a 2-week data assimilation cycle, show that the LSW-based QC procedure improves water vapor analyses in the moist lower troposphere.

The task of quantifying spatial and temporal variations in terrestrial water, energy, and vegetation conditions is challenging due to the significant complexity and heterogeneity of these conditions, all of which are impacted by climate change and anthropogenic activities. To address this challenge, Earth Observations (EOs) of the land and their utilization within data assimilation (DA) systems are vital. Satellite EOs are particularly relevant, as they offer quasi-global coverage, are non-intrusive, and provide uniformity, rapid measurements, and continuity. The past three decades have seen unprecedented growth in the number and variety of land remote sensing technologies launched by space agencies and commercial companies around the world. There have also been significant developments in land modeling and DA systems to provide tools that can exploit these measurements. Despite these advances, several important gaps remain in current land DA research and applications. This paper discusses these gaps, particularly in the context of using DA to improve model states for short-term numerical weather and sub-seasonal to seasonal predictions. We outline an agenda for land DA priorities so that the next generation of land DA systems will be better poised to take advantage of the significant current and anticipated shifts and advancements in remote sensing, modeling, computational technologies, and hardware resources.

Numerical weather prediction systems depend on Hyperspectral Infrared Sounder (HIS) data, yet the impacts of dust-contaminated HIS radiances on weather forecasts has not been quantified. To determine the impact of dust aerosol on HIS radiance assimilation, we use a modified radiance assimilation system employing a one-dimensional variational assimilation system (1DVAR) developed under the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Numerical Weather Prediction–Satellite Application Facility (NWP-SAF) project, which uses the Radiative Transfer for TOVS (RTTOV). Dust aerosol impacts on analyzed temperature and moisture fields are quantified using synthetic HIS observations from rawinsonde, Micropulse Lidar Network (MPLNET), and Aerosol Robotic Network (AERONET). Specifically, a unit dust aerosol optical depth (AOD) contamination at 550 nm can introduce larger than 2.4 and 8.6 K peak biases in analyzed temperature and dewpoint, respectively, over our test domain. We hypothesize that aerosol observations, or even possibly forecasts from aerosol predication models, may be used operationally to mitigate dust induced temperature and moisture analysis biases through forward radiative transfer modeling.

This paper describes the new global Navy Earth System Prediction Capability (Navy‐ESPC) coupled atmosphere‐ocean‐sea ice prediction system developed at the Naval Research Laboratory (NRL) for operational forecasting for timescales of days to the subseasonal. Two configurations of the system are validated: (1) a low‐resolution 16‐member ensemble system and (2) a high‐resolution deterministic system. The Navy‐ESPC ensemble system became operational in August 2020, and this is the first time the NRL operational partner, Fleet Numerical Meteorology and Oceanography Center, will provide global coupled atmosphere‐ocean‐sea ice forecasts, with atmospheric forecasts extending past 16 days, and ocean and sea ice ensemble forecasts. A unique aspect of the Navy‐ESPC is that the global ocean model is eddy resolving at 1/12° in the ensemble and at 1/25° in the deterministic configurations. The component models are current Navy operational systems: NAVy Global Environmental Model (NAVGEM) for the atmosphere, HYbrid Coordinate Ocean Model (HYCOM) for the ocean, and Community Ice CodE (CICE) for the sea ice. Physics updates to improve the simulation of equatorial phenomena, particularly the Madden‐Julian Oscillation (MJO), were introduced into NAVGEM. The low‐resolution ensemble configuration and high‐resolution deterministic configuration are evaluated based on analyses and forecasts from January 2017 to January 2018. Navy‐ESPC ensemble forecast skill for large‐scale atmospheric phenomena, such as the MJO, North Atlantic Oscillation (NAO), Antarctic Oscillation (AAO), and other indices, is comparable to that of other numerical weather prediction (NWP) centers. Ensemble forecasts of ocean sea surface temperatures perform better than climatology in the tropics and midlatitudes out to 60 days. In addition, the Navy‐ESPC Pan‐Arctic and Pan‐Antarctic sea ice extent predictions perform better than climatology out to about 45 days, although the skill is dependent on season. Key Points Navy‐ESPC was run for a full year of weekly 60 day forecasts and compared to other models, observations, and climatology Navy‐ESPC ensemble forecast skill for the MJO, NAO, AO, and AAO, and other indices, is comparable to that of other centers Ensemble forecasts of ocean sea surface temperatures perform better than climatology in the tropics and mid‐latitudes out to 60 days

We conceptualize aerosol radiative transfer processes arising from the hypothetical coupling of a global aerosol transport model and a global numerical weather prediction model by applying the US Naval Research Laboratory Navy Aerosol Analysis and Prediction System (NAAPS) and the Navy Global Environmental Model (NAVGEM) meteorological and surface reflectance fields. A unique experimental design during the 2013 NASA Studies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC4RS) field mission allowed for collocated airborne sampling by the high spectral resolution Lidar (HSRL), the Airborne Multi-angle SpectroPolarimetric Imager (AirMSPI), up/down shortwave (SW) and infrared (IR) broadband radiometers, as well as NASA A-Train support from the Moderate Resolution Imaging Spectroradiometer (MODIS), to attempt direct aerosol forcing closure. The results demonstrate the sensitivity of modeled fields to aerosol radiative fluxes and heating rates, specifically in the SW, as induced in this event from transported smoke and regional urban aerosols. Limitations are identified with respect to aerosol attribution, vertical distribution, and the choice of optical and surface polarimetric properties, which are discussed within the context of their influence on numerical weather prediction output that is particularly important as the community propels forward towards inline aerosol modeling within global forecast systems.