This study assesses the impact of assimilating high-volume Radio Occultation (RO) data from the RO Modeling EXperiment (ROMEX) on the Navy's global operational Naval Global Environment Model (NAVGEM). A series of observing system experiments were conducted, including a control run, a standard assimilation of all ROMEX data, and two sensitivity tests: one with an empirical bias correction and another with a modified refractivity coefficient. Results indicate that while the standard assimilation of ROMEX data improved free-tropospheric moisture forecasts, it amplified existing model biases in temperature and geopotential height, leading to forecast degradation. In contrast, both sensitivity experiments led to substantial improvements in forecast skill. The empirical bias correction method proved most effective, yielding consistent forecast improvements across temperature, moisture, and geopotential height. A Forecast Sensitivity to Observation Impact (FSOI) analysis confirmed the positive contribution of all ROMEX missions, with Spire missions providing the largest total impact and COSMIC-2 showing the highest per-observation effectiveness. The findings underscore that an adjustment to the current treatment of observations was critical to fully realize the benefits of the large volume of RO observations. While the empirical bias correction delivers the greatest forecast improvements, it may obscure and reinforce persistent model biases. The refractivity coefficient adjustment offers an alternative that preserves the unbiased nature of RO observations.

Abstract. This study extends the all-sky radiance data assimilation capability in MPAS-JEDI (data assimilation system for the Model for Prediction Across Scales-Atmosphere based upon the Joint Effort for Data assimilation Integration), previously implemented for the Advanced Microwave Sounding Unit-A (AMSU-A), to the Advanced Technology Microwave Sounder (ATMS). Compared with AMSU-A, ATMS covers a broad frequency range, including high-frequency humidity-sounding channels, in addition to the temperature-sounding and low-frequency imager channels. In contrast to the previous AMSU-A implementation, which assimilated only imaging channels under all-sky conditions, this work assimilates all ATMS channels using the all-sky approach. A situation-dependent observation error model is employed, with distinct cloud predictors over land and ocean surfaces for both temperature- and humidity-sounding channels. The analysis variables, radiance observation operator, and bias correction method are inherited from the AMSU-A all-sky assimilation. The impact of assimilating all-sky ATMS radiances is evaluated with three month-long global hybrid three-dimensional ensemble-variational (hybrid-3DEnVar) experiments: a benchmark experiment without ATMS data, an experiment assimilating only ATMS temperature-sounding channels, and an experiment assimilating all ATMS channels. The 6-hour background forecasts during the assimilation cycling and extended 5-day forecasts are verified against conventional observations, satellite radiances, and Global Forecast System (GFS) analysis. The results show that the background fits to radiosonde observations, satellite radiances, and GFS analyses have improved. Forecast verifications against GFS analyses and independent radiance observations demonstrate statistically significant improvements relative to the benchmark for up to 3 days in both ATMS experiments, across dynamic, thermodynamic, moisture, and cloud fields.

While understanding that quality control (QC) processes crucially affect the statistics of the observations compared to short-range numerical weather prediction (NWP) forecasts and the subsequent analyses, we also realize the fact that many centers implement entirely different QC methods from each other in operations. Here, we catalog the QC methods of Global Navigation Satellite System (GNSS) radio occultation (RO) observations used for data assimilation (DA) by NWP centers. The QC methods are categorized into four groups: preliminary checks which often refer to the metadata of the observed profile, background checks where the observation is compared to a short-range forecast, superrefraction checks which attempt to look for the presence of superrefraction in the profile, and miscellaneous checks such as running a one-dimensional variational scheme or using the variational QC method. This survey is the first published catalog of QC checks used in GNSS-RO observations. It also sets up the second part of this paper, where various methods for diagnosing superrefraction are compared.

First JEDI Space Weather Data Assimilation Workshop What: The workshop gathered 52 participants from 17 affiliations, including government agencies, research centers, and universities, to review current modeling and data assimilation (DA) capabilities in the United States and to discuss requirements for a unified space weather DA system. When: 28-29 August 2024 Where: Boulder, Colorado

The international collaborative Radio Occultation Modeling EXperiment (ROMEX) project marks the first time using a large volume of real data to assess the impact of increased Global Navigation Satellite System radio occultation (GNSS-RO) observations beyond current operational levels, moving past previous theoretical simulation-based studies. The ROMEX project enabled the use of approximately 35 000 daily RO profiles - nearly triple the number typically available to operational centers, which is about 8000 to 12 000 per day. This study investigates the impact of increased RO profiles on numerical weather prediction (NWP) with the Joint Effort for Data assimilation Integration (JEDI) and the global forecast system (GFS), as part of the ROMEX effort. A series of experiments were conducted assimilating varying amounts of RO data along with a common set of other key observations. The results confirm that assimilating additional RO data further improves forecasts across all major meteorological fields, including temperature, humidity, geopotential height, and wind speed, for most of vertical levels. These improvements are significantly evident in verification against both critical observations and the European Centre for Medium-Range Weather Forecasts (ECMWF) analyses, with beneficial impacts lasting up to 5 d. Conversely, withholding RO data resulted in forecast degradations. The results also suggest that forecast improvements scale approximately logarithmically with the number of assimilated profiles, and no evidence of saturation was observed. Biases in the forecast of temperature and geopotential height over the lower stratosphere are discussed, and they are consistent with findings from other studies in the ROMEX community.

The international radio occultation (RO) community is conducting a collaborative effort to explore the impact of a large number of RO observations on numerical weather prediction (NWP). This effort, the Radio Occultation Modeling Experiment (ROMEX), has been endorsed by the International Radio Occultation Working Group, a scientific working group under the auspices of the Coordination Group for Meteorological Satellites (CGMS). ROMEX seeks to inform strategies for future RO missions and acquisitions. ROMEX is planned to consist of at least one three-month period during which all available RO data are collected, processed, archived, and made available to the global community free of charge for research and testing. Although the primary purpose is to test the impact of varying numbers of RO observations on NWP, the three months of RO observations during the first ROMEX period (ROMEX-1, September-November 2022) will be a rich data set for research on many atmospheric phenomena. The RO data providers have sent their data to EUMETSAT for processing. The total number of RO profiles averages between 30,000 and 40,000 per day for ROMEX-1. The processed data (phase, bending angle, refractivity, temperature, and water vapor) will be distributed to ROMEX participants by the Radio Occultation Meteorology Satellite Applications Facility (ROM SAF). The data will also be processed independently by the UCAR COSMIC Data Analysis and Archive Center (CDAAC) and available via ROM SAF. The data are freely available to all participants who agree to the conditions that the providers be acknowledged and the data are not used for commercial or operational purposes.

The Joint Center for Satellite Data Assimilation (JCSDA) is a multiagency research center established to improve the quantitative use of satellite data in atmosphere, ocean, climate and environmental analysis and prediction systems. At the JCSDA, scientists and software engineers within the Joint Effort for Data Assimilation Integration (JEDI) are developing a unified data assimilation framework for research and operational use. To harness the full potential of ever-increasing volumes of data from new and evolving Earth observation systems, a new online database software system has been developed and deployed by the JCSDA. As a core component of JEDI, the Research Repository for Data and Diagnostics (R2D2) performs data registration, management, and configuration services for data assimilation computational workflows. We present an overview of R2D2's distributed system of data stores, SQL data model, intuitive python API, and user support efforts. In addition, we will detail R2D2's utilization by environmental prediction applications developed by the JCSDA and its partners.

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 obser-vations. 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 (COD532nm) 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 COD532nm 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 Kin temperature and 11 Kin dewpoint are possible for a cloud with COD532nm of 0.10 and cloud-top temperature of 210 K. When normalized by the contamination statistics, global differences of nearly 0.11 K in tempera-ture 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, differ-ences are likely much larger in regions with high cirrus frequency.

A new data source from ground‐based stations that track global navigation satellite system (GNSS) transmitters has been implemented in the Navy Global Environmental Model (NAVGEM) with the NRL Atmospheric Variational Data Assimilation System‐Accelerated Representer. The observable is the ground‐based zenith total delay (ZTD) at each ground station. This ground‐based GNSS ZTD represents the tropospheric delay of the propagation of GNSS radio L‐band signal between the transmitters and the ground stations after correcting for ionospheric effects. Here, we present the implementation procedure, quality control, single observation test, bias correction scheme, and data impact assessment. One of the critical elements in the quality control scheme is to correct for the difference between the observing ground station elevation and the numerical model terrain. A single observation test shows comparable impact from this new data source to a single radiosonde observation at a single level. The ZTD biases estimated from a 6‐month experiment run are generally small, but have a dependency on the processing center and are computed separately for each station. The forecast sensitivity to observation impact diagnosis from a 3‐month experiment demonstrates that a single ZTD observation shows similar impact as the average impact from a single tangent point of a GNSS Radio Occultation profile. The assimilation of ZTD observations also significantly improves the forecast skill by 0.25%–0.75% for wind, temperature, and precipitable water and by 1%–2% for geopotential height in the Southern Hemisphere beyond 3 days.

Abstract 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.

The Suomi National Polar-orbiting Partnership (SNPP) cross-track infrared sounder (CrIS) has provided critical observations for environmental applications for nearly ten years. However, on 26 March 2019, the Joint Polar Satellite System (JPSS) interface data processing segment (IDPS) stopped producing the operational SNPP CrIS sensor data record (SDR) product due to a failure of the midwave infrared (MWIR) band. Following a comprehensive risk assessment, the switch from primary Side-1 to redundant Side-2 electronics was made on 24 June 2019, successfully recovering the full capabilities of the sensor. Comprehensive assessment results demonstrate the high quality of the CrIS SDR product resulting from the sensor recalibration, thus meeting the JPSS Level-1 requirements with margin. The spectral calibration prioritized consistency with the CrIS SDR product prior to the side switch to minimize the impact on users. The results show that the radiometric impact on the CrIS SDR product resulting from the side switch is not significant and is within the calibration radiometric uncertainty. It is demonstrated that after instrument restoration, the SNPP CrIS SDR product recovers the quality needed to be used as radiometric reference for calibration and validation of infrared remote sensing instruments. The recovery of the SNPP CrIS MWIR band is expected to support improvements in numerical weather forecasting by restoring the MWIR band channels sensitive to tropospheric water vapor. This should also help maintain continuity and redundancy of one of the backbone observations of the global observing system.

We compare two seemingly different methods of estimating random error statistics (uncertainties) of observations, the three-cornered hat (3CH) method and Desroziers method and show several examples of estimated uncertainties of COSMIC-2 (C2) radio occultation (RO) observations. The two methods yield similar results, attesting to the validity of both. The small differences provide insight into the sensitivity of the methods to the assumptions and computational details. These estimates of RO error statistics differ considerably from several RO error models used by operational weather forecast centers, suggesting that the impact of RO observations on forecasts can be improved by adjusting the RO error models to agree more closely with the RO error statistics. Both methods show RO uncertainty estimates that vary with latitude. In the troposphere, uncertainties are higher in the tropics than subtropics and middle latitudes. In the upper stratosphere-lower mesosphere, we find the reverse, with tropical uncertainties slightly less than in the subtropics and higher latitudes. The uncertainty estimates from the two techniques also show similar variations between a 31-day period during Northern Hemisphere tropical cyclone season (16 August to 15 September 2020) and a month near the vernal Equinox (April 2021). Finally, we find a relationship between the vertical variation of the C2 estimated uncertainties and atmospheric variability, as measured by the standard deviation of the C2 sample. The convergence of the error estimates and the standard deviations above 40 km indicates a lessening impact of assimilating RO above this level.

Abstract 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.

Abstract 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.

Coupled data assimilation (DA) provides a consistent framework for assimilating satellite observations that are sensitive to several components of the Earth system. In this paper, we focus on low-peaking infrared satellite channels that are sensitive to the lower atmosphere and Earth surface temperature (EST) over both ocean and land. Our atmospheric hybrid-4DVAR system [the Navy Global Environmental Model (NAVGEM)] is extended to include the following: 1) variability in the sea surface temperature (both diurnal variability and climatological perturbations to the ensemble members), 2) the coupled Jacobians of the radiative transfer model for the infrared sensors, and 3) the coupled covariances between the EST and the atmosphere. Our coupling approach is found to improve forecast accuracy and to provide corrections to the EST that are in balance with the atmospheric analysis. The largest impact of the coupling is found on near-surface atmospheric temperature and humidity in the tropics, but the impact extends all the way to the stratosphere. The role of each coupling element on the performance of the global atmospheric circulation model is investigated. Inclusion of variability in the sea surface temperature has the strongest positive impact on the forecast quality. Additional inclusion of the coupled Jacobian and ensemble-based coupled covariances led to further improvements in scores and to modification of the corrections to the ocean boundary layer. Coupled DA had significant impact on latent and sensible heat fluxes over land, locations of western boundary currents, and along the ice edge.

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.

The presented work will illustrate the impact of analysis correction based additive inflation (ACAI) on atmospheric forecasts. ACAI uses analysis corrections from the NAVGEM data assimilation system as a representation of model error and is shown to simultaneously improve ensemble spread-skill, reduce model bias and improve the RMS error in the ensemble mean. Results are presented from a myriad of experiments exercising ACAI in stand-alone NAVGEM forecasts using two different ensemble systems; (1) the current operational EPS at FNMOC based on the ensemble transform method and (2) the Navy-ESPC EPS based on perturbed observations. The method of relaxation-to-prior-perturbations (RTPP) has also been implemented in the Navy-ESPC EPS and is shown to further improve the ensemble spread-skill relationship by allowing variance generated during the forecast to impact the initial-time ensemble variance in the subsequent cycle. Results from a simplified implementation of ACAI in the NAVGEM deterministic system will also be shown and indicate positive impact to model biases and RMSE.