Global Observing System Simulation Experiments Research Articles

Using OSSEs to Evaluate GXS Impact in the Context of International Coordination

Abstract The Geostationary Extended Observations (GeoXO) program plans to include a hyperspectral infrared (IR) sounder on its central satellite, expected to launch in the mid-2030s. As part of the follow-on to the GOES program, the NOAA/NASA GeoXO Sounder (GXS) instrument will join several international counterparts in a geostationary orbit. In preparation, the NASA Global Modeling and Assimilation Office (GMAO) assessed the potential effectiveness of GXS both as a single GEO IR sounder and as part of a global ring that includes international partners. Using a global observing system simulation experiment (OSSE) framework, GXS was assessed from a numerical weather prediction (NWP) perspective. Evaluation of the ability of GXS, both alone and as part of a global ring of GEO sounders, to improve weather prediction of thermodynamic variables was performed globally and regionally. GXS dominated regional analysis and forecast improvements and contributed significantly to global increases in forecast skill relative to a Control. However, more sustained global improvements, on the order of 4 days, relied on international partnerships. Additionally, GXS showed the capability to improve hurricane forecast track errors on the time scales necessary for evacuation warnings. The FSOI metric over CONUS showed that the GXS observations provided the largest radiance impact on the moist energy error norm reduction. The high-temporal-resolution atmospheric profile information over much of the Western Hemisphere from GXS provides an opportunity to improve the representation of weather systems and their forecasts. Significance Statement NOAA and NASA are currently planning the GeoXO mission as a follow-on to the GOES program. As part of this process, NASA’s Global Modeling and Assimilation Office has performed several experiments using an observing system simulation experiment (OSSE) framework to assess the potential impact of the GeoXO Sounder (GXS) on numerical weather prediction within the context of international partners launching similar instruments. As part of this assessment, it was found that assimilation of GXS data has the ability to improve both the model analyzed weather and forecasts of the weather, specifically over the domain that GXS observes. Global improvements relied more heavily on a solution consisting of multiple instruments to form a global ring.

Impacts of Marine Surface Pressure Observations from a Spaceborne Differential Absorption Radar Investigated with an Observing System Simulation Experiment

Abstract A new instrument has been proposed for measuring surface air pressure over the marine surface with a combined active/passive scanning multichannel differential absorption radar to provide an estimate of the total atmospheric column oxygen content. A demonstrator instrument, the Microwave Barometric Radar and Sounder (MBARS), has been funded by the National Aeronautics and Space Administration for airborne test missions. Here, a proof-of-concept study to evaluate the potential impact of spaceborne surface pressure data on numerical weather prediction is performed using the Goddard Modeling and Assimilation Office global observing system simulation experiment (OSSE) framework. This OSSE framework employs the Goddard Earth Observing System model and the hybrid 4D ensemble variational Gridpoint Statistical Interpolation data assimilation system. Multiple flight and scanning configurations of potential spaceborne orbits are examined. Swath width and observation spacing for the surface pressure data are varied to explore a range of sampling strategies. For wider swaths, the addition of surface pressures reduces the root-mean-square surface pressure analysis error by as much as 20% over some ocean regions. The forecast sensitivity observation impact tool estimates impacts on the Pacific Ocean basin boundary layer 24-h forecast temperatures for spaceborne surface pressures that are on par with rawinsondes and aircraft and estimates greater impacts than the current network of ships and buoys. The largest forecast impacts are found in the Southern Hemisphere extratropics.

Investigation of the Potential Saturation of Information from Global Navigation Satellite System Radio Occultation Observations with an Observing System Simulation Experiment

Abstract The potential impact of large numbers of Global Navigation Satellite System radio occultation (GNSS-RO) observations on numerical weather prediction is investigated using a global observing system simulation experiment (OSSE). The hybrid four-dimensional ensemble variational Gridpoint Statistical Interpolation (GSI) data assimilation system and Global Earth Observing System (GEOS) model are used to ingest up to 100 000 GNSS-RO soundings per day in addition to the current suite of conventional and radiance data. Analysis quality, forecast skill, and forecast sensitivity to observation impact are examined with differing quantities of additional GNSS-RO profiles. It is found that saturation of information from additional RO soundings has not been reached with 100 000 soundings per day. There are some indications of suboptimal performance of the GSI in handling GNSS-RO observations particularly in the middle- and lower-tropospheric extratropics.

Assessing the Forecast Impact of a Geostationary Microwave Sounder Using Regional and Global OSSEs

Abstract Forecast observing system simulation experiments (OSSEs) are conducted to assess the potential impact of geostationary microwave (GeoMW) sounder observations on numerical weather prediction forecasts. A regional OSSE is conducted using a tropical cyclone (TC) case that is very similar to Hurricane Harvey (2017), as hurricanes are among the most devastating of weather-related natural disasters, and hurricane intensity continues to pose a significant challenge for numerical weather prediction. A global OSSE is conducted to assess the potential impact of a single GeoMW sounder centered over the continental United States versus two sounders positioned at the current locations of the National Oceanic and Atmospheric Administration Geostationary Operational Environmental Satellites (GOES) East and West. It is found that assimilation of GeoMW soundings result in better characterization of the TC environment, especially before and during intensification, which leads to significant improvements in forecasts of TC track and intensity. TC vertical structure (warm core thermal perturbation and horizontal wind distribution) is also substantially improved, as are the surface wind and precipitation extremes. In the global OSSE, assimilation of GeoMW soundings leads to slight improvement globally and significant improvement regionally, with regional impact equal to or greater than nearly all other observation types. Significance Statement This work seeks to determine the impact of a new geostationary microwave (GeoMW) sounder on tropical cyclone forecasts in particular, and on weather forecasts in general. It does so by assimilating simulated GeoMW sounder data into two different forecast models: one global and one regional. The data have a small positive impact globally, and a significant positive impact over the region viewed by the GeoMW instrument. In particular, assimilation of GeoMW data has a significant and positive impact on forecasts of tropical cyclone track, strength, and structure.

Orbit Simulator for Satellite and Near-Space Platforms Supporting Observing System Simulation Experiments

Abstract Developed at the National Oceanic and Atmospheric Administration (NOAA) and the Joint Center for Satellite Data Assimilation (JCSDA), the Community Global Observing System Simulation Experiment (OSSE) Package (CGOP) provides a vehicle to quantitatively evaluate the impacts of emerging environmental observing systems or emerging in situ or remote sensing instruments on NOAA numerical weather prediction (NWP) forecast skill. The typical first step for the OSSE is to simulate observations from the so-called nature run. Therefore, the observation spatial, temporal, and view geometry are needed to extract the atmospheric and surface variables from the nature run, which are then input to the observation forward operator (e.g., radiative transfer models) to simulate the new observations. This is a challenge for newly proposed systems for which instruments are not yet built or platforms are not yet deployed. To address this need, this study introduces an orbit simulator to compute these parameters based on the specific hosting platform and onboard instrument characteristics, which has been recently developed by the NOAA Center for Satellite Applications and Research (STAR) and added to the GCOP framework. In addition to simulating existing polar-orbiting and geostationary orbits, it is also applicable to emerging near-space platforms (e.g., stratospheric balloons), cube satellite constellations, and Tundra orbits. The observation geometry simulator includes not only passive microwave and infrared sounders but also global navigation satellite system/radio occultation (GNSS/RO) instruments. For passive atmospheric sounders, it calculates the geometric parameters of proposed instruments on different platforms, such as time varying location (latitude and longitude), scan geometry (satellite zenith and azimuth angles), and ground instantaneous field of view (GIFOV) parameters for either cross-track or conical scanning mechanisms. For RO observations, it determines the geometry of the transmitters and receivers either on satellites or stratospheric balloons and computes their slant paths. The simulator has been successfully applied for recent OSSE studies (e.g., evaluating the impacts of future geostationary hyperspectral infrared sounders and RO observations from stratospheric balloons). Significance Statement An orbit simulator for satellite and near-space platforms for supporting observing system simulation experiments (OSSE) is developed in this study. It can compute spatial, temporal, and view geometry parameters of the new observations based on the specific hosting platform and onboard instrument characteristics. These parameters are important for the OSSE to extract atmospheric profiles and surface properties from nature run data and simulate new observations. In addition to simulating existing polar-orbiting and geostationary orbits, it is applicable to emerging near-space platforms (e.g., stratospheric balloons), cube satellite constellations, and Tundra orbits. The observation geometry simulator includes not only passive microwave and infrared sounders but also radio occultation instruments.

A preliminary assessment of the value and impact of multiple configurations of constellations of EON-MW, a proposed 12U microwave sounder CubeSat for global NWP

Observing system assessments were made for the Earth Observing Nanosatellite-Microwave (EON-MW), a 12U CubeSat analog of the Advanced Technology Microwave Sounder (ATMS). Since the EON-MW channels and sensor specifications closely follow those of ATMS, the sensor characteristics and geophysical capabilities assessments indicate that the value of information for humidity (temperature) of EON-MW observations will be similar (very similar) to that of ATMS. Eight global observing system simulation experiments (OSSEs) were carried out to evaluate several different EON-MW constellations for data gap mitigation and/or replacement of existing sensors. In these OSSEs, adding 2 EON-MW sensors in different orbits, compared to adding a single EON-MW sensor, improves forecasts generally, and improves the analysis of at least wind and humidity. In terms of the overall OSSE impacts in the scenarios considered, a single EON-MW sensor is a close substitute for ATMS and two EON-MW sensors are a close substitute for the Special Sensor Microwave Imager Sounder (SSMIS). The analysis and forecast impacts indicate that EON-MW provides improved humidity profiles compared to SSMIS and ATMS.

Impact of Refractivity Profiles from a Proposed GNSS-RO Constellation on Tropical Cyclone Forecasts in a Global Modeling System

Abstract A global observing system simulation experiment (OSSE) was used to assess the potential impact of a proposed Global Navigation Satellite System (GNSS) radio occultation (RO) constellation on tropical cyclone (TC) track, maximum 10-m wind speed (Vmax), and integrated kinetic energy (IKE) forecasts. The OSSE system was based on the 7-km NASA nature run and simulated RO refractivity determined by the spatial distribution of observations from the original planned (i.e., including both equatorial and polar orbits) Constellation Observing System for Meteorology, Ionosphere, and Climate-2 (COSMIC-2). Data were assimilated using the NOAA operational weather analysis and forecasting system. Three experiments generated global TC track, Vmax, and IKE forecasts over 6 weeks of the North Atlantic hurricane season in the North Atlantic, east Pacific, and west Pacific basins. Confidence in our results was bolstered because track forecast errors were similar to those of official National Hurricane Center forecasts, and Vmax errors and IKE errors showed similar results. GNSS-RO assimilation did not significantly impact global track forecasts, but did slightly degrade Vmax and IKE forecasts in the first 30–60 h of lead time. Global forecast error statistics show adding or excluding explicit random errors to RO profiles made little difference to forecasts. There was large forecast-to-forecast variability in RO impact. For two cases studied in depth, track and Vmax improvements and degradations were traced backward through the previous 24 h of assimilation cycles. The largest Vmax degradation was traced to particularly good control analyses rather than poor analyses caused by GNSS-RO.

An Observing System Simulation Experiment with a Constellation of Radio Occultation Satellites

Abstract Experiments with a global observing system simulation experiment (OSSE) system based on the recent 7-km-resolution NASA nature run (G5NR) were conducted to determine the potential value of proposed Global Navigation Satellite System (GNSS) radio occultation (RO) constellations in current operational numerical weather prediction systems. The RO observations were simulated with the geographic sampling expected from the original planned Constellation Observing System for Meteorology, Ionosphere, and Climate-2 (COSMIC-2) system, with six equatorial (total of ~6000 soundings per day) and six polar (total of ~6000 soundings per day) receiver satellites. The experiments also accounted for the expected improved vertical coverage provided by the Jet Propulsion Laboratory RO receivers on board COSMIC-2. Except that RO observations were simulated and assimilated as refractivities, the 2015 version of the NCEP’s operational data assimilation system was used to run the OSSEs. The OSSEs quantified the impact of RO observations on global weather analyses and forecasts and the impact of adding explicit errors to the simulation of perfect RO profiles. The inclusion or exclusion of explicit errors had small, statistically insignificant impacts on results. The impact of RO observations was found to increase the length of the useful forecasts. In experiments with explicit errors, these increases were found to be 0.6 h in the Northern Hemisphere extratropics (a 0.4% improvement), 5.9 h in the Southern Hemisphere extratropics (a significant 4.0% improvement), and 12.1 h in the tropics (a very substantial 28.4% improvement).

Community Global Observing System Simulation Experiment (OSSE) Package (CGOP): Assessment and Validation of the OSSE System Using an OSSE–OSE Intercomparison of Summary Assessment Metrics

AbstractObserving system simulation experiments (OSSEs) are used to simulate and assess the impacts of new observing systems planned for the future or the impacts of adopting new techniques for exploiting data or for forecasting. This study focuses on the impacts of satellite data on global numerical weather prediction (NWP) systems. Since OSSEs are based on simulations of nature and observations, reliable results require that the OSSE system be validated. This validation involves cycles of assessment and calibration of the individual system components, as well as the complete system, with the end goal of reproducing the behavior of real-data observing system experiments (OSEs). This study investigates the accuracy of the calibration of an OSSE system—here, the Community Global OSSE Package (CGOP) system—before any explicit tuning has been performed by performing an intercomparison of the OSSE summary assessment metrics (SAMs) with those obtained from parallel real-data OSEs. The main conclusion reached in this study is that, based on the SAMs, the CGOP is able to reproduce aspects of the analysis and forecast performance of parallel OSEs despite the simplifications employed in the OSSEs. This conclusion holds even when the SAMs are stratified by various subsets (the tropics only, temperature only, etc.).

Community Global Observing System Simulation Experiment (OSSE) Package (CGOP): Perfect Observations Simulation Validation

AbstractThe simulation of observations—a critical Community Global Observing System Simulation Experiment (OSSE) Package (CGOP) component—is validated first by a comparison of error-free simulated observations for the first 24 h at the start of the nature run (NR) to the real observations for those sensors that operated during that period. Sample results of this validation are presented here for existing low-Earth-orbiting (LEO) infrared (IR) and microwave (MW) brightness temperature (BT) observations, for radio occultation (RO) bending angle observations, and for various types of conventional observations. For sensors not operating at the start of the NR, a qualitative validation is obtained by comparing geographic and statistical characteristics of observations over the initial day for such a sensor and an existing similar sensor. The comparisons agree, with no significant unexplained bias, and to within the uncertainties caused by real observation errors, time and space collocation differences, radiative transfer uncertainties, and differences between the NR and reality. To validate channels of a proposed future MW sensor with no equivalent existing spaceborne sensor channel, multiple linear regression is used to relate these channels to existing similar channels. The validation then compares observations simulated from the NR to observations predicted by the regression relationship applied to actual real observations of the existing channels. Overall, the CGOP simulations of error-free observations from conventional and satellite platforms that make up the global observing system are found to be reasonably accurate and suitable as a starting point for creating realistic simulated observations for OSSEs. These findings complete a critical step in the CGOP validation, thereby reducing the caveats required when interpreting the OSSE results.

Community Global Observing System Simulation Experiment (OSSE) Package (CGOP): Description and Usage

AbstractA modular extensible framework for conducting observing system simulation experiments (OSSEs) has been developed with the goals of 1) supporting decision-makers with quantitative assessments of proposed observing systems investments, 2) supporting readiness for new sensors, 3) enhancing collaboration across the community by making the most up-to-date OSSE components accessible, and 4) advancing the theory and practical application of OSSEs. This first implementation, the Community Global OSSE Package (CGOP), is for short- to medium-range global numerical weather prediction applications. The CGOP is based on a new mesoscale global nature run produced by NASA using the 7-km cubed sphere version of the Goddard Earth Observing System, version 5 (GEOS-5), atmospheric general circulation model and the January 2015 operational version of the NOAA global data assimilation (DA) system. CGOP includes procedures to simulate the full suite of observing systems used operationally in the global DA system, including conventional in situ, satellite-based radiance, and radio occultation observations. The methodology of adding a new proposed observation type is documented and illustrated with examples of current interest. The CGOP is designed to evolve, both to improve its realism and to keep pace with the advance of operational systems.

Observing System Simulation Experiments (OSSEs) to Evaluate the Potential Impact of an Optical Autocovariance Wind Lidar (OAWL) on Numerical Weather Prediction

AbstractThe potential impact of Doppler wind lidar (DWL) observations from a proposed optical autocovariance wind lidar (OAWL) instrument is quantified in observing system simulation experiments (OSSEs). The OAWL design would provide profiles of useful wind vectors along a ground track to the left of the International Space Station (ISS), which is in a 51.6° inclination low-Earth orbit (LEO). These observations are simulated realistically, accounting for cloud and aerosol distributions inferred from the OSSE nature runs (NRs), and measurement and sampling error sources. The impact of the simulated observations is determined in both global and regional OSSE frameworks. The global OSSE uses the ECMWF T511 NR and the NCEP operational Global Data Assimilation System at T382 resolution. The regional OSSE uses an embedded hurricane NR and the NCEP operational HWRF data assimilation system with outer and inner domains of 9- and 3-km resolution, respectively.The global OSSE results show improved analyses and forecasts of tropical winds and extratropical geopotential heights. The tropical wind RMSEs are significantly reduced in the analyses and in short-term forecasts. The tropical wind improvement decays as the forecasts lengthen. The regional OSSEs are limited but show some improvements in hurricane track and intensity forecasts.

An Observing System Simulation Experiment for the Unmanned Aircraft System Data Impact on Tropical Cyclone Track Forecasts

Abstract High-altitude, long-endurance unmanned aircraft systems (HALE UAS) are capable of extended flights for atmospheric sampling. A case study was conducted to evaluate the potential impact of dropwindsonde observations from HALE UAS on tropical cyclone track prediction; tropical cyclone intensity was not addressed. This study employs a global observing system simulation experiment (OSSE) developed at the National Oceanic and Atmospheric Administration/Earth System Research Laboratory (NOAA/ESRL) that is based on the NOAA/National Centers for Environmental Prediction gridpoint statistical interpolation (GSI) data assimilation system and Global Forecast System (GFS) model. Different strategies for dropwindsonde deployment and UAS flight paths were compared. The introduction of UAS-deployed dropwindsondes was found to consistently improve the track forecast skill during the early forecast up to 96 h, with the caveat that the experiments omitted both vortex relocation and dropwindsondes from manned flights in the tropical cyclone region. The more effective UAS dropwindsonde deployment patterns sampled both the environment and the body of the tropical cyclone.

The role of model and initial condition error in numerical weather forecasting investigated with an observing system simulation experiment

A series of experiments that explore the roles of model and initial condition error in numerical weather prediction are performed using an observing system simulation experiment (OSSE) framework developed at the National Aeronautics and Space Administration Global Modeling and Assimilation Office (NASA/GMAO). The use of an OSSE allows the analysis and forecast errors to be explicitly calculated, and different hypothetical observing networks can be tested with ease. In these experiments, both a full global OSSE framework and an ‘identical twin’ OSSE setup are used to compare the behaviour of the data assimilation system (DAS) and evolution of forecast skill with and without model error. The initial condition error is manipulated by varying the distribution and quality of the observing network and the magnitude of observation errors. The results show that model error has a strong impact on both the quality of the analysis field and the evolution of forecast skill, including both systematic and unsystematic model error components. With a realistic observing network, the analysis state retains a significant quantity of error due to systematic model error. If errors of the analysis state are minimised, model error acts to rapidly degrade forecast skill during the first 24–48 hours of forward integration. In the presence of model error, the impact of observation errors on forecast skill is small, but in the absence of model error, observation errors cause a substantial degradation of the skill of medium-range forecasts.

Validation of the forecast skill of the Global Modeling and Assimilation Office Observing System Simulation Experiment

AbstractA global Observing System Simulation Experiment (OSSE) framework has been developed at the National Aeronautics and Space Administration Global Modeling and Assimilation Office (NASA/GMAO). The OSSE uses a 13‐month integration of the European Centre for Medium‐Range Weather Forecasts (ECMWF) operational model as the Nature Run, and the Goddard Earth Observing System version‐5 (GEOS‐5) forecast model with Gridpoint Statistical Interpolation (GSI) data assimilation as the forecast model. Synthetic observations have been developed with correlated observation errors to replicate the observing network from 2005–2006.The performance of the GMAO OSSE in terms of forecast skill and observation impacts is evaluated against real observational data for the same period. Metrics of anomaly correlation of 500 hPa geopotential and root‐mean‐square error of temperature and wind fields for 120 h forecasts are calculated for once‐daily forecasts from July 2005, and an adjoint is used to measure observation impacts of different data types. The forecast skill of the OSSE is found to be slightly higher than for real data, with smaller observation impacts overall, possibly due to insufficient model error in the OSSE. While there is similar relative ranking of observation impact for most data types in the OSSE compared with real data, for individual satellite channels the agreement is not as good. Some caveats and difficulties of using the OSSE system are discussed along with recommendations of how to avoid potential pitfalls when performing OSSEs.