Joint Center For Satellite Data Assimilation Research Articles

The prototype NOAA Aerosol Reanalysis version 1.0: description of the modeling system and its evaluation

Abstract. In this study, we describe the first prototype version of global aerosol reanalysis at the National Oceanic and Atmospheric Administration (NOAA), the prototype NOAA Aerosol Reanalysis version 1.0 (pNARA v1.0) that was produced for the year 2016. In pNARA v1.0, the forecast model is an early version of the operational Global Ensemble Forecast System-Aerosols (GEFS-Aerosols) model. The three-dimensional ensemble-variational (3D-EnVar) data assimilation (DA) system configuration is built using elements of the Joint Effort for Data Assimilation Integration (JEDI) framework being developed at the Joint Center for Satellite Data Assimilation (JCSDA). The Neural Network Retrievals (NNR) of aerosol optical depth (AOD) at 550 nm from the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments are assimilated to provide reanalysis of aerosol mass mixing ratios. We evaluate pNARA v1.0 against a wide variety of Aerosol Robotic Network (AERONET) observations, against the National Aeronautics and Space Administration's Modern-Era Retrospective Analysis for Research and Applications 2 (MERRA-2; Gelaro et al., 2017; Randles et al., 2017; Buchard et al., 2017) and the European Centre for Medium-Range Weather Forecasts' Copernicus Atmosphere Monitoring Service Reanalysis (CAMSRA; Inness et al., 2019), and against measurements of surface concentrations of particulate matter 2.5 (PM2.5) and aerosol species. Overall, the 3D-EnVar DA system significantly improves AOD simulations compared with observations, but the assimilation has limited impact on chemical composition and size distributions of aerosols. We also identify deficiencies in the model's representations of aerosol chemistry and their optical properties elucidated from evaluation of pNARA v1.0 against AERONET observations. A comparison of seasonal profiles of aerosol species from pNARA v1.0 with the other two reanalyses exposes significant differences among datasets. These differences reflect uncertainties in simulating aerosols in general.

Reducing Biases in Thermal Infrared Surface Radiance Calculations Over Global Oceans

Thermal infrared (IR) environmental satellite data assimilation and remote sensing of the surface and lower troposphere depend on accurate specification of the spectral surface emissivity within clear-sky forward calculations. Over ocean surfaces, accurate modeling of surface-leaving radiances over the sensor scanning swaths is complicated by a quasi-specular bidirectional reflectance distribution function (BRDF). Recent findings at the Joint Center for Satellite Data Assimilation (JCSDA) have also revealed significant zonally varying systematic biases (≈ |0.5| K) on a global scale over cold ocean waters, these the result of temperature dependence in the thermal IR optical constants. This paper proposes practical solutions to these problems by modeling thermal IR “effective emissivity” in a manner that accounts for both surface emission and quasi-specular reflectance, along with temperature dependence, while meeting the latency and computational constraints of operational global data assimilation and retrieval systems. We overview the theoretical basis of the model and validate it against ship-based Marine Atmospheric Emitted Radiance Interferometer (MAERI) spectra obtained from cold and warm water ocean campaigns.

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.

Roles of TAO/TRITON and Argo in tropical Pacific observing system: An OSSE study for multiple time scale variability

AbstractIn this study, a series of ocean observing system simulation experiments (OSSEs) are conducted in support of the tropical Pacific observing system (TPOS) 2020 Project (TPOS 2020) which was established in 2014, with aims to develop a more sustainable and resilient observing system for the tropical Pacific. The experiments are based on an ocean data assimilation system that is under development at the Joint Center for Satellite Data Assimilation (JCSDA) and the Environmental Modeling Center (EMC)/National Centers for Environmental Prediction (NCEP). The atmospheric forcing and synthetic ocean observations are generated from a nature run, which is based on a modified CFSv2 with a vertical ocean resolution of 1-meter near the ocean surface. To explore the efficacy of TAO/TRITON and Argo observations in TPOS, synthetic ocean temperature and salinity observations were constructed by sampling the nature run following their present distributions. Our experiments include a free run with no “observations” assimilated, and assimilation runs with the TAO/TRITON and Argo synthetic observations assimilated separately or jointly. These experiments were analyzed by comparing their long-term mean states and variabilities at different time scales [i.e., low-frequency (>90 days), intraseasonal (20~90 days), and high-frequency (<20 days)]. It was found that (1) both TAO/TRITON and especially Argo effectively improve the estimation of mean states and low-frequency variations; (2) on the intraseasonal time scale, Argo has more significant improvements than TAO/TRITON (except for regions close to TAO/TRITON sites); (3) on the high-frequency time scale, both TAO/TRITON and Argo have evident deficits (although for TAO/TRITON, limited improvements were present close to TAO/TRITON sites).

Impact of Different Satellite Wind Lidar Telescope Configurations on NCEP GFS Forecast Skill in Observing System Simulation Experiments

AbstractThe Global Wind Observing Sounder (GWOS) concept, which has been developed as a hypothetical space-based hybrid wind lidar system by NASA in response to the 2007 National Research Council (NRC) decadal survey, is expected to provide global wind profile observations with high vertical resolution, precision, and accuracy when realized. The assimilation of Doppler wind lidar (DWL) observations anticipated from the GWOS is being conducted as a series of observing system simulation experiments (OSSEs) at the Joint Center for Satellite Data Assimilation (JCSDA). A companion paper (Riishøjgaard et al.) describes the simulation of this lidar wind data and evaluates the impact on global numerical weather prediction (NWP) of the baseline GWOS using a four-telescope configuration to provide independent line-of-sight wind speeds, while this paper sets out to assess the NWP impact of GWOS equipped with alternative paired configurations of telescopes. The National Centers for Environmental Prediction (NCEP) Gridpoint Statistical Interpolation (GSI) analysis system and the Global Forecast System (GFS) were used, at a resolution of T382 with 64 layers, as the assimilation system and the forecast model, respectively, in these lidar OSSEs. A set of 45-day assimilation and forecast experiments from 2 July to 15 August 2005 was set up and executed.In this OSSE study, a control simulation utilizing all of the data types assimilated in the operational GSI/GFS system was compared to three OSSE simulations that added lidar wind data from the different configurations of telescopes (one-, two-, and four-look configurations). First, the root-mean-square error (RMSE) of wind analysis is compared against the nature run. A significant reduction of the stratospheric RMSE of wind analyses is found for all latitudes when lidar wind profiles are used in the assimilation system. The forecast impacts of lidar data on the wind and mass forecasts are also presented. In addition, the anomaly correlations (AC) of geopotential height forecasts at 500 hPa were evaluated to compare the control and different GWOS telescope configuration experiments. The results show that the assimilation of lidar data from the GWOS (one, two, or four looks) can improve the NCEP GFS wind and mass field forecasts. The addition of the simulated lidar wind observations leads to a statistically significant increase in AC scores.

Striping in the Suomi NPP VIIRS Thermal Bands through Anisotropic Surface Reflection

Abstract The Visible Infrared Imaging Radiometer Suite (VIIRS) thermal emissive band (TEB) M12 images centered at 3.7 μm were analyzed and unexpected striping was found. The striping was seen from ascending orbit (daytime) over uniform oceans and has a magnitude of ±0.5 K aligned with the VIIRS 16 detectors in a track direction of 12 km. From the ocean surface, reflected solar radiation can significantly increase the M12 radiance under certain geometric conditions in which bidirectional reflectance distribution function (BRDF) becomes important. Using the Community Radiative Transfer Model (CRTM), developed at the U.S. Joint Center for Satellite Data Assimilation (JCSDA), M12 band image striping over a uniform ocean was found that was caused by the difference of sensor azimuthal angles among detectors and the contamination of solar radiation. By analyzing the VIIRS M10 and M11 bands, which are two reflective bands, similar striping images over the uniform oceans were found. The M10 and M11 radiance/reflectance can be used to determine the BRDF effect on the thermal emissive band M12, and eventually be used to remove the solar radiation contamination from the M12 band. This study demonstrated that the M12 image striping is a real instrument artifact. Whether to remove the striping or to utilize the striping information fully depends on the application.

Assimilating Satellite Observations of Clouds and Precipitation into NWP Models

No Abstract available.

Validation of the community radiative transfer model

To validate the Community Radiative Transfer Model (CRTM) developed by the U.S. Joint Center for Satellite Data Assimilation (JCSDA), the discrete ordinate radiative transfer (DISORT) model and the line-by-line radiative transfer model (LBLRTM) are combined in order to provide a reference benchmark. Compared with the benchmark, the CRTM appears quite accurate for both clear sky and ice cloud radiance simulations with RMS errors below 0.2 K, except for clouds with small ice particles. In a computer CPU run time comparison, the CRTM is faster than DISORT by approximately two orders of magnitude. Using the operational MODIS cloud products and the European Center for Medium-range Weather Forecasting (ECMWF) atmospheric profiles as an input, the CRTM is employed to simulate the Atmospheric Infrared Sounder (AIRS) radiances. The CRTM simulations are shown to be in reasonably close agreement with the AIRS measurements (the discrepancies are within 2 K in terms of brightness temperature difference). Furthermore, the impact of uncertainties in the input cloud properties and atmospheric profiles on the CRTM simulations has been assessed. The CRTM-based brightness temperatures (BTs) at the top of the atmosphere (TOA), for both thin ( τ<5) and thick ( τ>30) clouds, are highly sensitive to uncertainties in atmospheric temperature and cloud top pressure. However, for an optically thick cloud, the CRTM-based BTs are not sensitive to the uncertainties of cloud optical thickness, effective particle size, and atmospheric humidity profiles. On the contrary, the uncertainties of the CRTM-based TOA BTs resulting from effective particle size and optical thickness are not negligible in an optically thin cloud.

Effect of Out-of-Band Response in NOAA-16 AVHRR Channel 3b on Top-of-Atmosphere Radiances Calculated with the Community Radiative Transfer Model

Abstract The Community Radiative Transfer Model (CRTM) developed at the Joint Center for Satellite Data Assimilation (JCSDA) is used in conjunction with a daily sea surface temperature (SST) and the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) atmospheric data and surface wind to calculate clear-sky top-of-atmosphere (TOA) brightness temperatures (BTs) in three Advanced Very High Resolution Radiometer (AVHRR) thermal infrared channels over global oceans. CRTM calculations are routinely performed by the sea surface temperature team for four AVHRR instruments on board the National Oceanic and Atmospheric Administration (NOAA) satellites NOAA-16, NOAA-17, and NOAA-18 and the Meteorological Operation (MetOp) satellite MetOp-A, and they are compared with clear-sky TOA BTs produced by the operational AVHRR Clear-Sky Processor for Oceans (ACSPO). It was observed that the model minus observation (M−O) bias in the NOAA-16 AVHRR channel 3b (Ch3b) centered at 3.7 μm experienced a discontinuity of ∼0.3 K when a new CRTM version 1.1 (v.1.1) was implemented in ACSPO processing in September 2008. No anomalies occurred in any other AVHRR channel or for any other platform. This study shows that this discontinuity is caused by the out-of-band response in NOAA-16 AVHRR Ch3b and by using a single layer to the NCEP GFS temperature profiles above 10 hPa for the alpha version of CRTM. The problem has been solved in CRTM v.1.1, which uses one of the six standard atmospheres to fill in the missing data above the top pressure level in the input NCEP GFS data. It is found that, because of the out-of-band response, the NOAA-16 AVHRR Ch3b has sensitivity to atmospheric temperature at high altitudes. This analysis also helped to resolve another anomaly in the absorption bands of the High Resolution Infrared Radiation Sounder (HIRS) sensor, whose radiances and Jacobians were affected to a much greater extent by this CRTM inconsistency.

Weather Forecasts by the WRF-ARW Model with the GSI Data Assimilation System in the Complex Terrain Areas of Southwest Asia

Abstract This paper will first describe the forecasting errors encountered from running the National Center for Atmospheric Research (NCAR) mesoscale model (the Advanced Research Weather Research and Forecasting model; ARW) in the complex terrain of southwest Asia from 1 to 31 May 2006. The subsequent statistical evaluation is designed to assess the model’s surface and upper-air forecast accuracy. Results show that the model biases caused by inadequate parameterization of physical processes are relatively small, except for the 2-m temperature, as compared to the nonsystematic errors resulting in part from the uncertainty in the initial conditions. The total model forecast errors at the surface show a substantial spatial heterogeneity; the errors are relatively larger in higher mountain areas. The performance of 2-m temperature forecasts is different from the other surface variables’ forecasts; the model forecast errors in 2-m temperature forecasts are closely related to the terrain configuration. The diurnal cycle variation of these near-surface temperature forecasts from the model is much smaller than what is observed. Second, in order to understand the role of the initial conditions in relation to the accuracy of the model forecasts, this study assimilated a form of satellite radiance data into this model through the Joint Center for Satellite Data Assimilation (JCSDA) analysis system called the Gridpoint Statistical Interpolation (GSI). The results indicate that on average over a 30-day experiment for the 24- and 48-h (second 24 h) forecasts, the satellite data provide beneficial information for improving the initial conditions and the model errors are reduced to some degree over some of the study locations. The diurnal cycle for some forecasting variables can be improved after satellite data assimilation; however, the improvement is very limited.

Estimates of radiation over clouds and dust aerosols: Optimized number of terms in phase function expansion

The bulk-scattering properties of dust aerosols and clouds are computed for the community radiative transfer model (CRTM) that is a flagship effort of the Joint Center for Satellite Data Assimilation (JCSDA). The delta-fit method is employed to truncate the forward peaks of the scattering phase functions and to compute the Legendre expansion coefficients for re-constructing the truncated phase function. Use of more terms in the expansion gives more accurate re-construction of the phase function, but the issue remains as to how many terms are necessary for different applications. To explore this issue further, the bidirectional reflectances associated with dust aerosols, water clouds, and ice clouds are simulated with various numbers of Legendre expansion terms. To have relative numerical errors smaller than 5%, the present analyses indicate that, in the visible spectrum, 16 Legendre polynomials should be used for dust aerosols, while 32 Legendre expansion terms should be used for both water and ice clouds. In the infrared spectrum, the brightness temperatures at the top of the atmosphere are computed by using the scattering properties of dust aerosols, water clouds and ice clouds. Although small differences of brightness temperatures compared with the counterparts computed with 4, 8, 128 expansion terms are observed at large viewing angles for each layer, it is shown that 4 terms of Legendre polynomials are sufficient in the radiative transfer computation at infrared wavelengths for practical applications.

THE JOINT CENTER FOR SATELLITE DATA ASSIMILATION

The Joint Center for Satellite Data Assimilation (JCSDA) was established by NASA and NOAA in 2001, with Department of Defense (DoD) agencies becoming partners in 2002. The goal of JCSDA is to accelerate the use of observations from Earth-orbiting satellites in operational environmental analysis and prediction models for the purpose of improving weather, ocean, climate, and air quality forecasts and the accuracy of climate datasets. Advanced instruments of current and planned satellite missions do and will increasingly provide large volumes of data related to the atmospheric, oceanic, and land surface state. During this decade, this will result in a five order of magnitude increase in the volume of data available for use by the operational and research weather, ocean, and climate communities. These data will exhibit accuracies and spatial, spectral, and temporal resolutions never before achieved. JCSDA will help ensure that the maximum benefit from investment in the space-based global observation system is realized. JCSDA will accelerate the use of satellite data from both operational and experimental spacecraft for weather and climate prediction systems. To this end, the advancement of data assimilation science by JCSDA has included the establishment of the JCSDA Community Radiative Transfer Model (CRTM), which has continual upgrades to allow for the effective use of current and many future satellite instruments. This and other activity within JCSDA have been supported by both internal and external (generally university based) research. Another key activity within JCSDA has been to lay the groundwork for and to establish common NWP model and data assimilation infrastructure for accessing new satellite data and optimizing the use of these data in operational models. As a result of this activity, common assimilation infrastructure has been established at NOAA and NASA and this will assist in a coordinated and integrated move to four-dimensional assimilation among the partner agencies. This paper discusses the establishment of JCSDA and its mission, goals, and science priorities. It also discusses recent advances made by JCSDA, and planned future developments.

Impact of atmospheric infrared sounder observations on weather forecasts

Experimental weather forecasts at the Joint Center for Satellite Data Assimilation (JCSDA) using Atmospheric Infrared Sounder (AIRS) radiance observations indicate significant improvements in global forecast skill compared with the operational system without AIRS data. The improvement in forecast skill at six days is equivalent to gaining an extension of forecast capability of several hours.This magnitude of improvement is quite significant when compared with the rate of general forecast improvement over the last decade. A several hour increase in forecast range at five or six days normally takes several years to achieve at operational weather centers.