Le service des États-Unis pour la météorologie et l’océanographie, la NOAA, est responsable des satellites météorologiques opérationnels américains. A l’heure actuelle, il opère les satellites en orbite polaire Suomi-NPP, NOAA-20 et NOAA-21, dont les instruments perfectionnés mesurent de nombreux paramètres atmosphériques globaux. Il opère également les satellites géostationnaires GOES-16, -17 et -18 qui fournissent en temps réel des images météorologiques à haute résolution du continent américain.

Accurate atmospheric 3D wind observations are one of the top priorities for the global scientific community. To address this requirement, and to support researchers’ needs to acquire and analyze wind data from multiple sources, the System for Analysis of Wind Collocations (SAWC) was jointly developed by NOAA/NESDIS/STAR, UMD/ESSIC/CISESS, and UW-Madison/CIMSS. SAWC encompasses the following: a multi-year archive of global 3D winds observed by Aeolus, sondes, aircraft, stratospheric superpressure balloons, and satellite-derived atmospheric motion vectors, archived and uniformly formatted in netCDF for public consumption; identified pairings between select datasets collocated in space and time; and a downloadable software application developed for users to interactively collocate and statistically compare wind observations based on their research needs. The utility of SAWC is demonstrated by conducting a one-year (September 2019–August 2020) evaluation of Aeolus level-2B (L2B) winds (Baseline 11 L2B processor version). Observations from four archived conventional wind datasets are collocated with Aeolus. The recommended quality controls are applied. Wind comparisons are assessed using the SAWC collocation application. Comparison statistics are stratified by season, geographic region, and Aeolus observing mode. The results highlight the value of SAWC’s capabilities, from product validation through intercomparison studies to the evaluation of data usage in applications and advances in the global Earth observing architecture.

The operational Aeolus Level-2B (L2B) horizontal line-of-sight (HLOS) retrieved Rayleigh winds, produced by the European Space Agency (ESA), utilize European Centre for Medium-Range Weather Forecasts (ECMWF) short-term forecasts of temperature, pressure, and horizontal winds in the Rayleigh-Brillouin and M1 correction procedures. These model fields or backgrounds can contain ECMWF model-specific errors, which may propagate to the retrieved Rayleigh winds. This study examines the sensitivity of the retrieved Rayleigh winds to the changes in the model backgrounds, and the potential benefit of using the same system, in this case the National Oceanic and Atmospheric Administration's Finite-Volume Cubed Sphere Global Forecast System (FV3GFS), for both the corrections and the data assimilation and forecast procedures. It is shown that the differences in the model backgrounds (FV3GFS minus ECMWF) can propagate through the Level-2B horizontal line-of-sight Rayleigh wind retrieval process, mainly the M1 correction, resulting in differences in the retrieved Rayleigh winds with mean and standard deviation of magnitude as large as 0.2 ms(-1). The differences reach up to 0.4, 0.6, and 0.7 ms(-1) for the 95th, 99th, and 99.5th percentiles of the sample distribution with maxima of similar to 1.4 ms(-1). The numbers of the large differences for the combined lower and upper 5th, 1st, and 0.5th percentile pairs are similar to 6,100, 1,220, and 610 between 2.5 and 25 km height globally per day respectively. The ESA-disseminated Rayleigh wind product (based on the ECMWF corrections) already shows a significant positive impact on the FV3GFS global forecasts. In the observing system experiments performed, compared with the ESA Rayleigh winds, the use of the FV3GFS-corrected Rayleigh winds lead to similar to 0.5% more Rayleigh winds assimilated in the lower troposphere and show enhanced positive impact on FV3GFS forecasts at the day 1-10 range but limited to the Southern Hemisphere.

Atmospheric motion vector (AMV) winds have positive impacts in operational numerical weather prediction (NWP) systems. These impacts might be improved with better treatment of the following error characteristics of AMVs. First, AMVs may have wind errors due to height assignment errors. Second, AMVs may have additional wind-speed biases in addition to those due to height assignment errors. Third, AMVs are representative of motion in a possibly thick atmospheric layer, not a single atmospheric level. Previous work proposed a variational feature track correction (FTC) method in which an observation operator is implemented that averages the NWP background winds optimally in the vertical. Here, a prototype feature track correction observation operator (FTC-OO) is implemented in the NOAA/NCEP data assimilation (DA) system. The parameters describing the vertical averaging are determined offline based on previous DA cycles. The FTC-OO reduces the observation minus background standard deviation by about 4%. Global observing-system experiments (OSEs) are performed comparing the FTC-OO with the operational observation operator. The forecast verification sample is 41 10-day forecasts. The OSEs show that the FTC-OO improves forecast skill, primarily for tropical geopotential height. Additional OSEs are performed that include Aeolus wind observations. The hypothesis that the Aeolus winds would enhance the impact of the FTC-OO was not borne out in these experiments-the Aeolus observations alone have a significant positive impact, but the impact of the FTC method in the presence of the Aeolus observations is neither enhanced nor degraded compared with the impact of the FTC method alone.

The operational Aeolus Level‐2B (L2B) Horizontal Line‐of‐Sight (HLOS) retrieved Rayleigh winds, produced by the European Space Agency (ESA), utilize ECMWF short‐term forecasts of temperature, pressure, and horizontal winds in the Rayleigh–Brillouin and M1 correction procedures. These model fields or backgrounds can contain ECMWF model‐specific errors, which may propagate to the retrieved Rayleigh winds. This study examines the sensitivity of the retrieved Rayleigh winds to the changes in the model backgrounds, and the potential benefit of using the same system, in this case the NOAA Finite‐Volume Cubed Sphere Global Forecast System (FV3GFS), for both the corrections and the data assimilation and forecast procedures.It is shown that the differences in the model backgrounds (FV3GFS‐ECMWF) can propagate through the L2B HLOS Rayleigh wind retrieval process, mainly the M1 correction, resulting in differences in the retrieved Rayleigh winds with mean and standard deviation of magnitude as large as 0.2 m s−1. The differences reach up to 0.4, 0.6, and 0.7 m/s for the 95th, 99th, and 99.5th percentiles of the sample distribution with maxima of ~1.4 m/s. The numbers of the large differences for the combined lower and upper 5th, 1st, and 0.5th percentile pairs are ~6100, 1220, and 610 between 2.5‐25 km height globally per day respectively. The ESA disseminated Rayleigh wind product (based on the ECMWF corrections) already shows a significant positive impact on the FV3GFS global forecasts (Garrett et al, 2022). In the observing system experiments (OSEs) performed, compared to the ESA Rayleigh winds, the use of the FV3GFS corrected Rayleigh winds lead to ~0.5% more Rayleigh winds assimilated in the lower troposphere and show enhanced positive impact on FV3GFS forecasts at the day 1‐10 range but limited to the Southern Hemisphere.This article is protected by copyright. All rights reserved.

A method to apply an empirical feature track correction (FTC) in a new observation operator for atmospheric motion vectors (AMVs) is proposed. The FTC AMV observation operator determines the background estimate of the observed AMV vector wind, adjusting the background profile by determining an optimal height adjustment, averaging the profile over a layer of optimal thickness, and applying a linear correction to the averaged profile wind. The FTC observation operator is tested in the context of a collocation study between AMVs projected onto the collocated Aeolus horizontal line‐of‐sight (HLOS) and the Aeolus HLOS wind profiles. This study is a prototype for a variational FTC for numerical weather prediction data assimilation systems in which the Aeolus wind profiles take the place of the background in the FTC observation operator. Compared to a collocation where the Aeolus profile is interpolated linearly in height to the AMV height, a simple ad hoc averaging approach and the FTC approach reduce the mean square difference between the AMV observation and the Aeolus estimated AMV observation by 38% and 43%, respectively.

The Advanced Systems Performance Evaluation tool for NOAA (ASPEN) is developed to help support designing and evaluating existing and planned observing systems in terms of comparative assessment, trade-offs analysis, and design optimization studies. ASPEN is a dynamic tool that rapidly assesses the benefit and cost effectiveness of environmental data obtained from any set of observing systems, whether ground-based or space-based, whether an individual sensor or a collection of sensors. The ASPEN assessed cost effectiveness accounts for the level of ability to measure the environment, the cost(s) associated with acquiring these measurements, and the degree of usefulness of these measurements to users and applications. It computes both the use benefit, measured as a requirements-satisfaction metric, and the cost effectiveness (equal to the benefit-to-cost ratio). ASPEN provides a uniform interface to compare the performance of different observing systems and to capture the requirements and priorities of applications. This interface describes the environment in terms of geophysical observables and their attributes. A prototype implementation of ASPEN is described and demonstrated in this study to assess the benefits of several observing systems for a range of applications. ASPEN could be extended to other types of studies, such as assessing the cost effectiveness of commercial data to applications in all the NOAA mission service areas, and ultimately to societal application areas, and thereby become a valuable addition to the observing systems assessment toolbox.

Abstract. The need for highly accurate atmospheric wind observations is a high priority in the science community, and in particular numerical weather prediction (NWP). To address this requirement, this study leverages Aeolus wind LIDAR Level-2B data provided by the European Space Agency (ESA) to better characterize atmospheric motion vector (AMV) bias and uncertainty, with the eventual goal of potentially improving AMV algorithms. AMV products from geostationary (GEO) and low-Earth polar orbiting (LEO) satellites are compared with reprocessed Aeolus horizontal line-of-sight (HLOS) global winds observed in August and September 2019. Winds from two of the four Aeolus observing modes are utilized for comparison with AMVs: Rayleigh-clear (derived from the molecular scattering signal) and Mie-cloudy (derived from particle scattering). For the most direct comparison, quality controlled (QC’d) Aeolus winds are collocated with quality controlled AMVs in space and time, and the AMVs are projected onto the Aeolus HLOS direction. Mean collocation differences (MCD) and standard deviation (SD) of those differences (SDCD) are determined from comparisons based on a number of conditions, and their relation to known AMV bias and uncertainty estimates is discussed. GOES-16 and LEO AMV characterizations based on Aeolus winds are described in more detail. Overall, QC’d AMVs correspond well with QC’d Aeolus HLOS wind velocities (HLOSV) for both Rayleigh-clear and Mie-cloudy observing modes, despite remaining biases in Aeolus winds after reprocessing. Comparisons with Aeolus HLOSV are consistent with known AMV bias and uncertainty in the tropics, NH extratropics, and in the Arctic, and at mid- to upper-levels in both clear and cloudy scenes. SH comparisons generally exhibit larger than expected SDCD, which could be attributed to height assignment errors in regions of high winds and enhanced vertical wind shear. GOES-16 water vapor clear-sky AMVs perform best relative to Rayleigh-clear winds, with small MCD (-0.6 m s-1 to 0.1 m s-1) and SDCD (5.4–5.6 m s-1) in the NH and tropics that fall within the accepted range of AMV error values relative to radiosonde winds. Compared to Mie-cloudy winds, AMVs exhibit similar MCD and smaller SDCD (~4.4–4.8 m s-1) throughout the troposphere. In polar regions, Mie-cloudy comparisons have smaller SDCD (5.2 m s-1 in the Arctic, 6.7 m s-1 in the Antarctic) relative to Rayleigh-clear comparisons, which are larger by 1–2 m s-1. The level of agreement between AMVs and Aeolus winds varies per combination of conditions including the Aeolus observing mode coupled with AMV derivation method, geographic region, and height of the collocated winds. It is advised that these stratifications be considered in future comparison studies and impact assessments involving 3D winds. Additional bias corrections to the Aeolus dataset are anticipated to further refine the results.

In this study we apply the Advanced Systems Performance Evaluation tool for NOAA (ASPEN), to support design activities for GeoXO, NOAA's program for the next-generation operational geostationary meteorological and space weather satellites. ASPEN is a dynamic and user friendly tool that rapidly assesses the value of environmental data obtained from observing systems. ASPEN was designed to help optimize and evaluate observing systems architecture solutions to meet as many needs as possible across a wide range of environmental applications. ASPEN provides a uniform interface in terms of geophysical observables and their attributes to compare the capabilities of different individual sensors, or constellations of sensors and to capture the requirements and priorities of applications. ASPEN fundamentally tries to answer the following questions: What knowledge of the environment is required by the multitude of applications? How well can observing systems measure the environment in general? To what extent do these observing system capabilities match the applications requirements? How can we account for varying levels of prioritization among observables and their attributes, when considering applications and their varying level of importance to the mission? A preliminary version of ASPEN is described here and applied to rank multiple potential configurations of GeoXO. In the application to GeoXO, ASPEN was an additional novel tool for assessing the relative benefit of an ensemble of proposed GeoXO sensor constellations.

The European Space Agency Aeolus mission launched a first-of-its-kind spaceborne Doppler wind lidar in August 2018. To optimize the assimilation of the Aeolus Level-2B (B10) horizontal line-of-sight (HLOS) winds, significant systematic differences between the observations and numerical weather prediction (NWP) background winds should be removed. Total least squares (TLS) regression is used to estimate speed-dependent systematic differences between the Aeolus HLOS winds and the National Oceanic and Atmospheric Administration (NOAA) Finite-Volume Cubed-Sphere Global Forecast System (FV3GFS) 6h forecast winds. Unlike ordinary least squares regression, TLS regression optimally accounts for random errors in both predictors and predictands. Large, well-defined, speed-dependent systematic differences are found in the lower stratosphere and troposphere in the tropics and Southern Hemisphere. Correction of these systematic differences improves the forecast impact of Aeolus data assimilated into the NOAA global NWP system.

The European Space Agency Aeolus mission launched the first‐of‐its‐kind space‐borne Doppler wind lidar in August 2018. The Aeolus Level‐2B (L2B) Horizontal Line‐of‐Sight (HLOS) wind observations are integrated into the NOAA Finite‐Volume Cubed‐Sphere Global Forecast System (FV3GFS). Components of the data assimilation system are optimized to increase the forecast impact from these Aeolus observations. Three observing‐system experiments (OSEs) are performed using the Aeolus L2B HLOS winds for the period of August 2–September 16, 2019: a baseline experiment assimilating all observations that are operationally assimilated in NOAA's FV3GFS but without Aeolus; an experiment adding the Aeolus L2B HLOS winds on top of the baseline configuration; and an experiment adding the Aeolus L2B HLOS winds on top of the baseline but also including a total least‐squares (TLS) regression bias correction applied to the HLOS winds. The variances of the Aeolus HLOS wind random errors (i.e., observation errors) are estimated using the Hollingsworth–Lonnberg (HL) method. Results from both OSEs demonstrate positive impact of Aeolus L2B HLOS winds on the NOAA global forecast. The largest impact is seen in the tropical upper troposphere and lower stratosphere where the Day 1–3 wind vector forecast root‐mean‐square error (RMSE) is reduced by up to 4%. Additionally, the assimilation of Aeolus impacts the steering currents ambient to tropical cyclones, resulting in a 15% reduction in track forecast error in the Eastern Pacific basin Day 2–5 forecasts, and a 5% and 20% reduction in track forecast error in the Atlantic basin at Day 2 and Day 5, respectively. In most cases, the additional TLS bias correction increases the positive impact of Aeolus data assimilation in the NOAA global numerical weather prediction (NWP) system when compared to the assimilation of Aeolus without bias correction.

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).& nbsp;

Recent efforts have focused on evaluation of the reprocessed Aeolus Level 2B (L2B) wind data with ESA M1 bias correction and its impact on NOAA global forecast. Aeolus wind quality especially the remaining biases vs NOAA global model background is examined. As a result, a revised bias correction taking account of noises in both Aeolus and GFS winds is implemented in the NOAA global data assimilation system to improve Aeolus wind assimilation. In this study we will present impact from Aeolus wind on NOAA global forecast, focusing on synoptic and mesoscale scale events, e.g., tropical cyclones track and intensity in Eastern Pacific, and heavy rainfalls over the Western Coast of US.

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.

The global Earth-observing satellite constellation (EOSC) is a major international asset that has developed since 1960 with a dramatic growth in size and complexity in the recent past. This high-level review article takes stock and summarizes, from a meteorological perspective, the current constellation's capabilities, in order to increase awareness, document the value chain of satellite data from measurements to decision making, and illustrate the interconnected and evolving nature of those processes. When assessed in terms of application areas, the constellation is highly interdependent, and the observations it provides complement each other.

Direct remote-sensing observations (e.g., radar backscatter, radiometer brightness temperature, or radio occultation bending angle) are often more effective for use in data assimilation (DA) than the corresponding geophysical retrievals (e.g., ocean surface winds, soil moisture, or atmospheric water vapor). In the particular case of Global Navigation Satellite System Reflectometry (GNSS-R), the lower-level delay-Doppler map (DDM) observable shows a complicated relationship with the ocean surface wind field. Prior studies have demonstrated DA using GNSS-R wind retrievals inferred from DDMs. The complexity of the DDM dependence on winds, however, suggests that the alternative approach of ingesting DDM observables directly into DA systems, without performing a wind retrieval, may be beneficial. We demonstrate assimilation of DDM observables from the NASA Cyclone Global Navigation Satellite System (CYGNSS) mission into global ocean surface wind analyses using a two-dimensional variational analysis method. Bias correction and quality-control methods are described. Several models for the required observation-error covariance matrix are developed and evaluated, with the conclusion that a diagonal matrix performs as well as a fully populated matrix empirically tuned to a large ensemble of CYGNSS observation data. The 10-m surface winds from the European Centre for Medium-Range Weather Forecasts (ECMWF) operational forecast are used as the background (i.e., prior in the variational analysis). Results are compared with independent scatterometer (the advanced scatterometer (ASCAT), the oceansat-2 Scatterometer (OSCAT)) winds. For one month (June 2017) of data, the root-mean-square difference (RMSD) was reduced from 1.17 to 1.07 m center dot s(-1) and bias from -0.14 to -0.08 m center dot s(-1) for the wind speed at the specular point. Within a 150-km wide swath along the specular point track, the RMSD was reduced from 1.20 to 1.13 m center dot s(-1). These RMSD and bias statistics are smaller than other CYGNSS wind products available at this time.

In this study we propose and test a feature track correction (FTC) observation operator for atmospheric motion vectors (AMVs). The FTC has four degrees of freedom corresponding to wind speed multiplicative and additive corrections (γ and δV), a vertical height assignment correction (h), and an estimate of the depth of the layer that contributes to the AMV (Δz). Since the effect of the FTC observation operator is to add a bias correction to a weighted average of the profile of background winds an alternate formulation is in terms of a profile of weights (wk) and δV . The FTC observation operator is tested in the context of a collocation study between AMVs projected onto the collocated Aeolus horizontal line-of-sight (HLOS) and the Aeolus HLOS wind profiles. This is a prototype for an implementation in a variational data assimilation system and here the Aeolus profiles act as the background in the FTC observation operator. Results were obtained for ten days of data using modest QC. The overall OMB or collocation difference SD for a global solution applied to the independent sample is 5.49 m/s with negligible mean. For comparison the corresponding simple (or pure) collocation SD is 7.85 m/s, and the null solution, which only interpolates the Aeolus profile to the reported height of the AMV and removes the overall bias, has an OMB SD of 7.23 m/s. These values correspond to reductions of variance of 51.0% and 42.3%, due to the FTC observation operator in comparison to the simple collocation and null solution, respectively. These preliminary tests demonstrate the potential for the FTC observation operator for * Improving AMV collocations (including triple collocation) with profile wind data. * Characterizing AMVs. For example, summary results for the HLOS winds show that AMVs compare best with wind profiles averaged over a 4.5 km layer centered 0.5 km above the reported AMV height. * Improving AMV observation usage within data assimilation (DA) systems. Lower estimated error and more realistic representation of AMVs with variational FTC (VarFTC) should result in greater information extracted. The FTC observation operator accomplishes this by accounting for the effects of h and Δz.

Delay-Doppler maps (DDMs) are generally the lowest level of calibrated observables produced from global navigation satellite system reflectometry (GNSS-R). A forward model is presented to relate the DDM, in units of absolute power at the receiver, to the ocean surface wind field. This model and the related Jacobian are designed for use in assimilating DDM observables into weather forecast models. Given that the forward model represents a full set of DDM measurements, direct assimilation of this lower level data product is expected to be more effective than using individual specular-point wind speed retrievals. The forward model is assessed by comparing DDMs computed from hurricane weather research and forecasting (HWRF) model winds against measured DDMs from the Cyclone Global Navigation Satellite System (CYGNSS) Level 1a data. Quality controls are proposed as a result of observed discrepancies due to the effect of swell, power calibration bias, inaccurate specular point position, and model representativeness error. DDM assimilation is demonstrated using a variational analysis method (VAM) applied to three cases from June 2017, specifically selected due to the large deviation between scatterometer winds and European Centre for Medium-Range Weather Forecasts (ECMWF) predictions. DDM assimilation reduced the root-mean-square error (RMSE) by 15%, 28%, and 48%, respectively, in each of the three examples.

Promising new opportunities to apply artificial intelligence (AI) to the Earth and environmental sciences are identified, informed by an overview of current efforts in the community. Community input was collected at the first National Oceanic and Atmospheric Administration (NOAA) workshop on “Leveraging AI in the Exploitation of Satellite Earth Observations and Numerical Weather Prediction” held in April 2019. This workshop brought together over 400 scientists, program managers, and leaders from the public, academic, and private sectors in order to enable experts involved in the development and adaptation of AI tools and applications to meet and exchange experiences with NOAA experts. Paths are described to actualize the potential of AI to better exploit the massive volumes of environmental data from satellite and in situ sources that are critical for numerical weather prediction (NWP) and other Earth and environmental science applications. The main lessons communicated from community input via active workshop discussions and polling are reported. Finally, recommendations are presented for both scientists and decision-makers to address some of the challenges facing the adoption of AI across all Earth science.