Atmospheric Motion Vectors Research Articles

New impact diagnostics for cross‐validation of different observation types

AbstractNew cross‐validation diagnostics have been derived by further partitioning well‐established impact diagnostics. They are related to consistency relations, the most prominent of which indicates whether the first‐guess departures of a given observation type pull the model state into the direction of the verifying data (when processed with the ensemble estimated model error covariances). Alternatively, this can be regarded as cross‐validation between model error covariance estimates from the ensemble (which are used in the data assimilation system) and estimates diagnosed directly from the observations. A statistical cross‐validation tool has been developed that includes an indicator of statistical significance as well as a normalization that makes the statistical comparison largely independent from the total number of data and the closeness of their collocation. We also present a version of these diagnostics related to single‐observation experiments that exploits the same consistency relations but is easier to compute. Diagnostics computed within the Deutscher Wetterdienst's localized ensemble transform Kalman filter (LETKF) are presented for various kinds of bins. Results from well‐established in‐situ measurements are taken as a benchmark for more complex observations. Good agreement is found for radio‐occultation bending angle measurements, whereas atmospheric motion vectors are generally also beneficial but substantially less optimal than the corresponding in‐situ measurements. This is consistent with reported atmospheric motion vector height assignment problems. To illustrate its potential, a recent example is given where the method allowed identifying bias problems of a subgroup of aircraft measurements.Another diagnostic relationship compares the information content of the analysis increments with a theoretical optimum. From this, the information content of the LETKF increments is found to be considerably lower than those of the deterministic hybrid ensemble–variational system, which is consistent with the LETKF's limitation to the comparably low‐dimensional ensemble space for finding the optimal analysis.

Optimizing the Assimilation of the GOES-16/-17 Atmospheric Motion Vectors in the Hurricane Weather Forecasting (HWRF) Model

Hourly and 15 min GOES-16 and -17 atmospheric motion vectors (AMVs) are evaluated using the 2020 version of the operational HWRF to assess their impact on tropical cyclone forecasting. The evaluation includes infrared (IR), visible (VIS), shortwave (SWIR), clear air, and cloud top water vapor (CAWV and CTWV) AMVs derived from the ABI imagery. Several changes are made to optimize the assimilation of these winds. The observational error profile is inflated to avoid overweighting of the AMVs. The range of allowable AMV wind speeds entering the assimilation system is increased to include larger wind speeds observed in tropical cyclones. Two data quality checks, commonly used for rejecting AMVs, namely QI and PCT1, have been removed. These changes resulted in a 20–40% increase in the number of AMVs assimilated. One additional change, specific to infrared AMVs, is narrowing the atmospheric layer where IR AMVs are rejected from 400–800 hPa to 400–600 hPa. The AMVs’ impact on forecast skill is assessed using storms from the North Atlantic and the Eastern Pacific, respectively. Overall, GOES-16 and -17 AMVs are beneficial for improving tropical cyclone forecasting. Positive analysis and forecast impact are obtained for track error, intensity error, minimum central pressure error, and storm size.

The sensitivity of the WRF-4DVar data assimilation system to the control variables: A study on heavy rainfall events over India

The impact of different formulations of background error covariances (BECs) is examined for three heavy rainfall episodes over north India with a regional 4-dimensional variational (4DVar) data assimilation (DA) system. Three BEC formulations are analyzed, in which two of them employ stream function and velocity potential (ψ and χ) and the third one uses zonal and meridional velocity components (v and v) as momentum control variables. The uv-based formulation is completely univariate whereas, the correlations among the control variables are taken into account in the ψχ-based formulations through linear regression relations. Among the two ψχ-based BECs, one uses univariate relation and the other one uses multivariate relations for the moisture field. The multivariate relationship allows for impacting the moisture analysis through the assimilation of temperature or wind observations. Three experiments are carried out for each case with cyclic 4DVar assimilation. The conventional surface and upper-air observations are assimilated in combination with atmospheric motion vectors (AMVs) and ocean surface winds. Free forecast for 48 h is performed from respective final analysis fields for all the experiments. The results indicate that the uv-based analysis fields are closer to the observations. A comparative analysis of the 4DVar experiments with the 3DVar DA system provided a critical insight on the role of the 4DVar DA system on implicitly accounting for the multivariate correlations. The precipitation forecasts confirm the improved performance of the ψχ-based experiment, when multivariate nature of the humidity is incorporated. The time evolution of the intense rainfall episodes over the location of maximum rainfall are relatively well reproduced in the uv-based experiment. The results indicate that the inclusion of multivariate humidity variable in the BEC formulation does have a significant impact on suppressing the excessive overestimation in rainfall intensity.

Exploiting Aeolus level-2b winds to better characterize atmospheric motion vector bias and uncertainty

Abstract. The need for highly accurate atmospheric wind observations is a high priority in the science community, particularly for numerical weather prediction (NWP). To address this need, this study leverages Aeolus wind lidar level-2B data provided by the European Space Agency (ESA) as a potential comparison standard to better characterize atmospheric motion vector (AMV) bias and uncertainty. AMV products from geostationary (GEO) and low Earth orbiting (LEO) satellites are compared with reprocessed Aeolus horizontal line-of-sight (HLOS) global winds observed in August–September 2019. Winds from two Aeolus observing modes are compared with AMVs, namely Rayleigh-clear (RAY; derived from the molecular scattering signal) and Mie-cloudy (MIE; derived from the particle scattering signal). Quality-controlled (QC'd) Aeolus winds are co-located with QC'd AMVs in space and time, and the AMVs are projected onto the Aeolus HLOS direction. Mean co-location differences (MCDs) and the standard deviation (SD) of those differences (SDCDs) are determined and analyzed. As shown in other comparison studies, the level of agreement between AMV and Aeolus wind velocities (HLOSVs) varies with the AMV type, geographic region, and height of the co-located winds, as well as with the Aeolus observing mode. In terms of global statistics, QC'd AMVs and QC'd Aeolus HLOSVs are highly correlated for both observing modes. Aeolus MIE winds are shown to have great potential value as a comparison standard to characterize AMVs, as MIE co-locations generally exhibit smaller biases and uncertainties compared to RAY co-locations. Aeolus RAY winds contribute a substantial fraction of the total SDCDs in the presence of clouds where co-location/representativeness errors are also large. Stratified comparisons with Aeolus HLOSVs are consistent with known AMV bias and uncertainty in the tropics, NH extratropics, the Arctic, and at mid- to upper-levels in clear and cloudy scenes. AMVs in the SH/Antarctic generally exhibit larger-than-expected MCDs and SDCDs, most probably due to larger AMV height assignment errors and co-location/representativeness errors in the presence of high wind speeds and strong vertical wind shear, particularly for RAY comparisons.

Improving the Assimilation of Enhanced Atmospheric Motion Vectors for Hurricane Intensity Predictions with HWRF

The initial conditions for hurricanes are difficult to improve due to the lack of inner-core observations over the ocean. An enhanced atmospheric motion vectors (AMVs) dataset from the Cooperative Institute for Meteorological Satellite Studies (CIMSS) has recently become available and covers the inner-core region of hurricanes. This study tries to find an optimal data assimilation (DA) configuration to better utilize the observations for the Hurricane Weather Research and Forecasting (HWRF) model with hurricane Irma (2017). The results show that (a) without vortex relocation (VR), the hourly three-dimensional ensemble–variational (3DEnVar) outperforms the 6-hourly 3DEnVar DA configuration in almost all aspects, except for long-term track predictions. The assimilation of inner-core AMVs further improves the corresponding intensity forecasts for both hourly and 6-hourly 3DEnVar DA. (b) The 6-hourly 3DEnVar DA predictions with VR can be significantly improved upon their non-VR counterparts. However, VR can be detrimental to hourly 3DEnVar minimum sea level pressure (MSLP) predictions due to the spuriously enhanced upper-level warm core. The improvements from the assimilation of additional inner-core AMVs are thus limited under hourly VR. Reducing VR frequency can reduce the detrimental effects of hourly 3DEnVar. (c) An updated observation error profile for the enhanced AMVs benefits the hourly 3DEnVar DA more than the 6-hourly 3DEnVar DA.

Impact of the Aeolus Level‐2B horizontal line‐of‐sight winds in the Environment and Climate Change Canada global forecast system

AbstractThe European Space Agency Aeolus mission was launched in August 2018. This satellite carries the first Doppler lidar able to provide global measurements of wind profiles. Aeolus Level‐2B products have been generated and monitored by the European Centre for Medium‐Range Weather Forecasts (ECMWF) in near real‐time since a few weeks after the launch. These products include the horizontal line‐of‐sight (HLOS) winds that are suitable for data assimilation in numerical weather prediction systems. This article presents a series of observing system experiments conducted over summer 2019 to assess the value of the Level‐2B HLOS winds and their impact on the Environment and Climate Change Canada global forecasts. The impact of atmospheric motion vectors (AMVs) on forecasts is also examined and compared with the impact of HLOS winds. Two datasets are used: the HLOS winds produced in near real‐time at ECMWF and those reprocessed later in fall 2020. It is found that the near real‐time data are significantly biased and should be corrected. A look‐up table bias correction based on observation minus background departures is applied to this dataset as initially proposed by ECMWF. The reprocessed data are of better quality and bias corrected using the telescope's primary mirror temperature variations as predictor. The impacts of the near real‐time and reprocessed HLOS winds on forecasts are generally positive for both temperature and wind. The impacts are largest in the troposphere over the Tropics and polar regions. The positive impacts on forecasts are larger with the reprocessed data, particularly in the stratosphere, where a significant degradation over the Southern Hemisphere is found from assimilating the near real‐time data. The normalized forecast error reductions at days 1 and 2 for the wind are ∼1.25% over the Tropics and Southern Hemisphere. The positive impact of the HLOS winds on forecasts is enhanced by ∼40% when the AMVs are not assimilated in the control experiment. The forecast error reduction from assimilating AMVs is, however, two times larger than from assimilating HLOS winds in the extratropics. Conversely, the impact of HLOS winds on forecasts is generally larger in the Tropics.

Assessing the quality of novel Aeolus winds for NWP applications at NCMRWF

AbstractThis study illustrates the validation of Aeolus Horizontal Line‐Of‐Sight (HLOS) winds, both Rayleigh‐clear and Mie‐cloudy, using in situ satellite wind (Atmospheric Motion Vectors, AMVs) observations, and NWP equivalents for three months, June–August 2020, covering the Indian summer monsoon season. Estimated errors in the Mie‐cloudy (Rayleigh‐clear) winds are clustered around 0.5–4 m·s−1 (3–8 m·s−1), and the differences between Aeolus Mie‐cloudy (Rayleigh‐clear) and sonde winds are within ±5 m·s−1 (± 8 m·s−1), but the systematic error is close to zero over the Northern Hemisphere where there are more sonde reports. Validation shows the quality of Mie‐cloudy winds is better than Rayleigh‐clear winds. Though the comparison against the observations like sonde (radiosonde and pilot balloons together) and aircraft indicate the quality of the Aeolus winds, their sparse spatial and temporal coverage limits the validation. Validation of Aeolus winds against AMVs provides similar results but with a better and nearly complete picture of the quality and quantity, with more information over the data‐sparse and remote regions. Statistical scores suggest the characteristics of the Aeolus winds at different vertical levels and geographical regions remain the same irrespective of the validation reference datasets. The Indian summer monsoon features like Low‐Level Jet (LLJ) and Tropical Easterly Jet (TEJ) are well represented in the Aeolus winds. This study also investigated the impact of the Aeolus HLOS winds over the Indian region through the collocated radiosonde and ALADIN wind profile assimilation experiments. Observing System Experiments (OSEs) suggest assimilation of Aeolus winds produced marginal improvement in the simulation of north Indian Ocean cyclones.

Compact Midwave Imaging System: Results from an Airborne Demonstration

The Compact Midwave Imaging System (CMIS) is a wide field of view, multi-angle, multi-spectral pushframe imager that relies on the forward motion of the satellite to create a two-dimensional (2D) image swath. An airborne demonstration of CMIS was successfully completed in January–February 2021 on the NASA Langley Research Center Gulfstream III. The primary objective of the four-flight campaign was to demonstrate the capability of this unique instrument to perform stereo observations of clouds and other particulates (e.g., smoke) in the atmosphere. It is shown that the midwave infrared (MWIR) spectral bands of CMIS provide a unique 24/7 capability with high resolution for accurate stereo sensing. The instrument relies on new focal plane array (FPA) technology, which provides excellent sensitivity at much warmer detector temperatures than traditional technologies. This capability enabled a compact, low-cost design that can provide atmospheric motion vectors and cloud heights to support requirements for atmospheric winds in the 2017–2027 Earth Science Decadal Survey. Applications include day/night observations of the planetary boundary layer, severe weather, and wildfires. A comparison with current space-based earth science instruments demonstrates that the SWIR/MWIR multi-spectral capability of CMIS is competitive with larger, more expensive instrumentation. Imagery obtained over a controlled burn and operating nuclear power plant demonstrated the sensitivity of the instrument to temperature variations. The system relies on a mature stereoscopic imaging technique applied to the same scene from two independent platforms to unambiguously retrieve atmospheric motion vectors (AMVs) with accurate height assignment. This capability has been successfully applied to geostationary and low-earth orbit satellites to achieve excellent accuracy. When applied to a ground-point validation case, the accuracy for the CMIS aircraft observations was 20 m and 0.3 m/s for cloud heights and motion vectors, respectively. This result was confirmed by a detailed error analysis with analytical and covariance models. The results for CMIS cases with underflights of Aeolus, CALIPSO, and Aqua provided a good validation of expected accuracies. The paper also showed the feasibility of accommodating CMIS on CubeSats to enable multiple instruments to be flown in a leader–follower mode.

Impact of Assimilating Atmospheric Motion Vectors from Himawari-8 and Clear-Sky Radiance from Fy-4a Giirs on Binary Typhoons in a Convection-Permitting Model

Impact of Assimilating Atmospheric Motion Vectors from Himawari-8 and Clear-Sky Radiance from Fy-4a Giirs on Binary Typhoons in a Convection-Permitting Model

Assimilation of satellite data in numerical weather prediction. Part II: Recent years

AbstractDevelopments in the assimilation of satellite data in numerical weather prediction (NWP), from the first experiments in the late 1960s to the present day, are presented in a two‐part review article. This part, Part II, reviews the progress in recent years, from about 2000. It includes summaries of advances in the relevant satellite remote‐sensing technologies and in methods to assimilate observations from these instruments into NWP systems. It also summarises impacts on forecast skill. Continued progress has been made on the assimilation of passive infrared (IR) sounding data and microwave (MW) sounding and imaging data. This has included data from hyperspectral IR sounders, which first became available during this period. Advances in the use of cloud‐affected radiances, from both IR and MW instruments, have been made. In support of this progress, further developments have been made in fast radiative transfer models and in bias correction techniques, and work has continued to improve understanding and representation of observation uncertainties. Continued progress has also been made on the use of wind information from satellites, including atmospheric motion vectors and scatterometer data. A new source of temperature and humidity information, from radio occultation observations, has become available during the period and has been exploited by many NWP centres. The impact of satellite data on NWP accuracy is continually assessed using a range of methods and metrics. Some results from recent Observing System Experiments (OSEs) and Forecast Sensitivity to Observation Impact (FSOI) assessment are presented and other methods are discussed. The role of satellite data in NWP‐based atmospheric reanalysis systems is also described.

Impact of Assimilating Atmospheric Motion Vectors from Himawari-8 and Clear-Sky Radiance from Fy-4a Giirs on Binary Typhoons in a Convection-Permitting Model

Impact of Assimilating Atmospheric Motion Vectors from Himawari-8 and Clear-Sky Radiance from Fy-4a Giirs on Binary Typhoons in a Convection-Permitting Model

Two-Stage Artificial Intelligence Algorithm for Calculating Moisture-Tracking Atmospheric Motion Vectors

Abstract Much of the errors of atmospheric motion vectors (AMV) may be a consequence of algorithms not incorporating dynamical information. A physics-informed, artificial intelligence algorithm was developed that corrects errors of moisture tracking AMV (from the movement of water vapor) using numerical weather prediction (NWP) fields. The University of Arizona (UA) algorithm uses a variational method as a first step (fsUA); the second step then filters the first-stage AMVs using a random forest model that learns the error correction from NWP fields. The UA algorithm is compared with a traditional image feature tracking algorithm (JPL) using a global nature run as the “ground truth.” Experiments use global all-sky humidity fields at 500 and 850 hPa for 1–3 January 2006 and 1–3 July 2006. UA outputs AMVs with root-mean-square vector differences (RMSVDs) of 2 m s−1 for the tropics and ∼2–3 m s−1 for midlatitudes and the poles, whereas JPL outputs much higher RMSVDs of ∼3 m s−1 for the tropics and ∼3–9 m s−1 for the midlatitudes and poles. Although the algorithm fsUA produces approximately the same global RMSVDs as the JPL algorithm, fsUA has a higher resolution since it outputs an AMV per pixel, whereas the JPL algorithm uses a target box that effectively smooths the vectors. Furthermore, UA’s RMSVDs are lower than the intrinsic error (calculated from the differences between two reanalysis datasets). Even for error-prone regions with low moisture gradients and where winds are oriented along moisture isolines, UA’s absolute speed difference with “truth” stays within ∼3 m s−1.

A collocation study of atmospheric motion vectors (AMVs) compared to Aeolus wind profiles with a feature track correction (FTC) observation operator

AbstractA 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.

Validation and assimilation of INSAT atmospheric motion vectors: Case studies for tropical cyclones

Validation and assimilation of INSAT atmospheric motion vectors: Case studies for tropical cyclones

Paradigm of surface wind fields generated using Scatsat-1 and low-level adjusted INSAT-3D winds during tropical cyclone Amphan

ABSTRACT Ocean surface vector winds are of paramount importance for studying almost all meteorological and oceanographic processes. Wind vector products over the oceans are available from polar orbiting scatterometers and infrared imagers onboard geostationary satellites. The scatterometers provide wind vectors at the surface level, whereas imagers provide winds at different pressure levels in the form of Atmospheric Motion Vectors (AMVs). In this study, an attempt has been made to first project the low-level AMVs from INSAT-3D to the sea surface level followed by combining winds from Scatsat-1 scatterometer for typical case of a super cyclone Amphan. Swath-based observations from a scatterometer cannot cover the entire area affected by a cyclone, whereas AMV products have limited coverage during cyclones because of cloud cover. Thus, the wind products generated by combining the two types of satellite observations surely have sufficient merit in terms of enhanced number of observations to cater to various meteorological aspects related to the cyclones. The generated winds are subsequently validated using operational products from the National Centre for Medium Range Weather Forecast numerical weather prediction model, yielding root mean square errors of ~2 m s−1 and ~20° in wind speed and direction respectively.

Impact of Assimilating High‐Resolution Atmospheric Motion Vectors on Convective Scale Short‐Term Forecasts: 1. Observing System Simulation Experiment (OSSE)

AbstractThis research investigates to what extent the high‐spatiotemporal‐resolution Atmospheric Motion Vector (AMV) product derived from the new‐generation Geostationary Operational Environmental Satellites‐Series R can benefit convective‐scale data assimilation (DA) and forecasts of potential high impact weather events. In the first part of this two‐part study, the impact of AMV DA on convective‐scale numerical weather prediction (NWP) is evaluated with an idealized supercell storm. The simulated AMV observations are synthesized from the idealized supercell storm generated by the Weather Research and Forecasting model and then the data are assimilated with a three‐dimensional variational analysis and forecast system. A baseline DA experiment demonstrates that the wind errors within and around the storms are remarkably reduced by assimilating the AMV data, consequently enhancing the storm top‐level divergence and low‐level convergence signatures associated with two strong splitting supercells. Three sets of sensitivity experiments are then performed to test the impact of observation resolution, DA cycling frequency and horizontal correlation length scale respectively. Generally, assimilating higher‐spatial‐resolution AMVs at higher cycling frequency is able to produce reasonable divergence analysis as well as the subsequent 90 min forecasts. However, too frequent DA cycling tends to produce a warm bias and overestimate nonprecipitating hydrometeor and storm‐relative helicity features, resulting in more spurious cells in the short‐term forecasts. It is also found that the correlation length scale of 20 km produces the best results when assimilating the AMV data.

Impact of Assimilating High‐Resolution Atmospheric Motion Vectors on Convective Scale Short‐Term Forecasts: 2. Assimilation Experiments of GOES‐16 Satellite Derived Winds

AbstractBuilding on the results from the observing system simulation experiments in Part I, this study investigates the impact of assimilating Geostationary Operational Environmental Satellite‐16 (GOES‐16) derived atmospheric motion vector (AMV) data on the convective scale numerical weather prediction (NWP) by using the National Severe Storms Laboratory (NSSL) three‐dimensional variational (3DVAR) data assimilation (DA) system. The benefit of the AMV DA for short‐term severe weather forecast is assessed with three high‐impact weather events that occurred in spring 2018 and 2019 over the Great Plains of the United States. The results show that the wind and equivalent potential temperature fields associated with the storm environment and the nearby ongoing convection are improved by the AMV DA, which yields better simulation of the boundaries and the subsequent forecasts of storm evolution. For the quasi‐linear or mesoscale convective system, the assimilation of AMVs has a positive impact on the 0–3 h forecasts of composite reflectivity and accumulated precipitation in terms of the shape, location, and magnitude. However, the AMV DA has difficulty in capturing the sharp moisture gradient associated with the dryline and mostly underpredicts the associated scattered storms.

The Potential of Satellite Sounding Observations for Deriving Atmospheric Wind in All-Weather Conditions

Atmospheric wind is an essential parameter in the global observing system. In this study, the water vapor field in Typhoon Lekima and its surrounding areas simulated by the Weather Research and Forecasting (WRF) model is utilized to track the atmospheric motion wind through the Farneback Optical Flow (OF) algorithm. A series of experiments are conducted to investigate the influence of temporal and spatial resolutions on the errors of tracked winds. It is shown that the wind accuracy from tracking the specific humidity is higher than that from tracking the relative humidity. For fast-evolving weather systems such as typhoons, the shorter time step allows for more accurate wind retrievals, whereas for slow to moderate evolving weather conditions, the longer time step is needed for smaller retrieval errors. Compared to the traditional atmospheric motion vectors (AMVs) algorithm, the Farneback OF wind algorithm achieves a pixel-wise feature tracking and obtains a higher spatial resolution of wind field. It also works well under some special circumstances such as very low water vapor content or the region where the wind direction is parallel to the moisture gradient direction. This study has some significant implications for the configuration of satellite microwave sounding missions through their derived water vapor fields. The required temporal and spatial resolutions in the OF algorithm critically determine the satellite revisiting time and the field of view size. The brightness temperature (BT) simulated through Community Radiative Transfer Model (CRTM) is also used to track winds. It is shown that the error of tracking BT is generally larger than that of tracking water vapor. This increased error may result from the uncertainty in simulations of brightness temperatures at 183 GHz.

The EUMETSAT Polar System: 13+ Successful Years of Global Observations for Operational Weather Prediction and Climate Monitoring

AbstractAfter successful launch in November 2018 and successful commissioning of Metop-C, all three satellites of the EUMETSAT Polar System (EPS) are in orbit together and operational. EPS is part of the Initial Joint Polar System (IJPS) with the United States (NOAA) and provides the service in the midmorning orbit. The Metop satellites carry a mission payload of sounding and imaging instruments, which allow provision of support to operational meteorology and climate monitoring, which are the main mission objectives for EPS. Applications include numerical weather prediction, atmospheric composition monitoring, and marine meteorology. Climate monitoring is supported through the generation of long time series through the program duration of 20+ years. The payload was developed and contributed by partners, including NOAA, CNES, and ESA. EUMETSAT and ESA developed the space segment in cooperation. The system has proven its value since the first satellite Metop-A, with enhanced products at high reliability for atmospheric sounding, delivered a very strong positive impact on NWP and results beyond expectations for atmospheric composition and chemistry applications. Having multiple satellites in orbit—now three—has enabled enhanced and additional products with increased impact, like atmospheric motion vector products at latitudes not accessible to geostationary observations or increased probability of radio occultations and hence atmospheric soundings with the Global Navigation Satellite System (GNSS) Radio-Occultation Atmospheric Sounder (GRAS) instruments. The paper gives an overview of the system and the embarked payload and discusses the benefits of generated products for applications and services. The conclusions point to the follow-on system, currently under development and assuring continuity for another 20+ years.

Assessment of FY‐2G Atmospheric Motion Vector Data and Assimilating Impacts on Typhoon Forecasts

AbstractAtmospheric motion vectors (AMVs) have produced positive impacts on global weather forecasts, but few studies have evaluated the impacts of AMV data from the Fengyun (FY) geostationary satellite series, especially from FY‐2G and FY‐4, on typhoon forecasts in a regional model. In this study, the qualities of FY‐2G and Himawari‐8 AMV data are comparatively evaluated with several preprocesses (e.g., height assignment, quality control, channel merging, thinning, and observation error assignment) employed, and a super typhoon (Typhoon Hato, which occurred in 2017) is selected to assess the forecasting performances with the AMV data assimilated in the Weather Research and Forecasting data assimilation (WRFDA) system. The results show that the AMV data from Himawari‐8 are better overall than those from FY‐2G, with smaller RMSEs and biases for full wind speeds, and the preprocesses improve the qualities of these two geostationary satellite AMV data sets, as they both depict the same general atmospheric circulations. On the other hand, assimilation of AMV data improves the forecasts of environmental fields, resulting in the simulated track and intensity being closer to the observations. Generally, assimilating FY‐2G AMV data has comparably positive impacts with Himawari‐8 AMVs, and the AMV data retrieved from multiple channels provide even better forecasts. Experiments involving another typhoon case are also performed to further provide evidence that the derived AMV data from the Fengyun geostationary satellite series have value in the operational applications.