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The high observation efficiency, scanning speed and observation frequency of the Fengyun-4A (FY-4A) satellite indicates the progress of Chinese geostationary meteorological satellites. The characteristics of FY-4A atmospheric motion vectors (AMVs) derived from the high-level water vapor (WV-High) channel, mid-level water vapor (WV-Mid) channel, and infrared (IR) channel of FY-4A are analyzed, and their corresponding observation errors estimated. Then, the impacts of single-channel and multi-channel FY-4A AMVs on RMAPS-ST (the Rapid-refresh Multi-scale Analysis and Prediction System—Short Term) are evaluated based on one-month data assimilation cycling and forecasting experiments. Results show that the observation errors of FY-4A AMVs from the three channels have an explicit vertical structure. Results from the cycling experiments indicate that the assimilation of AMVs from WV-High produces more apparent improvement of the wind in the upper layer, while a more positive effect in the lower layer is achieved by the assimilation of AMVs from IR. Furthermore, the assimilation of AMVs from IR is more skillful for medium and moderate precipitation than from other channels owing to the good quality of data in the lower layer in the AMVs from IR. Assimilation of FY-4A AMVs from the three channels could combine the advantages of assimilation from each individual channel to improve the wind in the upper, middle and lower layers simultaneously.

This 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. Plain Language Summary Though the high‐spatiotemporal‐resolution Atmospheric Motion Vectors (AMVs) retrieved from newly launched Geostationary Operational Environmental Satellite‐16/17 could benefit mesoscale and convective‐scale weather forecasts, few studies have been done so far to explore this potential. In this study, a three‐dimensional variational data assimilation scheme developed at NOAA/National Severe Storms Laboratory is used to assimilate the simulated AMVs into the Weather Research and Forecasting (WRF) model. The potential impacts of AMV product on convective‐scale analysis and short‐term severe weather prediction are examined through observing system simulation experiments (OSSEs). It is illustrated that assimilating the simulated AMVs noticeably improves the analysis for key model variables within and around the storms, especially for the wind field. As a result, assimilation of AMVs can help improve short‐term severe weather forecasts in comparison to when no AMVs are assimilated. Key Points GOES‐R derived Atmospheric Motion Vector (AMV) product has the potential to benefit convective scale data assimilation and forecasts The assimilation of GOES‐R derived AMVs is performed through observing system simulation experiments of a supercell storm The short‐term forecast of the simulated severe weather event is improved when assimilating the AMV data

The present study delineates the relative performance of 3D‐Var and 4D‐Var data assimilation (DA) techniques in the regional NCUM‐R model to simulate three heavy rainfall events (HREs) over the Indian region. Four numerical experiments for three extreme rainfall cases were conducted by assimilating different combinations of observations from surface, aircraft, upper‐air and satellite‐derived Atmospheric Motion Vectors (AMVs) using 3D‐Var and 4D‐Var techniques. These experiments generated initial conditions (ICs) for the NCUM‐R forecast model to simulate HREs. Key atmospheric variables, such as wind speed and direction, vertically integrated moisture transport (VIMT: kg.m−1.s−1), vertical profiles of relative humidity and temperature as well as various stability indices are analysed during the HREs. Forecast verification was performed using statistical skill scores and object‐based methods from the METplus tool, comparing NCUM‐R output against GPM rainfall data. The results demonstrate that the 4D‐Var technique improves simulation accuracy compared to 3D‐Var, particularly when assimilating satellite wind data. Incorporating satellite‐derived AMVs improved the representation of rainfall intensity and spatial patterns, as well as other atmospheric variables. It is found that rainfall for Case‐01, the VIMT was notably high along the eastern coast of India and southwest of BoB, with the 4DVS simulation better capturing moisture transport patterns compared to 3DVS and 3DV. The SWEAT index ranged from 205 to 250 J·kg−1 in the morning, rising to 250–300 J·kg−1 by noon, indicating increasing convective instability. On 18 March 2023 (Day‐1), the K‐index exceeded 30, signalling scattered thunderstorms, consistent with the IMD's reports of isolated to scattered rainfall on 19th and 20th March 2023. Similarly, it is found that satellite wind assimilation improved the statistical skill scores in predicting heavy precipitation in all three cases. Overall, the study suggested that the performance of the NCUM‐R model integrated with the 4D‐Var technique improved the model's forecast skill in the simulation of HREs. This study evaluates the 3D‐Var and 4D‐Var data assimilation using high resolution model (NCUM‐R). The surface, upper‐air and satellite wind data are assimilated to simulate three heavy rainfall cases with different experiments carried out over Indian region. The 3D‐Var technique reduced the event intensity and energy in all forecast days as well as in analysis. But 4D‐Var gives better simulation as compared to 3D‐Var and 3D‐Var with satellite wind data. The 4D‐Var with satellite wind data have better simulated the dynamics and thermodynamics variables of atmosphere, and enhance the predictability of model also.

The atmospheric motion vectors (AMVs) from Indian geostationary meteorological satellites are derived operationally at Space Applications Centre (SAC), Ahmedabad. Over the years, many improvements in the operational retrieval algorithm have taken place with the advancement in the retrieval techniques as well as in the sensors. Two Indian geostationary meteorological satellites INSAT-3D and INSAT-3DR launched on 26 July 2013 (placed at 82° E) and 08 September 2016 (placed at 74° E), respectively, are providing continuous coverage at every 15 min. The data from these satellites have enhanced the scope for better understanding of atmospheric processes over the Indian Ocean region. The retrieval techniques and accuracy of AMVs have improved significantly with improved spatial resolution data along with more number of spectral channels available in both satellites. In this work, quality assessment of operational AMVs retrieved using four different spectral channels (visible, mid-infrared, infrared and water vapour) of both the satellites at 4-km spatial resolution is discussed. It also discusses the retrieval of staggering AMVs with simultaneous usage of INSAT-3D and INSAT-3DR data. The derivation of different atmospheric parameters (viz. convergence, divergence, vorticity, wind shear etc.) using AMVs of both the satellites and their possible applications during tropical cyclogenesis are also discussed. Recently, the algorithm for the retrieval of high-resolution (HR) visible AMVs and rapid-scan (RS) AMV using INSAT-3DR data has been developed and tested; however, these new algorithms are yet to be implemented operationally. To demonstrate the initial applications, AMVs are assimilated into numerical model to assess their impact for forecast improvement. The quality and impact assessment from this study will provide useful guidance to the operational meteorological agencies for applications of existing and new INSAT-3D and INSAT-3DR AMV data for future applications in the numerical weather prediction (NWP) model over the South Asian region.

Atmospheric motion vectors (AMVs), known as cloud track winds, have positive impacts on global numerical weather forecasts (NWP). In this study, AMVs that were retrieved from Fengyun-2G and Fengyun-4A were compared in their data quality and impacts on the typhoon forecasts in order to investigate the differences between the first and second generation of the geostationary meteorological satellites of China. This report conducted data evaluation and assimilation-forecasting experiments on FY-2G and FY-4A atmospheric motion vectors (AMVs), respectively. The results showed that the AMVs data of FY-4A are of better quality than those of FY-2G and assimilating the AMVs of FY-2G and FY-4A have a neutral to slightly positive impacts on typhoon forecasts, which is quite encouraging for their operational use in the future.

Four-dimensional COAMPS Dynamic Initialization (FCDI) analyses that include high-temporal- and high-spatial-resolution GOES-16 Atmospheric Motion Vector (AMV) datasets are utilized to understand and predict why pre-Bonnie (2022), designated as a Potential Tropical Cyclone (PTC 2), did not undergo rapid intensification (RI) while passing along the coast of Venezuela during late June 2022. A tropical cyclone lifecycle-prediction model based on the ECMWF ensemble indicated that no RI should be expected for the trifurcation southern cluster of tracks along the coast, similar to PTC 2, but would likely occur for two other track clusters farther offshore. Displaying the GOES-16 mesodomain AMVs in 50 mb layers illustrates the outflow burst domes associated with the PTC 2 circulation well. The FCDI analyses forced by thousands of AMVs every 15 min document the 13,910 m wind-mass field responses and the subsequent 540 m wind field adjustments in the PTC 2 circulation. The long-lasting outflow burst domes on both 28 June and 29 June were mainly to the north of PTC 2, and the 13,910 m FCDI analyses document conditions over the PTC 2 which were not favorable for an RI event. The 540 m FCDI analyses demonstrated that the intensity was likely less than 35 kt because of the PTC 2 interactions with land. The FCDI analyses and two model forecasts initialized from the FCDI analyses document how the PTC 2 moved offshore to become Tropical Storm Bonnie; however, they reveal another cyclonic circulation farther west along the Venezuelan coast that has some of the characteristics of a Caribbean False Alarm event.

Building 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. Plain Language Summary The high‐spatiotemporal‐resolution atmospheric motion vectors (AMVs) derived from newly launched Geostationary Operational Environmental Satellites‐16/17 (GOES‐16/17; also named GOES East/West) may have the potential to improve short‐range severe weather forecasts. However, this has not been extensively explored. In this study, a three‐dimensional variational data assimilation scheme developed at NOAA/National Severe Storms Laboratory and the Weather Research and Forecasting model are used to investigate the impact of assimilating GOES‐R derived AMVs on short‐range severe thunderstorm forecasts. It is demonstrated that, compared to the control experiment without assimilating any observations, the short‐range forecasts of the quasi‐linear or mesoscale convective systems in three severe weather events are all improved based on verification against radar reflectivity and precipitation observations when assimilating the AMV data. Key Points Geostationary Operational Environmental Satellite‐16 (GOES‐R) derived atmospheric motion vector (AMV) product has the potential to benefit convective scale data assimilation and forecasts The impact of assimilating GOES‐R derived AMVs on short‐range severe thunderstorm forecasts is evaluated The short‐range forecasts of three severe weather events are improved by AMV DA compared to the control experiment without any observations

Based on the idealized supercell and real case studies in Part I and II, the purpose of this subsequent study is to further investigate the impact of assimilating Geostationary Operational Environmental Satellites‐16 (GOES‐16) derived atmospheric motion vectors (AMVs) in addition to WSR_88D Doppler radar observations on convective scale numerical weather prediction. Five high‐impact weather events that occurred in spring 2018 and 2019 are analyzed using the National Severe Storms Laboratory three‐dimensional variational data assimilation (DA) system. Four types of experiments are implemented and compared: (a) the control experiment (NoDA) without assimilating any observation, (b) the radar DA experiment (RAD), (c) the GOES‐16 AMV DA experiment (AMV), and (d) the experiment assimilating AMVs together with radar data (AMV_RAD). Score metrics aggregated over all cases indicate that AMV_RAD performs slightly better than RAD in 0–3 hr reflectivity and precipitation forecasts especially at higher thresholds, suggesting the added value of GOES‐16 AMVs on radar data. Detailed case examinations also show that AMV_RAD generally exhibits slightly more skillful storm forecast in terms of the areal coverage, storm mode, and storm orientations, owing to improvements in the analysis of boundary locations and localized enhanced divergence signatures. In spite of encouraging objective and subjective evaluation results, AMV_RAD has difficulty in adjusting the moisture gradient associated with dryline and tends to underpredict the associated weak discrete storms. Plain Language Summary With the launch of Geostationary Operational Environmental Satellites‐16 (GOES‐16) in November 2016, the impact of its derived high‐spatiotemporal‐resolution atmospheric motion vectors (AMVs) product in convective‐scale numerical weather prediction has not been extensively explored. In this study, the GOES‐16 AMVs together with WSR‐88D Doppler radar observations are effectively assimilated by a three‐dimensional variational data assimilation scheme developed at NOAA/National Severe Storms Laboratory. Both subjective and objective assessment results for five severe weather events suggest the slightly added forecast skill of GOES‐16 AMVs on conventional radar data for 0–3 hr reflectivity and precipitation forecasts. Key Points GOES‐R derived atmospheric motion vector (AMV) product has the potential to benefit convective scale data assimilation and forecasts The impact of assimilating GOES‐R derived AMVs together with NEXRAD observations on short‐range severe storm forecasts is evaluated The verification results for five severe weather events suggest the added forecast skill of GOES‐16 AMVs over conventional radar data

Based on atmospheric motion vectors (AMVs) derived from the Fengyun-4 meteorological satellite (FY-4), in this paper, integrated multi-satellite retrievals for GPM precipitation and reanalysis datasets and the vertical distribution characteristics of FY-4 AMVs, their application in the identification of the South Asian high (SAH) anticyclone and their application in the real-time monitoring of rainstorm disasters are studied. The results show that the AMVs’ vertical distribution characteristics are different across regions and seasons. AMVs from 150 to 350 hPa can be chosen as the upper troposphere wind (the total number accounts for about 77.2% on average). The center and shape of the upper tropospheric anticyclone obtained from AMVs are close to or slightly southward compared with those of the SAH at 200 hPa obtained from the ERA5 geopotential height. The SAH ridge line identified using the upper troposphere AMV zonal wind (the zonal wind is equal to zero) is slightly southward by about 1–2 degrees of latitude from that identified using ERA5 at 200 hPa but with a similar seasonal advance. The upper troposphere AMV can be used to monitor the location of the SAH and the evolution of its ridge line. The abnormally strong precipitation in South China is related to the location of the SAH and its ridge line. When the precipitation is abnormally strong/weak, the upper troposphere AMV deviation airflow shows divergence/convergence. During the “Dragon Boat Water” period in South China in 2022, strong precipitation occurred in the strong westerly winds or divergent flow on the northeast side of the upper troposphere anticyclone obtained from AMVs, and the precipitation intensity was the strongest when the divergence reached its peak, but this is not shown clearly in the EAR5 dataset.

In recent years, atmospheric numerical modeling frameworks and satellite observing systems have both undergone significant advances. While these developments offer considerable potential for improving forecasts of high-impact weather events such as tropical cyclones (TC), much work remains to be done regarding the targeted processing and optimal use of observations now becoming available with high spatiotemporal resolution. Using the 2019 version of NCEP’s HWRF model, we explore several different strategies for the assimilation of TC-scale, high-density atmospheric motion vectors (AMVs) derived from the new-generation GOES-R series of geostationary satellites. Using 2017’s Atlantic Hurricane Irma as a case study, we examine the HWRF forecast impacts of observation pre-processing, including thinning and adjustments to observation errors. It is demonstrated that enhanced vortex-scale GOES-16 AMVs contribute to notable improvements in HWRF track forecast error compared to a baseline control experiment that does not incorporate the high-density AMVs. Impacts on TC intensity and structure (i.e., wind radii) forecast errors are less robust, but results from the optimization experiments suggest that further work (both with regard to data assimilation strategies and advancements in the methods themselves) should lead to improvements in these forecast variables as well.